minor...

modules/deeplearning/icing_cnn.py

@@ -8,8 +8,7 @@ import numpy as np
 import pickle
 import h5py
 
-from icing.pirep_goes import split_data, normalize
-from util.plot_cm import plot_confusion_matrix
+from icing.pirep_goes import normalize
 
 LOG_DEVICE_PLACEMENT = False
 
@@ -151,6 +150,10 @@ class IcingIntensityNN:
         self.test_recall = None
         self.test_precision = None
         self.test_confusion_matrix = None
+        self.test_true_pos = None
+        self.test_true_neg = None
+        self.test_false_pos = None
+        self.test_false_neg = None
 
         self.test_labels = []
         self.test_preds = []
@@ -479,12 +482,20 @@ class IcingIntensityNN:
             self.test_auc = tf.keras.metrics.AUC(name='test_auc')
             self.test_recall = tf.keras.metrics.Recall(name='test_recall')
             self.test_precision = tf.keras.metrics.Precision(name='test_precision')
+            self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
+            self.test_true_pos = tf.keras.metrics.TrueNegatives(name='test_true_pos')
+            self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
+            self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
         else:
             self.train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
             self.test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
             self.test_auc = tf.keras.metrics.AUC(name='test_auc')
             self.test_recall = tf.keras.metrics.Recall(name='test_recall')
             self.test_precision = tf.keras.metrics.Precision(name='test_precision')
+            self.test_true_neg = tf.keras.metrics.TrueNegatives(name='test_true_neg')
+            self.test_true_pos = tf.keras.metrics.TrueNegatives(name='test_true_pos')
+            self.test_false_neg = tf.keras.metrics.FalseNegatives(name='test_false_neg')
+            self.test_false_pos = tf.keras.metrics.FalsePositives(name='test_false_pos')
 
     def build_predict(self):
         _, pred = tf.nn.top_k(self.logits)
@@ -526,6 +537,10 @@ class IcingIntensityNN:
             self.test_auc(labels, pred)
             self.test_recall(labels, pred)
             self.test_precision(labels, pred)
+            self.test_true_neg(labels, pred)
+            self.test_true_pos(labels, pred)
+            self.test_false_neg(labels, pred)
+            self.test_false_pos(labels, pred)
 
     def predict(self, mini_batch):
         inputs = [mini_batch[0]]
@@ -541,6 +556,10 @@ class IcingIntensityNN:
         self.test_auc(labels, pred)
         self.test_recall(labels, pred)
         self.test_precision(labels, pred)
+        self.test_true_neg(labels, pred)
+        self.test_true_pos(labels, pred)
+        self.test_false_neg(labels, pred)
+        self.test_false_pos(labels, pred)
 
     def do_training(self, ckpt_dir=None):
 
@@ -587,6 +606,10 @@ class IcingIntensityNN:
                         self.test_auc.reset_states()
                         self.test_recall.reset_states()
                         self.test_precision.reset_states()
+                        self.test_true_neg.reset_states()
+                        self.test_true_pos.reset_states()
+                        self.test_false_neg.reset_states()
+                        self.test_false_pos.reset_states()
 
                         for data0_tst, label_tst in self.test_dataset:
                             tst_ds = tf.data.Dataset.from_tensor_slices((data0_tst, label_tst))
@@ -621,6 +644,10 @@ class IcingIntensityNN:
             self.test_auc.reset_states()
             self.test_recall.reset_states()
             self.test_precision.reset_states()
+            self.test_true_neg.reset_states()
+            self.test_true_pos.reset_states()
+            self.test_false_neg.reset_states()
+            self.test_false_pos.reset_states()
 
             for data0, label in self.test_dataset:
                 ds = tf.data.Dataset.from_tensor_slices((data0, label))
@@ -632,8 +659,15 @@ class IcingIntensityNN:
             precsn = self.test_precision.result().numpy()
             f1 = 2 * (precsn * recall) / (precsn + recall)
 
-            print('loss, acc, recall, precision, auc, f1: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
-                  recall, precsn, self.test_auc.result().numpy(), f1)
+            tn = self.test_true_neg.result().numpy()
+            tp = self.test_true_pos.result().numpy()
+            fn = self.test_false_neg.result().numpy()
+            fp = self.test_false_pos.result().numpy()
+
+            mcc = (tp*tn) - (fp*fn) / np.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
+
+            print('loss, acc, recall, precision, auc, f1, mcc: ', self.test_loss.result().numpy(), self.test_accuracy.result().numpy(),
+                  recall, precsn, self.test_auc.result().numpy(), f1, mcc)
             print('--------------------------------------------------')
             ckpt_manager.save()