add rtGender loader

rtGender.py

@@ -0,0 +1,272 @@
+# coding=utf-8
+# 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.
+"""Class for loading datafrom rtGender"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+from enum import Enum
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{voigt-etal-2018-rtgender,
+    title = "{R}t{G}ender: A Corpus for Studying Differential Responses to Gender",
+    author = "Voigt, Rob  and
+      Jurgens, David  and
+      Prabhakaran, Vinodkumar  and
+      Jurafsky, Dan  and
+      Tsvetkov, Yulia",
+    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
+    month = may,
+    year = "2018",
+    address = "Miyazaki, Japan",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "https://www.aclweb.org/anthology/L18-1445",
+}
+"""
+
+_DESCRIPTION = """\
+RtGender is a corpus for studying responses to gender online, including posts and responses from Facebook, TED, Fitocracy, and Reddit where the gender of the source poster/speaker is known.
+"""
+
+_HOMEPAGE = "https://nlp.stanford.edu/robvoigt/rtgender/#contact"
+
+_LICENSE = "Research Only"
+
+_URL = "https://nlp.stanford.edu/robvoigt/rtgender/rtgender.tar.gz"
+
+
+class Config(Enum):
+    ANNOTATIONS = "annotations"
+    POSTS = "posts"
+    RESPONSES = "responses"
+    FB_POLI = "fb_politicians"
+    FB_PUB = "fb_public"
+    TED = "ted"
+    FITOCRACY = "fitocracy"
+    REDDIT = "reddit"
+
+
+class rtGender(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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=str(Config.ANNOTATIONS),
+            version=VERSION,
+            description="Covers 30k annotations",
+        ),
+        datasets.BuilderConfig(
+            name=str(Config.POSTS),
+            version=VERSION,
+            description="This part of my dataset covers a second domain",
+        ),
+        datasets.BuilderConfig(
+            name=str(Config.RESPONSES),
+            version=VERSION,
+            description="This part of my dataset covers a second domain",
+        )
+    ]
+    DEFAULT_CONFIG_NAME = str(Config.ANNOTATIONS)  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    POSTS_FEATURES = {
+        "source": datasets.Value("string"),
+        "op_id": datasets.Value("string"),
+        "op_gender": datasets.Value("string"),
+        "post_id": datasets.Value("string"),
+        "post_text": datasets.Value("string"),
+        "post_type": datasets.Value("string"),  # only for fb
+        "subreddit": datasets.Value("string"),  # only for reddit
+        "op_gender_visible": datasets.Value("string"),  # only for reddit
+    }
+
+    RESPONSES_FEATURES = {
+        "source": datasets.Value("string"),
+        "op_id": datasets.Value("string"),
+        "op_gender": datasets.Value("string"),
+        "post_id": datasets.Value("string"),
+        "responder_id": datasets.Value("string"),
+        "response_text": datasets.Value("string"),
+        "op_name": datasets.Value("string"),  # only for fb
+        "op_category": datasets.Value("string"),  # only for fb
+        "responder_gender": datasets.Value("string"),  # only for fitocracy and reddit
+        "responder_gender_visible": datasets.Value("string"),  # only for reddit
+        "subreddit": datasets.Value("string"),
+    }
+
+    ANNOTATION_FEATURES = {
+        "source": datasets.Value("string"),
+        "op_gender": datasets.Value("string"),
+        "post_text": datasets.Value("string"),
+        "response_text": datasets.Value("string"),
+        "sentiment": datasets.Value("string"),
+        "relevance": datasets.Value("string"),
+    }
+
+    def _info(self):
+
+        if (
+            self.config.name == Config.ANNOTATIONS
+        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(self.ANNOTATION_FEATURES)
+        elif self.config.name == Config.POSTS:
+            features = datasets.Features(self.POSTS_FEATURES)
+        else:
+            features = datasets.Features(self.RESPONSES_FEATURES)
+        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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # 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
+        data_dir = dl_manager.download_and_extract(_URL)
+        if self.config.name == Config.ANNOTATIONS:
+            files = ["annotations.csv"]
+        elif self.config.name == Config.POSTS:
+            files = [
+                "facebook_congress_posts.csv",
+                "facebook_wiki_posts.csv",
+                "fitocracy_posts.csv",
+                "reddit_posts.csv",
+            ]
+        else:
+            files = [
+                "facebook_congress_responses.csv",
+                "facebook_wiki_responses.csv",
+                "fitocracy_responses.csv",
+                "reddit_responses.csv",
+                "ted_responses.csv",
+            ]
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": files,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        filepaths,
+        split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        files = []
+        readers = {}
+        for fp in filepaths:
+            f = open(fp, encoding="utf-8")
+            reader = csv.reader(f)
+            next(reader)
+            readers[fp.replace(".csv", "")] = reader
+            files.append(f)
+
+        id_ = 0
+        for reader_name, reader in readers.items():
+            for row in reader:
+                if self.config.name == Config.ANNOTATIONS:
+                    yield id_, {
+                        "source": row[0],
+                        "op_gender": row[1],
+                        "post_text": row[2],
+                        "response_text": row[3],
+                        "sentiment": row[4],
+                        "relevance": row[5],
+                    }
+                elif self.config.name == Config.POSTS:
+                    r = {
+                        "source": reader_name,
+                        "op_id": row[0],
+                        "op_gender": row[1],
+                        "post_id": row[2],
+                        "post_text": row[3],
+                        "post_type": None,
+                        "subreddit": None,
+                        "op_gender_visible": None,
+                    }
+                    if "facebook" in reader_name:
+                        r["post_type"] = row[4]
+                    elif "reddit" in reader_name:
+                        r["subreddit"] = row[4]
+                        r["op_gender_visible"] = row[5]
+
+                    yield id_, r
+
+                else:
+                    r = {
+                        "source": reader_name,
+                        "op_id": row[0],
+                        "op_gender": row[1],
+                        "post_id": row[2],
+                        "responder_id": row[3],
+                        "response_text": row[4],
+                        "op_name": None,
+                        "op_category": None,
+                        "responder_gender": None,
+                        "responder_gender_visible": None,
+                        "subreddit": None
+                    }
+                    if "facebook" in reader_name:
+                        r["op_name"] = row[5]
+                        r["op_category"] = row[6]
+                    elif "fitocracy" in reader_name:
+                        r["responder_gender"] = row[5]
+                    elif "reddit" in reader_name:
+                        r["subreddit"] = row[5]
+                        r["responder_gender"] = row[6]
+                        r["responder_gender_visible"] = row[7]
+                    yield id_, r
+                id_ += 1
+
+        for fd in files:
+            fd.close()