Merge branch 'feature/fix_docs' into dev

Author: maks-sh

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 -------------------------------------------------

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.

sklift/models/models.py

@@ -222,10 +222,14 @@ 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.

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.