Minor changes to model and Added Results to Presentation Slides

classifier/linear_model.py

@@ -212,8 +212,8 @@ def get_sample_weights_with_features(theta_bias: float = None, random_state: int
     :return: returns a dictionary of random weights for each feature
     """
     weights = {THETA_BIAS_FEATURE: theta_bias}
-    random.seed(random_state)
     for feature in FEATURE_LIST:
+        random.seed(random_state)
         weights[feature] = round(random.uniform(-1.0, 1.0), 5)
 
     return weights

eval/metrics.py

@@ -177,4 +177,9 @@ class Result:
 
     def print_result(self):
         """ Prints F1 Score"""
-        print('F1 Score :: ', self.f1_score, ' Label :: ', self.label, ' Average :: ', self.average)
+        print_line = 'F1 Score :: ' + str(self.f1_score)
+        if self.label:
+            print_line += ' Label :: ' + self.label
+        if self.average:
+            print_line += ' Average :: ' + self.average
+        print(print_line)

presentation/presentation.tex

@@ -98,7 +98,6 @@ Base Classifier: {\bf {\color{red} Perceptron}}
 	\item Linear Classifier
 	\item Binary Classifier
 \end{itemize}
-\bigskip
 
 \begin{minted}[autogobble, breaklines,breakanywhere, fontfamily=helvetica, fontsize=\small]{python}
 class Perceptron:
@@ -112,6 +111,8 @@ class MultiClassPerceptron:
    def predict(self, X_test: list)
 
 \end{minted}
+\bigskip
+- {\bf Parameters} and {\bf Hyperparameters}
 
 \end{frame}
 
@@ -134,6 +135,7 @@ Lexicons and Regular Expressions ($\approx$ 30 Features)
 		    .....
 		}
 		\end{minted}
+	\bigskip
 	\item REGEX
 		\begin{itemize}
 		    \item $ACRONYM$
@@ -185,13 +187,13 @@ Lexicons and Regular Expressions ($\approx$ 30 Features)
 	\begin{tabular}{| l | c | c |} \hline
 		 {\bf Averaging} & {\bf Score} \\
 		 \hline \hline
-		 MACRO & 0.59 \\
+		 MICRO & 0.64 \\
 		  \hline
-		 MICRO & 0.57 \\
+		 MACRO & 0.57 \\
 		  \hline
-		 background & 0.63 \\
-		 method & 0.48 \\
-		 result & 0.55 \\
+		 background & 0.72 \\
+		 method & 0.54 \\
+		 result & 0.46 \\
 		  \hline
 	 \end{tabular}}
 

testing/model_testing.py

@@ -18,10 +18,10 @@ labels = set([inst.true_label for inst in X_train_inst])
 X_test_inst = read_csv_file(test_file_path, '\t')
 
 # number of training iterations
-epochs = int(len(X_train_inst)*0.9)
+epochs = int(len(X_train_inst)*1.5)
 
 # create MultiClassPerceptron classifier object
-clf = MultiClassPerceptron(epochs=epochs, learning_rate=0.9, random_state=42)
+clf = MultiClassPerceptron(epochs=epochs, learning_rate=0.75, random_state=101)
 
 # train the model
 clf.fit(X_train=X_train_inst, labels=list(labels))
@@ -34,9 +34,9 @@ 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)
 
 # Print F1 Score
-for result in f1_score_micro:
+for result in f1_score_micro + f1_score_macro + f1_score_none:
     result.print_result()