Initial commit

This dataset is a quick-and-dirty benchmark for predicting ratings across
different domains and on different rating scales based on text. It pulls in a
bunch of rating datasets, takes at most 1000 instances from each and combines
them into a big dataset.

.gitignore

@@ -0,0 +1 @@
+/__pycache__

README.md

@@ -1,3 +1,8 @@
----
-license: unknown
----
+----
+-license: unknown
+----
+
+This dataset is a quick-and-dirty benchmark for predicting ratings across
+different domains and on different rating scales based on text. It pulls in a
+bunch of rating datasets, takes at most 1000 instances from each and combines
+them into a big dataset.

cross_domain_review_benchmark.py

@@ -0,0 +1,228 @@
+# 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.
+
+"""
+This dataset is a quick-and-dirty benchmark for predicting ratings across
+different domains and on different rating scales based on text. It pulls in a
+bunch of rating datasets, takes at most 1000 instances from each and combines
+them into a big dataset.
+"""
+
+import datasets
+from dataclasses import dataclass
+from typing import Callable, Any
+import operator
+import shutil
+import os
+import sys
+from os.path import join as pjoin
+
+from datasets import concatenate_datasets, load_dataset
+from more_itertools.recipes import take
+
+
+@dataclass
+class SubDataset:
+    source: Any
+    nick: str
+    out_of: int
+    get_review: Callable[[Any], Any]
+    get_rating: Callable[[Any], Any]
+
+
+gi = operator.itemgetter
+
+
+def warn(msg):
+    print(file=sys.stderr)
+    print(f" ** Warning: {msg} **", file=sys.stderr)
+    print(file=sys.stderr)
+
+
+def round_near(x, eps=0.001):
+    x_rnd = int(x + 0.5)
+    if abs(x_rnd - x) > eps:
+        warn("got {x_rnd} when rounding {x}")
+    return x_rnd
+
+
+@dataclass
+class SplitHFSrc:
+    name: str
+
+    def load(self):
+        return load_dataset(self.name, streaming=True)
+
+
+@dataclass
+class TrainOnlyHFSrc:
+    name: str
+
+    def load(self):
+        if hasattr(self, "_cached"):
+            return self._cached
+        self._cached = load_dataset(self.name)["train"].train_test_split(test_size=0.5, seed=42)
+        return self._cached
+
+
+_kaggle_api = None
+
+
+def get_kaggle_api():
+    global _kaggle_api 
+    if _kaggle_api is not None:
+        return _kaggle_api 
+    from kaggle.api.kaggle_api_extended import KaggleApi
+    _kaggle_api = KaggleApi()
+    _kaggle_api.authenticate()
+    return _kaggle_api
+
+
+@dataclass
+class KaggleSrc:
+    name: str
+    file: str
+
+    def load(self):
+        if hasattr(self, "_cached"):
+            return self._cached
+        kaggle_api = get_kaggle_api()
+        dir_name = self.name.replace("/", "__")
+        if os.path.exists(dir_name):
+            shutil.rmtree(dir_name)
+        os.mkdir(dir_name)
+        try:
+            file_path = pjoin(dir_name, self.file.rsplit("/", 1)[-1])
+            kaggle_api.dataset_download_file(self.name, self.file, path=dir_name)
+            dataset = load_dataset("csv", data_files=file_path + ".zip")
+            return dataset["train"].train_test_split(test_size=0.5, seed=42)
+        finally:
+            shutil.rmtree(dir_name)
+
+
+def int_or_drop(col):
+    def inner(row):
+        try:
+            n = float(row[col])
+        except ValueError:
+            return None
+        return round_near(n)
+    return inner
+
+
+DATASETS = [
+    SubDataset(SplitHFSrc("juliensimon/amazon-shoe-reviews"), "amazon-shoes", 5, gi("text"), lambda row: row["labels"] + 1),
+    # TODO: Appears to be corrupt
+    #SubDataset("florentgbelidji/edmunds-car-ratings", "car-ratings", 40, lambda row: row["Review"].strip(), lambda row: round_near(row["Rating"] * 8) - 7),
+    SubDataset(TrainOnlyHFSrc("florentgbelidji/car-reviews"), "car-ratings", 5, gi("Review"), gi("Rating")),
+    SubDataset(SplitHFSrc("codyburker/yelp_review_sampled"), "yelp", 5, gi("text"), gi("stars")),
+    SubDataset(SplitHFSrc("kkotkar1/course-reviews"), "course-reviews", 5, gi("review"), gi("label")),
+    SubDataset(TrainOnlyHFSrc("app_reviews"), "app-reviews", 5, gi("review"), gi("star")),
+    SubDataset(TrainOnlyHFSrc("LoganKells/amazon_product_reviews_video_games"), "amazon-games", 5, gi("reviewText"), lambda row: round_near(row["overall"]) + 1),
+    SubDataset(KaggleSrc("zynicide/wine-reviews", "winemag-data-130k-v2.csv"), "wine-reviews", 100, gi("description"), gi("points")),
+    SubDataset(KaggleSrc("sadmadlad/imdb-user-reviews", "Pulp Fiction/movieReviews.csv"), "imdb-user-reviews", 10, gi("Review"), int_or_drop("User's Rating out of 10")),
+    # TODO: Unicode decoding error
+    #SubDataset(KaggleSrc("arushchillar/disneyland-reviews", "DisneylandReviews.csv"), "disneyland-reviews", 5, gi("Review_Text"), gi("Rating")),
+]
+
+_DESCRIPTION = __doc__
+
+_HOMEPAGE = ""
+
+_LICENSE = "Mixed"
+
+
+class CrossDomainReviewBenchmarkDataset(datasets.GeneratorBasedBuilder):
+    _DESCRIPTION
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "rating": datasets.Value("uint8"),
+                "out_of": datasets.Value("uint8"),
+                "dataset": datasets.Value("string")
+            }
+        )
+        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", "rating"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation="",
+        )
+
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split": "test"
+                },
+            ),
+        ]
+
+    def _generate_examples(self, split):
+        key = 0
+        for ds_info in DATASETS:
+            subdataset = ds_info.source.load()
+            lowest = float("inf")
+            highest = float("-inf")
+            got = 0
+            for row in subdataset[split]:
+                review = ds_info.get_review(row)
+                if review is None:
+                    continue
+                rating = ds_info.get_rating(row)
+                if rating is None:
+                    continue
+                assert 1 <= rating <= ds_info.out_of, f"Expected {rating} (inclusively) between 1 and {ds_info.out_of}"
+                lowest = min(lowest, rating)
+                highest = max(highest, rating)
+                yield key, {
+                    "text": review,
+                    "rating": rating,
+                    "out_of": ds_info.out_of,
+                    "dataset": ds_info.nick
+                }
+                key += 1
+                got += 1
+                if got >= 1000:
+                    break
+            if lowest != 1:
+                warn(
+                    f"Lowest rating in {ds_info.nick} was {lowest}, "
+                    "would suppose it would be 1"
+                )
+            if highest != ds_info.out_of:
+                warn(
+                    f"Highest rating in {ds_info.nick} was {highest}, "
+                    f"would suppose it would be {ds_info.out_of}"
+                )

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pyproject.toml

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "cross_domain_reviews"
+version = "0.1.0"
+description = "Hugging face dataset combining a bunch of review datasets from different domains"
+authors = ["Frankie Robertson <frankie@robertson.name>"]
+license = "Apache-v2"
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.8"
+datasets = "^2.5.2"
+more-itertools = "^8.14.0"
+kaggle = "^1.5.12"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"