a5e0d49ea4234a6692f3504f24d558902f9a91c2,tslearn/datasets.py,UCR_UEA_datasets,load_dataset,#UCR_UEA_datasets#Any#,179

Before Change

                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:])
        y_train = data_train[:, 0].astype(numpy.int)
        X_test = to_time_series_dataset(data_test[:, 1:])

After Change

        full_path = os.path.join(self._data_dir, dataset_name)
        fname_train = self._filenames.get(dataset_name, dataset_name) + "_TRAIN.txt"
        fname_test = self._filenames.get(dataset_name, dataset_name) + "_TEST.txt"
        if not os.path.exists(os.path.join(full_path, fname_train)) or \
            not os.path.exists(os.path.join(full_path, fname_test)):
            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)
        try:
            data_train = numpy.loadtxt(os.path.join(full_path, fname_train), delimiter=",")
            data_test = numpy.loadtxt(os.path.join(full_path, fname_test), delimiter=",")
        except:

Instances