added copyright & license

src/01-Load-Data.ipynb

@@ -16,6 +16,16 @@
     }
   },
   "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Copyright (c) Microsoft Corporation.\n",
+        "# Licensed under the MIT License."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -27,8 +37,7 @@
         "2. Load the datasets\n",
         "3. Convert string to list and remove punctuations from text\n",
         "4. Store datasets\n",
-        "\n",
-        ""
+        "\n"
       ]
     },
     {
@@ -49,8 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Define variables (input folder, datasets, ....)\n",
-        ""
+        "### Define variables (input folder, datasets, ....)\n"
       ]
     },
     {
@@ -72,8 +80,7 @@
         "\n",
         "# Azure Storage path\n",
         "adls_path = \"abfss://%s@%s.dfs.core.windows.net/MicrosoftNewsDataset/\" % (container, account_name)\n",
-        "\n",
-        ""
+        "\n"
       ]
     },
     {
@@ -253,16 +260,14 @@
         "    df_preprocess = df_preprocess.dropna()\n",
         "\n",
         "    # Keep results in dictionary\n",
-        "    results[key] = df_preprocess\n",
-        ""
+        "    results[key] = df_preprocess\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Store datasets\n",
-        ""
+        "## Store datasets\n"
       ]
     },
     {
@@ -274,9 +279,8 @@
         "# Write dataset to spark table\n",
         "results['train'].write.mode('overwrite').saveAsTable('default.ActivityTrain')\n",
         "results['test'].write.mode('overwrite').saveAsTable('default.ActivityTest')\n",
-        "results['dev'].write.mode('overwrite').saveAsTable('default.ActivityDev')\n",
-        ""
+        "results['dev'].write.mode('overwrite').saveAsTable('default.ActivityDev')\n"
       ]
     }
   ]
-}
+}

src/02-Train-Model.ipynb

@@ -16,6 +16,16 @@
     }
   },
   "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Copyright (c) Microsoft Corporation.\n",
+        "# Licensed under the MIT License."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -29,8 +39,7 @@
         "4. Train model\n",
         "5. Test model\n",
         "6. Store model and transformer\n",
-        "\n",
-        ""
+        "\n"
       ]
     },
     {
@@ -58,8 +67,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define variables (input folder, datasets, ....)\n",
-        ""
+        "## Define variables (input folder, datasets, ....)\n"
       ]
     },
     {
@@ -187,8 +195,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define and Train the model\n",
-        ""
+        "## Define and Train the model\n"
       ]
     },
     {
@@ -277,16 +284,14 @@
         "f1, weightedPrecision, weightedRecall, auc = evaluate_model(model,df_dev_feature)\n",
         "print('DEV AUC:', auc)\n",
         "print('DEV F1:', f1)\n",
-        "print('DEV Precision:', weightedPrecision, 'DEV Recall:',weightedRecall)\n",
-        ""
+        "print('DEV Precision:', weightedPrecision, 'DEV Recall:',weightedRecall)\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Store fitted feature processor and model\n",
-        ""
+        "## Store fitted feature processor and model\n"
       ]
     },
     {
@@ -297,8 +302,7 @@
       "source": [
         "# store model\n",
         "fitted_processor.write().overwrite().save(feature_processor_name)\n",
-        "model.write().overwrite().save(model_name)\n",
-        ""
+        "model.write().overwrite().save(model_name)\n"
       ]
     }
   ]

src/03-Recommendations.ipynb

@@ -16,6 +16,16 @@
     }
   },
   "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Copyright (c) Microsoft Corporation.\n",
+        "# Licensed under the MIT License."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -29,8 +39,7 @@
         "3. Apply model to dataset\n",
         "4. Cleanup results and store model\n",
         "5. Sample queries\n",
-        "\n",
-        ""
+        "\n"
       ]
     },
     {
@@ -49,8 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Define variables \n",
-        ""
+        "## Define variables \n"
       ]
     },
     {
@@ -65,16 +73,14 @@
         "feature_processor_name = 'feature_proprecssor.mml'\n",
         "col_user = 'User_ID'\n",
         "col_item = 'Article_ID'\n",
-        "dataset_test = 'default.activitytest'\n",
-        ""
+        "dataset_test = 'default.activitytest'\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Load dataset, feature processor and model\n",
-        ""
+        "## Load dataset, feature processor and model\n"
       ]
     },
     {
@@ -104,8 +110,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Apply feature processor and model to dataset\n",
-        ""
+        "## Apply feature processor and model to dataset\n"
       ]
     },
     {
@@ -249,4 +254,4 @@
       ]
     }
   ]
-}
+}