| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # TODO: Address all TODOs and remove all explanatory comments | |
| """TODO: Add a description here.""" | |
| import numpy as np | |
| #import json | |
| import os | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {A great new dataset}, | |
| author={huggingface, Inc. | |
| }, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DATASET = "WISDM" | |
| _SHAPE = (3, 100) | |
| _DESCRIPTION = """\ | |
| This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLS = { | |
| 'data': f"{_DATASET}_X.npy", | |
| 'labels': f"{_DATASET}_y.npy", | |
| 'fold_0': "test_indices_fold_0.txt", | |
| 'fold_1': "test_indices_fold_1.txt", | |
| 'fold_2': "test_indices_fold_2.txt", | |
| 'fold_3': "test_indices_fold_3.txt", | |
| 'fold_4': "test_indices_fold_4.txt", | |
| } | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class Monster(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name="full", version=VERSION, description="All data"), | |
| datasets.BuilderConfig(name="fold_0", version=VERSION, description="Cross-validation fold 0"), | |
| datasets.BuilderConfig(name="fold_1", version=VERSION, description="Cross-validation fold 1"), | |
| datasets.BuilderConfig(name="fold_2", version=VERSION, description="Cross-validation fold 2"), | |
| datasets.BuilderConfig(name="fold_3", version=VERSION, description="Cross-validation fold 3"), | |
| datasets.BuilderConfig(name="fold_4", version=VERSION, description="Cross-validation fold 4"), | |
| ] | |
| DEFAULT_CONFIG_NAME = "full" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| features = datasets.Features( | |
| { | |
| "X": datasets.Array2D(_SHAPE, "float32"), | |
| "y": datasets.Value("int64") | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| supervised_keys=("X", "y"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| data = dl_manager.download_and_extract(_URLS['data']) | |
| labels = dl_manager.download_and_extract(_URLS['labels']) | |
| if self.config.name == "full": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "data": data, | |
| "labels": labels, | |
| "fold": None, | |
| "split": "all", | |
| }, | |
| ), | |
| ] | |
| else: | |
| fold = dl_manager.download_and_extract(_URLS[self.config.name]) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "data": data, | |
| "labels": labels, | |
| "fold": fold, | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "data": data, | |
| "labels": labels, | |
| "fold": fold, | |
| "split": "test" | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, data, labels, fold, split): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| X = np.load(data) | |
| y = np.load(labels) | |
| if self.config.name == "full": | |
| for row in range(y.shape[0]): | |
| yield(row, {"X": X[row], "y": y[row]}) | |
| else: | |
| test_indices = np.loadtxt(fold, dtype='int') | |
| if split == "test": | |
| for row in test_indices: | |
| yield(int(row), {"X": X[row], "y": y[row]}) | |
| elif split == "train": | |
| train_indices = np.delete(np.arange(y.shape[0]), test_indices) | |
| for row in train_indices: | |
| yield(int(row), {"X": X[row], "y": y[row]}) | |