def __plot_classification_report(self, y_test_pred):
test_report = classification_report(self.__y_test, y_test_pred, output_dict=True) # dict
if self.__testing_mode is False:
testing_report_as_array = self.__helper_plot_classification_report(test_report)
self.__experiment.log_chart("Test Set - Classification Report", data=Heatmap(z=testing_report_as_array), y_ticks=self.__labels, x_ticks=["precision", "recall", "f1-score", "support"])
else:
print(test_report)
def __plot_confusion_matrix(self, y_test_pred=None):
if self.__y_test is not None and y_test_pred is not None:
confusion_mat_test = confusion_matrix(self.__y_test, y_test_pred) # array
confusion_mat_test = self.__helper_plot_confusion_matrix(confusion_mat_test)
if self.__testing_mode is False:
self.__experiment.log_chart("Test Set - confusion matrix", data=Heatmap(z=confusion_mat_test))
else:
print(confusion_mat_test)
def __plot_confusion_matrix(self, labels, predictions):
""" Plots the confusion matrix. """
confusion_mat_test = confusion_matrix(labels, predictions) # array
confusion_mat_test = TensorflowTrainer.__helper_plot_confusion_matrix(
confusion_mat_test,
mat_x_ticks=self.__classes,
mat_y_ticks=self.__classes)
self.__experiment.log_chart("confusion matrix",
data=Heatmap(z=confusion_mat_test))