From 37168c29327e794bcfcab2011fe2a80f982f4ece Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Tue, 28 Apr 2020 16:55:34 +0300
Subject: [PATCH 01/17] :hammer: Fix necessary imports

---
 sklift/viz/base.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index 2d2bb75..dbdc63f 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -1,5 +1,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
+from sklearn.utils.validation import check_consistent_length
+import warnings
 from ..metrics import uplift_curve, auuc, qini_curve, auqc, response_rate_by_percentile, treatment_balance_curve
 
 

From 61f68a1a2194fc9a6f611813c049aab8d6ebd94f Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Tue, 28 Apr 2020 22:23:43 +0300
Subject: [PATCH 02/17] :cop: Refactor models module

---
 sklift/models/models.py | 51 +++++++++++++++++++----------------------
 1 file changed, 23 insertions(+), 28 deletions(-)

diff --git a/sklift/models/models.py b/sklift/models/models.py
index 2b8b676..c49e4d3 100644
--- a/sklift/models/models.py
+++ b/sklift/models/models.py
@@ -44,7 +44,6 @@ class SoloModel(BaseEstimator):
 
     .. _SoloModel in documentation:
         https://scikit-uplift.readthedocs.io/en/latest/api/models.html#one-model-with-treatment-as-feature
-
     """
 
     def __init__(self, estimator):
@@ -54,8 +53,7 @@ def __init__(self, estimator):
         self._type_of_target = None
 
     def fit(self, X, y, treatment, estimator_fit_params=None):
-        """
-        Fit the model according to the given training data.
+        """Fit the model according to the given training data.
 
         For each test example calculate predictions on new set twice: by the first and second models.
         After that calculate uplift as a delta between these predictions.
@@ -63,8 +61,8 @@ def fit(self, X, y, treatment, estimator_fit_params=None):
         Return delta of predictions for each example.
 
         Args:
-            X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples and
-                n_features is the number of features.
+            X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of
+                samples and n_features is the number of features.
             y (array-like, shape (n_samples,)): Target vector relative to X.
             treatment (array-like, shape (n_samples,)): Binary treatment vector relative to X.
             estimator_fit_params (dict, optional): Parameters to pass to the fit method of the estimator.
@@ -80,7 +78,7 @@ def fit(self, X, y, treatment, estimator_fit_params=None):
 
         if isinstance(X, np.ndarray):
             X_mod = np.column_stack((X, treatment))
-        elif isinstance(X, pd.core.frame.DataFrame):
+        elif isinstance(X, pd.DataFrame):
             X_mod = X.assign(treatment=treatment)
         else:
             raise TypeError("Expected numpy.ndarray or pandas.DataFrame in training vector X, got %s" % type(X))
@@ -93,8 +91,7 @@ def fit(self, X, y, treatment, estimator_fit_params=None):
         return self
 
     def predict(self, X):
-        """
-        Perform uplift on samples in X.
+        """Perform uplift on samples in X.
 
         Args:
             X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples
@@ -106,7 +103,7 @@ def predict(self, X):
         if isinstance(X, np.ndarray):
             X_mod_trmnt = np.column_stack((X, np.ones(X.shape[0])))
             X_mod_ctrl = np.column_stack((X, np.zeros(X.shape[0])))
-        elif isinstance(X, pd.core.frame.DataFrame):
+        elif isinstance(X, pd.DataFrame):
             X_mod_trmnt = X.assign(treatment=np.ones(X.shape[0]))
             X_mod_ctrl = X.assign(treatment=np.zeros(X.shape[0]))
         else:
@@ -167,8 +164,7 @@ def __init__(self, estimator):
         self._type_of_target = None
 
     def fit(self, X, y, treatment, estimator_fit_params=None):
-        """
-        Fit the model according to the given training data.
+        """Fit the model according to the given training data.
 
         Args:
             X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples and
@@ -187,7 +183,6 @@ def fit(self, X, y, treatment, estimator_fit_params=None):
 
         if self._type_of_target != 'binary':
             raise ValueError("This approach is only suitable for binary classification problem")
-        # TODO: Заменить raise на Warning
         _, treatment_counts = np.unique(treatment, return_counts=True)
         if treatment_counts[0] != treatment_counts[1]:
             warnings.warn(
@@ -204,8 +199,7 @@ def fit(self, X, y, treatment, estimator_fit_params=None):
         return self
 
     def predict(self, X):
-        """
-        Perform uplift on samples in X.
+        """Perform uplift on samples in X.
 
         Args:
             X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples
@@ -220,6 +214,7 @@ def predict(self, X):
 
 class TwoModels(BaseEstimator):
     """aka naïve approach, or difference score method, or double classifier approach.
+
     Fit two separate models: on the treatment data and on the control data.
 
     See more details about `TwoModels in documentation`_.
@@ -293,8 +288,7 @@ def __init__(self, estimator_trmnt, estimator_ctrl, method='vanilla'):
             raise ValueError('Control and Treatment estimators should be different objects.')
 
     def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_fit_params=None):
-        """
-        Fit the model according to the given training data.
+        """Fit the model according to the given training data.
 
         For each test example calculate predictions on new set twice: by the first and second models.
         After that calculate uplift as a delta between these predictions.
@@ -302,12 +296,14 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
         Return delta of predictions for each example.
 
         Args:
-            X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples and
-                n_features is the number of features.
+            X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number
+                of samples and n_features is the number of features.
             y (array-like, shape (n_samples,)): Target vector relative to X.
             treatment (array-like, shape (n_samples,)): Binary treatment vector relative to X.
-            estimator_trmnt_fit_params (dict, optional): Parameters to pass to the fit method of the treatment estimator.
-            estimator_ctrl_fit_params (dict, optional): Parameters to pass to the fit method of the control estimator.
+            estimator_trmnt_fit_params (dict, optional): Parameters to pass to the fit method
+                of the treatment estimator.
+            estimator_ctrl_fit_params (dict, optional): Parameters to pass to the fit method
+                of the control estimator.
 
         Returns:
             object: self
@@ -343,7 +339,7 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
 
             if isinstance(X_trmnt, np.ndarray):
                 X_trmnt_mod = np.column_stack((X_trmnt, ddr_control))
-            elif isinstance(X_trmnt, pd.core.frame.DataFrame):
+            elif isinstance(X_trmnt, pd.DataFrame):
                 X_trmnt_mod = X_trmnt.assign(ddr_control=ddr_control)
             else:
                 raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X_trmnt))
@@ -363,7 +359,7 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
 
             if isinstance(X_ctrl, np.ndarray):
                 X_ctrl_mod = np.column_stack((X_ctrl, ddr_treatment))
-            elif isinstance(X_trmnt, pd.core.frame.DataFrame):
+            elif isinstance(X_trmnt, pd.DataFrame):
                 X_ctrl_mod = X_ctrl.assign(ddr_treatment=ddr_treatment)
             else:
                 raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X_ctrl))
@@ -375,8 +371,7 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
         return self
 
     def predict(self, X):
-        """
-        Perform uplift on samples in X.
+        """Perform uplift on samples in X.
 
         Args:
             X (array-like, shape (n_samples, n_features)): Training vector, where n_samples is the number of samples
@@ -394,10 +389,10 @@ def predict(self, X):
 
             if isinstance(X, np.ndarray):
                 X_mod = np.column_stack((X, self.ctrl_preds_))
-            elif isinstance(X, pd.core.frame.DataFrame):
+            elif isinstance(X, pd.DataFrame):
                 X_mod = X.assign(ddr_control=self.ctrl_preds_)
             else:
-                raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X_mod))
+                raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X))
             self.trmnt_preds_ = self.estimator_trmnt.predict_proba(X_mod)[:, 1]
 
         elif self.method == 'ddr_treatment':
@@ -408,10 +403,10 @@ def predict(self, X):
 
             if isinstance(X, np.ndarray):
                 X_mod = np.column_stack((X, self.trmnt_preds_))
-            elif isinstance(X, pd.core.frame.DataFrame):
+            elif isinstance(X, pd.DataFrame):
                 X_mod = X.assign(ddr_treatment=self.trmnt_preds_)
             else:
-                raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X_mod))
+                raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X))
             self.ctrl_preds_ = self.estimator_ctrl.predict_proba(X_mod)[:, 1]
 
         else:

From 794a07786c8f89093681d3ff5203d9db44ffedad Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Tue, 28 Apr 2020 22:35:31 +0300
Subject: [PATCH 03/17] :cop: Refactor metrics module

---
 sklift/metrics/metrics.py | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/sklift/metrics/metrics.py b/sklift/metrics/metrics.py
index efbf185..a1651ed 100644
--- a/sklift/metrics/metrics.py
+++ b/sklift/metrics/metrics.py
@@ -25,9 +25,7 @@ def uplift_curve(y_true, uplift, treatment):
         :func:`plot_uplift_qini_curves`: Plot Uplift and Qini curves.
     """
 
-    # ToDo: Добавить проверки на наличие обоих классов в столбце treatment
-    # ToDo: Добавить проверку на наличие обоих классов в  y_true для каждого уникального значения из столбца treatment
-
+    # TODO: check the treatment is binary
     y_true, uplift, treatment = np.array(y_true), np.array(uplift), np.array(treatment)
     desc_score_indices = np.argsort(uplift, kind="mergesort")[::-1]
     y_true, uplift, treatment = y_true[desc_score_indices], uplift[desc_score_indices], treatment[desc_score_indices]
@@ -79,9 +77,7 @@ def qini_curve(y_true, uplift, treatment):
 
         :func:`plot_uplift_qini_curves`: Plot Uplift and Qini curves.
     """
-    # ToDo: Добавить проверки на наличие обоих классов в столбце treatment
-    # ToDo: Добавить проверку на наличие обоих классов в столбце y_true для каждого уникального значения из столбца treatment
-
+    # TODO: check the treatment is binary
     y_true, uplift, treatment = np.array(y_true), np.array(uplift), np.array(treatment)
 
     desc_score_indices = np.argsort(uplift, kind="mergesort")[::-1]
@@ -127,7 +123,8 @@ def uplift_auc_score(y_true, uplift, treatment):
     Returns:
         float: Area Under the Uplift Curve.
     """
-    # ToDO: Добавить бейзлайн
+    # ToDO: Add normalization
+    # ToDO: Add baseline
     return auc(*uplift_curve(y_true, uplift, treatment))
 
 
@@ -147,7 +144,6 @@ def auuc(y_true, uplift, treatment):
         Metric `auuc` was renamed to :func:`uplift_auc_score`
         in version 0.1.0 and will be removed in 0.2.0
     """
-    # ToDO: Добавить бейзлайн
     warnings.warn(
         'Metric `auuc` was renamed to `uplift_auc_score`'
         'in version 0.1.0 and will be removed in 0.2.0',
@@ -167,7 +163,8 @@ def qini_auc_score(y_true, uplift, treatment):
     Returns:
         float: Area Under the Qini Curve.
     """
-    # ToDO: Добавить бейзлайн
+    # ToDO: Add normalization
+    # ToDO: Add baseline
     return auc(*qini_curve(y_true, uplift, treatment))
 
 
@@ -187,7 +184,6 @@ def auqc(y_true, uplift, treatment):
         Metric `auqc` was renamed to :func:`qini_auc_score`
         in version 0.1.0 and will be removed in 0.2.0
     """
-    # ToDO: Добавить бейзлайн
     warnings.warn(
         'Metric `auqc` was renamed to `qini_auc_score`'
         'in version 0.1.0 and will be removed in 0.2.0',
@@ -259,7 +255,7 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
         else:
             n_size = k
 
-        # ToDo: _checker_ there are obervations among two groups among first k
+        # ToDo: _checker_ there are observations among two groups among first k
         score_ctrl = y_true[order][:n_size][treatment[order][:n_size] == 0].mean()
         score_trmnt = y_true[order][:n_size][treatment[order][:n_size] == 1].mean()
 
@@ -331,7 +327,7 @@ def response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins
                          f' got {strategy}.')
     
     if not isinstance(bins, int) or bins <= 0:
-        raise ValueError(f'bins should be positive integer.'
+        raise ValueError(f'Bins should be positive integer.'
                          f' Invalid value bins: {bins}')
           
     if bins >= n_samples:

From 66345d0fa4a7eca7090aa4a74aaee2f1ea597683 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Tue, 28 Apr 2020 22:50:46 +0300
Subject: [PATCH 04/17] :cop: Refactor viz module

---
 sklift/viz/base.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index dbdc63f..bca69bc 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -20,7 +20,12 @@ def plot_uplift_preds(trmnt_preds, ctrl_preds, log=False, bins=100):
     Returns:
         Object that stores computed values.
     """
-    # ToDo: Добавить квантиль как параметр
+    # ToDo: Add k as parameter: vertical line on plots
+    check_consistent_length(trmnt_preds, ctrl_preds)
+
+    if not isinstance(bins, int) or bins <= 0:
+        raise ValueError(f'Bins should be positive integer. Invalid value for bins: {bins}')
+
     if log:
         trmnt_preds = np.log(trmnt_preds + 1)
         ctrl_preds = np.log(ctrl_preds + 1)
@@ -58,6 +63,9 @@ def plot_uplift_qini_curves(y_true, uplift, treatment, random=True, perfect=Fals
     Returns:
         Object that stores computed values.
     """
+    check_consistent_length(y_true, uplift, treatment)
+    y_true, uplift, treatment = np.array(y_true), np.array(uplift), np.array(treatment)
+
     x_up, y_up = uplift_curve(y_true, uplift, treatment)
     x_qi, y_qi = qini_curve(y_true, uplift, treatment)
 
@@ -67,11 +75,11 @@ def plot_uplift_qini_curves(y_true, uplift, treatment, random=True, perfect=Fals
     axes[1].plot(x_qi, y_qi, label='Model', color='b')
 
     if random:
-        up_ratio_random = y_true[treatment == 1].sum() / len(y_true[treatment == 1]) - \
-                          y_true[treatment == 0].sum() / len(y_true[treatment == 0])
+        up_ratio_random = (y_true[treatment == 1].sum() / len(y_true[treatment == 1]) -
+                           y_true[treatment == 0].sum() / len(y_true[treatment == 0]))
         y_up_random = x_up * up_ratio_random
 
-        qi_ratio_random = (y_true[treatment == 1].sum() - len(y_true[treatment == 1]) * \
+        qi_ratio_random = (y_true[treatment == 1].sum() - len(y_true[treatment == 1]) *
                            y_true[treatment == 0].sum() / len(y_true[treatment == 0])) / len(y_true)
         y_qi_random = x_qi * qi_ratio_random
 
@@ -137,9 +145,8 @@ def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, bins=10):
                          f' got {strategy}.')
     
     if not isinstance(bins, int) or bins <= 0:
-        raise ValueError(f'bins should be positive integer.'
-                         f' Invalid value bins: {bins}')
-          
+        raise ValueError(f'Bins should be positive integer. Invalid value bins: {bins}')
+
     if bins >= n_samples:
         raise ValueError(f'Number of bins = {bins} should be smaller than the length of y_true {n_samples}')
     

From 3dfe235508b45f91e28fd4ad8505a870dfc7e10e Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Thu, 30 Apr 2020 01:23:48 +0300
Subject: [PATCH 05/17] :hammer: Fix typo in predict method in ddr_control regr
 model

---
 sklift/models/models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklift/models/models.py b/sklift/models/models.py
index c49e4d3..7592715 100644
--- a/sklift/models/models.py
+++ b/sklift/models/models.py
@@ -335,7 +335,7 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
             if self._type_of_target == 'binary':
                 ddr_control = self.estimator_ctrl.predict_proba(X_trmnt)[:, 1]
             else:
-                ddr_control = self.estimator_ctrl.predict_(X_trmnt)
+                ddr_control = self.estimator_ctrl.predict(X_trmnt)
 
             if isinstance(X_trmnt, np.ndarray):
                 X_trmnt_mod = np.column_stack((X_trmnt, ddr_control))

From 1781f7a3a87dc2d12723137d9bef69ba627cffe6 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Thu, 30 Apr 2020 01:32:38 +0300
Subject: [PATCH 06/17] Add changes in changelog

---
 docs/changelog.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/docs/changelog.md b/docs/changelog.md
index 977495d..729a75a 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -8,6 +8,23 @@
 * 🔨 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 0.1.2
+_in development_
+
+### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
+
+* 🔨 Fix bug in [TwoModels](https://scikit-uplift.readthedocs.io/en/latest/api/models.html#sklift.models.models.TwoModels) (ddr_control) in regression models.
+* 📝 Minor code refactoring.
+
+### [sklift.metrics](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html)
+
+* 📝 Minor code refactoring.
+
+### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
+
+* 🔨 Fix bug in [plot_uplift_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_uplift_by_percentile).
+* 📝 Minor code refactoring.
+
 ## Version 0.1.1
 
 ### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)

From 4652f9444551c3a93df2dad7094193d274a2ed1a Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Thu, 30 Apr 2020 18:11:04 +0300
Subject: [PATCH 07/17] :hammer: Fix Two models approach for regression problem

---
 sklift/models/models.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/sklift/models/models.py b/sklift/models/models.py
index 7592715..8cdc438 100644
--- a/sklift/models/models.py
+++ b/sklift/models/models.py
@@ -355,7 +355,7 @@ def fit(self, X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_f
             if self._type_of_target == 'binary':
                 ddr_treatment = self.estimator_trmnt.predict_proba(X_ctrl)[:, 1]
             else:
-                ddr_treatment = self.estimator_trmnt.predict(X_ctrl)[:, 1]
+                ddr_treatment = self.estimator_trmnt.predict(X_ctrl)
 
             if isinstance(X_ctrl, np.ndarray):
                 X_ctrl_mod = np.column_stack((X_ctrl, ddr_treatment))
@@ -393,13 +393,17 @@ def predict(self, X):
                 X_mod = X.assign(ddr_control=self.ctrl_preds_)
             else:
                 raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X))
-            self.trmnt_preds_ = self.estimator_trmnt.predict_proba(X_mod)[:, 1]
+
+            if self._type_of_target == 'binary':
+                self.trmnt_preds_ = self.estimator_trmnt.predict_proba(X_mod)[:, 1]
+            else:
+                self.trmnt_preds_ = self.estimator_trmnt.predict(X_mod)
 
         elif self.method == 'ddr_treatment':
             if self._type_of_target == 'binary':
                 self.trmnt_preds_ = self.estimator_trmnt.predict_proba(X)[:, 1]
             else:
-                self.trmnt_preds_ = self.estimator_trmnt.predict_proba(X)[:, 1]
+                self.trmnt_preds_ = self.estimator_trmnt.predict(X)
 
             if isinstance(X, np.ndarray):
                 X_mod = np.column_stack((X, self.trmnt_preds_))
@@ -407,7 +411,11 @@ def predict(self, X):
                 X_mod = X.assign(ddr_treatment=self.trmnt_preds_)
             else:
                 raise TypeError("Expected numpy.ndarray or pandas.DataFrame, got %s" % type(X))
-            self.ctrl_preds_ = self.estimator_ctrl.predict_proba(X_mod)[:, 1]
+
+            if self._type_of_target == 'binary':
+                self.ctrl_preds_ = self.estimator_ctrl.predict_proba(X_mod)[:, 1]
+            else:
+                self.ctrl_preds_ = self.estimator_ctrl.predict(X_mod)
 
         else:
             if self._type_of_target == 'binary':

From 27173ff5a5dc72d23856e9d78c8d6b4b294f15dc Mon Sep 17 00:00:00 2001
From: Irina Elisova <iraelisova@gmail.com>
Date: Sat, 2 May 2020 18:06:45 +0300
Subject: [PATCH 08/17] Bar plot added in plot_uplift_by_percentile function
 (#11)

Add kind == 'bar' in viz.plot_uplift_by_percentile
 Fix docstring params to look nice in rtd :sparkles:
---
 sklift/metrics/metrics.py |  57 ++++++++++--------
 sklift/viz/base.py        | 122 +++++++++++++++++++++++++-------------
 2 files changed, 112 insertions(+), 67 deletions(-)

diff --git a/sklift/metrics/metrics.py b/sklift/metrics/metrics.py
index a1651ed..5fdc46d 100644
--- a/sklift/metrics/metrics.py
+++ b/sklift/metrics/metrics.py
@@ -286,83 +286,90 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
 
 
 def response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins=10):
-    """Compute response rate (target mean in the control or treatment group) at each percentile.
-    
+    """Compute response rate and its variance at each percentile.
+
+    Response rate ia a target mean in the group.
+
     Args:
         y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
         group (string, ['treatment', 'control']): Group type for computing response rate: treatment or control.
+
             * ``'treatment'``:
-                Values equal 1 in the treatment column. 
+                Values equal 1 in the treatment column.
+
             * ``'control'``:
-                Values equal 0 in the treatment column. 
-        strategy (string, ['overall', 'by_group']): Determines the calculating strategy. 
+                Values equal 0 in the treatment column.
+
+        strategy (string, ['overall', 'by_group']): Determines the calculating strategy.
+
             * ``'overall'``:
                 The first step is taking the first k observations of all test data ordered by uplift prediction
                 (overall both groups - control and treatment) and conversions in treatment and control groups
                 calculated only on them. Then the difference between these conversions is calculated.
+
             * ``'by_group'``:
                 Separately calculates conversions in top k observations in each group (control and treatment)
-                sorted by uplift predictions. Then the difference between these conversions is calculated
-        bins (int): Determines the number of bins (and relative percentile) in the test data.  
-        
+                sorted by uplift predictions. Then the difference between these conversions is calculated.
+
+        bins (int): Determines а number of bins (and а relative percentile) in the test data. Default is 10.
+
     Returns:
         array: Response rate at each percentile for control or treatment group
-        array: Variance of the response rate at each percentile 
+        array: Variance of the response rate at each percentile
     """
-    
+
     group_types = ['treatment', 'control']
     strategy_methods = ['overall', 'by_group']
-    
+
     n_samples = len(y_true)
     check_consistent_length(y_true, uplift, treatment)
-    
+
     if group not in group_types:
         raise ValueError(f'Response rate supports only group types in {group_types},'
-                         f' got {group}.') 
+                         f' got {group}.')
 
     if strategy not in strategy_methods:
         raise ValueError(f'Response rate supports only calculating methods in {strategy_methods},'
                          f' got {strategy}.')
-    
+
     if not isinstance(bins, int) or bins <= 0:
-        raise ValueError(f'Bins should be positive integer.'
-                         f' Invalid value bins: {bins}')
-          
+        raise ValueError(f'Bins should be positive integer. Invalid value bins: {bins}')
+
     if bins >= n_samples:
         raise ValueError(f'Number of bins = {bins} should be smaller than the length of y_true {n_samples}')
-    
+
     if bins == 1:
         warnings.warn(f'You will get the only one bin of {n_samples} samples'
                       f' which is the length of y_true.' 
                       f'\nPlease consider using uplift_at_k function instead',
                       UserWarning)
-    
+
     y_true, uplift, treatment = np.array(y_true), np.array(uplift), np.array(treatment)
     order = np.argsort(uplift, kind='mergesort')[::-1]
-    
+
     if group == 'treatment':
         trmnt_flag = 1
     else:  # group == 'control'
         trmnt_flag = 0
-    
+
     if strategy == 'overall':
         y_true_bin = np.array_split(y_true[order], bins)
         trmnt_bin = np.array_split(treatment[order], bins)
-        
+
         group_size = np.array([len(y[trmnt == trmnt_flag]) for y, trmnt in zip(y_true_bin, trmnt_bin)])
         response_rate = np.array([np.mean(y[trmnt == trmnt_flag]) for y, trmnt in zip(y_true_bin, trmnt_bin)])
 
     else:  # strategy == 'by_group'
         y_bin = np.array_split(y_true[order][treatment[order] == trmnt_flag], bins)
-        
+
         group_size = np.array([len(y) for y in y_bin])
         response_rate = np.array([np.mean(y) for y in y_bin])
 
     variance = np.multiply(response_rate, np.divide((1 - response_rate), group_size))
-                            
-    return response_rate, variance          
+
+    return response_rate, variance
 
 
 def treatment_balance_curve(uplift, treatment, winsize):
diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index bca69bc..c45135e 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -1,7 +1,7 @@
-import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.utils.validation import check_consistent_length
 import warnings
+import matplotlib.pyplot as plt
+from sklearn.utils.validation import check_consistent_length
 from ..metrics import uplift_curve, auuc, qini_curve, auqc, response_rate_by_percentile, treatment_balance_curve
 
 
@@ -20,7 +20,7 @@ def plot_uplift_preds(trmnt_preds, ctrl_preds, log=False, bins=100):
     Returns:
         Object that stores computed values.
     """
-    # ToDo: Add k as parameter: vertical line on plots
+    # TODO: Add k as parameter: vertical line on plots
     check_consistent_length(trmnt_preds, ctrl_preds)
 
     if not isinstance(bins, int) or bins <= 0:
@@ -112,78 +112,116 @@ def plot_uplift_qini_curves(y_true, uplift, treatment, random=True, perfect=Fals
     return axes
 
 
-def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, bins=10):
-    """Plot Uplift score at each percentile, 
-    Treatment response rate (target mean in the treatment group) 
-    and Control response rate (target mean in the control group) at each percentile.
-    
+def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, kind='line', bins=10):
+    """Plot uplift score, treatment response rate and control response rate at each percentile.
+
+    Treatment response rate ia a target mean in the treatment group.
+    Control response rate is a target mean in the control group.
+    Uplift score is a difference between treatment response rate and control response rate.
+
     Args:
         y_true (1d array-like): Correct (true) target values.
         uplift (1d array-like): Predicted uplift, as returned by a model.
         treatment (1d array-like): Treatment labels.
-        strategy (string, ['overall', 'by_group']): Determines the calculating strategy. Defaults to 'first'.
+        strategy (string, ['overall', 'by_group']): Determines the calculating strategy.
+
             * ``'overall'``:
                 The first step is taking the first k observations of all test data ordered by uplift prediction
                 (overall both groups - control and treatment) and conversions in treatment and control groups
                 calculated only on them. Then the difference between these conversions is calculated.
+
             * ``'by_group'``:
                 Separately calculates conversions in top k observations in each group (control and treatment)
-                sorted by uplift predictions. Then the difference between these conversions is calculated
-        bins (int): Determines the number of bins (and relative percentile) in the test data. 
-        
+                sorted by uplift predictions. Then the difference between these conversions is calculated.
+
+        kind (string, ['line', 'bar']): The type of plot to draw. Default is 'line'.
+
+            * ``'line'``:
+                Generates a line plot.
+
+            * ``'bar'``:
+                Generates a traditional bar-style plot.
+
+        bins (int): Determines а number of bins (and а relative percentile) in the test data. Default is 10.
+
     Returns:
         Object that stores computed values.
     """
-    
+
     strategy_methods = ['overall', 'by_group']
-    
+    kind_methods = ['line', 'bar']
+
     n_samples = len(y_true)
     check_consistent_length(y_true, uplift, treatment)
-    
+
     if strategy not in strategy_methods:
         raise ValueError(f'Response rate supports only calculating methods in {strategy_methods},'
                          f' got {strategy}.')
-    
+
+    if kind not in kind_methods:
+        raise ValueError(f'Function supports only types of plots in {kind_methods},'
+                         f' got {kind}.')
+
     if not isinstance(bins, int) or bins <= 0:
         raise ValueError(f'Bins should be positive integer. Invalid value bins: {bins}')
 
     if bins >= n_samples:
         raise ValueError(f'Number of bins = {bins} should be smaller than the length of y_true {n_samples}')
-    
-    if bins == 1:
-        warnings.warn(f'You will get the only one bin of {n_samples} samples'
-                      f' which is the length of y_true.' 
-                      f'\nPlease consider using uplift_at_k function instead',
-                      UserWarning)
-        
+
     rspns_rate_trmnt, var_trmnt = response_rate_by_percentile(y_true, uplift,
                                                               treatment, group='treatment',
                                                               strategy=strategy, bins=bins)
-    
+
     rspns_rate_ctrl, var_ctrl = response_rate_by_percentile(y_true, uplift,
                                                             treatment, group='control',
                                                             strategy=strategy, bins=bins)
 
     uplift_score, uplift_variance = np.subtract(rspns_rate_trmnt, rspns_rate_ctrl), np.add(var_trmnt, var_ctrl)
-    
+
     percentiles = [p * 100 / bins for p in range(1, bins + 1)]
-    
-    _, axes = plt.subplots(ncols=1, nrows=1, figsize=(8, 6))
-    
-    axes.errorbar(percentiles, uplift_score, yerr=np.sqrt(uplift_variance), 
-                  linewidth=2, color='red', label='uplift')
-    axes.errorbar(percentiles, rspns_rate_trmnt, yerr=np.sqrt(var_trmnt),
-                  linewidth=2, color='forestgreen', label='treatment\nresponse rate')
-    axes.errorbar(percentiles, rspns_rate_ctrl, yerr=np.sqrt(var_ctrl),
-                  linewidth=2, color='orange', label='control\nresponse rate')
-    axes.fill_between(percentiles, rspns_rate_ctrl, rspns_rate_trmnt, alpha=0.1, color='red')
-    
-    axes.set_xticks(percentiles)
-    axes.legend(loc='upper right')
-    axes.set_title('Uplift by percentile')
-    axes.set_xlabel('Percentile')
-    axes.set_ylabel('Uplift = treatment response rate - control response rate')
-    
+
+    if kind == 'line':
+        _, axes = plt.subplots(ncols=1, nrows=1, figsize=(8, 6))
+        axes.errorbar(percentiles, uplift_score, yerr=np.sqrt(uplift_variance),
+                      linewidth=2, color='red', label='uplift')
+        axes.errorbar(percentiles, rspns_rate_trmnt, yerr=np.sqrt(var_trmnt),
+                      linewidth=2, color='forestgreen', label='treatment\nresponse rate')
+        axes.errorbar(percentiles, rspns_rate_ctrl, yerr=np.sqrt(var_ctrl),
+                      linewidth=2, color='orange', label='control\nresponse rate')
+        axes.fill_between(percentiles, rspns_rate_ctrl, rspns_rate_trmnt, alpha=0.1, color='red')
+
+        if np.amin(uplift_score) < 0:
+            axes.axhline(y=0, color='black', linewidth=1)
+        axes.set_xticks(percentiles)
+        axes.legend(loc='upper right')
+        axes.set_title('Uplift by percentile')
+        axes.set_xlabel('Percentile')
+        axes.set_ylabel('Uplift = treatment response rate - control response rate')
+
+    else:  # kind == 'bar'
+        delta = percentiles[0]
+        fig, axes = plt.subplots(ncols=1, nrows=2, figsize=(8, 6), sharex=True, sharey=True)
+        fig.text(0.04, 0.5, 'Uplift = treatment response rate - control response rate',
+                 va='center', ha='center', rotation='vertical')
+
+        axes[0].bar(np.array(percentiles), uplift_score, delta / 1.5,
+                    yerr=np.sqrt(uplift_variance), color='red', label='uplift')
+        axes[1].bar(np.array(percentiles) - delta / 6, rspns_rate_trmnt, delta / 3,
+                    yerr=np.sqrt(var_trmnt), color='forestgreen', label='treatment\nresponse rate')
+        axes[1].bar(np.array(percentiles) + delta / 6, rspns_rate_ctrl, delta / 3,
+                    yerr=np.sqrt(var_ctrl), color='orange', label='control\nresponse rate')
+
+        axes[0].legend(loc='upper right')
+        axes[0].tick_params(axis='x', bottom=False)
+        axes[0].axhline(y=0, color='black', linewidth=1)
+        axes[0].set_title('Uplift by percentile')
+
+        axes[1].set_xticks(percentiles)
+        axes[1].legend(loc='upper right')
+        axes[1].axhline(y=0, color='black', linewidth=1)
+        axes[1].set_xlabel('Percentile')
+        axes[1].set_title('Response rate by percentile')
+
     return axes
 
 

From 253112384c129200681aa940332851efbf954071 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 18:19:21 +0300
Subject: [PATCH 09/17] :memo: Add changes to changelog

---
 docs/changelog.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index 729a75a..2bd61d7 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -13,7 +13,7 @@ _in development_
 
 ### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
 
-* 🔨 Fix bug in [TwoModels](https://scikit-uplift.readthedocs.io/en/latest/api/models.html#sklift.models.models.TwoModels) (ddr_control) in regression models.
+* 🔨 Fix bugs in [TwoModels](https://scikit-uplift.readthedocs.io/en/latest/api/models.html#sklift.models.models.TwoModels) for regression problem.
 * 📝 Minor code refactoring.
 
 ### [sklift.metrics](https://scikit-uplift.readthedocs.io/en/latest/api/metrics.html)
@@ -22,6 +22,7 @@ _in development_
 
 ### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)
 
+* 💥 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).
 * 🔨 Fix bug in [plot_uplift_by_percentile](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html#sklift.viz.base.plot_uplift_by_percentile).
 * 📝 Minor code refactoring.
 

From a7d28b3cabb1b983b5ceee12bb8b0da289ca15cc Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 18:22:08 +0300
Subject: [PATCH 10/17] :memo: Prettify docs/conf.py

---
 docs/conf.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index b2406a2..44dcb23 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -33,7 +33,7 @@ def get_version():
 # -- Project information -----------------------------------------------------
 
 project = 'scikit-uplift'
-author = 'Maksim Shevchenko'
+author = 'Maksim Shevchenko and Contributors'
 copyright = "{}, {}".format(datetime.datetime.now().year, author)
 
 # The full version, including alpha/beta/rc tags
@@ -46,8 +46,11 @@ def get_version():
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.mathjax", "sphinx.ext.napoleon",
-    "recommonmark"
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.mathjax",
+    "sphinx.ext.napoleon",
+    "recommonmark",
 ]
 
 master_doc = 'index'
@@ -58,7 +61,7 @@ def get_version():
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'Readme.rst']
 
 
 # -- Options for HTML output -------------------------------------------------

From b10ff872c8a933bc15bf43ed70e2034956dcd552 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 19:33:44 +0300
Subject: [PATCH 11/17] :sparkles: Fix docsting in TwoModels

---
 sklift/models/models.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/sklift/models/models.py b/sklift/models/models.py
index 8cdc438..e34fed2 100644
--- a/sklift/models/models.py
+++ b/sklift/models/models.py
@@ -222,10 +222,11 @@ class TwoModels(BaseEstimator):
     Args:
         estimator_trmnt (estimator object implementing 'fit'): The object to use to fit the treatment data.
         estimator_ctrl (estimator object implementing 'fit'): The object to use to fit the control data.
-        method (string, ‘vanilla’, ’ddr_control’ or ‘ddr_treatment’, default='vanilla'): Specifies the approach:
-            * ‘vanilla’ - two independent models
-            * ’ddr_control’ -  dependent data representation (First train control estimator)
-            * ’ddr_treatment’ -  dependent data representation (First train treatment estimator)
+        method (string, 'vanilla', 'ddr_control' or 'ddr_treatment', default='vanilla'): Specifies the approach:
+
+            * ``'vanilla'`` - two independent models;
+            * ``'ddr_control'`` -  dependent data representation (First train control estimator);
+            * ``'ddr_treatment'`` -  dependent data representation (First train treatment estimator).
 
     Attributes:
         trmnt_preds_ (array-like, shape (n_samples, )): Estimator predictions on samples when treatment.

From 4fc5ee858ca41a712a9ed7c6632f194fdc0e9353 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 19:36:25 +0300
Subject: [PATCH 12/17] :sparkles: Fix docsting in uplift_at_k,
 response_rate_by_percentile

---
 sklift/metrics/metrics.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/sklift/metrics/metrics.py b/sklift/metrics/metrics.py
index 5fdc46d..2f2ee9e 100644
--- a/sklift/metrics/metrics.py
+++ b/sklift/metrics/metrics.py
@@ -207,10 +207,9 @@ def uplift_at_k(y_true, uplift, treatment, strategy, k=0.3):
                 The first step is taking the first k observations of all test data ordered by uplift prediction
                 (overall both groups - control and treatment) and conversions in treatment and control groups
                 calculated only on them. Then the difference between these conversions is calculated.
-
             * ``'by_group'``:
                 Separately calculates conversions in top k observations in each group (control and treatment)
-                sorted by uplift predictions. Then the difference between these conversions is calculated
+                sorted by uplift predictions. Then the difference between these conversions is calculated.
 
     .. versionchanged:: 0.1.0
 
@@ -298,7 +297,6 @@ def response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins
 
             * ``'treatment'``:
                 Values equal 1 in the treatment column.
-
             * ``'control'``:
                 Values equal 0 in the treatment column.
 
@@ -308,7 +306,6 @@ def response_rate_by_percentile(y_true, uplift, treatment, group, strategy, bins
                 The first step is taking the first k observations of all test data ordered by uplift prediction
                 (overall both groups - control and treatment) and conversions in treatment and control groups
                 calculated only on them. Then the difference between these conversions is calculated.
-
             * ``'by_group'``:
                 Separately calculates conversions in top k observations in each group (control and treatment)
                 sorted by uplift predictions. Then the difference between these conversions is calculated.

From 5097b81cb8763e279c0e92ef763fb24c34720598 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 19:38:58 +0300
Subject: [PATCH 13/17] :sparkles: Fix docsting in TwoModels

---
 sklift/models/models.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sklift/models/models.py b/sklift/models/models.py
index e34fed2..ea43c66 100644
--- a/sklift/models/models.py
+++ b/sklift/models/models.py
@@ -224,9 +224,12 @@ class TwoModels(BaseEstimator):
         estimator_ctrl (estimator object implementing 'fit'): The object to use to fit the control data.
         method (string, 'vanilla', 'ddr_control' or 'ddr_treatment', default='vanilla'): Specifies the approach:
 
-            * ``'vanilla'`` - two independent models;
-            * ``'ddr_control'`` -  dependent data representation (First train control estimator);
-            * ``'ddr_treatment'`` -  dependent data representation (First train treatment estimator).
+            * ``'vanilla'``:
+                Two independent models;
+            * ``'ddr_control'``:
+                Dependent data representation (First train control estimator).
+            * ``'ddr_treatment'``:
+                Dependent data representation (First train treatment estimator).
 
     Attributes:
         trmnt_preds_ (array-like, shape (n_samples, )): Estimator predictions on samples when treatment.

From 40cc7967347052e9b73fbfa9115de84b154e0a47 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 19:40:54 +0300
Subject: [PATCH 14/17] :sparkles: Fix docsting plot_uplift_by_percentile

---
 sklift/viz/base.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index c45135e..41753a9 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -129,7 +129,6 @@ def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, kind='line',
                 The first step is taking the first k observations of all test data ordered by uplift prediction
                 (overall both groups - control and treatment) and conversions in treatment and control groups
                 calculated only on them. Then the difference between these conversions is calculated.
-
             * ``'by_group'``:
                 Separately calculates conversions in top k observations in each group (control and treatment)
                 sorted by uplift predictions. Then the difference between these conversions is calculated.
@@ -138,7 +137,6 @@ def plot_uplift_by_percentile(y_true, uplift, treatment, strategy, kind='line',
 
             * ``'line'``:
                 Generates a line plot.
-
             * ``'bar'``:
                 Generates a traditional bar-style plot.
 

From ab70f0e11c92a854c01e10df8204defbc6fce9bb Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 19:55:48 +0300
Subject: [PATCH 15/17] :rocket: Bump version to 0.1.2

---
 docs/changelog.md  | 1 -
 sklift/__init__.py | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index 2bd61d7..ab46e1a 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -9,7 +9,6 @@
 * ❗️ 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 0.1.2
-_in development_
 
 ### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
 
diff --git a/sklift/__init__.py b/sklift/__init__.py
index df9144c..10939f0 100644
--- a/sklift/__init__.py
+++ b/sklift/__init__.py
@@ -1 +1 @@
-__version__ = '0.1.1'
+__version__ = '0.1.2'

From a44ff91c56d1bd25d0d119e64d5d6aa3b2835a77 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 20:10:56 +0300
Subject: [PATCH 16/17] :rocket: Update notebooks to 0.1.2

---
 notebooks/RetailHero.ipynb        | 71 +++++++++++++++++--------------
 notebooks/RetailHero_EN.ipynb     | 69 ++++++++++++++++--------------
 notebooks/pipeline_usage_EN.ipynb | 36 ++++++++--------
 notebooks/pipeline_usage_RU.ipynb | 36 ++++++++--------
 4 files changed, 111 insertions(+), 101 deletions(-)

diff --git a/notebooks/RetailHero.ipynb b/notebooks/RetailHero.ipynb
index e2c424c..40e7eb6 100644
--- a/notebooks/RetailHero.ipynb
+++ b/notebooks/RetailHero.ipynb
@@ -107,8 +107,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:37.492036Z",
-     "start_time": "2020-04-28T09:24:37.488584Z"
+     "end_time": "2020-05-02T17:07:22.372375Z",
+     "start_time": "2020-05-02T17:07:22.368436Z"
     },
     "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==0.1.1 catboost=0.22"
+    "!pip install scikit-uplift==0.1.2 catboost=0.22"
    ]
   },
   {
@@ -144,8 +144,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:40.783897Z",
-     "start_time": "2020-04-28T09:24:37.503470Z"
+     "end_time": "2020-05-02T17:07:25.384054Z",
+     "start_time": "2020-05-02T17:07:22.383222Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -196,8 +196,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:42.100096Z",
-     "start_time": "2020-04-28T09:24:40.786498Z"
+     "end_time": "2020-05-02T17:07:26.483716Z",
+     "start_time": "2020-05-02T17:07:25.386480Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -251,8 +251,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:44.483576Z",
-     "start_time": "2020-04-28T09:24:42.102707Z"
+     "end_time": "2020-05-02T17:07:28.491581Z",
+     "start_time": "2020-05-02T17:07:26.486312Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -313,8 +313,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:44.511016Z",
-     "start_time": "2020-04-28T09:24:44.486035Z"
+     "end_time": "2020-05-02T17:07:28.514717Z",
+     "start_time": "2020-05-02T17:07:28.494500Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -484,8 +484,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:45.715602Z",
-     "start_time": "2020-04-28T09:24:44.514353Z"
+     "end_time": "2020-05-02T17:07:29.570605Z",
+     "start_time": "2020-05-02T17:07:28.518362Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -545,8 +545,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:47.941480Z",
-     "start_time": "2020-04-28T09:24:45.719641Z"
+     "end_time": "2020-05-02T17:07:31.489869Z",
+     "start_time": "2020-05-02T17:07:29.572733Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -632,8 +632,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:50.571779Z",
-     "start_time": "2020-04-28T09:24:47.944822Z"
+     "end_time": "2020-05-02T17:07:33.865281Z",
+     "start_time": "2020-05-02T17:07:31.494251Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -694,8 +694,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:52.942803Z",
-     "start_time": "2020-04-28T09:24:50.576741Z"
+     "end_time": "2020-05-02T17:07:36.709646Z",
+     "start_time": "2020-05-02T17:07:33.871512Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -758,8 +758,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:52.964396Z",
-     "start_time": "2020-04-28T09:24:52.945544Z"
+     "end_time": "2020-05-02T17:07:36.726223Z",
+     "start_time": "2020-05-02T17:07:36.712564Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -858,8 +858,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:56.505700Z",
-     "start_time": "2020-04-28T09:24:53.019392Z"
+     "end_time": "2020-05-02T17:07:39.436995Z",
+     "start_time": "2020-05-02T17:07:36.729508Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -874,7 +874,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/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",
+      "/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",
       "  \n"
      ]
     },
@@ -891,10 +891,15 @@
     }
    ],
    "source": [
-    "cm_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
-    "cm_full = cm_full.fit(X_train_full, y_train_full, treat_train_full, estimator_fit_params={'cat_features': [1]})\n",
+    "ct_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
+    "ct_full = ct_full.fit(\n",
+    "    X_train_full, \n",
+    "    y_train_full, \n",
+    "    treat_train_full, \n",
+    "    estimator_fit_params={'cat_features': cat_features}\n",
+    ")\n",
     "\n",
-    "X_test.loc[:, 'uplift'] = cm_full.predict(X_test.values)\n",
+    "X_test.loc[:, 'uplift'] = ct_full.predict(X_test.values)\n",
     "\n",
     "sub = X_test[['uplift']].to_csv('sub1.csv')\n",
     "\n",
@@ -906,8 +911,8 @@
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:24:56.560018Z",
-     "start_time": "2020-04-28T09:24:56.508541Z"
+     "end_time": "2020-05-02T17:07:39.478855Z",
+     "start_time": "2020-05-02T17:07:39.440546Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -988,12 +993,12 @@
     }
    ],
    "source": [
-    "cm_full_fi = pd.DataFrame({\n",
-    "    'feature_name': cm_full.estimator.feature_names_,\n",
-    "    'feature_score': cm_full.estimator.feature_importances_\n",
+    "ct_full_fi = pd.DataFrame({\n",
+    "    'feature_name': ct_full.estimator.feature_names_,\n",
+    "    'feature_score': ct_full.estimator.feature_importances_\n",
     "}).sort_values('feature_score', ascending=False).reset_index(drop=True)\n",
     "\n",
-    "cm_full_fi"
+    "ct_full_fi"
    ]
   },
   {
diff --git a/notebooks/RetailHero_EN.ipynb b/notebooks/RetailHero_EN.ipynb
index 45eacf9..465abb5 100644
--- a/notebooks/RetailHero_EN.ipynb
+++ b/notebooks/RetailHero_EN.ipynb
@@ -98,8 +98,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:26:59.973637Z",
-     "start_time": "2020-04-28T09:26:59.969856Z"
+     "end_time": "2020-05-02T17:03:42.686542Z",
+     "start_time": "2020-05-02T17:03:42.682766Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -117,7 +117,7 @@
     "urllib.request.urlretrieve(url, '/content/retail_hero.zip')\n",
     "\n",
     "!unzip /content/retail_hero.zip\n",
-    "!pip install scikit-uplift==0.1.1 catboost=0.22"
+    "!pip install scikit-uplift==0.1.2 catboost=0.22"
    ]
   },
   {
@@ -135,8 +135,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:03.353098Z",
-     "start_time": "2020-04-28T09:26:59.984369Z"
+     "end_time": "2020-05-02T17:03:45.696618Z",
+     "start_time": "2020-05-02T17:03:42.697098Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -187,8 +187,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:04.563554Z",
-     "start_time": "2020-04-28T09:27:03.355432Z"
+     "end_time": "2020-05-02T17:03:46.792933Z",
+     "start_time": "2020-05-02T17:03:45.698939Z"
     },
     "colab": {},
     "colab_type": "code",
@@ -241,8 +241,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:06.789462Z",
-     "start_time": "2020-04-28T09:27:04.570306Z"
+     "end_time": "2020-05-02T17:03:48.848922Z",
+     "start_time": "2020-05-02T17:03:46.795574Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -305,8 +305,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:06.813310Z",
-     "start_time": "2020-04-28T09:27:06.792837Z"
+     "end_time": "2020-05-02T17:03:48.871869Z",
+     "start_time": "2020-05-02T17:03:48.851856Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -476,8 +476,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:07.960855Z",
-     "start_time": "2020-04-28T09:27:06.816440Z"
+     "end_time": "2020-05-02T17:03:49.905064Z",
+     "start_time": "2020-05-02T17:03:48.875206Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -538,8 +538,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:10.062915Z",
-     "start_time": "2020-04-28T09:27:07.972200Z"
+     "end_time": "2020-05-02T17:03:51.782971Z",
+     "start_time": "2020-05-02T17:03:49.907481Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -627,8 +627,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:12.177386Z",
-     "start_time": "2020-04-28T09:27:10.065653Z"
+     "end_time": "2020-05-02T17:03:53.820694Z",
+     "start_time": "2020-05-02T17:03:51.785856Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -689,8 +689,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:14.459477Z",
-     "start_time": "2020-04-28T09:27:12.181749Z"
+     "end_time": "2020-05-02T17:03:56.085589Z",
+     "start_time": "2020-05-02T17:03:53.824592Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -753,8 +753,8 @@
    "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:14.480794Z",
-     "start_time": "2020-04-28T09:27:14.463302Z"
+     "end_time": "2020-05-02T17:03:56.140144Z",
+     "start_time": "2020-05-02T17:03:56.091196Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -853,8 +853,8 @@
    "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:17.097949Z",
-     "start_time": "2020-04-28T09:27:14.483691Z"
+     "end_time": "2020-05-02T17:03:59.864286Z",
+     "start_time": "2020-05-02T17:03:56.151623Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -886,10 +886,15 @@
     }
    ],
    "source": [
-    "cm_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
-    "cm_full = cm_full.fit(X_train_full, y_train_full, treat_train_full, estimator_fit_params={'cat_features': [1]})\n",
+    "ct_full = ClassTransformation(CatBoostClassifier(iterations=20, thread_count=2, random_state=42, silent=True))\n",
+    "ct_full = ct_full.fit(\n",
+    "    X_train_full, \n",
+    "    y_train_full, \n",
+    "    treat_train_full, \n",
+    "    estimator_fit_params={'cat_features': cat_features}\n",
+    ")\n",
     "\n",
-    "X_test.loc[:, 'uplift'] = cm_full.predict(X_test.values)\n",
+    "X_test.loc[:, 'uplift'] = ct_full.predict(X_test.values)\n",
     "\n",
     "sub = X_test[['uplift']].to_csv('sub1.csv')\n",
     "\n",
@@ -901,8 +906,8 @@
    "execution_count": 12,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:27:17.138755Z",
-     "start_time": "2020-04-28T09:27:17.101433Z"
+     "end_time": "2020-05-02T17:03:59.898275Z",
+     "start_time": "2020-05-02T17:03:59.868331Z"
     },
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -983,12 +988,12 @@
     }
    ],
    "source": [
-    "cm_full_fi = pd.DataFrame({\n",
-    "    'feature_name': cm_full.estimator.feature_names_,\n",
-    "    'feature_score': cm_full.estimator.feature_importances_\n",
+    "ct_full = pd.DataFrame({\n",
+    "    'feature_name': ct_full.estimator.feature_names_,\n",
+    "    'feature_score': ct_full.estimator.feature_importances_\n",
     "}).sort_values('feature_score', ascending=False).reset_index(drop=True)\n",
     "\n",
-    "cm_full_fi"
+    "ct_full"
    ]
   },
   {
diff --git a/notebooks/pipeline_usage_EN.ipynb b/notebooks/pipeline_usage_EN.ipynb
index 5627162..c295bae 100644
--- a/notebooks/pipeline_usage_EN.ipynb
+++ b/notebooks/pipeline_usage_EN.ipynb
@@ -50,13 +50,13 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:10.044500Z",
-     "start_time": "2020-04-28T09:14:10.037045Z"
+     "end_time": "2020-05-02T16:59:19.201984Z",
+     "start_time": "2020-05-02T16:59:19.196511Z"
     }
    },
    "outputs": [],
    "source": [
-    "!pip install scikit-uplift==0.1.1 xgboost==1.0.2 category_encoders==2.1.0"
+    "!pip install scikit-uplift==0.1.2 xgboost==1.0.2 category_encoders==2.1.0"
    ]
   },
   {
@@ -71,15 +71,15 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:14.101729Z",
-     "start_time": "2020-04-28T09:14:10.048276Z"
+     "end_time": "2020-05-02T16:59:34.848414Z",
+     "start_time": "2020-05-02T16:59:19.206449Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117d3b0f0>)"
+       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117de0438>)"
       ]
      },
      "execution_count": 2,
@@ -113,8 +113,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:14.638574Z",
-     "start_time": "2020-04-28T09:14:14.106920Z"
+     "end_time": "2020-05-02T16:59:35.421058Z",
+     "start_time": "2020-05-02T16:59:34.851896Z"
     }
    },
    "outputs": [
@@ -281,8 +281,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:15.419422Z",
-     "start_time": "2020-04-28T09:14:14.642252Z"
+     "end_time": "2020-05-02T16:59:36.153705Z",
+     "start_time": "2020-05-02T16:59:35.425904Z"
     }
    },
    "outputs": [],
@@ -313,8 +313,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:15.432871Z",
-     "start_time": "2020-04-28T09:14:15.421629Z"
+     "end_time": "2020-05-02T16:59:36.173267Z",
+     "start_time": "2020-05-02T16:59:36.155955Z"
     }
    },
    "outputs": [
@@ -343,8 +343,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:15.562848Z",
-     "start_time": "2020-04-28T09:14:15.437086Z"
+     "end_time": "2020-05-02T16:59:36.269784Z",
+     "start_time": "2020-05-02T16:59:36.179904Z"
     }
    },
    "outputs": [],
@@ -377,8 +377,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:16.816735Z",
-     "start_time": "2020-04-28T09:14:15.568337Z"
+     "end_time": "2020-05-02T16:59:37.436032Z",
+     "start_time": "2020-05-02T16:59:36.275110Z"
     }
    },
    "outputs": [
@@ -416,8 +416,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:14:16.960683Z",
-     "start_time": "2020-04-28T09:14:16.819117Z"
+     "end_time": "2020-05-02T16:59:37.583995Z",
+     "start_time": "2020-05-02T16:59:37.438748Z"
     }
    },
    "outputs": [
diff --git a/notebooks/pipeline_usage_RU.ipynb b/notebooks/pipeline_usage_RU.ipynb
index 5e306b5..5277541 100644
--- a/notebooks/pipeline_usage_RU.ipynb
+++ b/notebooks/pipeline_usage_RU.ipynb
@@ -44,13 +44,13 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:06.767010Z",
-     "start_time": "2020-04-28T09:23:06.762871Z"
+     "end_time": "2020-05-02T17:00:49.841653Z",
+     "start_time": "2020-05-02T17:00:49.835556Z"
     }
    },
    "outputs": [],
    "source": [
-    "!pip install scikit-uplift==0.1.1 xgboost==1.0.2 category_encoders==2.1.0"
+    "!pip install scikit-uplift==0.1.2 xgboost==1.0.2 category_encoders==2.1.0"
    ]
   },
   {
@@ -70,15 +70,15 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:10.820274Z",
-     "start_time": "2020-04-28T09:23:06.776057Z"
+     "end_time": "2020-05-02T17:01:04.035180Z",
+     "start_time": "2020-05-02T17:00:49.846452Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117e7b0b8>)"
+       "('./content/Hilstorm.csv', <http.client.HTTPMessage at 0x117ee04a8>)"
       ]
      },
      "execution_count": 2,
@@ -112,8 +112,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:11.319111Z",
-     "start_time": "2020-04-28T09:23:10.823302Z"
+     "end_time": "2020-05-02T17:01:04.476684Z",
+     "start_time": "2020-05-02T17:01:04.038420Z"
     }
    },
    "outputs": [
@@ -280,8 +280,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:11.919143Z",
-     "start_time": "2020-04-28T09:23:11.324058Z"
+     "end_time": "2020-05-02T17:01:05.037943Z",
+     "start_time": "2020-05-02T17:01:04.480545Z"
     }
    },
    "outputs": [],
@@ -312,8 +312,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:11.936024Z",
-     "start_time": "2020-04-28T09:23:11.921716Z"
+     "end_time": "2020-05-02T17:01:05.052632Z",
+     "start_time": "2020-05-02T17:01:05.040798Z"
     }
    },
    "outputs": [
@@ -342,8 +342,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:12.019176Z",
-     "start_time": "2020-04-28T09:23:11.939728Z"
+     "end_time": "2020-05-02T17:01:05.144900Z",
+     "start_time": "2020-05-02T17:01:05.059079Z"
     }
    },
    "outputs": [],
@@ -381,8 +381,8 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:13.244343Z",
-     "start_time": "2020-04-28T09:23:12.021559Z"
+     "end_time": "2020-05-02T17:01:06.298101Z",
+     "start_time": "2020-05-02T17:01:05.149361Z"
     }
    },
    "outputs": [
@@ -415,8 +415,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-28T09:23:13.383577Z",
-     "start_time": "2020-04-28T09:23:13.246513Z"
+     "end_time": "2020-05-02T17:01:06.418688Z",
+     "start_time": "2020-05-02T17:01:06.300295Z"
     }
    },
    "outputs": [

From fd17ee2ed0898dc84b68d61b7363a9e96ee552c8 Mon Sep 17 00:00:00 2001
From: Maksim Shevchenko <maxik08@list.ru>
Date: Sat, 2 May 2020 20:20:47 +0300
Subject: [PATCH 17/17] :memo: Remove unused import

---
 sklift/viz/base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sklift/viz/base.py b/sklift/viz/base.py
index 41753a9..91397b6 100644
--- a/sklift/viz/base.py
+++ b/sklift/viz/base.py
@@ -1,5 +1,4 @@
 import numpy as np
-import warnings
 import matplotlib.pyplot as plt
 from sklearn.utils.validation import check_consistent_length
 from ..metrics import uplift_curve, auuc, qini_curve, auqc, response_rate_by_percentile, treatment_balance_curve