Upload WISDM.py

WISDM.py

@@ -11,45 +11,21 @@
 # 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."""
+"""Monster-Monash custom downloader"""
 
 
 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 = ""
+#_DESCRIPTION = ""
+#_CITATION = ""
+#_HOMEPAGE = ""
+#_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",
@@ -61,23 +37,11 @@ _URLS = {
     }
 
 
-# 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."""
+    """Generic Monster class for all downloader."""
 
-    VERSION = datasets.Version("1.1.0")
+    VERSION = datasets.Version("1.0.0")
 
-    # 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"),
@@ -87,47 +51,31 @@ class Monster(datasets.GeneratorBasedBuilder):
         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.
+    DEFAULT_CONFIG_NAME = "full"  # By default all data is returned in a single split.
 
     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.
+#            description=_DESCRIPTION,
+            features=features,
             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,
+#            homepage=_HOMEPAGE,
+#            license=_LICENSE,
+#            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,
@@ -141,7 +89,6 @@ class Monster(datasets.GeneratorBasedBuilder):
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
                         "data": data,
                         "labels": labels,
@@ -151,7 +98,6 @@ class Monster(datasets.GeneratorBasedBuilder):
                 ),
                 datasets.SplitGenerator(
                     name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
                     gen_kwargs={
                         "data": data,
                         "labels": labels,
@@ -161,10 +107,7 @@ class Monster(datasets.GeneratorBasedBuilder):
                 ),
             ]
 
-    # 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":