add deepnote link

Author: khuyentran1401

data_science_tools/Datapane_new_features/analyzing_articles.ipynb

@@ -22,8 +22,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "!pip install datapane",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -40,8 +40,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "from sklearn.datasets import load_iris\nimport plotly.express as px \nimport datapane as dp ",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -55,7 +55,6 @@
     "deepnote_cell_type": "code"
    },
    "source": "import os\n\nTOKEN = os.environ[\"DATAPANE_TOKEN\"]\ndp.login(TOKEN)",
-   "execution_count": null,
    "outputs": [
     {
      "name": "stdout",
@@ -70,7 +69,8 @@
      },
      "metadata": {}
     }
-   ]
+   ],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -87,7 +87,6 @@
     "deepnote_cell_type": "code"
    },
    "source": "X, y = load_iris(as_frame=True, return_X_y=True)\nX.head(10)",
-   "execution_count": null,
    "outputs": [
     {
      "output_type": "execute_result",
@@ -429,7 +428,8 @@
      },
      "metadata": {}
     }
-   ]
+   ],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -446,8 +446,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "fig = px.scatter(data_frame=X,\n          x='sepal length (cm)',\n          y='sepal width (cm)')",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -465,8 +465,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "table = dp.DataTable(X)\nplot = dp.Plot(fig)",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -479,8 +479,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "group = dp.Group(table, plot,\n                columns=2)",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -493,14 +493,14 @@
     "deepnote_cell_type": "code"
    },
    "source": "dp.Report(group).publish(name='Iris', description='Show Iris data')",
-   "execution_count": null,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": "Publishing report and associated data - please wait..\nReport successfully published at https://datapane.com/u/khuyentran1401/reports/iris/\n"
     }
-   ]
+   ],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",

data_science_tools/Datapane_new_features/iris_project.ipynb

@@ -7,9 +7,7 @@
     "tags": [],
     "cell_id": "00000-0d62bc9e-286e-448d-8d2b-486fa48f69e4",
     "deepnote_cell_type": "markdown"
-   },
-   "outputs": [],
-   "execution_count": null
+   }
   },
   {
    "cell_type": "markdown",
@@ -30,8 +28,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "!pip install --upgrade pip && pip install hyperdash",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -40,8 +38,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "!hd signup",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -50,8 +48,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "!hyperdash login",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -68,8 +66,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "import numpy as np \nimport pandas as pd \n\ndisease = pd.read_csv('heart.csv')\ndisease.describe()\n\n#Split the data into train and test set\nfrom sklearn.model_selection import train_test_split\n\ntrain, test = train_test_split(disease, test_size = 0.2, random_state = 1)\n\nX_train = train.drop(['target','fbs'],axis=1)\ny_train = train['target']\n\nX_test = train.drop(['target','fbs'],axis=1)\ny_test = train['target']",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
@@ -78,8 +76,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "from hyperdash import Experiment\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.metrics import confusion_matrix\n\n#Declare your Experiment object each run\nexp = Experiment('SGDClassifier')\n\n#Record the value of the hyperparameter alpha\nalpha = exp.param('alpha', 0.01)\n\nsgd_model = SGDClassifier(random_state=1, alpha = alpha)\nsgd_model.fit(X_train, y_train)\n\n#Evaluate with cross validation score\nfrom sklearn.model_selection import cross_val_score\n\nscore = cross_val_score(sgd_model, X_train, y_train).mean()\nexp.metric('Accuracy', score)\n\n#Evaluate with confusion matrix\n\nsgd_predictions = cross_val_predict(sgd_model, X_train, y_train)\ntn, fp, fn, tp  = confusion_matrix(y_train, sgd_predictions).ravel()\n\nexp.metric('True Negative', tn)\nexp.metric('False Positive', fp)\nexp.metric('False Negative', fn)\nexp.metric('True Positive', tp) \n\nexp.end()\n",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -96,8 +94,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn import metrics\nfrom hyperdash import monitor\n\n@monitor('RandomForestClassifier')\ndef random_forest(exp):\n    \n    n_estimators = 100\n    forest_clf = RandomForestClassifier(random_state=1, n_estimators= n_estimators)\n    forest_predictions = cross_val_predict(forest_clf, X_train, y_train )\n    f1 = metrics.f1_score\n    exp.metric('f1', f1)",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -114,8 +112,8 @@
     "deepnote_cell_type": "code"
    },
    "source": "from hyperdash import Experiment\nexp = Experiment(\"Dogs vs. Cats\")\n\n# Parameters\nestimators = exp.param(\"Estimators\", 500)\nepochs = exp.param(\"Epochs\", 5)\nbatch = exp.param(\"Batch Size\", 64)\n\nfor epoch in xrange(1, epochs + 1):\n    accuracy = 1. - 1./epoch\n    loss = float(epochs - epoch)/epochs\n    print(\"Training model (epoch {})\".format(epoch))\n    time.sleep(1)\n\n    # Metrics\n    exp.metric(\"Accuracy\", accuracy)\n    exp.metric(\"Loss\", loss)\n\nexp.end()",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",