Merge branch 'release/v0.1.1'

.gitignore

@@ -207,7 +207,7 @@ fabric.properties
 # Android studio 3.1+ serialized cache file
 .idea/caches/build_file_checksums.ser
 
-notebooks/RetailHero-data/*
+notebooks/content/*
 notebooks/catboost_info
 notebooks/*.tmp
 

Readme.rst

@@ -11,15 +11,18 @@
 .. |Docs| image:: https://readthedocs.org/projects/scikit-uplift/badge/?version=latest
 .. _Docs: https://scikit-uplift.readthedocs.io/en/latest/
 
-.. _RU: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
-.. _EN: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
-
 .. |Open In Colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
 .. _Open In Colab1: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
 
 .. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
 .. _Open In Colab2: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
-
+
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab3: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab4: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
 .. _scikit-uplift.readthedocs.io: https://scikit-uplift.readthedocs.io/en/latest/
 .. _Part 1: https://habr.com/ru/company/ru_mts/blog/485980/
 .. _Part 2: https://habr.com/ru/company/ru_mts/blog/485976/
@@ -50,9 +53,13 @@ More about uplift modelling problem read in russian on habr.com: `Part 1`_ and `
 
 * Applying any estimator adheres to scikit-learn conventions;
 
+* All approaches can be used in sklearn.pipeline (see example (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__ |Open In Colab3|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__ |Open In Colab4|_))
+
 * Almost all implemented approaches solve both the problem of classification and regression;
 
-* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model.
+* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model;
+
+* Useful graphs for analyzing the built model.
 
 Installation
 -------------
@@ -89,7 +96,7 @@ And if you now point your browser to ``_build/html/index.html``, you should see
 Quick Start
 -----------
 
-See the **RetailHero tutorial notebook** (`EN`_ |Open In Colab1|_, `RU`_ |Open In Colab2|_) for details.
+See the **RetailHero tutorial notebook** (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__ |Open In Colab1|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__ |Open In Colab2|_) for details.
 
 **Train and predict uplift model**
 
@@ -157,7 +164,7 @@ Important links
 
 - Official source code repo: https://github.com/maks-sh/scikit-uplift/
 - Issue tracker: https://github.com/maks-sh/scikit-uplift/issues
-
+- Release History: https://scikit-uplift.readthedocs.io/en/latest/changelog.html
 
 ===============
 

docs/changelog.md

@@ -8,6 +8,24 @@
 * 🔨 something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
 * ❗️ you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
 
+## Version 0.1.1
+
+### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
+
+* 💥 Add [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).
+* 🔨 Fix bug with import [plot_treatment_balance_curve](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_treatment_balance_curve).
+
+### [sklift.metrics](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html)
+
+* 💥 Add [response_rate_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.metrics.metrics.response_rate_by_percentile) by [@ElisovaIra](https://github.com/ElisovaIra).
+* 🔨 Fix bug with import [uplift_auc_score](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.uplift_auc_score) and [qini_auc_score](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.qini_auc_score).
+* 📝 Fix typos in docstrings.
+
+### Miscellaneous
+
+* 💥 Add tutorial ["Example of usage model from sklift.models in sklearn.pipeline"](https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb).
+* 📝 Add link to Release History in main Readme.md.
+
 ## Version 0.1.0
 
 ### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
@@ -19,6 +37,7 @@
 
 * 💥 Add [treatment_balance_curve](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.treatment_balance_curve) by [@spiaz](https://github.com/spiaz).
 * ❗️ The metrics `auuc` and `auqc` are now respectively renamed to [uplift_auc_score](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html#sklift.metrics.metrics.uplift_auc_score) and [qini_auc_score](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.qini_auc_score). So, `auuc` and `auqc` will be removed in 0.2.0.
+* ❗️ Add a new parameter `startegy` in [uplift_at_k](https://scikit-uplift.readthedocs.io/en/latest/metrics.html#sklift.metrics.metrics.uplift_at_k).
 
 ### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
 

docs/index.rst

@@ -1,6 +1,12 @@
 .. _Part 1: https://habr.com/ru/company/ru_mts/blog/485980/
 .. _Part 2: https://habr.com/ru/company/ru_mts/blog/485976/
 
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab3: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+.. _Open In Colab4: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
 **************
 scikit-uplift
 **************
@@ -18,9 +24,13 @@ Features
 
 * Applying any estimator adheres to scikit-learn conventions;
 
+* All approaches can be used in sklearn.pipeline (see example (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_ |Open In Colab3|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_ |Open In Colab4|_))
+
 * Almost all implemented approaches solve both the problem of classification and regression;
 
-* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model.
+* A lot of metrics (Such as *Area Under Uplift Curve* or *Area Under Qini Curve*) are implemented to evaluate your uplift model;
+
+* Useful graphs for analyzing the built model.
 
 
 **The package currently supports the following methods:**

docs/tutorials.rst

@@ -4,6 +4,11 @@
 .. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
    :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
 
+.. _Example of usage model from sklift.models in sklearn.pipeline: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
 
 **********
 Tutorials
@@ -12,10 +17,12 @@ Tutorials
 Basic
 ########
 
-It is better to start scikit-uplift from the basic tutorial.
+It is better to start scikit-uplift from the basic tutorials.
 
 * `The overview of the basic approaches to solving the Uplift Modeling problem`_
-    * In Englsih: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_   |Open In Colab1|
-    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_   |Open In Colab2|
-
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__   |Open In Colab1|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__   |Open In Colab2|
 
+* `Example of usage model from sklift.models in sklearn.pipeline`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__   |Open In Colab3|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__   |Open In Colab4|

notebooks/Readme.rst

@@ -0,0 +1,28 @@
+.. _The overview of the basic approaches to solving the Uplift Modeling problem: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
+.. |Open In Colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb
+.. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb
+
+.. _Example of usage model from sklift.models in sklearn.pipeline: https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb
+.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb
+
+**********
+Tutorials
+**********
+
+Basic
+########
+
+It is better to start scikit-uplift from the basic tutorials.
+
+* `The overview of the basic approaches to solving the Uplift Modeling problem`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`_   |Open In Colab1|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`_   |Open In Colab2|
+
+* `Example of usage model from sklift.models in sklearn.pipeline`_
+    * In English: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`_   |Open In Colab3|
+    * In Russian: `nbviewer <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_ | `github <https://github.com/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`_   |Open In Colab4|

notebooks/RetailHero.ipynb

@@ -107,8 +107,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:07.634319Z",
-     "start_time": "2020-04-16T20:41:07.630711Z"
+     "end_time": "2020-04-28T09:24:37.492036Z",
+     "start_time": "2020-04-28T09:24:37.488584Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -126,7 +126,7 @@
     "urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
     "\n",
     "!unzip /content/retail_hero.zip\n",
-    "!pip install scikit-uplift==0.1.0 catboost=0.22"
+    "!pip install scikit-uplift==0.1.1 catboost=0.22"
    ]
   },
   {
@@ -144,8 +144,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:10.715080Z",
-     "start_time": "2020-04-16T20:41:07.644388Z"
+     "end_time": "2020-04-28T09:24:40.783897Z",
+     "start_time": "2020-04-28T09:24:37.503470Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -196,8 +196,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:11.873021Z",
-     "start_time": "2020-04-16T20:41:10.717336Z"
+     "end_time": "2020-04-28T09:24:42.100096Z",
+     "start_time": "2020-04-28T09:24:40.786498Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -251,8 +251,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:14.061983Z",
-     "start_time": "2020-04-16T20:41:11.875565Z"
+     "end_time": "2020-04-28T09:24:44.483576Z",
+     "start_time": "2020-04-28T09:24:42.102707Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -313,8 +313,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:14.084652Z",
-     "start_time": "2020-04-16T20:41:14.064998Z"
+     "end_time": "2020-04-28T09:24:44.511016Z",
+     "start_time": "2020-04-28T09:24:44.486035Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -484,8 +484,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:15.254843Z",
-     "start_time": "2020-04-16T20:41:14.087893Z"
+     "end_time": "2020-04-28T09:24:45.715602Z",
+     "start_time": "2020-04-28T09:24:44.514353Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -545,8 +545,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:17.230517Z",
-     "start_time": "2020-04-16T20:41:15.257156Z"
+     "end_time": "2020-04-28T09:24:47.941480Z",
+     "start_time": "2020-04-28T09:24:45.719641Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -632,8 +632,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:19.361999Z",
-     "start_time": "2020-04-16T20:41:17.233889Z"
+     "end_time": "2020-04-28T09:24:50.571779Z",
+     "start_time": "2020-04-28T09:24:47.944822Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -694,8 +694,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:21.519463Z",
-     "start_time": "2020-04-16T20:41:19.366080Z"
+     "end_time": "2020-04-28T09:24:52.942803Z",
+     "start_time": "2020-04-28T09:24:50.576741Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -758,8 +758,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:21.536036Z",
-     "start_time": "2020-04-16T20:41:21.522709Z"
+     "end_time": "2020-04-28T09:24:52.964396Z",
+     "start_time": "2020-04-28T09:24:52.945544Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -858,8 +858,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:24.150095Z",
-     "start_time": "2020-04-16T20:41:21.539068Z"
+     "end_time": "2020-04-28T09:24:56.505700Z",
+     "start_time": "2020-04-28T09:24:53.019392Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -906,8 +906,8 @@
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-16T20:41:24.171639Z",
-     "start_time": "2020-04-16T20:41:24.153353Z"
+     "end_time": "2020-04-28T09:24:56.560018Z",
+     "start_time": "2020-04-28T09:24:56.508541Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",