Model Testing Code added

classifier/linear_model.py

@@ -130,7 +130,7 @@ class MultiClassPerceptron:
         for epoch in range(self.epochs):
 
             # get a random number within the size of training set
-            rand_num = random.randint(0, train_len)
+            rand_num = random.randint(0, train_len-1)
             # pick a random data instance with the generated random number
             inst = X_train[rand_num]
 

testing/feature_testing.py

@@ -4,6 +4,8 @@ from utils.csv import read_csv_file
 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'
+
 print(train_file_path)
 
 data = read_csv_file(csv_file_path=train_file_path, delimiter='\t')

testing/model_testing.py

@@ -1,3 +1,31 @@
-from classifier.linear_model import get_sample_weights_with_features
+from classifier.linear_model import MultiClassPerceptron
+from utils.csv import read_csv_file
+from eval.metrics import f1_score
+import utils.constants as const
+import os
 
-print(get_sample_weights_with_features())
+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'
+
+
+X_train_inst = read_csv_file(train_file_path, '\t')
+
+labels = set([inst.true_label for inst in X_train_inst])
+
+X_test_inst = read_csv_file(test_file_path, '\t')
+
+epochs = int(len(X_train_inst)*0.75)
+
+clf = MultiClassPerceptron(epochs, 1)
+
+clf.fit(X_train=X_train_inst, labels=list(labels))
+
+y_test = clf.predict(X_test_inst)
+
+y_true = [inst.true_label for inst in X_test_inst]
+
+f1_score_list = f1_score(y_true, y_test, labels, const.AVG_MICRO)
+
+for result in f1_score_list:
+    result.print_result()