diff --git a/week2/friday/you_do_4/features/FeatureExtractor.py b/week2/friday/you_do_4/features/FeatureExtractor.py
new file mode 100644
index 0000000..6f4a801
--- /dev/null
+++ b/week2/friday/you_do_4/features/FeatureExtractor.py
@@ -0,0 +1,198 @@
+import pandas as pd
+from pandas import DataFrame
+from typing import Literal, List
+import numpy as np
+
+class FeatureExtractor:
+    def __init__(
+        self,
+        past_knowledge: DataFrame,
+        cyclical_feature_names: List[str],
+        freq: str = "D",
+        lag_size: int = 30,
+        window_size: int = 30,
+    ):
+        self.PAST_KNOWLEDGE = past_knowledge.sort_values(by="datetime")
+        self.cyclical_feature_names = cyclical_feature_names
+        self.lag_size = lag_size
+        self.window_size = window_size
+        self.freq = freq
+
+    def transform(self, dates_to_predict: pd.DatetimeIndex) -> DataFrame:
+        df = self._get_all_ranges(dates_to_predict)
+        self.full_df = df.join(self.PAST_KNOWLEDGE, how="left")
+
+        return (
+            df.pipe(self._start_pipeline)
+            .pipe(self._add_lag_features)
+            .pipe(self._add_rolling_window_features)
+            .pipe(self._add_exponential_moving_features)
+            .pipe(self._drop_columns_with_same_values)
+            .pipe(self._expand_datetime)
+            .pipe(self._add_fourier_features)
+            .pipe(
+                lambda df: df.astype(
+                    {
+                        col: "int32"
+                        for col in df.select_dtypes(["int", "uint32"]).columns
+                    }
+                )
+            )
+            .pipe(
+                lambda df: df.astype(
+                    {col: "float32" for col in df.select_dtypes("float").columns}
+                )
+            )
+            .bfill()
+            .loc[dates_to_predict, :]
+        )
+
+    def _start_pipeline(self, df: pd.DataFrame) -> pd.DataFrame:
+        return df.copy().sort_index()
+
+    def _get_all_ranges(self, dates_to_predict: pd.DatetimeIndex) -> pd.DataFrame:
+        start_date = min(dates_to_predict.min(), self.PAST_KNOWLEDGE.index.min())
+        end_date = max(dates_to_predict.max(), self.PAST_KNOWLEDGE.index.max())
+        complete_date_range = pd.date_range(
+            start=start_date, end=end_date, freq=self.freq
+        )
+        return pd.DataFrame(index=complete_date_range)
+
+    def _add_lag_features(
+        self,
+        df: DataFrame,
+        fillna_with: Literal["ffill", "bfill"] | None = "bfill",
+    ) -> DataFrame:
+        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(
+            include=["number", "object"]
+        ).columns.tolist()
+
+        created_features = [
+            self.full_df[col].shift(i).rename(f"{col}_lag_{i}")
+            for i in range(1, self.lag_size + 1)
+            for col in columns_to_use
+        ]
+
+        lags_df = pd.concat(created_features, axis=1)
+
+        df = df.join(
+            lags_df,
+            how="left",
+        )
+
+        if fillna_with == "ffill":
+            df = df.ffill()
+        elif fillna_with == "bfill":
+            df = df.bfill()
+
+        return df
+
+    def _add_rolling_window_features(
+        self,
+        df: DataFrame,
+        fillna_with: Literal["ffill", "bfill"] | None = "ffill",
+    ) -> DataFrame:
+        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(
+            include=["float"]
+        ).columns.tolist()
+
+        metrics = ["mean", "std", "min", "max", "median", "var"]
+
+        created_features = [
+            (
+                self.full_df[col]
+                .rolling(window=size, min_periods=1)
+                .agg(metrics)
+                .rename(columns=lambda metric: f"{col}_rw{size}_{metric}")
+            )
+            for size in range(2, self.window_size + 1)
+            for col in columns_to_use
+        ]
+
+        window_df = pd.concat(created_features, axis=1)
+
+        df = df.join(
+            window_df,
+            how="left",
+        )
+
+        if fillna_with == "ffill":
+            df = df.ffill()
+        elif fillna_with == "bfill":
+            df = df.bfill()
+
+        return df
+
+    def _drop_columns_with_same_values(self, df: DataFrame, threshold=0.9) -> DataFrame:
+        to_drop = [
+            col
+            for col in df.columns
+            if df[col].value_counts(normalize=True, dropna=False).values[0] >= threshold
+        ]
+        return df.drop(columns=to_drop)
+
+    def _add_exponential_moving_features(
+        self, df: pd.DataFrame, up_to: int = 30
+    ) -> pd.DataFrame:
+        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(
+            include=["float"]
+        ).columns.tolist()
+
+        metrics = ["mean", "std", "var"]
+
+        created_features = [
+            (
+                self.full_df[col]
+                .ewm(span=span, adjust=False)
+                .agg(metrics)
+                .rename(columns=lambda metric: f"{col}_em_{span}_{metric}")
+            )
+            for span in range(2, up_to + 1)
+            for col in columns_to_use
+        ]
+
+        exponential_moving_df = pd.concat(created_features, axis=1)
+
+        df = df.join(
+            exponential_moving_df,
+            how="left",
+        )
+        return df
+
+    def _expand_datetime(self, df: DataFrame) -> DataFrame:
+        return df.assign(
+            **{
+                "year": lambda a_df: a_df.index.year,
+                "month": lambda a_df: a_df.index.month,
+                "day": lambda a_df: a_df.index.day,
+                "hour": lambda a_df: a_df.index.hour,
+                "day_of_year": lambda a_df: a_df.index.dayofyear,
+                "week_of_year": lambda a_df: a_df.index.isocalendar().week,
+                "quarter": lambda a_df: a_df.index.quarter,
+                # "season": lambda a_df: a_df.index.month % 12 // 3 + 1,
+                "is_weekend": lambda a_df: np.vectorize({True: 1, False: 0}.get)(
+                    a_df.index.weekday >= 5
+                ),
+            }
+        )
+
+    def _add_fourier_features(self, df: pd.DataFrame, num_terms: int = 7) -> DataFrame:
+        for col, max_val in self.cyclical_feature_names.items():
+            source = self._get_column_source(df, col)
+
+            for i in range(1, num_terms + 1):
+                operation = 2 * np.pi * i * source[col] / max_val
+
+                df[f"fourier_sin_{col}_{i}"] = np.sin(operation)
+                df[f"fourier_cos_{col}_{i}"] = np.cos(operation)
+
+        return df
+
+    def _get_column_source(self, df: DataFrame, col: str) -> List[str]:
+        if col in df.columns:
+            source = df
+        elif col in self.PAST_KNOWLEDGE.columns:
+            source = self.PAST_KNOWLEDGE
+        else:
+            raise KeyError(f"{col} not found both in df and past knowledge.")
+        return source
diff --git a/week2/friday/you_do_4/features/TargetEncoder.py b/week2/friday/you_do_4/features/TargetEncoder.py
new file mode 100644
index 0000000..df572e2
--- /dev/null
+++ b/week2/friday/you_do_4/features/TargetEncoder.py
@@ -0,0 +1,175 @@
+import pandas as pd
+from sklearn.model_selection import KFold, train_test_split
+from typing import Any, List, Union, Literal, Optional, Protocol, runtime_checkable
+from sktime.split.base import BaseSplitter
+
+
+@runtime_checkable
+class FoldProtocol(Protocol):
+    def split(self, X, y=None, groups=None) -> Any:
+        pass
+
+
+class TargetEncoder:
+    def __init__(
+        self,
+        random_state: Optional[int] = None,
+        columns: Union[List[str], str, Literal["auto"]] = "auto",
+        smoothing: Union[float, int] = 20,
+        cv: Union[int, FoldProtocol] = 5,
+        drop_after_transform: bool = True,
+        fill_zeros_with_mean: bool = True,
+        fillna_with_mean: bool = True,
+        epsilon: Union[float | int] = 1e-10,
+        stats_to_include: List[str] = [
+            "mean",
+            "median",
+            "var",
+            "std",
+            "skew",
+            "min",
+            "max",
+        ],
+    ):
+        self.SEED = random_state
+        self.smoothing = smoothing
+        self.columns = columns
+        self.drop_after_transform = drop_after_transform
+        self.fillna_with_mean = fillna_with_mean
+        self.fill_zeros_with_mean = fill_zeros_with_mean
+        self.cv = cv
+        self.EPSILON = epsilon
+        self.mappings = {}
+        self.stats_to_include = stats_to_include
+
+    def _get_column_names_to_process(
+        self,
+        X: Optional[pd.DataFrame] = None,
+    ) -> List[str]:
+        if isinstance(self.columns, str) and self.columns == "auto":
+            if X is None:
+                raise ValueError("X must be provided when columns='auto'")
+
+            LOWER_COUNT_LIMIT = 10
+            categorical_columns = X.select_dtypes(include="object").columns
+            high_cardinality_columns = [
+                col
+                for col in categorical_columns
+                if X[col].nunique() >= LOWER_COUNT_LIMIT
+            ]
+            return high_cardinality_columns
+
+        elif isinstance(self.columns, str):
+            return [self.columns]
+        elif isinstance(self.columns, list):
+            return self.columns
+
+    def _get_fold(self) -> FoldProtocol:
+        if isinstance(self.cv, int):
+            return KFold(n_splits=self.cv, shuffle=True, random_state=self.SEED)
+        elif isinstance(self.cv, FoldProtocol):
+            return self.cv
+        raise ValueError(
+            "cv must be an integer or an object implementing a 'split' method"
+        )
+
+    def fit(self, X: pd.DataFrame, y: pd.DataFrame) -> "TargetEncoder":
+        X = X.copy()
+        y = y.copy()
+        self.columns = self._get_column_names_to_process(X)
+        self.TARGET = y.columns[0]
+        fold = self._get_fold()
+
+        for column in self.columns:
+            splitter = self._get_splitter(X, y, fold)
+
+            if column not in X.columns:
+                raise ValueError(f"Column '{column}' not found in X")
+
+            encodings = [
+                self._generate_encodings(X.iloc[train_idx], y.iloc[train_idx], column)
+                for train_idx, _ in splitter
+            ]
+
+            self.mappings[column] = self._calculate_weighted_means_of_encodings(
+                encodings, column
+            )
+
+        return self
+
+    def _get_splitter(
+        self,
+        X: Optional[pd.DataFrame],
+        y: pd.DataFrame,
+        fold: FoldProtocol,
+    ):
+        return fold.split(y) if isinstance(fold, BaseSplitter) else fold.split(X, y)
+
+    def _generate_encodings(
+        self, X_train_fold: pd.DataFrame, y_train_fold, column: str
+    ):
+        dataset = pd.concat([X_train_fold, y_train_fold], axis=1)
+        global_mean = dataset[self.TARGET].agg(self.stats_to_include)
+
+        grouped_target = dataset.groupby(column)[self.TARGET]
+        grouped_target_count = grouped_target.count().iloc[0]
+
+        numerator = grouped_target.agg(self.stats_to_include) + (
+            (self.smoothing * global_mean) + self.EPSILON
+        )
+        denominator = (grouped_target_count * self.smoothing) + self.EPSILON
+
+        result = numerator / denominator
+
+        count_by_category = grouped_target.count().rename("count")
+        return pd.concat([result, count_by_category], axis=1)
+
+    def _calculate_weighted_means_of_encodings(
+        self, encodings: List[pd.DataFrame], column: str
+    ) -> dict:
+        combined_encodings = pd.concat(encodings)
+        group = combined_encodings.groupby(column)
+
+        return {
+            stat: group.apply(lambda x: (x[stat] * x["count"]).sum() / x["count"].sum())
+            for stat in self.stats_to_include
+        }
+
+    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
+        X = X.copy()
+
+        for column in self.columns:
+            column_mappings = self.mappings[column]
+
+            for stat in self.stats_to_include:
+                generated_column = f"te_{column}_{stat}"
+
+                X[generated_column] = (
+                    X[column].map(column_mappings[stat]).astype("float32")
+                )
+
+                if self.fillna_with_mean:
+                    mean_value = column_mappings[stat].mean().astype("float32")
+                    X[generated_column] = X[generated_column].fillna(mean_value)
+
+                if self.fill_zeros_with_mean:
+                    mean_value = column_mappings[stat].mean().astype("float32")
+                    X.loc[X[generated_column] == 0, generated_column] = mean_value
+
+        if self.drop_after_transform:
+            X.drop(columns=self.columns, inplace=True)
+
+        return X
+
+    def fit_transform(self, X: pd.DataFrame, y: pd.DataFrame) -> pd.DataFrame:
+        self.fit(X, y)
+        return self.transform(X)
+
+
+def get_data(address: str, target: str, SEED: int):
+    auto_df = pd.read_csv(address)
+
+    X = auto_df.loc[:, auto_df.columns != target]
+    y = auto_df.loc[:, auto_df.columns == target]
+
+    return train_test_split(X, y, test_size=0.2, random_state=SEED)
diff --git a/week2/friday/you_do_4/features/__init__.py b/week2/friday/you_do_4/features/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/week2/friday/you_do_4/notebooks/4-data-cleaning.ipynb b/week2/friday/you_do_4/notebooks/4-data-cleaning.ipynb
index a6e439c..a6553e9 100644
--- a/week2/friday/you_do_4/notebooks/4-data-cleaning.ipynb
+++ b/week2/friday/you_do_4/notebooks/4-data-cleaning.ipynb
@@ -6,8 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from ydata_profiling import ProfileReport"
+    "import pandas as pd"
    ]
   },
   {
@@ -16,10 +15,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dam_address = \"../dataset/raw/dam_occupancy.csv\"\n",
-    "weather_address = \"../dataset/external/weather.csv\"\n",
-    "flood_address = \"../dataset/external/flood.csv\"\n",
-    "climate_change_address = \"../dataset/external/climate_change.csv\""
+    "dam_address = \"dataset/raw/dam_occupancy.csv\"\n",
+    "weather_address = \"dataset/external/weather.csv\"\n",
+    "flood_address = \"dataset/external/flood.csv\"\n",
+    "climate_change_address = \"dataset/external/climate_change.csv\""
    ]
   },
   {
@@ -48,6 +47,338 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "DATE",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "GENERAL_DAM_OCCUPANCY_RATE",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "GENERAL_DAM_RESERVED_WATER",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "37ab2f17-431f-44a0-9c46-ac0672d2d9e2",
+       "rows": [
+        [
+         "0",
+         "2005-01-01",
+         "44.62",
+         "388"
+        ],
+        [
+         "1",
+         "2005-01-02",
+         "44.62",
+         "388"
+        ],
+        [
+         "2",
+         "2005-01-03",
+         "44.47",
+         "386"
+        ],
+        [
+         "3",
+         "2005-01-04",
+         "44.42",
+         "386"
+        ],
+        [
+         "4",
+         "2005-01-05",
+         "44.35",
+         "385"
+        ],
+        [
+         "5",
+         "2005-01-06",
+         "44.26",
+         "385"
+        ],
+        [
+         "6",
+         "2005-01-07",
+         "44.16",
+         "384"
+        ],
+        [
+         "7",
+         "2005-01-08",
+         "44.01",
+         "382"
+        ],
+        [
+         "8",
+         "2005-01-09",
+         "43.85",
+         "381"
+        ],
+        [
+         "9",
+         "2005-01-10",
+         "43.67",
+         "379"
+        ],
+        [
+         "10",
+         "2005-01-11",
+         "43.52",
+         "378"
+        ],
+        [
+         "11",
+         "2005-01-12",
+         "43.35",
+         "377"
+        ],
+        [
+         "12",
+         "2005-01-13",
+         "43.15",
+         "375"
+        ],
+        [
+         "13",
+         "2005-01-14",
+         "42.93",
+         "373"
+        ],
+        [
+         "14",
+         "2005-01-15",
+         "42.75",
+         "371"
+        ],
+        [
+         "15",
+         "2005-01-16",
+         "42.91",
+         "373"
+        ],
+        [
+         "16",
+         "2005-01-17",
+         "44.46",
+         "386"
+        ],
+        [
+         "17",
+         "2005-01-18",
+         "47.08",
+         "409"
+        ],
+        [
+         "18",
+         "2005-01-19",
+         "48.18",
+         "419"
+        ],
+        [
+         "19",
+         "2005-01-20",
+         "48.36",
+         "420"
+        ],
+        [
+         "20",
+         "2005-01-21",
+         "48.38",
+         "420"
+        ],
+        [
+         "21",
+         "2005-01-22",
+         "48.41",
+         "421"
+        ],
+        [
+         "22",
+         "2005-01-23",
+         "48.8",
+         "424"
+        ],
+        [
+         "23",
+         "2005-01-24",
+         "48.9",
+         "425"
+        ],
+        [
+         "24",
+         "2005-01-25",
+         "48.87",
+         "424"
+        ],
+        [
+         "25",
+         "2005-01-26",
+         "49.4",
+         "429"
+        ],
+        [
+         "26",
+         "2005-01-27",
+         "49.64",
+         "431"
+        ],
+        [
+         "27",
+         "2005-01-28",
+         "50.17",
+         "436"
+        ],
+        [
+         "28",
+         "2005-01-29",
+         "53.13",
+         "461"
+        ],
+        [
+         "29",
+         "2005-01-30",
+         "55.05",
+         "478"
+        ],
+        [
+         "30",
+         "2005-01-31",
+         "56.28",
+         "489"
+        ],
+        [
+         "31",
+         "2005-02-01",
+         "56.69",
+         "492"
+        ],
+        [
+         "32",
+         "2005-02-02",
+         "56.89",
+         "494"
+        ],
+        [
+         "33",
+         "2005-02-03",
+         "58.21",
+         "506"
+        ],
+        [
+         "34",
+         "2005-02-04",
+         "62.87",
+         "546"
+        ],
+        [
+         "35",
+         "2005-02-05",
+         "66.46",
+         "577"
+        ],
+        [
+         "36",
+         "2005-02-06",
+         "67.64",
+         "588"
+        ],
+        [
+         "37",
+         "2005-02-07",
+         "68.3",
+         "593"
+        ],
+        [
+         "38",
+         "2005-02-08",
+         "68.84",
+         "598"
+        ],
+        [
+         "39",
+         "2005-02-09",
+         "69.12",
+         "600"
+        ],
+        [
+         "40",
+         "2005-02-10",
+         "69.28",
+         "602"
+        ],
+        [
+         "41",
+         "2005-02-11",
+         "69.6",
+         "605"
+        ],
+        [
+         "42",
+         "2005-02-12",
+         "69.78",
+         "606"
+        ],
+        [
+         "43",
+         "2005-02-13",
+         "70.13",
+         "609"
+        ],
+        [
+         "44",
+         "2005-02-14",
+         "71.29",
+         "619"
+        ],
+        [
+         "45",
+         "2005-02-15",
+         "74.45",
+         "647"
+        ],
+        [
+         "46",
+         "2005-02-16",
+         "76.76",
+         "667"
+        ],
+        [
+         "47",
+         "2005-02-17",
+         "77.69",
+         "675"
+        ],
+        [
+         "48",
+         "2005-02-18",
+         "78.34",
+         "681"
+        ],
+        [
+         "49",
+         "2005-02-19",
+         "79.08",
+         "687"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 5941
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -214,11 +545,10 @@
    "outputs": [],
    "source": [
     "dam_df = (\n",
-    "    dam_df\n",
-    "    .pipe(lower_column_names)\n",
+    "    dam_df.pipe(lower_column_names)\n",
     "    .assign(**{\"datetime\": lambda df: pd.to_datetime(df[\"date\"])})\n",
     "    .drop(columns=\"date\")\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -235,6 +565,200 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "date",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "weather_code",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sunrise",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "sunset",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "daylight_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sunshine_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "rain_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "snowfall_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_hours",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_speed_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_gusts_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_direction_10m_dominant",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "shortwave_radiation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "et0_fao_evapotranspiration",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "82add7ac-063f-441e-9e0e-731fce1e19c3",
+       "rows": [
+        [
+         "0",
+         "2004-12-31 21:00:00+00:00",
+         "53.0",
+         "10.563",
+         "7.363",
+         "8.990084",
+         "9.071384",
+         "4.1469555",
+         "6.0338445",
+         "0",
+         "0",
+         "33432.535",
+         "25582.63",
+         "3.5",
+         "3.5",
+         "0.0",
+         "10.0",
+         "23.507751",
+         "37.44",
+         "31.536366",
+         "5.19",
+         "0.8144206"
+        ],
+        [
+         "1",
+         "2005-01-01 21:00:00+00:00",
+         "51.0",
+         "7.813",
+         "4.713",
+         "6.660917",
+         "4.8168144",
+         "2.1813393",
+         "3.76984",
+         "0",
+         "0",
+         "33479.133",
+         "22339.408",
+         "2.8000002",
+         "2.8000002",
+         "0.0",
+         "17.0",
+         "13.276144",
+         "25.919998",
+         "349.9135",
+         "3.92",
+         "0.6443958"
+        ],
+        [
+         "2",
+         "2005-01-02 21:00:00+00:00",
+         "51.0",
+         "8.613",
+         "2.263",
+         "6.0317497",
+         "5.408062",
+         "-1.0330892",
+         "3.0097349",
+         "0",
+         "0",
+         "33529.406",
+         "27602.402",
+         "0.1",
+         "0.1",
+         "0.0",
+         "1.0",
+         "13.70839",
+         "26.28",
+         "260.3468",
+         "6.45",
+         "0.8633633"
+        ]
+       ],
+       "shape": {
+        "columns": 21,
+        "rows": 3
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -414,7 +938,8 @@
     "    .assign(**{\n",
     "        \"datetime\": lambda df: pd.to_datetime(\n",
     "            pd.to_datetime(df[\"date\"]).dt.tz_localize(None).dt.date\n",
-    "        )\n",
+    "        ),\n",
+    "        \"weather_code\": lambda df: df[\"weather_code\"].astype(\"int64\")\n",
     "    })\n",
     "    .drop(columns=\"date\")\n",
     ")"
@@ -427,6 +952,200 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "weather_code",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "temperature_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sunrise",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "sunset",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "daylight_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sunshine_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "rain_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "snowfall_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_hours",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_speed_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_gusts_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_direction_10m_dominant",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "shortwave_radiation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "et0_fao_evapotranspiration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "datetime",
+         "rawType": "datetime64[ns]",
+         "type": "datetime"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "d0604ccf-2cb9-433d-a58b-b9e13917ef30",
+       "rows": [
+        [
+         "0",
+         "53",
+         "10.563",
+         "7.363",
+         "8.990084",
+         "9.071384",
+         "4.1469555",
+         "6.0338445",
+         "0",
+         "0",
+         "33432.535",
+         "25582.63",
+         "3.5",
+         "3.5",
+         "0.0",
+         "10.0",
+         "23.507751",
+         "37.44",
+         "31.536366",
+         "5.19",
+         "0.8144206",
+         "2004-12-31 00:00:00"
+        ],
+        [
+         "1",
+         "51",
+         "7.813",
+         "4.713",
+         "6.660917",
+         "4.8168144",
+         "2.1813393",
+         "3.76984",
+         "0",
+         "0",
+         "33479.133",
+         "22339.408",
+         "2.8000002",
+         "2.8000002",
+         "0.0",
+         "17.0",
+         "13.276144",
+         "25.919998",
+         "349.9135",
+         "3.92",
+         "0.6443958",
+         "2005-01-01 00:00:00"
+        ],
+        [
+         "2",
+         "51",
+         "8.613",
+         "2.263",
+         "6.0317497",
+         "5.408062",
+         "-1.0330892",
+         "3.0097349",
+         "0",
+         "0",
+         "33529.406",
+         "27602.402",
+         "0.1",
+         "0.1",
+         "0.0",
+         "1.0",
+         "13.70839",
+         "26.28",
+         "260.3468",
+         "6.45",
+         "0.8633633",
+         "2005-01-02 00:00:00"
+        ]
+       ],
+       "shape": {
+        "columns": 21,
+        "rows": 3
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -472,7 +1191,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>53.0</td>\n",
+       "      <td>53</td>\n",
        "      <td>10.563</td>\n",
        "      <td>7.363</td>\n",
        "      <td>8.990084</td>\n",
@@ -496,7 +1215,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>51.0</td>\n",
+       "      <td>51</td>\n",
        "      <td>7.813</td>\n",
        "      <td>4.713</td>\n",
        "      <td>6.660917</td>\n",
@@ -520,7 +1239,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>51.0</td>\n",
+       "      <td>51</td>\n",
        "      <td>8.613</td>\n",
        "      <td>2.263</td>\n",
        "      <td>6.031750</td>\n",
@@ -549,9 +1268,9 @@
       ],
       "text/plain": [
        "   weather_code  temperature_2m_max  temperature_2m_min  temperature_2m_mean  \\\n",
-       "0          53.0              10.563               7.363             8.990084   \n",
-       "1          51.0               7.813               4.713             6.660917   \n",
-       "2          51.0               8.613               2.263             6.031750   \n",
+       "0            53              10.563               7.363             8.990084   \n",
+       "1            51               7.813               4.713             6.660917   \n",
+       "2            51               8.613               2.263             6.031750   \n",
        "\n",
        "   apparent_temperature_max  apparent_temperature_min  \\\n",
        "0                  9.071384                  4.146955   \n",
@@ -604,7 +1323,7 @@
       "Data columns (total 21 columns):\n",
       " #   Column                       Non-Null Count  Dtype         \n",
       "---  ------                       --------------  -----         \n",
-      " 0   weather_code                 5941 non-null   float64       \n",
+      " 0   weather_code                 5941 non-null   int64         \n",
       " 1   temperature_2m_max           5941 non-null   float64       \n",
       " 2   temperature_2m_min           5941 non-null   float64       \n",
       " 3   temperature_2m_mean          5941 non-null   float64       \n",
@@ -625,7 +1344,7 @@
       " 18  shortwave_radiation_sum      5941 non-null   float64       \n",
       " 19  et0_fao_evapotranspiration   5941 non-null   float64       \n",
       " 20  datetime                     5941 non-null   datetime64[ns]\n",
-      "dtypes: datetime64[ns](1), float64(18), int64(2)\n",
+      "dtypes: datetime64[ns](1), float64(17), int64(3)\n",
       "memory usage: 974.8 KB\n"
      ]
     }
@@ -648,6 +1367,48 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "date",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "river_discharge",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "d71d8bec-a9d8-4cb3-9c09-c53fe6845598",
+       "rows": [
+        [
+         "0",
+         "2005-01-01 00:00:00+00:00",
+         "0.35831344"
+        ],
+        [
+         "1",
+         "2005-01-02 00:00:00+00:00",
+         "0.35831344"
+        ],
+        [
+         "2",
+         "2005-01-03 00:00:00+00:00",
+         "0.31219995"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 3
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -763,6 +1524,238 @@
    "outputs": [
     {
      "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "date",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "climate_change_temperature_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_cloud_cover_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_shortwave_radiation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_precipitation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_rain_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_snowfall_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_pressure_msl_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_soil_moisture_0_to_10cm_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_et0_fao_evapotranspiration_sum",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "a9bb947c-e9ba-4433-8689-0ef465d1c5b0",
+       "rows": [
+        [
+         "0",
+         "2005-01-01 00:00:00+00:00",
+         "9.559418",
+         "12.070409",
+         "6.5758615",
+         "11.667466",
+         "28.228834",
+         null,
+         null,
+         "71.42315",
+         "82.39349",
+         "62.762245",
+         "4.387951",
+         "2.5541499",
+         "6.4470377",
+         "0.1275738",
+         null,
+         null,
+         "1015.54956",
+         null,
+         "1.4364268"
+        ],
+        [
+         "1",
+         "2005-01-02 00:00:00+00:00",
+         "9.612512",
+         "10.779707",
+         "6.923346",
+         "21.014172",
+         "23.266617",
+         null,
+         null,
+         "74.50186",
+         "80.47813",
+         "65.83115",
+         "4.551526",
+         "2.793407",
+         "5.660715",
+         "1.5306889",
+         null,
+         null,
+         "1009.44135",
+         null,
+         "0.9909548"
+        ],
+        [
+         "2",
+         "2005-01-03 00:00:00+00:00",
+         "3.6156096",
+         "7.0890045",
+         "1.4208312",
+         "15.315918",
+         "26.408207",
+         null,
+         null,
+         "57.580574",
+         "67.56276",
+         "51.900047",
+         "-3.4126642",
+         "-4.796935",
+         "-1.2502497",
+         "13.774413",
+         null,
+         null,
+         "1021.83325",
+         null,
+         "1.0985928"
+        ],
+        [
+         "3",
+         "2005-01-04 00:00:00+00:00",
+         "3.7187057",
+         "5.1983027",
+         "1.4183159",
+         "19.740993",
+         "17.782825",
+         null,
+         null,
+         "70.659294",
+         "84.647385",
+         "58.968933",
+         "-1.5296472",
+         "-3.9674487",
+         "0.95512724",
+         "0.12752424",
+         null,
+         null,
+         "1025.425",
+         null,
+         "0.7778741"
+        ],
+        [
+         "4",
+         "2005-01-05 00:00:00+00:00",
+         "6.9218006",
+         "9.3076",
+         "4.4158006",
+         "15.074694",
+         "29.294376",
+         null,
+         null,
+         "80.73801",
+         "87.732025",
+         "75.03782",
+         "3.7574437",
+         "2.7207286",
+         "4.9444194",
+         "0.3825224",
+         null,
+         null,
+         "1018.5167",
+         null,
+         "1.0821611"
+        ]
+       ],
+       "shape": {
+        "columns": 20,
+        "rows": 5
+       }
+      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -1121,6 +2114,16 @@
     "cli_change_df.info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_duplicate_datetime_values(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    return df.loc[df[\"datetime\"].drop_duplicates().index, :]\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1130,7 +2133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1141,29 +2144,30 @@
     "    .merge(cli_change_df, how=\"left\", on=\"datetime\")\n",
     "    .dropna()\n",
     "    .drop_duplicates()\n",
+    "    .pipe(drop_duplicate_datetime_values)\n",
     "    .drop(columns=[\"sunrise\", \"sunset\"]) # constant value columns\n",
     "    .drop(columns=\"snowfall_sum\") # 96% of values are 0\n",
-    "    .astype({\"weather_code\": \"int64\"}) # categorical column\n",
-    ")\n",
-    "# precipitation_hours is cyclic"
+    "    .drop(columns=\"general_dam_reserved_water\") # just another target column\n",
+    "    .assign(weather_code=lambda df: df[\"weather_code\"].astype(\"int64\"))\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# ProfileReport(final_df)"
+    "final_df = final_df.set_index(\"datetime\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "final_df.to_csv(\"../dataset/interim/cleaned_dataset.csv\", index=False)"
+    "final_df.to_csv(\"dataset/interim/past_dataset.csv\")"
    ]
   }
  ],
diff --git a/week2/friday/you_do_4/notebooks/5.1-feature-engineering-functional.ipynb b/week2/friday/you_do_4/notebooks/5.1-feature-engineering-functional.ipynb
new file mode 100644
index 0000000..000388b
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/5.1-feature-engineering-functional.ipynb
@@ -0,0 +1,1502 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas import DataFrame\n",
+    "from typing import Literal, List\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"../dataset/interim/past_dataset.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = pd.read_csv(cleaned_dataset_address, parse_dates=[\"datetime\"], converters = {'weather_code': str})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5939 entries, 0 to 5938\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                                         Non-Null Count  Dtype         \n",
+      "---  ------                                         --------------  -----         \n",
+      " 0   general_dam_occupancy_rate                     5939 non-null   float64       \n",
+      " 1   datetime                                       5939 non-null   datetime64[ns]\n",
+      " 2   weather_code                                   5939 non-null   object        \n",
+      " 3   temperature_2m_max                             5939 non-null   float64       \n",
+      " 4   temperature_2m_min                             5939 non-null   float64       \n",
+      " 5   temperature_2m_mean                            5939 non-null   float64       \n",
+      " 6   apparent_temperature_max                       5939 non-null   float64       \n",
+      " 7   apparent_temperature_min                       5939 non-null   float64       \n",
+      " 8   apparent_temperature_mean                      5939 non-null   float64       \n",
+      " 9   daylight_duration                              5939 non-null   float64       \n",
+      " 10  sunshine_duration                              5939 non-null   float64       \n",
+      " 11  precipitation_sum                              5939 non-null   float64       \n",
+      " 12  rain_sum                                       5939 non-null   float64       \n",
+      " 13  precipitation_hours                            5939 non-null   float64       \n",
+      " 14  wind_speed_10m_max                             5939 non-null   float64       \n",
+      " 15  wind_gusts_10m_max                             5939 non-null   float64       \n",
+      " 16  wind_direction_10m_dominant                    5939 non-null   float64       \n",
+      " 17  shortwave_radiation_sum                        5939 non-null   float64       \n",
+      " 18  et0_fao_evapotranspiration                     5939 non-null   float64       \n",
+      " 19  river_discharge                                5939 non-null   float64       \n",
+      " 20  climate_change_temperature_2m_mean             5939 non-null   float64       \n",
+      " 21  climate_change_temperature_2m_max              5939 non-null   float64       \n",
+      " 22  climate_change_temperature_2m_min              5939 non-null   float64       \n",
+      " 23  climate_change_wind_speed_10m_mean             5939 non-null   float64       \n",
+      " 24  climate_change_wind_speed_10m_max              5939 non-null   float64       \n",
+      " 25  climate_change_relative_humidity_2m_mean       5939 non-null   float64       \n",
+      " 26  climate_change_relative_humidity_2m_max        5939 non-null   float64       \n",
+      " 27  climate_change_relative_humidity_2m_min        5939 non-null   float64       \n",
+      " 28  climate_change_dew_point_2m_mean               5939 non-null   float64       \n",
+      " 29  climate_change_dew_point_2m_min                5939 non-null   float64       \n",
+      " 30  climate_change_dew_point_2m_max                5939 non-null   float64       \n",
+      " 31  climate_change_precipitation_sum               5939 non-null   float64       \n",
+      " 32  climate_change_pressure_msl_mean               5939 non-null   float64       \n",
+      " 33  climate_change_et0_fao_evapotranspiration_sum  5939 non-null   float64       \n",
+      "dtypes: datetime64[ns](1), float64(32), object(1)\n",
+      "memory usage: 1.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "past_knowledge.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def expand_datetime(df: DataFrame, column: str = \"datetime\") -> DataFrame:\n",
+    "    return df.assign(\n",
+    "        **{\n",
+    "            \"year\": lambda a_df: a_df[column].dt.year,\n",
+    "            \"month\": lambda a_df: a_df[column].dt.month,\n",
+    "            \"day\": lambda a_df: a_df[column].dt.day,\n",
+    "            \"hour\": lambda a_df: a_df[column].dt.hour,\n",
+    "            \"day_of_year\": lambda a_df: a_df[column].dt.dayofyear,\n",
+    "            \"week_of_year\": lambda a_df: a_df[column].dt.isocalendar().week,\n",
+    "            \"quarter\": lambda a_df: a_df[column].dt.quarter,\n",
+    "            # \"season\": lambda a_df: a_df[column].dt.month % 12 // 3 + 1,\n",
+    "            \"is_weekend\": lambda a_df: (a_df[column].dt.weekday >= 5).map(\n",
+    "                {True: 1, False: 0}\n",
+    "            ),\n",
+    "        }\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_rolling_window_features(\n",
+    "    prediction_df: DataFrame,\n",
+    "    past_knowledge: DataFrame,\n",
+    "    window_size: int = 7,\n",
+    "    fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"ffill\",\n",
+    ") -> DataFrame:\n",
+    "    past_knowledge[\"datetime\"] = pd.to_datetime(past_knowledge[\"datetime\"])\n",
+    "    prediction_df[\"datetime\"] = pd.to_datetime(prediction_df[\"datetime\"])\n",
+    "\n",
+    "    past_knowledge = past_knowledge.sort_values(\"datetime\")\n",
+    "    prediction_df = prediction_df.sort_values(\"datetime\")\n",
+    "\n",
+    "    full_date_range = pd.date_range(\n",
+    "        start=past_knowledge[\"datetime\"].min(),\n",
+    "        end=prediction_df[\"datetime\"].max(),\n",
+    "        freq=\"D\",\n",
+    "    )\n",
+    "    full_df = pd.DataFrame({\"datetime\": full_date_range}).merge(\n",
+    "        past_knowledge, on=\"datetime\", how=\"left\"\n",
+    "    )\n",
+    "\n",
+    "    columns_to_use = past_knowledge.select_dtypes(include=[\"number\"]).columns.tolist()\n",
+    "\n",
+    "    metrics = [\"mean\", \"std\", \"min\", \"max\", \"median\", \"var\"]\n",
+    "\n",
+    "    created_features = []\n",
+    "    for col in columns_to_use:\n",
+    "        for size in range(2, window_size + 1):\n",
+    "            rolling_window_feature = (\n",
+    "                full_df[col]\n",
+    "                .rolling(window=size, min_periods=1)\n",
+    "                .agg(metrics)\n",
+    "                .rename(columns=lambda metric: f\"{col}_rw{size}_{metric}\")\n",
+    "            )\n",
+    "            created_features.append(rolling_window_feature)\n",
+    "\n",
+    "    window_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "    prediction_df = prediction_df.merge(\n",
+    "        window_df,\n",
+    "        on=\"datetime\",\n",
+    "        how=\"left\",\n",
+    "    )\n",
+    "\n",
+    "    if fillna_with == \"ffill\":\n",
+    "        prediction_df = prediction_df.ffill()\n",
+    "    elif fillna_with == \"bfill\":\n",
+    "        prediction_df = prediction_df.bfill()\n",
+    "\n",
+    "    return prediction_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_lag_features(\n",
+    "    prediction_df: DataFrame,\n",
+    "    past_knowledge: DataFrame,\n",
+    "    lag_size: int = 30,\n",
+    "    fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"ffill\",\n",
+    "):\n",
+    "    past_knowledge[\"datetime\"] = pd.to_datetime(past_knowledge[\"datetime\"])\n",
+    "    prediction_df[\"datetime\"] = pd.to_datetime(prediction_df[\"datetime\"])\n",
+    "\n",
+    "    past_knowledge = past_knowledge.sort_values(\"datetime\")\n",
+    "    prediction_df = prediction_df.sort_values(\"datetime\")\n",
+    "\n",
+    "    full_date_range = pd.date_range(\n",
+    "        start=past_knowledge[\"datetime\"].min(),\n",
+    "        end=prediction_df[\"datetime\"].max(),\n",
+    "        freq=\"D\",\n",
+    "    )\n",
+    "    full_df = pd.DataFrame({\"datetime\": full_date_range})\n",
+    "    full_df = full_df.merge(past_knowledge, on=\"datetime\", how=\"left\")\n",
+    "\n",
+    "    past_knowledge_columns_to_use = past_knowledge.select_dtypes(\n",
+    "        include=\"number\"\n",
+    "    ).columns.tolist()\n",
+    "\n",
+    "    created_features = []\n",
+    "    for col in past_knowledge_columns_to_use:\n",
+    "        for i in range(1, lag_size + 1):\n",
+    "            created_col_name = f\"{col}_lag_{i}\"\n",
+    "            created_features.append(full_df[col].shift(i).rename(created_col_name))\n",
+    "\n",
+    "    lags_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "    prediction_df = prediction_df.merge(\n",
+    "        lags_df,\n",
+    "        on=\"datetime\",\n",
+    "        how=\"left\",\n",
+    "    )\n",
+    "\n",
+    "    if fillna_with == \"ffill\":\n",
+    "        prediction_df = prediction_df.ffill()\n",
+    "    elif fillna_with == \"bfill\":\n",
+    "        prediction_df = prediction_df.bfill()\n",
+    "\n",
+    "    return prediction_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_cyclical(df: pd.DataFrame, cyclical_feature_names: dict[str, int]) -> DataFrame:\n",
+    "    for col, max_val in cyclical_feature_names.items():\n",
+    "        df[col + \"_sin\"] = np.sin(2 * np.pi * df[col] / max_val)\n",
+    "        df[col + \"_cos\"] = np.cos(2 * np.pi * df[col] / max_val)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def drop_columns_with_same_values(df: DataFrame, threshold=0.9) -> DataFrame:\n",
+    "    to_drop = [\n",
+    "        col\n",
+    "        for col in df.columns\n",
+    "        if df[col].value_counts(normalize=True, dropna=False).values[0] >= threshold\n",
+    "    ]\n",
+    "    return df.drop(columns=to_drop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cyclical_features = {\n",
+    "    \"month\": 12,\n",
+    "    \"day\": 31,\n",
+    "    \"day_of_year\": 365,\n",
+    "    \"week_of_year\": 52,\n",
+    "    \"quarter\": 4,\n",
+    "    # \"season\": 4,\n",
+    "    \"is_weekend\": 2,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction_df = (\n",
+    "    DataFrame(index=pd.date_range(\"2021-04-07\", \"2021-05-07\", freq=\"D\"))\n",
+    "    .reset_index()\n",
+    "    .rename(columns={\"index\": \"datetime\"})\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction_df = (\n",
+    "    prediction_df\n",
+    "    .sort_values(\"datetime\")\n",
+    "    .pipe(add_lag_features, past_knowledge=past_knowledge, lag_size=30)\n",
+    "    .pipe(add_rolling_window_features, past_knowledge=past_knowledge, window_size=30)\n",
+    "    .pipe(drop_columns_with_same_values)\n",
+    "    .pipe(expand_datetime)\n",
+    "    .pipe(encode_cyclical, cyclical_feature_names=cyclical_features)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 31 entries, 0 to 30\n",
+      "Columns: 5307 entries, datetime to is_weekend_cos\n",
+      "dtypes: Float64(2), UInt32(1), datetime64[ns](1), float64(5296), int32(6), int64(1)\n",
+      "memory usage: 1.3 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], dtype: int64)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_df_null_counts = prediction_df.isna().sum()\n",
+    "prediction_df_null_counts[prediction_df_null_counts > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "datetime",
+         "rawType": "datetime64[ns]",
+         "type": "datetime"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_5",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_6",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_7",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_8",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_9",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_10",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_11",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_12",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_13",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_14",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_15",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_16",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_17",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_18",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_19",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_20",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_21",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_22",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_23",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_24",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_25",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_26",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_27",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_28",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_29",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_30",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_5",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_6",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_7",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_8",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_9",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_10",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_11",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_12",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_13",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_14",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_15",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_16",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_17",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "f323fbca-8efa-4143-ade5-ab1553711a3e",
+       "rows": [
+        [
+         "0",
+         "2021-04-07 00:00:00",
+         "73.46",
+         "73.18",
+         "72.83",
+         "72.24",
+         "71.96",
+         "71.56",
+         "71.14",
+         "70.63",
+         "69.83",
+         "68.64",
+         "66.84",
+         "65.21",
+         "64.04",
+         "63.74",
+         "63.34",
+         "63.03",
+         "62.71",
+         "62.4",
+         "62.34",
+         "62.23",
+         "61.96",
+         "61.64",
+         "61.08",
+         "60.71",
+         "60.37",
+         "59.93",
+         "17.4955",
+         "11.445499",
+         "9.945499",
+         "10.195499",
+         "9.3455",
+         "10.7955",
+         "13.1455",
+         "10.9955",
+         "9.0455",
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+         "5.3455",
+         "7.3954997",
+         "12.7455"
+        ],
+        [
+         "1",
+         "2021-04-08 00:00:00",
+         "73.66",
+         "73.46",
+         "73.18",
+         "72.83",
+         "72.24",
+         "71.96",
+         "71.56",
+         "71.14",
+         "70.63",
+         "69.83",
+         "68.64",
+         "66.84",
+         "65.21",
+         "64.04",
+         "63.74",
+         "63.34",
+         "63.03",
+         "62.71",
+         "62.4",
+         "62.34",
+         "62.23",
+         "61.96",
+         "61.64",
+         "61.08",
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+         "9.0455",
+         "6.7454996",
+         "5.3455",
+         "7.3954997"
+        ],
+        [
+         "2",
+         "2021-04-09 00:00:00",
+         "73.76",
+         "73.66",
+         "73.46",
+         "73.18",
+         "72.83",
+         "72.24",
+         "71.96",
+         "71.56",
+         "71.14",
+         "70.63",
+         "69.83",
+         "68.64",
+         "66.84",
+         "65.21",
+         "64.04",
+         "63.74",
+         "63.34",
+         "63.03",
+         "62.71",
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+         "13.1455",
+         "10.9955",
+         "9.0455",
+         "6.7454996",
+         "5.3455"
+        ],
+        [
+         "3",
+         "2021-04-10 00:00:00",
+         "74.7",
+         "73.76",
+         "73.66",
+         "73.46",
+         "73.18",
+         "72.83",
+         "72.24",
+         "71.96",
+         "71.56",
+         "71.14",
+         "70.63",
+         "69.83",
+         "68.64",
+         "66.84",
+         "65.21",
+         "64.04",
+         "63.74",
+         "63.34",
+         "63.03",
+         "62.71",
+         "62.4",
+         "62.34",
+         "62.23",
+         "61.96",
+         "61.64",
+         "61.08",
+         "18.1455",
+         "8.7455",
+         "14.8955",
+         "17.4955",
+         "11.445499",
+         "9.945499",
+         "10.195499",
+         "9.3455",
+         "10.7955",
+         "13.1455",
+         "10.9955",
+         "9.0455",
+         "6.7454996"
+        ],
+        [
+         "4",
+         "2021-04-11 00:00:00",
+         "75.88",
+         "74.7",
+         "73.76",
+         "73.66",
+         "73.46",
+         "73.18",
+         "72.83",
+         "72.24",
+         "71.96",
+         "71.56",
+         "71.14",
+         "70.63",
+         "69.83",
+         "68.64",
+         "66.84",
+         "65.21",
+         "64.04",
+         "63.74",
+         "63.34",
+         "63.03",
+         "62.71",
+         "62.4",
+         "62.34",
+         "62.23",
+         "61.96",
+         "61.64",
+         "19.0455",
+         "18.1455",
+         "8.7455",
+         "14.8955",
+         "17.4955",
+         "11.445499",
+         "9.945499",
+         "10.195499",
+         "9.3455",
+         "10.7955",
+         "13.1455",
+         "10.9955",
+         "9.0455"
+        ]
+       ],
+       "shape": {
+        "columns": 40,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>datetime</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_5</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_6</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_7</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_8</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_9</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_10</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_11</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_12</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_13</th>\n",
+       "      <th>...</th>\n",
+       "      <th>temperature_2m_max_lag_8</th>\n",
+       "      <th>temperature_2m_max_lag_9</th>\n",
+       "      <th>temperature_2m_max_lag_10</th>\n",
+       "      <th>temperature_2m_max_lag_11</th>\n",
+       "      <th>temperature_2m_max_lag_12</th>\n",
+       "      <th>temperature_2m_max_lag_13</th>\n",
+       "      <th>temperature_2m_max_lag_14</th>\n",
+       "      <th>temperature_2m_max_lag_15</th>\n",
+       "      <th>temperature_2m_max_lag_16</th>\n",
+       "      <th>temperature_2m_max_lag_17</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-04-07</td>\n",
+       "      <td>73.46</td>\n",
+       "      <td>73.18</td>\n",
+       "      <td>72.83</td>\n",
+       "      <td>72.24</td>\n",
+       "      <td>71.96</td>\n",
+       "      <td>71.56</td>\n",
+       "      <td>71.14</td>\n",
+       "      <td>70.63</td>\n",
+       "      <td>69.83</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.195499</td>\n",
+       "      <td>9.345500</td>\n",
+       "      <td>10.795500</td>\n",
+       "      <td>13.145500</td>\n",
+       "      <td>10.995500</td>\n",
+       "      <td>9.0455</td>\n",
+       "      <td>6.7455</td>\n",
+       "      <td>5.3455</td>\n",
+       "      <td>7.3955</td>\n",
+       "      <td>12.7455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-04-08</td>\n",
+       "      <td>73.66</td>\n",
+       "      <td>73.46</td>\n",
+       "      <td>73.18</td>\n",
+       "      <td>72.83</td>\n",
+       "      <td>72.24</td>\n",
+       "      <td>71.96</td>\n",
+       "      <td>71.56</td>\n",
+       "      <td>71.14</td>\n",
+       "      <td>70.63</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.945499</td>\n",
+       "      <td>10.195499</td>\n",
+       "      <td>9.345500</td>\n",
+       "      <td>10.795500</td>\n",
+       "      <td>13.145500</td>\n",
+       "      <td>10.9955</td>\n",
+       "      <td>9.0455</td>\n",
+       "      <td>6.7455</td>\n",
+       "      <td>5.3455</td>\n",
+       "      <td>7.3955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-04-09</td>\n",
+       "      <td>73.76</td>\n",
+       "      <td>73.66</td>\n",
+       "      <td>73.46</td>\n",
+       "      <td>73.18</td>\n",
+       "      <td>72.83</td>\n",
+       "      <td>72.24</td>\n",
+       "      <td>71.96</td>\n",
+       "      <td>71.56</td>\n",
+       "      <td>71.14</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.445499</td>\n",
+       "      <td>9.945499</td>\n",
+       "      <td>10.195499</td>\n",
+       "      <td>9.345500</td>\n",
+       "      <td>10.795500</td>\n",
+       "      <td>13.1455</td>\n",
+       "      <td>10.9955</td>\n",
+       "      <td>9.0455</td>\n",
+       "      <td>6.7455</td>\n",
+       "      <td>5.3455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-04-10</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>73.76</td>\n",
+       "      <td>73.66</td>\n",
+       "      <td>73.46</td>\n",
+       "      <td>73.18</td>\n",
+       "      <td>72.83</td>\n",
+       "      <td>72.24</td>\n",
+       "      <td>71.96</td>\n",
+       "      <td>71.56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.495500</td>\n",
+       "      <td>11.445499</td>\n",
+       "      <td>9.945499</td>\n",
+       "      <td>10.195499</td>\n",
+       "      <td>9.345500</td>\n",
+       "      <td>10.7955</td>\n",
+       "      <td>13.1455</td>\n",
+       "      <td>10.9955</td>\n",
+       "      <td>9.0455</td>\n",
+       "      <td>6.7455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-04-11</td>\n",
+       "      <td>75.88</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>73.76</td>\n",
+       "      <td>73.66</td>\n",
+       "      <td>73.46</td>\n",
+       "      <td>73.18</td>\n",
+       "      <td>72.83</td>\n",
+       "      <td>72.24</td>\n",
+       "      <td>71.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.895500</td>\n",
+       "      <td>17.495500</td>\n",
+       "      <td>11.445499</td>\n",
+       "      <td>9.945499</td>\n",
+       "      <td>10.195499</td>\n",
+       "      <td>9.3455</td>\n",
+       "      <td>10.7955</td>\n",
+       "      <td>13.1455</td>\n",
+       "      <td>10.9955</td>\n",
+       "      <td>9.0455</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    datetime  general_dam_occupancy_rate_lag_5  \\\n",
+       "0 2021-04-07                             73.46   \n",
+       "1 2021-04-08                             73.66   \n",
+       "2 2021-04-09                             73.76   \n",
+       "3 2021-04-10                             74.70   \n",
+       "4 2021-04-11                             75.88   \n",
+       "\n",
+       "   general_dam_occupancy_rate_lag_6  general_dam_occupancy_rate_lag_7  \\\n",
+       "0                             73.18                             72.83   \n",
+       "1                             73.46                             73.18   \n",
+       "2                             73.66                             73.46   \n",
+       "3                             73.76                             73.66   \n",
+       "4                             74.70                             73.76   \n",
+       "\n",
+       "   general_dam_occupancy_rate_lag_8  general_dam_occupancy_rate_lag_9  \\\n",
+       "0                             72.24                             71.96   \n",
+       "1                             72.83                             72.24   \n",
+       "2                             73.18                             72.83   \n",
+       "3                             73.46                             73.18   \n",
+       "4                             73.66                             73.46   \n",
+       "\n",
+       "   general_dam_occupancy_rate_lag_10  general_dam_occupancy_rate_lag_11  \\\n",
+       "0                              71.56                              71.14   \n",
+       "1                              71.96                              71.56   \n",
+       "2                              72.24                              71.96   \n",
+       "3                              72.83                              72.24   \n",
+       "4                              73.18                              72.83   \n",
+       "\n",
+       "   general_dam_occupancy_rate_lag_12  general_dam_occupancy_rate_lag_13  ...  \\\n",
+       "0                              70.63                              69.83  ...   \n",
+       "1                              71.14                              70.63  ...   \n",
+       "2                              71.56                              71.14  ...   \n",
+       "3                              71.96                              71.56  ...   \n",
+       "4                              72.24                              71.96  ...   \n",
+       "\n",
+       "   temperature_2m_max_lag_8  temperature_2m_max_lag_9  \\\n",
+       "0                 10.195499                  9.345500   \n",
+       "1                  9.945499                 10.195499   \n",
+       "2                 11.445499                  9.945499   \n",
+       "3                 17.495500                 11.445499   \n",
+       "4                 14.895500                 17.495500   \n",
+       "\n",
+       "   temperature_2m_max_lag_10  temperature_2m_max_lag_11  \\\n",
+       "0                  10.795500                  13.145500   \n",
+       "1                   9.345500                  10.795500   \n",
+       "2                  10.195499                   9.345500   \n",
+       "3                   9.945499                  10.195499   \n",
+       "4                  11.445499                   9.945499   \n",
+       "\n",
+       "   temperature_2m_max_lag_12  temperature_2m_max_lag_13  \\\n",
+       "0                  10.995500                     9.0455   \n",
+       "1                  13.145500                    10.9955   \n",
+       "2                  10.795500                    13.1455   \n",
+       "3                   9.345500                    10.7955   \n",
+       "4                  10.195499                     9.3455   \n",
+       "\n",
+       "   temperature_2m_max_lag_14  temperature_2m_max_lag_15  \\\n",
+       "0                     6.7455                     5.3455   \n",
+       "1                     9.0455                     6.7455   \n",
+       "2                    10.9955                     9.0455   \n",
+       "3                    13.1455                    10.9955   \n",
+       "4                    10.7955                    13.1455   \n",
+       "\n",
+       "   temperature_2m_max_lag_16  temperature_2m_max_lag_17  \n",
+       "0                     7.3955                    12.7455  \n",
+       "1                     5.3455                     7.3955  \n",
+       "2                     6.7455                     5.3455  \n",
+       "3                     9.0455                     6.7455  \n",
+       "4                    10.9955                     9.0455  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_df.iloc[:, : 40].head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "series = pd.Series()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2021-04-07', '2021-04-08', '2021-04-09', '2021-04-10',\n",
+       "               '2021-04-11', '2021-04-12', '2021-04-13', '2021-04-14',\n",
+       "               '2021-04-15', '2021-04-16', '2021-04-17', '2021-04-18',\n",
+       "               '2021-04-19', '2021-04-20', '2021-04-21', '2021-04-22',\n",
+       "               '2021-04-23', '2021-04-24', '2021-04-25', '2021-04-26',\n",
+       "               '2021-04-27', '2021-04-28', '2021-04-29', '2021-04-30',\n",
+       "               '2021-05-01', '2021-05-02', '2021-05-03', '2021-05-04',\n",
+       "               '2021-05-05', '2021-05-06', '2021-05-07'],\n",
+       "              dtype='datetime64[ns]', freq='D')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.date_range(\"2021-04-07\", \"2021-05-07\", freq=\"D\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = past_knowledge.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = a.set_index(\"datetime\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for date in a.index:\n",
+    "    delta = date - pd.offsets.MonthBegin(1)\n",
+    "    train = a.loc[delta:date-pd.offsets.Day(1)]\n",
+    "    valid = a.loc[date:date+pd.offsets.MonthEnd(1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "datetime",
+         "rawType": "datetime64[ns]",
+         "type": "datetime"
+        },
+        {
+         "name": "general_dam_occupancy_rate",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "weather_code",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "temperature_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "daylight_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sunshine_duration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "rain_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_hours",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_speed_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_gusts_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "wind_direction_10m_dominant",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "shortwave_radiation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "et0_fao_evapotranspiration",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "river_discharge",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_precipitation_sum",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_pressure_msl_mean",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_et0_fao_evapotranspiration_sum",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "96b1b388-568c-4277-bde0-1ddd2c55d47c",
+       "rows": [
+        [
+         "2021-04-06 00:00:00",
+         "75.88",
+         "53.0",
+         "19.0455",
+         "8.1455",
+         "14.087169",
+         "17.12777",
+         "3.7007232",
+         "11.177331",
+         "46667.355",
+         "42191.03",
+         "1.0",
+         "1.0",
+         "6.0",
+         "30.173496",
+         "53.639996",
+         "248.04117",
+         "21.28",
+         "3.2731962",
+         "1.0941124",
+         "8.147371",
+         "11.601972",
+         "5.2488275",
+         "8.0458555",
+         "13.493123",
+         "74.78501",
+         "87.00573",
+         "62.43915",
+         "4.169358",
+         "1.6218177",
+         "6.3477397",
+         "0.22038391",
+         "1029.3667",
+         "2.7141848"
+        ]
+       ],
+       "shape": {
+        "columns": 33,
+        "rows": 1
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>general_dam_occupancy_rate</th>\n",
+       "      <th>weather_code</th>\n",
+       "      <th>temperature_2m_max</th>\n",
+       "      <th>temperature_2m_min</th>\n",
+       "      <th>temperature_2m_mean</th>\n",
+       "      <th>apparent_temperature_max</th>\n",
+       "      <th>apparent_temperature_min</th>\n",
+       "      <th>apparent_temperature_mean</th>\n",
+       "      <th>daylight_duration</th>\n",
+       "      <th>sunshine_duration</th>\n",
+       "      <th>...</th>\n",
+       "      <th>climate_change_wind_speed_10m_max</th>\n",
+       "      <th>climate_change_relative_humidity_2m_mean</th>\n",
+       "      <th>climate_change_relative_humidity_2m_max</th>\n",
+       "      <th>climate_change_relative_humidity_2m_min</th>\n",
+       "      <th>climate_change_dew_point_2m_mean</th>\n",
+       "      <th>climate_change_dew_point_2m_min</th>\n",
+       "      <th>climate_change_dew_point_2m_max</th>\n",
+       "      <th>climate_change_precipitation_sum</th>\n",
+       "      <th>climate_change_pressure_msl_mean</th>\n",
+       "      <th>climate_change_et0_fao_evapotranspiration_sum</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2021-04-06</th>\n",
+       "      <td>75.88</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>19.0455</td>\n",
+       "      <td>8.1455</td>\n",
+       "      <td>14.087169</td>\n",
+       "      <td>17.12777</td>\n",
+       "      <td>3.700723</td>\n",
+       "      <td>11.177331</td>\n",
+       "      <td>46667.355</td>\n",
+       "      <td>42191.03</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.493123</td>\n",
+       "      <td>74.78501</td>\n",
+       "      <td>87.00573</td>\n",
+       "      <td>62.43915</td>\n",
+       "      <td>4.169358</td>\n",
+       "      <td>1.621818</td>\n",
+       "      <td>6.34774</td>\n",
+       "      <td>0.220384</td>\n",
+       "      <td>1029.3667</td>\n",
+       "      <td>2.714185</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            general_dam_occupancy_rate weather_code  temperature_2m_max  \\\n",
+       "datetime                                                                  \n",
+       "2021-04-06                       75.88         53.0             19.0455   \n",
+       "\n",
+       "            temperature_2m_min  temperature_2m_mean  apparent_temperature_max  \\\n",
+       "datetime                                                                        \n",
+       "2021-04-06              8.1455            14.087169                  17.12777   \n",
+       "\n",
+       "            apparent_temperature_min  apparent_temperature_mean  \\\n",
+       "datetime                                                          \n",
+       "2021-04-06                  3.700723                  11.177331   \n",
+       "\n",
+       "            daylight_duration  sunshine_duration  ...  \\\n",
+       "datetime                                          ...   \n",
+       "2021-04-06          46667.355           42191.03  ...   \n",
+       "\n",
+       "            climate_change_wind_speed_10m_max  \\\n",
+       "datetime                                        \n",
+       "2021-04-06                          13.493123   \n",
+       "\n",
+       "            climate_change_relative_humidity_2m_mean  \\\n",
+       "datetime                                               \n",
+       "2021-04-06                                  74.78501   \n",
+       "\n",
+       "            climate_change_relative_humidity_2m_max  \\\n",
+       "datetime                                              \n",
+       "2021-04-06                                 87.00573   \n",
+       "\n",
+       "            climate_change_relative_humidity_2m_min  \\\n",
+       "datetime                                              \n",
+       "2021-04-06                                 62.43915   \n",
+       "\n",
+       "            climate_change_dew_point_2m_mean  climate_change_dew_point_2m_min  \\\n",
+       "datetime                                                                        \n",
+       "2021-04-06                          4.169358                         1.621818   \n",
+       "\n",
+       "            climate_change_dew_point_2m_max  climate_change_precipitation_sum  \\\n",
+       "datetime                                                                        \n",
+       "2021-04-06                          6.34774                          0.220384   \n",
+       "\n",
+       "            climate_change_pressure_msl_mean  \\\n",
+       "datetime                                       \n",
+       "2021-04-06                         1029.3667   \n",
+       "\n",
+       "            climate_change_et0_fao_evapotranspiration_sum  \n",
+       "datetime                                                   \n",
+       "2021-04-06                                       2.714185  \n",
+       "\n",
+       "[1 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week2/friday/you_do_4/notebooks/5.2-feature-engineering-oop.ipynb b/week2/friday/you_do_4/notebooks/5.2-feature-engineering-oop.ipynb
new file mode 100644
index 0000000..f51e928
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/5.2-feature-engineering-oop.ipynb
@@ -0,0 +1,1135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas import DataFrame\n",
+    "from typing import Literal, List\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"dataset/interim/past_dataset.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = pd.read_csv(cleaned_dataset_address, parse_dates=[\"datetime\"], converters={\"weather_code\": str}).set_index(\"datetime\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "DatetimeIndex: 5938 entries, 2005-01-01 to 2021-04-06\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                                         Non-Null Count  Dtype  \n",
+      "---  ------                                         --------------  -----  \n",
+      " 0   general_dam_occupancy_rate                     5938 non-null   float64\n",
+      " 1   weather_code                                   5938 non-null   int64  \n",
+      " 2   temperature_2m_max                             5938 non-null   float64\n",
+      " 3   temperature_2m_min                             5938 non-null   float64\n",
+      " 4   temperature_2m_mean                            5938 non-null   float64\n",
+      " 5   apparent_temperature_max                       5938 non-null   float64\n",
+      " 6   apparent_temperature_min                       5938 non-null   float64\n",
+      " 7   apparent_temperature_mean                      5938 non-null   float64\n",
+      " 8   daylight_duration                              5938 non-null   float64\n",
+      " 9   sunshine_duration                              5938 non-null   float64\n",
+      " 10  precipitation_sum                              5938 non-null   float64\n",
+      " 11  rain_sum                                       5938 non-null   float64\n",
+      " 12  precipitation_hours                            5938 non-null   float64\n",
+      " 13  wind_speed_10m_max                             5938 non-null   float64\n",
+      " 14  wind_gusts_10m_max                             5938 non-null   float64\n",
+      " 15  wind_direction_10m_dominant                    5938 non-null   float64\n",
+      " 16  shortwave_radiation_sum                        5938 non-null   float64\n",
+      " 17  et0_fao_evapotranspiration                     5938 non-null   float64\n",
+      " 18  river_discharge                                5938 non-null   float64\n",
+      " 19  climate_change_temperature_2m_mean             5938 non-null   float64\n",
+      " 20  climate_change_temperature_2m_max              5938 non-null   float64\n",
+      " 21  climate_change_temperature_2m_min              5938 non-null   float64\n",
+      " 22  climate_change_wind_speed_10m_mean             5938 non-null   float64\n",
+      " 23  climate_change_wind_speed_10m_max              5938 non-null   float64\n",
+      " 24  climate_change_relative_humidity_2m_mean       5938 non-null   float64\n",
+      " 25  climate_change_relative_humidity_2m_max        5938 non-null   float64\n",
+      " 26  climate_change_relative_humidity_2m_min        5938 non-null   float64\n",
+      " 27  climate_change_dew_point_2m_mean               5938 non-null   float64\n",
+      " 28  climate_change_dew_point_2m_min                5938 non-null   float64\n",
+      " 29  climate_change_dew_point_2m_max                5938 non-null   float64\n",
+      " 30  climate_change_precipitation_sum               5938 non-null   float64\n",
+      " 31  climate_change_pressure_msl_mean               5938 non-null   float64\n",
+      " 32  climate_change_et0_fao_evapotranspiration_sum  5938 non-null   float64\n",
+      "dtypes: float64(32), int64(1)\n",
+      "memory usage: 1.5 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "past_knowledge.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureExtractor:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        past_knowledge: DataFrame,\n",
+    "        cyclical_feature_names: List[str],\n",
+    "        freq: str = \"D\",\n",
+    "        lag_size: int = 30,\n",
+    "        window_size: int = 30,\n",
+    "    ):\n",
+    "        self.PAST_KNOWLEDGE = past_knowledge.sort_values(by=\"datetime\")\n",
+    "        self.cyclical_feature_names = cyclical_feature_names\n",
+    "        self.lag_size = lag_size\n",
+    "        self.window_size = window_size\n",
+    "        self.freq = freq\n",
+    "\n",
+    "    def transform(self, dates_to_predict: pd.DatetimeIndex) -> DataFrame:\n",
+    "        df = self._get_all_ranges(dates_to_predict)\n",
+    "        self.full_df = df.join(self.PAST_KNOWLEDGE, how=\"left\")\n",
+    "\n",
+    "        return (\n",
+    "            df.pipe(self._start_pipeline)\n",
+    "            .pipe(self._add_lag_features)\n",
+    "            .pipe(self._add_rolling_window_features)\n",
+    "            .pipe(self._add_exponential_moving_features)\n",
+    "            .pipe(self._drop_columns_with_same_values)\n",
+    "            .pipe(self._expand_datetime)\n",
+    "            .pipe(self._add_fourier_features)\n",
+    "            .pipe(\n",
+    "                lambda df: df.astype(\n",
+    "                    {\n",
+    "                        col: \"int32\"\n",
+    "                        for col in df.select_dtypes([\"int\", \"uint32\"]).columns\n",
+    "                    }\n",
+    "                )\n",
+    "            )\n",
+    "            .pipe(\n",
+    "                lambda df: df.astype(\n",
+    "                    {col: \"float32\" for col in df.select_dtypes(\"float\").columns}\n",
+    "                )\n",
+    "            )\n",
+    "            .bfill()\n",
+    "            .loc[dates_to_predict, :]\n",
+    "        )\n",
+    "\n",
+    "    def _start_pipeline(self, df: pd.DataFrame) -> pd.DataFrame:\n",
+    "        return df.copy().sort_index()\n",
+    "\n",
+    "    def _get_all_ranges(self, dates_to_predict: pd.DatetimeIndex) -> pd.DataFrame:\n",
+    "        start_date = min(dates_to_predict.min(), self.PAST_KNOWLEDGE.index.min())\n",
+    "        end_date = max(dates_to_predict.max(), self.PAST_KNOWLEDGE.index.max())\n",
+    "        complete_date_range = pd.date_range(\n",
+    "            start=start_date, end=end_date, freq=self.freq\n",
+    "        )\n",
+    "        return pd.DataFrame(index=complete_date_range)\n",
+    "\n",
+    "    def _add_lag_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"bfill\",\n",
+    "    ) -> DataFrame:\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"number\", \"object\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        created_features = [\n",
+    "            self.full_df[col].shift(i).rename(f\"{col}_lag_{i}\")\n",
+    "            for i in range(1, self.lag_size + 1)\n",
+    "            for col in columns_to_use\n",
+    "        ]\n",
+    "\n",
+    "        lags_df = pd.concat(created_features, axis=1)\n",
+    "\n",
+    "        df = df.join(\n",
+    "            lags_df,\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _add_rolling_window_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"ffill\",\n",
+    "    ) -> DataFrame:\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"float\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        metrics = [\"mean\", \"std\", \"min\", \"max\", \"median\", \"var\"]\n",
+    "\n",
+    "        created_features = [\n",
+    "            (\n",
+    "                self.full_df[col]\n",
+    "                .rolling(window=size, min_periods=1)\n",
+    "                .agg(metrics)\n",
+    "                .rename(columns=lambda metric: f\"{col}_rw{size}_{metric}\")\n",
+    "            )\n",
+    "            for size in range(2, self.window_size + 1)\n",
+    "            for col in columns_to_use\n",
+    "        ]\n",
+    "\n",
+    "        window_df = pd.concat(created_features, axis=1)\n",
+    "\n",
+    "        df = df.join(\n",
+    "            window_df,\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _drop_columns_with_same_values(self, df: DataFrame, threshold=0.9) -> DataFrame:\n",
+    "        to_drop = [\n",
+    "            col\n",
+    "            for col in df.columns\n",
+    "            if df[col].value_counts(normalize=True, dropna=False).values[0] >= threshold\n",
+    "        ]\n",
+    "        return df.drop(columns=to_drop)\n",
+    "\n",
+    "    def _add_exponential_moving_features(\n",
+    "        self, df: pd.DataFrame, up_to: int = 30\n",
+    "    ) -> pd.DataFrame:\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"float\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        metrics = [\"mean\", \"std\", \"var\"]\n",
+    "\n",
+    "        created_features = [\n",
+    "            (\n",
+    "                    self.full_df[col]\n",
+    "                    .ewm(span=span, adjust=False)\n",
+    "                    .agg(metrics)\n",
+    "                    .rename(columns=lambda metric: f\"{col}_em_{span}_{metric}\")\n",
+    "            )\n",
+    "            for span in range(2, up_to + 1)\n",
+    "            for col in columns_to_use\n",
+    "        ]\n",
+    "\n",
+    "        exponential_moving_df = pd.concat(created_features, axis=1)\n",
+    " \n",
+    "        df = df.join(\n",
+    "            exponential_moving_df,\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "        return df\n",
+    "\n",
+    "    def _expand_datetime(self, df: DataFrame) -> DataFrame:\n",
+    "        return df.assign(\n",
+    "            **{\n",
+    "                \"year\": lambda a_df: a_df.index.year,\n",
+    "                \"month\": lambda a_df: a_df.index.month,\n",
+    "                \"day\": lambda a_df: a_df.index.day,\n",
+    "                \"hour\": lambda a_df: a_df.index.hour,\n",
+    "                \"day_of_year\": lambda a_df: a_df.index.dayofyear,\n",
+    "                \"week_of_year\": lambda a_df: a_df.index.isocalendar().week,\n",
+    "                \"quarter\": lambda a_df: a_df.index.quarter,\n",
+    "                # \"season\": lambda a_df: a_df.index.month % 12 // 3 + 1,\n",
+    "                \"is_weekend\": lambda a_df: np.vectorize({True: 1, False: 0}.get)(a_df.index.weekday >= 5),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "    def _add_fourier_features(self, df: pd.DataFrame, num_terms: int = 7) -> DataFrame:\n",
+    "        for col, max_val in self.cyclical_feature_names.items():\n",
+    "            source = self._get_column_source(df, col)\n",
+    "\n",
+    "            for i in range(1, num_terms + 1):\n",
+    "                operation = 2 * np.pi * i * source[col] / max_val\n",
+    "\n",
+    "                df[f\"fourier_sin_{col}_{i}\"] = np.sin(operation)\n",
+    "                df[f\"fourier_cos_{col}_{i}\"] = np.cos(operation)\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _get_column_source(self, df: DataFrame, col: str) -> List[str]:\n",
+    "        if col in df.columns:\n",
+    "            source = df\n",
+    "        elif col in self.PAST_KNOWLEDGE.columns:\n",
+    "            source = self.PAST_KNOWLEDGE\n",
+    "        else:\n",
+    "            raise KeyError(f\"{col} not found both in df and past knowledge.\")\n",
+    "        return source\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    \"past_knowledge\": past_knowledge,\n",
+    "    \"cyclical_feature_names\": {\n",
+    "        \"month\": 12,\n",
+    "        \"day\": 31,\n",
+    "        \"day_of_year\": 365,\n",
+    "        \"week_of_year\": 52,\n",
+    "        \"quarter\": 4,\n",
+    "        # \"season\": 4,\n",
+    "        \"is_weekend\": 2,\n",
+    "        \"precipitation_hours\": 24,\n",
+    "    },\n",
+    "    \"lag_size\": 30,\n",
+    "    \"window_size\": 30,\n",
+    "}\n",
+    "\n",
+    "dates_to_predict = past_knowledge.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_extractor = FeatureExtractor(**parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction_df = feature_extractor.transform(dates_to_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "DatetimeIndex: 5938 entries, 2005-01-01 to 2021-04-06\n",
+      "Columns: 9348 entries, general_dam_occupancy_rate_lag_1 to fourier_cos_precipitation_hours_7\n",
+      "dtypes: float32(9340), int32(8)\n",
+      "memory usage: 211.8 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Series([], dtype: int64)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_df_null_counts = prediction_df.isna().sum().sort_values(ascending=False)\n",
+    "prediction_df_null_counts[prediction_df_null_counts > 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "datetime",
+         "rawType": "datetime64[ns]",
+         "type": "datetime"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "weather_code_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_min_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_min_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "daylight_duration_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "sunshine_duration_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_sum_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "rain_sum_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "precipitation_hours_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "wind_speed_10m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "wind_gusts_10m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "wind_direction_10m_dominant_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "shortwave_radiation_sum_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "et0_fao_evapotranspiration_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "river_discharge_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_temperature_2m_min_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_wind_speed_10m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_relative_humidity_2m_min_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_min_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_dew_point_2m_max_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_precipitation_sum_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_pressure_msl_mean_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "climate_change_et0_fao_evapotranspiration_sum_lag_1",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "general_dam_occupancy_rate_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "weather_code_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_max_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_min_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "temperature_2m_mean_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_max_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        },
+        {
+         "name": "apparent_temperature_min_lag_2",
+         "rawType": "float32",
+         "type": "float"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "1d332503-6cc5-434d-b501-0f0f721e7dc4",
+       "rows": [
+        [
+         "2005-01-01 00:00:00",
+         "44.62",
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+         "17.782825",
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+         "5.3880005",
+         "3.9873095",
+         "-1.2693028"
+        ]
+       ],
+       "shape": {
+        "columns": 40,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>general_dam_occupancy_rate_lag_1</th>\n",
+       "      <th>weather_code_lag_1</th>\n",
+       "      <th>temperature_2m_max_lag_1</th>\n",
+       "      <th>temperature_2m_min_lag_1</th>\n",
+       "      <th>temperature_2m_mean_lag_1</th>\n",
+       "      <th>apparent_temperature_max_lag_1</th>\n",
+       "      <th>apparent_temperature_min_lag_1</th>\n",
+       "      <th>apparent_temperature_mean_lag_1</th>\n",
+       "      <th>daylight_duration_lag_1</th>\n",
+       "      <th>sunshine_duration_lag_1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>climate_change_precipitation_sum_lag_1</th>\n",
+       "      <th>climate_change_pressure_msl_mean_lag_1</th>\n",
+       "      <th>climate_change_et0_fao_evapotranspiration_sum_lag_1</th>\n",
+       "      <th>general_dam_occupancy_rate_lag_2</th>\n",
+       "      <th>weather_code_lag_2</th>\n",
+       "      <th>temperature_2m_max_lag_2</th>\n",
+       "      <th>temperature_2m_min_lag_2</th>\n",
+       "      <th>temperature_2m_mean_lag_2</th>\n",
+       "      <th>apparent_temperature_max_lag_2</th>\n",
+       "      <th>apparent_temperature_min_lag_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>datetime</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2005-01-01</th>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>7.813000</td>\n",
+       "      <td>4.713</td>\n",
+       "      <td>6.660917</td>\n",
+       "      <td>4.816814</td>\n",
+       "      <td>2.181339</td>\n",
+       "      <td>3.769840</td>\n",
+       "      <td>33479.132812</td>\n",
+       "      <td>22339.408203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.127574</td>\n",
+       "      <td>1015.549561</td>\n",
+       "      <td>1.436427</td>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>7.813</td>\n",
+       "      <td>4.713</td>\n",
+       "      <td>6.660917</td>\n",
+       "      <td>4.816814</td>\n",
+       "      <td>2.181339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-01-02</th>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>7.813000</td>\n",
+       "      <td>4.713</td>\n",
+       "      <td>6.660917</td>\n",
+       "      <td>4.816814</td>\n",
+       "      <td>2.181339</td>\n",
+       "      <td>3.769840</td>\n",
+       "      <td>33479.132812</td>\n",
+       "      <td>22339.408203</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.127574</td>\n",
+       "      <td>1015.549561</td>\n",
+       "      <td>1.436427</td>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>7.813</td>\n",
+       "      <td>4.713</td>\n",
+       "      <td>6.660917</td>\n",
+       "      <td>4.816814</td>\n",
+       "      <td>2.181339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-01-03</th>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>8.613000</td>\n",
+       "      <td>2.263</td>\n",
+       "      <td>6.031750</td>\n",
+       "      <td>5.408062</td>\n",
+       "      <td>-1.033089</td>\n",
+       "      <td>3.009735</td>\n",
+       "      <td>33529.406250</td>\n",
+       "      <td>27602.402344</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.530689</td>\n",
+       "      <td>1009.441345</td>\n",
+       "      <td>0.990955</td>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>7.813</td>\n",
+       "      <td>4.713</td>\n",
+       "      <td>6.660917</td>\n",
+       "      <td>4.816814</td>\n",
+       "      <td>2.181339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-01-04</th>\n",
+       "      <td>44.470001</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.763000</td>\n",
+       "      <td>2.463</td>\n",
+       "      <td>5.388000</td>\n",
+       "      <td>3.987309</td>\n",
+       "      <td>-1.269303</td>\n",
+       "      <td>1.578146</td>\n",
+       "      <td>33583.265625</td>\n",
+       "      <td>4671.306152</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.774413</td>\n",
+       "      <td>1021.833252</td>\n",
+       "      <td>1.098593</td>\n",
+       "      <td>44.619999</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>8.613</td>\n",
+       "      <td>2.263</td>\n",
+       "      <td>6.031750</td>\n",
+       "      <td>5.408062</td>\n",
+       "      <td>-1.033089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005-01-05</th>\n",
+       "      <td>44.419998</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>9.662999</td>\n",
+       "      <td>4.363</td>\n",
+       "      <td>7.417167</td>\n",
+       "      <td>6.653760</td>\n",
+       "      <td>-0.225449</td>\n",
+       "      <td>3.145541</td>\n",
+       "      <td>33640.636719</td>\n",
+       "      <td>25796.974609</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.127524</td>\n",
+       "      <td>1025.425049</td>\n",
+       "      <td>0.777874</td>\n",
+       "      <td>44.470001</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>6.763</td>\n",
+       "      <td>2.463</td>\n",
+       "      <td>5.388000</td>\n",
+       "      <td>3.987309</td>\n",
+       "      <td>-1.269303</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            general_dam_occupancy_rate_lag_1  weather_code_lag_1  \\\n",
+       "datetime                                                           \n",
+       "2005-01-01                         44.619999                51.0   \n",
+       "2005-01-02                         44.619999                51.0   \n",
+       "2005-01-03                         44.619999                51.0   \n",
+       "2005-01-04                         44.470001                53.0   \n",
+       "2005-01-05                         44.419998                51.0   \n",
+       "\n",
+       "            temperature_2m_max_lag_1  temperature_2m_min_lag_1  \\\n",
+       "datetime                                                         \n",
+       "2005-01-01                  7.813000                     4.713   \n",
+       "2005-01-02                  7.813000                     4.713   \n",
+       "2005-01-03                  8.613000                     2.263   \n",
+       "2005-01-04                  6.763000                     2.463   \n",
+       "2005-01-05                  9.662999                     4.363   \n",
+       "\n",
+       "            temperature_2m_mean_lag_1  apparent_temperature_max_lag_1  \\\n",
+       "datetime                                                                \n",
+       "2005-01-01                   6.660917                        4.816814   \n",
+       "2005-01-02                   6.660917                        4.816814   \n",
+       "2005-01-03                   6.031750                        5.408062   \n",
+       "2005-01-04                   5.388000                        3.987309   \n",
+       "2005-01-05                   7.417167                        6.653760   \n",
+       "\n",
+       "            apparent_temperature_min_lag_1  apparent_temperature_mean_lag_1  \\\n",
+       "datetime                                                                      \n",
+       "2005-01-01                        2.181339                         3.769840   \n",
+       "2005-01-02                        2.181339                         3.769840   \n",
+       "2005-01-03                       -1.033089                         3.009735   \n",
+       "2005-01-04                       -1.269303                         1.578146   \n",
+       "2005-01-05                       -0.225449                         3.145541   \n",
+       "\n",
+       "            daylight_duration_lag_1  sunshine_duration_lag_1  ...  \\\n",
+       "datetime                                                      ...   \n",
+       "2005-01-01             33479.132812             22339.408203  ...   \n",
+       "2005-01-02             33479.132812             22339.408203  ...   \n",
+       "2005-01-03             33529.406250             27602.402344  ...   \n",
+       "2005-01-04             33583.265625              4671.306152  ...   \n",
+       "2005-01-05             33640.636719             25796.974609  ...   \n",
+       "\n",
+       "            climate_change_precipitation_sum_lag_1  \\\n",
+       "datetime                                             \n",
+       "2005-01-01                                0.127574   \n",
+       "2005-01-02                                0.127574   \n",
+       "2005-01-03                                1.530689   \n",
+       "2005-01-04                               13.774413   \n",
+       "2005-01-05                                0.127524   \n",
+       "\n",
+       "            climate_change_pressure_msl_mean_lag_1  \\\n",
+       "datetime                                             \n",
+       "2005-01-01                             1015.549561   \n",
+       "2005-01-02                             1015.549561   \n",
+       "2005-01-03                             1009.441345   \n",
+       "2005-01-04                             1021.833252   \n",
+       "2005-01-05                             1025.425049   \n",
+       "\n",
+       "            climate_change_et0_fao_evapotranspiration_sum_lag_1  \\\n",
+       "datetime                                                          \n",
+       "2005-01-01                                           1.436427     \n",
+       "2005-01-02                                           1.436427     \n",
+       "2005-01-03                                           0.990955     \n",
+       "2005-01-04                                           1.098593     \n",
+       "2005-01-05                                           0.777874     \n",
+       "\n",
+       "            general_dam_occupancy_rate_lag_2  weather_code_lag_2  \\\n",
+       "datetime                                                           \n",
+       "2005-01-01                         44.619999                51.0   \n",
+       "2005-01-02                         44.619999                51.0   \n",
+       "2005-01-03                         44.619999                51.0   \n",
+       "2005-01-04                         44.619999                51.0   \n",
+       "2005-01-05                         44.470001                53.0   \n",
+       "\n",
+       "            temperature_2m_max_lag_2  temperature_2m_min_lag_2  \\\n",
+       "datetime                                                         \n",
+       "2005-01-01                     7.813                     4.713   \n",
+       "2005-01-02                     7.813                     4.713   \n",
+       "2005-01-03                     7.813                     4.713   \n",
+       "2005-01-04                     8.613                     2.263   \n",
+       "2005-01-05                     6.763                     2.463   \n",
+       "\n",
+       "            temperature_2m_mean_lag_2  apparent_temperature_max_lag_2  \\\n",
+       "datetime                                                                \n",
+       "2005-01-01                   6.660917                        4.816814   \n",
+       "2005-01-02                   6.660917                        4.816814   \n",
+       "2005-01-03                   6.660917                        4.816814   \n",
+       "2005-01-04                   6.031750                        5.408062   \n",
+       "2005-01-05                   5.388000                        3.987309   \n",
+       "\n",
+       "            apparent_temperature_min_lag_2  \n",
+       "datetime                                    \n",
+       "2005-01-01                        2.181339  \n",
+       "2005-01-02                        2.181339  \n",
+       "2005-01-03                        2.181339  \n",
+       "2005-01-04                       -1.033089  \n",
+       "2005-01-05                       -1.269303  \n",
+       "\n",
+       "[5 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction_df.iloc[:, :40].head(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ds-study",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week2/friday/you_do_4/notebooks/6-ml-baseline.ipynb b/week2/friday/you_do_4/notebooks/6-ml-baseline.ipynb
new file mode 100644
index 0000000..39090f4
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/6-ml-baseline.ipynb
@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas import DataFrame\n",
+    "import numpy as np\n",
+    "from typing import Literal, List\n",
+    "from sklearn.metrics import mean_absolute_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"../dataset/interim/past_dataset.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = pd.read_csv(cleaned_dataset_address, parse_dates=[\"datetime\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    \"past_knowledge\": past_knowledge,\n",
+    "    \"cyclical_feature_names\": {\n",
+    "        \"month\": 12,\n",
+    "        \"day\": 31,\n",
+    "        \"day_of_year\": 365,\n",
+    "        \"week_of_year\": 52,\n",
+    "        \"quarter\": 4,\n",
+    "        # \"season\": 4,\n",
+    "        \"is_weekend\": 2,\n",
+    "    },\n",
+    "    \"lag_size\": 30,\n",
+    "    \"window_size\": 30,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "known_dates = DataFrame(past_knowledge[\"datetime\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TARGET = \"general_dam_occupancy_rate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureExtractor:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        past_knowledge: DataFrame,\n",
+    "        cyclical_feature_names: List[str],\n",
+    "        lag_size: int = 30,\n",
+    "        window_size: int = 30,\n",
+    "    ):\n",
+    "        self.PAST_KNOWLEDGE = past_knowledge.sort_values(by=\"datetime\")\n",
+    "        self.cyclical_feature_names = cyclical_feature_names\n",
+    "        self.lag_size = lag_size\n",
+    "        self.window_size = window_size\n",
+    "\n",
+    "    def transform(self, df: DataFrame) -> DataFrame:\n",
+    "        return (\n",
+    "            df.sort_values(\"datetime\")\n",
+    "            .pipe(self._add_lag_features)\n",
+    "            .pipe(self._add_rolling_window_features)\n",
+    "            .pipe(self._add_exponential_moving_features)\n",
+    "            .pipe(self._drop_columns_with_same_values)\n",
+    "            .pipe(self._expand_datetime)\n",
+    "            .pipe(self._add_fourier_features)\n",
+    "            .pipe(\n",
+    "                lambda df: df.astype(\n",
+    "                    {col: \"float32\" for col in df.select_dtypes(\"number\").columns}\n",
+    "                )\n",
+    "            )\n",
+    "            .bfill()\n",
+    "        )\n",
+    "\n",
+    "    def _add_lag_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"bfill\",\n",
+    "    ) -> DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range})\n",
+    "        full_df = full_df.merge(self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\")\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=\"number\"\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        created_features = []\n",
+    "        for col in columns_to_use:\n",
+    "            for i in range(1, self.lag_size + 1):\n",
+    "                created_col_name = f\"{col}_lag_{i}\"\n",
+    "                created_features.append(full_df[col].shift(i).rename(created_col_name))\n",
+    "\n",
+    "        lags_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            lags_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _add_rolling_window_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"ffill\",\n",
+    "    ) -> DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range}).merge(\n",
+    "            self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\"\n",
+    "        )\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"number\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        metrics = [\"mean\", \"std\", \"min\", \"max\", \"median\", \"var\"]\n",
+    "\n",
+    "        created_features = []\n",
+    "        for col in columns_to_use:\n",
+    "            for size in range(2, self.window_size + 1):\n",
+    "                rolling_window_feature = (\n",
+    "                    full_df[col]\n",
+    "                    .rolling(window=size, min_periods=1)\n",
+    "                    .agg(metrics)\n",
+    "                    .rename(columns=lambda metric: f\"{col}_rw{size}_{metric}\")\n",
+    "                )\n",
+    "                created_features.append(rolling_window_feature)\n",
+    "\n",
+    "        window_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            window_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _drop_columns_with_same_values(self, df: DataFrame, threshold=0.9) -> DataFrame:\n",
+    "        to_drop = [\n",
+    "            col\n",
+    "            for col in df.columns\n",
+    "            if df[col].value_counts(normalize=True, dropna=False).values[0] >= threshold\n",
+    "        ]\n",
+    "        return df.drop(columns=to_drop)\n",
+    "\n",
+    "    def _add_exponential_moving_features(\n",
+    "        self, df: pd.DataFrame, up_to: int = 30\n",
+    "    ) -> pd.DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range}).merge(\n",
+    "            self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\"\n",
+    "        )\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"number\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(include=\"number\").columns\n",
+    "        metrics = [\"mean\"]\n",
+    "        created_features = []\n",
+    "\n",
+    "        for col in columns_to_use:\n",
+    "            for span in range(2, up_to + 1):\n",
+    "                feature = (\n",
+    "                    full_df[col]\n",
+    "                    .ewm(span=span, adjust=False)\n",
+    "                    .agg(metrics)\n",
+    "                    .rename(columns=lambda metric: f\"{col}_em_{span}_{metric}\")\n",
+    "                )\n",
+    "                created_features.append(feature)\n",
+    "\n",
+    "        pd.concat([df, *created_features], axis=1)\n",
+    "\n",
+    "        exponential_moving_df = pd.concat(\n",
+    "            [full_df[\"datetime\"], *created_features], axis=1\n",
+    "        )\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            exponential_moving_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "        return df\n",
+    "\n",
+    "    def _expand_datetime(self, df: DataFrame, column: str = \"datetime\") -> DataFrame:\n",
+    "        return df.assign(\n",
+    "            **{\n",
+    "                \"year\": lambda a_df: a_df[column].dt.year,\n",
+    "                \"month\": lambda a_df: a_df[column].dt.month,\n",
+    "                \"day\": lambda a_df: a_df[column].dt.day,\n",
+    "                \"hour\": lambda a_df: a_df[column].dt.hour,\n",
+    "                \"day_of_year\": lambda a_df: a_df[column].dt.dayofyear,\n",
+    "                \"week_of_year\": lambda a_df: a_df[column].dt.isocalendar().week,\n",
+    "                \"quarter\": lambda a_df: a_df[column].dt.quarter,\n",
+    "                # \"season\": lambda a_df: a_df[column].dt.month % 12 // 3 + 1,\n",
+    "                \"is_weekend\": lambda a_df: (a_df[column].dt.weekday >= 5).map(\n",
+    "                    {True: 1, False: 0}\n",
+    "                ),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "    def _add_fourier_features(self, df: pd.DataFrame, num_terms: int = 7) -> DataFrame:\n",
+    "        for col, max_val in self.cyclical_feature_names.items():\n",
+    "            source = self._get_column_source(df, col)\n",
+    "\n",
+    "            for i in range(1, num_terms + 1):\n",
+    "                operation = 2 * np.pi * i * source[col] / max_val\n",
+    "\n",
+    "                df[f\"fourier_sin_{col}_{i}\"] = np.sin(operation)\n",
+    "                df[f\"fourier_cos_{col}_{i}\"] = np.cos(operation)\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _get_column_source(self, df: DataFrame, col: str) -> List[str]:\n",
+    "        if col in df.columns:\n",
+    "            source = df\n",
+    "        elif col in self.PAST_KNOWLEDGE.columns:\n",
+    "            source = self.PAST_KNOWLEDGE\n",
+    "        else:\n",
+    "            raise KeyError(f\"{col} not found both in df and past knowledge.\")\n",
+    "        return source\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_extractor = FeatureExtractor(**parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_values = known_dates.merge(past_knowledge, on=\"datetime\", how=\"left\").loc[:, [\"datetime\", TARGET]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_values = feature_extractor.transform(known_dates).loc[:, [\"datetime\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size = int(len(X_values) * 0.7)\n",
+    "val_size = int(len(X_values) * 0.15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = X_values.iloc[:train_size].merge(y_values, on=\"datetime\", how=\"inner\")\n",
+    "\n",
+    "val_df = X_values.iloc[train_size : train_size + val_size].merge(\n",
+    "    y_values, on=\"datetime\", how=\"inner\"\n",
+    ")\n",
+    "\n",
+    "test_df = X_values.iloc[train_size + val_size :].merge(\n",
+    "    y_values, on=\"datetime\", how=\"inner\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = (\n",
+    "    train_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    train_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_val, y_val = (\n",
+    "    val_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    val_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_test, y_test = (\n",
+    "    test_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    test_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dummy_forecaster(y):\n",
+    "    return np.full(len(y), y.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.46453160118681"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, dummy_forecaster(y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.062895669528846"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_val, dummy_forecaster(y_val))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.998338269300987"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, dummy_forecaster(y_test))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week2/friday/you_do_4/notebooks/6.1-ml-catboost.ipynb b/week2/friday/you_do_4/notebooks/6.1-ml-catboost.ipynb
new file mode 100644
index 0000000..68d3a90
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/6.1-ml-catboost.ipynb
@@ -0,0 +1,77066 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Literal\n",
+    "import pandas as pd\n",
+    "from pandas import DataFrame\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from lightgbm import LGBMRegressor\n",
+    "import joblib\n",
+    "from catboost import CatBoostRegressor\n",
+    "import json\n",
+    "import optuna\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"../dataset/interim/past_dataset.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SEED = 87\n",
+    "TARGET = \"general_dam_occupancy_rate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    with open(\"../models/best_features_by_lightgbm.json\", errors=\"FileNotFoundError\") as f:\n",
+    "        best_features = json.load(f)\n",
+    "        \n",
+    "except FileNotFoundError:\n",
+    "    best_features = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = pd.read_csv(cleaned_dataset_address, parse_dates=[\"datetime\"])\n",
+    "past_knowledge = past_knowledge.sort_values(\"datetime\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FeatureExtractor:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        past_knowledge: DataFrame,\n",
+    "        cyclical_feature_names: List[str],\n",
+    "        lag_size: int = 30,\n",
+    "        window_size: int = 30,\n",
+    "    ):\n",
+    "        self.PAST_KNOWLEDGE = past_knowledge.sort_values(by=\"datetime\")\n",
+    "        self.cyclical_feature_names = cyclical_feature_names\n",
+    "        self.lag_size = lag_size\n",
+    "        self.window_size = window_size\n",
+    "\n",
+    "    def transform(self, df: DataFrame) -> DataFrame:\n",
+    "        return (\n",
+    "            df.sort_values(\"datetime\")\n",
+    "            .pipe(self._add_lag_features)\n",
+    "            .pipe(self._add_rolling_window_features)\n",
+    "            .pipe(self._add_exponential_moving_features)\n",
+    "            .pipe(self._drop_columns_with_same_values)\n",
+    "            .pipe(self._expand_datetime)\n",
+    "            .pipe(self._add_fourier_features)\n",
+    "            .pipe(\n",
+    "                lambda df: df.astype(\n",
+    "                    {col: \"float32\" for col in df.select_dtypes(\"number\").columns}\n",
+    "                )\n",
+    "            )\n",
+    "            .bfill()\n",
+    "        )\n",
+    "\n",
+    "    def _add_lag_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"bfill\",\n",
+    "    ) -> DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range})\n",
+    "        full_df = full_df.merge(self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\")\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=\"number\"\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        created_features = []\n",
+    "        for col in columns_to_use:\n",
+    "            for i in range(1, self.lag_size + 1):\n",
+    "                created_col_name = f\"{col}_lag_{i}\"\n",
+    "                created_features.append(full_df[col].shift(i).rename(created_col_name))\n",
+    "\n",
+    "        lags_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            lags_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _add_rolling_window_features(\n",
+    "        self,\n",
+    "        df: DataFrame,\n",
+    "        fillna_with: Literal[\"ffill\", \"bfill\"] | None = \"ffill\",\n",
+    "    ) -> DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range}).merge(\n",
+    "            self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\"\n",
+    "        )\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"number\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        metrics = [\"mean\", \"std\", \"min\", \"max\", \"median\", \"var\"]\n",
+    "\n",
+    "        created_features = []\n",
+    "        for col in columns_to_use:\n",
+    "            for size in range(2, self.window_size + 1):\n",
+    "                rolling_window_feature = (\n",
+    "                    full_df[col]\n",
+    "                    .rolling(window=size, min_periods=1)\n",
+    "                    .agg(metrics)\n",
+    "                    .rename(columns=lambda metric: f\"{col}_rw{size}_{metric}\")\n",
+    "                )\n",
+    "                created_features.append(rolling_window_feature)\n",
+    "\n",
+    "        window_df = pd.concat([full_df[\"datetime\"], *created_features], axis=1)\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            window_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        if fillna_with == \"ffill\":\n",
+    "            df = df.ffill()\n",
+    "        elif fillna_with == \"bfill\":\n",
+    "            df = df.bfill()\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _drop_columns_with_same_values(self, df: DataFrame, threshold=0.9) -> DataFrame:\n",
+    "        to_drop = [\n",
+    "            col\n",
+    "            for col in df.columns\n",
+    "            if df[col].value_counts(normalize=True, dropna=False).values[0] >= threshold\n",
+    "        ]\n",
+    "        return df.drop(columns=to_drop)\n",
+    "\n",
+    "    def _add_exponential_moving_features(\n",
+    "        self, df: pd.DataFrame, up_to: int = 30\n",
+    "    ) -> pd.DataFrame:\n",
+    "        df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
+    "\n",
+    "        df = df.sort_values(\"datetime\")\n",
+    "\n",
+    "        full_date_range = pd.date_range(\n",
+    "            start=self.PAST_KNOWLEDGE[\"datetime\"].min(),\n",
+    "            end=df[\"datetime\"].max(),\n",
+    "            freq=\"D\",\n",
+    "        )\n",
+    "        full_df = pd.DataFrame({\"datetime\": full_date_range}).merge(\n",
+    "            self.PAST_KNOWLEDGE, on=\"datetime\", how=\"left\"\n",
+    "        )\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(\n",
+    "            include=[\"number\"]\n",
+    "        ).columns.tolist()\n",
+    "\n",
+    "        columns_to_use = self.PAST_KNOWLEDGE.select_dtypes(include=\"number\").columns\n",
+    "        metrics = [\"mean\"]\n",
+    "        created_features = []\n",
+    "\n",
+    "        for col in columns_to_use:\n",
+    "            for span in range(2, up_to + 1):\n",
+    "                feature = (\n",
+    "                    full_df[col]\n",
+    "                    .ewm(span=span, adjust=False)\n",
+    "                    .agg(metrics)\n",
+    "                    .rename(columns=lambda metric: f\"{col}_em_{span}_{metric}\")\n",
+    "                )\n",
+    "                created_features.append(feature)\n",
+    "\n",
+    "        pd.concat([df, *created_features], axis=1)\n",
+    "\n",
+    "        exponential_moving_df = pd.concat(\n",
+    "            [full_df[\"datetime\"], *created_features], axis=1\n",
+    "        )\n",
+    "\n",
+    "        df = df.merge(\n",
+    "            exponential_moving_df,\n",
+    "            on=\"datetime\",\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "        return df\n",
+    "\n",
+    "    def _expand_datetime(self, df: DataFrame, column: str = \"datetime\") -> DataFrame:\n",
+    "        return df.assign(\n",
+    "            **{\n",
+    "                \"year\": lambda a_df: a_df[column].dt.year,\n",
+    "                \"month\": lambda a_df: a_df[column].dt.month,\n",
+    "                \"day\": lambda a_df: a_df[column].dt.day,\n",
+    "                \"hour\": lambda a_df: a_df[column].dt.hour,\n",
+    "                \"day_of_year\": lambda a_df: a_df[column].dt.dayofyear,\n",
+    "                \"week_of_year\": lambda a_df: a_df[column].dt.isocalendar().week,\n",
+    "                \"quarter\": lambda a_df: a_df[column].dt.quarter,\n",
+    "                # \"season\": lambda a_df: a_df[column].dt.month % 12 // 3 + 1,\n",
+    "                \"is_weekend\": lambda a_df: (a_df[column].dt.weekday >= 5).map(\n",
+    "                    {True: 1, False: 0}\n",
+    "                ),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "    def _add_fourier_features(self, df: pd.DataFrame, num_terms: int = 7) -> DataFrame:\n",
+    "        for col, max_val in self.cyclical_feature_names.items():\n",
+    "            source = self._get_column_source(df, col)\n",
+    "\n",
+    "            for i in range(1, num_terms + 1):\n",
+    "                operation = 2 * np.pi * i * source[col] / max_val\n",
+    "\n",
+    "                df[f\"fourier_sin_{col}_{i}\"] = np.sin(operation)\n",
+    "                df[f\"fourier_cos_{col}_{i}\"] = np.cos(operation)\n",
+    "\n",
+    "        return df\n",
+    "\n",
+    "    def _get_column_source(self, df: DataFrame, col: str) -> List[str]:\n",
+    "        if col in df.columns:\n",
+    "            source = df\n",
+    "        elif col in self.PAST_KNOWLEDGE.columns:\n",
+    "            source = self.PAST_KNOWLEDGE\n",
+    "        else:\n",
+    "            raise KeyError(f\"{col} not found both in df and past knowledge.\")\n",
+    "        return source\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    \"past_knowledge\": past_knowledge,\n",
+    "    \"cyclical_feature_names\": {\n",
+    "        \"month\": 12,\n",
+    "        \"day\": 31,\n",
+    "        \"day_of_year\": 365,\n",
+    "        \"week_of_year\": 52,\n",
+    "        \"quarter\": 4,\n",
+    "        # \"season\": 4,\n",
+    "        \"is_weekend\": 2,\n",
+    "        \"precipitation_hours\": 24,\n",
+    "    },\n",
+    "    \"lag_size\": 30,\n",
+    "    \"window_size\": 30,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_extractor = FeatureExtractor(**parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "known_dates = pd.DataFrame(past_knowledge[\"datetime\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_values = past_knowledge.loc[:, [\"datetime\", TARGET]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if best_features:\n",
+    "    X_values = feature_extractor.transform(known_dates).filter(items=[\"datetime\", *best_features])\n",
+    "else:\n",
+    "    X_values = feature_extractor.transform(known_dates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size = int(len(X_values) * 0.7)\n",
+    "val_size = int(len(X_values) * 0.15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = X_values.iloc[:train_size].merge(y_values, on=\"datetime\", how=\"inner\")\n",
+    "\n",
+    "val_df = X_values.iloc[train_size : train_size + val_size].merge(\n",
+    "    y_values, on=\"datetime\", how=\"inner\"\n",
+    ")\n",
+    "\n",
+    "test_df = X_values.iloc[train_size + val_size :].merge(\n",
+    "    y_values, on=\"datetime\", how=\"inner\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = (\n",
+    "    train_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    train_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_val, y_val = (\n",
+    "    val_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    val_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_test, y_test = (\n",
+    "    test_df.drop(columns=[\"datetime\", \"general_dam_occupancy_rate\"]),\n",
+    "    test_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(best_features) == 0:\n",
+    "    lgb = LGBMRegressor(importance_type=\"gain\", random_state=SEED)\n",
+    "    model = lgb.fit(X_train, y_train)\n",
+    "    feature_importance = {\n",
+    "        name: importance\n",
+    "        for name, importance in zip(model.feature_names_in_, model.feature_importances_)\n",
+    "    }\n",
+    "    number_of_features_to_select = round(len(X_train.columns) ** (1 / 2))\n",
+    "    best_columns = (\n",
+    "        pd.DataFrame(feature_importance, index=[0]).T.sort_values(0, ascending=False)[0:number_of_features_to_select].index.to_list()\n",
+    "    )\n",
+    "    with open(\"../models/best_features_by_lightgbm.json\", \"w\") as f:\n",
+    "        json.dump(best_columns, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.0001, 0.1, log=True),\n",
+    "        \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000),\n",
+    "        \"max_depth\": trial.suggest_int(\"max_depth\", 3, 8),\n",
+    "        \"l2_leaf_reg\": trial.suggest_float(\"l2_leaf_reg\", 0.1, 10.0, log=True),\n",
+    "        \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.5, 1.0),\n",
+    "        \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 1, 100),\n",
+    "        \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0),\n",
+    "        \"max_bin\": trial.suggest_int(\"max_bin\", 100, 500),\n",
+    "        \"od_type\": trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
+    "        \"random_strength\": trial.suggest_float(\"random_strength\", 0.0, 100.0),\n",
+    "        \"leaf_estimation_method\": trial.suggest_categorical(\n",
+    "            \"leaf_estimation_method\", [\"Newton\", \"Gradient\"]\n",
+    "        ),\n",
+    "        \"grow_policy\": trial.suggest_categorical(\n",
+    "            \"grow_policy\", [\"SymmetricTree\", \"Depthwise\", \"Lossguide\"]\n",
+    "        ),\n",
+    "        \"feature_border_type\": trial.suggest_categorical(\n",
+    "            \"feature_border_type\", [\"GreedyLogSum\", \"MinEntropy\", \"Uniform\"]\n",
+    "        ),\n",
+    "        \"one_hot_max_size\": trial.suggest_int(\"one_hot_max_size\", 2, 10),\n",
+    "        \"max_ctr_complexity\": trial.suggest_int(\"max_ctr_complexity\", 1, 8),\n",
+    "        \"bagging_temperature\": trial.suggest_float(\"bagging_temperature\", 0.0, 1.0),\n",
+    "        \"leaf_estimation_iterations\": trial.suggest_int(\n",
+    "            \"leaf_estimation_iterations\", 1, 10\n",
+    "        ),\n",
+    "        \"leaf_estimation_backtracking\": trial.suggest_categorical(\n",
+    "            \"leaf_estimation_backtracking\", [\"No\", \"AnyImprovement\"]\n",
+    "        ),\n",
+    "        \"random_score_type\": trial.suggest_categorical(\n",
+    "            \"random_score_type\", [\"NormalWithModelSizeDecrease\", \"Gumbel\"]\n",
+    "        ),\n",
+    "    }\n",
+    "\n",
+    "    model = CatBoostRegressor(**params, random_state=SEED, early_stopping_rounds=100)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_val)\n",
+    "\n",
+    "    dummy_forecaster_mae_error = 14.062895669528846\n",
+    "\n",
+    "    return (\n",
+    "        mean_absolute_error(y_val, y_pred)\n",
+    "        if mean_absolute_error(y_val, y_pred) < dummy_forecaster_mae_error\n",
+    "        else float(np.inf)\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-02-17 19:02:49,880] A new study created in memory with name: no-name-1410ba4b-d254-4dc1-8105-cdac75205d53\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d00ce73c314a48fe9c556518400efa67",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "[I 2025-02-17 19:02:52,022] Trial 7 finished with value: 1.187162818770461 and parameters: {'learning_rate': 0.032868053934415324, 'n_estimators': 119, 'max_depth': 3, 'l2_leaf_reg': 0.8483514913112786, 'colsample_bylevel': 0.900095438512206, 'min_data_in_leaf': 78, 'subsample': 0.7869353519090272, 'max_bin': 341, 'od_type': 'IncToDec', 'random_strength': 71.39953038607949, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 8, 'max_ctr_complexity': 6, 'bagging_temperature': 0.8867781941914316, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 7 with value: 1.187162818770461.\n",
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+      "[I 2025-02-17 19:02:54,996] Trial 3 finished with value: 13.989459631077816 and parameters: {'learning_rate': 0.00010270844429328331, 'n_estimators': 196, 'max_depth': 5, 'l2_leaf_reg': 0.30964752055761907, 'colsample_bylevel': 0.9978304757201426, 'min_data_in_leaf': 23, 'subsample': 0.5276787671466538, 'max_bin': 456, 'od_type': 'Iter', 'random_strength': 28.873833600295818, 'leaf_estimation_method': 'Newton', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 3, 'bagging_temperature': 0.6171083835905685, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 7 with value: 1.187162818770461.\n",
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+      "[I 2025-02-17 19:02:57,152] Trial 6 finished with value: 11.319568977040039 and parameters: {'learning_rate': 0.00043912256174528295, 'n_estimators': 573, 'max_depth': 4, 'l2_leaf_reg': 5.712000112813942, 'colsample_bylevel': 0.5417394122059508, 'min_data_in_leaf': 15, 'subsample': 0.886344413611105, 'max_bin': 116, 'od_type': 'Iter', 'random_strength': 81.74849770767288, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 2, 'bagging_temperature': 0.6299243817927815, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 7 with value: 1.187162818770461.\n",
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+      "[I 2025-02-17 19:02:58,301] Trial 0 finished with value: 1.3233455085645802 and parameters: {'learning_rate': 0.07076685410829123, 'n_estimators': 178, 'max_depth': 7, 'l2_leaf_reg': 0.10117566532515954, 'colsample_bylevel': 0.7118071967191799, 'min_data_in_leaf': 77, 'subsample': 0.8990864516079964, 'max_bin': 444, 'od_type': 'Iter', 'random_strength': 88.99603483429505, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 7, 'bagging_temperature': 0.01896132360936298, 'leaf_estimation_iterations': 1, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 7 with value: 1.187162818770461.\n",
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+      "76:\tlearn: 14.3743961\ttotal: 1.61s\tremaining: 3.93s\n",
+      "[I 2025-02-17 19:02:58,823] Trial 5 finished with value: 13.47585963216164 and parameters: {'learning_rate': 0.0005757991447554214, 'n_estimators': 102, 'max_depth': 8, 'l2_leaf_reg': 5.496032801794013, 'colsample_bylevel': 0.9801932966290812, 'min_data_in_leaf': 42, 'subsample': 0.520516263809031, 'max_bin': 498, 'od_type': 'IncToDec', 'random_strength': 45.29297559677431, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Lossguide', 'feature_border_type': 'Uniform', 'one_hot_max_size': 4, 'max_ctr_complexity': 4, 'bagging_temperature': 0.3617439711549574, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 7 with value: 1.187162818770461.\n",
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+      "19:\tlearn: 24.9020005\ttotal: 158ms\tremaining: 836ms\n",
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+      "22:\tlearn: 24.8929133\ttotal: 174ms\tremaining: 778ms\n",
+      "[I 2025-02-17 19:02:59,021] Trial 4 finished with value: 1.1354384951799756 and parameters: {'learning_rate': 0.008937274603395887, 'n_estimators': 262, 'max_depth': 6, 'l2_leaf_reg': 3.929924850137719, 'colsample_bylevel': 0.6806463804648453, 'min_data_in_leaf': 53, 'subsample': 0.5788657338850107, 'max_bin': 434, 'od_type': 'IncToDec', 'random_strength': 58.27477978578632, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 4, 'bagging_temperature': 0.14424335992483173, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 4 with value: 1.1354384951799756.\n",
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+      "39:\tlearn: 24.8570542\ttotal: 281ms\tremaining: 603ms\n",
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+      "90:\tlearn: 13.0386326\ttotal: 1.91s\tremaining: 3.64s\n",
+      "152:\tlearn: 23.9848887\ttotal: 8.99s\tremaining: 24.3s\n",
+      "[I 2025-02-17 19:02:59,141] Trial 2 finished with value: 13.180723045666033 and parameters: {'learning_rate': 0.00018586455266239954, 'n_estimators': 385, 'max_depth': 4, 'l2_leaf_reg': 0.5912610176007219, 'colsample_bylevel': 0.7906665892860871, 'min_data_in_leaf': 100, 'subsample': 0.6924347076802507, 'max_bin': 295, 'od_type': 'IncToDec', 'random_strength': 15.180230032014052, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 2, 'bagging_temperature': 0.12016906358622759, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 4 with value: 1.1354384951799756.\n",
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+      "[I 2025-02-17 19:02:59,853] Trial 11 finished with value: inf and parameters: {'learning_rate': 0.00015551014783040328, 'n_estimators': 126, 'max_depth': 3, 'l2_leaf_reg': 0.735163040637181, 'colsample_bylevel': 0.6810174783667614, 'min_data_in_leaf': 25, 'subsample': 0.707758571547001, 'max_bin': 145, 'od_type': 'Iter', 'random_strength': 92.18422253210136, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 2, 'bagging_temperature': 0.5117949507423676, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 4 with value: 1.1354384951799756.\n",
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+      "[I 2025-02-17 19:03:00,536] Trial 12 finished with value: 3.0502576888124464 and parameters: {'learning_rate': 0.006248330818678558, 'n_estimators': 252, 'max_depth': 3, 'l2_leaf_reg': 0.8828185085219409, 'colsample_bylevel': 0.6466878139545311, 'min_data_in_leaf': 15, 'subsample': 0.8645248963075425, 'max_bin': 139, 'od_type': 'Iter', 'random_strength': 76.76350104364292, 'leaf_estimation_method': 'Newton', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 3, 'bagging_temperature': 0.9153504204923991, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 4 with value: 1.1354384951799756.\n",
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+      "[I 2025-02-17 19:03:02,481] Trial 10 finished with value: 1.123609617536902 and parameters: {'learning_rate': 0.00936892818310661, 'n_estimators': 265, 'max_depth': 4, 'l2_leaf_reg': 0.5335036767053155, 'colsample_bylevel': 0.6812997589295144, 'min_data_in_leaf': 32, 'subsample': 0.9901041891045841, 'max_bin': 347, 'od_type': 'IncToDec', 'random_strength': 76.05404412616214, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Lossguide', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 6, 'bagging_temperature': 0.055112137376369885, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 10 with value: 1.123609617536902.\n",
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+      "[I 2025-02-17 19:03:02,542] Trial 16 finished with value: 1.0633919292051854 and parameters: {'learning_rate': 0.03142490527786905, 'n_estimators': 315, 'max_depth': 5, 'l2_leaf_reg': 0.9698386242000169, 'colsample_bylevel': 0.5639625156558972, 'min_data_in_leaf': 2, 'subsample': 0.5710162343677754, 'max_bin': 135, 'od_type': 'IncToDec', 'random_strength': 95.55894136220236, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 2, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7989950239636116, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 16 with value: 1.0633919292051854.203:\tlearn: 0.8590988\ttotal: 2.25s\tremaining: 951ms\n",
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+      "[I 2025-02-17 19:03:03,543] Trial 9 finished with value: 10.858788676018936 and parameters: {'learning_rate': 0.0016498453422783294, 'n_estimators': 171, 'max_depth': 6, 'l2_leaf_reg': 0.10717953032631095, 'colsample_bylevel': 0.7060353873853202, 'min_data_in_leaf': 79, 'subsample': 0.5218318300640836, 'max_bin': 495, 'od_type': 'Iter', 'random_strength': 80.95580611341566, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 1, 'bagging_temperature': 0.2805907689215421, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 16 with value: 1.0633919292051854.\n",
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+      "[I 2025-02-17 19:03:03,679] Trial 13 finished with value: 0.8031173115630049 and parameters: {'learning_rate': 0.06561004564880747, 'n_estimators': 290, 'max_depth': 4, 'l2_leaf_reg': 7.079495859658732, 'colsample_bylevel': 0.9187382224316711, 'min_data_in_leaf': 32, 'subsample': 0.7555876214935411, 'max_bin': 354, 'od_type': 'Iter', 'random_strength': 16.32612242919326, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 6, 'max_ctr_complexity': 3, 'bagging_temperature': 0.9188343572729143, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 13 with value: 0.8031173115630049.\n",
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+      "[I 2025-02-17 19:03:09,920] Trial 20 finished with value: 0.44962510099304254 and parameters: {'learning_rate': 0.03298175254209879, 'n_estimators': 764, 'max_depth': 5, 'l2_leaf_reg': 2.3119191531864036, 'colsample_bylevel': 0.5115740053693892, 'min_data_in_leaf': 51, 'subsample': 0.6193721262194385, 'max_bin': 223, 'od_type': 'IncToDec', 'random_strength': 10.785666790449756, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7607498210021819, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:19,667] Trial 21 finished with value: 0.5406758333434724 and parameters: {'learning_rate': 0.07735778749090998, 'n_estimators': 822, 'max_depth': 5, 'l2_leaf_reg': 2.508959510415277, 'colsample_bylevel': 0.8263202610576319, 'min_data_in_leaf': 58, 'subsample': 0.6411769128439825, 'max_bin': 231, 'od_type': 'Iter', 'random_strength': 3.74441956241683, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7507213413125378, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:20,546] Trial 1 finished with value: 12.083279913096463 and parameters: {'learning_rate': 0.00030041460408013025, 'n_estimators': 567, 'max_depth': 8, 'l2_leaf_reg': 9.779369690720529, 'colsample_bylevel': 0.6726053778421132, 'min_data_in_leaf': 53, 'subsample': 0.9231578078401074, 'max_bin': 315, 'od_type': 'IncToDec', 'random_strength': 75.34150346488558, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'Uniform', 'one_hot_max_size': 3, 'max_ctr_complexity': 3, 'bagging_temperature': 0.8822993229718848, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:21,345] Trial 19 finished with value: 0.9152125314947764 and parameters: {'learning_rate': 0.018422837665243663, 'n_estimators': 850, 'max_depth': 5, 'l2_leaf_reg': 1.9870677606829448, 'colsample_bylevel': 0.5011090054928258, 'min_data_in_leaf': 7, 'subsample': 0.9953770702911939, 'max_bin': 246, 'od_type': 'IncToDec', 'random_strength': 97.41165283821032, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 6, 'max_ctr_complexity': 6, 'bagging_temperature': 0.7515897990145628, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:21,599] Trial 17 finished with value: 2.1852643752625798 and parameters: {'learning_rate': 0.0019370895134014027, 'n_estimators': 881, 'max_depth': 5, 'l2_leaf_reg': 2.1648769773979386, 'colsample_bylevel': 0.5300445021148046, 'min_data_in_leaf': 52, 'subsample': 0.9955080706535815, 'max_bin': 265, 'od_type': 'IncToDec', 'random_strength': 50.0853573817114, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 8, 'bagging_temperature': 0.30585205510256686, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:23,822] Trial 18 finished with value: 0.9071186841876399 and parameters: {'learning_rate': 0.019726664247677057, 'n_estimators': 912, 'max_depth': 5, 'l2_leaf_reg': 1.9841480751744078, 'colsample_bylevel': 0.5202913246630452, 'min_data_in_leaf': 2, 'subsample': 0.9884032658259235, 'max_bin': 254, 'od_type': 'IncToDec', 'random_strength': 98.84066245092907, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 6, 'bagging_temperature': 0.743358379490301, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:36,001] Trial 25 finished with value: 0.4657640588143375 and parameters: {'learning_rate': 0.08030262690622493, 'n_estimators': 750, 'max_depth': 6, 'l2_leaf_reg': 2.4001149302118017, 'colsample_bylevel': 0.7960261447272343, 'min_data_in_leaf': 65, 'subsample': 0.6321407217604559, 'max_bin': 199, 'od_type': 'Iter', 'random_strength': 2.2694248271695354, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7035373251917928, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:36,671] Trial 22 finished with value: 0.46581050231923293 and parameters: {'learning_rate': 0.02085855367224748, 'n_estimators': 807, 'max_depth': 6, 'l2_leaf_reg': 2.191613537180601, 'colsample_bylevel': 0.8138724580204357, 'min_data_in_leaf': 57, 'subsample': 0.6387697999887856, 'max_bin': 223, 'od_type': 'IncToDec', 'random_strength': 6.074413829087393, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 7, 'bagging_temperature': 0.7265220044178085, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:36,815] Trial 15 finished with value: 7.829328679460251 and parameters: {'learning_rate': 0.0005557046148954664, 'n_estimators': 985, 'max_depth': 8, 'l2_leaf_reg': 3.425465340747884, 'colsample_bylevel': 0.6823157947924912, 'min_data_in_leaf': 90, 'subsample': 0.8975788946465796, 'max_bin': 196, 'od_type': 'IncToDec', 'random_strength': 35.05567130871371, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 8, 'max_ctr_complexity': 5, 'bagging_temperature': 0.07865534163790677, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 20 with value: 0.44962510099304254.\n",
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+      "[I 2025-02-17 19:03:37,903] Trial 23 finished with value: 0.27413487163003625 and parameters: {'learning_rate': 0.025349430198845137, 'n_estimators': 840, 'max_depth': 6, 'l2_leaf_reg': 2.164853442485411, 'colsample_bylevel': 0.7996990631607371, 'min_data_in_leaf': 63, 'subsample': 0.6443127973177656, 'max_bin': 221, 'od_type': 'IncToDec', 'random_strength': 0.5404869025742265, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 8, 'bagging_temperature': 0.7531001637362406, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:03:40,382] Trial 24 finished with value: 0.7724152362192134 and parameters: {'learning_rate': 0.0034650194305389376, 'n_estimators': 876, 'max_depth': 6, 'l2_leaf_reg': 2.18148748991295, 'colsample_bylevel': 0.803025318817098, 'min_data_in_leaf': 62, 'subsample': 0.6425974603632751, 'max_bin': 214, 'od_type': 'Iter', 'random_strength': 0.48468517571147984, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 8, 'bagging_temperature': 0.709292783177425, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:03:46,157] Trial 26 finished with value: 0.5191986455565964 and parameters: {'learning_rate': 0.08396063899013505, 'n_estimators': 707, 'max_depth': 7, 'l2_leaf_reg': 2.880035220128265, 'colsample_bylevel': 0.7850567720209878, 'min_data_in_leaf': 67, 'subsample': 0.6312776499208671, 'max_bin': 190, 'od_type': 'Iter', 'random_strength': 2.033358290341866, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 5, 'bagging_temperature': 0.6250307960532578, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:03:46,216] Trial 14 finished with value: 7.282327702982175 and parameters: {'learning_rate': 0.0008229178705453533, 'n_estimators': 836, 'max_depth': 6, 'l2_leaf_reg': 2.326564993785765, 'colsample_bylevel': 0.6302940404086608, 'min_data_in_leaf': 18, 'subsample': 0.6893509999583907, 'max_bin': 445, 'od_type': 'Iter', 'random_strength': 58.413945119543165, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 3, 'max_ctr_complexity': 8, 'bagging_temperature': 0.8953346115352595, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:03:57,127] Trial 27 finished with value: 0.7504816050078768 and parameters: {'learning_rate': 0.004081594343143977, 'n_estimators': 709, 'max_depth': 7, 'l2_leaf_reg': 3.4473002437904814, 'colsample_bylevel': 0.6043346461154794, 'min_data_in_leaf': 64, 'subsample': 0.6349017434537672, 'max_bin': 190, 'od_type': 'Iter', 'random_strength': 2.216875802690865, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 8, 'bagging_temperature': 0.5441405552075482, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:03:59,276] Trial 30 finished with value: 0.5344115686708478 and parameters: {'learning_rate': 0.09875609902143997, 'n_estimators': 652, 'max_depth': 7, 'l2_leaf_reg': 1.4613848432993186, 'colsample_bylevel': 0.7641758389559921, 'min_data_in_leaf': 66, 'subsample': 0.6388129947129407, 'max_bin': 180, 'od_type': 'Iter', 'random_strength': 0.6158736556038384, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 8, 'bagging_temperature': 0.990596295310549, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:04:00,201] Trial 31 finished with value: 0.7685515916482738 and parameters: {'learning_rate': 0.0440680494811972, 'n_estimators': 690, 'max_depth': 7, 'l2_leaf_reg': 1.329435755727133, 'colsample_bylevel': 0.5971834135634422, 'min_data_in_leaf': 68, 'subsample': 0.5880334148715105, 'max_bin': 181, 'od_type': 'IncToDec', 'random_strength': 13.565398823715476, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 7, 'bagging_temperature': 0.5210580689794647, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 23 with value: 0.27413487163003625.\n",
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+      "[I 2025-02-17 19:04:01,767] Trial 28 finished with value: 0.1796486494136596 and parameters: {'learning_rate': 0.0339414347464316, 'n_estimators': 700, 'max_depth': 7, 'l2_leaf_reg': 3.2830029310819624, 'colsample_bylevel': 0.8385702042700233, 'min_data_in_leaf': 66, 'subsample': 0.6216595098536191, 'max_bin': 195, 'od_type': 'IncToDec', 'random_strength': 0.028793756568040063, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 8, 'bagging_temperature': 0.6229015981433335, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:02,209] Trial 29 finished with value: 0.38165207418849373 and parameters: {'learning_rate': 0.04099494282776948, 'n_estimators': 727, 'max_depth': 7, 'l2_leaf_reg': 1.4136927607985987, 'colsample_bylevel': 0.8159332097394186, 'min_data_in_leaf': 66, 'subsample': 0.6380179271156395, 'max_bin': 193, 'od_type': 'IncToDec', 'random_strength': 1.4909206261735486, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 9, 'max_ctr_complexity': 8, 'bagging_temperature': 0.6346802486799202, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:06,009] Trial 33 finished with value: 0.6525635789510263 and parameters: {'learning_rate': 0.04042835027704546, 'n_estimators': 720, 'max_depth': 7, 'l2_leaf_reg': 1.4455440506778379, 'colsample_bylevel': 0.5844150135856749, 'min_data_in_leaf': 42, 'subsample': 0.5941379332226139, 'max_bin': 172, 'od_type': 'IncToDec', 'random_strength': 15.894513285137826, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.5136439265047742, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:06,202] Trial 32 finished with value: 0.5464641329363378 and parameters: {'learning_rate': 0.032528494127738686, 'n_estimators': 717, 'max_depth': 7, 'l2_leaf_reg': 1.378989078805158, 'colsample_bylevel': 0.5887406679818495, 'min_data_in_leaf': 68, 'subsample': 0.6893141365961504, 'max_bin': 166, 'od_type': 'IncToDec', 'random_strength': 14.814318140328886, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.9942118319100189, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:13,685] Trial 35 finished with value: 0.7152654168396103 and parameters: {'learning_rate': 0.041024403826175705, 'n_estimators': 464, 'max_depth': 6, 'l2_leaf_reg': 1.489392838538738, 'colsample_bylevel': 0.8815040012367849, 'min_data_in_leaf': 42, 'subsample': 0.5834340409965025, 'max_bin': 284, 'od_type': 'IncToDec', 'random_strength': 14.7860784153365, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.8135685337199967, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:15,167] Trial 36 finished with value: 0.6005812934080071 and parameters: {'learning_rate': 0.051008460097243725, 'n_estimators': 483, 'max_depth': 6, 'l2_leaf_reg': 1.3666986327018973, 'colsample_bylevel': 0.8506097045261343, 'min_data_in_leaf': 45, 'subsample': 0.7986290519517103, 'max_bin': 267, 'od_type': 'IncToDec', 'random_strength': 24.384565085096888, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.8138368514320204, 'leaf_estimation_iterations': 1, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:22,776] Trial 34 finished with value: 0.637575363253211 and parameters: {'learning_rate': 0.041049603381738006, 'n_estimators': 707, 'max_depth': 7, 'l2_leaf_reg': 1.2058401416658442, 'colsample_bylevel': 0.8600330232657103, 'min_data_in_leaf': 43, 'subsample': 0.5906744589935073, 'max_bin': 169, 'od_type': 'IncToDec', 'random_strength': 19.97648154659614, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.8257411722161453, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:25,699] Trial 37 finished with value: 0.7790848706864915 and parameters: {'learning_rate': 0.0115841063832788, 'n_estimators': 463, 'max_depth': 7, 'l2_leaf_reg': 1.438666644607945, 'colsample_bylevel': 0.8534458338885691, 'min_data_in_leaf': 42, 'subsample': 0.7493853322878252, 'max_bin': 286, 'od_type': 'IncToDec', 'random_strength': 25.017909084972782, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.38726629117534883, 'leaf_estimation_iterations': 1, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:27,562] Trial 38 finished with value: 0.813897911020744 and parameters: {'learning_rate': 0.01418271107468579, 'n_estimators': 491, 'max_depth': 7, 'l2_leaf_reg': 1.3561713772614388, 'colsample_bylevel': 0.8616014192593332, 'min_data_in_leaf': 44, 'subsample': 0.7293217611579632, 'max_bin': 284, 'od_type': 'IncToDec', 'random_strength': 23.00445240579097, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.4281088226207367, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:38,899] Trial 39 finished with value: 0.7274351834601509 and parameters: {'learning_rate': 0.014574378917671528, 'n_estimators': 616, 'max_depth': 7, 'l2_leaf_reg': 4.719469929744807, 'colsample_bylevel': 0.8558322789772658, 'min_data_in_leaf': 45, 'subsample': 0.7379116392298349, 'max_bin': 280, 'od_type': 'IncToDec', 'random_strength': 24.203490955608924, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 8, 'max_ctr_complexity': 7, 'bagging_temperature': 0.42004406577139863, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:39,454] Trial 40 finished with value: 0.8612434932291856 and parameters: {'learning_rate': 0.011512581938546424, 'n_estimators': 613, 'max_depth': 7, 'l2_leaf_reg': 4.061141709746762, 'colsample_bylevel': 0.8539213898618838, 'min_data_in_leaf': 74, 'subsample': 0.7268818437321048, 'max_bin': 282, 'od_type': 'IncToDec', 'random_strength': 23.920159151046878, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.419516104216613, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:44,428] Trial 42 finished with value: 0.5430982182181826 and parameters: {'learning_rate': 0.011821323373707854, 'n_estimators': 628, 'max_depth': 8, 'l2_leaf_reg': 4.84447983258246, 'colsample_bylevel': 0.7456941689358358, 'min_data_in_leaf': 85, 'subsample': 0.722651705353105, 'max_bin': 105, 'od_type': 'IncToDec', 'random_strength': 8.13052729288349, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.4197412118481797, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:54,179] Trial 44 finished with value: 0.6223008291758257 and parameters: {'learning_rate': 0.012593360012948326, 'n_estimators': 622, 'max_depth': 8, 'l2_leaf_reg': 4.625372632462684, 'colsample_bylevel': 0.7328082270390107, 'min_data_in_leaf': 74, 'subsample': 0.555323794123169, 'max_bin': 112, 'od_type': 'IncToDec', 'random_strength': 7.634560350902109, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.586978253762204, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:55,169] Trial 45 finished with value: 0.5171573992375064 and parameters: {'learning_rate': 0.024684209578548066, 'n_estimators': 637, 'max_depth': 8, 'l2_leaf_reg': 9.362973536395478, 'colsample_bylevel': 0.7275257170290724, 'min_data_in_leaf': 74, 'subsample': 0.6656402323616252, 'max_bin': 102, 'od_type': 'IncToDec', 'random_strength': 10.05578668861796, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.6681847805309097, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:57,073] Trial 43 finished with value: 0.6352596932726792 and parameters: {'learning_rate': 0.01115792518333805, 'n_estimators': 646, 'max_depth': 7, 'l2_leaf_reg': 9.977240998387321, 'colsample_bylevel': 0.7361719194582979, 'min_data_in_leaf': 86, 'subsample': 0.5515137936881789, 'max_bin': 303, 'od_type': 'IncToDec', 'random_strength': 11.118191349589743, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.5857732103153321, 'leaf_estimation_iterations': 3, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:04:59,622] Trial 41 finished with value: 0.7026372323162111 and parameters: {'learning_rate': 0.0121864252467468, 'n_estimators': 623, 'max_depth': 8, 'l2_leaf_reg': 4.560626965888524, 'colsample_bylevel': 0.737562259384802, 'min_data_in_leaf': 85, 'subsample': 0.5495971659939346, 'max_bin': 278, 'od_type': 'IncToDec', 'random_strength': 24.260066164576955, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.42814505763434896, 'leaf_estimation_iterations': 1, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:07,267] Trial 8 finished with value: 10.899926077283252 and parameters: {'learning_rate': 0.00029452857627441687, 'n_estimators': 908, 'max_depth': 8, 'l2_leaf_reg': 6.916186210212988, 'colsample_bylevel': 0.9354912680954889, 'min_data_in_leaf': 12, 'subsample': 0.6339605758046187, 'max_bin': 283, 'od_type': 'Iter', 'random_strength': 46.47659672892247, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 3, 'max_ctr_complexity': 3, 'bagging_temperature': 0.5165191332705843, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "2:\tlearn: 24.5048426\ttotal: 73.7ms\tremaining: 18.8s\n",
+      "[I 2025-02-17 19:05:07,552] Trial 48 finished with value: 0.38309231121694715 and parameters: {'learning_rate': 0.0237859447644015, 'n_estimators': 774, 'max_depth': 6, 'l2_leaf_reg': 8.002444425253527, 'colsample_bylevel': 0.7295261931919543, 'min_data_in_leaf': 74, 'subsample': 0.5554989059023433, 'max_bin': 210, 'od_type': 'IncToDec', 'random_strength': 9.018848418592867, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.581759394206053, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:09,786] Trial 46 finished with value: 0.5110647895513947 and parameters: {'learning_rate': 0.006681071022364863, 'n_estimators': 776, 'max_depth': 8, 'l2_leaf_reg': 0.3631732060984863, 'colsample_bylevel': 0.7363932636892351, 'min_data_in_leaf': 73, 'subsample': 0.5463919490628746, 'max_bin': 103, 'od_type': 'IncToDec', 'random_strength': 8.849790558178254, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 7, 'max_ctr_complexity': 8, 'bagging_temperature': 0.6654529126751142, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:16,563] Trial 51 finished with value: 1.0239151562289415 and parameters: {'learning_rate': 0.061983754688831966, 'n_estimators': 766, 'max_depth': 6, 'l2_leaf_reg': 0.3304961978700337, 'colsample_bylevel': 0.7759283819689818, 'min_data_in_leaf': 58, 'subsample': 0.6086824247704744, 'max_bin': 210, 'od_type': 'IncToDec', 'random_strength': 30.480288861364897, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.6534741030600674, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:19,706] Trial 50 finished with value: 0.8453996569321668 and parameters: {'learning_rate': 0.06113735112822022, 'n_estimators': 775, 'max_depth': 6, 'l2_leaf_reg': 0.48881678001962764, 'colsample_bylevel': 0.941792001579987, 'min_data_in_leaf': 59, 'subsample': 0.6157888377483172, 'max_bin': 211, 'od_type': 'IncToDec', 'random_strength': 33.29129528184573, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.6707943610735122, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:24,601] Trial 49 finished with value: 0.5684702491220294 and parameters: {'learning_rate': 0.025426131299998527, 'n_estimators': 772, 'max_depth': 6, 'l2_leaf_reg': 6.746026887198573, 'colsample_bylevel': 0.7767754644173331, 'min_data_in_leaf': 58, 'subsample': 0.6657155881590967, 'max_bin': 313, 'od_type': 'IncToDec', 'random_strength': 31.867952360749683, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.6729802802071936, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:32,599] Trial 47 finished with value: 0.48564645897438924 and parameters: {'learning_rate': 0.005952488038950016, 'n_estimators': 768, 'max_depth': 8, 'l2_leaf_reg': 0.6555712313420774, 'colsample_bylevel': 0.7209789302257696, 'min_data_in_leaf': 84, 'subsample': 0.5418005809103904, 'max_bin': 309, 'od_type': 'IncToDec', 'random_strength': 8.565551570617842, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 4, 'bagging_temperature': 0.5722091842794362, 'leaf_estimation_iterations': 6, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:34,177] Trial 52 finished with value: 0.8374278598400838 and parameters: {'learning_rate': 0.006820116496291826, 'n_estimators': 782, 'max_depth': 6, 'l2_leaf_reg': 0.5757965526791913, 'colsample_bylevel': 0.9358517825487285, 'min_data_in_leaf': 59, 'subsample': 0.605164496027257, 'max_bin': 208, 'od_type': 'Iter', 'random_strength': 35.4845971736227, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.6406219150942284, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:36,231] Trial 56 finished with value: 0.5975112259903598 and parameters: {'learning_rate': 0.02681960576676953, 'n_estimators': 952, 'max_depth': 5, 'l2_leaf_reg': 7.666679031873355, 'colsample_bylevel': 0.9565002393162307, 'min_data_in_leaf': 98, 'subsample': 0.5067104403438522, 'max_bin': 154, 'od_type': 'IncToDec', 'random_strength': 35.20003274144928, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 4, 'bagging_temperature': 0.5657035126571206, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:36,604] Trial 53 finished with value: 0.9186879392945543 and parameters: {'learning_rate': 0.007134061536099737, 'n_estimators': 769, 'max_depth': 6, 'l2_leaf_reg': 0.4463881944175536, 'colsample_bylevel': 0.7764752169930568, 'min_data_in_leaf': 58, 'subsample': 0.6122594988387092, 'max_bin': 210, 'od_type': 'IncToDec', 'random_strength': 33.133551736762264, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 4, 'bagging_temperature': 0.6884281403373359, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:40,917] Trial 54 finished with value: 0.7325780752881307 and parameters: {'learning_rate': 0.006336198441064424, 'n_estimators': 765, 'max_depth': 6, 'l2_leaf_reg': 6.26480578745957, 'colsample_bylevel': 0.9546830848387289, 'min_data_in_leaf': 58, 'subsample': 0.611202513690979, 'max_bin': 242, 'od_type': 'IncToDec', 'random_strength': 29.564605304916768, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 1, 'bagging_temperature': 0.6500097336819182, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:42,728] Trial 55 finished with value: 0.816089933525424 and parameters: {'learning_rate': 0.06024723979316211, 'n_estimators': 953, 'max_depth': 6, 'l2_leaf_reg': 6.805575574473758, 'colsample_bylevel': 0.951009469455264, 'min_data_in_leaf': 58, 'subsample': 0.5012293066115163, 'max_bin': 206, 'od_type': 'IncToDec', 'random_strength': 38.49326321185181, 'leaf_estimation_method': 'Newton', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 4, 'bagging_temperature': 0.579737937551776, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:42,968] Trial 57 finished with value: 0.5760089968587268 and parameters: {'learning_rate': 0.007556981148257469, 'n_estimators': 998, 'max_depth': 5, 'l2_leaf_reg': 6.179805088193052, 'colsample_bylevel': 0.8274869319911021, 'min_data_in_leaf': 52, 'subsample': 0.6737215075003697, 'max_bin': 153, 'od_type': 'IncToDec', 'random_strength': 39.36554009248461, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 1, 'bagging_temperature': 0.5649130100805222, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.431:\tlearn: 0.3418526\ttotal: 9.48s\tremaining: 11.3s\n",
+      "\n",
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+      "[I 2025-02-17 19:05:43,356] Trial 61 finished with value: 0.48916818231936265 and parameters: {'learning_rate': 0.05511507127221351, 'n_estimators': 559, 'max_depth': 4, 'l2_leaf_reg': 3.117167143352032, 'colsample_bylevel': 0.8256927036768041, 'min_data_in_leaf': 49, 'subsample': 0.6676129819731355, 'max_bin': 240, 'od_type': 'Iter', 'random_strength': 5.445764582216375, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 1, 'bagging_temperature': 0.4697882654965878, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:44,681] Trial 58 finished with value: 0.526431321805941 and parameters: {'learning_rate': 0.053939061423786155, 'n_estimators': 978, 'max_depth': 5, 'l2_leaf_reg': 1.7965848024489142, 'colsample_bylevel': 0.8247076252385602, 'min_data_in_leaf': 49, 'subsample': 0.5031141020923031, 'max_bin': 153, 'od_type': 'Iter', 'random_strength': 5.0803984650790985, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'Uniform', 'one_hot_max_size': 9, 'max_ctr_complexity': 1, 'bagging_temperature': 0.6053091302792842, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:48,548] Trial 63 finished with value: 0.4823784406020743 and parameters: {'learning_rate': 0.05195783085584585, 'n_estimators': 564, 'max_depth': 4, 'l2_leaf_reg': 2.855228319297404, 'colsample_bylevel': 0.8172786372367683, 'min_data_in_leaf': 49, 'subsample': 0.6735303579202876, 'max_bin': 233, 'od_type': 'Iter', 'random_strength': 4.981828889631467, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 6, 'bagging_temperature': 0.8679592196417745, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:51,289] Trial 60 finished with value: 0.3954864891699767 and parameters: {'learning_rate': 0.028015045768803898, 'n_estimators': 964, 'max_depth': 5, 'l2_leaf_reg': 2.9308411708474997, 'colsample_bylevel': 0.808488516879249, 'min_data_in_leaf': 100, 'subsample': 0.5213975127092639, 'max_bin': 239, 'od_type': 'IncToDec', 'random_strength': 4.735336540781672, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 6, 'bagging_temperature': 0.8597586856547232, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:52,560] Trial 62 finished with value: 0.562222182403834 and parameters: {'learning_rate': 0.05067993711601402, 'n_estimators': 857, 'max_depth': 5, 'l2_leaf_reg': 3.1644003850970877, 'colsample_bylevel': 0.8148036600467019, 'min_data_in_leaf': 50, 'subsample': 0.6790410737319768, 'max_bin': 236, 'od_type': 'Iter', 'random_strength': 6.11302098762132, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 6, 'bagging_temperature': 0.7902860344415097, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:55,286] Trial 59 finished with value: 0.3844715563183522 and parameters: {'learning_rate': 0.028797826886160924, 'n_estimators': 945, 'max_depth': 5, 'l2_leaf_reg': 1.7617506424744354, 'colsample_bylevel': 0.8246445769033132, 'min_data_in_leaf': 97, 'subsample': 0.665347453835291, 'max_bin': 155, 'od_type': 'Iter', 'random_strength': 5.096475316176093, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Depthwise', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 9, 'max_ctr_complexity': 1, 'bagging_temperature': 0.4697339921367873, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:55,976] Trial 64 finished with value: 0.38619679218515807 and parameters: {'learning_rate': 0.018764287524708913, 'n_estimators': 560, 'max_depth': 4, 'l2_leaf_reg': 1.737074010596414, 'colsample_bylevel': 0.8317976401859252, 'min_data_in_leaf': 52, 'subsample': 0.6635622685557461, 'max_bin': 231, 'od_type': 'Iter', 'random_strength': 4.849406042090289, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 6, 'bagging_temperature': 0.7650022459790307, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:05:57,239] Trial 65 finished with value: 0.3926127086302377 and parameters: {'learning_rate': 0.017028638725538465, 'n_estimators': 554, 'max_depth': 4, 'l2_leaf_reg': 2.92222373614964, 'colsample_bylevel': 0.8960892917582802, 'min_data_in_leaf': 50, 'subsample': 0.5734684591819362, 'max_bin': 229, 'od_type': 'Iter', 'random_strength': 4.5581987393851096, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 6, 'bagging_temperature': 0.4774064701074452, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:06:04,820] Trial 72 finished with value: 0.5507149596750632 and parameters: {'learning_rate': 0.016324338809264538, 'n_estimators': 914, 'max_depth': 3, 'l2_leaf_reg': 1.7664008663969524, 'colsample_bylevel': 0.9001709966922217, 'min_data_in_leaf': 98, 'subsample': 0.5654678587008338, 'max_bin': 131, 'od_type': 'Iter', 'random_strength': 18.205558405377243, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 2, 'bagging_temperature': 0.860288669213702, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:06:06,134] Trial 73 finished with value: 0.6653540664535066 and parameters: {'learning_rate': 0.019430405475683105, 'n_estimators': 918, 'max_depth': 3, 'l2_leaf_reg': 1.7499004079036142, 'colsample_bylevel': 0.9039058658043181, 'min_data_in_leaf': 93, 'subsample': 0.570332744270963, 'max_bin': 126, 'od_type': 'Iter', 'random_strength': 64.89555108555089, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 2, 'bagging_temperature': 0.4681684873047442, 'leaf_estimation_iterations': 8, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 28 with value: 0.1796486494136596.\n",
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+      "[I 2025-02-17 19:06:06,582] Trial 68 finished with value: 0.15108252231154506 and parameters: {'learning_rate': 0.01895273515775726, 'n_estimators': 812, 'max_depth': 6, 'l2_leaf_reg': 1.7716418250501087, 'colsample_bylevel': 0.7016220955969794, 'min_data_in_leaf': 70, 'subsample': 0.5651200947737074, 'max_bin': 225, 'od_type': 'IncToDec', 'random_strength': 0.020919644561239625, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'SymmetricTree', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7314139978785491, 'leaf_estimation_iterations': 4, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:07,956] Trial 67 finished with value: 0.5350866933451506 and parameters: {'learning_rate': 0.018527745588253132, 'n_estimators': 858, 'max_depth': 5, 'l2_leaf_reg': 2.749974743469422, 'colsample_bylevel': 0.8066435128192131, 'min_data_in_leaf': 78, 'subsample': 0.7016626917001394, 'max_bin': 226, 'od_type': 'Iter', 'random_strength': 18.615690881281623, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 6, 'bagging_temperature': 0.7775131217936974, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:15,749] Trial 66 finished with value: 0.6015310690622198 and parameters: {'learning_rate': 0.017963779326592, 'n_estimators': 866, 'max_depth': 5, 'l2_leaf_reg': 1.7103904899687654, 'colsample_bylevel': 0.7992099326472556, 'min_data_in_leaf': 80, 'subsample': 0.567956385714884, 'max_bin': 412, 'od_type': 'Iter', 'random_strength': 18.718098724940262, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 3, 'bagging_temperature': 0.7145271412662415, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:16,498] Trial 71 finished with value: 0.5268457777439478 and parameters: {'learning_rate': 0.0184528929572801, 'n_estimators': 916, 'max_depth': 5, 'l2_leaf_reg': 1.1235253823060876, 'colsample_bylevel': 0.8978329574578724, 'min_data_in_leaf': 96, 'subsample': 0.5723265857723294, 'max_bin': 132, 'od_type': 'IncToDec', 'random_strength': 18.455449040421854, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 2, 'bagging_temperature': 0.22500642535189175, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:19,479] Trial 75 finished with value: 0.6048934406207328 and parameters: {'learning_rate': 0.02262676155473838, 'n_estimators': 522, 'max_depth': 4, 'l2_leaf_reg': 1.1018535717860714, 'colsample_bylevel': 0.7050591927065113, 'min_data_in_leaf': 36, 'subsample': 0.6531615611019299, 'max_bin': 263, 'od_type': 'Iter', 'random_strength': 13.079894450003195, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 3, 'bagging_temperature': 0.3187509106408818, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:21,686] Trial 70 finished with value: 0.5729257203410217 and parameters: {'learning_rate': 0.019799032459054296, 'n_estimators': 904, 'max_depth': 5, 'l2_leaf_reg': 0.13472694900322632, 'colsample_bylevel': 0.7587612668501139, 'min_data_in_leaf': 80, 'subsample': 0.5334633189381878, 'max_bin': 375, 'od_type': 'IncToDec', 'random_strength': 18.29079045064132, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.7716404124919631, 'leaf_estimation_iterations': 2, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.709:\tlearn: 0.0804120\ttotal: 28.7s\tremaining: 6.17s\n",
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+      "[I 2025-02-17 19:06:22,922] Trial 76 finished with value: 0.27709587062378493 and parameters: {'learning_rate': 0.034656977712420045, 'n_estimators': 804, 'max_depth': 4, 'l2_leaf_reg': 1.0502549787595326, 'colsample_bylevel': 0.699648467579323, 'min_data_in_leaf': 70, 'subsample': 0.6518873699049352, 'max_bin': 258, 'od_type': 'Iter', 'random_strength': 0.8748869392695278, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 3, 'bagging_temperature': 0.17469921218081008, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.191:\tlearn: 0.2531807\ttotal: 5.04s\tremaining: 8.64s\n",
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+      "42:\tlearn: 5.6044084\ttotal: 969ms\tremaining: 8.11s\n",
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+      "[I 2025-02-17 19:06:23,631] Trial 77 finished with value: 0.15531344265612546 and parameters: {'learning_rate': 0.036791750185780965, 'n_estimators': 817, 'max_depth': 4, 'l2_leaf_reg': 0.9139835750788172, 'colsample_bylevel': 0.6491110432633207, 'min_data_in_leaf': 70, 'subsample': 0.6568577390468678, 'max_bin': 255, 'od_type': 'Iter', 'random_strength': 0.06453276295219812, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 3, 'bagging_temperature': 0.7192507465635307, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:25,802] Trial 74 finished with value: 0.8748991100728837 and parameters: {'learning_rate': 0.022122199931308557, 'n_estimators': 520, 'max_depth': 4, 'l2_leaf_reg': 1.091755240276478, 'colsample_bylevel': 0.896190276935693, 'min_data_in_leaf': 37, 'subsample': 0.6524726994585253, 'max_bin': 400, 'od_type': 'Iter', 'random_strength': 65.81666148826473, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 6, 'max_ctr_complexity': 3, 'bagging_temperature': 0.3091614677168046, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 68 with value: 0.15108252231154506.\n",
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+      "[I 2025-02-17 19:06:26,855] Trial 69 finished with value: 0.09630055656967536 and parameters: {'learning_rate': 0.01813069428779062, 'n_estimators': 863, 'max_depth': 5, 'l2_leaf_reg': 1.7958191080688655, 'colsample_bylevel': 0.7993981849787956, 'min_data_in_leaf': 96, 'subsample': 0.5705997571695023, 'max_bin': 397, 'od_type': 'IncToDec', 'random_strength': 0.014435490289824493, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 10, 'max_ctr_complexity': 5, 'bagging_temperature': 0.8629206662618324, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'Gumbel'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:29,547] Trial 81 finished with value: 0.17207417625739907 and parameters: {'learning_rate': 0.034600832383072315, 'n_estimators': 403, 'max_depth': 4, 'l2_leaf_reg': 0.8348368901959793, 'colsample_bylevel': 0.6533366782925941, 'min_data_in_leaf': 71, 'subsample': 0.6490461862235457, 'max_bin': 183, 'od_type': 'Iter', 'random_strength': 0.1705186899551503, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 6, 'max_ctr_complexity': 6, 'bagging_temperature': 0.4979564751822225, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:30,227] Trial 79 finished with value: 0.1987306437765178 and parameters: {'learning_rate': 0.033330331041820704, 'n_estimators': 521, 'max_depth': 4, 'l2_leaf_reg': 1.0389114562617403, 'colsample_bylevel': 0.7590149244913659, 'min_data_in_leaf': 37, 'subsample': 0.652298494112468, 'max_bin': 256, 'od_type': 'Iter', 'random_strength': 0.3817670057224078, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 5, 'bagging_temperature': 0.5411219197576509, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:33,690] Trial 78 finished with value: 0.6114734492168871 and parameters: {'learning_rate': 0.035714126914281774, 'n_estimators': 821, 'max_depth': 4, 'l2_leaf_reg': 0.9458637507946048, 'colsample_bylevel': 0.6965214420170914, 'min_data_in_leaf': 36, 'subsample': 0.5298077093293367, 'max_bin': 258, 'od_type': 'Iter', 'random_strength': 12.336120546923752, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 4, 'max_ctr_complexity': 5, 'bagging_temperature': 0.3338873684483473, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:39,618] Trial 83 finished with value: 0.2584195974313434 and parameters: {'learning_rate': 0.03642979857651298, 'n_estimators': 810, 'max_depth': 4, 'l2_leaf_reg': 0.8331498302799191, 'colsample_bylevel': 0.6466542639994328, 'min_data_in_leaf': 70, 'subsample': 0.6517669323461349, 'max_bin': 253, 'od_type': 'Iter', 'random_strength': 0.9609562170876905, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 6, 'max_ctr_complexity': 3, 'bagging_temperature': 0.6176381867593161, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
+      "[I 2025-02-17 19:06:39,690] Trial 80 finished with value: 0.22806994824736251 and parameters: {'learning_rate': 0.035253165274072865, 'n_estimators': 816, 'max_depth': 4, 'l2_leaf_reg': 0.7941308428697843, 'colsample_bylevel': 0.7573847293975434, 'min_data_in_leaf': 70, 'subsample': 0.5948126868204439, 'max_bin': 331, 'od_type': 'Iter', 'random_strength': 0.35463656748889427, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 6, 'max_ctr_complexity': 5, 'bagging_temperature': 0.4911034146753929, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "813:\tlearn: 0.1399604\ttotal: 15.9s\tremaining: 0us\n",
+      "[I 2025-02-17 19:06:40,468] Trial 82 finished with value: 0.21715191626541128 and parameters: {'learning_rate': 0.038017028125797526, 'n_estimators': 814, 'max_depth': 4, 'l2_leaf_reg': 0.8225781898762904, 'colsample_bylevel': 0.6996292464800894, 'min_data_in_leaf': 69, 'subsample': 0.7043824401255886, 'max_bin': 257, 'od_type': 'Iter', 'random_strength': 0.29348078514044706, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 3, 'bagging_temperature': 0.23281557893756422, 'leaf_estimation_iterations': 7, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:43,540] Trial 87 finished with value: 0.17372742742755534 and parameters: {'learning_rate': 0.03569833556960258, 'n_estimators': 379, 'max_depth': 4, 'l2_leaf_reg': 0.8216620811045015, 'colsample_bylevel': 0.6599335491288428, 'min_data_in_leaf': 71, 'subsample': 0.625072747939172, 'max_bin': 487, 'od_type': 'Iter', 'random_strength': 0.15454754893593523, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.0049948880710593, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:46,727] Trial 86 finished with value: 0.3452447170286215 and parameters: {'learning_rate': 0.035820979398133154, 'n_estimators': 822, 'max_depth': 4, 'l2_leaf_reg': 0.8434766364390066, 'colsample_bylevel': 0.6612242016721342, 'min_data_in_leaf': 71, 'subsample': 0.5936265741233338, 'max_bin': 257, 'od_type': 'Iter', 'random_strength': 2.2316520481269, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.18813681508948002, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:48,815] Trial 88 finished with value: 0.22280178106618964 and parameters: {'learning_rate': 0.03811214510260399, 'n_estimators': 407, 'max_depth': 4, 'l2_leaf_reg': 0.7959084115981556, 'colsample_bylevel': 0.6609602668800726, 'min_data_in_leaf': 25, 'subsample': 0.6280253461110031, 'max_bin': 475, 'od_type': 'Iter', 'random_strength': 0.34945253233018164, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 5, 'bagging_temperature': 0.5318398291979516, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:50,846] Trial 91 finished with value: 0.5168784331351608 and parameters: {'learning_rate': 0.07377099064361861, 'n_estimators': 355, 'max_depth': 4, 'l2_leaf_reg': 0.8065560992862141, 'colsample_bylevel': 0.6627296213021573, 'min_data_in_leaf': 70, 'subsample': 0.6261085590142348, 'max_bin': 333, 'od_type': 'Iter', 'random_strength': 2.7191544515386687, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.5419386498554987, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:52,048] Trial 89 finished with value: 0.21104366594043256 and parameters: {'learning_rate': 0.03302480756918949, 'n_estimators': 384, 'max_depth': 4, 'l2_leaf_reg': 0.7798599147496693, 'colsample_bylevel': 0.6645714640025184, 'min_data_in_leaf': 71, 'subsample': 0.5955588228868535, 'max_bin': 334, 'od_type': 'Iter', 'random_strength': 0.2844578190320068, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.2506219730784064, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:53,033] Trial 92 finished with value: 0.45325744939905305 and parameters: {'learning_rate': 0.0734543956147, 'n_estimators': 408, 'max_depth': 3, 'l2_leaf_reg': 0.7554189078132445, 'colsample_bylevel': 0.6600884155859301, 'min_data_in_leaf': 71, 'subsample': 0.6257899427491422, 'max_bin': 479, 'od_type': 'Iter', 'random_strength': 2.883174243058605, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.016210152586589546, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:54,957] Trial 90 finished with value: 6.545473035519732 and parameters: {'learning_rate': 0.002100749341664993, 'n_estimators': 378, 'max_depth': 4, 'l2_leaf_reg': 0.645654829076853, 'colsample_bylevel': 0.6549356839269879, 'min_data_in_leaf': 70, 'subsample': 0.5918775271492928, 'max_bin': 325, 'od_type': 'Iter', 'random_strength': 0.03096282998322765, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.9274530858292772, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:56,222] Trial 93 finished with value: 0.5026552889273644 and parameters: {'learning_rate': 0.09464854713934855, 'n_estimators': 368, 'max_depth': 3, 'l2_leaf_reg': 0.7000710242750434, 'colsample_bylevel': 0.6703643022616594, 'min_data_in_leaf': 82, 'subsample': 0.625263387699477, 'max_bin': 460, 'od_type': 'Iter', 'random_strength': 2.8499366082461304, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.02715767027912038, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.66:\tlearn: 22.0251228\ttotal: 1.51s\tremaining: 5.51s\n",
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+      "[I 2025-02-17 19:06:57,860] Trial 84 finished with value: 0.3007396826029225 and parameters: {'learning_rate': 0.03392735988965163, 'n_estimators': 807, 'max_depth': 6, 'l2_leaf_reg': 0.9240628666918584, 'colsample_bylevel': 0.6483678363270362, 'min_data_in_leaf': 69, 'subsample': 0.6252473134911628, 'max_bin': 258, 'od_type': 'Iter', 'random_strength': 0.8280880207249266, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 3, 'bagging_temperature': 0.17763398570081357, 'leaf_estimation_iterations': 9, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:59,040] Trial 94 finished with value: 8.713595134037721 and parameters: {'learning_rate': 0.0012399698176919932, 'n_estimators': 362, 'max_depth': 3, 'l2_leaf_reg': 0.6750649373517269, 'colsample_bylevel': 0.6188339224394578, 'min_data_in_leaf': 22, 'subsample': 0.6269347115621348, 'max_bin': 476, 'od_type': 'Iter', 'random_strength': 2.9299379755921295, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 3, 'max_ctr_complexity': 5, 'bagging_temperature': 0.04514375082685826, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "[I 2025-02-17 19:06:59,246] Trial 85 finished with value: 0.21365341968919374 and parameters: {'learning_rate': 0.041606464074547406, 'n_estimators': 820, 'max_depth': 6, 'l2_leaf_reg': 0.8847890062450529, 'colsample_bylevel': 0.6547304435589824, 'min_data_in_leaf': 70, 'subsample': 0.5963250871807675, 'max_bin': 332, 'od_type': 'IncToDec', 'random_strength': 0.27449429241344786, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'GreedyLogSum', 'one_hot_max_size': 3, 'max_ctr_complexity': 3, 'bagging_temperature': 0.01256570666907153, 'leaf_estimation_iterations': 5, 'leaf_estimation_backtracking': 'No', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
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+      "369:\tlearn: 11.0621657\ttotal: 6.4s\tremaining: 640ms\n",
+      "87:\tlearn: 0.9774207\ttotal: 2.09s\tremaining: 7.8s\n",
+      "395:\tlearn: 0.4687927\ttotal: 7.47s\tremaining: 396ms\n",
+      "370:\tlearn: 11.0369586\ttotal: 6.41s\tremaining: 622ms\n",
+      "121:\tlearn: 1.9560667\ttotal: 3.35s\tremaining: 8.31s\n",
+      "309:\tlearn: 14.0447629\ttotal: 5.29s\tremaining: 17.1ms\n",
+      "371:\tlearn: 11.0134560\ttotal: 6.41s\tremaining: 603ms\n",
+      "396:\tlearn: 0.4674437\ttotal: 7.48s\tremaining: 377ms\n",
+      "88:\tlearn: 0.9598196\ttotal: 2.1s\tremaining: 7.74s\n",
+      "310:\tlearn: 14.0194006\ttotal: 5.3s\tremaining: 0us\n",
+      "372:\tlearn: 10.9896863\ttotal: 6.42s\tremaining: 585ms\n",
+      "122:\tlearn: 1.9514038\ttotal: 3.36s\tremaining: 8.25s\n",
+      "397:\tlearn: 0.4660885\ttotal: 7.48s\tremaining: 357ms\n",
+      "398:\tlearn: 0.4657986\ttotal: 7.49s\tremaining: 338ms\n",
+      "[I 2025-02-17 19:07:00,150] Trial 97 finished with value: 6.62078027259102 and parameters: {'learning_rate': 0.002180803771368469, 'n_estimators': 311, 'max_depth': 3, 'l2_leaf_reg': 0.6887207394995578, 'colsample_bylevel': 0.626106658300844, 'min_data_in_leaf': 62, 'subsample': 0.6856006235561786, 'max_bin': 432, 'od_type': 'Iter', 'random_strength': 10.68264149391283, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 5, 'bagging_temperature': 0.11736980651325285, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
+      "399:\tlearn: 0.4655190\ttotal: 7.5s\tremaining: 319ms\n",
+      "374:\tlearn: 10.9399493\ttotal: 6.43s\tremaining: 549ms\n",
+      "89:\tlearn: 0.9476074\ttotal: 2.12s\tremaining: 7.68s\n",
+      "123:\tlearn: 1.9323375\ttotal: 3.37s\tremaining: 8.19s\n",
+      "400:\tlearn: 0.4649315\ttotal: 7.5s\tremaining: 299ms\n",
+      "375:\tlearn: 10.9148243\ttotal: 6.45s\tremaining: 532ms\n",
+      "401:\tlearn: 0.4643602\ttotal: 7.54s\tremaining: 282ms\n",
+      "90:\tlearn: 0.9330780\ttotal: 2.17s\tremaining: 7.77s\n",
+      "376:\tlearn: 10.8915532\ttotal: 6.48s\tremaining: 516ms\n",
+      "124:\tlearn: 1.9244258\ttotal: 3.43s\tremaining: 8.24s\n",
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+      "411:\tlearn: 0.4583302\ttotal: 7.6s\tremaining: 92.3ms\n",
+      "95:\tlearn: 0.8689996\ttotal: 2.22s\tremaining: 7.43s\n",
+      "386:\tlearn: 10.6682802\ttotal: 6.54s\tremaining: 338ms\n",
+      "130:\tlearn: 1.8682745\ttotal: 3.48s\tremaining: 7.82s\n",
+      "412:\tlearn: 0.4577106\ttotal: 7.61s\tremaining: 73.7ms\n",
+      "387:\tlearn: 10.6471417\ttotal: 6.54s\tremaining: 320ms\n",
+      "413:\tlearn: 0.4575253\ttotal: 7.61s\tremaining: 55.2ms\n",
+      "96:\tlearn: 0.8600747\ttotal: 2.23s\tremaining: 7.37s\n",
+      "131:\tlearn: 1.8613817\ttotal: 3.49s\tremaining: 7.76s\n",
+      "388:\tlearn: 10.6228141\ttotal: 6.55s\tremaining: 303ms\n",
+      "414:\tlearn: 0.4567039\ttotal: 7.62s\tremaining: 36.7ms\n",
+      "389:\tlearn: 10.5994546\ttotal: 6.55s\tremaining: 286ms\n",
+      "97:\tlearn: 0.8503489\ttotal: 2.25s\tremaining: 7.31s\n",
+      "132:\tlearn: 1.8434724\ttotal: 3.5s\tremaining: 7.69s\n",
+      "415:\tlearn: 0.4559311\ttotal: 7.63s\tremaining: 18.3ms\n",
+      "390:\tlearn: 10.5745906\ttotal: 6.56s\tremaining: 268ms\n",
+      "416:\tlearn: 0.4557067\ttotal: 7.63s\tremaining: 0us\n",
+      "391:\tlearn: 10.5526898\ttotal: 6.57s\tremaining: 251ms\n",
+      "133:\tlearn: 1.8369134\ttotal: 3.51s\tremaining: 7.63s\n",
+      "[I 2025-02-17 19:07:00,301] Trial 95 finished with value: 0.5667494390297843 and parameters: {'learning_rate': 0.04547694590563112, 'n_estimators': 417, 'max_depth': 3, 'l2_leaf_reg': 0.6805435824735477, 'colsample_bylevel': 0.6259546813603812, 'min_data_in_leaf': 21, 'subsample': 0.693255172142272, 'max_bin': 483, 'od_type': 'Iter', 'random_strength': 7.870256740175291, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 5, 'bagging_temperature': 0.2613033602687341, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
+      "99:\tlearn: 0.8366281\ttotal: 2.26s\tremaining: 7.17s\n",
+      "393:\tlearn: 10.5078712\ttotal: 6.58s\tremaining: 217ms\n",
+      "134:\tlearn: 1.8332740\ttotal: 3.52s\tremaining: 7.57s\n",
+      "394:\tlearn: 10.4845417\ttotal: 6.63s\tremaining: 201ms\n",
+      "100:\tlearn: 0.8290525\ttotal: 2.32s\tremaining: 7.26s\n",
+      "135:\tlearn: 1.8153603\ttotal: 3.58s\tremaining: 7.61s\n",
+      "395:\tlearn: 10.4607232\ttotal: 6.64s\tremaining: 184ms\n",
+      "396:\tlearn: 10.4379338\ttotal: 6.65s\tremaining: 167ms\n",
+      "101:\tlearn: 0.8207583\ttotal: 2.34s\tremaining: 7.22s\n",
+      "397:\tlearn: 10.4186174\ttotal: 6.65s\tremaining: 150ms\n",
+      "136:\tlearn: 1.7972357\ttotal: 3.6s\tremaining: 7.56s\n",
+      "398:\tlearn: 10.3946388\ttotal: 6.65s\tremaining: 133ms\n",
+      "102:\tlearn: 0.8133133\ttotal: 2.35s\tremaining: 7.16s\n",
+      "137:\tlearn: 1.7805950\ttotal: 3.6s\tremaining: 7.5s\n",
+      "399:\tlearn: 10.3714248\ttotal: 6.66s\tremaining: 117ms\n",
+      "400:\tlearn: 10.3480736\ttotal: 6.7s\tremaining: 100ms\n",
+      "138:\tlearn: 1.7626873\ttotal: 3.65s\tremaining: 7.52s\n",
+      "103:\tlearn: 0.8069680\ttotal: 2.4s\tremaining: 7.21s\n",
+      "401:\tlearn: 10.3244932\ttotal: 6.71s\tremaining: 83.5ms\n",
+      "104:\tlearn: 0.8020140\ttotal: 2.4s\tremaining: 7.15s\n",
+      "402:\tlearn: 10.3024794\ttotal: 6.72s\tremaining: 66.7ms\n",
+      "139:\tlearn: 1.7583266\ttotal: 3.66s\tremaining: 7.46s\n",
+      "403:\tlearn: 10.2805803\ttotal: 6.72s\tremaining: 49.9ms\n",
+      "105:\tlearn: 0.7972622\ttotal: 2.41s\tremaining: 7.08s\n",
+      "140:\tlearn: 1.7554245\ttotal: 3.67s\tremaining: 7.39s\n",
+      "404:\tlearn: 10.2566696\ttotal: 6.72s\tremaining: 33.2ms\n",
+      "405:\tlearn: 10.2368056\ttotal: 6.73s\tremaining: 16.6ms\n",
+      "106:\tlearn: 0.7905855\ttotal: 2.42s\tremaining: 7.01s\n",
+      "406:\tlearn: 10.2140679\ttotal: 6.73s\tremaining: 0us\n",
+      "141:\tlearn: 1.7433406\ttotal: 3.68s\tremaining: 7.33s\n",
+      "[I 2025-02-17 19:07:00,472] Trial 96 finished with value: 4.4723395335419465 and parameters: {'learning_rate': 0.0025251202768658398, 'n_estimators': 407, 'max_depth': 3, 'l2_leaf_reg': 0.6805179318296192, 'colsample_bylevel': 0.6307127749747744, 'min_data_in_leaf': 62, 'subsample': 0.6929858460660119, 'max_bin': 365, 'od_type': 'Iter', 'random_strength': 8.009157205975768, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 5, 'bagging_temperature': 0.040550018420108414, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
+      "[I 2025-02-17 19:07:02,150] Trial 98 finished with value: 1.1228179368955464 and parameters: {'learning_rate': 0.04202783374933014, 'n_estimators': 425, 'max_depth': 4, 'l2_leaf_reg': 0.6781980696572001, 'colsample_bylevel': 0.6263769918916625, 'min_data_in_leaf': 27, 'subsample': 0.7109873842999416, 'max_bin': 476, 'od_type': 'Iter', 'random_strength': 86.01000827054426, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 5, 'max_ctr_complexity': 5, 'bagging_temperature': 0.04400227210944854, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n",
+      "[I 2025-02-17 19:07:02,279] Trial 99 finished with value: 0.6035174557748251 and parameters: {'learning_rate': 0.04818750095235205, 'n_estimators': 417, 'max_depth': 4, 'l2_leaf_reg': 0.9431361903912845, 'colsample_bylevel': 0.6226530130468801, 'min_data_in_leaf': 24, 'subsample': 0.6894084370038897, 'max_bin': 488, 'od_type': 'Iter', 'random_strength': 7.3502174269309, 'leaf_estimation_method': 'Gradient', 'grow_policy': 'Lossguide', 'feature_border_type': 'MinEntropy', 'one_hot_max_size': 2, 'max_ctr_complexity': 5, 'bagging_temperature': 0.07787690744890884, 'leaf_estimation_iterations': 10, 'leaf_estimation_backtracking': 'AnyImprovement', 'random_score_type': 'NormalWithModelSizeDecrease'}. Best is trial 69 with value: 0.09630055656967536.\n"
+     ]
+    }
+   ],
+   "source": [
+    "study = optuna.create_study(direction=\"minimize\")\n",
+    "study.optimize(objective, n_trials=100, show_progress_bar=True, n_jobs=-1)\n",
+    "best_params = study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'learning_rate': 0.01813069428779062,\n",
+       " 'n_estimators': 863,\n",
+       " 'max_depth': 5,\n",
+       " 'l2_leaf_reg': 1.7958191080688655,\n",
+       " 'colsample_bylevel': 0.7993981849787956,\n",
+       " 'min_data_in_leaf': 96,\n",
+       " 'subsample': 0.5705997571695023,\n",
+       " 'max_bin': 397,\n",
+       " 'od_type': 'IncToDec',\n",
+       " 'random_strength': 0.014435490289824493,\n",
+       " 'leaf_estimation_method': 'Gradient',\n",
+       " 'grow_policy': 'Lossguide',\n",
+       " 'feature_border_type': 'MinEntropy',\n",
+       " 'one_hot_max_size': 10,\n",
+       " 'max_ctr_complexity': 5,\n",
+       " 'bagging_temperature': 0.8629206662618324,\n",
+       " 'leaf_estimation_iterations': 5,\n",
+       " 'leaf_estimation_backtracking': 'AnyImprovement',\n",
+       " 'random_score_type': 'Gumbel'}"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:\tlearn: 24.5002877\ttotal: 6.48ms\tremaining: 5.58s\n",
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+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<catboost.core.CatBoostRegressor at 0x30ed17390>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = CatBoostRegressor(**best_params, random_state=SEED, early_stopping_rounds=100)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "{'learning_rate': 0.01813069428779062,\n",
+    " 'n_estimators': 863,\n",
+    " 'max_depth': 5,\n",
+    " 'l2_leaf_reg': 1.7958191080688655,\n",
+    " 'colsample_bylevel': 0.7993981849787956,\n",
+    " 'min_data_in_leaf': 96,\n",
+    " 'subsample': 0.5705997571695023,\n",
+    " 'max_bin': 397,\n",
+    " 'od_type': 'IncToDec',\n",
+    " 'random_strength': 0.014435490289824493,\n",
+    " 'leaf_estimation_method': 'Gradient',\n",
+    " 'grow_policy': 'Lossguide',\n",
+    " 'feature_border_type': 'MinEntropy',\n",
+    " 'one_hot_max_size': 10,\n",
+    " 'max_ctr_complexity': 5,\n",
+    " 'bagging_temperature': 0.8629206662618324,\n",
+    " 'leaf_estimation_iterations': 5,\n",
+    " 'leaf_estimation_backtracking': 'AnyImprovement',\n",
+    " 'random_score_type': 'Gumbel'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['../models/catboost-1.pkl.gz']"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(model, \"../models/catboost-1.pkl.gz\", compress=\"gzip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.058644801110443644"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_pred = model.predict(X_train)\n",
+    "mean_absolute_error(y_train, train_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_pred = model.predict(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09630055656967536"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_val, val_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3261406422763714"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_pred = pd.concat([train_df[\"datetime\"], pd.Series(train_pred, name=\"predictions\"), y_train], axis=1)\n",
+    "val_pred = pd.concat([val_df[\"datetime\"], pd.Series(val_pred, name=\"predictions\"), y_val], axis=1)\n",
+    "test_pred = pd.concat([test_df[\"datetime\"], pd.Series(test_pred, name=\"predictions\"), y_test], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
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+         },
+         "yaxis": "y"
+        },
+        {
+         "mode": "lines",
+         "name": "Train Predictions",
+         "type": "scatter",
+         "x": [
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",
+          "dtype": "f8"
+         },
+         "yaxis": "y"
+        },
+        {
+         "mode": "lines",
+         "name": "Validation True",
+         "type": "scatter",
+         "x": [
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+         "xaxis": "x2",
+         "y": {
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",
+          "dtype": "f8"
+         },
+         "yaxis": "y2"
+        },
+        {
+         "mode": "lines",
+         "name": "Test True",
+         "type": "scatter",
+         "x": [
+          "2018-11-08T00:00:00.000000000",
+          "2018-11-09T00:00:00.000000000",
+          "2018-11-10T00:00:00.000000000",
+          "2018-11-11T00:00:00.000000000",
+          "2018-11-12T00:00:00.000000000",
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+          "dtype": "f8"
+         },
+         "yaxis": "y3"
+        },
+        {
+         "mode": "lines",
+         "name": "Test Predictions",
+         "type": "scatter",
+         "x": [
+          "2018-11-08T00:00:00.000000000",
+          "2018-11-09T00:00:00.000000000",
+          "2018-11-10T00:00:00.000000000",
+          "2018-11-11T00:00:00.000000000",
+          "2018-11-12T00:00:00.000000000",
+          "2018-11-13T00:00:00.000000000",
+          "2018-11-14T00:00:00.000000000",
+          "2018-11-15T00:00:00.000000000",
+          "2018-11-16T00:00:00.000000000",
+          "2018-11-17T00:00:00.000000000",
+          "2018-11-18T00:00:00.000000000",
+          "2018-11-19T00:00:00.000000000",
+          "2018-11-20T00:00:00.000000000",
+          "2018-11-21T00:00:00.000000000",
+          "2018-11-22T00:00:00.000000000",
+          "2018-11-23T00:00:00.000000000",
+          "2018-11-24T00:00:00.000000000",
+          "2018-11-25T00:00:00.000000000",
+          "2018-11-26T00:00:00.000000000",
+          "2018-11-27T00:00:00.000000000",
+          "2018-11-28T00:00:00.000000000",
+          "2018-11-29T00:00:00.000000000",
+          "2018-11-30T00:00:00.000000000",
+          "2018-12-01T00:00:00.000000000",
+          "2018-12-02T00:00:00.000000000",
+          "2018-12-03T00:00:00.000000000",
+          "2018-12-04T00:00:00.000000000",
+          "2018-12-05T00:00:00.000000000",
+          "2018-12-06T00:00:00.000000000",
+          "2018-12-07T00:00:00.000000000",
+          "2018-12-08T00:00:00.000000000",
+          "2018-12-09T00:00:00.000000000",
+          "2018-12-10T00:00:00.000000000",
+          "2018-12-11T00:00:00.000000000",
+          "2018-12-12T00:00:00.000000000",
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+         "yaxis": "y3"
+        }
+       ],
+       "layout": {
+        "annotations": [
+         {
+          "font": {
+           "size": 16
+          },
+          "showarrow": false,
+          "text": "Train",
+          "x": 0.14444444444444446,
+          "xanchor": "center",
+          "xref": "paper",
+          "y": 1,
+          "yanchor": "bottom",
+          "yref": "paper"
+         },
+         {
+          "font": {
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+          },
+          "showarrow": false,
+          "text": "Validation",
+          "x": 0.5,
+          "xanchor": "center",
+          "xref": "paper",
+          "y": 1,
+          "yanchor": "bottom",
+          "yref": "paper"
+         },
+         {
+          "font": {
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+          "showarrow": false,
+          "text": "Test",
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+          "xanchor": "center",
+          "xref": "paper",
+          "y": 1,
+          "yanchor": "bottom",
+          "yref": "paper"
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+        ],
+        "height": 400,
+        "template": {
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+             "color": "#2a3f5f"
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+            "error_y": {
+             "color": "#2a3f5f"
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+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
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+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
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+            "type": "bar"
+           }
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+             "pattern": {
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+              "solidity": 0.2
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+            },
+            "type": "barpolar"
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+          "carpet": [
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+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
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+            "baxis": {
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+             "linecolor": "white",
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+            "type": "carpet"
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+          "choropleth": [
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+             "ticks": ""
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+          "contour": [
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+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
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+           "title": {
+            "standoff": 15
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+           "zerolinecolor": "white",
+           "zerolinewidth": 2
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+         }
+        },
+        "title": {
+         "text": "Predictions vs. True Values"
+        },
+        "width": 1200,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          0.2888888888888889
+         ]
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "domain": [
+          0.35555555555555557,
+          0.6444444444444445
+         ]
+        },
+        "xaxis3": {
+         "anchor": "y3",
+         "domain": [
+          0.7111111111111111,
+          1
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+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
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+        "yaxis2": {
+         "anchor": "x2",
+         "domain": [
+          0,
+          1
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+        "yaxis3": {
+         "anchor": "x3",
+         "domain": [
+          0,
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+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = make_subplots(rows=1, cols=3, subplot_titles=(\"Train\", \"Validation\", \"Test\"))\n",
+    "\n",
+    "for i, (df, name) in enumerate(zip([train_pred, val_pred, test_pred], [\"Train\", \"Validation\", \"Test\"])):\n",
+    "    fig.add_trace(go.Scatter(x=df.datetime, y=df[TARGET], mode=\"lines\", name=f\"{name} True\"), row=1, col=i+1)\n",
+    "    fig.add_trace(go.Scatter(x=df.datetime, y=df[\"predictions\"], mode=\"lines\", name=f\"{name} Predictions\"), row=1, col=i+1)\n",
+    "\n",
+    "fig.update_layout(height=400, width=1200, title_text=\"Predictions vs. True Values\")\n",
+    "fig.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week2/friday/you_do_4/notebooks/6.2-ml-catboost-with-te-and-cv.ipynb b/week2/friday/you_do_4/notebooks/6.2-ml-catboost-with-te-and-cv.ipynb
new file mode 100644
index 0000000..76de5af
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/6.2-ml-catboost-with-te-and-cv.ipynb
@@ -0,0 +1,25135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sktime.split import ExpandingWindowSplitter\n",
+    "from lightgbm import LGBMRegressor\n",
+    "import joblib\n",
+    "from catboost import CatBoostRegressor\n",
+    "import json\n",
+    "import optuna\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "from features.FeatureExtractor import FeatureExtractor\n",
+    "from features.TargetEncoder import TargetEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"dataset/interim/past_dataset.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SEED = 87\n",
+    "TARGET = \"general_dam_occupancy_rate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    with open(\"models/best_features_by_lightgbm_with_te.json\", errors=\"FileNotFoundError\") as f:\n",
+    "        best_features = json.load(f)\n",
+    "        \n",
+    "except FileNotFoundError:\n",
+    "    best_features = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = (\n",
+    "    pd.read_csv(\n",
+    "        cleaned_dataset_address,\n",
+    "        parse_dates=[\"datetime\"],\n",
+    "        converters={\"weather_code\": str},\n",
+    "    )\n",
+    "    .set_index(\"datetime\")\n",
+    "    .sort_index()\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    \"past_knowledge\": past_knowledge,\n",
+    "    \"cyclical_feature_names\": {\n",
+    "        \"month\": 12,\n",
+    "        \"day\": 31,\n",
+    "        \"day_of_year\": 365,\n",
+    "        \"week_of_year\": 52,\n",
+    "        \"quarter\": 4,\n",
+    "        # \"season\": 4,\n",
+    "        \"is_weekend\": 2,\n",
+    "        \"precipitation_hours\": 24,\n",
+    "    },\n",
+    "    \"lag_size\": 30,\n",
+    "    \"window_size\": 30,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_extractor = FeatureExtractor(**parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "known_dates = past_knowledge.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_values = past_knowledge.loc[:, [TARGET]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if best_features:\n",
+    "    X_values = feature_extractor.transform(known_dates).filter(items=[*best_features])\n",
+    "else:\n",
+    "    X_values = feature_extractor.transform(known_dates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size = int(len(X_values) * 0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = X_values.iloc[:train_size].merge(y_values, on=\"datetime\", how=\"inner\")\n",
+    "test_df = X_values.iloc[train_size:].merge(y_values, on=\"datetime\", how=\"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = (\n",
+    "    train_df.drop(columns=[\"general_dam_occupancy_rate\"]),\n",
+    "    train_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_test, y_test = (\n",
+    "    test_df.drop(columns=[\"general_dam_occupancy_rate\"]),\n",
+    "    test_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_expending_window_splitter(df: pd.DataFrame, n_fold: int = 5):\n",
+    "    initial_window = len(df) // 2\n",
+    "    step_length = (len(df) - initial_window) // n_fold\n",
+    "    fh = np.arange(1, step_length + 1)\n",
+    "\n",
+    "    return ExpandingWindowSplitter(\n",
+    "        initial_window=initial_window, step_length=step_length, fh=fh\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(best_features) == 0:\n",
+    "    lgb = LGBMRegressor(importance_type=\"gain\", random_state=SEED)\n",
+    "    splitter = get_expending_window_splitter(X_train)\n",
+    "    te = TargetEncoder(cv=splitter)\n",
+    "    X_train = te.fit_transform(X_train, pd.DataFrame(y_train))\n",
+    "    model = lgb.fit(X_train, y_train)\n",
+    "    feature_importance = {\n",
+    "        name: importance\n",
+    "        for name, importance in zip(model.feature_names_in_, model.feature_importances_)\n",
+    "    }\n",
+    "    number_of_features_to_select = round(len(X_train.columns) ** (1 / 2))\n",
+    "    best_columns = (\n",
+    "        pd.DataFrame(feature_importance, index=[0])\n",
+    "        .T.sort_values(0, ascending=False)[0:number_of_features_to_select]\n",
+    "        .index.to_list()\n",
+    "    )\n",
+    "    with open(\"models/best_features_by_lightgbm_with_te.json\", \"w\") as f:\n",
+    "        json.dump(best_columns, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def objective(trial):\n",
+    "    params = {\n",
+    "        \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.0001, 0.1, log=True),\n",
+    "        \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000),\n",
+    "        \"max_depth\": trial.suggest_int(\"max_depth\", 3, 8),\n",
+    "        \"l2_leaf_reg\": trial.suggest_float(\"l2_leaf_reg\", 0.1, 10.0, log=True),\n",
+    "        \"colsample_bylevel\": trial.suggest_float(\"colsample_bylevel\", 0.5, 1.0),\n",
+    "        \"min_data_in_leaf\": trial.suggest_int(\"min_data_in_leaf\", 1, 100),\n",
+    "        \"subsample\": trial.suggest_float(\"subsample\", 0.5, 1.0),\n",
+    "        \"max_bin\": trial.suggest_int(\"max_bin\", 100, 500),\n",
+    "        \"od_type\": trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
+    "        \"random_strength\": trial.suggest_float(\"random_strength\", 0.0, 100.0),\n",
+    "        \"leaf_estimation_method\": trial.suggest_categorical(\n",
+    "            \"leaf_estimation_method\", [\"Newton\", \"Gradient\"]\n",
+    "        ),\n",
+    "        \"grow_policy\": trial.suggest_categorical(\n",
+    "            \"grow_policy\", [\"SymmetricTree\", \"Depthwise\", \"Lossguide\"]\n",
+    "        ),\n",
+    "        \"feature_border_type\": trial.suggest_categorical(\n",
+    "            \"feature_border_type\", [\"GreedyLogSum\", \"MinEntropy\", \"Uniform\"]\n",
+    "        ),\n",
+    "        \"one_hot_max_size\": trial.suggest_int(\"one_hot_max_size\", 2, 10),\n",
+    "        \"max_ctr_complexity\": trial.suggest_int(\"max_ctr_complexity\", 1, 8),\n",
+    "        \"bagging_temperature\": trial.suggest_float(\"bagging_temperature\", 0.0, 1.0),\n",
+    "        \"leaf_estimation_iterations\": trial.suggest_int(\n",
+    "            \"leaf_estimation_iterations\", 1, 10\n",
+    "        ),\n",
+    "        \"leaf_estimation_backtracking\": trial.suggest_categorical(\n",
+    "            \"leaf_estimation_backtracking\", [\"No\", \"AnyImprovement\"]\n",
+    "        ),\n",
+    "        \"random_score_type\": trial.suggest_categorical(\n",
+    "            \"random_score_type\", [\"NormalWithModelSizeDecrease\", \"Gumbel\"]\n",
+    "        ),\n",
+    "    }\n",
+    "\n",
+    "    splitter = get_expending_window_splitter(X_train)\n",
+    "\n",
+    "    results = []\n",
+    "\n",
+    "    for train_ind, val_ind in splitter.split(y_train):\n",
+    "        X_train_fold, X_val_fold = X_train.iloc[train_ind], X_train.iloc[val_ind]\n",
+    "        y_train_fold, y_val_fold = y_train.iloc[train_ind], y_train.iloc[val_ind]\n",
+    "\n",
+    "        te_cv = get_expending_window_splitter(X_train_fold)\n",
+    "        te = TargetEncoder(cv=te_cv)\n",
+    "        X_train_fold = te.fit_transform(X_train_fold, pd.DataFrame(y_train_fold))\n",
+    "        X_val_fold = te.transform(X_val_fold)\n",
+    "\n",
+    "        model = CatBoostRegressor(**params, random_state=SEED, early_stopping_rounds=100, logging_level=\"Silent\")\n",
+    "        model.fit(X_train_fold, y_train_fold)\n",
+    "        y_pred = model.predict(X_val_fold)\n",
+    "        results.append(mean_absolute_error(y_val_fold, y_pred))\n",
+    "\n",
+    "    dummy_forecaster_mae_error = 14.062895669528846\n",
+    "\n",
+    "    return np.mean(results) if np.mean(results) < dummy_forecaster_mae_error else np.inf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# study = optuna.create_study(direction=\"minimize\", storage=\"sqlite:///study.db\")\n",
+    "# study.optimize(objective, n_trials=50, show_progress_bar=True, n_jobs=-1)\n",
+    "# best_params = study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model = CatBoostRegressor(**study.best_params, random_state=SEED, early_stopping_rounds=100)\n",
+    "# model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joblib.dump(model, \"models/catboost-2-with-te-and-cv.pkl.gz\", compress=\"gzip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = joblib.load(\"models/catboost-2-with-te-and-cv.pkl.gz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08612799811786172"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_pred = model.predict(X_train)\n",
+    "mean_absolute_error(y_train, train_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.23486103173474923"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_pred_series = pd.Series(train_pred, name=\"predictions\", index=y_train.index)\n",
+    "test_pred_series = pd.Series(test_pred, name=\"predictions\", index=y_test.index)\n",
+    "\n",
+    "train_pred_df = pd.concat(\n",
+    "    [train_pred_series, y_train], axis=1\n",
+    ")\n",
+    "\n",
+    "test_pred_df = pd.concat(\n",
+    "    [test_pred_series, y_test], axis=1\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
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+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
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+            "ticks": "",
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+           "zaxis": {
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Predictions vs. True Values"
+        },
+        "width": 1200,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          0.45
+         ]
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "domain": [
+          0.55,
+          1
+         ]
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ]
+        },
+        "yaxis2": {
+         "anchor": "x2",
+         "domain": [
+          0,
+          1
+         ]
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Train\", \"Test\"))\n",
+    "\n",
+    "for i, (df, name) in enumerate(zip([train_pred_df, test_pred_df], [\"Train\", \"Test\"])):\n",
+    "    fig.add_trace(go.Scatter(x=df.index, y=df[TARGET], mode=\"lines\", name=f\"{name} True\"), row=1, col=i+1)\n",
+    "    fig.add_trace(go.Scatter(x=df.index, y=df[\"predictions\"], mode=\"lines\", name=f\"{name} Predictions\"), row=1, col=i+1)\n",
+    "\n",
+    "fig.update_layout(height=400, width=1200, title_text=\"Predictions vs. True Values\")\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ds-study",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week2/friday/you_do_4/notebooks/6.3-ml-linear.ipynb b/week2/friday/you_do_4/notebooks/6.3-ml-linear.ipynb
new file mode 100644
index 0000000..2f45fbc
--- /dev/null
+++ b/week2/friday/you_do_4/notebooks/6.3-ml-linear.ipynb
@@ -0,0 +1,27307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.linear_model import Lasso, Ridge\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sktime.split import ExpandingWindowSplitter\n",
+    "import joblib\n",
+    "import json\n",
+    "import optuna\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "from features.FeatureExtractor import FeatureExtractor\n",
+    "from features.TargetEncoder import TargetEncoder\n",
+    "from statsmodels.tsa.stattools import adfuller"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_dataset_address = \"dataset/interim/past_dataset.csv\"\n",
+    "SEED = 99\n",
+    "TARGET = \"general_dam_occupancy_rate\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    with open(\"models/best_features_by_lasso.json\", errors=\"FileNotFoundError\") as f:\n",
+    "        best_features = json.load(f)\n",
+    "        \n",
+    "except FileNotFoundError:\n",
+    "    best_features = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['general_dam_occupancy_rate_lag_1',\n",
+       " 'general_dam_occupancy_rate_rw2_mean',\n",
+       " 'general_dam_occupancy_rate_rw2_max']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "past_knowledge = (\n",
+    "    pd.read_csv(\n",
+    "        cleaned_dataset_address,\n",
+    "        parse_dates=[\"datetime\"],\n",
+    "        converters={\"weather_code\": str},\n",
+    "    )\n",
+    "    .set_index(\"datetime\")\n",
+    "    .sort_index()\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    \"past_knowledge\": past_knowledge,\n",
+    "    \"cyclical_feature_names\": {\n",
+    "        \"month\": 12,\n",
+    "        \"day\": 31,\n",
+    "        \"day_of_year\": 365,\n",
+    "        \"week_of_year\": 52,\n",
+    "        \"quarter\": 4,\n",
+    "        # \"season\": 4,\n",
+    "        \"is_weekend\": 2,\n",
+    "        \"precipitation_hours\": 24,\n",
+    "    },\n",
+    "    \"lag_size\": 30,\n",
+    "    \"window_size\": 30,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_extractor = FeatureExtractor(**parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "known_dates = past_knowledge.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_values = past_knowledge.loc[:, TARGET]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We can deny that dataset is non-stationary\n"
+     ]
+    }
+   ],
+   "source": [
+    "p_value = adfuller(y_values)[1]\n",
+    "\n",
+    "if p_value >= 0.05:\n",
+    "    print(\"We can't deny that dataset is non-stationary\")\n",
+    "elif p_value < 0.05:\n",
+    "    print(\"We can deny that dataset is non-stationary\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if best_features:\n",
+    "    X_values = feature_extractor.transform(known_dates).filter(items=[*best_features])\n",
+    "else:\n",
+    "    X_values = feature_extractor.transform(known_dates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size = int(len(X_values) * 0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = X_values.iloc[:train_size].merge(y_values, on=\"datetime\", how=\"inner\")\n",
+    "test_df = X_values.iloc[train_size:].merge(y_values, on=\"datetime\", how=\"inner\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, y_train = (\n",
+    "    train_df.drop(columns=[\"general_dam_occupancy_rate\"]),\n",
+    "    train_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n",
+    "\n",
+    "X_test, y_test = (\n",
+    "    test_df.drop(columns=[\"general_dam_occupancy_rate\"]),\n",
+    "    test_df[\"general_dam_occupancy_rate\"],\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_expending_window_splitter(df: pd.DataFrame, n_fold: int = 5):\n",
+    "    initial_window = len(df) // 2\n",
+    "    step_length = (len(df) - initial_window) // n_fold\n",
+    "    fh = np.arange(1, step_length + 1)\n",
+    "\n",
+    "    return ExpandingWindowSplitter(\n",
+    "        initial_window=initial_window, step_length=step_length, fh=fh\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def winsorize_iqr(\n",
+    "    df: pd.DataFrame,\n",
+    "    lower_quantile: float = 0.25,\n",
+    "    upper_quantile: float = 0.75,\n",
+    "    multiplier: float = 1.5,\n",
+    ") -> pd.DataFrame:\n",
+    "    if not (0 <= lower_quantile < upper_quantile <= 1):\n",
+    "        raise ValueError(\"Quantiles must satisfy 0 <= lower < upper <= 1\")\n",
+    "    if multiplier <= 0:\n",
+    "        raise ValueError(\"Multiplier must be positive\")\n",
+    "    \n",
+    "    df = df.copy()\n",
+    "    numerical_cols = df.select_dtypes(include=[\"number\"]).columns\n",
+    "\n",
+    "    for col in numerical_cols:\n",
+    "        Q1 = df[col].quantile(lower_quantile)\n",
+    "        Q3 = df[col].quantile(upper_quantile)\n",
+    "        IQR = Q3 - Q1\n",
+    "\n",
+    "        lower_bound = Q1 - multiplier * IQR\n",
+    "        upper_bound = Q3 + multiplier * IQR\n",
+    "\n",
+    "        is_col_integer = pd.api.types.is_integer_dtype(df[col])\n",
+    "        is_col_float = pd.api.types.is_float_dtype(df[col])\n",
+    "        \n",
+    "        if is_col_integer:\n",
+    "            df[col] = df[col].clip(lower=lower_bound, upper=upper_bound)\n",
+    "        elif is_col_float:\n",
+    "            df[col] = df[col].clip(lower=lower_bound, upper=upper_bound)\n",
+    "\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = winsorize_iqr(X_train)\n",
+    "X_test = winsorize_iqr(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if len(best_features) == 0:\n",
+    "    splitter = get_expending_window_splitter(X_train)\n",
+    "\n",
+    "    te = TargetEncoder(cv=splitter)\n",
+    "    X_train_fs = te.fit_transform(X_train, pd.DataFrame(y_train))\n",
+    "    X_test_fs = te.transform(X_test)\n",
+    "\n",
+    "    ss = StandardScaler().set_output(transform=\"pandas\")\n",
+    "    X_train_fs_scaled = ss.fit_transform(X_train_fs)\n",
+    "    X_test_fs_scaled = ss.transform(X_test_fs)\n",
+    "\n",
+    "    lasso = Lasso(random_state=SEED)\n",
+    "    model = lasso.fit(X_train_fs_scaled, y_train)\n",
+    "\n",
+    "    feature_importance = (\n",
+    "        pd.DataFrame(lasso.coef_, index=lasso.feature_names_in_, columns=[\"coef\"])\n",
+    "        .abs()\n",
+    "        .sort_values(\"coef\", ascending=False)\n",
+    "    )\n",
+    "\n",
+    "    best_cols = feature_importance.loc[\n",
+    "        feature_importance[\"coef\"] > 0\n",
+    "    ].index.to_list()\n",
+    "    \n",
+    "    with open(\"models/best_features_by_lasso.json\", \"w\") as f:\n",
+    "        json.dump(best_cols, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def optimize_ridge(trial: optuna.Trial):\n",
+    "    params = {\n",
+    "        \"alpha\": trial.suggest_float(\"alpha\", 1e-5, 1e3),\n",
+    "        \"fit_intercept\": trial.suggest_categorical(\"fit_intercept\", [True, False]),\n",
+    "        \"solver\": trial.suggest_categorical(\n",
+    "            \"solver\", [\"auto\", \"cholesky\", \"lsqr\", \"sag\"]\n",
+    "        ),\n",
+    "    }\n",
+    "\n",
+    "    splitter = get_expending_window_splitter(X_train)\n",
+    "    results = []\n",
+    "\n",
+    "    for train_ind, val_ind in splitter.split(y_train):\n",
+    "        X_train_fold, X_val_fold = X_train.iloc[train_ind], X_train.iloc[val_ind]\n",
+    "        y_train_fold, y_val_fold = y_train.iloc[train_ind], y_train.iloc[val_ind]\n",
+    "\n",
+    "        te_cv = get_expending_window_splitter(X_train_fold)\n",
+    "        te = TargetEncoder(cv=te_cv)\n",
+    "        X_train_fold = te.fit_transform(X_train_fold, pd.DataFrame(y_train_fold))\n",
+    "        X_val_fold = te.transform(X_val_fold)\n",
+    "\n",
+    "        ss = StandardScaler().set_output(transform=\"pandas\")\n",
+    "        X_train_scaled = ss.fit_transform(X_train_fold)\n",
+    "        X_val_scaled = ss.transform(X_val_fold)\n",
+    "\n",
+    "        model = Ridge(**params, random_state=SEED)\n",
+    "        model.fit(X_train_scaled, y_train_fold)\n",
+    "        y_pred = model.predict(X_val_scaled)\n",
+    "        results.append(mean_absolute_error(y_val_fold, y_pred))\n",
+    "\n",
+    "    dummy_forecaster_mae_error = 14.062895669528846\n",
+    "\n",
+    "    return np.mean(results) if np.mean(results) < dummy_forecaster_mae_error else np.inf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:40:16,304] A new study created in RDB with name: no-name-47cc4b4f-3d38-4212-9660-b43ddc8d89f4\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "11b96e4481e6453589c8019bf89500f0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:40:17,883] Trial 4 finished with value: inf and parameters: {'alpha': 774.7555219635001, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 4 with value: inf.\n",
+      "[I 2025-03-06 14:40:17,984] Trial 2 finished with value: 0.8183825556383635 and parameters: {'alpha': 438.1037099244699, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 2 with value: 0.8183825556383635.\n",
+      "[I 2025-03-06 14:40:18,291] Trial 1 finished with value: 1.5444382234272205 and parameters: {'alpha': 885.1915372328981, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 2 with value: 0.8183825556383635.\n",
+      "[I 2025-03-06 14:40:18,294] Trial 3 finished with value: inf and parameters: {'alpha': 198.0328933649226, 'fit_intercept': False, 'solver': 'sag'}. Best is trial 2 with value: 0.8183825556383635.\n",
+      "[I 2025-03-06 14:40:18,298] Trial 7 finished with value: 1.2102923481830796 and parameters: {'alpha': 675.6226516705339, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 2 with value: 0.8183825556383635.\n",
+      "[I 2025-03-06 14:40:18,362] Trial 5 finished with value: 1.510838022525185 and parameters: {'alpha': 863.7693335745743, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,416] Trial 0 finished with value: 1.5177614844392475 and parameters: {'alpha': 868.0785050010205, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,425] Trial 6 finished with value: 0.32475407849763566 and parameters: {'alpha': 137.77503280554123, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,569] Trial 8 finished with value: 0.7924352600097656 and parameters: {'alpha': 422.6114171099939, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,714] Trial 9 finished with value: 0.5853620883018091 and parameters: {'alpha': 299.76247201802175, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,866] Trial 11 finished with value: inf and parameters: {'alpha': 18.731087867813415, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,894] Trial 10 finished with value: 1.1917328067819695 and parameters: {'alpha': 664.1833154467741, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:18,983] Trial 12 finished with value: inf and parameters: {'alpha': 14.67907453461413, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,029] Trial 13 finished with value: inf and parameters: {'alpha': 42.34238164804435, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,105] Trial 14 finished with value: inf and parameters: {'alpha': 7.007825475635315, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,115] Trial 16 finished with value: inf and parameters: {'alpha': 0.9216106761081448, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,144] Trial 17 finished with value: 0.4888062668328537 and parameters: {'alpha': 240.91626593914853, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,262] Trial 18 finished with value: 0.5453304477410568 and parameters: {'alpha': 274.81068018939067, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,340] Trial 19 finished with value: 0.49828686844675163 and parameters: {'alpha': 246.58957922716607, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,385] Trial 21 finished with value: 0.40309334305612665 and parameters: {'alpha': 187.9293960466103, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,447] Trial 22 finished with value: 0.45237786942331415 and parameters: {'alpha': 218.43221137287728, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,505] Trial 23 finished with value: 0.3644254820492393 and parameters: {'alpha': 163.32507715851744, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,513] Trial 24 finished with value: 0.392442090807463 and parameters: {'alpha': 181.24054663584258, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,515] Trial 20 finished with value: 0.3769079864180715 and parameters: {'alpha': 171.34107369486313, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,558] Trial 15 finished with value: inf and parameters: {'alpha': 77.30900423089787, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 6 with value: 0.32475407849763566.\n",
+      "[I 2025-03-06 14:40:19,623] Trial 25 finished with value: 0.28289261095548934 and parameters: {'alpha': 108.95637226875041, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 25 with value: 0.28289261095548934.\n",
+      "[I 2025-03-06 14:40:19,898] Trial 26 finished with value: 0.3071587216025905 and parameters: {'alpha': 125.91189939854357, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 25 with value: 0.28289261095548934.\n",
+      "[I 2025-03-06 14:40:19,900] Trial 27 finished with value: 0.7115404312936884 and parameters: {'alpha': 374.36535823113854, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 25 with value: 0.28289261095548934.\n",
+      "[I 2025-03-06 14:40:19,959] Trial 28 finished with value: 0.2843575619988693 and parameters: {'alpha': 110.0866113304338, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 25 with value: 0.28289261095548934.\n",
+      "[I 2025-03-06 14:40:20,296] Trial 31 finished with value: 0.6664841735197369 and parameters: {'alpha': 347.4385060445358, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 29 with value: 0.28154216938219584.\n",
+      "[I 2025-03-06 14:40:20,299] Trial 29 finished with value: 0.28154216938219584 and parameters: {'alpha': 108.10163378060123, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 29 with value: 0.28154216938219584.\n",
+      "[I 2025-03-06 14:40:20,299] Trial 30 finished with value: 0.6535251110197369 and parameters: {'alpha': 339.6936938612545, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 29 with value: 0.28154216938219584.\n",
+      "[I 2025-03-06 14:40:20,349] Trial 33 finished with value: 0.6952855680124383 and parameters: {'alpha': 364.6552276641282, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 29 with value: 0.28154216938219584.\n",
+      "[I 2025-03-06 14:40:20,442] Trial 32 finished with value: 0.28844607897306745 and parameters: {'alpha': 112.9745926595704, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 29 with value: 0.28154216938219584.\n",
+      "[I 2025-03-06 14:40:20,737] Trial 36 finished with value: 0.24424514680561277 and parameters: {'alpha': 79.94368911467656, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:20,738] Trial 34 finished with value: 1.0089647487278988 and parameters: {'alpha': 552.7062369560683, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:20,905] Trial 35 finished with value: 1.7078118063193872 and parameters: {'alpha': 990.6038115866195, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:20,919] Trial 38 finished with value: 0.30133353265059626 and parameters: {'alpha': 121.92389257180197, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:20,953] Trial 37 finished with value: 0.2520288639108759 and parameters: {'alpha': 86.12297756054366, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:21,057] Trial 40 finished with value: 0.27456170127467117 and parameters: {'alpha': 103.09409725923248, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:21,081] Trial 41 finished with value: 0.2555523217130963 and parameters: {'alpha': 88.87164177652912, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:21,100] Trial 39 finished with value: 0.24890780613949434 and parameters: {'alpha': 83.66649972904396, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 36 with value: 0.24424514680561277.\n",
+      "[I 2025-03-06 14:40:21,235] Trial 43 finished with value: 0.2401582827919409 and parameters: {'alpha': 76.61373516082699, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 42 with value: 0.2268274900416325.\n",
+      "[I 2025-03-06 14:40:21,251] Trial 42 finished with value: 0.2268274900416325 and parameters: {'alpha': 65.21027265005992, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 46 with value: 0.22370508840460532.\n",
+      "[I 2025-03-06 14:40:21,272] Trial 46 finished with value: 0.22370508840460532 and parameters: {'alpha': 62.42796897936867, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 46 with value: 0.22370508840460532.\n",
+      "[I 2025-03-06 14:40:21,299] Trial 45 finished with value: 0.25442384450812094 and parameters: {'alpha': 87.99332805684321, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 46 with value: 0.22370508840460532.\n",
+      "[I 2025-03-06 14:40:21,393] Trial 44 finished with value: 0.220453078934519 and parameters: {'alpha': 59.492658255929065, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 44 with value: 0.220453078934519.\n",
+      "[I 2025-03-06 14:40:21,499] Trial 48 finished with value: 0.21304004747892685 and parameters: {'alpha': 52.64223042860597, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,544] Trial 47 finished with value: 0.21984395784076893 and parameters: {'alpha': 58.936885113016615, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,546] Trial 49 finished with value: 0.21456737593801406 and parameters: {'alpha': 54.067597514424136, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,581] Trial 51 finished with value: inf and parameters: {'alpha': 40.824376503615746, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,595] Trial 50 finished with value: inf and parameters: {'alpha': 37.775956535209325, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,674] Trial 52 finished with value: inf and parameters: {'alpha': 53.75157558718454, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,681] Trial 53 finished with value: inf and parameters: {'alpha': 513.2077908568433, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,687] Trial 54 finished with value: inf and parameters: {'alpha': 540.8645127160693, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 48 with value: 0.21304004747892685.\n",
+      "[I 2025-03-06 14:40:21,915] Trial 55 finished with value: 0.35584737150493423 and parameters: {'alpha': 158.1536506497236, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:21,919] Trial 59 finished with value: 0.10334399459035777 and parameters: {'alpha': 0.2504752569161752, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:21,931] Trial 58 finished with value: 0.33628156070106907 and parameters: {'alpha': 145.41266444279506, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:21,969] Trial 57 finished with value: 0.3519428619063528 and parameters: {'alpha': 155.6320680072489, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,004] Trial 56 finished with value: 0.14406082166169829 and parameters: {'alpha': 1.3324516845029066, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,082] Trial 62 finished with value: 0.16411411543996718 and parameters: {'alpha': 4.116291635171714, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,138] Trial 60 finished with value: 0.16525772448088002 and parameters: {'alpha': 4.496194748659292, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,150] Trial 61 finished with value: 0.4418733885433799 and parameters: {'alpha': 212.02606246779112, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,221] Trial 63 finished with value: 0.4430187091385691 and parameters: {'alpha': 212.72801093793268, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,280] Trial 66 finished with value: 0.17025760909231097 and parameters: {'alpha': 6.948181224476624, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,308] Trial 64 finished with value: 0.1777083829538446 and parameters: {'alpha': 14.210933366402077, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,308] Trial 65 finished with value: 0.14095584896689975 and parameters: {'alpha': 1.1636951454613222, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,309] Trial 67 finished with value: 0.16342972990337182 and parameters: {'alpha': 3.9111721306744807, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,461] Trial 68 finished with value: 0.16959633140162428 and parameters: {'alpha': 6.530707046027071, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,489] Trial 69 finished with value: 0.16548389346474107 and parameters: {'alpha': 4.577613164369761, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,551] Trial 70 finished with value: 0.11023576351768097 and parameters: {'alpha': 0.33696475967860806, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,669] Trial 72 finished with value: 0.17038070736533728 and parameters: {'alpha': 7.0308417176749085, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,690] Trial 73 finished with value: 0.17509116011770157 and parameters: {'alpha': 11.1317854009054, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,712] Trial 74 finished with value: 0.15519664531507 and parameters: {'alpha': 2.312248371389704, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,731] Trial 75 finished with value: 0.18301940815172704 and parameters: {'alpha': 20.868664661565806, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,740] Trial 71 finished with value: 0.134180644402755 and parameters: {'alpha': 0.8777348454516098, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,853] Trial 77 finished with value: 0.11938616005345401 and parameters: {'alpha': 0.48822386900222803, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,865] Trial 76 finished with value: 1.1895430982730262 and parameters: {'alpha': 662.8353459205555, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:22,871] Trial 78 finished with value: 1.2883521578176398 and parameters: {'alpha': 723.9548142513876, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,003] Trial 79 finished with value: 1.1054550044330798 and parameters: {'alpha': 611.295007870101, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,064] Trial 82 finished with value: 0.8330849315763775 and parameters: {'alpha': 446.88862827676104, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,070] Trial 81 finished with value: 1.1640656392629523 and parameters: {'alpha': 647.1731817442213, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,110] Trial 80 finished with value: 0.1951268687037418 and parameters: {'alpha': 34.82079511417113, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,137] Trial 83 finished with value: 0.19415450028268916 and parameters: {'alpha': 33.76423488536264, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,323] Trial 85 finished with value: 0.1974614650364926 and parameters: {'alpha': 37.29954309231036, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,432] Trial 84 finished with value: 0.1952991009200247 and parameters: {'alpha': 35.00605634982649, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,615] Trial 87 finished with value: 0.1943329756887336 and parameters: {'alpha': 33.95129215793602, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,765] Trial 86 finished with value: 0.1971157288882607 and parameters: {'alpha': 36.93528489981726, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,830] Trial 89 finished with value: 1.4742497003495063 and parameters: {'alpha': 840.5286104983779, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,925] Trial 88 finished with value: 0.31623246299342106 and parameters: {'alpha': 132.05241503973676, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:23,971] Trial 91 finished with value: 0.31745644987407484 and parameters: {'alpha': 132.87542154536823, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,042] Trial 90 finished with value: 0.2921364197419819 and parameters: {'alpha': 115.55591297238301, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,064] Trial 92 finished with value: 0.31069348285875825 and parameters: {'alpha': 128.31547963005855, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,206] Trial 93 finished with value: 0.246499778153269 and parameters: {'alpha': 81.090078768516, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,252] Trial 96 finished with value: 0.25847270321494664 and parameters: {'alpha': 91.12652951265984, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,265] Trial 95 finished with value: 0.239571498830695 and parameters: {'alpha': 76.12995684654186, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,349] Trial 94 finished with value: 0.2632660108706827 and parameters: {'alpha': 94.27570632026332, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,375] Trial 98 finished with value: 0.16138590229235206 and parameters: {'alpha': 3.3841295837079035, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,380] Trial 97 finished with value: 0.2675876135896382 and parameters: {'alpha': 98.00006770368694, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,433] Trial 99 finished with value: 0.2691782788085938 and parameters: {'alpha': 99.1730274325476, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,455] Trial 100 finished with value: 0.16346967420076078 and parameters: {'alpha': 3.922829090237416, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,613] Trial 101 finished with value: inf and parameters: {'alpha': 55.045859958287814, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,617] Trial 103 finished with value: inf and parameters: {'alpha': 66.69790733978522, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,639] Trial 102 finished with value: 0.2167389609888981 and parameters: {'alpha': 56.082598969636805, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,701] Trial 105 finished with value: 0.2198554196006374 and parameters: {'alpha': 58.94753893937777, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,719] Trial 104 finished with value: 0.21623598684210532 and parameters: {'alpha': 55.61811330615304, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,777] Trial 106 finished with value: 0.22449201557360204 and parameters: {'alpha': 63.13493986983576, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,814] Trial 107 finished with value: 0.1207758512155634 and parameters: {'alpha': 0.5161070526327971, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,880] Trial 108 finished with value: 0.18645260543020153 and parameters: {'alpha': 25.076371142318493, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:24,902] Trial 109 finished with value: 0.18265037469161188 and parameters: {'alpha': 20.404866344973506, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,011] Trial 110 finished with value: 0.18434227551912014 and parameters: {'alpha': 22.50639845414195, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,039] Trial 111 finished with value: 0.1840984959010075 and parameters: {'alpha': 22.209582640378834, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,108] Trial 113 finished with value: 0.184022379792866 and parameters: {'alpha': 22.11605428794933, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,109] Trial 112 finished with value: 0.1845200773540297 and parameters: {'alpha': 22.72429872728618, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,135] Trial 114 finished with value: 0.18437921785053463 and parameters: {'alpha': 22.552569776950126, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,178] Trial 115 finished with value: 0.14249297723067442 and parameters: {'alpha': 1.2437547263702002, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,261] Trial 116 finished with value: 0.1721246730443052 and parameters: {'alpha': 8.316768668808125, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,321] Trial 117 finished with value: 0.17365252049496308 and parameters: {'alpha': 9.66257460174827, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,400] Trial 118 finished with value: 0.11292859169407901 and parameters: {'alpha': 0.3765239087743445, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,443] Trial 121 finished with value: 0.1553736381450453 and parameters: {'alpha': 2.3358740780614857, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,475] Trial 120 finished with value: 0.20265349712171057 and parameters: {'alpha': 42.62580933402299, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,481] Trial 119 finished with value: 0.16009734721936686 and parameters: {'alpha': 3.105842963735018, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,533] Trial 122 finished with value: 0.2046611726459704 and parameters: {'alpha': 44.60542651041772, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,594] Trial 123 finished with value: 0.210642906172903 and parameters: {'alpha': 50.376852781471996, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,705] Trial 124 finished with value: 0.23938705958316212 and parameters: {'alpha': 75.9776559613845, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,763] Trial 125 finished with value: 0.236698621954667 and parameters: {'alpha': 73.73563610894307, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 59 with value: 0.10334399459035777.\n",
+      "[I 2025-03-06 14:40:25,869] Trial 126 finished with value: 0.14615870573344994 and parameters: {'alpha': 1.4660352899302787, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:25,882] Trial 128 finished with value: 0.10984442556280842 and parameters: {'alpha': 0.3315072133265913, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:25,891] Trial 127 finished with value: 0.05527907573499179 and parameters: {'alpha': 0.024471227852889132, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:25,894] Trial 130 finished with value: 0.15513882414165306 and parameters: {'alpha': 2.3055587512173483, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:25,903] Trial 129 finished with value: 0.12613717240182987 and parameters: {'alpha': 0.6373272361916968, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:25,940] Trial 131 finished with value: 0.1634561774002879 and parameters: {'alpha': 3.919384436317083, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,041] Trial 132 finished with value: 0.5916583292429071 and parameters: {'alpha': 302.7080703956732, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,051] Trial 133 finished with value: 0.2019798200426604 and parameters: {'alpha': 41.94938713958505, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,247] Trial 135 finished with value: 0.1987920566277755 and parameters: {'alpha': 38.6887463678519, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,255] Trial 134 finished with value: 0.19347828889545643 and parameters: {'alpha': 33.01844147542636, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,264] Trial 139 finished with value: 0.1891070664576481 and parameters: {'alpha': 28.159849677061608, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,308] Trial 136 finished with value: 0.19072316875256995 and parameters: {'alpha': 29.974990557460515, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,348] Trial 138 finished with value: 0.19410800434313322 and parameters: {'alpha': 33.713370229978615, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,393] Trial 141 finished with value: 0.19664205315840871 and parameters: {'alpha': 36.43595304308811, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,406] Trial 140 finished with value: 0.19350065506784545 and parameters: {'alpha': 33.04315772368391, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,416] Trial 137 finished with value: 0.19660135395250822 and parameters: {'alpha': 36.39289486130484, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,575] Trial 142 finished with value: inf and parameters: {'alpha': 407.4430259115233, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,639] Trial 145 finished with value: inf and parameters: {'alpha': 2.3498523841558177, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,687] Trial 143 finished with value: inf and parameters: {'alpha': 0.5774766790156458, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,689] Trial 144 finished with value: inf and parameters: {'alpha': 2.281740020438936, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,718] Trial 146 finished with value: inf and parameters: {'alpha': 1.1971301920622994, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,735] Trial 147 finished with value: inf and parameters: {'alpha': 70.35408714299031, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,814] Trial 150 finished with value: 1.6397850674599095 and parameters: {'alpha': 946.4671760089169, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,901] Trial 148 finished with value: 0.1384197975560239 and parameters: {'alpha': 1.0446879452709878, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,953] Trial 149 finished with value: 0.2358566725560239 and parameters: {'alpha': 73.02426209578195, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:26,991] Trial 151 finished with value: 0.22354072374845813 and parameters: {'alpha': 62.28006003014002, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,134] Trial 154 finished with value: 0.21830925537109383 and parameters: {'alpha': 57.320235788012795, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,138] Trial 153 finished with value: 0.2295219342362254 and parameters: {'alpha': 67.42167161345782, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,156] Trial 152 finished with value: 0.21742352070055512 and parameters: {'alpha': 56.714217169478744, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,163] Trial 155 finished with value: 0.21826215177837177 and parameters: {'alpha': 57.487083347959384, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,178] Trial 156 finished with value: 0.18410946295487263 and parameters: {'alpha': 22.22353419386447, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,235] Trial 158 finished with value: 0.1829759575452303 and parameters: {'alpha': 20.81299621547064, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,301] Trial 157 finished with value: 0.21049545647872128 and parameters: {'alpha': 50.236648776745355, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,335] Trial 159 finished with value: 0.21175606978567027 and parameters: {'alpha': 51.43337221048968, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,444] Trial 160 finished with value: 0.1833070121684828 and parameters: {'alpha': 21.22871443782053, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,541] Trial 161 finished with value: 0.12112077688116785 and parameters: {'alpha': 0.523131587685892, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,569] Trial 162 finished with value: 0.17944726260536603 and parameters: {'alpha': 16.36983212910252, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,594] Trial 164 finished with value: 0.18345883763363494 and parameters: {'alpha': 21.41712968696201, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,613] Trial 165 finished with value: 0.10814927130448196 and parameters: {'alpha': 0.30851057126754244, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,670] Trial 163 finished with value: 0.18369705309416126 and parameters: {'alpha': 21.712979062767324, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,718] Trial 167 finished with value: 0.15915157772666538 and parameters: {'alpha': 2.9224250460869725, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,723] Trial 166 finished with value: 0.12721614225688743 and parameters: {'alpha': 0.6651161063488027, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:27,981] Trial 168 finished with value: 0.18711026482833065 and parameters: {'alpha': 25.86118736292016, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,010] Trial 172 finished with value: 0.2537256643194902 and parameters: {'alpha': 87.4469106103262, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,048] Trial 170 finished with value: 0.13966230468750007 and parameters: {'alpha': 1.1013419652931362, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,135] Trial 171 finished with value: 0.20184092278731497 and parameters: {'alpha': 41.80344491801507, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,215] Trial 169 finished with value: 0.25720604350842935 and parameters: {'alpha': 90.15102708709506, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,541] Trial 173 finished with value: 0.2668063617264598 and parameters: {'alpha': 97.40606776759095, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,542] Trial 174 finished with value: 0.14731638883891868 and parameters: {'alpha': 1.5474226849452708, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,639] Trial 176 finished with value: 0.12464069978412837 and parameters: {'alpha': 0.6007859479590407, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,687] Trial 175 finished with value: 0.14022862568102396 and parameters: {'alpha': 1.1282668598261254, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,691] Trial 177 finished with value: 0.12456863653885697 and parameters: {'alpha': 0.5989451551458354, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,771] Trial 178 finished with value: 0.07793427907843341 and parameters: {'alpha': 0.07221337937391792, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,782] Trial 179 finished with value: 0.185607988152755 and parameters: {'alpha': 24.052312166691376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,802] Trial 180 finished with value: 0.18256750089946552 and parameters: {'alpha': 20.30056052778425, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:28,883] Trial 182 finished with value: 0.20276093884919816 and parameters: {'alpha': 42.73268080203452, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,021] Trial 183 finished with value: 0.2018450447805305 and parameters: {'alpha': 41.81378641030034, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,034] Trial 181 finished with value: 0.20287992688630757 and parameters: {'alpha': 42.85075722236433, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,045] Trial 184 finished with value: 0.19667742463764393 and parameters: {'alpha': 36.47405830068767, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,048] Trial 185 finished with value: 0.19852819213867187 and parameters: {'alpha': 38.413998574886456, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,075] Trial 186 finished with value: 0.20010657862613077 and parameters: {'alpha': 40.04517541235499, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,163] Trial 188 finished with value: 0.1865057895379318 and parameters: {'alpha': 25.14064200562783, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,166] Trial 187 finished with value: 0.1843728926809211 and parameters: {'alpha': 22.54462477364979, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,257] Trial 189 finished with value: 0.17896785426089643 and parameters: {'alpha': 15.772717280294684, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,400] Trial 191 finished with value: 0.18058110961914067 and parameters: {'alpha': 17.80667591172643, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,444] Trial 193 finished with value: 0.12392552997789892 and parameters: {'alpha': 0.5839057830439315, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,453] Trial 194 finished with value: 0.18076790501644743 and parameters: {'alpha': 18.04375768775621, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,483] Trial 190 finished with value: 0.15085065828022215 and parameters: {'alpha': 1.8370756683235523, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,502] Trial 195 finished with value: 0.14423616120990962 and parameters: {'alpha': 1.3434882374733403, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,508] Trial 192 finished with value: 0.1241545884945519 and parameters: {'alpha': 0.5895495259318841, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,562] Trial 196 finished with value: 0.12600936799701898 and parameters: {'alpha': 0.6341227396692847, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,673] Trial 197 finished with value: 0.16318700111790715 and parameters: {'alpha': 3.8415430916307987, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,730] Trial 198 finished with value: 0.18446493530273442 and parameters: {'alpha': 22.657247037711983, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,829] Trial 202 finished with value: 0.18684186561986024 and parameters: {'alpha': 25.542717175010804, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,861] Trial 204 finished with value: 0.8755382779733758 and parameters: {'alpha': 472.3145705818117, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,880] Trial 201 finished with value: 0.21175630428916534 and parameters: {'alpha': 51.43365036082671, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,908] Trial 199 finished with value: 0.18652864399157076 and parameters: {'alpha': 25.16805433775193, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,909] Trial 203 finished with value: 1.3683054490260074 and parameters: {'alpha': 773.8076288975997, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:29,990] Trial 200 finished with value: 0.1862651709626851 and parameters: {'alpha': 24.85106391090803, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,112] Trial 205 finished with value: 0.10188822426243838 and parameters: {'alpha': 0.2348976708186531, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,198] Trial 207 finished with value: 0.12050649414062506 and parameters: {'alpha': 0.5108448097153624, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,217] Trial 206 finished with value: 0.1474992164370889 and parameters: {'alpha': 1.560827986006273, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,306] Trial 208 finished with value: 0.1359348287803249 and parameters: {'alpha': 0.9428749630448152, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,310] Trial 211 finished with value: 0.11922305002312919 and parameters: {'alpha': 0.4851565078081862, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,327] Trial 210 finished with value: 0.20884663889031665 and parameters: {'alpha': 48.65944792723088, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,351] Trial 209 finished with value: 0.20919624106959298 and parameters: {'alpha': 48.995236439051666, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,437] Trial 212 finished with value: 0.15828896844161194 and parameters: {'alpha': 2.771295847442847, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,440] Trial 213 finished with value: 0.5020872306100946 and parameters: {'alpha': 248.7057511201377, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,544] Trial 215 finished with value: 0.18207317710474924 and parameters: {'alpha': 19.679478761284596, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,629] Trial 214 finished with value: 0.07410436337119657 and parameters: {'alpha': 0.05974598792599828, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,657] Trial 216 finished with value: 0.1830631559030634 and parameters: {'alpha': 20.922924221532888, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,688] Trial 217 finished with value: 0.18301087929173526 and parameters: {'alpha': 20.857563534091497, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,805] Trial 218 finished with value: 0.1848415379574425 and parameters: {'alpha': 23.11756969832936, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,806] Trial 221 finished with value: 0.1867372834858142 and parameters: {'alpha': 25.417244241047282, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,854] Trial 219 finished with value: 0.18514695865028788 and parameters: {'alpha': 23.49059085255219, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,891] Trial 222 finished with value: 0.05667857743112666 and parameters: {'alpha': 0.026060703045593693, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:30,922] Trial 220 finished with value: 0.18593784500925173 and parameters: {'alpha': 24.4545274635662, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,044] Trial 223 finished with value: 0.10664692145096635 and parameters: {'alpha': 0.28932837622816376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,072] Trial 225 finished with value: 0.20300489662571958 and parameters: {'alpha': 43.8897325098721, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,079] Trial 224 finished with value: 0.12613717240182987 and parameters: {'alpha': 0.6371234416473304, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,172] Trial 228 finished with value: 0.12289695203279201 and parameters: {'alpha': 0.5608432221130311, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,191] Trial 227 finished with value: 0.20220464047080594 and parameters: {'alpha': 42.175539068931386, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,219] Trial 229 finished with value: 0.15361001708984384 and parameters: {'alpha': 2.119838971643089, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,229] Trial 226 finished with value: 0.11208779065583889 and parameters: {'alpha': 0.3636776007869524, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,262] Trial 230 finished with value: 0.20380244603207237 and parameters: {'alpha': 43.76363208083915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,370] Trial 232 finished with value: 0.19750972296463817 and parameters: {'alpha': 37.35036317359278, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,442] Trial 231 finished with value: 0.19412694772820727 and parameters: {'alpha': 33.73411317963377, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,524] Trial 233 finished with value: 0.13673127441406258 and parameters: {'alpha': 0.974119472397357, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,580] Trial 234 finished with value: 0.1365138371839022 and parameters: {'alpha': 0.9652633498496976, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,594] Trial 236 finished with value: 0.11645357460423526 and parameters: {'alpha': 0.4339778086460589, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,599] Trial 235 finished with value: 0.181043953022204 and parameters: {'alpha': 18.391679750311646, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,642] Trial 237 finished with value: 0.18185375308388166 and parameters: {'alpha': 19.405739365285957, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,657] Trial 238 finished with value: 0.18094477584035779 and parameters: {'alpha': 18.2661533317939, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,749] Trial 239 finished with value: 0.1811226996813323 and parameters: {'alpha': 18.490844424625507, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,772] Trial 240 finished with value: 0.1853047719212583 and parameters: {'alpha': 23.682089422141473, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:31,788] Trial 241 finished with value: 0.18187683722245074 and parameters: {'alpha': 19.434060144496353, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,042] Trial 242 finished with value: 0.12113667011461768 and parameters: {'alpha': 0.5234415416864074, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,117] Trial 246 finished with value: 0.10283671971371304 and parameters: {'alpha': 0.24523201883787993, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,119] Trial 244 finished with value: 0.09892611822831006 and parameters: {'alpha': 0.20522143191104403, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,121] Trial 245 finished with value: 1.0904798267244036 and parameters: {'alpha': 602.1668247058882, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,173] Trial 243 finished with value: 0.13911150737561684 and parameters: {'alpha': 1.0758787224543576, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,238] Trial 248 finished with value: 0.07696801083213406 and parameters: {'alpha': 0.0688757524999804, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,244] Trial 249 finished with value: 0.14805033016807162 and parameters: {'alpha': 1.602015081692872, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,245] Trial 247 finished with value: 0.15076453446237675 and parameters: {'alpha': 1.8292053324229016, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,363] Trial 250 finished with value: 0.19682971396998356 and parameters: {'alpha': 36.63429113121762, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,449] Trial 254 finished with value: 0.20136704397101152 and parameters: {'alpha': 41.330787994963764, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,490] Trial 252 finished with value: 0.19601632292094986 and parameters: {'alpha': 35.77210840350188, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,563] Trial 253 finished with value: 0.19476334318462174 and parameters: {'alpha': 34.42900136450045, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,592] Trial 251 finished with value: 0.2213825578870272 and parameters: {'alpha': 60.33516216436692, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,598] Trial 255 finished with value: 0.22271667583264806 and parameters: {'alpha': 61.537481316725916, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,653] Trial 257 finished with value: 0.2258280071700247 and parameters: {'alpha': 64.32704028564343, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,718] Trial 258 finished with value: 0.22875064131887343 and parameters: {'alpha': 66.9030534684847, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,746] Trial 259 finished with value: 0.22529046168277142 and parameters: {'alpha': 63.84973243180095, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,773] Trial 256 finished with value: 0.22716249408922704 and parameters: {'alpha': 65.50600140395531, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,834] Trial 260 finished with value: 0.1828477663060239 and parameters: {'alpha': 19.555474083356476, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,948] Trial 263 finished with value: 0.1831523475406045 and parameters: {'alpha': 21.03498188842221, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:32,983] Trial 262 finished with value: 0.18303971037212177 and parameters: {'alpha': 20.893356214612258, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,065] Trial 261 finished with value: 0.18237104010331012 and parameters: {'alpha': 18.876678852500106, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,076] Trial 264 finished with value: 0.18314225585937507 and parameters: {'alpha': 21.02217128907785, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,104] Trial 265 finished with value: 0.18248754645096632 and parameters: {'alpha': 20.19974483418378, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,158] Trial 266 finished with value: 0.1826262272563734 and parameters: {'alpha': 20.374271762061333, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,219] Trial 268 finished with value: 0.18183731214021387 and parameters: {'alpha': 19.385115841381545, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,246] Trial 267 finished with value: 0.07164869384765628 and parameters: {'alpha': 0.052747419637579285, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,386] Trial 270 finished with value: 0.1478294230571547 and parameters: {'alpha': 1.585491088248252, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,427] Trial 269 finished with value: 0.19395440995065794 and parameters: {'alpha': 33.544416834509484, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,674] Trial 271 finished with value: 0.20122050980417355 and parameters: {'alpha': 41.17571482364507, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,815] Trial 272 finished with value: 0.2071540574244449 and parameters: {'alpha': 47.027964364615414, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,849] Trial 274 finished with value: 0.12723145411441208 and parameters: {'alpha': 0.6647079656947035, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:33,933] Trial 273 finished with value: 0.12781775461297293 and parameters: {'alpha': 0.6803138242109399, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,166] Trial 275 finished with value: 0.11043975528114724 and parameters: {'alpha': 0.34006968944067123, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,203] Trial 276 finished with value: inf and parameters: {'alpha': 44.0942301166019, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,269] Trial 278 finished with value: inf and parameters: {'alpha': 41.158429743842355, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,325] Trial 277 finished with value: inf and parameters: {'alpha': 43.052248217020185, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,327] Trial 279 finished with value: inf and parameters: {'alpha': 1.1802887499942245, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,363] Trial 281 finished with value: 0.19478211952611021 and parameters: {'alpha': 34.44956002559979, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,443] Trial 280 finished with value: 0.19451459703947369 and parameters: {'alpha': 34.15822282499368, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,478] Trial 282 finished with value: 0.1829342159231086 and parameters: {'alpha': 20.761169420456852, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,615] Trial 284 finished with value: 0.18535594989977391 and parameters: {'alpha': 23.744452502385876, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,630] Trial 283 finished with value: 0.17917728785464643 and parameters: {'alpha': 16.03360920100578, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,698] Trial 286 finished with value: 0.5818090867213199 and parameters: {'alpha': 296.7891814234757, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,715] Trial 285 finished with value: 0.18268189453125006 and parameters: {'alpha': 20.44431214804274, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,806] Trial 287 finished with value: 0.18009395456414481 and parameters: {'alpha': 17.1874081488546, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,854] Trial 288 finished with value: 0.18038501220703132 and parameters: {'alpha': 17.556569560127087, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,957] Trial 290 finished with value: 0.606120755807977 and parameters: {'alpha': 311.38013367701166, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,962] Trial 289 finished with value: 0.1812384637129935 and parameters: {'alpha': 18.635806942961942, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,967] Trial 291 finished with value: 0.17938405748869252 and parameters: {'alpha': 16.290713598733504, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:34,995] Trial 292 finished with value: 0.21073301333778788 and parameters: {'alpha': 50.46262825449625, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,019] Trial 293 finished with value: 0.15436489656147215 and parameters: {'alpha': 2.2089271132192008, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,124] Trial 294 finished with value: 0.1408219635331004 and parameters: {'alpha': 1.1567176855284083, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,155] Trial 296 finished with value: 0.07867010337428045 and parameters: {'alpha': 0.07517024781998441, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,200] Trial 295 finished with value: 0.14676463725842936 and parameters: {'alpha': 1.5077776920539365, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,398] Trial 298 finished with value: 0.14500432880602393 and parameters: {'alpha': 1.390044520851332, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,402] Trial 297 finished with value: 0.1247296986790708 and parameters: {'alpha': 0.602765569502054, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,415] Trial 299 finished with value: 0.13551108096474102 and parameters: {'alpha': 0.9266032811027279, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,429] Trial 301 finished with value: 0.19718210462068259 and parameters: {'alpha': 37.00569828776481, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,437] Trial 300 finished with value: 0.19601387669613493 and parameters: {'alpha': 35.769776574829066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,465] Trial 302 finished with value: 0.7263073189504523 and parameters: {'alpha': 383.17983068065865, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,492] Trial 304 finished with value: 0.19738001471268504 and parameters: {'alpha': 37.213818732534904, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,641] Trial 303 finished with value: 0.21308106471011518 and parameters: {'alpha': 52.68124798787404, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,708] Trial 306 finished with value: 0.21367049618369655 and parameters: {'alpha': 53.23163249213255, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,710] Trial 305 finished with value: 0.21604063457288242 and parameters: {'alpha': 55.22683118734645, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,763] Trial 308 finished with value: 0.21789108372738494 and parameters: {'alpha': 56.93243134007896, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,843] Trial 309 finished with value: 0.21373959440532486 and parameters: {'alpha': 53.2963292744988, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,861] Trial 307 finished with value: 0.21436003951223276 and parameters: {'alpha': 53.76130026754308, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,874] Trial 310 finished with value: 0.18550572760331008 and parameters: {'alpha': 23.292917453095026, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:35,972] Trial 311 finished with value: 0.39590189119037833 and parameters: {'alpha': 183.4174670797242, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,053] Trial 313 finished with value: 0.18289278268914483 and parameters: {'alpha': 20.70936260053848, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,079] Trial 312 finished with value: 0.4127003648617393 and parameters: {'alpha': 194.05424061098392, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,106] Trial 315 finished with value: 0.18331825388055106 and parameters: {'alpha': 21.243150259343913, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,163] Trial 314 finished with value: 1.2560108243600943 and parameters: {'alpha': 703.8918567335395, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,232] Trial 318 finished with value: 0.18360418489155023 and parameters: {'alpha': 21.59796974647719, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,237] Trial 316 finished with value: 0.18561670333059216 and parameters: {'alpha': 24.06189633515353, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,269] Trial 317 finished with value: 0.18183346210680515 and parameters: {'alpha': 19.379832363141553, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,365] Trial 319 finished with value: 0.10689744667454774 and parameters: {'alpha': 0.29239519288889737, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,522] Trial 321 finished with value: 0.19578099230314555 and parameters: {'alpha': 35.521056332154366, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,526] Trial 323 finished with value: 0.19357819059673115 and parameters: {'alpha': 33.12877468295078, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,535] Trial 320 finished with value: 0.24503578516909963 and parameters: {'alpha': 80.57918924718074, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,562] Trial 322 finished with value: 0.19586917692485611 and parameters: {'alpha': 35.61558240977111, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,587] Trial 324 finished with value: 0.059957367136102 and parameters: {'alpha': 0.03024348071364108, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,648] Trial 326 finished with value: 0.2438382847193669 and parameters: {'alpha': 79.61563099234962, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,725] Trial 327 finished with value: 0.9974422132632605 and parameters: {'alpha': 545.7494516863541, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,774] Trial 325 finished with value: 0.12868048012181338 and parameters: {'alpha': 0.7049508015076345, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,945] Trial 328 finished with value: 1.5306487508994653 and parameters: {'alpha': 876.3914863499597, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:36,987] Trial 329 finished with value: 1.4293554833984374 and parameters: {'alpha': 812.156786205398, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,002] Trial 330 finished with value: 0.1155180518220601 and parameters: {'alpha': 0.41788634932379, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,029] Trial 332 finished with value: 1.474427516640111 and parameters: {'alpha': 840.6452033434507, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,068] Trial 331 finished with value: 0.18038922523900086 and parameters: {'alpha': 17.5614677047838, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,075] Trial 333 finished with value: 0.09537794870476975 and parameters: {'alpha': 0.17392947995712066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,190] Trial 335 finished with value: 1.5094087145353616 and parameters: {'alpha': 862.8600744035679, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,203] Trial 334 finished with value: 1.6973395602898846 and parameters: {'alpha': 983.7893034238102, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,370] Trial 337 finished with value: 0.18164916715923113 and parameters: {'alpha': 19.148320983444716, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,378] Trial 338 finished with value: 0.17985086117393098 and parameters: {'alpha': 16.878430378761575, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,386] Trial 336 finished with value: 0.11631656352796058 and parameters: {'alpha': 0.4316237152492175, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,444] Trial 340 finished with value: 0.12532158048930928 and parameters: {'alpha': 0.6172662791115298, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,539] Trial 343 finished with value: 0.1951007842053865 and parameters: {'alpha': 34.79244924001258, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,551] Trial 339 finished with value: 0.1818873641807155 and parameters: {'alpha': 19.446653313826662, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,553] Trial 342 finished with value: 0.10055405260587995 and parameters: {'alpha': 0.2212074746315932, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,557] Trial 341 finished with value: 0.18181669671309628 and parameters: {'alpha': 19.359228144851677, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,718] Trial 347 finished with value: 0.19774899632504112 and parameters: {'alpha': 37.60101777979872, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,757] Trial 346 finished with value: 0.19701047054893092 and parameters: {'alpha': 36.82503867056096, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,767] Trial 344 finished with value: 0.0930946066123561 and parameters: {'alpha': 0.1554119405735025, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,771] Trial 345 finished with value: 0.192355213751542 and parameters: {'alpha': 31.779749936501535, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,873] Trial 348 finished with value: 0.0708166001490543 and parameters: {'alpha': 0.050756969983562286, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,901] Trial 350 finished with value: 0.9567448522306744 and parameters: {'alpha': 521.1893197770136, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,928] Trial 351 finished with value: inf and parameters: {'alpha': 0.6084569683639662, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:37,950] Trial 349 finished with value: 0.10184393683182572 and parameters: {'alpha': 0.2343604335207957, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,039] Trial 352 finished with value: inf and parameters: {'alpha': 0.1382154818558658, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,086] Trial 354 finished with value: inf and parameters: {'alpha': 497.55114172363403, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,154] Trial 353 finished with value: 0.23360611077559632 and parameters: {'alpha': 71.1076067863253, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,197] Trial 355 finished with value: 0.23617740266498774 and parameters: {'alpha': 73.29529201164449, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,225] Trial 356 finished with value: 0.1820629328356292 and parameters: {'alpha': 19.667109578523586, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,268] Trial 357 finished with value: 0.22614240729081012 and parameters: {'alpha': 64.60528792294154, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,364] Trial 359 finished with value: 0.208120895546361 and parameters: {'alpha': 47.95978256650252, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,372] Trial 358 finished with value: 0.2076463745117188 and parameters: {'alpha': 47.502215393773284, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,387] Trial 361 finished with value: 0.20979726350483147 and parameters: {'alpha': 49.570932132653724, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,498] Trial 360 finished with value: 0.20597497982627472 and parameters: {'alpha': 45.886882484881525, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,624] Trial 365 finished with value: 0.18296388061523444 and parameters: {'alpha': 20.798222230554344, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,665] Trial 362 finished with value: 0.18355138626901735 and parameters: {'alpha': 21.532121644131784, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,723] Trial 363 finished with value: 0.18207854498612258 and parameters: {'alpha': 19.686601893132455, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,807] Trial 364 finished with value: 0.18251001541940798 and parameters: {'alpha': 20.22817733873838, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,827] Trial 366 finished with value: 0.1823161344829359 and parameters: {'alpha': 19.96932722457449, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:38,892] Trial 367 finished with value: 0.18301410933645157 and parameters: {'alpha': 20.846534786959822, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,186] Trial 369 finished with value: 0.11300677599455186 and parameters: {'alpha': 0.37762431800395124, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,208] Trial 368 finished with value: 0.18248230706465876 and parameters: {'alpha': 20.177589761762945, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,292] Trial 370 finished with value: 0.1268376584344162 and parameters: {'alpha': 0.6551416433608219, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,425] Trial 371 finished with value: 0.2008067103335732 and parameters: {'alpha': 40.76176041218448, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,426] Trial 372 finished with value: 0.19701397685803865 and parameters: {'alpha': 36.82918510789897, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,517] Trial 373 finished with value: 0.10006621665553043 and parameters: {'alpha': 0.216484786206173, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,544] Trial 375 finished with value: 0.19872812872635692 and parameters: {'alpha': 38.62233695655628, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,571] Trial 374 finished with value: 0.19574216430664065 and parameters: {'alpha': 35.479895239657424, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,591] Trial 376 finished with value: 0.19549368138363488 and parameters: {'alpha': 35.21378877460567, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,669] Trial 378 finished with value: 0.19596708200555102 and parameters: {'alpha': 35.720223883799335, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,715] Trial 377 finished with value: 1.0413183411287008 and parameters: {'alpha': 572.2981182153292, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,853] Trial 380 finished with value: 0.8080631242290297 and parameters: {'alpha': 431.94260187833225, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,894] Trial 379 finished with value: 0.10366883332905022 and parameters: {'alpha': 0.2543392616730623, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,921] Trial 383 finished with value: 0.12202079326428875 and parameters: {'alpha': 0.5418814289585745, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,923] Trial 382 finished with value: 1.0902187793611227 and parameters: {'alpha': 602.0076529067952, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,961] Trial 384 finished with value: 0.07379209466231498 and parameters: {'alpha': 0.05892280806882874, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:39,971] Trial 381 finished with value: 0.1245204990748356 and parameters: {'alpha': 0.5980344062675832, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,087] Trial 386 finished with value: 0.17919482421875005 and parameters: {'alpha': 16.05484851485297, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,142] Trial 387 finished with value: 0.17999070993523855 and parameters: {'alpha': 17.056982028972392, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,190] Trial 385 finished with value: 0.18096326146175992 and parameters: {'alpha': 18.29108697276981, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,298] Trial 388 finished with value: 0.18116672690943675 and parameters: {'alpha': 18.54675648044369, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,320] Trial 390 finished with value: 0.1812118106239721 and parameters: {'alpha': 18.602671454451567, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,330] Trial 391 finished with value: 0.22215220182719989 and parameters: {'alpha': 61.02923633183115, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,366] Trial 389 finished with value: 0.1810820157020971 and parameters: {'alpha': 18.440324354468448, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,384] Trial 392 finished with value: 0.22338169722707651 and parameters: {'alpha': 62.13717081110034, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,517] Trial 394 finished with value: 0.22617196115594168 and parameters: {'alpha': 64.63109804199317, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,536] Trial 393 finished with value: 0.22119684236225332 and parameters: {'alpha': 60.16713626414791, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,605] Trial 395 finished with value: 0.19657000436883224 and parameters: {'alpha': 36.359838525237215, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,624] Trial 396 finished with value: 0.1957346987433183 and parameters: {'alpha': 35.471547609800474, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,734] Trial 398 finished with value: 0.1975914201274671 and parameters: {'alpha': 37.435830722577975, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,789] Trial 400 finished with value: 0.19478613499691616 and parameters: {'alpha': 34.4535113394431, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,791] Trial 399 finished with value: 0.1949553946725946 and parameters: {'alpha': 34.63623097318353, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,810] Trial 397 finished with value: 0.1966449218107525 and parameters: {'alpha': 36.43907840392098, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,924] Trial 404 finished with value: 0.1405200964997945 and parameters: {'alpha': 1.1418739596607177, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,930] Trial 401 finished with value: 0.1276883295641448 and parameters: {'alpha': 0.6775541624895249, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:40,953] Trial 402 finished with value: 0.17964321957236848 and parameters: {'alpha': 16.615747612496907, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,001] Trial 403 finished with value: 0.18086146285207652 and parameters: {'alpha': 18.162210518431912, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,155] Trial 408 finished with value: 0.17864643715306341 and parameters: {'alpha': 15.375940186027929, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,206] Trial 405 finished with value: 0.47507555548416935 and parameters: {'alpha': 232.2972876679396, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,231] Trial 407 finished with value: inf and parameters: {'alpha': 1.3028459155139647, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,284] Trial 406 finished with value: 0.1793475149054277 and parameters: {'alpha': 16.245310019586807, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,330] Trial 411 finished with value: inf and parameters: {'alpha': 1.3622182296451841, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,346] Trial 409 finished with value: 0.1508903054327715 and parameters: {'alpha': 1.840581418326356, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,406] Trial 410 finished with value: inf and parameters: {'alpha': 86.19217543528457, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,438] Trial 412 finished with value: 0.2836652357884458 and parameters: {'alpha': 109.61133152084443, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,531] Trial 413 finished with value: 0.24946623168945323 and parameters: {'alpha': 84.10942411346257, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,560] Trial 414 finished with value: 0.26678738139905434 and parameters: {'alpha': 97.4069666839091, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,641] Trial 415 finished with value: 1.5806091138337783 and parameters: {'alpha': 908.3585591795813, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,712] Trial 417 finished with value: 0.21078030915912835 and parameters: {'alpha': 50.507414668060854, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,769] Trial 416 finished with value: 0.21201621633429282 and parameters: {'alpha': 51.67858977094753, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,844] Trial 419 finished with value: 0.21040226703844578 and parameters: {'alpha': 50.14685373213592, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,859] Trial 418 finished with value: 0.21276874749434627 and parameters: {'alpha': 52.38760047185726, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,877] Trial 420 finished with value: 0.2114659788111637 and parameters: {'alpha': 51.0360274831832, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,973] Trial 422 finished with value: 0.18415600791529613 and parameters: {'alpha': 21.53009001100528, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:41,977] Trial 421 finished with value: 0.21500948203638987 and parameters: {'alpha': 54.10240297883997, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,025] Trial 423 finished with value: 0.18336414197419826 and parameters: {'alpha': 20.283099419576104, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,071] Trial 424 finished with value: 0.1824782096782484 and parameters: {'alpha': 20.189206283294066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,114] Trial 425 finished with value: 0.18110811549136524 and parameters: {'alpha': 18.47298133732165, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,205] Trial 428 finished with value: 0.18136054995888165 and parameters: {'alpha': 18.788468055035924, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,293] Trial 426 finished with value: 0.1065109046052632 and parameters: {'alpha': 0.28750539034713335, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,331] Trial 431 finished with value: 0.12946730520148034 and parameters: {'alpha': 0.7273281202411556, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,343] Trial 427 finished with value: 0.13126517353258643 and parameters: {'alpha': 0.7815033922730144, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,372] Trial 429 finished with value: 0.13646122969777968 and parameters: {'alpha': 0.9633826729073699, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,440] Trial 430 finished with value: 0.1260475760048315 and parameters: {'alpha': 0.634727683394804, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,499] Trial 432 finished with value: 0.19392587440892273 and parameters: {'alpha': 33.51291568043298, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,527] Trial 433 finished with value: 0.19212525095086355 and parameters: {'alpha': 31.525707656360698, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,591] Trial 435 finished with value: 0.1926228352436267 and parameters: {'alpha': 32.065121766426586, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:42,684] Trial 434 finished with value: 0.13766736578690383 and parameters: {'alpha': 1.01265167695933, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,046] Trial 438 finished with value: 0.6551693868857936 and parameters: {'alpha': 340.6761755418537, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,074] Trial 436 finished with value: 0.19054197008634874 and parameters: {'alpha': 29.76381682403256, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,104] Trial 437 finished with value: 0.08774000475431745 and parameters: {'alpha': 0.11873969200580381, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,116] Trial 439 finished with value: 0.18948677753649265 and parameters: {'alpha': 28.57992302822617, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,403] Trial 441 finished with value: 0.0657658829538446 and parameters: {'alpha': 0.03976620433576783, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,405] Trial 442 finished with value: 0.18239420166015632 and parameters: {'alpha': 20.08243191008919, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,503] Trial 440 finished with value: 0.18084161839535368 and parameters: {'alpha': 18.119738545374382, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:43,566] Trial 443 finished with value: 0.1823335873252468 and parameters: {'alpha': 19.990852287108044, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 127 with value: 0.05527907573499179.\n",
+      "[I 2025-03-06 14:40:44,088] Trial 444 finished with value: 0.028335061292146406 and parameters: {'alpha': 0.006937664848638253, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,109] Trial 445 finished with value: 0.1978991878469367 and parameters: {'alpha': 37.75074935883521, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,164] Trial 450 finished with value: 0.1308382072368422 and parameters: {'alpha': 0.7681362211301017, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,256] Trial 447 finished with value: 0.11051547742341698 and parameters: {'alpha': 0.3408450079328657, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,411] Trial 446 finished with value: 0.09986491782740545 and parameters: {'alpha': 0.21394610333086858, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,516] Trial 449 finished with value: 0.19739504702919408 and parameters: {'alpha': 37.22226959976518, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,518] Trial 451 finished with value: 0.10951530877364316 and parameters: {'alpha': 0.3263753354808478, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:44,533] Trial 448 finished with value: 0.19696357389751232 and parameters: {'alpha': 36.76779696570166, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,014] Trial 453 finished with value: 0.23174300672029205 and parameters: {'alpha': 69.49998347325354, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,049] Trial 452 finished with value: 0.23428719720137758 and parameters: {'alpha': 71.68712572237162, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,122] Trial 455 finished with value: 0.182307968621505 and parameters: {'alpha': 19.95896332839552, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,150] Trial 454 finished with value: 0.22924496299342115 and parameters: {'alpha': 67.33267645115812, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,212] Trial 457 finished with value: 0.23570147139699843 and parameters: {'alpha': 72.889626440337, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,339] Trial 456 finished with value: 1.3145289957468131 and parameters: {'alpha': 740.2245554824464, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,661] Trial 458 finished with value: 0.22488994706003296 and parameters: {'alpha': 63.48807021872705, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,685] Trial 459 finished with value: 0.23465205161646802 and parameters: {'alpha': 71.99787597188067, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,685] Trial 462 finished with value: 0.18319937673468345 and parameters: {'alpha': 21.079370092972546, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,912] Trial 460 finished with value: 0.18117780157791946 and parameters: {'alpha': 18.54383267997232, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:45,997] Trial 461 finished with value: 0.18009547883686275 and parameters: {'alpha': 17.172108960836272, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,101] Trial 463 finished with value: 0.18153197965923118 and parameters: {'alpha': 18.986459231220433, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,205] Trial 465 finished with value: inf and parameters: {'alpha': 18.89950492002775, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,301] Trial 464 finished with value: 0.18475470259817028 and parameters: {'alpha': 22.997725802919305, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,380] Trial 466 finished with value: inf and parameters: {'alpha': 404.05626734004977, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,389] Trial 467 finished with value: inf and parameters: {'alpha': 406.83249839180775, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,691] Trial 468 finished with value: 0.20392410612407486 and parameters: {'alpha': 43.87682813678748, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,782] Trial 469 finished with value: 0.20353434056332237 and parameters: {'alpha': 43.493159113000864, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,848] Trial 470 finished with value: 0.21001602815326895 and parameters: {'alpha': 49.7801216196262, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:46,974] Trial 471 finished with value: 0.35772167833830176 and parameters: {'alpha': 159.36051683352446, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,117] Trial 472 finished with value: 0.12228791683799349 and parameters: {'alpha': 0.5478752046908294, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,145] Trial 473 finished with value: 0.8465837878417968 and parameters: {'alpha': 454.9644943804261, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,148] Trial 474 finished with value: 0.10553941284179694 and parameters: {'alpha': 0.27579524665998706, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,159] Trial 476 finished with value: 0.14409953883120896 and parameters: {'alpha': 1.3354939399796362, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,307] Trial 475 finished with value: 0.19649078394839642 and parameters: {'alpha': 36.27636077518358, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,345] Trial 477 finished with value: 0.12222142867238905 and parameters: {'alpha': 0.5463128434648643, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,348] Trial 478 finished with value: 0.14689047466077315 and parameters: {'alpha': 1.5164898594358032, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,439] Trial 479 finished with value: 0.5383368541195518 and parameters: {'alpha': 270.60979324318873, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,524] Trial 480 finished with value: 0.19431627936112256 and parameters: {'alpha': 33.94207494215399, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,616] Trial 482 finished with value: 1.1620255086477178 and parameters: {'alpha': 645.9198083652531, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,627] Trial 481 finished with value: 0.19441754150390628 and parameters: {'alpha': 34.052777873011856, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,638] Trial 483 finished with value: 0.18639586207339648 and parameters: {'alpha': 25.00893514243025, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,717] Trial 485 finished with value: 0.19497885787160776 and parameters: {'alpha': 34.661839935408125, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,773] Trial 484 finished with value: 0.1947176521381579 and parameters: {'alpha': 34.37942671665707, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,829] Trial 487 finished with value: 0.18425278513055104 and parameters: {'alpha': 22.397372626143543, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,851] Trial 486 finished with value: 0.1832511119320519 and parameters: {'alpha': 21.159144113779853, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,879] Trial 488 finished with value: 0.182405124897204 and parameters: {'alpha': 20.09622830056596, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:47,990] Trial 491 finished with value: 0.18064666349712177 and parameters: {'alpha': 17.890664893824624, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,024] Trial 490 finished with value: 0.18015153468081835 and parameters: {'alpha': 17.26037374206739, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,027] Trial 489 finished with value: 0.17994796926398032 and parameters: {'alpha': 17.002612875389303, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,092] Trial 492 finished with value: 0.17892156873201076 and parameters: {'alpha': 15.716371521432455, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,202] Trial 494 finished with value: 0.09142266897101155 and parameters: {'alpha': 0.1433781722696224, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,224] Trial 493 finished with value: 0.2125596380293997 and parameters: {'alpha': 52.191233442760584, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,247] Trial 496 finished with value: 0.21537941072162833 and parameters: {'alpha': 54.822911481762645, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,251] Trial 495 finished with value: 0.10987874903628705 and parameters: {'alpha': 0.33199094969297116, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,420] Trial 497 finished with value: 0.2124830581825658 and parameters: {'alpha': 52.11877609186591, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,441] Trial 498 finished with value: 0.21234641652960531 and parameters: {'alpha': 51.99006296694497, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,488] Trial 499 finished with value: 0.2091485517321135 and parameters: {'alpha': 48.949439992611225, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,508] Trial 500 finished with value: 0.20360295371607734 and parameters: {'alpha': 43.56721033014059, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,537] Trial 501 finished with value: 0.20593445890727796 and parameters: {'alpha': 45.84778648440877, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,597] Trial 502 finished with value: 0.20934362009148852 and parameters: {'alpha': 49.13700468053705, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,696] Trial 504 finished with value: 0.11072170390881994 and parameters: {'alpha': 0.3438147282146849, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,771] Trial 503 finished with value: 0.1377277873792147 and parameters: {'alpha': 1.014784253181044, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,837] Trial 507 finished with value: 0.1799536840820313 and parameters: {'alpha': 17.009869749752905, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,839] Trial 505 finished with value: 0.13988148193359382 and parameters: {'alpha': 1.111177094076188, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,884] Trial 506 finished with value: 0.18212737150493424 and parameters: {'alpha': 19.748294586102293, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:48,997] Trial 509 finished with value: 0.18362799020867607 and parameters: {'alpha': 21.627028832676835, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,108] Trial 508 finished with value: 0.18059266453793182 and parameters: {'alpha': 17.820715008217753, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,186] Trial 510 finished with value: 0.18131342921206833 and parameters: {'alpha': 18.72895179888311, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,401] Trial 511 finished with value: 0.18159491011770157 and parameters: {'alpha': 19.065093188209524, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,534] Trial 512 finished with value: 0.19174443661338408 and parameters: {'alpha': 31.096439061791184, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,605] Trial 513 finished with value: 0.19319834536903785 and parameters: {'alpha': 32.70030418907077, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,718] Trial 514 finished with value: 0.1932336686626234 and parameters: {'alpha': 32.73924026060034, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:49,920] Trial 516 finished with value: 0.19948712839226973 and parameters: {'alpha': 39.40920266779712, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,014] Trial 515 finished with value: 0.19401484882555514 and parameters: {'alpha': 33.60247961088734, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,115] Trial 517 finished with value: 0.09665128822728208 and parameters: {'alpha': 0.18468791721771605, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,215] Trial 521 finished with value: 0.14348983231393925 and parameters: {'alpha': 1.2994617472393795, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,443] Trial 520 finished with value: 0.1470743458958677 and parameters: {'alpha': 1.5299079316122335, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,532] Trial 522 finished with value: inf and parameters: {'alpha': 14.66239017851323, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,537] Trial 519 finished with value: 0.1397778940301193 and parameters: {'alpha': 1.106533619365152, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,546] Trial 518 finished with value: 0.06755365504214642 and parameters: {'alpha': 0.04289012030118053, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,586] Trial 523 finished with value: inf and parameters: {'alpha': 0.08883738291503626, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,598] Trial 524 finished with value: inf and parameters: {'alpha': 17.471235999704984, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:50,767] Trial 525 finished with value: 0.18042468666478212 and parameters: {'alpha': 17.605921835718902, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,171] Trial 526 finished with value: 0.1951658251953125 and parameters: {'alpha': 34.854797682760534, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,210] Trial 528 finished with value: 0.24476973934775917 and parameters: {'alpha': 80.36298020675946, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,212] Trial 529 finished with value: 0.19577729325143917 and parameters: {'alpha': 35.509025103961086, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,369] Trial 527 finished with value: 0.24877203504060455 and parameters: {'alpha': 83.55679171333871, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,602] Trial 531 finished with value: 0.19430906384919824 and parameters: {'alpha': 33.92558000169546, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,615] Trial 530 finished with value: 0.1944940073113693 and parameters: {'alpha': 34.127160632907014, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,866] Trial 532 finished with value: 0.19267542988024264 and parameters: {'alpha': 32.12304558256706, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,911] Trial 533 finished with value: 0.1346580986585116 and parameters: {'alpha': 0.8918169376130909, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:51,965] Trial 535 finished with value: 0.21796795911287012 and parameters: {'alpha': 57.21134395975368, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,006] Trial 534 finished with value: 0.18022671592310863 and parameters: {'alpha': 17.33832130197396, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,242] Trial 537 finished with value: 0.21790237201891455 and parameters: {'alpha': 57.15569145915572, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,292] Trial 536 finished with value: 0.10385793951737259 and parameters: {'alpha': 0.25343256350817, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,321] Trial 539 finished with value: 0.17973810032894744 and parameters: {'alpha': 16.73583259841613, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,322] Trial 538 finished with value: 0.17924078125000006 and parameters: {'alpha': 14.230238924463217, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,416] Trial 540 finished with value: 0.13845901906866784 and parameters: {'alpha': 1.046616256795783, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,454] Trial 541 finished with value: 0.1295097246350741 and parameters: {'alpha': 0.7254551692186515, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,461] Trial 542 finished with value: 0.18094888761821554 and parameters: {'alpha': 16.790526145914434, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,474] Trial 543 finished with value: 0.12771795731393923 and parameters: {'alpha': 0.6783805619848513, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,614] Trial 544 finished with value: 0.0916561862664474 and parameters: {'alpha': 0.14483431158776536, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,738] Trial 545 finished with value: 0.2076298982319079 and parameters: {'alpha': 47.4868260524833, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,748] Trial 546 finished with value: 0.18150933651572787 and parameters: {'alpha': 18.974823834030254, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,774] Trial 547 finished with value: 0.20828541581003296 and parameters: {'alpha': 48.118696648578435, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,775] Trial 548 finished with value: 0.20872367232473277 and parameters: {'alpha': 48.540791593506384, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,914] Trial 549 finished with value: 0.20815799849660777 and parameters: {'alpha': 47.99544211341265, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,975] Trial 550 finished with value: 0.20896407297234787 and parameters: {'alpha': 48.772553274601265, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:52,998] Trial 551 finished with value: 0.2125372943436472 and parameters: {'alpha': 52.170071301668216, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,062] Trial 552 finished with value: 0.19561311594109787 and parameters: {'alpha': 35.341857859450656, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,138] Trial 554 finished with value: 0.19135834106445318 and parameters: {'alpha': 30.67921691714915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,191] Trial 555 finished with value: 0.18783012380499592 and parameters: {'alpha': 26.70070398503513, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,221] Trial 553 finished with value: 0.1891880034436678 and parameters: {'alpha': 28.252050406274144, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,222] Trial 556 finished with value: 0.18568862844366785 and parameters: {'alpha': 24.149803162427254, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,349] Trial 557 finished with value: 0.18250003938373777 and parameters: {'alpha': 20.21605089739, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,388] Trial 559 finished with value: 0.18292919819078954 and parameters: {'alpha': 20.75485278188887, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,399] Trial 558 finished with value: 0.18169929873817853 and parameters: {'alpha': 19.212533767610196, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,411] Trial 560 finished with value: 0.1221973984888981 and parameters: {'alpha': 0.5459774961329326, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,464] Trial 561 finished with value: 0.17839594964278377 and parameters: {'alpha': 15.068056233806779, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,806] Trial 562 finished with value: 0.13726359252929693 and parameters: {'alpha': 0.98957389458461, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:53,975] Trial 563 finished with value: 0.18051038111636517 and parameters: {'alpha': 17.699097386734884, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,006] Trial 564 finished with value: 0.07320701165450248 and parameters: {'alpha': 0.05664538531985111, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,222] Trial 566 finished with value: 0.11793302220394744 and parameters: {'alpha': 0.46013925531716865, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,268] Trial 565 finished with value: 0.22435607903731505 and parameters: {'alpha': 63.008992640392364, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,482] Trial 567 finished with value: 0.3167954391318874 and parameters: {'alpha': 132.43097357018257, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,486] Trial 569 finished with value: 0.12576967008891865 and parameters: {'alpha': 0.6274044642309584, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,498] Trial 568 finished with value: 0.18051177689401735 and parameters: {'alpha': 17.701127004274326, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,623] Trial 570 finished with value: 0.14149115459241374 and parameters: {'alpha': 1.179548631556761, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:54,981] Trial 572 finished with value: 0.12208593062551405 and parameters: {'alpha': 0.5427635309105445, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,048] Trial 571 finished with value: 0.24115616140265222 and parameters: {'alpha': 77.43207604245765, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,187] Trial 574 finished with value: 0.2408317323704771 and parameters: {'alpha': 77.16608648446037, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,227] Trial 573 finished with value: 0.23841544285824434 and parameters: {'alpha': 75.16973720906954, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,416] Trial 575 finished with value: 0.23473023765162426 and parameters: {'alpha': 72.06441147738632, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,511] Trial 576 finished with value: 0.24501448068719175 and parameters: {'alpha': 80.55932519102025, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,690] Trial 577 finished with value: 0.19649282862613077 and parameters: {'alpha': 36.27080402763826, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,840] Trial 578 finished with value: 0.1951053200812089 and parameters: {'alpha': 34.78918921665891, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,870] Trial 579 finished with value: 0.19793655260587995 and parameters: {'alpha': 37.7898716197836, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,930] Trial 580 finished with value: inf and parameters: {'alpha': 35.4926904648241, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:55,938] Trial 582 finished with value: 0.19620746575606499 and parameters: {'alpha': 35.9758675218551, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,085] Trial 581 finished with value: inf and parameters: {'alpha': 33.72257681154626, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,120] Trial 584 finished with value: inf and parameters: {'alpha': 30.599278097346637, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,206] Trial 583 finished with value: inf and parameters: {'alpha': 32.566414431732674, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,241] Trial 585 finished with value: inf and parameters: {'alpha': 0.11356424759850976, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,281] Trial 586 finished with value: 0.18012790135433804 and parameters: {'alpha': 17.23067393376435, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,353] Trial 587 finished with value: 0.18050363563939148 and parameters: {'alpha': 17.707473568990952, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,395] Trial 588 finished with value: 1.3806243263646176 and parameters: {'alpha': 781.5249488195897, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,418] Trial 589 finished with value: 0.1257755455258019 and parameters: {'alpha': 0.6281201475124043, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,563] Trial 590 finished with value: 0.1792803344726563 and parameters: {'alpha': 16.161971808874327, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,593] Trial 591 finished with value: 0.17923641325298115 and parameters: {'alpha': 16.106192862079133, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,597] Trial 592 finished with value: 0.07653419703433391 and parameters: {'alpha': 0.06747972975263254, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,617] Trial 593 finished with value: 0.6783682718056127 and parameters: {'alpha': 354.55054168909464, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,654] Trial 594 finished with value: 0.11462359085886109 and parameters: {'alpha': 0.40342734511233297, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,786] Trial 595 finished with value: 1.0405063229851974 and parameters: {'alpha': 571.8058499997035, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,853] Trial 596 finished with value: 0.6139491322728208 and parameters: {'alpha': 316.0648233740822, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,911] Trial 597 finished with value: 0.21784467773437505 and parameters: {'alpha': 57.102514185092915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:56,952] Trial 598 finished with value: 0.21991207468133228 and parameters: {'alpha': 58.99950974097281, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,045] Trial 600 finished with value: 0.21652019332082656 and parameters: {'alpha': 55.88060449719251, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,050] Trial 601 finished with value: 0.21664759296618014 and parameters: {'alpha': 55.99804477707864, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,076] Trial 602 finished with value: 0.21675910259046058 and parameters: {'alpha': 56.100760634602814, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,080] Trial 599 finished with value: 0.4393596813322368 and parameters: {'alpha': 210.48473215145023, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,287] Trial 603 finished with value: 0.18117974827816619 and parameters: {'alpha': 18.56305505509285, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,399] Trial 605 finished with value: 0.18172113968698608 and parameters: {'alpha': 19.223607150456026, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,564] Trial 604 finished with value: 0.18182529785156257 and parameters: {'alpha': 19.353720065103584, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,893] Trial 606 finished with value: 0.18184810411955188 and parameters: {'alpha': 19.382504033798813, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,911] Trial 608 finished with value: 0.8966523357833059 and parameters: {'alpha': 484.98285047141457, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:57,999] Trial 607 finished with value: 0.11006376124331829 and parameters: {'alpha': 0.33441115905670005, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,130] Trial 609 finished with value: 0.18044083528217522 and parameters: {'alpha': 17.610255190114387, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,192] Trial 610 finished with value: 0.18119183163291536 and parameters: {'alpha': 18.577969055961148, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,193] Trial 611 finished with value: 0.09456731278268915 and parameters: {'alpha': 0.16712784742485118, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,234] Trial 612 finished with value: 0.28881175665604447 and parameters: {'alpha': 113.2306645073283, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,245] Trial 613 finished with value: 0.11473711303710943 and parameters: {'alpha': 0.40512677167227484, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,443] Trial 616 finished with value: 0.2034097229003906 and parameters: {'alpha': 43.37635187906485, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,451] Trial 615 finished with value: 0.20286144608347043 and parameters: {'alpha': 42.83246889114706, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,466] Trial 614 finished with value: 0.19596325606496712 and parameters: {'alpha': 35.71618293014876, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,516] Trial 617 finished with value: 0.19971945376747535 and parameters: {'alpha': 39.6481779627392, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,593] Trial 618 finished with value: 0.19722990478515628 and parameters: {'alpha': 37.056434637756816, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,646] Trial 619 finished with value: 0.19641554199218753 and parameters: {'alpha': 36.19630323375425, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,690] Trial 620 finished with value: 0.20303722347861847 and parameters: {'alpha': 43.007337461448365, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,737] Trial 621 finished with value: 0.18367709157843345 and parameters: {'alpha': 20.72731261458671, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,858] Trial 622 finished with value: 0.18296001638312095 and parameters: {'alpha': 19.718867382341244, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:58,966] Trial 624 finished with value: 0.18075703465511933 and parameters: {'alpha': 16.5150547137066, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,061] Trial 623 finished with value: 0.1134409336451481 and parameters: {'alpha': 0.3817584786180873, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,275] Trial 629 finished with value: 1.2218631232653165 and parameters: {'alpha': 682.7629382024181, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,285] Trial 627 finished with value: 0.12534562834087176 and parameters: {'alpha': 0.6170064147736374, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,291] Trial 625 finished with value: 0.18473068899054285 and parameters: {'alpha': 22.296224202947336, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,410] Trial 628 finished with value: 0.1455142646869861 and parameters: {'alpha': 1.4088184491926645, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,431] Trial 626 finished with value: 0.14459471223530024 and parameters: {'alpha': 1.3505353382783802, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,700] Trial 630 finished with value: 0.17950063303094166 and parameters: {'alpha': 16.41849557140651, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,843] Trial 632 finished with value: 0.18315740060906666 and parameters: {'alpha': 21.041232839823902, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:40:59,962] Trial 631 finished with value: 1.6325486333264798 and parameters: {'alpha': 941.7933888333627, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,005] Trial 633 finished with value: 0.19103622378700663 and parameters: {'alpha': 30.32247266368188, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,037] Trial 636 finished with value: 0.19125854402240958 and parameters: {'alpha': 30.56873623069784, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,057] Trial 637 finished with value: 0.11389692074424349 and parameters: {'alpha': 0.39166028757200044, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,064] Trial 634 finished with value: 0.5249485738975123 and parameters: {'alpha': 262.53950866724756, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,076] Trial 635 finished with value: 0.5223020737176192 and parameters: {'alpha': 260.94246839776474, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,230] Trial 638 finished with value: 0.13446060013620487 and parameters: {'alpha': 0.8876076386567042, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,235] Trial 639 finished with value: 0.18249821635999183 and parameters: {'alpha': 20.212886592894534, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,285] Trial 640 finished with value: 0.22396359477796057 and parameters: {'alpha': 62.66062967119066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,403] Trial 643 finished with value: 0.22336590678967932 and parameters: {'alpha': 62.12257568505527, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,463] Trial 642 finished with value: 0.22039645437140218 and parameters: {'alpha': 59.44079724535553, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,513] Trial 644 finished with value: 0.2698608391370272 and parameters: {'alpha': 99.67367396068755, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,527] Trial 645 finished with value: 0.2134386011384663 and parameters: {'alpha': 53.01548724736348, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,554] Trial 641 finished with value: 0.7141080839458265 and parameters: {'alpha': 375.8969073227406, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,645] Trial 646 finished with value: 0.06687275506270561 and parameters: {'alpha': 0.04180562719552835, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,658] Trial 647 finished with value: 0.09464668579101562 and parameters: {'alpha': 0.16770363284828, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,721] Trial 648 finished with value: 0.13156692324989727 and parameters: {'alpha': 0.7907247731469386, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,733] Trial 649 finished with value: 0.12782978335731915 and parameters: {'alpha': 0.6812840612821297, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,767] Trial 650 finished with value: 0.12554536749588824 and parameters: {'alpha': 0.6226316731945233, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:00,931] Trial 651 finished with value: 0.19536875738846632 and parameters: {'alpha': 35.080871679485064, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,052] Trial 655 finished with value: 0.19202601581774265 and parameters: {'alpha': 31.4164380507922, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,067] Trial 652 finished with value: 0.19242525480571548 and parameters: {'alpha': 31.856971917181305, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,083] Trial 653 finished with value: 0.19181058317485614 and parameters: {'alpha': 31.179370464843398, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,109] Trial 654 finished with value: 0.19514108186420645 and parameters: {'alpha': 34.83685053277589, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,115] Trial 657 finished with value: 0.1842822297748767 and parameters: {'alpha': 22.433936977245114, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,119] Trial 656 finished with value: 0.19139479029605266 and parameters: {'alpha': 30.718828014131685, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,179] Trial 658 finished with value: 0.1912970636307566 and parameters: {'alpha': 30.611413043609843, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,356] Trial 659 finished with value: 0.18072628546463823 and parameters: {'alpha': 17.99045109502104, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,400] Trial 660 finished with value: 0.09550563746402138 and parameters: {'alpha': 0.174844948820408, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,538] Trial 662 finished with value: 0.1802187315609581 and parameters: {'alpha': 17.345737501308957, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,543] Trial 665 finished with value: 0.1807175606496711 and parameters: {'alpha': 17.98028692773777, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,545] Trial 663 finished with value: 0.18311722180818263 and parameters: {'alpha': 20.990721724750845, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,573] Trial 664 finished with value: 0.1793011635228208 and parameters: {'alpha': 16.1869112721828, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,578] Trial 661 finished with value: 0.18398951075503706 and parameters: {'alpha': 22.07542714164167, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,656] Trial 666 finished with value: 0.17941753205951896 and parameters: {'alpha': 16.33336903093593, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,855] Trial 667 finished with value: 0.11741986270302227 and parameters: {'alpha': 0.4513100591377664, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,873] Trial 668 finished with value: inf and parameters: {'alpha': 54.947743276523916, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,877] Trial 669 finished with value: inf and parameters: {'alpha': 48.80031781055484, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,928] Trial 670 finished with value: inf and parameters: {'alpha': 42.5989417819338, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:01,967] Trial 672 finished with value: 0.10733096493369661 and parameters: {'alpha': 0.29777362261987306, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,013] Trial 671 finished with value: inf and parameters: {'alpha': 44.757918719804145, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,033] Trial 673 finished with value: inf and parameters: {'alpha': 48.738313943049356, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,037] Trial 674 finished with value: 0.20590766768606086 and parameters: {'alpha': 45.82196816270613, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,250] Trial 676 finished with value: 0.18338053023488904 and parameters: {'alpha': 21.320417494252233, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,255] Trial 678 finished with value: 0.07575185000770972 and parameters: {'alpha': 0.06494218144495094, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,260] Trial 675 finished with value: 0.10825495367752884 and parameters: {'alpha': 0.3098162989154417, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,309] Trial 679 finished with value: 0.17998604074578545 and parameters: {'alpha': 17.050604620219357, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,389] Trial 677 finished with value: 0.11963544208727392 and parameters: {'alpha': 0.4931363682550123, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,427] Trial 682 finished with value: 0.13628779830129528 and parameters: {'alpha': 0.9564376197322692, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,492] Trial 680 finished with value: 0.13312715833162014 and parameters: {'alpha': 0.8415716411894212, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,565] Trial 681 finished with value: 0.13137718435187096 and parameters: {'alpha': 0.7848794919324535, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,653] Trial 684 finished with value: 0.18097384303042768 and parameters: {'alpha': 18.303391316401754, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,675] Trial 683 finished with value: 0.26220903551603625 and parameters: {'alpha': 93.98039506320521, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,768] Trial 687 finished with value: 0.23000799843236033 and parameters: {'alpha': 68.00085784114962, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,808] Trial 685 finished with value: 0.2394337787828948 and parameters: {'alpha': 76.01589334552227, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,808] Trial 688 finished with value: 0.23597886172645982 and parameters: {'alpha': 73.12760014873915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,834] Trial 686 finished with value: 0.2318411046720807 and parameters: {'alpha': 69.58789360845137, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,934] Trial 689 finished with value: 0.2611754828202098 and parameters: {'alpha': 93.19628610624481, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,967] Trial 691 finished with value: 0.3784460546875 and parameters: {'alpha': 172.59613511731115, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:02,973] Trial 690 finished with value: 0.19677833039936266 and parameters: {'alpha': 36.58039075043064, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,154] Trial 693 finished with value: 0.1969766932437294 and parameters: {'alpha': 36.789752195852515, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,192] Trial 692 finished with value: 1.1328970981316817 and parameters: {'alpha': 628.062797196157, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,199] Trial 694 finished with value: 0.19641458149157073 and parameters: {'alpha': 36.19522399161134, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,225] Trial 696 finished with value: 0.1858763344212583 and parameters: {'alpha': 24.37939852912497, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,278] Trial 695 finished with value: 0.18294031140779202 and parameters: {'alpha': 20.769264639842866, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,412] Trial 699 finished with value: 0.18164914627878298 and parameters: {'alpha': 19.149062972065202, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,414] Trial 697 finished with value: 0.1861623502390009 and parameters: {'alpha': 24.727405217600058, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,439] Trial 698 finished with value: 0.18238972039473691 and parameters: {'alpha': 20.077131508118143, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,588] Trial 704 finished with value: 0.1804037018143504 and parameters: {'alpha': 17.581045025684862, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,709] Trial 703 finished with value: 0.10845036094263985 and parameters: {'alpha': 0.31253281129702043, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,711] Trial 701 finished with value: 0.06572276373612257 and parameters: {'alpha': 0.039431283544496376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,754] Trial 700 finished with value: 0.18028057302374595 and parameters: {'alpha': 17.42400162209223, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,926] Trial 702 finished with value: 0.10184393683182572 and parameters: {'alpha': 0.23436418312907265, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:03,975] Trial 707 finished with value: 0.20073331395199429 and parameters: {'alpha': 40.67926165753248, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,028] Trial 706 finished with value: 0.21095147743626647 and parameters: {'alpha': 50.66482323263295, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,131] Trial 705 finished with value: 0.20722445826480268 and parameters: {'alpha': 47.0962788191085, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,395] Trial 708 finished with value: 0.959890891820004 and parameters: {'alpha': 523.0808588792737, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,474] Trial 710 finished with value: 0.17947584312037426 and parameters: {'alpha': 16.40509824942753, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,496] Trial 712 finished with value: 0.1808612877775494 and parameters: {'alpha': 18.16138685592198, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,591] Trial 713 finished with value: 0.08999598343698605 and parameters: {'alpha': 0.13351638677373817, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,595] Trial 711 finished with value: 0.10910585121556338 and parameters: {'alpha': 0.32123294147548054, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,601] Trial 714 finished with value: 0.18097996421412424 and parameters: {'alpha': 18.312031378204633, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,620] Trial 709 finished with value: 0.9565325767758018 and parameters: {'alpha': 521.061280185055, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,723] Trial 715 finished with value: 0.09893354524311268 and parameters: {'alpha': 0.20537704531442746, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,810] Trial 716 finished with value: 0.20272098883377881 and parameters: {'alpha': 42.693077944679885, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,826] Trial 718 finished with value: 0.20272222238641038 and parameters: {'alpha': 42.69431311464774, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:04,836] Trial 717 finished with value: 0.20010719540244656 and parameters: {'alpha': 40.045330116229714, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,016] Trial 722 finished with value: 0.19831021767064144 and parameters: {'alpha': 38.186599525491836, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,027] Trial 719 finished with value: 0.19788228913959705 and parameters: {'alpha': 37.740436117745915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,039] Trial 720 finished with value: 0.1989581927168997 and parameters: {'alpha': 38.86155330755732, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,057] Trial 721 finished with value: 0.5685563013337787 and parameters: {'alpha': 288.81060633513465, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,085] Trial 723 finished with value: 0.19916582500256994 and parameters: {'alpha': 39.077673141217346, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,436] Trial 725 finished with value: 0.18372914152446554 and parameters: {'alpha': 21.739135576258782, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,472] Trial 726 finished with value: 0.1823336258737665 and parameters: {'alpha': 19.990989091976793, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:05,625] Trial 724 finished with value: 0.19135541780170645 and parameters: {'alpha': 30.666171054549782, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,090] Trial 729 finished with value: 0.18081189588044824 and parameters: {'alpha': 18.082269119722653, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,094] Trial 731 finished with value: 0.18074214978669825 and parameters: {'alpha': 17.993790702085146, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,105] Trial 727 finished with value: 0.18193495875308394 and parameters: {'alpha': 19.49110837506994, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,130] Trial 732 finished with value: 0.22045236096833887 and parameters: {'alpha': 59.287654897981156, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,149] Trial 728 finished with value: 0.18137512129934216 and parameters: {'alpha': 18.790144876232535, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,276] Trial 730 finished with value: 0.17970509155273445 and parameters: {'alpha': 16.67628314585356, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,296] Trial 733 finished with value: 0.1179037735145971 and parameters: {'alpha': 0.46024723677956453, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,382] Trial 734 finished with value: 0.2271191976768093 and parameters: {'alpha': 65.46798818907111, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,665] Trial 739 finished with value: 0.21616746132298523 and parameters: {'alpha': 55.55399982010921, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,672] Trial 738 finished with value: 0.1446819812654195 and parameters: {'alpha': 1.3706843391001033, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,674] Trial 735 finished with value: 0.10517780768143507 and parameters: {'alpha': 0.27140774545332014, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,679] Trial 736 finished with value: 0.22315860248766453 and parameters: {'alpha': 61.936014821035656, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,765] Trial 737 finished with value: 0.21727836303710943 and parameters: {'alpha': 56.58023216205445, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,773] Trial 740 finished with value: 0.22086018997995485 and parameters: {'alpha': 59.8625443438915, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,790] Trial 741 finished with value: 0.059054092953330604 and parameters: {'alpha': 0.02901567341585304, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:06,820] Trial 742 finished with value: 1.3307471933465256 and parameters: {'alpha': 750.335395897836, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,024] Trial 743 finished with value: 0.18125840923108558 and parameters: {'alpha': 18.66034259390946, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,039] Trial 745 finished with value: 0.10485359940378294 and parameters: {'alpha': 0.2677580391328632, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,051] Trial 746 finished with value: 0.19099919703433393 and parameters: {'alpha': 30.28152929940203, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,052] Trial 744 finished with value: 0.10855986925627062 and parameters: {'alpha': 0.31378568500273274, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,088] Trial 747 finished with value: 0.18116178145559217 and parameters: {'alpha': 18.541165432668354, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,237] Trial 749 finished with value: 0.18100851408305924 and parameters: {'alpha': 18.347625209530324, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,255] Trial 748 finished with value: 0.17997440872995485 and parameters: {'alpha': 17.03627312703603, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,257] Trial 750 finished with value: 0.18217411640368014 and parameters: {'alpha': 19.806164317419487, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,440] Trial 754 finished with value: 0.1965847642115543 and parameters: {'alpha': 35.84181756294265, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,472] Trial 751 finished with value: 0.19398370162160777 and parameters: {'alpha': 33.57631415815072, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,473] Trial 755 finished with value: 0.19454101562500004 and parameters: {'alpha': 34.18713241981594, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,486] Trial 753 finished with value: 0.1951820444849918 and parameters: {'alpha': 34.88053398703327, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,487] Trial 752 finished with value: 0.19873806300113078 and parameters: {'alpha': 38.632086847352895, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,657] Trial 756 finished with value: 0.19857409243935034 and parameters: {'alpha': 38.09964024742141, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,661] Trial 758 finished with value: 0.14618057398745896 and parameters: {'alpha': 1.4674982865999309, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,661] Trial 757 finished with value: 0.11760197715357737 and parameters: {'alpha': 0.45458772939064646, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,783] Trial 759 finished with value: 0.08640519402754937 and parameters: {'alpha': 0.11137034279011443, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,927] Trial 763 finished with value: inf and parameters: {'alpha': 15.377303267240634, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,965] Trial 762 finished with value: inf and parameters: {'alpha': 18.276042308828675, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,989] Trial 761 finished with value: inf and parameters: {'alpha': 17.879495125576614, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:07,992] Trial 760 finished with value: 0.17897944258840467 and parameters: {'alpha': 15.787799173864704, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,011] Trial 764 finished with value: inf and parameters: {'alpha': 460.494381873974, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,098] Trial 765 finished with value: 0.059054092953330604 and parameters: {'alpha': 0.029040819843516218, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,099] Trial 767 finished with value: 0.6253749364591898 and parameters: {'alpha': 322.8945161913117, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,124] Trial 766 finished with value: 0.845344411171361 and parameters: {'alpha': 454.2225744996376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,322] Trial 769 finished with value: 0.21120374717310858 and parameters: {'alpha': 50.91002156616603, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,518] Trial 771 finished with value: 0.21598636821546058 and parameters: {'alpha': 55.38184116949982, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,547] Trial 768 finished with value: 0.4695916051603618 and parameters: {'alpha': 228.95474903266484, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,753] Trial 770 finished with value: 0.21852427368164068 and parameters: {'alpha': 57.723802146455895, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,929] Trial 774 finished with value: 0.08702339985094573 and parameters: {'alpha': 0.114505516079966, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,932] Trial 773 finished with value: 0.21091869673879526 and parameters: {'alpha': 50.633833905114955, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:08,945] Trial 772 finished with value: 0.21405397685803867 and parameters: {'alpha': 53.58452242960012, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,065] Trial 775 finished with value: 0.19209218756424756 and parameters: {'alpha': 31.48019791205593, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,513] Trial 777 finished with value: 0.1907043747430099 and parameters: {'alpha': 29.944251451766736, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,605] Trial 776 finished with value: 0.139005431486431 and parameters: {'alpha': 1.0709533965181395, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,800] Trial 779 finished with value: 0.1809887145353619 and parameters: {'alpha': 18.305778851633384, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,871] Trial 778 finished with value: 0.18778253565738082 and parameters: {'alpha': 26.633622540819573, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,913] Trial 783 finished with value: 0.12314632748252471 and parameters: {'alpha': 0.5658801902709144, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,916] Trial 780 finished with value: 0.17979211162366368 and parameters: {'alpha': 16.786477539767482, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,947] Trial 782 finished with value: 0.1821902746582032 and parameters: {'alpha': 19.811215912315113, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:09,993] Trial 781 finished with value: 0.18044591565583887 and parameters: {'alpha': 17.61688004129411, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:10,666] Trial 785 finished with value: 1.6993367430355673 and parameters: {'alpha': 985.085777512118, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:10,725] Trial 784 finished with value: 0.23921663715563332 and parameters: {'alpha': 75.83366655980521, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:10,886] Trial 786 finished with value: 0.1962886898000617 and parameters: {'alpha': 36.05398419568366, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:10,910] Trial 787 finished with value: 1.5784560167814556 and parameters: {'alpha': 906.9735734765846, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:10,957] Trial 788 finished with value: 0.14233964503238083 and parameters: {'alpha': 1.2243573604004023, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,029] Trial 789 finished with value: 0.131495124897204 and parameters: {'alpha': 0.7852549464560061, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,182] Trial 791 finished with value: 0.25014290032637754 and parameters: {'alpha': 84.64164628056383, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,295] Trial 790 finished with value: 0.11952614900287836 and parameters: {'alpha': 0.4903819323842343, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,470] Trial 792 finished with value: 0.13206025011564565 and parameters: {'alpha': 0.8032182974723932, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,513] Trial 793 finished with value: 0.1410746699604236 and parameters: {'alpha': 1.1583387983077316, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,620] Trial 795 finished with value: 0.12202079326428875 and parameters: {'alpha': 0.541964054740111, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,692] Trial 796 finished with value: 0.1939450362356086 and parameters: {'alpha': 33.53387579982321, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,741] Trial 794 finished with value: 0.09682694419459294 and parameters: {'alpha': 0.18625769338992954, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,762] Trial 799 finished with value: 0.34027789242393097 and parameters: {'alpha': 148.03284554877558, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,801] Trial 798 finished with value: 0.19346045378032486 and parameters: {'alpha': 32.999456665372904, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,804] Trial 797 finished with value: 0.194911172838713 and parameters: {'alpha': 34.58855568564372, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:11,956] Trial 800 finished with value: 0.1957325512695313 and parameters: {'alpha': 35.46918494007413, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,009] Trial 801 finished with value: 0.1961988774671053 and parameters: {'alpha': 35.96631283558685, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,042] Trial 803 finished with value: 0.19333703105725747 and parameters: {'alpha': 32.86255177056311, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,097] Trial 802 finished with value: 1.4146193110737046 and parameters: {'alpha': 802.8761831723159, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,140] Trial 804 finished with value: 1.4232960415810034 and parameters: {'alpha': 808.8085339296148, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,150] Trial 805 finished with value: 0.1821775593647205 and parameters: {'alpha': 18.599725477281794, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,333] Trial 807 finished with value: 0.1810336879368833 and parameters: {'alpha': 16.923393731353187, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,360] Trial 806 finished with value: 0.18226255814401732 and parameters: {'alpha': 18.719925725861657, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,367] Trial 808 finished with value: 0.18184403609426403 and parameters: {'alpha': 18.110376086324827, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,402] Trial 809 finished with value: 0.18261839137027142 and parameters: {'alpha': 20.364443884498606, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,525] Trial 810 finished with value: 0.18074424104389397 and parameters: {'alpha': 18.013679532369498, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,561] Trial 811 finished with value: 0.21290246190121304 and parameters: {'alpha': 52.51367132037977, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,600] Trial 813 finished with value: 0.1843944894891037 and parameters: {'alpha': 22.57110885407259, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,626] Trial 812 finished with value: 0.2320391460860404 and parameters: {'alpha': 69.75894090162575, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,801] Trial 815 finished with value: 0.10553941284179694 and parameters: {'alpha': 0.27595010630888706, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,804] Trial 814 finished with value: 0.2128204410593134 and parameters: {'alpha': 52.43641432577642, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,881] Trial 817 finished with value: 0.23001299207185455 and parameters: {'alpha': 68.00591050561208, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,910] Trial 818 finished with value: 0.10585394788240136 and parameters: {'alpha': 0.2794277576746795, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:12,915] Trial 816 finished with value: 0.09790120470548933 and parameters: {'alpha': 0.19570893312275653, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,083] Trial 820 finished with value: 0.2082485891241777 and parameters: {'alpha': 48.08256168938295, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,084] Trial 821 finished with value: 0.20381863962273852 and parameters: {'alpha': 43.77931040252829, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,210] Trial 819 finished with value: 0.20838204731188323 and parameters: {'alpha': 48.21133747232747, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,403] Trial 822 finished with value: 0.20258985743472452 and parameters: {'alpha': 42.56177150243459, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,635] Trial 823 finished with value: 0.20708362285413245 and parameters: {'alpha': 46.95418741819124, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,646] Trial 825 finished with value: 0.20436049258583472 and parameters: {'alpha': 44.304724468971656, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,671] Trial 824 finished with value: 0.19586564009817026 and parameters: {'alpha': 35.603606038465706, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:13,724] Trial 826 finished with value: 0.18139406519839646 and parameters: {'alpha': 18.81414675891509, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,126] Trial 828 finished with value: 0.18186013125770978 and parameters: {'alpha': 19.39757010017678, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,142] Trial 827 finished with value: 0.18149519563373773 and parameters: {'alpha': 18.95621071135295, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,142] Trial 830 finished with value: 0.1815976711554277 and parameters: {'alpha': 19.08453231075783, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,145] Trial 829 finished with value: 0.18276148116262342 and parameters: {'alpha': 20.544992647141722, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,256] Trial 831 finished with value: 0.18127481965717523 and parameters: {'alpha': 18.68167456004098, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,258] Trial 833 finished with value: 0.18292767552425998 and parameters: {'alpha': 20.752985969988572, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,265] Trial 832 finished with value: 0.18007561992444496 and parameters: {'alpha': 17.164517970996766, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,384] Trial 834 finished with value: 0.17951788991827722 and parameters: {'alpha': 16.457723472823876, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,561] Trial 835 finished with value: inf and parameters: {'alpha': 1.0414572834372149, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,578] Trial 840 finished with value: 0.7938528583084908 and parameters: {'alpha': 423.4647851210622, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,623] Trial 839 finished with value: inf and parameters: {'alpha': 2.0941781848837655, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,630] Trial 836 finished with value: inf and parameters: {'alpha': 0.06791101196616807, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,639] Trial 838 finished with value: inf and parameters: {'alpha': 689.1873434725554, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,658] Trial 837 finished with value: inf and parameters: {'alpha': 97.71893123118903, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,688] Trial 841 finished with value: inf and parameters: {'alpha': 1.1533000199574404, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:14,770] Trial 842 finished with value: inf and parameters: {'alpha': 94.50618461110302, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,015] Trial 843 finished with value: 0.2284541793662624 and parameters: {'alpha': 66.63930847280622, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,128] Trial 844 finished with value: 0.23435229029605273 and parameters: {'alpha': 71.742604195462, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,382] Trial 846 finished with value: 0.22965342304430517 and parameters: {'alpha': 67.68978809026807, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,509] Trial 848 finished with value: 0.10311569695723688 and parameters: {'alpha': 0.24760654437318694, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,548] Trial 845 finished with value: 0.19592742855674344 and parameters: {'alpha': 35.66965587641797, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,676] Trial 847 finished with value: 0.19272099101819493 and parameters: {'alpha': 32.17340139893725, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,721] Trial 850 finished with value: 0.19351252640573607 and parameters: {'alpha': 33.05667638443236, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,722] Trial 849 finished with value: 0.19386449231599512 and parameters: {'alpha': 33.43611804920239, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,769] Trial 851 finished with value: 0.11278645045230269 and parameters: {'alpha': 0.3742807087778772, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,879] Trial 852 finished with value: 0.1947228802811472 and parameters: {'alpha': 34.38523076505034, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:15,933] Trial 853 finished with value: 0.1920848071289063 and parameters: {'alpha': 31.481340398190834, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,072] Trial 854 finished with value: 0.18207643124229037 and parameters: {'alpha': 19.683045856886327, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,129] Trial 859 finished with value: 0.17972037764699844 and parameters: {'alpha': 16.713317427796323, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,163] Trial 858 finished with value: 0.11191161428351157 and parameters: {'alpha': 0.3608216396828654, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,164] Trial 855 finished with value: 0.1808577493446752 and parameters: {'alpha': 18.15680305335293, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,185] Trial 857 finished with value: 0.18124971493369663 and parameters: {'alpha': 18.65136735628426, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,283] Trial 856 finished with value: 0.18150840974506588 and parameters: {'alpha': 18.973029434127003, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,332] Trial 861 finished with value: 0.14139786396227394 and parameters: {'alpha': 1.1859072079248258, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,340] Trial 860 finished with value: 0.18241918065121304 and parameters: {'alpha': 20.11543633175713, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,442] Trial 862 finished with value: 0.20519214766652963 and parameters: {'alpha': 45.12475996061779, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,576] Trial 863 finished with value: 0.2121157180946752 and parameters: {'alpha': 51.7725615896711, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,580] Trial 864 finished with value: 0.10664108616879117 and parameters: {'alpha': 0.28926096399116535, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,596] Trial 865 finished with value: 0.2117445245040091 and parameters: {'alpha': 51.42229737102613, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,725] Trial 867 finished with value: 0.20720773623817848 and parameters: {'alpha': 47.080032393053045, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,756] Trial 866 finished with value: 0.21027684782329362 and parameters: {'alpha': 50.02812277323494, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,775] Trial 868 finished with value: 0.20905172266909955 and parameters: {'alpha': 48.856225489229026, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,780] Trial 869 finished with value: 0.7397198988743832 and parameters: {'alpha': 391.1865020679916, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,823] Trial 870 finished with value: 0.12094782322933809 and parameters: {'alpha': 0.5194574036080417, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,974] Trial 871 finished with value: 0.09889178113435446 and parameters: {'alpha': 0.2049457560687906, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:16,981] Trial 873 finished with value: 1.0457706662469162 and parameters: {'alpha': 574.9978034116122, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,093] Trial 872 finished with value: 0.09584274908768506 and parameters: {'alpha': 0.177441790118392, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,310] Trial 874 finished with value: 0.1903927727950247 and parameters: {'alpha': 29.597306252125463, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,492] Trial 877 finished with value: 1.044616004767167 and parameters: {'alpha': 574.2974724290917, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,689] Trial 875 finished with value: 0.18887722778320318 and parameters: {'alpha': 27.88842097520107, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,798] Trial 876 finished with value: 0.19138575709292768 and parameters: {'alpha': 30.699504844195094, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,872] Trial 878 finished with value: 0.1898317550498561 and parameters: {'alpha': 28.9679099842671, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,937] Trial 879 finished with value: 0.30180989277086767 and parameters: {'alpha': 122.25037479696046, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,980] Trial 880 finished with value: 0.1903969520970395 and parameters: {'alpha': 29.612396967822814, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:17,998] Trial 882 finished with value: 0.19014130910773028 and parameters: {'alpha': 29.32542573321555, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,009] Trial 881 finished with value: 0.1881685776238693 and parameters: {'alpha': 27.092082142053794, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,161] Trial 883 finished with value: 0.1799127090614721 and parameters: {'alpha': 16.957366910357887, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,207] Trial 885 finished with value: 0.1799935737690173 and parameters: {'alpha': 17.060526502625702, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,271] Trial 884 finished with value: 0.1799383498021177 and parameters: {'alpha': 16.990000561031568, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,295] Trial 886 finished with value: 1.1755015024285567 and parameters: {'alpha': 654.1999257905485, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,421] Trial 887 finished with value: 0.179064988178454 and parameters: {'alpha': 15.893561437794283, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,440] Trial 890 finished with value: 0.24405182263826078 and parameters: {'alpha': 79.78774653131028, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,449] Trial 889 finished with value: 0.12609873631527557 and parameters: {'alpha': 0.6361353252410527, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,499] Trial 888 finished with value: 0.0692287540475946 and parameters: {'alpha': 0.04697156375884849, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,645] Trial 891 finished with value: 1.2700371369371914 and parameters: {'alpha': 712.5899797212136, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,805] Trial 892 finished with value: 0.11649825715717524 and parameters: {'alpha': 0.43478263664892886, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:18,882] Trial 894 finished with value: 0.22146849699321552 and parameters: {'alpha': 60.40835928785114, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,033] Trial 893 finished with value: 0.4342511431563528 and parameters: {'alpha': 207.337115402934, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,102] Trial 895 finished with value: 0.09513562307257403 and parameters: {'alpha': 0.17125097219253302, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,187] Trial 896 finished with value: 0.22418651855468755 and parameters: {'alpha': 62.85639739168005, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,418] Trial 898 finished with value: 0.22672986270302226 and parameters: {'alpha': 65.12082565936592, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,687] Trial 899 finished with value: 0.20135829024465463 and parameters: {'alpha': 41.32186357099183, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,706] Trial 902 finished with value: 0.2050959626850329 and parameters: {'alpha': 45.0304788014281, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,708] Trial 897 finished with value: 0.21883907682719989 and parameters: {'alpha': 58.013742872392264, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,751] Trial 900 finished with value: 0.20138652703536186 and parameters: {'alpha': 41.35030814687066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,860] Trial 903 finished with value: 0.2009833341257196 and parameters: {'alpha': 40.941319329654824, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,869] Trial 901 finished with value: 0.19596486867804278 and parameters: {'alpha': 35.71728420851097, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,883] Trial 905 finished with value: 0.08686390374434624 and parameters: {'alpha': 0.11411168097244588, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:19,936] Trial 904 finished with value: 0.11750680888928873 and parameters: {'alpha': 0.45258780639601714, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,079] Trial 907 finished with value: 0.11408517244037834 and parameters: {'alpha': 0.3946270537739516, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,120] Trial 909 finished with value: 0.1118869737484581 and parameters: {'alpha': 0.3605197983384966, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,132] Trial 908 finished with value: 0.12498567832545238 and parameters: {'alpha': 0.608708881004138, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,159] Trial 906 finished with value: 0.1786155903063323 and parameters: {'alpha': 15.338333104925358, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,329] Trial 911 finished with value: 0.18261834479081007 and parameters: {'alpha': 20.3646841619158, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,384] Trial 912 finished with value: 0.18091562512849513 and parameters: {'alpha': 18.230084045111994, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,497] Trial 913 finished with value: 0.18062655986585124 and parameters: {'alpha': 17.864836335549608, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,507] Trial 910 finished with value: 0.18075230892783725 and parameters: {'alpha': 18.02442108986519, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,511] Trial 914 finished with value: 0.18206653551603624 and parameters: {'alpha': 19.67179169405512, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,538] Trial 915 finished with value: 0.18402721441971634 and parameters: {'alpha': 22.122573485619284, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,583] Trial 916 finished with value: 0.18119948512027145 and parameters: {'alpha': 18.587763351909143, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,612] Trial 917 finished with value: 0.18301449964021388 and parameters: {'alpha': 20.862043297963712, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,741] Trial 918 finished with value: 0.19541152979800577 and parameters: {'alpha': 34.5484201982945, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:41:20,872] Trial 920 finished with value: 0.19893605944181744 and parameters: {'alpha': 38.83834515623717, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,896] Trial 921 finished with value: 0.19861793482730267 and parameters: {'alpha': 38.148279380791216, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,984] Trial 923 finished with value: 0.19710829878957647 and parameters: {'alpha': 36.51874183891525, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:20,994] Trial 922 finished with value: 0.19795759849146793 and parameters: {'alpha': 37.81929716734245, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,028] Trial 925 finished with value: 0.1963774415989926 and parameters: {'alpha': 36.15580054901287, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,052] Trial 924 finished with value: 0.04772015663548522 and parameters: {'alpha': 0.01746059997143721, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,328] Trial 926 finished with value: 0.24363445633737676 and parameters: {'alpha': 79.44887029515252, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,360] Trial 927 finished with value: 0.1811431432462994 and parameters: {'alpha': 18.500104468142084, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,446] Trial 919 finished with value: 0.06412462845651729 and parameters: {'alpha': 0.03392523465594991, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,608] Trial 929 finished with value: 0.24997324205900506 and parameters: {'alpha': 84.51034141392871, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,620] Trial 930 finished with value: 0.1376743623432361 and parameters: {'alpha': 1.0130093542025513, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,663] Trial 928 finished with value: 0.11068454152960532 and parameters: {'alpha': 0.3427631946523764, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,695] Trial 931 finished with value: 0.06993221570466697 and parameters: {'alpha': 0.048538637693889275, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,830] Trial 932 finished with value: 0.08953153320312503 and parameters: {'alpha': 0.13043624804324122, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:21,922] Trial 933 finished with value: 0.22309754323858969 and parameters: {'alpha': 61.69756287519065, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,084] Trial 934 finished with value: 0.18101635067588412 and parameters: {'alpha': 18.35661402376408, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,266] Trial 940 finished with value: 0.18039357588918592 and parameters: {'alpha': 15.977329107706275, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,271] Trial 935 finished with value: 0.2166071550549959 and parameters: {'alpha': 55.74005421313499, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,281] Trial 936 finished with value: 1.497480718415913 and parameters: {'alpha': 855.1732909319167, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,282] Trial 937 finished with value: 0.1839895685778167 and parameters: {'alpha': 21.2085344383542, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,295] Trial 939 finished with value: 0.1828344445800782 and parameters: {'alpha': 19.5306497877853, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,304] Trial 938 finished with value: 0.18269531005859382 and parameters: {'alpha': 19.349969402273462, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,446] Trial 942 finished with value: 0.21347018670333062 and parameters: {'alpha': 52.93111199379186, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,446] Trial 941 finished with value: 0.1833783567408512 and parameters: {'alpha': 20.308050658935294, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,666] Trial 947 finished with value: 0.21197910348992605 and parameters: {'alpha': 51.520178532517036, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,700] Trial 943 finished with value: 0.08098328112150496 and parameters: {'alpha': 0.08446786032787892, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,707] Trial 944 finished with value: 0.2111226908794203 and parameters: {'alpha': 50.833455019987866, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,775] Trial 945 finished with value: 0.10810338250411189 and parameters: {'alpha': 0.30484131133226583, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,824] Trial 946 finished with value: 0.19505071321186268 and parameters: {'alpha': 34.15466980398527, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,870] Trial 950 finished with value: 0.19657106503135285 and parameters: {'alpha': 35.82423913943993, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:22,945] Trial 948 finished with value: 0.19972120945980676 and parameters: {'alpha': 40.630024332257136, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,014] Trial 949 finished with value: 0.19352291523180512 and parameters: {'alpha': 32.83255017094088, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,053] Trial 952 finished with value: 0.19529425826223273 and parameters: {'alpha': 35.000846160224505, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,096] Trial 953 finished with value: 0.19474725418893915 and parameters: {'alpha': 34.41184417318322, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,114] Trial 951 finished with value: 0.18195180972450664 and parameters: {'alpha': 19.528396049445025, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,221] Trial 954 finished with value: 0.09767209639699836 and parameters: {'alpha': 0.1938751631990676, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,259] Trial 955 finished with value: 0.1809835138903167 and parameters: {'alpha': 18.316506835659684, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,333] Trial 956 finished with value: 0.18024972457082655 and parameters: {'alpha': 17.38490934485546, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,397] Trial 957 finished with value: 0.18045080810546882 and parameters: {'alpha': 17.640764072753104, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,408] Trial 958 finished with value: 0.12554536749588824 and parameters: {'alpha': 0.6225014730969407, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,528] Trial 959 finished with value: 0.18073281622635698 and parameters: {'alpha': 17.9998141843949, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,567] Trial 960 finished with value: 0.08918920082493834 and parameters: {'alpha': 0.1280671792603303, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,570] Trial 961 finished with value: 0.17976938405890222 and parameters: {'alpha': 16.776084204795566, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,580] Trial 962 finished with value: 0.1809664979312295 and parameters: {'alpha': 18.294611339737855, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,747] Trial 963 finished with value: inf and parameters: {'alpha': 0.5555096052348114, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,788] Trial 964 finished with value: inf and parameters: {'alpha': 55.34229118920164, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,791] Trial 965 finished with value: inf and parameters: {'alpha': 62.36178161417071, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,793] Trial 966 finished with value: 0.22282428563168183 and parameters: {'alpha': 61.634845108044985, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,819] Trial 967 finished with value: 0.22807631071391868 and parameters: {'alpha': 66.31095675785393, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:23,970] Trial 969 finished with value: 0.04947196070620891 and parameters: {'alpha': 0.018939786025627264, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,000] Trial 968 finished with value: 0.19685726973684212 and parameters: {'alpha': 36.66353594099999, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,116] Trial 970 finished with value: 0.19408078189247535 and parameters: {'alpha': 33.683373902122, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,230] Trial 973 finished with value: 0.19456054366262338 and parameters: {'alpha': 34.20882222682711, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,233] Trial 972 finished with value: 0.1921813454718339 and parameters: {'alpha': 31.58816983697982, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,263] Trial 971 finished with value: 0.911273597861842 and parameters: {'alpha': 493.781290473122, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,319] Trial 975 finished with value: 0.19411536068564972 and parameters: {'alpha': 33.721784242225326, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,325] Trial 974 finished with value: 0.12221839618883643 and parameters: {'alpha': 0.5462109491199838, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,519] Trial 978 finished with value: 0.07738909398129114 and parameters: {'alpha': 0.07020383563601543, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,520] Trial 976 finished with value: 0.18007424502724106 and parameters: {'alpha': 17.163052714994063, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,547] Trial 977 finished with value: 0.18108823807565794 and parameters: {'alpha': 18.447367755879906, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,555] Trial 979 finished with value: 0.18085167474044 and parameters: {'alpha': 18.14899187620704, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,635] Trial 980 finished with value: 0.18198833765933395 and parameters: {'alpha': 19.57374606255203, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,722] Trial 983 finished with value: 0.24762584042197786 and parameters: {'alpha': 82.64887848750165, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,740] Trial 982 finished with value: 0.17960636037726158 and parameters: {'alpha': 16.568794698975008, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,796] Trial 981 finished with value: 0.17963109407123773 and parameters: {'alpha': 16.59999719596176, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,942] Trial 984 finished with value: 0.23983182180304286 and parameters: {'alpha': 76.3446124170376, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:24,975] Trial 987 finished with value: 1.6330012588019123 and parameters: {'alpha': 942.0854688184643, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,002] Trial 986 finished with value: 0.20491575799239312 and parameters: {'alpha': 44.854357401326034, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,070] Trial 985 finished with value: 0.11670515547902967 and parameters: {'alpha': 0.4387932863448345, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,112] Trial 988 finished with value: 0.20616173025030843 and parameters: {'alpha': 46.06909360035628, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,174] Trial 990 finished with value: 0.21272489373458064 and parameters: {'alpha': 52.34672287183267, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,217] Trial 989 finished with value: 0.4054987144711143 and parameters: {'alpha': 189.5683010824321, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,260] Trial 991 finished with value: 0.12442173937345816 and parameters: {'alpha': 0.5957329155630401, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,326] Trial 992 finished with value: 0.17984092529296886 and parameters: {'alpha': 16.865250693453802, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,422] Trial 993 finished with value: 0.18038474397358145 and parameters: {'alpha': 17.55643841377524, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,429] Trial 994 finished with value: 0.1804035444078948 and parameters: {'alpha': 17.57955625681521, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,463] Trial 995 finished with value: 0.07929693603515628 and parameters: {'alpha': 0.07747684744786962, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,585] Trial 996 finished with value: 0.18116192999588823 and parameters: {'alpha': 17.112203815308007, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,623] Trial 997 finished with value: 0.1927695878520765 and parameters: {'alpha': 31.972880565194323, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,739] Trial 1000 finished with value: 0.19540317280016448 and parameters: {'alpha': 34.54563983594786, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,749] Trial 999 finished with value: 0.12422748535156256 and parameters: {'alpha': 0.587264287631882, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,751] Trial 998 finished with value: 0.10702255428916536 and parameters: {'alpha': 0.2908677239882119, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,850] Trial 1003 finished with value: 0.12990884797748775 and parameters: {'alpha': 0.7401828259636927, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,916] Trial 1002 finished with value: 0.19734518773129114 and parameters: {'alpha': 37.17793865092307, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:25,981] Trial 1001 finished with value: 0.1329181000719573 and parameters: {'alpha': 0.834398515615306, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,042] Trial 1004 finished with value: 0.28570926449424344 and parameters: {'alpha': 111.05365292263326, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,044] Trial 1005 finished with value: 0.2106161695620888 and parameters: {'alpha': 50.3515631405982, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,116] Trial 1006 finished with value: 0.28630178222656255 and parameters: {'alpha': 111.46993048199806, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,175] Trial 1007 finished with value: 0.2124337337453742 and parameters: {'alpha': 52.0724870479078, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,233] Trial 1008 finished with value: 0.2739002440763775 and parameters: {'alpha': 102.61589187941553, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,310] Trial 1009 finished with value: 0.20806030530427636 and parameters: {'alpha': 47.90102219091738, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,373] Trial 1010 finished with value: 0.20799214509662836 and parameters: {'alpha': 47.835770744187656, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,417] Trial 1011 finished with value: 1.1340296263363487 and parameters: {'alpha': 628.7558445996481, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,558] Trial 1012 finished with value: 0.19225940140573608 and parameters: {'alpha': 31.67404215643978, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,580] Trial 1013 finished with value: 0.6416403004214637 and parameters: {'alpha': 332.60836525048376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,611] Trial 1014 finished with value: 0.0977047983912418 and parameters: {'alpha': 0.19408613360272814, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,652] Trial 1015 finished with value: 0.192424549689042 and parameters: {'alpha': 31.85580029874972, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,718] Trial 1017 finished with value: 0.18999039666426812 and parameters: {'alpha': 29.156425268737063, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,727] Trial 1016 finished with value: 0.19042688984118014 and parameters: {'alpha': 29.64565803976316, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,847] Trial 1018 finished with value: 0.1132450026341489 and parameters: {'alpha': 0.3812681476406796, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:26,902] Trial 1019 finished with value: inf and parameters: {'alpha': 17.948981129095213, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,040] Trial 1025 finished with value: 0.23637425710577725 and parameters: {'alpha': 73.46221271062353, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,064] Trial 1021 finished with value: inf and parameters: {'alpha': 17.192474545100627, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,066] Trial 1022 finished with value: inf and parameters: {'alpha': 17.43610771983702, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,066] Trial 1020 finished with value: 0.18030362503854858 and parameters: {'alpha': 17.453831192405776, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,068] Trial 1023 finished with value: inf and parameters: {'alpha': 17.929619710401404, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,174] Trial 1024 finished with value: 0.22859646233809633 and parameters: {'alpha': 66.76761893298153, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,343] Trial 1027 finished with value: 0.684636480520148 and parameters: {'alpha': 358.2950438696822, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,365] Trial 1026 finished with value: 0.10265059994346222 and parameters: {'alpha': 0.24299031743601146, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,391] Trial 1028 finished with value: 0.6962309463661596 and parameters: {'alpha': 365.21973942344516, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,453] Trial 1032 finished with value: 0.12741163632041536 and parameters: {'alpha': 0.6701268103318904, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,504] Trial 1031 finished with value: 0.1962985341282895 and parameters: {'alpha': 36.07236460928889, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,516] Trial 1030 finished with value: 0.19486508968955593 and parameters: {'alpha': 34.53938699567795, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,554] Trial 1029 finished with value: 0.19587467972604852 and parameters: {'alpha': 35.62134553293419, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,604] Trial 1033 finished with value: 0.19729558182565793 and parameters: {'alpha': 37.124982587031226, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,828] Trial 1034 finished with value: 0.2188875114360609 and parameters: {'alpha': 58.0627958240864, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,849] Trial 1035 finished with value: 0.18124652183131174 and parameters: {'alpha': 18.646360504528307, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,865] Trial 1037 finished with value: 0.09780794941149262 and parameters: {'alpha': 0.19499010030677796, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,881] Trial 1036 finished with value: 0.18114422902960534 and parameters: {'alpha': 18.517945923655844, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,890] Trial 1038 finished with value: 0.4919241753186677 and parameters: {'alpha': 242.5318905647443, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,932] Trial 1039 finished with value: 0.13321901302939973 and parameters: {'alpha': 0.844629563512316, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:27,983] Trial 1040 finished with value: 0.17949640714946552 and parameters: {'alpha': 16.431602706053912, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,134] Trial 1041 finished with value: 0.18090810656095813 and parameters: {'alpha': 18.219998580697123, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,156] Trial 1042 finished with value: 0.18117086284436684 and parameters: {'alpha': 18.551056115991333, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,295] Trial 1045 finished with value: 0.2126996299984581 and parameters: {'alpha': 52.32281146283508, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,333] Trial 1043 finished with value: 0.2121206137566818 and parameters: {'alpha': 51.777183070703764, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,362] Trial 1046 finished with value: 0.21454586907637752 and parameters: {'alpha': 54.04768135415311, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,364] Trial 1047 finished with value: 0.21019335012335527 and parameters: {'alpha': 49.94886244802854, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,395] Trial 1048 finished with value: 0.09039782445004114 and parameters: {'alpha': 0.13608589077619637, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,442] Trial 1044 finished with value: 0.11612950522974926 and parameters: {'alpha': 0.4285335296453718, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,543] Trial 1049 finished with value: 0.21119803074886928 and parameters: {'alpha': 50.904258649492284, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,577] Trial 1050 finished with value: 0.10321328619706009 and parameters: {'alpha': 0.24910957066203315, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,737] Trial 1051 finished with value: 0.19190016909950663 and parameters: {'alpha': 30.987925351551166, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,890] Trial 1056 finished with value: 0.19292948229338 and parameters: {'alpha': 32.161514519050506, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,900] Trial 1055 finished with value: 0.19085683535927225 and parameters: {'alpha': 29.80361202755888, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,927] Trial 1052 finished with value: 0.08942450940583883 and parameters: {'alpha': 0.12662864222243692, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:28,928] Trial 1057 finished with value: 0.19253738120631173 and parameters: {'alpha': 31.980559318479937, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,017] Trial 1053 finished with value: 0.09860241994757403 and parameters: {'alpha': 0.199526436192823, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,030] Trial 1054 finished with value: 0.07911260311729032 and parameters: {'alpha': 0.07262034050168296, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,046] Trial 1058 finished with value: 0.18750379593698605 and parameters: {'alpha': 26.321086618244774, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,085] Trial 1059 finished with value: 0.18957992360968343 and parameters: {'alpha': 28.695588495886177, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,293] Trial 1063 finished with value: 0.23946420641447377 and parameters: {'alpha': 76.04096545342561, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,334] Trial 1061 finished with value: 0.2441645257889598 and parameters: {'alpha': 79.87846623949956, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,364] Trial 1062 finished with value: 0.5767725553813733 and parameters: {'alpha': 293.7597215555122, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,369] Trial 1060 finished with value: 0.2500411224686473 and parameters: {'alpha': 84.56401690053487, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,444] Trial 1064 finished with value: 0.24486707114771802 and parameters: {'alpha': 80.44357126375822, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,475] Trial 1066 finished with value: 0.24445007555509876 and parameters: {'alpha': 80.10806860410415, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,567] Trial 1065 finished with value: 1.3323086685983756 and parameters: {'alpha': 751.3092492841408, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,628] Trial 1067 finished with value: 0.18131885331003297 and parameters: {'alpha': 18.737167980179485, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,672] Trial 1068 finished with value: 0.1810411164936267 and parameters: {'alpha': 18.38882674653853, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,772] Trial 1071 finished with value: 0.18083180940326898 and parameters: {'alpha': 18.12479511856849, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,792] Trial 1069 finished with value: 0.18065428646689977 and parameters: {'alpha': 17.89988178640343, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,837] Trial 1070 finished with value: 0.18012416054173527 and parameters: {'alpha': 17.224856059945132, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,851] Trial 1072 finished with value: 0.18074556936163658 and parameters: {'alpha': 18.01650516868132, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,906] Trial 1073 finished with value: 0.9957708907920436 and parameters: {'alpha': 544.7399197485939, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:29,957] Trial 1074 finished with value: 0.17811780678196962 and parameters: {'alpha': 14.720361680708894, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,011] Trial 1075 finished with value: 0.10973793598375828 and parameters: {'alpha': 0.3298377060668323, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,168] Trial 1076 finished with value: 0.2041535753752056 and parameters: {'alpha': 44.1086143743113, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,243] Trial 1080 finished with value: 0.09585056801243834 and parameters: {'alpha': 0.17758078289615103, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,255] Trial 1078 finished with value: 0.19979258994654608 and parameters: {'alpha': 39.723504476934174, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,258] Trial 1079 finished with value: 0.12058287359940385 and parameters: {'alpha': 0.5123155827380259, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,267] Trial 1081 finished with value: 0.20232235319438735 and parameters: {'alpha': 42.29369142306898, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,326] Trial 1077 finished with value: 1.552796639532792 and parameters: {'alpha': 890.5386685689477, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,453] Trial 1083 finished with value: 0.09935490491365133 and parameters: {'alpha': 0.20961587489619893, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,454] Trial 1082 finished with value: 0.203707005807977 and parameters: {'alpha': 43.66968234474946, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,591] Trial 1084 finished with value: 0.19450152427271794 and parameters: {'alpha': 34.14414631280545, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,622] Trial 1088 finished with value: 0.18113631212736436 and parameters: {'alpha': 18.50820551868178, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,676] Trial 1085 finished with value: 0.19392551462273852 and parameters: {'alpha': 33.51270939533713, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,692] Trial 1087 finished with value: 0.1815136973170231 and parameters: {'alpha': 18.980253284936584, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,699] Trial 1086 finished with value: 0.19358924438476566 and parameters: {'alpha': 33.140875247701224, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,868] Trial 1089 finished with value: 0.18195156076531668 and parameters: {'alpha': 19.52677927338448, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,978] Trial 1090 finished with value: 0.22409321256938744 and parameters: {'alpha': 62.77676759804145, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:30,995] Trial 1091 finished with value: 0.2251434145636308 and parameters: {'alpha': 63.71918699547787, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,022] Trial 1093 finished with value: inf and parameters: {'alpha': 61.976226011275294, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,031] Trial 1092 finished with value: inf and parameters: {'alpha': 55.4567342889412, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,166] Trial 1094 finished with value: inf and parameters: {'alpha': 0.016734696776000625, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,178] Trial 1095 finished with value: inf and parameters: {'alpha': 50.80093400629504, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,179] Trial 1096 finished with value: inf and parameters: {'alpha': 966.077681503542, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,186] Trial 1097 finished with value: inf and parameters: {'alpha': 17.216425666230624, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,398] Trial 1101 finished with value: 0.18198277703536186 and parameters: {'alpha': 19.567336975323244, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,454] Trial 1099 finished with value: 0.09026254163240136 and parameters: {'alpha': 0.13534994177126505, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,477] Trial 1098 finished with value: 0.11973043206465879 and parameters: {'alpha': 0.4951816829135888, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,517] Trial 1100 finished with value: 0.13049815680252888 and parameters: {'alpha': 0.7576786242038789, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,560] Trial 1102 finished with value: 0.17995146754214644 and parameters: {'alpha': 17.00692550357491, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,584] Trial 1103 finished with value: 0.18193111353824015 and parameters: {'alpha': 19.50155391160039, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,651] Trial 1104 finished with value: 0.1814682357627468 and parameters: {'alpha': 18.922679422103712, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,717] Trial 1105 finished with value: 0.18337418418482737 and parameters: {'alpha': 21.31265766873181, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,875] Trial 1106 finished with value: 0.1965288987972862 and parameters: {'alpha': 36.316569001498515, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,875] Trial 1107 finished with value: 0.19415352693256585 and parameters: {'alpha': 33.763790132679695, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,876] Trial 1108 finished with value: 0.19602035284745067 and parameters: {'alpha': 35.77661792961546, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,948] Trial 1111 finished with value: 0.19811980584395558 and parameters: {'alpha': 37.98774456236426, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 444 with value: 0.028335061292146406.\n",
+      "[I 2025-03-06 14:41:31,981] Trial 1109 finished with value: 0.018637291002775534 and parameters: {'alpha': 0.00404048137916589, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,004] Trial 1110 finished with value: 0.19840054006476154 and parameters: {'alpha': 38.28125633106448, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,109] Trial 1112 finished with value: 0.3332619516473068 and parameters: {'alpha': 143.4222219888704, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,199] Trial 1113 finished with value: 0.08352145668431335 and parameters: {'alpha': 0.0962478105297932, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,241] Trial 1114 finished with value: 0.18236835487767275 and parameters: {'alpha': 20.049863430852163, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,268] Trial 1115 finished with value: 0.18155942299290712 and parameters: {'alpha': 19.03752174720714, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,346] Trial 1116 finished with value: 0.13252081260279613 and parameters: {'alpha': 0.8213696362561905, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,440] Trial 1118 finished with value: 0.1354895580412213 and parameters: {'alpha': 0.9255978495237202, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,519] Trial 1117 finished with value: 0.18148978489925993 and parameters: {'alpha': 17.59435087792164, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,520] Trial 1119 finished with value: 0.11413926243832244 and parameters: {'alpha': 0.3933389038083477, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,567] Trial 1121 finished with value: 0.20973680394222866 and parameters: {'alpha': 50.2849049850656, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,657] Trial 1120 finished with value: 0.1282929004548726 and parameters: {'alpha': 0.6914051441004239, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,693] Trial 1124 finished with value: 0.21502290013363493 and parameters: {'alpha': 54.10836643301609, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,789] Trial 1122 finished with value: 0.1123614514160157 and parameters: {'alpha': 0.3645028022347915, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,880] Trial 1125 finished with value: 0.1959199662057977 and parameters: {'alpha': 35.66932787629122, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,916] Trial 1123 finished with value: 0.08739615921823604 and parameters: {'alpha': 0.11381114552415704, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,932] Trial 1128 finished with value: 0.1938517343621505 and parameters: {'alpha': 33.43051770406471, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,944] Trial 1127 finished with value: 0.19243981490285778 and parameters: {'alpha': 31.873039913378317, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:32,968] Trial 1126 finished with value: 0.19385109670538653 and parameters: {'alpha': 33.43025703453503, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,105] Trial 1130 finished with value: 0.1932352202405428 and parameters: {'alpha': 32.74970755028958, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,106] Trial 1129 finished with value: 0.19332931010999183 and parameters: {'alpha': 32.85416522562173, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,254] Trial 1131 finished with value: 0.18143754953484795 and parameters: {'alpha': 18.88459439020314, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,319] Trial 1134 finished with value: 0.18402195736533722 and parameters: {'alpha': 22.115111332894188, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,387] Trial 1133 finished with value: 0.22554104517886517 and parameters: {'alpha': 64.07249078327757, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,422] Trial 1136 finished with value: 0.22626359265779197 and parameters: {'alpha': 64.71247321570708, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,447] Trial 1135 finished with value: 0.18038946134868428 and parameters: {'alpha': 17.561297804355238, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,500] Trial 1132 finished with value: 0.8797393438399466 and parameters: {'alpha': 474.83495985160226, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,579] Trial 1137 finished with value: 0.22906086008172294 and parameters: {'alpha': 67.17445067430363, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,635] Trial 1138 finished with value: 0.22392885774311275 and parameters: {'alpha': 62.6288701049729, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,653] Trial 1139 finished with value: 0.18113838411030025 and parameters: {'alpha': 18.51008930740894, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,778] Trial 1140 finished with value: 0.1148121621864721 and parameters: {'alpha': 0.4064664261334806, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,799] Trial 1141 finished with value: 0.1802327037931744 and parameters: {'alpha': 17.36324882434066, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,803] Trial 1142 finished with value: 0.18010368967156667 and parameters: {'alpha': 17.199133785252393, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,905] Trial 1144 finished with value: 0.17901432578638984 and parameters: {'alpha': 15.829497321585901, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:33,993] Trial 1145 finished with value: 0.06855760074013159 and parameters: {'alpha': 0.04540148930430242, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,021] Trial 1143 finished with value: 0.37510509457236835 and parameters: {'alpha': 170.48260862540764, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,077] Trial 1146 finished with value: 0.18136054995888165 and parameters: {'alpha': 18.7885613984683, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,113] Trial 1147 finished with value: 0.20568922607421875 and parameters: {'alpha': 45.609505345571215, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,182] Trial 1148 finished with value: 0.11210363088507407 and parameters: {'alpha': 0.36396970116191446, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,295] Trial 1151 finished with value: 0.20947964959395562 and parameters: {'alpha': 49.261796503339774, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,345] Trial 1149 finished with value: 0.2054793516781456 and parameters: {'alpha': 45.40536030229081, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,768] Trial 1152 finished with value: 0.18100869397615138 and parameters: {'alpha': 18.348460242800503, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,775] Trial 1150 finished with value: 0.20722162976716696 and parameters: {'alpha': 47.087361035375764, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:34,923] Trial 1153 finished with value: 0.20642628090306334 and parameters: {'alpha': 46.31907028466449, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,078] Trial 1154 finished with value: 0.1800703307462994 and parameters: {'alpha': 17.157141750000594, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,151] Trial 1158 finished with value: 0.19162010800010285 and parameters: {'alpha': 30.9689578608278, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,200] Trial 1157 finished with value: 0.18352986334549762 and parameters: {'alpha': 21.505970203559404, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,240] Trial 1156 finished with value: 0.18209635761461765 and parameters: {'alpha': 19.709082150360494, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,254] Trial 1155 finished with value: 0.1175648999023438 and parameters: {'alpha': 0.453896290543241, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,279] Trial 1159 finished with value: 0.12084491294459301 and parameters: {'alpha': 0.5172320425604705, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,417] Trial 1161 finished with value: 0.13360091642680927 and parameters: {'alpha': 0.8575643264343025, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,443] Trial 1160 finished with value: 0.1253335257118627 and parameters: {'alpha': 0.617496892588292, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,503] Trial 1162 finished with value: 0.1280682252261514 and parameters: {'alpha': 0.6875895987400378, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,613] Trial 1166 finished with value: 0.19373391081157487 and parameters: {'alpha': 33.300990906599175, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,669] Trial 1163 finished with value: 0.1932013119988693 and parameters: {'alpha': 32.712770301585316, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,675] Trial 1165 finished with value: 0.19335098883377883 and parameters: {'alpha': 32.878471383807714, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,744] Trial 1167 finished with value: 0.1958269695081209 and parameters: {'alpha': 35.57051926348273, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:35,744] Trial 1164 finished with value: 0.19523854280170644 and parameters: {'alpha': 34.941148834506905, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,004] Trial 1168 finished with value: 1.4619415749640212 and parameters: {'alpha': 832.738590303933, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,061] Trial 1169 finished with value: 0.19192930561266452 and parameters: {'alpha': 31.310079586676377, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,110] Trial 1170 finished with value: 0.19398499338250413 and parameters: {'alpha': 33.56908120705575, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,688] Trial 1171 finished with value: 0.11841428119860205 and parameters: {'alpha': 0.469057642457029, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,840] Trial 1174 finished with value: 0.18196931833367608 and parameters: {'alpha': 19.533953263650623, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:36,897] Trial 1172 finished with value: 0.18271195434570317 and parameters: {'alpha': 20.467116995732066, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,028] Trial 1173 finished with value: 0.18009437377929696 and parameters: {'alpha': 17.170439109594987, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,124] Trial 1176 finished with value: 0.18136348767732327 and parameters: {'alpha': 18.775545427803888, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,158] Trial 1177 finished with value: 0.18256018471165714 and parameters: {'alpha': 20.27567377686801, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,258] Trial 1175 finished with value: 0.8128207771381577 and parameters: {'alpha': 434.7755137936319, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,507] Trial 1181 finished with value: 0.2191910885459499 and parameters: {'alpha': 58.340991836813885, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,509] Trial 1178 finished with value: 0.1209797285541736 and parameters: {'alpha': 0.519636589815027, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,596] Trial 1179 finished with value: 0.2566553786107114 and parameters: {'alpha': 89.72591497135173, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,661] Trial 1180 finished with value: 0.21844775005139808 and parameters: {'alpha': 57.65821994736965, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,700] Trial 1182 finished with value: 0.21999306833367607 and parameters: {'alpha': 59.07271941494838, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,741] Trial 1184 finished with value: 0.10934645180150086 and parameters: {'alpha': 0.32478660324646197, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,742] Trial 1183 finished with value: 0.07469691194233144 and parameters: {'alpha': 0.0616129153412486, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,819] Trial 1185 finished with value: 0.11728041503906257 and parameters: {'alpha': 0.44901528891150305, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:37,995] Trial 1186 finished with value: 0.17955447246350748 and parameters: {'alpha': 16.504958388970643, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,005] Trial 1188 finished with value: 0.1799173750385486 and parameters: {'alpha': 16.963051428992415, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,056] Trial 1187 finished with value: 0.20472973407946132 and parameters: {'alpha': 44.672224893004376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,153] Trial 1190 finished with value: 0.20646066136410365 and parameters: {'alpha': 46.35891755791126, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,185] Trial 1189 finished with value: 0.20628935238486848 and parameters: {'alpha': 46.192802534904665, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,226] Trial 1191 finished with value: 0.20262998162520565 and parameters: {'alpha': 42.60195283371261, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,232] Trial 1192 finished with value: 0.20583280967310857 and parameters: {'alpha': 45.74882310108213, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,251] Trial 1193 finished with value: 0.20520512566817434 and parameters: {'alpha': 45.13706487597403, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,442] Trial 1194 finished with value: 0.18170867887798117 and parameters: {'alpha': 19.22407021033478, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,480] Trial 1195 finished with value: 0.12222142867238905 and parameters: {'alpha': 0.546402432338636, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,500] Trial 1196 finished with value: 0.17974212222450667 and parameters: {'alpha': 16.74059078290499, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,522] Trial 1197 finished with value: 0.18122299772563738 and parameters: {'alpha': 18.617141341861586, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,670] Trial 1198 finished with value: 0.11562801950555104 and parameters: {'alpha': 0.42006785899336135, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,723] Trial 1200 finished with value: 0.18056393143503296 and parameters: {'alpha': 17.784968635666008, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,723] Trial 1199 finished with value: 0.07887279309724508 and parameters: {'alpha': 0.07591193104083466, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,751] Trial 1201 finished with value: 0.18033374106959296 and parameters: {'alpha': 17.491760247213115, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,862] Trial 1202 finished with value: 0.1803653155196341 and parameters: {'alpha': 17.532495085133533, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:38,875] Trial 1203 finished with value: inf and parameters: {'alpha': 19.74344965222437, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,000] Trial 1205 finished with value: 0.0656401819490132 and parameters: {'alpha': 0.03942686037640545, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,020] Trial 1204 finished with value: inf and parameters: {'alpha': 73.9759247710227, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,117] Trial 1206 finished with value: inf and parameters: {'alpha': 79.40846375709705, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,196] Trial 1208 finished with value: 0.22945555689761524 and parameters: {'alpha': 67.36299475777277, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,237] Trial 1207 finished with value: 0.24459015554327723 and parameters: {'alpha': 79.54706890500748, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,278] Trial 1209 finished with value: 0.19243031590511928 and parameters: {'alpha': 31.59404006006088, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,405] Trial 1210 finished with value: 0.1101128176398027 and parameters: {'alpha': 0.33234899965713705, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,444] Trial 1212 finished with value: 1.0848178747558594 and parameters: {'alpha': 598.7095267769072, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,450] Trial 1213 finished with value: 0.18927912539833475 and parameters: {'alpha': 28.355132765574698, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,463] Trial 1211 finished with value: 0.19486028538754116 and parameters: {'alpha': 33.935949690464014, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,566] Trial 1214 finished with value: 0.19177824687756995 and parameters: {'alpha': 31.143891798944907, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,587] Trial 1215 finished with value: 1.3843832792583264 and parameters: {'alpha': 783.8817978902183, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,618] Trial 1216 finished with value: 0.07653419703433391 and parameters: {'alpha': 0.06740319836149611, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,753] Trial 1217 finished with value: 0.11581703414113904 and parameters: {'alpha': 0.4233786163936668, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,913] Trial 1221 finished with value: 0.1830938903166119 and parameters: {'alpha': 20.961596382570455, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,933] Trial 1218 finished with value: 0.18025374164782076 and parameters: {'alpha': 17.38931102028195, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,934] Trial 1219 finished with value: 0.11027612703022209 and parameters: {'alpha': 0.33730327936962556, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,960] Trial 1222 finished with value: 0.1799481828870272 and parameters: {'alpha': 17.00296917987379, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:39,973] Trial 1220 finished with value: 0.18062348729183805 and parameters: {'alpha': 17.86047084098494, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,018] Trial 1223 finished with value: 0.18000528288188739 and parameters: {'alpha': 17.07447347057729, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,081] Trial 1224 finished with value: 0.10060111713610198 and parameters: {'alpha': 0.22158271349566822, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,212] Trial 1225 finished with value: 0.18202637598941207 and parameters: {'alpha': 19.621491270347754, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,384] Trial 1228 finished with value: 0.20858322239925992 and parameters: {'alpha': 48.405043788658766, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,385] Trial 1229 finished with value: 0.21110776039525084 and parameters: {'alpha': 50.81910178844052, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,392] Trial 1226 finished with value: 0.21032165084035775 and parameters: {'alpha': 50.07133437690015, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,403] Trial 1231 finished with value: 0.21504259039627885 and parameters: {'alpha': 54.50968890595024, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,421] Trial 1227 finished with value: 0.2054526520096628 and parameters: {'alpha': 45.378997217545006, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,491] Trial 1230 finished with value: 0.21662423256321964 and parameters: {'alpha': 55.9764139980558, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,559] Trial 1232 finished with value: 0.2104331042480469 and parameters: {'alpha': 50.17691871810874, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,581] Trial 1233 finished with value: 0.20959766749331826 and parameters: {'alpha': 49.38027457404888, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:40,820] Trial 1234 finished with value: 0.19133827655993013 and parameters: {'alpha': 30.647215308334832, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,100] Trial 1236 finished with value: 0.10571424162212177 and parameters: {'alpha': 0.2774414221406922, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,131] Trial 1235 finished with value: 0.11359116673519742 and parameters: {'alpha': 0.38623687869617196, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,426] Trial 1237 finished with value: 0.19556964445415298 and parameters: {'alpha': 35.28773279054477, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,515] Trial 1239 finished with value: 0.19259964188425166 and parameters: {'alpha': 32.0395948811589, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,545] Trial 1238 finished with value: 1.292803974352385 and parameters: {'alpha': 726.7150802514772, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,582] Trial 1241 finished with value: 0.19384454506321963 and parameters: {'alpha': 33.41393374727229, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,678] Trial 1242 finished with value: 0.09961904258326482 and parameters: {'alpha': 0.2120227115117412, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,837] Trial 1240 finished with value: 0.08820486456620069 and parameters: {'alpha': 0.12201129471297298, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:41,918] Trial 1244 finished with value: 0.08424892199064558 and parameters: {'alpha': 0.09995281296684441, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,021] Trial 1245 finished with value: 0.1818984356368216 and parameters: {'alpha': 19.461394370248616, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,066] Trial 1243 finished with value: 0.19199382780376237 and parameters: {'alpha': 31.382010440494405, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,112] Trial 1246 finished with value: 0.1803933627801193 and parameters: {'alpha': 17.567105687625165, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,116] Trial 1247 finished with value: 0.10526265618575252 and parameters: {'alpha': 0.2726331093208425, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,127] Trial 1248 finished with value: 0.18349767533151734 and parameters: {'alpha': 21.465777136357605, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,194] Trial 1249 finished with value: 0.18342080521432985 and parameters: {'alpha': 21.370772058232276, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,273] Trial 1250 finished with value: 0.9377010706208881 and parameters: {'alpha': 509.7012175413188, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,322] Trial 1251 finished with value: 0.18262862850791534 and parameters: {'alpha': 20.378281127072064, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,373] Trial 1252 finished with value: 0.19150384887695318 and parameters: {'alpha': 30.839902113441276, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,518] Trial 1253 finished with value: 1.7205014076634455 and parameters: {'alpha': 998.8790004574095, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,539] Trial 1254 finished with value: 0.2658552814041941 and parameters: {'alpha': 96.7154518599414, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,611] Trial 1256 finished with value: 0.7503664611816406 and parameters: {'alpha': 397.53225819627, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,640] Trial 1257 finished with value: 0.27254821019222863 and parameters: {'alpha': 101.6354533889926, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,688] Trial 1258 finished with value: 0.45876426867033304 and parameters: {'alpha': 222.3497470327605, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,694] Trial 1255 finished with value: 0.5525379259611432 and parameters: {'alpha': 279.164517724378, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,790] Trial 1259 finished with value: 0.07701493241159542 and parameters: {'alpha': 0.06897556374024857, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,849] Trial 1260 finished with value: inf and parameters: {'alpha': 37.86127538056901, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:42,884] Trial 1261 finished with value: 0.19644783286646794 and parameters: {'alpha': 36.231166157889014, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,059] Trial 1262 finished with value: inf and parameters: {'alpha': 70.4064751856048, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,071] Trial 1263 finished with value: inf and parameters: {'alpha': 34.34007824284229, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,109] Trial 1264 finished with value: 0.18100383846885285 and parameters: {'alpha': 18.34086149974865, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,205] Trial 1266 finished with value: 0.18171276502107325 and parameters: {'alpha': 19.229322296107647, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,208] Trial 1267 finished with value: 0.18048403211091701 and parameters: {'alpha': 17.682512140654843, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,254] Trial 1265 finished with value: 1.2085946713738693 and parameters: {'alpha': 674.5752463676782, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,282] Trial 1269 finished with value: 0.18178019357781666 and parameters: {'alpha': 19.313136636770835, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,337] Trial 1268 finished with value: 0.18196079634817028 and parameters: {'alpha': 19.53930489318979, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,509] Trial 1272 finished with value: 0.132332599455181 and parameters: {'alpha': 0.8153298997761977, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,511] Trial 1271 finished with value: 0.1793447345934417 and parameters: {'alpha': 16.241175460608062, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,512] Trial 1270 finished with value: 0.119444758622019 and parameters: {'alpha': 0.48937086853796696, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,612] Trial 1273 finished with value: 0.13439172324732737 and parameters: {'alpha': 0.885484735204899, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,752] Trial 1274 finished with value: 0.2284724401212994 and parameters: {'alpha': 66.65899611894189, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,771] Trial 1275 finished with value: 0.2284432653166119 and parameters: {'alpha': 66.63318054911359, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,782] Trial 1276 finished with value: 0.2261383725457443 and parameters: {'alpha': 64.60171801823769, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:43,824] Trial 1277 finished with value: 0.22551460250051406 and parameters: {'alpha': 64.0492205270728, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,225] Trial 1278 finished with value: 0.22355862169767687 and parameters: {'alpha': 62.29579725522239, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,285] Trial 1280 finished with value: 0.20191443211605678 and parameters: {'alpha': 41.877292909283305, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,296] Trial 1279 finished with value: 0.2000393227025082 and parameters: {'alpha': 39.96968267070464, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,367] Trial 1281 finished with value: 0.20072854678505347 and parameters: {'alpha': 40.67431566849389, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,481] Trial 1283 finished with value: 0.19839509348016038 and parameters: {'alpha': 38.268086910682975, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,744] Trial 1284 finished with value: 0.19886270160875824 and parameters: {'alpha': 38.75452545428545, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:44,857] Trial 1285 finished with value: 0.11163931480006176 and parameters: {'alpha': 0.3571043802070577, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,077] Trial 1286 finished with value: 0.1809174047851563 and parameters: {'alpha': 18.23301142086595, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,113] Trial 1282 finished with value: 0.1956228831722862 and parameters: {'alpha': 35.34428218954577, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,115] Trial 1288 finished with value: 0.18399263318513573 and parameters: {'alpha': 22.078930804547735, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,125] Trial 1287 finished with value: 0.18048394377055926 and parameters: {'alpha': 17.682413605123326, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,132] Trial 1289 finished with value: 0.10190591160824429 and parameters: {'alpha': 0.23516533442480375, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,355] Trial 1290 finished with value: 0.08277608025801812 and parameters: {'alpha': 0.09263959392027424, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,393] Trial 1291 finished with value: 0.18301405112818675 and parameters: {'alpha': 19.78944904383098, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,523] Trial 1294 finished with value: 0.18210999498869251 and parameters: {'alpha': 18.50499156531818, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,524] Trial 1296 finished with value: 0.1865934173905223 and parameters: {'alpha': 24.74106575423479, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,562] Trial 1292 finished with value: 0.18286116031044414 and parameters: {'alpha': 19.585799586855227, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,584] Trial 1297 finished with value: 0.12759410895096635 and parameters: {'alpha': 0.6724585960462282, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,667] Trial 1293 finished with value: 0.183774870990954 and parameters: {'alpha': 20.90211212372562, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,670] Trial 1295 finished with value: 0.09786742007606909 and parameters: {'alpha': 0.19323362181761733, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,735] Trial 1298 finished with value: 0.18198527305201487 and parameters: {'alpha': 19.57019468200977, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,870] Trial 1299 finished with value: 0.12175199764854039 and parameters: {'alpha': 0.536355531669231, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,946] Trial 1300 finished with value: 0.2010657058876439 and parameters: {'alpha': 41.025838666425535, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:45,990] Trial 1303 finished with value: 0.20659259206671465 and parameters: {'alpha': 46.48635770909691, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,086] Trial 1304 finished with value: 0.2089082075580798 and parameters: {'alpha': 48.718485204359894, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,096] Trial 1301 finished with value: 0.20921386095548936 and parameters: {'alpha': 49.01173673646418, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,112] Trial 1305 finished with value: 0.20704677689401726 and parameters: {'alpha': 46.924650145667805, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,126] Trial 1302 finished with value: 0.20635207500256988 and parameters: {'alpha': 46.25374216399143, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,243] Trial 1306 finished with value: 0.20591394788240133 and parameters: {'alpha': 45.82802967622202, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,279] Trial 1308 finished with value: 0.09091599525853208 and parameters: {'alpha': 0.13953728536670146, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,329] Trial 1307 finished with value: 0.1956532931357936 and parameters: {'alpha': 35.385307076419444, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,543] Trial 1312 finished with value: 0.1818468416555305 and parameters: {'alpha': 19.396877795289793, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,569] Trial 1311 finished with value: 0.18079280112818671 and parameters: {'alpha': 18.07504053658951, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,585] Trial 1310 finished with value: 0.5121523665578741 and parameters: {'alpha': 254.8103360138247, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,606] Trial 1309 finished with value: 0.18347514050935448 and parameters: {'alpha': 21.438266798000196, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,607] Trial 1313 finished with value: 0.17998531153628708 and parameters: {'alpha': 17.049010640729968, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,687] Trial 1315 finished with value: 0.18003841899671058 and parameters: {'alpha': 17.11758564532486, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,816] Trial 1314 finished with value: 0.11362779104132407 and parameters: {'alpha': 0.38736725489014756, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,972] Trial 1316 finished with value: inf and parameters: {'alpha': 15.886726666462367, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:46,981] Trial 1317 finished with value: inf and parameters: {'alpha': 33.47917651606002, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,000] Trial 1318 finished with value: inf and parameters: {'alpha': 32.26011912204363, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,019] Trial 1319 finished with value: inf and parameters: {'alpha': 33.42281011346744, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,107] Trial 1320 finished with value: inf and parameters: {'alpha': 0.7624022219541199, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,255] Trial 1321 finished with value: inf and parameters: {'alpha': 0.36678931749512367, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,308] Trial 1322 finished with value: inf and parameters: {'alpha': 0.3196188326659759, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,437] Trial 1323 finished with value: 0.1401777465820313 and parameters: {'alpha': 1.1161270942209915, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,805] Trial 1325 finished with value: 0.24562447471217114 and parameters: {'alpha': 81.04881433440568, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:47,871] Trial 1324 finished with value: 0.1812528888903167 and parameters: {'alpha': 18.63740798489247, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,018] Trial 1326 finished with value: 0.11543826120476981 and parameters: {'alpha': 0.41629256965426076, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,092] Trial 1329 finished with value: 0.252407717606394 and parameters: {'alpha': 86.41772260169093, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,112] Trial 1327 finished with value: 0.31318663118061274 and parameters: {'alpha': 129.9937326436198, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,258] Trial 1330 finished with value: 0.2488532150107937 and parameters: {'alpha': 83.62108323574607, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,412] Trial 1328 finished with value: 0.23686534288908315 and parameters: {'alpha': 73.87303024279232, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,491] Trial 1331 finished with value: 0.31207504317434215 and parameters: {'alpha': 129.2403576816043, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,587] Trial 1332 finished with value: 0.2264185480700042 and parameters: {'alpha': 64.84931572596624, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,603] Trial 1334 finished with value: 0.19241248560855267 and parameters: {'alpha': 31.842302440678182, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,673] Trial 1333 finished with value: 0.19207739938836352 and parameters: {'alpha': 31.47396170086976, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,730] Trial 1335 finished with value: 0.18838382002981088 and parameters: {'alpha': 27.337837656015754, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,858] Trial 1336 finished with value: 0.19327097881116367 and parameters: {'alpha': 32.790401810356926, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,877] Trial 1337 finished with value: 0.19178486276726978 and parameters: {'alpha': 31.150828758137795, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,924] Trial 1338 finished with value: 0.19373319927014807 and parameters: {'alpha': 33.30082462492634, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:48,983] Trial 1339 finished with value: 0.18121639629163247 and parameters: {'alpha': 18.607986627711973, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,004] Trial 1341 finished with value: 0.17977865658408723 and parameters: {'alpha': 16.787058018530665, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,132] Trial 1340 finished with value: 0.18132512226305517 and parameters: {'alpha': 18.744127629975157, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,211] Trial 1342 finished with value: 0.18081291099146796 and parameters: {'alpha': 18.10161973153913, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,257] Trial 1343 finished with value: 0.18048832223992606 and parameters: {'alpha': 17.687712097127918, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,350] Trial 1345 finished with value: 0.1071547098581415 and parameters: {'alpha': 0.2955387473309564, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,352] Trial 1344 finished with value: 0.13211605391652967 and parameters: {'alpha': 0.8081426127359732, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,409] Trial 1347 finished with value: 0.10782506058542358 and parameters: {'alpha': 0.30425251093274763, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,425] Trial 1346 finished with value: 0.21758403513055105 and parameters: {'alpha': 56.86243755538197, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,468] Trial 1348 finished with value: 0.21746207885742191 and parameters: {'alpha': 56.75005653629554, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,596] Trial 1349 finished with value: 0.21555532528525911 and parameters: {'alpha': 54.98633158810247, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,681] Trial 1351 finished with value: 0.07923051070363901 and parameters: {'alpha': 0.07707751095885754, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,788] Trial 1353 finished with value: 0.20843972232216287 and parameters: {'alpha': 48.26677324229914, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,791] Trial 1350 finished with value: 0.21503320222553457 and parameters: {'alpha': 54.501189321871756, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,833] Trial 1352 finished with value: 0.20128473324424342 and parameters: {'alpha': 41.247447826189905, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,847] Trial 1355 finished with value: 0.19749045352333472 and parameters: {'alpha': 37.33036095828545, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,864] Trial 1356 finished with value: 0.18026609323601978 and parameters: {'alpha': 17.405653651645654, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:49,865] Trial 1354 finished with value: 0.20936427021227386 and parameters: {'alpha': 49.15684074368157, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,045] Trial 1357 finished with value: 0.11393322060032902 and parameters: {'alpha': 0.3922794533089181, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,185] Trial 1359 finished with value: 0.11900943340100748 and parameters: {'alpha': 0.48107175467499264, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,188] Trial 1358 finished with value: 0.18218642513877473 and parameters: {'alpha': 19.82330933412413, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,207] Trial 1361 finished with value: 1.5897005597887541 and parameters: {'alpha': 914.1968227106132, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,229] Trial 1360 finished with value: 0.1805705248380963 and parameters: {'alpha': 17.792944581940148, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,323] Trial 1363 finished with value: 0.18054106413188736 and parameters: {'alpha': 17.75484278263299, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,358] Trial 1364 finished with value: 0.10337843929893095 and parameters: {'alpha': 0.2507373165468396, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,413] Trial 1362 finished with value: 0.11569564003392277 and parameters: {'alpha': 0.42125024277212364, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,447] Trial 1365 finished with value: 0.0762340052554482 and parameters: {'alpha': 0.06645016243425489, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,681] Trial 1366 finished with value: 0.19189067761872947 and parameters: {'alpha': 31.267933458401217, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,694] Trial 1369 finished with value: 0.1927244362921464 and parameters: {'alpha': 32.18634285906889, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,725] Trial 1367 finished with value: 0.1937309907612048 and parameters: {'alpha': 33.29779659196509, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,725] Trial 1368 finished with value: 0.19453229562859786 and parameters: {'alpha': 34.1776566671195, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,750] Trial 1371 finished with value: 0.18785183439555925 and parameters: {'alpha': 26.725534078631036, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,762] Trial 1370 finished with value: 0.19592736109683392 and parameters: {'alpha': 35.67752007669586, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,871] Trial 1372 finished with value: 0.1909008774285568 and parameters: {'alpha': 30.172448301925673, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:50,919] Trial 1373 finished with value: 0.19203901791221217 and parameters: {'alpha': 31.430850006442725, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,142] Trial 1377 finished with value: 0.17932089715254942 and parameters: {'alpha': 16.211815666550798, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,169] Trial 1374 finished with value: 0.17982073229338 and parameters: {'alpha': 16.840811251255634, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,277] Trial 1375 finished with value: 0.0891167103335732 and parameters: {'alpha': 0.1276287439727691, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,283] Trial 1379 finished with value: 0.17977952874434627 and parameters: {'alpha': 16.78854758842717, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,284] Trial 1376 finished with value: 0.17870516383120896 and parameters: {'alpha': 15.44982744591962, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,287] Trial 1380 finished with value: 0.1800390245297081 and parameters: {'alpha': 17.118228655016498, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,292] Trial 1378 finished with value: 0.6534528839432565 and parameters: {'alpha': 339.66037049285336, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,351] Trial 1381 finished with value: 0.652322355443051 and parameters: {'alpha': 338.9849788932046, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,503] Trial 1382 finished with value: 0.2303661605674343 and parameters: {'alpha': 68.15540108753858, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,676] Trial 1383 finished with value: 0.23048910946494666 and parameters: {'alpha': 68.26186450908006, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,757] Trial 1387 finished with value: 0.2247045832262542 and parameters: {'alpha': 63.15001231619889, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,780] Trial 1384 finished with value: 0.23342779894377066 and parameters: {'alpha': 70.80858000130897, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,864] Trial 1386 finished with value: 0.21725025795384462 and parameters: {'alpha': 56.345774355500254, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:51,947] Trial 1388 finished with value: 0.10773951660156256 and parameters: {'alpha': 0.3026648252964501, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,012] Trial 1385 finished with value: 0.41856078304893096 and parameters: {'alpha': 197.67873026167723, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,137] Trial 1390 finished with value: 0.10966236206054691 and parameters: {'alpha': 0.32840730205208096, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,163] Trial 1389 finished with value: 0.2182011053788035 and parameters: {'alpha': 57.42622403375423, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,542] Trial 1392 finished with value: 0.11520773643092111 and parameters: {'alpha': 0.41301018738229894, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,553] Trial 1391 finished with value: 0.18281490928248362 and parameters: {'alpha': 20.61157056576477, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,573] Trial 1393 finished with value: 0.1812071225457443 and parameters: {'alpha': 18.596421975722695, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,594] Trial 1396 finished with value: 0.18235533672131995 and parameters: {'alpha': 20.03342912566316, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,601] Trial 1395 finished with value: 0.19523035927220395 and parameters: {'alpha': 34.93266275089909, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,620] Trial 1394 finished with value: 0.1827057785516037 and parameters: {'alpha': 20.47329905918144, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,678] Trial 1398 finished with value: 0.18277095446134875 and parameters: {'alpha': 20.55636548435189, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:52,678] Trial 1397 finished with value: 0.08944286659642273 and parameters: {'alpha': 0.1299053079899472, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,007] Trial 1400 finished with value: 0.2067248279451069 and parameters: {'alpha': 46.61379383385254, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,013] Trial 1401 finished with value: 0.07608900981702305 and parameters: {'alpha': 0.06581537876412638, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,085] Trial 1399 finished with value: 0.20402918893914473 and parameters: {'alpha': 43.98619422412581, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,139] Trial 1402 finished with value: 0.20485717548571136 and parameters: {'alpha': 44.79679793895131, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,203] Trial 1403 finished with value: 0.19743445646587174 and parameters: {'alpha': 37.27157880014944, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,216] Trial 1404 finished with value: 1.1394626373612253 and parameters: {'alpha': 632.0825034405387, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,263] Trial 1405 finished with value: 0.20390482543945315 and parameters: {'alpha': 43.864149676396764, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,360] Trial 1406 finished with value: 0.2039151419870477 and parameters: {'alpha': 43.87458545561665, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,437] Trial 1407 finished with value: 0.18197652575041123 and parameters: {'alpha': 19.559049218567797, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,498] Trial 1408 finished with value: 0.1808131278268915 and parameters: {'alpha': 18.101427644501037, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,570] Trial 1409 finished with value: 0.1793454027677838 and parameters: {'alpha': 16.2416129326498, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,677] Trial 1411 finished with value: 1.0179512553967929 and parameters: {'alpha': 558.1462832942037, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,707] Trial 1410 finished with value: 0.1814002056563529 and parameters: {'alpha': 18.83761858552729, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,850] Trial 1412 finished with value: 0.1778780590820313 and parameters: {'alpha': 14.421312065086486, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:53,858] Trial 1413 finished with value: 0.09798064035516037 and parameters: {'alpha': 0.19651071223232253, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,003] Trial 1415 finished with value: 0.6093864584189967 and parameters: {'alpha': 313.32501130759744, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,084] Trial 1414 finished with value: 0.18009885343852802 and parameters: {'alpha': 17.175749540320894, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,203] Trial 1416 finished with value: 0.18021633994654612 and parameters: {'alpha': 17.325231613595, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,289] Trial 1417 finished with value: 0.1314533241031045 and parameters: {'alpha': 0.783949156821083, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,631] Trial 1418 finished with value: 0.12824643503289485 and parameters: {'alpha': 0.6892253013734099, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,656] Trial 1421 finished with value: 0.1971414857241982 and parameters: {'alpha': 36.96277788754588, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,775] Trial 1420 finished with value: 0.19136475457442442 and parameters: {'alpha': 30.68629670974508, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,827] Trial 1425 finished with value: 0.09670993980006169 and parameters: {'alpha': 0.1849437477564687, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,852] Trial 1422 finished with value: 0.10337843929893095 and parameters: {'alpha': 0.2508048371813685, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,895] Trial 1419 finished with value: 0.19652665013363488 and parameters: {'alpha': 36.31368024000191, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,915] Trial 1423 finished with value: 0.19459449983295643 and parameters: {'alpha': 34.245048341771025, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:54,934] Trial 1424 finished with value: 0.1940416309878701 and parameters: {'alpha': 33.64054075171084, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,040] Trial 1426 finished with value: 0.2642181033485815 and parameters: {'alpha': 95.49242409851544, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,066] Trial 1427 finished with value: 0.0324326247044614 and parameters: {'alpha': 0.00888486692011389, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,116] Trial 1428 finished with value: 1.0777525350149053 and parameters: {'alpha': 594.4216692919248, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,215] Trial 1429 finished with value: 0.17967361508018098 and parameters: {'alpha': 16.655273308286812, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,266] Trial 1430 finished with value: 1.2457389108115748 and parameters: {'alpha': 697.5311516153637, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,384] Trial 1431 finished with value: 0.2110093877852591 and parameters: {'alpha': 50.72550712388618, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,393] Trial 1432 finished with value: 0.05567671637284131 and parameters: {'alpha': 0.02498163333194895, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,419] Trial 1433 finished with value: 0.18043707358912425 and parameters: {'alpha': 17.62222319425542, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,442] Trial 1434 finished with value: 0.2177902247378701 and parameters: {'alpha': 57.05243382294841, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,706] Trial 1435 finished with value: 0.0883296943102385 and parameters: {'alpha': 0.1227776814218238, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,814] Trial 1436 finished with value: 0.11928378321597458 and parameters: {'alpha': 0.48666966485244467, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,894] Trial 1437 finished with value: 0.12692106940018513 and parameters: {'alpha': 0.6569461242983228, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:55,905] Trial 1438 finished with value: 0.22631686189350333 and parameters: {'alpha': 64.75945106762384, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,050] Trial 1440 finished with value: 0.1924885514751234 and parameters: {'alpha': 31.92695846409702, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,051] Trial 1439 finished with value: 0.19241166645250826 and parameters: {'alpha': 31.842430096819896, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,112] Trial 1443 finished with value: 0.19532069933439558 and parameters: {'alpha': 35.02918409716156, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,119] Trial 1441 finished with value: 0.10018980963456006 and parameters: {'alpha': 0.21788907384408002, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,217] Trial 1442 finished with value: inf and parameters: {'alpha': 31.12134659584157, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,335] Trial 1444 finished with value: 0.19476769916735198 and parameters: {'alpha': 34.43425443063413, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,412] Trial 1445 finished with value: 0.18129000237715878 and parameters: {'alpha': 17.30156254900858, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,419] Trial 1446 finished with value: inf and parameters: {'alpha': 20.681500850107184, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,761] Trial 1447 finished with value: 0.1836993983218545 and parameters: {'alpha': 20.758150938943334, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,764] Trial 1450 finished with value: 0.18092411537571965 and parameters: {'alpha': 16.764644838231668, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,773] Trial 1448 finished with value: 0.18191362401058803 and parameters: {'alpha': 18.213088415903734, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,828] Trial 1451 finished with value: 0.21438568230879937 and parameters: {'alpha': 53.89348014590562, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:56,844] Trial 1449 finished with value: 0.1814765772126851 and parameters: {'alpha': 17.569439987919036, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,363] Trial 1452 finished with value: 0.21454638948139398 and parameters: {'alpha': 54.04327293221891, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,440] Trial 1453 finished with value: 0.09755421046206827 and parameters: {'alpha': 0.19220188598996846, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,647] Trial 1454 finished with value: 0.2109778381990132 and parameters: {'alpha': 50.690049360579486, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,660] Trial 1455 finished with value: 0.20302492238898026 and parameters: {'alpha': 42.995091211327065, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,769] Trial 1456 finished with value: 0.11932221930252887 and parameters: {'alpha': 0.487066235901892, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,823] Trial 1457 finished with value: 0.0924968861148232 and parameters: {'alpha': 0.15101100205372964, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,874] Trial 1458 finished with value: 0.20343353624845806 and parameters: {'alpha': 43.39975136911745, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,908] Trial 1459 finished with value: 0.11054807340923115 and parameters: {'alpha': 0.34136185950959796, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,961] Trial 1460 finished with value: 0.18050186722604858 and parameters: {'alpha': 17.706185762868486, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:57,976] Trial 1461 finished with value: 0.09223996665553044 and parameters: {'alpha': 0.1490999068016152, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,078] Trial 1462 finished with value: 0.35691407008120885 and parameters: {'alpha': 158.84050754929478, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,112] Trial 1463 finished with value: 0.08108640804893096 and parameters: {'alpha': 0.08489394634843553, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,181] Trial 1464 finished with value: 0.1815672820723685 and parameters: {'alpha': 19.046249012987204, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,279] Trial 1465 finished with value: 0.24652806210166536 and parameters: {'alpha': 81.77402719681885, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,360] Trial 1469 finished with value: 0.19156429938065384 and parameters: {'alpha': 30.906875457836353, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,459] Trial 1466 finished with value: 0.2466349619654606 and parameters: {'alpha': 81.85928676312932, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,511] Trial 1467 finished with value: 0.2448605982087789 and parameters: {'alpha': 80.43838667311942, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,560] Trial 1468 finished with value: 0.2569349902986226 and parameters: {'alpha': 89.9421885200535, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,613] Trial 1470 finished with value: 0.19454268284847867 and parameters: {'alpha': 34.18899992733807, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,686] Trial 1471 finished with value: 0.19513292724609377 and parameters: {'alpha': 34.82757020622733, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,817] Trial 1472 finished with value: 0.19124541503906253 and parameters: {'alpha': 30.55410137658096, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:58,956] Trial 1473 finished with value: 0.19296804205643509 and parameters: {'alpha': 32.45554395764798, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,035] Trial 1474 finished with value: 0.1931864210269326 and parameters: {'alpha': 32.69660750408587, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,085] Trial 1475 finished with value: 0.1805462344520971 and parameters: {'alpha': 17.762661702948606, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,220] Trial 1476 finished with value: 0.18407446892989318 and parameters: {'alpha': 22.180164540415042, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,422] Trial 1477 finished with value: 0.1833754289807772 and parameters: {'alpha': 21.3144970189584, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,471] Trial 1479 finished with value: 0.18035943365799761 and parameters: {'alpha': 17.506764356672704, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,498] Trial 1480 finished with value: inf and parameters: {'alpha': 63.23887245743457, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:41:59,812] Trial 1478 finished with value: 0.027556602847450686 and parameters: {'alpha': 0.006658647386017756, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,032] Trial 1482 finished with value: inf and parameters: {'alpha': 56.69896689606462, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,035] Trial 1483 finished with value: inf and parameters: {'alpha': 59.18555582638816, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,058] Trial 1481 finished with value: 0.21781573422080597 and parameters: {'alpha': 57.07145582445299, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,154] Trial 1485 finished with value: 0.9703140979646383 and parameters: {'alpha': 529.3724040736324, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,162] Trial 1484 finished with value: 0.208563776277241 and parameters: {'alpha': 48.3864115012087, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,474] Trial 1487 finished with value: 0.18006207930715465 and parameters: {'alpha': 17.12921414560272, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,758] Trial 1488 finished with value: 0.10771738172029202 and parameters: {'alpha': 0.30237858321779915, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,783] Trial 1486 finished with value: 0.12193559782329366 and parameters: {'alpha': 0.5395673471578555, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:00,878] Trial 1489 finished with value: 0.17940224275287836 and parameters: {'alpha': 16.295318355931258, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:01,098] Trial 1490 finished with value: 0.10822481194747127 and parameters: {'alpha': 0.3089914941866825, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:01,532] Trial 1492 finished with value: 0.1795374484734787 and parameters: {'alpha': 16.465197232598914, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:01,583] Trial 1491 finished with value: 0.12128912000154202 and parameters: {'alpha': 0.526013333334679, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:01,730] Trial 1493 finished with value: 0.09516947670384458 and parameters: {'alpha': 0.17153075355393954, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:01,734] Trial 1495 finished with value: 0.08028608135022618 and parameters: {'alpha': 0.08110670720506506, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,009] Trial 1498 finished with value: 0.7946005532355056 and parameters: {'alpha': 423.9034159421888, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,013] Trial 1494 finished with value: 0.10546967696340466 and parameters: {'alpha': 0.2745198766794777, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,338] Trial 1497 finished with value: 0.09685807854903374 and parameters: {'alpha': 0.18596679838498884, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,585] Trial 1496 finished with value: 0.10304175447162833 and parameters: {'alpha': 0.24679746415165654, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,716] Trial 1500 finished with value: 0.19800996504934212 and parameters: {'alpha': 37.86627696253272, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,719] Trial 1501 finished with value: 0.19959785098427224 and parameters: {'alpha': 39.51578079026307, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,737] Trial 1502 finished with value: 0.19601236687911186 and parameters: {'alpha': 35.760159850103264, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:02,743] Trial 1499 finished with value: 0.19887260536595397 and parameters: {'alpha': 38.76526088384835, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,282] Trial 1504 finished with value: 0.19791055323550574 and parameters: {'alpha': 37.76234796912138, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,283] Trial 1503 finished with value: 0.19930020340768914 and parameters: {'alpha': 39.20900018895041, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,451] Trial 1507 finished with value: 0.1831206172902961 and parameters: {'alpha': 20.980169910577217, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,581] Trial 1505 finished with value: 0.18194948190789478 and parameters: {'alpha': 19.508935380084225, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,585] Trial 1506 finished with value: 0.18083965563322374 and parameters: {'alpha': 18.117370121509005, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,783] Trial 1508 finished with value: 0.18012986251027965 and parameters: {'alpha': 17.2154798414495, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,883] Trial 1510 finished with value: 0.18137427644428458 and parameters: {'alpha': 18.789377140044294, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:03,986] Trial 1509 finished with value: 0.18237281847502063 and parameters: {'alpha': 20.040347516312302, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,036] Trial 1511 finished with value: 0.18297191155684628 and parameters: {'alpha': 20.793867798329742, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,325] Trial 1512 finished with value: 0.18235702482524677 and parameters: {'alpha': 20.020676308601164, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,396] Trial 1514 finished with value: 0.22680890965511935 and parameters: {'alpha': 65.19440931248165, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,482] Trial 1513 finished with value: 0.1826668766704359 and parameters: {'alpha': 20.425730441289218, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,484] Trial 1515 finished with value: 0.2265126530376234 and parameters: {'alpha': 64.77118948485601, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,485] Trial 1517 finished with value: 0.21380774979440792 and parameters: {'alpha': 53.359731403378284, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,543] Trial 1516 finished with value: 1.6659634298545434 and parameters: {'alpha': 963.4099982122186, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,547] Trial 1518 finished with value: 0.22749778750770977 and parameters: {'alpha': 65.80153170226774, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,762] Trial 1520 finished with value: 0.21299446211965467 and parameters: {'alpha': 52.600325054655094, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,765] Trial 1519 finished with value: 1.3638570258532072 and parameters: {'alpha': 771.02254865741, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:04,766] Trial 1521 finished with value: 0.20236527215254937 and parameters: {'alpha': 42.329927169338674, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,371] Trial 1522 finished with value: 0.20107415925678457 and parameters: {'alpha': 41.02703427860549, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,406] Trial 1523 finished with value: 0.1143732905016448 and parameters: {'alpha': 0.3992064544007257, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,524] Trial 1524 finished with value: 0.09746845927991368 and parameters: {'alpha': 0.1918254075223951, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,556] Trial 1527 finished with value: 0.18010531352796058 and parameters: {'alpha': 17.20204475315477, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,568] Trial 1525 finished with value: 0.11345389385022622 and parameters: {'alpha': 0.3846742711349025, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,597] Trial 1526 finished with value: 0.18026708264802638 and parameters: {'alpha': 17.407103939111035, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,736] Trial 1529 finished with value: 0.18286665565892277 and parameters: {'alpha': 20.677249244308705, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,770] Trial 1528 finished with value: 0.18247974840666123 and parameters: {'alpha': 20.190302400573536, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,883] Trial 1530 finished with value: 0.1944859603078742 and parameters: {'alpha': 34.126667689666, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,940] Trial 1531 finished with value: 0.19380842991879116 and parameters: {'alpha': 33.15044717023246, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:05,987] Trial 1532 finished with value: 0.1951901250899466 and parameters: {'alpha': 34.305664714497766, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,030] Trial 1535 finished with value: 0.19286988306949016 and parameters: {'alpha': 32.09490328565031, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,035] Trial 1534 finished with value: 0.19651060649671057 and parameters: {'alpha': 35.76201950747176, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,037] Trial 1533 finished with value: 0.19084144807514397 and parameters: {'alpha': 29.791421855266638, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,274] Trial 1536 finished with value: 0.7248197347861842 and parameters: {'alpha': 382.3684310841243, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,328] Trial 1537 finished with value: 0.18201612953587587 and parameters: {'alpha': 19.59250670038699, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,400] Trial 1538 finished with value: inf and parameters: {'alpha': 17.548706427427742, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,743] Trial 1539 finished with value: 0.1239595570775083 and parameters: {'alpha': 0.5842551805899218, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:06,939] Trial 1542 finished with value: inf and parameters: {'alpha': 17.42206247336261, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,092] Trial 1543 finished with value: 0.21066687532123768 and parameters: {'alpha': 50.39943132256319, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,092] Trial 1540 finished with value: inf and parameters: {'alpha': 14.276452410158647, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,149] Trial 1544 finished with value: 0.2035952504368832 and parameters: {'alpha': 43.559587565700085, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,195] Trial 1541 finished with value: 0.08824240118729443 and parameters: {'alpha': 0.12228605853209797, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,215] Trial 1546 finished with value: 0.12969835860402967 and parameters: {'alpha': 0.7338222234213801, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,299] Trial 1545 finished with value: 0.21046452611019745 and parameters: {'alpha': 50.20701821787648, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,346] Trial 1547 finished with value: 0.2908325303890831 and parameters: {'alpha': 114.64586714755565, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,502] Trial 1549 finished with value: 0.2084353502775494 and parameters: {'alpha': 48.26247544761331, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,526] Trial 1548 finished with value: 0.2092397077379729 and parameters: {'alpha': 49.03700897516284, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,550] Trial 1551 finished with value: 0.8356018683182566 and parameters: {'alpha': 448.39404817916164, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,675] Trial 1553 finished with value: 0.18027646439401734 and parameters: {'alpha': 17.419036285995837, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,688] Trial 1550 finished with value: 0.17981021015368015 and parameters: {'alpha': 16.827432884889028, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,714] Trial 1552 finished with value: 0.1807501164807772 and parameters: {'alpha': 18.01991596636323, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,721] Trial 1555 finished with value: 0.1227485025827509 and parameters: {'alpha': 0.5576825371678021, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,734] Trial 1554 finished with value: 0.1810293768631785 and parameters: {'alpha': 18.374454686073502, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,869] Trial 1556 finished with value: 0.1801700733064351 and parameters: {'alpha': 17.284438134562432, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,887] Trial 1557 finished with value: 0.1800612027780634 and parameters: {'alpha': 17.146546691939786, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:07,992] Trial 1558 finished with value: 1.4785373028243216 and parameters: {'alpha': 843.2498486222622, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:08,299] Trial 1559 finished with value: 0.19395077835886104 and parameters: {'alpha': 33.5315721397145, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:08,324] Trial 1563 finished with value: 0.23444587004009057 and parameters: {'alpha': 71.82229798894019, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:08,443] Trial 1562 finished with value: 0.2381827058490955 and parameters: {'alpha': 74.97507580992297, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:08,518] Trial 1561 finished with value: 0.2328938633968957 and parameters: {'alpha': 70.49216656210194, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:08,695] Trial 1560 finished with value: 0.22878517758018102 and parameters: {'alpha': 66.92935873138292, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,064] Trial 1565 finished with value: 0.23660050312243017 and parameters: {'alpha': 73.65296139591365, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,099] Trial 1564 finished with value: 0.23674451396741372 and parameters: {'alpha': 73.77105617224464, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,104] Trial 1566 finished with value: 0.13258124061986026 and parameters: {'alpha': 0.8232251976234249, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,131] Trial 1567 finished with value: 0.19721943404348274 and parameters: {'alpha': 37.045329571954255, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,165] Trial 1568 finished with value: 0.19413577855160366 and parameters: {'alpha': 33.74431366646848, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,236] Trial 1569 finished with value: 0.13302344996402146 and parameters: {'alpha': 0.8380264601032287, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,303] Trial 1570 finished with value: 0.19708949662057978 and parameters: {'alpha': 36.9082954410988, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,322] Trial 1571 finished with value: 0.13974781975997125 and parameters: {'alpha': 1.1055591193130228, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,566] Trial 1575 finished with value: 0.1807073838404606 and parameters: {'alpha': 17.96715039011262, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,576] Trial 1572 finished with value: 0.1805451133326481 and parameters: {'alpha': 17.76125913712367, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,585] Trial 1576 finished with value: 0.1822767539576481 and parameters: {'alpha': 19.93487090846809, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,600] Trial 1574 finished with value: 0.1823132321006374 and parameters: {'alpha': 19.9806686369886, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,629] Trial 1573 finished with value: 0.11173402530067851 and parameters: {'alpha': 0.35853662324020785, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,726] Trial 1577 finished with value: 0.18140727770353626 and parameters: {'alpha': 18.84697543782244, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,727] Trial 1578 finished with value: 0.18180237021998363 and parameters: {'alpha': 19.34093442240351, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,742] Trial 1579 finished with value: 0.18141566521895566 and parameters: {'alpha': 18.8575616974605, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:09,972] Trial 1580 finished with value: 0.2057807932642887 and parameters: {'alpha': 45.698526250856226, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,045] Trial 1583 finished with value: 0.20952819021124594 and parameters: {'alpha': 49.313742196691535, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,046] Trial 1584 finished with value: 0.13253291201942854 and parameters: {'alpha': 0.8220152359222679, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,077] Trial 1582 finished with value: 0.11072170390881994 and parameters: {'alpha': 0.34374372491414584, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,083] Trial 1581 finished with value: 0.0703291800729852 and parameters: {'alpha': 0.04946015968732924, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,125] Trial 1585 finished with value: 0.12045120592619252 and parameters: {'alpha': 0.509521544323273, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,190] Trial 1586 finished with value: 1.191886388903166 and parameters: {'alpha': 664.2776561400723, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,262] Trial 1587 finished with value: 0.20437949218750004 and parameters: {'alpha': 44.32951750437818, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,355] Trial 1588 finished with value: 0.19508714445415298 and parameters: {'alpha': 34.77843994867026, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,438] Trial 1590 finished with value: 0.19370952566046468 and parameters: {'alpha': 33.273917362912314, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,497] Trial 1593 finished with value: 0.1953624281070107 and parameters: {'alpha': 35.07444387802396, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,562] Trial 1592 finished with value: 0.1938160946494655 and parameters: {'alpha': 33.39152526630901, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,566] Trial 1591 finished with value: 0.19410339458264805 and parameters: {'alpha': 33.70878514015764, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,607] Trial 1589 finished with value: 0.189353111251028 and parameters: {'alpha': 28.4390015315904, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,646] Trial 1594 finished with value: 0.19163046309621717 and parameters: {'alpha': 30.980162109285004, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,760] Trial 1596 finished with value: inf and parameters: {'alpha': 877.6953683909378, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,854] Trial 1595 finished with value: inf and parameters: {'alpha': 0.44928865028970705, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,893] Trial 1597 finished with value: inf and parameters: {'alpha': 17.262296939311234, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:10,989] Trial 1598 finished with value: inf and parameters: {'alpha': 1.1929247504768068, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,009] Trial 1599 finished with value: inf and parameters: {'alpha': 54.66361567762058, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,011] Trial 1600 finished with value: 0.13700662970291946 and parameters: {'alpha': 0.9851815435704868, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,057] Trial 1601 finished with value: 0.8902535631681744 and parameters: {'alpha': 481.1466264721908, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,062] Trial 1602 finished with value: 0.11037304173519742 and parameters: {'alpha': 0.3388241587812779, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,276] Trial 1603 finished with value: 0.18155515374434628 and parameters: {'alpha': 19.031768629676947, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,394] Trial 1604 finished with value: 0.18088418238589643 and parameters: {'alpha': 18.190502022476437, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,465] Trial 1605 finished with value: 0.05814420660721631 and parameters: {'alpha': 0.027866200687322932, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,545] Trial 1606 finished with value: 0.38739643824527137 and parameters: {'alpha': 178.24463569053268, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,622] Trial 1608 finished with value: 0.2092716853412829 and parameters: {'alpha': 49.067857033861536, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,623] Trial 1609 finished with value: 0.10791085031609793 and parameters: {'alpha': 0.3055109308514075, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,724] Trial 1610 finished with value: 0.26546866551449433 and parameters: {'alpha': 96.42735280543393, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,750] Trial 1611 finished with value: 0.10661364283511518 and parameters: {'alpha': 0.2888416459492837, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,766] Trial 1607 finished with value: 0.2674371185623972 and parameters: {'alpha': 97.88878341265792, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,902] Trial 1612 finished with value: 0.21206914023951481 and parameters: {'alpha': 51.72851510897934, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,903] Trial 1613 finished with value: 0.21192228457802226 and parameters: {'alpha': 51.5899449963552, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:11,933] Trial 1614 finished with value: 0.2149541103001645 and parameters: {'alpha': 54.42771850794605, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,076] Trial 1616 finished with value: 0.08173327052066204 and parameters: {'alpha': 0.08787526946134769, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,087] Trial 1615 finished with value: 0.18371405157791948 and parameters: {'alpha': 20.768378150533177, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,149] Trial 1618 finished with value: 0.18028234786184216 and parameters: {'alpha': 17.42785291508551, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,198] Trial 1619 finished with value: 0.18168786267732323 and parameters: {'alpha': 19.197318963603614, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,285] Trial 1617 finished with value: 0.1820761533717106 and parameters: {'alpha': 19.683528617563578, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,307] Trial 1622 finished with value: 0.4815548061651932 and parameters: {'alpha': 236.23405931595715, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,356] Trial 1620 finished with value: 0.1810394155401933 and parameters: {'alpha': 18.38621603615493, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,387] Trial 1621 finished with value: 0.19073988917300583 and parameters: {'alpha': 29.99374078028201, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,526] Trial 1623 finished with value: 0.1985520472476357 and parameters: {'alpha': 38.43882411900307, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,589] Trial 1624 finished with value: 0.1948928237433183 and parameters: {'alpha': 34.56907969075078, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,605] Trial 1625 finished with value: 0.19686937095240542 and parameters: {'alpha': 36.676081379415365, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,650] Trial 1627 finished with value: 0.11598954840409134 and parameters: {'alpha': 0.42620395847043596, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,777] Trial 1626 finished with value: 0.19780764314350333 and parameters: {'alpha': 37.66285202984936, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,782] Trial 1628 finished with value: 0.1948864327199836 and parameters: {'alpha': 34.56184833191681, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,842] Trial 1629 finished with value: 0.09838902877004525 and parameters: {'alpha': 0.20030048682283969, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,905] Trial 1630 finished with value: 0.17966570781506996 and parameters: {'alpha': 16.64407179667369, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,944] Trial 1631 finished with value: 0.12670764230828543 and parameters: {'alpha': 0.6517508972524494, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:12,997] Trial 1632 finished with value: 0.18033638164319496 and parameters: {'alpha': 17.495642050165475, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,018] Trial 1633 finished with value: 0.1804151491185239 and parameters: {'alpha': 17.594519466657417, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,089] Trial 1634 finished with value: 0.08860201069078949 and parameters: {'alpha': 0.12447171942092713, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,263] Trial 1635 finished with value: 0.18018486347399268 and parameters: {'alpha': 17.302982076277818, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,296] Trial 1636 finished with value: 0.2226783039936267 and parameters: {'alpha': 61.50312380267366, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,398] Trial 1637 finished with value: 0.06775880544562092 and parameters: {'alpha': 0.043975785791559516, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,457] Trial 1638 finished with value: 0.22076529611687917 and parameters: {'alpha': 59.776419691934215, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,460] Trial 1639 finished with value: 0.21219122500770973 and parameters: {'alpha': 51.843656182598636, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,510] Trial 1640 finished with value: 0.22038738261975746 and parameters: {'alpha': 59.43271629497935, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,553] Trial 1641 finished with value: 0.22287167139956834 and parameters: {'alpha': 61.67741269691499, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,612] Trial 1642 finished with value: 0.21116941650390633 and parameters: {'alpha': 50.877276734642045, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,707] Trial 1644 finished with value: 0.12110470536081423 and parameters: {'alpha': 0.5228946489740603, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,768] Trial 1643 finished with value: 0.18098596795333066 and parameters: {'alpha': 18.319052854752204, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,840] Trial 1645 finished with value: 0.19260892083418998 and parameters: {'alpha': 32.058619385604864, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,886] Trial 1647 finished with value: 0.1810375122070313 and parameters: {'alpha': 18.383413005472185, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:13,942] Trial 1646 finished with value: 1.4445756436317845 and parameters: {'alpha': 821.7604665184863, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,005] Trial 1649 finished with value: 0.18068550273694498 and parameters: {'alpha': 17.939699248956934, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,010] Trial 1648 finished with value: 0.18068945717259463 and parameters: {'alpha': 17.944786479489675, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,041] Trial 1650 finished with value: 0.18004516498766454 and parameters: {'alpha': 17.125982610100667, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,191] Trial 1651 finished with value: 0.18279072824578546 and parameters: {'alpha': 20.58172263628962, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,207] Trial 1653 finished with value: 0.19240387965152142 and parameters: {'alpha': 31.83303404575721, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,260] Trial 1654 finished with value: 0.12473876079358559 and parameters: {'alpha': 0.6030165219114214, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,261] Trial 1652 finished with value: 0.1912720215486226 and parameters: {'alpha': 30.583629027464255, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,381] Trial 1657 finished with value: 0.19404794652035368 and parameters: {'alpha': 33.647093566740764, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,421] Trial 1655 finished with value: 0.5794794278757196 and parameters: {'alpha': 295.3884275821458, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,422] Trial 1658 finished with value: 0.20122050337942027 and parameters: {'alpha': 41.18211588369727, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,467] Trial 1656 finished with value: 0.5834776963404604 and parameters: {'alpha': 297.792151313207, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,669] Trial 1659 finished with value: 0.20669138228567024 and parameters: {'alpha': 46.58118895386588, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,702] Trial 1661 finished with value: 0.1314923863461144 and parameters: {'alpha': 0.7884702584272186, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,760] Trial 1663 finished with value: 0.10080063939144739 and parameters: {'alpha': 0.223902905819989, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,785] Trial 1660 finished with value: 0.1257894342362254 and parameters: {'alpha': 0.6283342512154588, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,791] Trial 1664 finished with value: 0.17898393509714233 and parameters: {'alpha': 15.793063354919655, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,820] Trial 1662 finished with value: 0.1805779952199836 and parameters: {'alpha': 17.802609612329253, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,918] Trial 1665 finished with value: 0.11208779065583889 and parameters: {'alpha': 0.3637935215952266, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:14,976] Trial 1666 finished with value: 0.17956599204615548 and parameters: {'alpha': 16.519735209695366, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,073] Trial 1667 finished with value: 0.07087646278782897 and parameters: {'alpha': 0.05094194899158269, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,105] Trial 1668 finished with value: 0.194716466771176 and parameters: {'alpha': 34.37856006635644, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,212] Trial 1671 finished with value: 0.197433984246505 and parameters: {'alpha': 37.27072553769067, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,262] Trial 1670 finished with value: 0.08317204583419 and parameters: {'alpha': 0.09459004811937763, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,267] Trial 1669 finished with value: 0.09093587183902141 and parameters: {'alpha': 0.13996926719561423, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,286] Trial 1673 finished with value: 0.19843959614000825 and parameters: {'alpha': 38.32150224820118, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,307] Trial 1672 finished with value: 0.08800502422131992 and parameters: {'alpha': 0.12079231354698533, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,465] Trial 1674 finished with value: 0.20669012759560035 and parameters: {'alpha': 47.419346872511575, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,542] Trial 1676 finished with value: 0.2087178876696135 and parameters: {'alpha': 49.33855397374493, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,625] Trial 1677 finished with value: 0.24144681261564566 and parameters: {'alpha': 77.55596934292436, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,674] Trial 1675 finished with value: 0.1073088123843545 and parameters: {'alpha': 0.29420840860162145, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,693] Trial 1680 finished with value: 0.21273978708367602 and parameters: {'alpha': 52.24211122738914, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,766] Trial 1679 finished with value: 0.235971702945107 and parameters: {'alpha': 73.12142801025361, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,770] Trial 1678 finished with value: 0.19200834106445316 and parameters: {'alpha': 31.11395882254151, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,857] Trial 1681 finished with value: 0.23971663561369255 and parameters: {'alpha': 76.24981685700733, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:15,871] Trial 1682 finished with value: 0.24670852712530844 and parameters: {'alpha': 81.25074058972503, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,003] Trial 1683 finished with value: 0.539877342914782 and parameters: {'alpha': 271.53812094577097, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,038] Trial 1684 finished with value: 0.18197185334858149 and parameters: {'alpha': 19.55262103869936, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,102] Trial 1685 finished with value: 0.18225736887078542 and parameters: {'alpha': 19.910460914744135, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,156] Trial 1687 finished with value: inf and parameters: {'alpha': 19.309488398816054, 'fit_intercept': False, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,204] Trial 1686 finished with value: 0.1891771139365749 and parameters: {'alpha': 28.238939885997137, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,214] Trial 1688 finished with value: 0.1805362600226152 and parameters: {'alpha': 17.748578936528542, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,243] Trial 1689 finished with value: 0.18053051950555105 and parameters: {'alpha': 17.7412910997237, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,409] Trial 1691 finished with value: 0.19210350958573194 and parameters: {'alpha': 31.5026244235155, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,442] Trial 1690 finished with value: inf and parameters: {'alpha': 31.42347667667862, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,451] Trial 1692 finished with value: 0.19507090589021384 and parameters: {'alpha': 34.76159329851906, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,517] Trial 1693 finished with value: 1.5969037795538648 and parameters: {'alpha': 918.8259631337883, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,585] Trial 1694 finished with value: 0.19295917750308395 and parameters: {'alpha': 32.44586828232706, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,698] Trial 1696 finished with value: 0.11655871569181749 and parameters: {'alpha': 0.4359446204460739, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,704] Trial 1695 finished with value: 0.1955973126541941 and parameters: {'alpha': 35.325035653589225, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,773] Trial 1698 finished with value: 0.18131998567280022 and parameters: {'alpha': 18.737920259755075, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,854] Trial 1697 finished with value: 1.298061450388055 and parameters: {'alpha': 729.9853655811892, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:16,908] Trial 1699 finished with value: 0.1343899869577509 and parameters: {'alpha': 0.8853703299295744, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,032] Trial 1701 finished with value: 0.21252114412006584 and parameters: {'alpha': 52.15481454142295, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,033] Trial 1702 finished with value: 0.1802411314633019 and parameters: {'alpha': 17.374269207076704, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,068] Trial 1700 finished with value: 0.1808773576917147 and parameters: {'alpha': 18.18224010402447, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,259] Trial 1704 finished with value: 0.21406745920281667 and parameters: {'alpha': 53.60241157650073, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,286] Trial 1703 finished with value: 0.21754366037469164 and parameters: {'alpha': 56.825186801089146, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,323] Trial 1705 finished with value: 0.1822036563270971 and parameters: {'alpha': 19.842849887925592, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,397] Trial 1706 finished with value: 0.2102304884097451 and parameters: {'alpha': 49.98387698690992, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,417] Trial 1707 finished with value: 0.2099620264956826 and parameters: {'alpha': 49.72839427122076, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,448] Trial 1708 finished with value: 0.18112174078690385 and parameters: {'alpha': 18.48988092438068, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,543] Trial 1709 finished with value: 0.21120532445004114 and parameters: {'alpha': 50.91168040804686, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,589] Trial 1710 finished with value: 0.21051232948704773 and parameters: {'alpha': 50.25198269843229, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,667] Trial 1711 finished with value: 0.1371419639185856 and parameters: {'alpha': 0.9906385955671809, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,669] Trial 1712 finished with value: 0.11470853734066618 and parameters: {'alpha': 0.40456170986756457, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,825] Trial 1714 finished with value: 0.18056886082699433 and parameters: {'alpha': 17.791347744693734, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,836] Trial 1715 finished with value: 0.18041619474712178 and parameters: {'alpha': 17.595838325657787, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,836] Trial 1716 finished with value: 0.1806459182257402 and parameters: {'alpha': 17.890040098788607, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,872] Trial 1713 finished with value: 0.18008732903731503 and parameters: {'alpha': 17.1793111825933, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:17,883] Trial 1717 finished with value: 0.11217558812191619 and parameters: {'alpha': 0.3652110873138898, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,023] Trial 1719 finished with value: 0.12351135022615138 and parameters: {'alpha': 0.5747203714537034, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,025] Trial 1718 finished with value: 0.18376794272974922 and parameters: {'alpha': 21.80167510986962, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,064] Trial 1720 finished with value: 0.19236541465357737 and parameters: {'alpha': 31.521881231364866, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,216] Trial 1721 finished with value: 0.19522880705180923 and parameters: {'alpha': 34.93077528101752, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,363] Trial 1724 finished with value: 0.19567690095600332 and parameters: {'alpha': 34.84123741336403, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,375] Trial 1725 finished with value: 0.19568453677528785 and parameters: {'alpha': 34.842384668465904, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,376] Trial 1723 finished with value: 0.1952666814221834 and parameters: {'alpha': 34.39333019372081, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,382] Trial 1722 finished with value: 0.10054821565326895 and parameters: {'alpha': 0.21795996628113573, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,498] Trial 1726 finished with value: inf and parameters: {'alpha': 0.21041639666266856, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,549] Trial 1728 finished with value: inf and parameters: {'alpha': 0.015260331000314872, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,559] Trial 1727 finished with value: inf and parameters: {'alpha': 0.4824895716736468, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,561] Trial 1729 finished with value: inf and parameters: {'alpha': 0.4899076908960671, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,781] Trial 1731 finished with value: 0.10750343422337587 and parameters: {'alpha': 0.3002032993437087, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,824] Trial 1732 finished with value: 0.18051122006064974 and parameters: {'alpha': 17.716944144688803, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,829] Trial 1730 finished with value: 0.18202530626798938 and parameters: {'alpha': 19.619208189746658, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,831] Trial 1733 finished with value: 0.18198974789268096 and parameters: {'alpha': 19.57552082220676, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,972] Trial 1734 finished with value: 0.10748191290604447 and parameters: {'alpha': 0.3000039299514454, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,991] Trial 1736 finished with value: 0.1804283311060856 and parameters: {'alpha': 17.612807591302396, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:18,994] Trial 1735 finished with value: 0.18143065256219168 and parameters: {'alpha': 18.876266590250435, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,048] Trial 1737 finished with value: 0.18171391665810038 and parameters: {'alpha': 19.230250043715248, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,253] Trial 1738 finished with value: 0.23003077900133645 and parameters: {'alpha': 68.02103471754833, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,257] Trial 1739 finished with value: 0.22160675447162834 and parameters: {'alpha': 60.53729849847942, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,260] Trial 1740 finished with value: 0.23231859304327723 and parameters: {'alpha': 70.00048672092989, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,264] Trial 1741 finished with value: 0.20237783736379528 and parameters: {'alpha': 42.34963783560815, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,443] Trial 1743 finished with value: 1.1142202198550577 and parameters: {'alpha': 616.6452793462258, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,469] Trial 1745 finished with value: 0.22475893670333064 and parameters: {'alpha': 63.374586301315105, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,556] Trial 1742 finished with value: 0.19484563393040708 and parameters: {'alpha': 34.51885804427827, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,655] Trial 1744 finished with value: 0.1971647866981908 and parameters: {'alpha': 36.98748225974202, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,699] Trial 1747 finished with value: 0.28091073197214234 and parameters: {'alpha': 107.65066916326063, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,739] Trial 1748 finished with value: 0.19353992797851566 and parameters: {'alpha': 33.08645996249662, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,756] Trial 1746 finished with value: 0.19577470568205185 and parameters: {'alpha': 35.51521352031392, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,799] Trial 1749 finished with value: 0.1974311509303043 and parameters: {'alpha': 37.267507873197886, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,871] Trial 1750 finished with value: 0.19704989925986843 and parameters: {'alpha': 36.86660834270478, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,960] Trial 1752 finished with value: 0.1939883499306127 and parameters: {'alpha': 33.5810198560566, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:19,968] Trial 1751 finished with value: 0.12289695203279201 and parameters: {'alpha': 0.5609115580510996, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,076] Trial 1753 finished with value: 0.10057901116622123 and parameters: {'alpha': 0.22138964079450157, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,177] Trial 1754 finished with value: 0.18313593550832655 and parameters: {'alpha': 21.01361148669259, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,239] Trial 1756 finished with value: 0.11033595484683394 and parameters: {'alpha': 0.3383988008054177, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,259] Trial 1755 finished with value: 0.18101742200349513 and parameters: {'alpha': 18.358121490320833, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,297] Trial 1758 finished with value: 0.08257063270970397 and parameters: {'alpha': 0.09184821929966053, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,379] Trial 1757 finished with value: 0.18409382992393103 and parameters: {'alpha': 22.203511688813922, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,452] Trial 1759 finished with value: 0.09060098639237255 and parameters: {'alpha': 0.13776534953182895, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,470] Trial 1760 finished with value: 0.18187818320826485 and parameters: {'alpha': 19.435863253168428, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,623] Trial 1762 finished with value: 0.4413453139776933 and parameters: {'alpha': 211.7020203880968, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,651] Trial 1761 finished with value: 0.43343584164268095 and parameters: {'alpha': 206.85351214570005, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,657] Trial 1763 finished with value: 0.20961924663342932 and parameters: {'alpha': 49.40087474511337, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,724] Trial 1764 finished with value: 0.2133854411235609 and parameters: {'alpha': 52.96560988434402, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,809] Trial 1765 finished with value: 0.43362732993678044 and parameters: {'alpha': 206.97089157911202, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,833] Trial 1766 finished with value: 0.7692579214638158 and parameters: {'alpha': 408.79429286387403, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:20,971] Trial 1767 finished with value: 0.20973018265573606 and parameters: {'alpha': 49.50713125326685, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,126] Trial 1768 finished with value: 0.18171222694798528 and parameters: {'alpha': 19.227040483041673, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,130] Trial 1769 finished with value: 0.255164955861945 and parameters: {'alpha': 88.56821798579794, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,513] Trial 1771 finished with value: 0.18117170448704778 and parameters: {'alpha': 18.535988092641116, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,645] Trial 1770 finished with value: 0.18073591938219577 and parameters: {'alpha': 17.986151926927004, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,670] Trial 1774 finished with value: 0.18106029277600744 and parameters: {'alpha': 18.412813864450655, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:21,702] Trial 1772 finished with value: 0.1815171256296259 and parameters: {'alpha': 18.96808876358481, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,001] Trial 1775 finished with value: 0.0832329027035362 and parameters: {'alpha': 0.0946499779506165, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,006] Trial 1776 finished with value: 0.18138504002621306 and parameters: {'alpha': 18.81892363032754, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,025] Trial 1773 finished with value: 0.17985583714535366 and parameters: {'alpha': 16.86747549908507, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,129] Trial 1777 finished with value: 0.180947821173417 and parameters: {'alpha': 18.27125708100176, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,323] Trial 1780 finished with value: 0.20593112446032075 and parameters: {'alpha': 45.84456610172047, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,343] Trial 1778 finished with value: 0.7001575114360609 and parameters: {'alpha': 367.5656329661922, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,400] Trial 1781 finished with value: 0.2015437648411801 and parameters: {'alpha': 41.50935216057565, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,473] Trial 1779 finished with value: 0.20271197169253702 and parameters: {'alpha': 42.68387790069326, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,496] Trial 1785 finished with value: 0.20210951814350336 and parameters: {'alpha': 42.0800972324354, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,520] Trial 1784 finished with value: 0.2050032261898643 and parameters: {'alpha': 44.93989807183365, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,528] Trial 1783 finished with value: 0.20502268194901316 and parameters: {'alpha': 44.95915311290742, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,531] Trial 1782 finished with value: 0.09477431351511102 and parameters: {'alpha': 0.168677340387958, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,713] Trial 1786 finished with value: 0.19482304372687093 and parameters: {'alpha': 33.89705938693945, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,786] Trial 1788 finished with value: 0.09584274908768506 and parameters: {'alpha': 0.17738068716163283, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,934] Trial 1787 finished with value: 0.34704549348530017 and parameters: {'alpha': 152.09779611199474, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,957] Trial 1791 finished with value: 0.2295942847964639 and parameters: {'alpha': 67.641896851379, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,989] Trial 1792 finished with value: 0.0925592335269326 and parameters: {'alpha': 0.15152951406737813, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,990] Trial 1793 finished with value: 0.18033921817177226 and parameters: {'alpha': 17.497816822751634, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:22,993] Trial 1789 finished with value: 0.23132591906095817 and parameters: {'alpha': 69.14311549421718, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,015] Trial 1790 finished with value: 0.23281388960988908 and parameters: {'alpha': 70.28472720226546, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,127] Trial 1794 finished with value: 0.23468294664884878 and parameters: {'alpha': 72.02725734676692, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,164] Trial 1795 finished with value: 0.23610526874743018 and parameters: {'alpha': 73.23426620526801, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,321] Trial 1796 finished with value: 0.1826415679610404 and parameters: {'alpha': 20.39358290580473, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,369] Trial 1797 finished with value: 0.1806775151624178 and parameters: {'alpha': 17.927786110439015, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,433] Trial 1799 finished with value: inf and parameters: {'alpha': 20.303100745725015, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,530] Trial 1800 finished with value: inf and parameters: {'alpha': 18.684825953493938, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,570] Trial 1798 finished with value: 0.180899042840255 and parameters: {'alpha': 18.210162571823453, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,594] Trial 1802 finished with value: 0.18146309435392688 and parameters: {'alpha': 18.91615995675989, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,608] Trial 1801 finished with value: inf and parameters: {'alpha': 20.981400454869906, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,621] Trial 1803 finished with value: inf and parameters: {'alpha': 19.884095318488963, 'fit_intercept': False, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,697] Trial 1804 finished with value: 0.11859977506938742 and parameters: {'alpha': 0.47350733464690054, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,763] Trial 1806 finished with value: 0.1297119164396588 and parameters: {'alpha': 0.7343840058649572, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:23,796] Trial 1805 finished with value: 0.19586519518400497 and parameters: {'alpha': 35.61141312404001, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,101] Trial 1807 finished with value: 0.19926946083470395 and parameters: {'alpha': 39.17721180415655, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,299] Trial 1809 finished with value: 0.1985805811831826 and parameters: {'alpha': 38.4611005894094, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,691] Trial 1811 finished with value: 0.19565606220446136 and parameters: {'alpha': 35.38001995814989, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,694] Trial 1808 finished with value: 0.13482868710166537 and parameters: {'alpha': 0.8980619275091225, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,723] Trial 1810 finished with value: 0.10832975708007817 and parameters: {'alpha': 0.3103726541900407, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,950] Trial 1812 finished with value: 0.19772545975534542 and parameters: {'alpha': 37.5691014890401, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:24,981] Trial 1814 finished with value: 0.1961146939247533 and parameters: {'alpha': 35.868475160213144, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,029] Trial 1813 finished with value: 0.13632807829204366 and parameters: {'alpha': 0.9518252251912708, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,111] Trial 1815 finished with value: 0.1946338315943668 and parameters: {'alpha': 34.28822379951033, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,196] Trial 1816 finished with value: 0.8630061287006578 and parameters: {'alpha': 464.8006539610775, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,368] Trial 1817 finished with value: 0.21615027832031256 and parameters: {'alpha': 55.53835785233138, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,390] Trial 1821 finished with value: 0.1814229059159129 and parameters: {'alpha': 18.86614957881669, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,417] Trial 1818 finished with value: 0.2155603317742599 and parameters: {'alpha': 54.990712253990715, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,421] Trial 1819 finished with value: 0.21538288811934625 and parameters: {'alpha': 54.82573963231106, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,508] Trial 1820 finished with value: 0.22190088809364727 and parameters: {'alpha': 60.80287003305921, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,538] Trial 1822 finished with value: 0.2194695086348685 and parameters: {'alpha': 58.59630659219904, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,576] Trial 1823 finished with value: 0.18020183767218348 and parameters: {'alpha': 17.32428487746888, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,689] Trial 1825 finished with value: 0.07052448775442025 and parameters: {'alpha': 0.05005274382362413, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,698] Trial 1824 finished with value: 0.18011126606188327 and parameters: {'alpha': 17.209186763605516, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,929] Trial 1828 finished with value: 0.11232757047954364 and parameters: {'alpha': 0.36732450034116515, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,945] Trial 1827 finished with value: 0.18105059949372948 and parameters: {'alpha': 16.940408226959327, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:25,993] Trial 1830 finished with value: 0.12052144691868838 and parameters: {'alpha': 0.5074827455139498, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,002] Trial 1826 finished with value: 0.0967298597476357 and parameters: {'alpha': 0.18205013661572206, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,053] Trial 1829 finished with value: 0.1825085592490749 and parameters: {'alpha': 19.08143719308706, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,097] Trial 1831 finished with value: 0.11374626991673527 and parameters: {'alpha': 0.3892140592262466, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,165] Trial 1833 finished with value: 1.3577995034308183 and parameters: {'alpha': 767.23234114427, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,258] Trial 1832 finished with value: 0.14330959549753297 and parameters: {'alpha': 1.2889326503799672, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,340] Trial 1834 finished with value: 0.1930671808182566 and parameters: {'alpha': 32.56507633276793, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,402] Trial 1835 finished with value: 0.256969311330695 and parameters: {'alpha': 89.96898350422111, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,472] Trial 1836 finished with value: 0.11292859169407901 and parameters: {'alpha': 0.3765111639742967, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,568] Trial 1838 finished with value: 0.9212622098581414 and parameters: {'alpha': 499.7946490445485, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,580] Trial 1839 finished with value: 0.25887351639597045 and parameters: {'alpha': 91.43465550940026, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,609] Trial 1840 finished with value: 0.1940616280967311 and parameters: {'alpha': 33.66204589074185, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,691] Trial 1837 finished with value: 0.27161931595651734 and parameters: {'alpha': 100.95926079288584, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,718] Trial 1841 finished with value: 0.19296822837428046 and parameters: {'alpha': 32.455023019914684, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,746] Trial 1842 finished with value: 0.1945667339124178 and parameters: {'alpha': 34.215198705009186, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,769] Trial 1843 finished with value: 0.19255235570004117 and parameters: {'alpha': 31.997415177387108, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,935] Trial 1844 finished with value: 0.19248443802682982 and parameters: {'alpha': 31.922271373168012, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,976] Trial 1846 finished with value: 1.0170501243189762 and parameters: {'alpha': 557.6014472101274, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:26,980] Trial 1845 finished with value: 0.18192038420024675 and parameters: {'alpha': 19.48740735201212, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,155] Trial 1847 finished with value: 0.18059296810752473 and parameters: {'alpha': 17.82126751171022, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,168] Trial 1849 finished with value: 0.18077479896946963 and parameters: {'alpha': 18.05219680375923, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,183] Trial 1848 finished with value: 0.18107080604954776 and parameters: {'alpha': 18.426485974033803, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,205] Trial 1850 finished with value: 0.1800386278011925 and parameters: {'alpha': 17.117859163863873, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,246] Trial 1851 finished with value: 0.18207188412314973 and parameters: {'alpha': 19.677846999234212, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,317] Trial 1852 finished with value: 0.1813082267680922 and parameters: {'alpha': 18.723034608413037, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,331] Trial 1853 finished with value: 0.09919144152189557 and parameters: {'alpha': 0.20790872779862696, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,342] Trial 1854 finished with value: 0.07322921720805922 and parameters: {'alpha': 0.05718950748888425, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,583] Trial 1856 finished with value: 0.20880550922594576 and parameters: {'alpha': 48.61948214456827, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,618] Trial 1855 finished with value: 0.21108005364669 and parameters: {'alpha': 50.79260468740348, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,645] Trial 1857 finished with value: 0.0995898581414474 and parameters: {'alpha': 0.21173313491246412, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,693] Trial 1858 finished with value: 1.4062453691020764 and parameters: {'alpha': 797.6095507088394, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,695] Trial 1859 finished with value: 0.2089918423622533 and parameters: {'alpha': 48.79850579928203, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,778] Trial 1861 finished with value: 0.2184655996222246 and parameters: {'alpha': 57.67471750790109, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,828] Trial 1860 finished with value: 0.2106098459986637 and parameters: {'alpha': 50.34525435060767, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:27,879] Trial 1862 finished with value: 0.21133523064864312 and parameters: {'alpha': 51.03467952631148, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,120] Trial 1863 finished with value: 1.166488126220703 and parameters: {'alpha': 648.6608928350189, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,165] Trial 1864 finished with value: 0.06151701338918587 and parameters: {'alpha': 0.03258747306203311, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,166] Trial 1865 finished with value: 0.11357280157791946 and parameters: {'alpha': 0.3863475064502567, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,228] Trial 1866 finished with value: 0.09773365998920644 and parameters: {'alpha': 0.19437393354343427, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,232] Trial 1867 finished with value: 0.191632784038343 and parameters: {'alpha': 30.982290737532573, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,264] Trial 1868 finished with value: 0.1344027066843134 and parameters: {'alpha': 0.8858529878129043, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,405] Trial 1871 finished with value: 0.1958388167531867 and parameters: {'alpha': 35.58264511519033, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,406] Trial 1869 finished with value: 0.19147623849969167 and parameters: {'alpha': 30.809704660915884, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,458] Trial 1870 finished with value: 0.18885057790655843 and parameters: {'alpha': 27.86902695345086, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:28,709] Trial 1873 finished with value: 0.19301815834446961 and parameters: {'alpha': 32.51025346689277, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,055] Trial 1875 finished with value: inf and parameters: {'alpha': 17.461221824022847, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,059] Trial 1872 finished with value: 0.08864979800575659 and parameters: {'alpha': 0.1243218788570835, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,062] Trial 1874 finished with value: 0.19266305419921878 and parameters: {'alpha': 32.10997007599546, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,364] Trial 1876 finished with value: inf and parameters: {'alpha': 18.522529253724244, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,424] Trial 1877 finished with value: inf and parameters: {'alpha': 0.39845214494383835, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,728] Trial 1879 finished with value: 0.18156193667763165 and parameters: {'alpha': 19.023854910165067, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,767] Trial 1880 finished with value: 0.18147118041992197 and parameters: {'alpha': 17.57276491858047, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,772] Trial 1878 finished with value: inf and parameters: {'alpha': 18.955722954017862, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,951] Trial 1882 finished with value: 0.18168857325503704 and parameters: {'alpha': 17.883807934657895, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:29,960] Trial 1881 finished with value: 0.18217938560084299 and parameters: {'alpha': 18.59427522569843, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,011] Trial 1883 finished with value: 0.1818491951711555 and parameters: {'alpha': 18.117639697923053, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,177] Trial 1884 finished with value: 0.499238217002467 and parameters: {'alpha': 246.9741296761684, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,185] Trial 1885 finished with value: 0.19648957127621297 and parameters: {'alpha': 36.27503214739851, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,207] Trial 1887 finished with value: 0.11577640560752474 and parameters: {'alpha': 0.422720844520268, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,223] Trial 1886 finished with value: 0.11371503597861847 and parameters: {'alpha': 0.3886828774945679, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,354] Trial 1888 finished with value: 0.11037304173519742 and parameters: {'alpha': 0.3387962848096385, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,372] Trial 1889 finished with value: 0.20859875905890216 and parameters: {'alpha': 48.42012435720081, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,383] Trial 1891 finished with value: 0.2062370404373972 and parameters: {'alpha': 46.14196818335097, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,386] Trial 1890 finished with value: 0.22615881450452308 and parameters: {'alpha': 64.61980410486984, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,546] Trial 1892 finished with value: 0.22328214246247952 and parameters: {'alpha': 62.04760116913432, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,589] Trial 1894 finished with value: 0.21957015252364315 and parameters: {'alpha': 58.68770614554669, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,617] Trial 1895 finished with value: 0.19902918463456004 and parameters: {'alpha': 38.93572167167171, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,624] Trial 1893 finished with value: 0.19848862529553865 and parameters: {'alpha': 38.37293740182332, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,748] Trial 1896 finished with value: 0.19390307456568673 and parameters: {'alpha': 33.48723697408739, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,849] Trial 1899 finished with value: 0.1949877850663035 and parameters: {'alpha': 34.67112056444961, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,900] Trial 1898 finished with value: 0.18014452045641455 and parameters: {'alpha': 17.252283042814547, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:30,901] Trial 1897 finished with value: 0.18155515374434628 and parameters: {'alpha': 19.031647678331115, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,029] Trial 1900 finished with value: 0.09855397493061269 and parameters: {'alpha': 0.20170534093157194, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,098] Trial 1902 finished with value: 0.17981724525853213 and parameters: {'alpha': 16.836365277309262, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,125] Trial 1903 finished with value: 0.1816553975637336 and parameters: {'alpha': 19.156962307988145, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,137] Trial 1901 finished with value: 1.055861199372944 and parameters: {'alpha': 581.1204318312907, 'fit_intercept': True, 'solver': 'cholesky'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,226] Trial 1904 finished with value: 0.06587293174342107 and parameters: {'alpha': 0.04006676380428462, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,273] Trial 1907 finished with value: 0.18253108057925582 and parameters: {'alpha': 20.254860203534047, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,290] Trial 1906 finished with value: 0.1824085653525905 and parameters: {'alpha': 20.1009702708582, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,298] Trial 1905 finished with value: 0.18177039582905025 and parameters: {'alpha': 19.300983980295783, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,454] Trial 1908 finished with value: 0.19493186080129526 and parameters: {'alpha': 34.610717211576514, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,549] Trial 1910 finished with value: 0.19388139905427634 and parameters: {'alpha': 33.463284537348784, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,600] Trial 1909 finished with value: 0.19652615542763158 and parameters: {'alpha': 36.31334815982004, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,683] Trial 1912 finished with value: 0.2483883046361021 and parameters: {'alpha': 83.25464862041017, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,712] Trial 1911 finished with value: 0.23732081028988494 and parameters: {'alpha': 74.25824399907266, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,819] Trial 1914 finished with value: 0.2501460693359376 and parameters: {'alpha': 84.64627553297167, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,864] Trial 1915 finished with value: 0.24070152671412423 and parameters: {'alpha': 77.06170554229591, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:31,950] Trial 1913 finished with value: 0.23550925659179697 and parameters: {'alpha': 72.7301982460997, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,101] Trial 1916 finished with value: 0.1794005161004318 and parameters: {'alpha': 16.31187294344145, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,223] Trial 1917 finished with value: 0.13881919234426404 and parameters: {'alpha': 1.0625078056276642, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,266] Trial 1918 finished with value: 0.07272508769788241 and parameters: {'alpha': 0.05555139753857304, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,273] Trial 1919 finished with value: 0.09771947734631992 and parameters: {'alpha': 0.19410967227248657, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,295] Trial 1920 finished with value: 0.1287647584292764 and parameters: {'alpha': 0.7073130261443864, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,384] Trial 1921 finished with value: 0.10540619102076484 and parameters: {'alpha': 0.27414980526503363, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,434] Trial 1922 finished with value: 0.20997197843852797 and parameters: {'alpha': 49.73801330612891, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,632] Trial 1923 finished with value: 0.205988603515625 and parameters: {'alpha': 45.90058890444896, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,656] Trial 1926 finished with value: 1.2604876101202716 and parameters: {'alpha': 706.666543967677, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,715] Trial 1927 finished with value: 0.19412389436420646 and parameters: {'alpha': 33.730742262008555, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,717] Trial 1925 finished with value: 0.21434782605622948 and parameters: {'alpha': 53.863103033673234, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,719] Trial 1924 finished with value: 0.2100375366210938 and parameters: {'alpha': 49.80005065142845, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,795] Trial 1929 finished with value: 0.19342837177477387 and parameters: {'alpha': 32.96374008110043, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,864] Trial 1928 finished with value: 0.19403705437911187 and parameters: {'alpha': 33.634838032454354, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:32,907] Trial 1930 finished with value: 0.1944891646535773 and parameters: {'alpha': 34.130529841106956, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,094] Trial 1934 finished with value: 0.18170544883326487 and parameters: {'alpha': 19.21951347981419, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,101] Trial 1933 finished with value: 0.1806131659256785 and parameters: {'alpha': 17.847064489299882, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,140] Trial 1932 finished with value: 0.12183177862870073 and parameters: {'alpha': 0.5381003657434809, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,196] Trial 1935 finished with value: 0.6269523290373149 and parameters: {'alpha': 323.8363306372111, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,219] Trial 1936 finished with value: 0.18375685039319498 and parameters: {'alpha': 21.78794536645947, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,241] Trial 1931 finished with value: 0.17965031731856504 and parameters: {'alpha': 16.625614059928804, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,338] Trial 1938 finished with value: 0.1798150239000823 and parameters: {'alpha': 16.83333148331208, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,361] Trial 1937 finished with value: 0.18081112169767685 and parameters: {'alpha': 18.098339362214702, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,552] Trial 1939 finished with value: 0.12809882632606914 and parameters: {'alpha': 0.6886699193017376, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,565] Trial 1941 finished with value: 0.18015816502621307 and parameters: {'alpha': 17.270001219750934, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,591] Trial 1940 finished with value: 0.13432874139083067 and parameters: {'alpha': 0.8832390448374502, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,616] Trial 1942 finished with value: 0.1803986182283101 and parameters: {'alpha': 17.574087777704708, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,696] Trial 1944 finished with value: 0.1812039198062295 and parameters: {'alpha': 18.593087974106716, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,701] Trial 1943 finished with value: 0.1817689438348068 and parameters: {'alpha': 19.299310202560555, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,750] Trial 1946 finished with value: 0.08620947831003292 and parameters: {'alpha': 0.11029903753946652, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:33,751] Trial 1945 finished with value: 0.1159407973761308 and parameters: {'alpha': 0.4252907993150902, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,360] Trial 1949 finished with value: inf and parameters: {'alpha': 47.530572583704604, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,408] Trial 1947 finished with value: inf and parameters: {'alpha': 53.07098839948913, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,465] Trial 1950 finished with value: inf and parameters: {'alpha': 0.048051004769961506, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,487] Trial 1948 finished with value: 0.21409391151829774 and parameters: {'alpha': 53.621836370928946, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,489] Trial 1951 finished with value: inf and parameters: {'alpha': 51.592917235128425, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,935] Trial 1952 finished with value: inf and parameters: {'alpha': 48.22936001540086, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:34,964] Trial 1953 finished with value: inf and parameters: {'alpha': 46.853297491438454, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,065] Trial 1954 finished with value: inf and parameters: {'alpha': 49.687440096841925, 'fit_intercept': False, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,189] Trial 1958 finished with value: 0.19246437352230678 and parameters: {'alpha': 31.90016966421654, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,261] Trial 1956 finished with value: 0.29719634489360613 and parameters: {'alpha': 119.07409262266374, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,261] Trial 1955 finished with value: 0.19446169401469984 and parameters: {'alpha': 34.101311251863045, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,301] Trial 1959 finished with value: 0.19397014860454362 and parameters: {'alpha': 33.561496991039604, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,327] Trial 1957 finished with value: 0.19384098735608557 and parameters: {'alpha': 33.41929375288297, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,403] Trial 1960 finished with value: 0.1923979174804688 and parameters: {'alpha': 31.826831220057795, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,509] Trial 1961 finished with value: 0.19172795230263165 and parameters: {'alpha': 31.08739497500956, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,513] Trial 1962 finished with value: 0.1812357042814557 and parameters: {'alpha': 18.63230435678422, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:42:35,637] Trial 1963 finished with value: 0.180822771381579 and parameters: {'alpha': 18.11299669746753, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,732] Trial 1967 finished with value: 0.18155426218133228 and parameters: {'alpha': 17.69230094615387, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,778] Trial 1965 finished with value: 0.12190003842002475 and parameters: {'alpha': 0.5396615053674965, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,795] Trial 1964 finished with value: 0.18117896638569086 and parameters: {'alpha': 17.14486726737753, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:42:35,894] Trial 1966 finished with value: 0.18133015284488085 and parameters: {'alpha': 18.750009305619642, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:35,967] Trial 1969 finished with value: 0.18193386487458887 and parameters: {'alpha': 17.929136127787487, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n",
+      "/opt/anaconda3/envs/ds-study/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[I 2025-03-06 14:42:36,116] Trial 1970 finished with value: 0.1802022299676193 and parameters: {'alpha': 15.691015283646722, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,126] Trial 1971 finished with value: 0.1793011635228208 and parameters: {'alpha': 16.186911282600946, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,235] Trial 1972 finished with value: 0.22661726035670235 and parameters: {'alpha': 65.02510041024576, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,253] Trial 1973 finished with value: 0.2288940365439968 and parameters: {'alpha': 67.02851328067216, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,281] Trial 1968 finished with value: 0.03307594180458474 and parameters: {'alpha': 0.0007384808423236558, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,285] Trial 1976 finished with value: 0.11182915739360615 and parameters: {'alpha': 0.3599119916133937, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,291] Trial 1975 finished with value: 0.22941801513671883 and parameters: {'alpha': 67.48738935050771, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,381] Trial 1974 finished with value: 0.10711500327662422 and parameters: {'alpha': 0.2952551301106258, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,482] Trial 1977 finished with value: 0.19186378360145975 and parameters: {'alpha': 31.237896365700003, 'fit_intercept': True, 'solver': 'auto'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,636] Trial 1978 finished with value: 0.19659464226973686 and parameters: {'alpha': 35.845402127229804, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:36,885] Trial 1982 finished with value: 0.1961822166041324 and parameters: {'alpha': 35.39807554982347, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,056] Trial 1980 finished with value: 0.819681207082648 and parameters: {'alpha': 438.87322803423086, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,103] Trial 1983 finished with value: 0.20102476414730677 and parameters: {'alpha': 40.97651287551859, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,110] Trial 1979 finished with value: 0.13251828767475338 and parameters: {'alpha': 0.8180756389097616, 'fit_intercept': True, 'solver': 'lsqr'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,173] Trial 1984 finished with value: 0.19227406352796056 and parameters: {'alpha': 31.497781964198538, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,267] Trial 1981 finished with value: 0.19541272158974096 and parameters: {'alpha': 34.54881578656695, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,318] Trial 1985 finished with value: 0.19513754169664885 and parameters: {'alpha': 34.24983447559493, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,452] Trial 1986 finished with value: 0.18048662237870072 and parameters: {'alpha': 16.109637338793284, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,600] Trial 1990 finished with value: 0.1809323422723068 and parameters: {'alpha': 16.769661899165033, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,614] Trial 1989 finished with value: 0.1804957198293586 and parameters: {'alpha': 16.127495137842487, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,616] Trial 1988 finished with value: 0.1317534815095601 and parameters: {'alpha': 0.7935462479728794, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,638] Trial 1987 finished with value: 0.1814559489360609 and parameters: {'alpha': 17.53470731108309, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,648] Trial 1991 finished with value: 0.13306511930766868 and parameters: {'alpha': 0.8355398929039739, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,677] Trial 1992 finished with value: 0.18020510183233968 and parameters: {'alpha': 15.693574916898747, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,768] Trial 1993 finished with value: 0.18337705572831006 and parameters: {'alpha': 20.30782904353639, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,809] Trial 1994 finished with value: 0.13735428755910778 and parameters: {'alpha': 0.995421339847869, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,966] Trial 1995 finished with value: 0.22540899548982324 and parameters: {'alpha': 63.789584332743985, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,973] Trial 1998 finished with value: 0.2206632358269943 and parameters: {'alpha': 59.48529887229445, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,985] Trial 1996 finished with value: 0.2126980053068462 and parameters: {'alpha': 52.19913404375244, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,991] Trial 1999 finished with value: 0.22276602930972458 and parameters: {'alpha': 61.394102785504664, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n",
+      "[I 2025-03-06 14:42:37,995] Trial 1997 finished with value: 0.13329739501953133 and parameters: {'alpha': 0.8439209391513478, 'fit_intercept': True, 'solver': 'sag'}. Best is trial 1109 with value: 0.018637291002775534.\n"
+     ]
+    }
+   ],
+   "source": [
+    "study = optuna.create_study(direction=\"minimize\", storage=\"sqlite:///example-study.db\")\n",
+    "study.optimize(optimize_ridge, n_trials=2000, show_progress_bar=True, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ridge = Ridge(**study.best_params, random_state=SEED)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = get_expending_window_splitter(y_train)\n",
+    "te = TargetEncoder(cv=cv)\n",
+    "X_train = te.fit_transform(X_train, pd.DataFrame(y_train))\n",
+    "X_test = te.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss = StandardScaler().set_output(transform=\"pandas\")\n",
+    "X_train_scaled = ss.fit_transform(X_train)\n",
+    "X_test_scaled = ss.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ridge.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['models/ridge-regression-1.pkl.gz']"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(model, \"models/ridge-regression-1.pkl.gz\", compress=\"gzip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.013298685816714558"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_pred = model.predict(X_train_scaled)\n",
+    "mean_absolute_error(y_train, train_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_pred = model.predict(X_test_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.015979958736535255"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_pred_series = pd.Series(train_pred, name=\"predictions\", index=y_train.index)\n",
+    "test_pred_series = pd.Series(test_pred, name=\"predictions\", index=y_test.index)\n",
+    "\n",
+    "train_pred_df = pd.concat([train_pred_series, y_train], axis=1)\n",
+    "test_pred_df = pd.concat([test_pred_series, y_test], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+            "gridwidth": 2,
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+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
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+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Predictions vs. True Values"
+        },
+        "width": 1200,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          0.45
+         ]
+        },
+        "xaxis2": {
+         "anchor": "y2",
+         "domain": [
+          0.55,
+          1
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+        "yaxis": {
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+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = make_subplots(rows=1, cols=2, subplot_titles=(\"Train\", \"Test\"))\n",
+    "\n",
+    "for i, (df, name) in enumerate(zip([train_pred_df, test_pred_df], [\"Train\", \"Test\"])):\n",
+    "    fig.add_trace(go.Scatter(x=df.index, y=df[TARGET], mode=\"lines\", name=f\"{name} True\"), row=1, col=i+1)\n",
+    "    fig.add_trace(go.Scatter(x=df.index, y=df[\"predictions\"], mode=\"lines\", name=f\"{name} Predictions\"), row=1, col=i+1)\n",
+    "\n",
+    "fig.update_layout(height=400, width=1200, title_text=\"Predictions vs. True Values\")\n",
+    "fig.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ds-study",
+   "language": "python",
+   "name": "python3"
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+   "version": "3.11.8"
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}