Merge branch 'dev' into feature/fix_docs

Author: maks-sh

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 0.1.2
+_in development_
+
+### [sklift.models](https://scikit-uplift.readthedocs.io/en/latest/api/models.html)
+
+* 🔨 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)
+
+* 📝 Minor code refactoring.
+
+### [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.
+
 ## Version 0.1.1
 
 ### [sklift.viz](https://scikit-uplift.readthedocs.io/en/latest/api/viz.html)

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()
 
@@ -290,83 +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):