Exemple #1
0
    def evaluate(self):
        classifier = Recurrent()
        classifier.retrieve_samples(Path.FEATURE_PATH)

        classifier.evaluate()
        classifier.relevance.output_confusion_matrix("{}confusion_matrix{}.png".format(Path.RESULT_PATH, self.run_name))
        classifier.relevance.output_statistics("{}statistics.md".format(Path.RESULT_PATH), self.run_name)
Exemple #2
0
    def predict(self, session_id):
        self.process(session_id)

        classifier = Recurrent()
        classifier.retrieve_sample("{}{}.data".format(Path.FEATURE_PATH,
                                                      session_id),
                                   is_labelled=False)
        classifier.evaluate(is_labelled=False)
Exemple #3
0
    def evaluate(self):
        classifier = Recurrent()
        classifier.retrieve_samples(Path.FEATURE_PATH)

        classifier.evaluate()
        classifier.relevance.output_confusion_matrix(
            "{}confusion_matrix{}.png".format(Path.RESULT_PATH, self.run_name))
        classifier.relevance.output_statistics(
            "{}statistics.md".format(Path.RESULT_PATH), self.run_name)
Exemple #4
0
    def predict(self, session_id):
        self.process(session_id)

        classifier = Recurrent()
        classifier.retrieve_sample("{}{}.data".format(Path.FEATURE_PATH, session_id), is_labelled=False)
        classifier.evaluate(is_labelled=False)
Exemple #5
0
    def get_classifier(self, neurons_per_layer):
        classifier = Recurrent(neurons_per_layer)
        classifier.retrieve_samples(Path.FEATURE_PATH)

        return classifier
Exemple #6
0
    def get_classifier(self, neurons_per_layer):
        classifier = Recurrent(neurons_per_layer)
        classifier.retrieve_samples(Path.FEATURE_PATH)

        return classifier