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🚀 Update notebooks to 0.1.2
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-101
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‎notebooks/RetailHero.ipynb‎

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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-28T09:24:37.492036Z",
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"start_time": "2020-04-28T09:24:37.488584Z"
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"start_time": "2020-05-02T17:07:22.368436Z"
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"colab": {
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"base_uri": "https://localhost:8080/",
@@ -126,7 +126,7 @@
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"urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
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"\n",
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"!unzip /content/retail_hero.zip\n",
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"!pip install scikit-uplift==0.1.1 catboost=0.22"
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"!pip install scikit-uplift==0.1.2 catboost=0.22"
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]
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},
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{
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"execution_count": 2,
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"start_time": "2020-05-02T17:07:22.383222Z"
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"colab": {},
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"colab_type": "code",
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"start_time": "2020-05-02T17:07:25.386480Z"
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"start_time": "2020-05-02T17:07:26.486312Z"
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"colab": {
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"start_time": "2020-05-02T17:07:31.494251Z"
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"execution_count": 9,
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"execution_count": 11,
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"metadata": {
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"start_time": "2020-05-02T17:07:36.729508Z"
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"colab": {
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/Maksim/Library/Python/3.6/lib/python/site-packages/ipykernel_launcher.py:2: UserWarning: It is recommended to use this approach on treatment balanced data. Current sample size is unbalanced.\n",
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"/Users/Maksim/Library/Python/3.6/lib/python/site-packages/ipykernel_launcher.py:6: UserWarning: It is recommended to use this approach on treatment balanced data. Current sample size is unbalanced.\n",
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" \n"
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},
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}
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],
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"source": [
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"cm_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
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"cm_full = cm_full.fit(X_train_full, y_train_full, treat_train_full, estimator_fit_params={'cat_features': [1]})\n",
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"ct_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
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"ct_full = ct_full.fit(\n",
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" X_train_full, \n",
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" y_train_full, \n",
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" treat_train_full, \n",
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" estimator_fit_params={'cat_features': cat_features}\n",
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")\n",
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"\n",
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"X_test.loc[:, 'uplift'] = cm_full.predict(X_test.values)\n",
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"X_test.loc[:, 'uplift'] = ct_full.predict(X_test.values)\n",
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"\n",
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"sub = X_test[['uplift']].to_csv('sub1.csv')\n",
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"\n",
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"execution_count": 12,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2020-05-02T17:07:39.440546Z"
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}
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],
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"source": [
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"cm_full_fi = pd.DataFrame({\n",
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" 'feature_name': cm_full.estimator.feature_names_,\n",
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" 'feature_score': cm_full.estimator.feature_importances_\n",
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"ct_full_fi = pd.DataFrame({\n",
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" 'feature_name': ct_full.estimator.feature_names_,\n",
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" 'feature_score': ct_full.estimator.feature_importances_\n",
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"}).sort_values('feature_score', ascending=False).reset_index(drop=True)\n",
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"\n",
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"cm_full_fi"
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"ct_full_fi"
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]
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{

‎notebooks/RetailHero_EN.ipynb‎

Lines changed: 37 additions & 32 deletions
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"execution_count": 1,
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"metadata": {
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"start_time": "2020-04-28T09:26:59.969856Z"
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"start_time": "2020-05-02T17:03:42.682766Z"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
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"\n",
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"!unzip /content/retail_hero.zip\n",
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"!pip install scikit-uplift==0.1.1 catboost=0.22"
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"!pip install scikit-uplift==0.1.2 catboost=0.22"
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]
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},
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"execution_count": 2,
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"source": [
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"cm_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
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"cm_full = cm_full.fit(X_train_full, y_train_full, treat_train_full, estimator_fit_params={'cat_features': [1]})\n",
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"ct_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
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"ct_full = ct_full.fit(\n",
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" X_train_full, \n",
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" y_train_full, \n",
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" treat_train_full, \n",
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" estimator_fit_params={'cat_features': cat_features}\n",
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")\n",
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"\n",
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"X_test.loc[:, 'uplift'] = cm_full.predict(X_test.values)\n",
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"X_test.loc[:, 'uplift'] = ct_full.predict(X_test.values)\n",
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"\n",
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"sub = X_test[['uplift']].to_csv('sub1.csv')\n",
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"execution_count": 12,
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"metadata": {
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"source": [
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"cm_full_fi = pd.DataFrame({\n",
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" 'feature_name': cm_full.estimator.feature_names_,\n",
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" 'feature_score': cm_full.estimator.feature_importances_\n",
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"ct_full = pd.DataFrame({\n",
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" 'feature_name': ct_full.estimator.feature_names_,\n",
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" 'feature_score': ct_full.estimator.feature_importances_\n",
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"}).sort_values('feature_score', ascending=False).reset_index(drop=True)\n",
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"\n",
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"ct_full"
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]
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},
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{

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