d56ae8d9f2af74d6dd6f9b7bf6f24915d7ca76ec,autokeras/classifier.py,ClassifierBase,fit,#ClassifierBase#Any#Any#Any#Any#Any#,126

Before Change

        pickle.dump(self, open(os.path.join(self.path, "classifier"), "wb"))
        pickle_to_file(self, os.path.join(self.path, "classifier"))

        if time_limit is None:
            while True:
                searcher = self.load_searcher()
                if len(searcher.history) >= constant.MAX_MODEL_NUM:
                    return
                p = multiprocessing.Process(target=run_searcher_once, args=(x_train, y_train, x_test, y_test, self.path))
                p.start()
                p.join()

        start_time = time.time()
        while time.time() - start_time <= time_limit:
            p = multiprocessing.Process(target=run_searcher_once, args=(x_train, y_train, x_test, y_test, self.path))
            p.start()
            // Kill the process if necessary.
            while time.time() - start_time <= time_limit:
                if p.is_alive():
                    time.sleep(1)
                else:
                    break
            else:
                // If break above the code in this else won"t run
                p.terminate()
                p.join()

    def predict(self, x_test):
        Return predict result for the testing data.

        Args:

After Change

        pickle.dump(self, open(os.path.join(self.path, "classifier"), "wb"))
        pickle_to_file(self, os.path.join(self.path, "classifier"))

        if time_limit is None:
            time_limit = 24*60*60

        start_time = time.time()
        while time.time() - start_time <= time_limit:
            run_searcher_once(x_train, y_train, x_test, y_test, self.path)

    def predict(self, x_test):
        Return predict result for the testing data.

Instances