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# Copyright 2020 The HuggingFace Datasets Authors.
# Copyright 2023 Cyril Zhang.
#
# 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 csv
import json
import os
import datasets
import numpy as np
_CITATION = """\
"""
_DESCRIPTION = """\
Online dataset mockup.
"""
_HOMEPAGE = ""
_LICENSE = ""
_URLS = {}
class MockupDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIGS = []
def __init__(self, name=None, data_config={}, **kwargs):
super().__init__(**kwargs)
if 'length' not in data_config:
data_config['length'] = 20
if 'size' not in data_config:
data_config['size'] = 100
self.data_config = data_config
self.sampler = AutomatonSampler(name, data_config)
def _info(self):
features = datasets.Features(
{
"x": datasets.Sequence(datasets.Value("int32"), length=-1),
"y": datasets.Sequence(datasets.Value("int32"), length=-1)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"split": "train",
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, split):
for i in range(self.data_config['size']):
x, y = self.sampler.sample()
yield i, {
"x": x,
"y": y
}
class AutomatonSampler:
def __init__(self, name, data_config):
self.name = name
self.data_config = data_config
if 'seed' in self.data_config:
self.np_rng = np.random.default_rng(self.data_config['seed'])
else:
self.np_rng = np.random.default_rng()
self.T = self.data_config['length']
def f(self, x):
return np.cumsum(x) % 2
def sample(self):
x = self.np_rng.binomial(1,0.5,size=self.T)
return x, self.f(x) |