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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 csv
import json
import os
import datasets
import synapseclient
_DESCRIPTION = """\
This is a test Synapse dataset.
"""
# 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 = ""
# Synapse ID for dataset
_SYN_ID = ""
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class SynapseTestDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# Custom download function for Synapse
def _download_from_synapse(syn_id, path):
syn = synapseclient.Synapse()
syn.login()
syn.get(entity=syn_id, downloadLocation=path)
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = datasets.Features(
{
"feature_a": datasets.Value("string"),
"feature_b": datasets.Value("string"),
"feature_c": datasets.Value("string")
# 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=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
)
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
# This is where we can call the custom download function for Synapse
data_dir = dl_manager.download_custom(_SYN_ID, self._download_from_synapse)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "train.jsonl"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "dev.jsonl"),
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "test.jsonl"),
"split": "test"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, 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.
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield key, {
"feature_a": data["feature_a"],
"feature_b": data["feature_b"],
"feature_c": "" if split == "test" else data["answer"],
} |