replace confusion matrix plot using plotly.express

requirements.txt

@@ -1 +1,3 @@
-requests
+requests
+sklearn
+plotly.express

scripts/analysis.py

@@ -1,4 +1,4 @@
-import matplotlib.pyplot as plt
+import plotly.express as px
 from sklearn.metrics import confusion_matrix
 import numpy as np
 import itertools
@@ -8,33 +8,21 @@ def get_confusion_matrix(prediction_label, true_label):
 
 def plot_confusion_matrix(cm, classes,
                           normalize=False,
-                          title='Confusion matrix',
-                          cmap=plt.cm.Blues):
+                          title='Confusion matrix'):
     """
     This function prints and plots the confusion matrix.
     Normalization can be applied by setting `normalize=True`.
     """
-    # Create plot
-    plt.imshow(cm, interpolation='nearest', cmap=cmap)
-    plt.title(title)
-    plt.colorbar()
+    # Normalize if wanted
+    if normalize:
+        cm = cm / np.sum(cm)
 
-    # Update axis value
-    tick_marks = np.arange(len(classes))
-    plt.xticks(tick_marks, classes, rotation=45)
-    plt.yticks(tick_marks, classes)
-
-    # Normalize if needed
-    fmt = '.2f' if normalize else 'd'
-    thresh = cm.max() *2./3
-    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
-        plt.text(j, i, format(cm[i, j], fmt),
-                 horizontalalignment="center",
-                 color="white" if cm[i, j] > thresh else "black")
-
-    # Update axis label
-    # plt.tight_layout()
-    plt.ylabel('True label')
-    plt.xlabel('Predicted label')
-
-    plt.show()
+    # Create confusion matrix graph
+    fig = px.imshow(cm,
+                    labels=dict(x="Predicted label", y="True label", color="value"),
+                    x=classes,
+                    y=classes,
+                    color_continuous_scale='Blues',
+                    title=title
+                )
+    fig.show()

scripts/test.py

@@ -7,6 +7,7 @@ def test1():
   x = np.arange(len(database.CLASSES))
   cm = np.dot(x.reshape(len(x),1), x.reshape(1,len(x)))
   print(cm)
+  print(database.CLASSES)
   analysis.plot_confusion_matrix(cm, database.CLASSES)
 
 def test():
@@ -40,10 +41,11 @@ def test():
   cm = analysis.get_confusion_matrix(test_predictions, test_labels)
 
   # Plot confusion matrix
-  analysis.plot_confusion_matrix(cm, database.CLASSES)
-
+  analysis.plot_confusion_matrix(cm, database.CLASSES, normalize=True)
 
+  # Plot confusion matrix (ignore other activities)
+  # analysis.plot_confusion_matrix(cm[1:, 1:], database.CLASSES[1:], normalize=True)
 
 
 if __name__ == '__main__':
-  test1()
+  test()