crowdflower.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Crowdflower datasets"""

from __future__ import absolute_import, division, print_function

import csv
import os
import textwrap

import six

import datasets


_crowdflower_CITATION = r"""
@inproceedings{van2012designing,
  title={Designing a scalable crowdsourcing platform},
  author={Van Pelt, Chris and Sorokin, Alex},
  booktitle={Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data},
  pages={765--766},
  year={2012}
}
"""

_crowdflower_DESCRIPTION = """
Collection of crowdflower classification datasets
"""

DATA_URL = "https://www.dropbox.com/s/ldrcdsv8d9qiwg0/crowdflower.zip?dl=1"

TASK_TO_LABELS = {'airline-sentiment': ['neutral', 'positive', 'negative'],
 'corporate-messaging': ['Information', 'Action', 'Exclude', 'Dialogue'],
 'economic-news': ['not sure', 'yes', 'no'],
 'political-media-audience': ['constituency', 'national'],
 'political-media-bias': ['partisan', 'neutral'],
 'political-media-message': ['information',
  'support',
  'policy',
  'constituency',
  'personal',
  'other',
  'media',
  'mobilization',
  'attack'],
 'sentiment_nuclear_power': ['Neutral / author is just sharing information',
  'Negative',
  'Tweet NOT related to nuclear energy',
  'Positive'],
 'text_emotion': ['sadness',
  'empty',
  'relief',
  'hate',
  'worry',
  'enthusiasm',
  'happiness',
  'neutral',
  'love',
  'fun',
  'anger',
  'surprise',
  'boredom'],
 'tweet_global_warming': ['Yes', 'No']}

def get_labels(task):
    return TASK_TO_LABELS[task]

class crowdflowerConfig(datasets.BuilderConfig):
    """BuilderConfig for crowdflower."""

    def __init__(
        self,
        text_features,
        label_classes=None,
        process_label=lambda x: x,
        **kwargs,
    ):
        """BuilderConfig for crowdflower.
        Args:
          text_features: `dict[string, string]`, map from the name of the feature
            dict for each text field to the name of the column in the tsv file
          label_column: `string`, name of the column in the tsv file corresponding
            to the label
          data_url: `string`, url to download the zip file from
          data_dir: `string`, the path to the folder containing the tsv files in the
            downloaded zip
          citation: `string`, citation for the data set
          url: `string`, url for information about the data set
          label_classes: `list[string]`, the list of classes if the label is
            categorical. If not provided, then the label will be of type
            `datasets.Value('float32')`.
          process_label: `Function[string, any]`, function  taking in the raw value
            of the label and processing it to the form required by the label feature
          **kwargs: keyword arguments forwarded to super.
        """

        super(crowdflowerConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)

        self.text_features = text_features
        self.label_column = "label"
        self.label_classes = get_labels(self.name)
        self.data_url = DATA_URL
        self.data_dir = os.path.join("crowdflower", self.name)
        self.citation = textwrap.dedent(_crowdflower_CITATION)
        def process_label(x):
            x=str(x)
            if x=="Y":
                return "Yes"
            if x=="N":
                return "No"
            return x
        self.process_label = process_label
        self.description = ""
        self.url = ""


class crowdflower(datasets.GeneratorBasedBuilder):

    """The General Language Understanding Evaluation (crowdflower) benchmark."""

    BUILDER_CONFIG_CLASS = crowdflowerConfig

    BUILDER_CONFIGS = [
    crowdflowerConfig(name="sentiment_nuclear_power",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="tweet_global_warming",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="airline-sentiment",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="corporate-messaging",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="economic-news",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="political-media-audience",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="political-media-bias",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="political-media-message",
                        text_features={"text": "text"},),
    crowdflowerConfig(name="text_emotion",
                        text_features={"text": "text"},),
    ]

    def _info(self):
        features = {text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)}
        if self.config.label_classes:
            features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
        else:
            features["label"] = datasets.Value("float32")
        features["idx"] = datasets.Value("int32")
        return datasets.DatasetInfo(
            description=_crowdflower_DESCRIPTION,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _crowdflower_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url)
        data_dir = os.path.join(dl_dir, self.config.data_dir)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(data_dir or "", "train.tsv"),
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, data_file, split):

        process_label = self.config.process_label
        label_classes = self.config.label_classes

        with open(data_file, encoding="latin-1") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)

            for n, row in enumerate(reader):

                example = {feat: row[col] for feat, col in six.iteritems(self.config.text_features)}
                example["idx"] = n
                #print(row)

                if self.config.label_column in row:
                    label = row[self.config.label_column]
                    label = process_label(label)
                    if label_classes and label not in label_classes:
                        continue
                    example["label"] = label
                else:
                    example["label"] = process_label(-1)
                if not example["label"] or not example["text"]:
                    continue
                yield example["idx"], example