train_test_split (#1)

- feat: dataloading script (d2a5d425462eb420f1e663e9443cba35df1dd36b)

GRCh38.py

@@ -0,0 +1,85 @@
+# 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.
+
+import json
+import random
+
+import datasets
+
+# You can copy an official description
+_DESCRIPTION = """\
+A dataset of all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 1 and reached a total of 3.2 billion nucleotides.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26"
+
+FILES = ["intervals.jsonl"]
+
+class PubchemSelfies(datasets.GeneratorBasedBuilder):
+    """A dataset of all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reached a total of 3.2 billion nucleotides."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # You will be able to load one or the other configurations in the following list with
+    BUILDER_CONFIG = datasets.BuilderConfig(
+        version=VERSION, description="A dataset of all autosomal and sex chromosomes sequences from reference assembly GRCh38/hg38 and reached a total of 3.2 billion nucleotides."
+    )
+
+    def _info(self):
+        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=datasets.Features(
+                {
+                    "chr": datasets.Value("string"),
+                    "description": datasets.Value("string"),
+                    "seq": datasets.Value("string"),
+                    "split": datasets.Value("string"),
+                }
+            ),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(FILES)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filename": downloaded_files[0]
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filename):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filename) as jsonl_file:
+            for row, line in enumerate(jsonl_file):
+                data = json.loads(line)
+
+                # 5% of the time the data is validation so we set the split accordingly
+                # This is kind of a hacky but it's so we can load in streaming 
+                split = "valid" if random.random() < 0.05 else "train"
+
+                yield row, {
+                    "chr": data["chr"],
+                    "description": data["description"],
+                    "seq": data["seq"],
+                    "split": split,
+                }

README.md

@@ -1,3 +1,19 @@
 ---
 license: mit
+dataset_info:
+  features:
+  - name: chr
+    dtype: string
+  - name: description
+    dtype: string
+  - name: seq
+    dtype: string
+  - name: split
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3158692879
+    num_examples: 510445
+  download_size: 3166859999
+  dataset_size: 3158692879
 ---