Add encoder

hiclass/HierarchicalClassifier.py

@@ -3,13 +3,13 @@
 import abc
 import hashlib
 import logging
-import pickle
-
 import networkx as nx
 import numpy as np
+import pickle
 from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator
 from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import LabelEncoder
 from sklearn.utils.validation import _check_sample_weight
 
 try:
@@ -215,7 +215,10 @@ def _disambiguate(self):
                     child = str(self.y_[i, j])
                     row.append(parent + self.separator_ + child)
                 new_y.append(np.asarray(row, dtype=np.str_))
-            self.y_ = np.array(new_y)
+            new_y = np.array(new_y)
+            self.label_encoder_ = LabelEncoder()
+            self.label_encoder_.fit(new_y)
+            self.y_ = self.label_encoder_.transform(new_y)
 
     def _create_digraph(self):
         # Create DiGraph

hiclass/LocalClassifierPerLevel.py

@@ -5,13 +5,13 @@
 """
 
 import hashlib
-import numpy as np
 import pickle
 from copy import deepcopy
-from joblib import Parallel, delayed
 from os.path import exists
+
+import numpy as np
+from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator
-from sklearn.preprocessing import LabelEncoder
 from sklearn.utils.validation import check_array, check_is_fitted
 
 from hiclass.ConstantClassifier import ConstantClassifier
@@ -273,9 +273,6 @@ def _fit_classifier(self, level, separator):
             classifier = ConstantClassifier()
         if not self.bert:
             try:
-                label_encoder = LabelEncoder()
-                label_encoder.fit(y)
-                y = label_encoder.transform(y)
                 classifier.fit(X, y, sample_weight)
             except TypeError:
                 classifier.fit(X, y)

hiclass/LocalClassifierPerParentNode.py

@@ -5,13 +5,13 @@
 """
 
 import hashlib
-import networkx as nx
-import numpy as np
 import pickle
 from copy import deepcopy
 from os.path import exists
+
+import networkx as nx
+import numpy as np
 from sklearn.base import BaseEstimator
-from sklearn.preprocessing import LabelEncoder
 from sklearn.utils.validation import check_array, check_is_fitted
 
 from hiclass.ConstantClassifier import ConstantClassifier
@@ -231,9 +231,6 @@ def _fit_classifier(self, node):
             classifier = ConstantClassifier()
         if not self.bert:
             try:
-                label_encoder = LabelEncoder()
-                label_encoder.fit(y)
-                y = label_encoder.transform(y)
                 classifier.fit(X, y, sample_weight)
             except TypeError:
                 classifier.fit(X, y)