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Merge branch 'release/v0.1.2'
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‎docs/changelog.md‎

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* 🔨 something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
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* ❗️ you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
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## Version 0.1.2
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### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
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* 🔨 Fix bugs in [TwoModels](https://scikit-uplift.readthedocs.io/en/latest/api/models.html#sklift.models.models.TwoModels) for regression problem.
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* 📝 Minor code refactoring.
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### [sklift.metrics](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html)
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* 📝 Minor code refactoring.
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### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
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* 💥 Add bar plot in [plot_uplift_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_uplift_by_percentile) by [@ElisovaIra](https://github.com/ElisovaIra).
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* 🔨 Fix bug in [plot_uplift_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_uplift_by_percentile).
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* 📝 Minor code refactoring.
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## Version 0.1.1
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### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)

‎docs/conf.py‎

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# -- Project information -----------------------------------------------------
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project = 'scikit-uplift'
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author = 'Maksim Shevchenko'
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author = 'Maksim Shevchenko and Contributors'
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copyright = "{}, {}".format(datetime.datetime.now().year, author)
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# The full version, including alpha/beta/rc tags
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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"sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinx.ext.napoleon",
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"recommonmark"
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"sphinx.ext.autodoc",
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"sphinx.ext.viewcode",
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"sphinx.ext.mathjax",
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"sphinx.ext.napoleon",
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"recommonmark",
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]
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master_doc = 'index'
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'Readme.rst']
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# -- Options for HTML output -------------------------------------------------

‎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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"end_time": "2020-05-02T17:07:22.372375Z",
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"start_time": "2020-05-02T17:07:22.368436Z"
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},
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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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"execution_count": 11,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-28T09:24:56.505700Z",
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"start_time": "2020-04-28T09:24:53.019392Z"
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"end_time": "2020-05-02T17:07:39.436995Z",
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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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"end_time": "2020-04-28T09:24:56.560018Z",
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"start_time": "2020-04-28T09:24:56.508541Z"
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"end_time": "2020-05-02T17:07:39.478855Z",
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"start_time": "2020-05-02T17:07:39.440546Z"
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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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"cm_full_fi"
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"ct_full_fi"
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]
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},
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{

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