fix #169 with suggestion from @davidefiocco

docs/index.html

@@ -275,7 +275,7 @@ <h2 id="support-for-different-tasks">Support for different tasks</h2>
 </tr>
 <tr>
 <td style="text-align: left;">Skope rule set</td>
-<td style="text-align: center;"><a href="https://csinva.io/imodels/rule_set/slipper.html#imodels.rule_set.slipper.SlipperClassifier">SkopeRulesClassifier</a></td>
+<td style="text-align: center;"><a href="https://csinva.io/imodels/rule_set/skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier">SkopeRulesClassifier</a></td>
 <td style="text-align: center;"></td>
 <td></td>
 </tr>

docs/rule_list/greedy_rule_list.html

@@ -34,10 +34,8 @@
 import math
 from copy import deepcopy
 
-import pandas as pd
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.utils import check_X_y
 from sklearn.utils.multiclass import unique_labels
 from sklearn.utils.validation import check_array, check_is_fitted
 from sklearn.tree import DecisionTreeClassifier
@@ -87,9 +85,9 @@
         elif np.all(y == y[0]):
             return [{'val': y[0], 'num_pts': y.size}]
 
-        # base case 3: max depth reached
-        elif depth >= self.max_depth:
-            return []
+         # base case 3: max depth reached
+        elif depth == self.max_depth:
+            return [{'val': np.mean(y), 'num_pts': y.size}]
 
         # recursively generate rule list
         else:
@@ -128,7 +126,7 @@
                 'col': self.feature_names_[col],
                 'index_col': col,
                 'cutoff': cutoff,
-                'val': np.mean(y),  # values before splitting
+                'val': np.mean(y_left),  # will be the values before splitting in the next lower level
                 'flip': flip,
                 'val_right': np.mean(y_right),
                 'num_pts': y.size,
@@ -155,7 +153,11 @@
             for j, rule in enumerate(self.rules_):
                 if j == len(self.rules_) - 1:
                     probs[i] = rule['val']
-                elif x[rule['index_col']] >= rule['cutoff']:
+                    continue
+                regular_condition = x[rule["index_col"]] >= rule["cutoff"]
+                flipped_condition = x[rule["index_col"]] < rule["cutoff"]
+                condition = flipped_condition if rule["flip"] else regular_condition
+                if condition:
                     probs[i] = rule['val_right']
                     break
         return np.vstack((1 - probs, probs)).transpose()  # probs (n, 2)
@@ -434,9 +436,9 @@ <h2 id="params">Params</h2>
         elif np.all(y == y[0]):
             return [{&#39;val&#39;: y[0], &#39;num_pts&#39;: y.size}]
 
-        # base case 3: max depth reached
-        elif depth &gt;= self.max_depth:
-            return []
+         # base case 3: max depth reached
+        elif depth == self.max_depth:
+            return [{&#39;val&#39;: np.mean(y), &#39;num_pts&#39;: y.size}]
 
         # recursively generate rule list
         else:
@@ -475,7 +477,7 @@ <h2 id="params">Params</h2>
                 &#39;col&#39;: self.feature_names_[col],
                 &#39;index_col&#39;: col,
                 &#39;cutoff&#39;: cutoff,
-                &#39;val&#39;: np.mean(y),  # values before splitting
+                &#39;val&#39;: np.mean(y_left),  # will be the values before splitting in the next lower level
                 &#39;flip&#39;: flip,
                 &#39;val_right&#39;: np.mean(y_right),
                 &#39;num_pts&#39;: y.size,
@@ -502,7 +504,11 @@ <h2 id="params">Params</h2>
             for j, rule in enumerate(self.rules_):
                 if j == len(self.rules_) - 1:
                     probs[i] = rule[&#39;val&#39;]
-                elif x[rule[&#39;index_col&#39;]] &gt;= rule[&#39;cutoff&#39;]:
+                    continue
+                regular_condition = x[rule[&#34;index_col&#34;]] &gt;= rule[&#34;cutoff&#34;]
+                flipped_condition = x[rule[&#34;index_col&#34;]] &lt; rule[&#34;cutoff&#34;]
+                condition = flipped_condition if rule[&#34;flip&#34;] else regular_condition
+                if condition:
                     probs[i] = rule[&#39;val_right&#39;]
                     break
         return np.vstack((1 - probs, probs)).transpose()  # probs (n, 2)
@@ -771,9 +777,9 @@ <h3>Methods</h3>
     elif np.all(y == y[0]):
         return [{&#39;val&#39;: y[0], &#39;num_pts&#39;: y.size}]
 
-    # base case 3: max depth reached
-    elif depth &gt;= self.max_depth:
-        return []
+     # base case 3: max depth reached
+    elif depth == self.max_depth:
+        return [{&#39;val&#39;: np.mean(y), &#39;num_pts&#39;: y.size}]
 
     # recursively generate rule list
     else:
@@ -812,7 +818,7 @@ <h3>Methods</h3>
             &#39;col&#39;: self.feature_names_[col],
             &#39;index_col&#39;: col,
             &#39;cutoff&#39;: cutoff,
-            &#39;val&#39;: np.mean(y),  # values before splitting
+            &#39;val&#39;: np.mean(y_left),  # will be the values before splitting in the next lower level
             &#39;flip&#39;: flip,
             &#39;val_right&#39;: np.mean(y_right),
             &#39;num_pts&#39;: y.size,
@@ -864,7 +870,11 @@ <h3>Methods</h3>
         for j, rule in enumerate(self.rules_):
             if j == len(self.rules_) - 1:
                 probs[i] = rule[&#39;val&#39;]
-            elif x[rule[&#39;index_col&#39;]] &gt;= rule[&#39;cutoff&#39;]:
+                continue
+            regular_condition = x[rule[&#34;index_col&#34;]] &gt;= rule[&#34;cutoff&#34;]
+            flipped_condition = x[rule[&#34;index_col&#34;]] &lt; rule[&#34;cutoff&#34;]
+            condition = flipped_condition if rule[&#34;flip&#34;] else regular_condition
+            if condition:
                 probs[i] = rule[&#39;val_right&#39;]
                 break
     return np.vstack((1 - probs, probs)).transpose()  # probs (n, 2)</code></pre>

docs/tree/c45_tree/c45_tree.html

@@ -43,6 +43,7 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.model_selection import cross_val_score
 from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
+from imodels.util.arguments import check_fit_arguments
 
 from ..c45_tree.c45_utils import decision, is_numeric_feature, gain, gain_ratio, get_best_split, \
     set_as_leaf_node
@@ -166,17 +167,20 @@
 
     def fit(self, X, y, feature_names: str = None):
         self.complexity_ = 0
-        X, y = check_X_y(X, y)
+        # X, y = check_X_y(X, y)
+        X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
         self.resultType = type(y[0])
         if feature_names is None:
             self.feature_names = [f'X_{x}' for x in range(X.shape[1])]
         else:
             # only include alphanumeric chars / replace spaces with underscores
             self.feature_names = [''.join([i for i in x if i.isalnum()]).replace(' ', '_')
                                   for x in feature_names]
-            self.feature_names = ['X_' + x if x[0].isdigit()
-                                  else x
-                                  for x in self.feature_names]
+            self.feature_names = [
+                'X_' + x if x[0].isdigit()
+                else x
+                for x in self.feature_names
+            ]
 
         assert len(self.feature_names) == X.shape[1]
 
@@ -535,17 +539,20 @@ <h2 id="parameters">Parameters</h2>
 
     def fit(self, X, y, feature_names: str = None):
         self.complexity_ = 0
-        X, y = check_X_y(X, y)
+        # X, y = check_X_y(X, y)
+        X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
         self.resultType = type(y[0])
         if feature_names is None:
             self.feature_names = [f&#39;X_{x}&#39; for x in range(X.shape[1])]
         else:
             # only include alphanumeric chars / replace spaces with underscores
             self.feature_names = [&#39;&#39;.join([i for i in x if i.isalnum()]).replace(&#39; &#39;, &#39;_&#39;)
                                   for x in feature_names]
-            self.feature_names = [&#39;X_&#39; + x if x[0].isdigit()
-                                  else x
-                                  for x in self.feature_names]
+            self.feature_names = [
+                &#39;X_&#39; + x if x[0].isdigit()
+                else x
+                for x in self.feature_names
+            ]
 
         assert len(self.feature_names) == X.shape[1]
 
@@ -766,17 +773,20 @@ <h3>Methods</h3>
 </summary>
 <pre><code class="python">def fit(self, X, y, feature_names: str = None):
     self.complexity_ = 0
-    X, y = check_X_y(X, y)
+    # X, y = check_X_y(X, y)
+    X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
     self.resultType = type(y[0])
     if feature_names is None:
         self.feature_names = [f&#39;X_{x}&#39; for x in range(X.shape[1])]
     else:
         # only include alphanumeric chars / replace spaces with underscores
         self.feature_names = [&#39;&#39;.join([i for i in x if i.isalnum()]).replace(&#39; &#39;, &#39;_&#39;)
                               for x in feature_names]
-        self.feature_names = [&#39;X_&#39; + x if x[0].isdigit()
-                              else x
-                              for x in self.feature_names]
+        self.feature_names = [
+            &#39;X_&#39; + x if x[0].isdigit()
+            else x
+            for x in self.feature_names
+        ]
 
     assert len(self.feature_names) == X.shape[1]