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README.md

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----
-license: mit
-task_categories:
-- time-series-forecasting
-size_categories:
-- n<1K
----

data/DFR/6_st_DFR_V=0.zip

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data/DFR/6_st_DFR_V=1.zip

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data/DFR/6_st_DFR_V=3.zip

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mjp.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.
+"""Collection of datasets for the MJP."""
+
+import pathlib
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Optional
+
+import datasets
+import torch
+
+from fim.data.utils import load_file
+from fim.typing import Path, Paths
+
+
+# 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
+_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 = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+_ROOT_URL = "data/DFR"
+
+
+@dataclass
+class MJPDatasetsBuilderConfig(datasets.BuilderConfig):
+    """MJPDatasets builder config.."""
+
+    file_name: Optional[str] = None
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class MJP(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 = MJPDatasetsBuilderConfig
+
+    # 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 = [
+        MJPDatasetsBuilderConfig(
+            name="DFR_V=0",
+            file_name="6_st_DFR_V=0.zip",
+            version=VERSION,
+            description="This part of my dataset covers a first domain",
+        ),
+        MJPDatasetsBuilderConfig(
+            name="DFR_V=1",
+            file_name="6_st_DFR_V=1.zip",
+            version=VERSION,
+            description="This part of my dataset covers a first domain",
+        ),
+        MJPDatasetsBuilderConfig(
+            name="DFR_V=2",
+            file_name="6_st_DFR_V=2.zip",
+            version=VERSION,
+            description="This part of my dataset covers a first domain",
+        ),
+        MJPDatasetsBuilderConfig(
+            name="DFR_V=3",
+            file_name="6_st_DFR_V=3.zip",
+            version=VERSION,
+            description="This part of my dataset covers a first domain",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "DFR_V=0"
+
+    files_to_load = {
+        "observation_times": "fine_grid_grid.pt",
+        "observation_values": "fine_grid_noisy_sample_paths.pt",
+        "time_normalization_factors": "fine_grid_time_normalization_factors.pt",
+        "sequence_lengths": "fine_grid_mask_seq_lengths.pt",
+        "ground_truth_intensity_matrices": "fine_grid_intensity_matrices.pt",
+        "adjacency_matrices": "fine_grid_adjacency_matrices.pt",
+        "ground_truth_initial_distributions": "fine_grid_initial_distributions.pt",
+    }
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "observation_times": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
+                "observation_values": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("uint32")))),
+                "time_normalization_factors": datasets.Value("float32"),
+                "sequence_lengths": datasets.Sequence(datasets.Sequence(datasets.Value("int32"))),
+                "ground_truth_intensity_matrices": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                "adjacency_matrices": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                "ground_truth_initial_distributions": datasets.Sequence(datasets.Value("uint64")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # 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
+        urls = f"{_ROOT_URL}/{self.config.file_name}"
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"datadir": pathlib.Path(data_dir) / self.config.file_name.split(".")[0]},
+            )
+        ]
+
+    def __get_files(self, path: Path) -> Paths:
+        files_to_load = [(key, pathlib.Path(path) / file_name) for key, file_name in self.files_to_load.items()]
+        return files_to_load
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, datadir):
+        data = defaultdict(list)
+        files_to_load = self.__get_files(datadir)
+        for key, file_path in files_to_load:
+            data[key].append(load_file(file_path))
+        for k, v in data.items():
+            data[k] = torch.cat(v)
+            print(k, data[k].shape)
+        for id in range(len(data["observation_times"])):
+            yield id, {k: v[id].tolist() for k, v in data.items() if k in self.info.features}