fix test

id3/id3.py

@@ -2,10 +2,8 @@
 This is a module to be used as a reference for building other modules
 """
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
-from sklearn.utils.multiclass import unique_labels
-from sklearn.metrics import euclidean_distances
 from sklearn.model_selection import train_test_split
 
 from .node import Node
@@ -48,8 +46,8 @@ def _build(self, examples_idx, features_idx):
         if features_idx.size == 0 or unique.size == 1:
             return Node(classification_name)
 
-        calc_record = self._splitter.calc(examples_idx, features_idx)
-        split_records = self._splitter.split(examples_idx, calc_record)
+        calc_record = self.splitter_.calc(examples_idx, features_idx)
+        split_records = self.splitter_.split(examples_idx, calc_record)
         new_features_idx = np.delete(features_idx,
                                      np.where(features_idx ==
                                               calc_record.feature_idx))
@@ -94,9 +92,9 @@ def fit(self, X, y, feature_names=None, check_input=True, pruner=None):
         self : object
             Returns self.
         """
+        X_, y = check_X_y(X, y)
         self.feature_names = feature_names
         self.pruner = pruner
-        X_, y = check_X_y(X, y)
         prune = isinstance(self.pruner, BasePruner)
         if prune:
             X_, X_test, y, y_test = train_test_split(X_, y, test_size=0.2)
@@ -121,7 +119,7 @@ def fit(self, X, y, feature_names=None, check_input=True, pruner=None):
         self.y_encoder = ExtendedLabelEncoder()
         self.y = self.y_encoder.fit_transform(y)
 
-        self._splitter = Splitter(self.X,
+        self.splitter_ = Splitter(self.X,
                                   self.y,
                                   self.is_numerical,
                                   self.X_encoders,
@@ -147,6 +145,7 @@ def predict(self, X):
         y : array of shape = [n_samples]
             Returns :math:`x^2` where :math:`x` is the first column of `X`.
         """
+        check_is_fitted(self, 'tree_')
         X = check_array(X)
         X_ = np.zeros(X.shape)
         ret = np.empty(X.shape[0], dtype=X.dtype)

id3/splitter.py

@@ -177,13 +177,14 @@ def calc(self, examples_idx, features_idx):
                 tmp_calc_record = self._info_numerical(feature, y_)
             else:
                 tmp_calc_record = self._info_nominal(feature, y_)
-            if self.feature_names is not None:
-                tmp_calc_record.feature_name = self.feature_names[idx]
             if tmp_calc_record < calc_record:
                 ft_idx = features_idx[idx]
                 calc_record = tmp_calc_record
                 calc_record.feature_idx = ft_idx
-                calc_record.feature_name = self.feature_names[ft_idx]
+                if self.feature_names is not None:
+                    calc_record.feature_name = self.feature_names[ft_idx]
+                else:
+                    calc_record.feature_name = str(ft_idx)
         calc_record.entropy, calc_record.class_counts = self._entropy(y_, True)
         return calc_record
 

id3/tests/test_common.py

@@ -1,12 +1,6 @@
 from sklearn.utils.estimator_checks import check_estimator
-from id3 import (Id3Estimator, TemplateClassifier)
+from id3 import Id3Estimator
 
 
 def test_estimator():
     return check_estimator(Id3Estimator)
-
-
-def test_classifier():
-    return check_estimator(TemplateClassifier)
-
-

id3/tests/test_id3.py

@@ -5,7 +5,7 @@
 from id3.data import load_contact_lenses, load_will_wait, load_weather
 from sklearn.preprocessing import LabelEncoder
 from sklearn.datasets import load_iris
-from id3 import export_pdf, export_graphviz
+from id3 import export_graphviz
 
 id3Estimator = Id3Estimator()