Improved plot confusion matrix

eval/metrics.py

@@ -1,6 +1,8 @@
 import utils.constants as const
 from sklearn.metrics import confusion_matrix
 import matplotlib.pyplot as plt
+import numpy as np
+import itertools
 
 
 def f1_score(y_true, y_pred, labels, average):
@@ -176,22 +178,31 @@ def get_confusion_matrix(y_true, y_pred):
 
 
 def plot_confusion_matrix(confusion_mat, classifier_name):
-    """
+
-    Takes Confusion Matrix as a parameter and plots the matrix using matplotlib
+    accuracy = np.trace(confusion_mat) / float(np.sum(confusion_mat))
-    :param confusion_mat: Confusion Matrix
+    mis_class = 1 - accuracy
-    :param classifier_name: Classifier Name to show it on the Top
+
-    """
+    plt.figure(figsize=(8, 6))
-    fig, ax = plt.subplots(2, 2)
+    plt.imshow(confusion_mat, interpolation='nearest', cmap=plt.get_cmap('Blues'))
+    plt.title(classifier_name)
+    plt.colorbar()
+
+    target_names = const.CLASS_LABELS_LIST
+    if target_names is not None:
+        tick_marks = np.arange(len(target_names))
+        plt.xticks(tick_marks, target_names, rotation=45)
+        plt.yticks(tick_marks, target_names)
+
+    thresh = confusion_mat.max() / confusion_mat.max() / 2
+    for i, j in itertools.product(range(confusion_mat.shape[0]), range(confusion_mat.shape[1])):
+        plt.text(j, i, "{:,}".format(confusion_mat[i, j]),
+                 horizontalalignment="center",
+                 color="white" if confusion_mat[i, j] > thresh else "black")
+
+    plt.tight_layout()
+    plt.ylabel('True/Gold')
+    plt.xlabel('Predicted \nAccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, mis_class))
     plt.show()
-    ax.matshow(confusion_mat, cmap='Greens')
-    for x in (0, 2):
-        for y in (0, 2):
-            ax.text(x, y, confusion_mat[y, x])
-    ax.set_xlabel('Predicted')
-    ax.set_ylabel('True/Gold')
-    ax.set_xticklabels([''] + const.CLASS_LABELS_LIST)
-    ax.set_yticklabels([''] + const.CLASS_LABELS_LIST)
-    ax.set_title(classifier_name)
 
 
 class Result:

predict.py

@@ -9,4 +9,4 @@ confusion_matrix = metrics.get_confusion_matrix(y_true, y_pred)
 
 print(confusion_matrix)
 
-# metrics.plot_confusion_matrix(confusion_matrix, "BiLSTM Classifier + Attention with ELMo")
+metrics.plot_confusion_matrix(confusion_matrix, "BiLSTM Classifier + Attention with ELMo")