Upload WISDM.py

WISDM.py

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+# 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]})