F1 score output & adaptation to renamed pos_feature

training_testing.py

@@ -6,7 +6,7 @@ import numpy as np
 from sklearn import svm
 from sklearn import tree
 from sklearn.model_selection import cross_val_score
-import postagger
+import pos_feature
 
 
 def create_vector(corpus_instance, vocabulary=None, pos_vocabulary=None):
@@ -17,12 +17,9 @@ def create_vector(corpus_instance, vocabulary=None, pos_vocabulary=None):
     Example for corpus instance: OrderedDict([('LABEL', '0'), ('FILENAME', '36_19_RPRRQDRSHDV6J.txt'), ('STARS', '5.0'), ('TITLE', etc.
     """
     f1 = ngram_feature.extract(corpus_instance, vocabulary)
-    f2 = postagger.extract(corpus_instance, pos_vocabulary)
+    f2 = pos_feature.extract(corpus_instance, pos_vocabulary)
     f4 = sent_rating_feature.extract(corpus_instance)
 
-    print(f2)
-    print(len(f2))
-
     return np.concatenate((f1, f2, f4))
 
 
@@ -44,8 +41,7 @@ if __name__ == '__main__':
     bigram_vocab = ngram_feature.get_vocabulary(train_set, 2)
     
     # pos_bags
-    pos_bigram_vocab = postagger.get_pos_vocabulary(train_set)
-    #print(pos_bigram_vocab) #already lookin' good
+    pos_bigram_vocab = pos_feature.get_pos_vocabulary(train_set)
     
     # inputs:
     train_inputs = [create_vector(el, unigram_vocab, pos_bigram_vocab)
@@ -75,12 +71,15 @@ if __name__ == '__main__':
     train_multiple([svm_clf, tree_clf], train_inputs, train_labels)
     
     # validation
-    svm_score = cross_val_score(svm_clf, train_inputs, train_labels, cv=5).mean()#, scoring='f1')
-    tree_score = cross_val_score(tree_clf, train_inputs, train_labels, cv=5).mean()#, scoring='f1')
+    svm_acc = cross_val_score(svm_clf, train_inputs, train_labels, cv=5, scoring='accuracy').mean()
+    tree_acc = cross_val_score(tree_clf, train_inputs, train_labels, cv=5, scoring='accuracy').mean()
+
+    svm_f1 = cross_val_score(svm_clf, train_inputs, train_labels, cv=5, scoring='f1').mean()
+    tree_f1 = cross_val_score(tree_clf, train_inputs, train_labels, cv=5, scoring='f1').mean()
 
     print("\n--Cross Validation Scores-- ")
-    print("\nSVM: {}".format(svm_score))
-    print("\nTree: {}".format(tree_score))
+    print("\nSVM: Accuracy: {}, F1-Score: {}".format(svm_acc, svm_f1))
+    print("\nTree: Accuracy: {}, F1-Score: {}".format(tree_acc, tree_f1))
 
     # testing
     # print("\nSVM: Score on test Data:")