[tools] comet: black

comet.py

 import tensorflow as tf
 from tools.keras import PlottingCallback
 
+
 class MulticlassificationConfusionMatrix(tf.keras.callbacks.Callback):
-    def __init__(self, comet_experiment, x=None, y=None, sample_weights=None, batch_size=None, labels=None, freq=100, verbose=0):
+    def __init__(
+        self,
+        comet_experiment,
+        x=None,
+        y=None,
+        sample_weights=None,
+        batch_size=None,
+        labels=None,
+        freq=100,
+        verbose=0,
+    ):
         self.comet_experiment = comet_experiment
-        self.data_x = x 
-        self.data_label = y 
-        self.data_weights = sample_weights 
+        self.data_x = x
+        self.data_label = y
+        self.data_weights = sample_weights
         self.batch_size = batch_size
         self.freq = freq
         self.labels = labels
         self.verbose = verbose
 
-
     def on_train_begin(self, logs=None):
         if self.verbose:
             print("Logging confusion matrix to comet experiment")
         pred = self.model.predict(x=self.data_x, batch_size=self.batch_size)
-        self.comet_experiment.log_confusion_matrix(y_true=self.data_label,
-                                                   y_predicted=pred,
-                                                   labels=self.labels,
-                                                   title="Confusion Start",
-                                                   epoch=0,
-                                                   file_name="confusion-matrix-0.json")
+        self.comet_experiment.log_confusion_matrix(
+            y_true=self.data_label,
+            y_predicted=pred,
+            labels=self.labels,
+            title="Confusion Start",
+            epoch=0,
+            file_name="confusion-matrix-0.json",
+        )
 
     def on_epoch_end(self, epoch, logs=None):
-        if epoch % self.freq  == 0 and epoch != 0:
+        if epoch % self.freq == 0 and epoch != 0:
             if self.verbose:
                 print("Logging confusion matrix to comet experiment")
             pred = self.model.predict(x=self.data_x, batch_size=self.batch_size)
-            self.comet_experiment.log_confusion_matrix(y_true=self.data_label,
-                                                       y_predicted=pred,
-                                                       labels=self.labels,
-                                                       title="Confusion Epoch {}".format(epoch),
-                                                       epoch=epoch,
-                                                       file_name="confusion-matrix-{}.json".format(epoch))
-
+            self.comet_experiment.log_confusion_matrix(
+                y_true=self.data_label,
+                y_predicted=pred,
+                labels=self.labels,
+                title="Confusion Epoch {}".format(epoch),
+                epoch=epoch,
+                file_name="confusion-matrix-{}.json".format(epoch),
+            )
 
     def on_train_end(self, logs=None):
         if self.verbose:
             print("Logging confusion matrix to comet experiment")
         pred = self.model.predict(x=self.data_x, batch_size=self.batch_size)
-        self.comet_experiment.log_confusion_matrix(y_true=self.data_label,
-                                                   y_predicted=pred,
-                                                   labels=self.labels,
-                                                   title="Confusion End",
-                                                   epoch=-1,
-                                                   file_name="confusion-matrix-end.json")
+        self.comet_experiment.log_confusion_matrix(
+            y_true=self.data_label,
+            y_predicted=pred,
+            labels=self.labels,
+            title="Confusion End",
+            epoch=-1,
+            file_name="confusion-matrix-end.json",
+        )