Create crowdflower.py

crowdflower.py

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+# 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.
+# 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
+"""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