7eda0ba9f842e7b125f397182abd513d5d22b17d,tslearn/datasets.py,UCR_UEA_datasets,load_dataset,#UCR_UEA_datasets#Any#,166

Before Change

        
        full_path = os.path.join(self._data_dir, dataset_name)
        if os.path.isdir(full_path) and dataset_name not in self._ignore_list:
            if os.path.exists(os.path.join(full_path, self._filenames.get(dataset_name, dataset_name) + "_TRAIN.txt")):
                fname_train = self._filenames.get(dataset_name, dataset_name) + "_TRAIN.txt"
                fname_test = self._filenames.get(dataset_name, dataset_name) + "_TEST.txt"
                data_train = numpy.loadtxt(os.path.join(full_path, fname_train), delimiter=",")
                data_test = numpy.loadtxt(os.path.join(full_path, fname_test), delimiter=",")
                X_train = to_time_series_dataset(data_train[:, 1:])
                y_train = data_train[:, 0].astype(numpy.int)
                X_test = to_time_series_dataset(data_test[:, 1:])
                y_test = data_test[:, 0].astype(numpy.int)
                return X_train, y_train, X_test, y_test
        return None, None, None, None


class CachedDatasets(object):

After Change

            url = "http://www.timeseriesclassification.com/Downloads/%s.zip" % dataset_name
            for fname in [fname_train, fname_test]:
                if os.path.exists(os.path.join(full_path, fname)):
                    os.remove(os.path.join(full_path, fname))
            extract_from_zip_url(url, target_dir=self._data_dir, verbose=False)
            data_train = numpy.loadtxt(os.path.join(full_path, fname_train), delimiter=",")
            data_test = numpy.loadtxt(os.path.join(full_path, fname_test), delimiter=",")
        X_train = to_time_series_dataset(data_train[:, 1:])

Instances