# 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, }