reviews_with_drift.py

# 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 new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# 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):
        # 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.
        label_mapping = {"Positive": 1, "Negative": 0}
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(csv_file)
            for id_, row in enumerate(csv_reader):
                prediction_ts,age,gender,context,text,label = row
                print(prediction_ts)
                print(type(prediction_ts))
                print(float(prediction_ts))
                print(type(float(prediction_ts)))
                print(age)
                print(type(label))
                print(label)
                yield id_, {
                    "prediction_ts":33.33,
                    "age":15,
                    "gender":gender,
                    "context":context,
                    "text": text,
                    "label":"Negative",
                }