Azure · harneetvirk · Oct 11, 2021 · Oct 11, 2021
diff --git a/configuration.ipynb b/configuration.ipynb
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/contrib/fairness/fairlearn-azureml-mitigation.yml b/contrib/fairness/fairlearn-azureml-mitigation.yml
@@ -6,4 +6,4 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.7.0
+  - raiwidgets~=0.11.0
diff --git a/contrib/fairness/upload-fairness-dashboard.yml b/contrib/fairness/upload-fairness-dashboard.yml
@@ -6,4 +6,4 @@ dependencies:
   - fairlearn>=0.6.2
   - joblib
   - liac-arff
-  - raiwidgets~=0.7.0
+  - raiwidgets~=0.11.0
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -22,9 +22,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.34.0
+  - azureml-widgets~=1.35.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.34.0/validated_win32_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.35.0/validated_win32_requirements.txt [--no-deps]
   - arch==4.14
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml
@@ -22,9 +22,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.34.0
+  - azureml-widgets~=1.35.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.34.0/validated_linux_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.35.0/validated_linux_requirements.txt [--no-deps]
   - arch==4.14
diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -23,9 +23,9 @@ dependencies:
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-widgets~=1.34.0
+  - azureml-widgets~=1.35.0
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
-  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.34.0/validated_darwin_requirements.txt [--no-deps]
+  - -r https://automlresources-prod.azureedge.net/validated-requirements/1.35.0/validated_darwin_requirements.txt [--no-deps]
   - arch==4.14
diff --git a/how-to-use-azureml/automated-machine-learning/check_conda_version.py b/how-to-use-azureml/automated-machine-learning/check_conda_version.py
@@ -3,7 +3,7 @@
 
 try:
     import conda
-except:
+except Exception:
     print('Failed to import conda.')
     print('This setup is usually run from the base conda environment.')
     print('You can activate the base environment using the command "conda activate base"')

diff --git a/...tion-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/...tion-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -104,7 +104,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
@@ -703,7 +703,7 @@
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(entry_script=script_file_name)\n",
+        "inference_config = InferenceConfig(environment = best_run.get_environment(), entry_script=script_file_name)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
         "                                               memory_gb = 2, \n",

diff --git a/...-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb b/...-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -93,7 +93,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/.../automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb b/.../automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb
@@ -96,7 +96,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -81,7 +81,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py b/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
@@ -31,7 +31,7 @@
     model = Model(ws, args.model_name)
     last_train_time = model.created_time
     print("Model was last trained on {0}.".format(last_train_time))
-except Exception as e:
+except Exception:
     print("Could not get last model train time.")
     last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
 

diff --git a/...dit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb b/...dit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb
@@ -92,7 +92,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb b/...machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb
@@ -91,7 +91,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/.../automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb b/.../automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb
@@ -113,7 +113,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...ml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb b/...ml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -88,7 +88,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...omated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb b/...omated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -99,7 +99,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/...to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py b/...to-use-azureml/automated-machine-learning/forecasting-energy-demand/forecasting_script.py
@@ -5,55 +5,10 @@
 """
 
 import argparse
-import pandas as pd
-import numpy as np
 from azureml.core import Dataset, Run
-from azureml.automl.core.shared.constants import TimeSeriesInternal
 from sklearn.externals import joblib
 from pandas.tseries.frequencies import to_offset
 
-
-def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
-                  predicted_column_name='predicted',
-                  horizon_colname='horizon_origin'):
-    """
-    Demonstrates how to get the output aligned to the inputs
-    using pandas indexes. Helps understand what happened if
-    the output's shape differs from the input shape, or if
-    the data got re-sorted by time and grain during forecasting.
-
-    Typical causes of misalignment are:
-    * we predicted some periods that were missing in actuals -> drop from eval
-    * model was asked to predict past max_horizon -> increase max horizon
-    * data at start of X_test was needed for lags -> provide previous periods
-    """
-
-    if (horizon_colname in X_trans):
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted,
-                                horizon_colname: X_trans[horizon_colname]})
-    else:
-        df_fcst = pd.DataFrame({predicted_column_name: y_predicted})
-
-    # y and X outputs are aligned by forecast() function contract
-    df_fcst.index = X_trans.index
-
-    # align original X_test to y_test
-    X_test_full = X_test.copy()
-    X_test_full[target_column_name] = y_test
-
-    # X_test_full's index does not include origin, so reset for merge
-    df_fcst.reset_index(inplace=True)
-    X_test_full = X_test_full.reset_index().drop(columns='index')
-    together = df_fcst.merge(X_test_full, how='right')
-
-    # drop rows where prediction or actuals are nan
-    # happens because of missing actuals
-    # or at edges of time due to lags/rolling windows
-    clean = together[together[[target_column_name,
-                               predicted_column_name]].notnull().all(axis=1)]
-    return(clean)
-
-
 parser = argparse.ArgumentParser()
 parser.add_argument(
     '--target_column_name', type=str, dest='target_column_name',
@@ -77,13 +32,25 @@ def align_outputs(y_predicted, X_trans, X_test, y_test, target_column_name,
 
 # generate forecast
 fitted_model = joblib.load('model.pkl')
-y_predictions, X_trans = fitted_model.forecast(X_test)
-
-# align output
-df_all = align_outputs(y_predictions, X_trans, X_test, y_test, target_column_name)
+# We have default quantiles values set as below(95th percentile)
+quantiles = [0.025, 0.5, 0.975]
+predicted_column_name = 'predicted'
+PI = 'prediction_interval'
+fitted_model.quantiles = quantiles
+pred_quantiles = fitted_model.forecast_quantiles(X_test)
+pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply(lambda x: '[{}, {}]'.format(x[0],
+                                                                                                        x[1]), axis=1)
+X_test[target_column_name] = y_test
+X_test[PI] = pred_quantiles[PI]
+X_test[predicted_column_name] = pred_quantiles[0.5]
+# drop rows where prediction or actuals are nan
+# happens because of missing actuals
+# or at edges of time due to lags/rolling windows
+clean = X_test[X_test[[target_column_name,
+                       predicted_column_name]].notnull().all(axis=1)]
 
 file_name = 'outputs/predictions.csv'
-export_csv = df_all.to_csv(file_name, header=True, index=False)  # added Index
+export_csv = clean.to_csv(file_name, header=True, index=False)  # added Index
 
 # Upload the predictions into artifacts
 run.upload_file(name=file_name, path_or_stream=file_name)
diff --git a/...tomated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb b/...tomated-machine-learning/forecasting-forecast-function/auto-ml-forecasting-function.ipynb
@@ -94,7 +94,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "print(\"This notebook was created using version 1.34.0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.35.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },