theta bias changed

classifier/linear_model.py

@@ -128,8 +128,8 @@ class MultiClassPerceptron:
 
         # Dictionary for storing label->Perceptron() objects, Create a new Perceptron object for each label
         for label in labels:
-            sample_weights = get_sample_weights_with_features(theta_bias=0.9, random_state=self.random_state)
-            self.perceptron_dict[label] = Perceptron(label, sample_weights, theta_bias=0.9)
+            sample_weights = get_sample_weights_with_features(theta_bias=-0.25, random_state=self.random_state)
+            self.perceptron_dict[label] = Perceptron(label, sample_weights, theta_bias=-0.25)
 
         next_print = int(self.epochs/10)
 

testing/model_testing.py

@@ -8,26 +8,35 @@ project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 train_file_path = project_root+'/data/tsv/train.tsv'
 test_file_path = project_root+'/data/tsv/test.tsv'
 
-
+# Read the training dataset
 X_train_inst = read_csv_file(train_file_path, '\t')
 
+# set of labels from Training data
 labels = set([inst.true_label for inst in X_train_inst])
 
+# Read test data set
 X_test_inst = read_csv_file(test_file_path, '\t')
 
-epochs = int(len(X_train_inst)*0.95)
+# number of training iterations
+epochs = int(len(X_train_inst)*0.9)
 
-clf = MultiClassPerceptron(epochs=epochs, learning_rate=1, random_state=42)
+# create MultiClassPerceptron classifier object
+clf = MultiClassPerceptron(epochs=epochs, learning_rate=0.9, random_state=42)
 
+# train the model
 clf.fit(X_train=X_train_inst, labels=list(labels))
 
+
+# predict
 y_test = clf.predict(X_test_inst)
 
 y_true = [inst.true_label for inst in X_test_inst]
 
+# Model Evaluation
 f1_score_micro = f1_score(y_true, y_test, labels, const.AVG_MICRO)
-f1_score_macro = f1_score(y_true, y_test, labels, const.AVG_MACRO)
-f1_score_none = f1_score(y_true, y_test, labels, None)
+# f1_score_macro = f1_score(y_true, y_test, labels, const.AVG_MACRO)
+# f1_score_none = f1_score(y_true, y_test, labels, None)
 
-for result in f1_score_micro+f1_score_macro+f1_score_none:
+# Print F1 Score
+for result in f1_score_micro:
     result.print_result()