Upload WISDM2.py

WISDM2.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.
+"""Monster-Monash custom downloader"""
+
+
+import numpy as np
+import os
+import datasets
+
+
+_DATASET = "WISDM2"
+_SHAPE = (3, 100)
+#_DESCRIPTION = ""
+#_CITATION = ""
+#_HOMEPAGE = ""
+#_LICENSE = ""
+
+_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",
+    }
+
+
+class Monster(datasets.GeneratorBasedBuilder):
+    """Generic Monster class for all downloader."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    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"  # By default all data is returned in a single split.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "X": datasets.Array2D(_SHAPE, "float32"),
+                "y": datasets.Value("int64")
+            }
+        )
+        return datasets.DatasetInfo(
+#            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("X", "y"),
+#            homepage=_HOMEPAGE,
+#            license=_LICENSE,
+#            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        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,
+                    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,
+                    gen_kwargs={
+                        "data": data,
+                        "labels": labels,
+                        "fold": fold,
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "data": data,
+                        "labels": labels,
+                        "fold": fold,
+                        "split": "test"
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, data, labels, fold, split):
+        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]})