Upload yelp_dataset.py

yelp_dataset.py

@@ -0,0 +1,219 @@
+# -*- coding: utf-8 -*-
+"""yelp_dataset.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/14UtK4YCjMSx4cVbUb9NBRHviWZg07dtY
+"""
+
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+!pip install datasets
+
+import csv
+import json
+import os
+from typing import List
+import datasets
+import logging
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_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 = "https://www.yelp.com/dataset/download"
+
+# 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://yelpdata.s3.us-west-2.amazonaws.com/"
+_URLS = {
+    "train": _URL + "yelp_train.csv",
+    "test": _URL + "yelp_test.csv",
+}
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class YelpDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    _URLS = _URLS
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+      raise ValueError('woops!')
+      return datasets.DatasetInfo(
+          description=_DESCRIPTION,
+          features=datasets.Features(
+              {
+                  "business_id": datasets.Value("string"),
+                  "name": datasets.Value("string"),
+                  "address": datasets.Value("string"),
+                  "city": datasets.Value("string"),
+                  "state": datasets.Value("string"),
+                  "postal_code": datasets.Value("string"),
+                  "latitude": datasets.Value("float64"),
+                  "longitude": datasets.Value("float64"),
+                  "stars_x": datasets.Value("float64"),
+                  "review_count": datasets.Value("int64"),
+                  "is_open": datasets.Value("int64"),
+                  "attributes": datasets.features.Sequence(
+                      { "RestaurantsDelivery":datasets.Value("boolean"),
+                        "OutdoorSeating":datasets.Value("boolean"),
+                        "BusinessAcceptsCreditCards":datasets.Value("boolean"),
+                        "BusinessParking": datasets.features.Sequence(
+                            {'garage':datasets.Value("boolean"),
+                             'street':datasets.Value("boolean"),
+                             'validated':datasets.Value("boolean"),
+                             'lot':datasets.Value("boolean"),
+                             'valet':datasets.Value("boolean")}),
+                        "BikeParking":datasets.Value("boolean"),
+                        "RestaurantsPriceRange2":datasets.Value("int64"),
+                        "RestaurantsTakeOut":datasets.Value("boolean"),
+                        "ByAppointmentOnly":datasets.Value("boolean"),
+                        "WiFi":datasets.Value("string"),
+                        "Alcohol":datasets.Value("string"),
+                        "Caters":datasets.Value("boolean"),
+                        'Corkage':datasets.Value("boolean"),
+                        'WheelchairAccessible':datasets.Value("boolean"),
+                        'HasTV':datasets.Value("boolean"),
+                        'Open24Hours':datasets.Value("boolean"),
+                        'BikeParking':datasets.Value("boolean"),
+                        'Ambience': datasets.features.Sequence(
+                            {'touristy': datasets.Value("boolean"),
+                             'hipster': datasets.Value("boolean"),
+                             'romantic': datasets.Value("boolean"),
+                             'divey': datasets.Value("boolean"),
+                             'intimate': datasets.Value("boolean"),
+                             'trendy': datasets.Value("boolean"),
+                             'upscale': datasets.Value("boolean"),
+                             'classy': datasets.Value("boolean"),
+                             'casual': datasets.Value("boolean")}),
+                        'RestaurantsAttire': datasets.Value("string"),
+                        'DriveThru':datasets.Value("boolean"),
+                        'BusinessAcceptsBitcoin':datasets.Value("boolean"),
+                        'NoiseLevel': datasets.Value("string"),
+                        'Smoking': datasets.Value("string"),
+                        'BestNights':datasets.features.Sequence(
+                            {u'monday': datasets.Value("boolean"),
+                             u'tuesday': datasets.Value("boolean"),
+                             u'wednesday': datasets.Value("boolean"),
+                             u'thursday': datasets.Value("boolean"),
+                             u'friday': datasets.Value("boolean"),
+                             u'saturday': datasets.Value("boolean"),
+                             u'sunday': datasets.Value("boolean")}),
+                        'GoodForMeal':datasets.features.Sequence(
+                            {'dessert': datasets.Value("boolean"),
+                             'latenight': datasets.Value("boolean"),
+                             'lunch': datasets.Value("boolean"),
+                             'dinner': datasets.Value("boolean"),
+                             'brunch': datasets.Value("boolean"),
+                             'breakfast': datasets.Value("boolean")}),
+                        'RestaurantsGoodForGroups':datasets.Value("boolean"),
+                        'GoodForDancing':datasets.Value("boolean"),
+                        'Music':datasets.features.Sequence(
+                            {'dj': datasets.Value("boolean"),
+                            'background_music': datasets.Value("boolean"),
+                            'no_music': datasets.Value("boolean"),
+                             'jukebox': datasets.Value("boolean"),
+                             'live': datasets.Value("boolean"),
+                             'video': datasets.Value("boolean"),
+                             'karaoke': datasets.Value("boolean")}),
+                        'DietaryRestrictions':datasets.features.Sequence(
+                            {'dairy-free': datasets.Value("boolean"),
+                             'gluten-free': datasets.Value("boolean"),
+                             'vegan': datasets.Value("boolean"),
+                             'kosher': datasets.Value("boolean"),
+                             'halal': datasets.Value("boolean"),
+                             'soy-free': datasets.Value("boolean"),
+                             'vegetarian': datasets.Value("boolean")}),
+                        'RestaurantsReservations':datasets.Value("boolean"),
+                        'HairSpecializesIn':datasets.features.Sequence(
+                          {'straightperms': datasets.Value("boolean"),
+                           'coloring': datasets.Value("boolean"),
+                           'extensions': datasets.Value("boolean"),
+                           'africanamerican': datasets.Value("boolean"),
+                           'curly': datasets.Value("boolean"),
+                           'kids': datasets.Value("boolean"),
+                           'perms': datasets.Value("boolean"),
+                           'asian': datasets.Value("boolean")}),
+                        'BYOBCorkage': datasets.Value("string"),
+                        'BYOB':datasets.Value("boolean"),
+                        'DogsAllowed':datasets.Value("boolean"),
+                        'RestaurantsCounterService':datasets.Value("boolean"),
+                        'RestaurantsTableService':datasets.Value("boolean"),
+                        'CoatCheck':datasets.Value("boolean"),
+                        'AgesAllowed': datasets.Value("string"),
+                        'AcceptsInsurance':datasets.Value("boolean"),
+                        'HappyHour':datasets.Value("boolean"),
+                        'GoodForKids':datasets.Value("boolean"),
+                      }
+                  ),
+                  "categories": datasets.Value("string"),
+                  "hours": datasets.Value("string"),
+                  "review_id": datasets.Value("string"),
+                  "user_id": datasets.Value("string"),
+                  "stars_y": datasets.Value("float64"),
+                  "useful": datasets.Value("int64"),
+                  "funny": datasets.Value("int64"),
+                  "cool": datasets.Value("int64"),
+                  "text": datasets.Value("string"),
+                  "date": datasets.Value("string"),
+
+              }
+          ),
+          # No default supervised_keys (as we have to pass both question
+          # and context as input).
+          supervised_keys=None,
+          homepage="https://www.yelp.com/dataset/download",
+          citation=_CITATION,
+      )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+      urls_to_download = self._URLS
+      downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+      return [
+          datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+          datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+      ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as csv_file:
+            reader = csv.DictReader(csv_file)
+            for i, row in enumerate(reader):
+                # Convert the row to a dictionary, removing any null values
+                example = {key: value for key, value in row.items() if value is not None}
+                yield i, example