Fixed bug in lime_tabular with no discretizer

marcotcr · Nov 17, 2016 · cf51676 · cf51676
1 parent 5a93a43
commit cf51676
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Showing 2 changed files with 16 additions and 18 deletions.
diff --git a/lime/lime_tabular.py b/lime/lime_tabular.py
@@ -133,24 +133,22 @@ def __init__(self, training_data, training_labels=None, feature_names=None,
         if self.categorical_features is None:
             self.categorical_features = []
         self.discretizer = None
-        if discretizer == 'quartile':
-            self.discretizer = QuartileDiscretizer(training_data,
-                                                   self.categorical_features,
-                                                   feature_names,
-                                                   labels=training_labels)
-        elif discretizer == 'decile':
-            self.discretizer = DecileDiscretizer(training_data,
-                                                 self.categorical_features,
-                                                 feature_names,
-                                                 labels=training_labels)
-        elif discretizer == 'entropy':
-            self.discretizer = EntropyDiscretizer(training_data,
-                                                  self.categorical_features,
-                                                  feature_names,
-                                                  labels=training_labels)
-        else:
-            raise '''Discretizer must be 'quartile', 'decile' or 'entropy' '''
         if discretize_continuous:
+            if discretizer == 'quartile':
+                self.discretizer = QuartileDiscretizer(
+                    training_data, self.categorical_features, feature_names,
+                    labels=training_labels)
+            elif discretizer == 'decile':
+                self.discretizer = DecileDiscretizer(
+                    training_data, self.categorical_features, feature_names,
+                    labels=training_labels)
+            elif discretizer == 'entropy':
+                self.discretizer = EntropyDiscretizer(
+                    training_data, self.categorical_features, feature_names,
+                    labels=training_labels)
+            else:
+                raise ('''Discretizer must be 'quartile', 'decile' ''' +
+                       '''or 'entropy' ''')
             self.categorical_features = range(training_data.shape[1])
             discretized_training_data = self.discretizer.discretize(
                 training_data)

diff --git a/setup.py b/setup.py
@@ -1,7 +1,7 @@
 from setuptools import setup
 
 setup(name='lime',
-      version='0.1.1.17',
+      version='0.1.1.18',
       description='Local Interpretable Model-Agnostic Explanations for machine learning classifiers',
       url='http://github.com/marcotcr/lime',
       author='Marco Tulio Ribeiro',