Datasets:

arize-ai
/

movie_reviews_with_context_drift

	# 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.

	# Lint as: python3
	"""IMDb movie revies dataset mixed with Trip Advisor Hotel Reviews to simulate drift accross time."""


	import csv
	import json
	import os

	import datasets
	from datasets.tasks import TextClassification



	# TODO: Add BibTeX citation to our BLOG
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = ""
	# _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 dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (age, gender, context) as well as a made up timestamp prediction_ts of when the inference took place.
	"""

	# 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)
	_URL = "https://huggingface.co/datasets/arize-ai/reviews_with_drift/resolve/main/"
	_URLS = {
	"training": _URL + "training.csv",
	"validation": _URL + "validation.csv",
	"production": _URL + "production.csv",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ReviewsWithDrift(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	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="default", version=VERSION, description="Default"),
	]

	DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	class_names = ["negative", "positive"]
	# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	# These are the features of your dataset like images, labels ...
	{
	"prediction_ts": datasets.Value("float"),
	"age":datasets.Value("int16"),
	"gender":datasets.Value("string"),
	"context":datasets.Value("string"),
	"text":datasets.Value("string"),
	"label":datasets.ClassLabel(names=class_names),
	}
	)

	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=("text", "label"),
	# Homepage of the dataset for documentation
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	# 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
	extracted_paths = dl_manager.download_and_extract(_URLS)
	return [
	datasets.SplitGenerator(
	name=datasets.Split("training"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": extracted_paths['training'],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("validation"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": extracted_paths['validation'],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split("production"),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": extracted_paths['production'],
	},
	),
	]


	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath):
	# 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.
	label_mapping = {"positive": 1, "negative": 0}
	with open(filepath) as csv_file:
	csv_reader = csv.reader(csv_file)
	for id_, row in enumerate(csv_reader):
	prediction_ts,age,gender,context,text,label = row
	if id_==0:
	continue
	yield id_, {
	"prediction_ts":prediction_ts,
	"age":age,
	"gender":gender,
	"context":context,
	"text": text,
	"label":label,
	}