Additional Text Classification Datasets

ludwig/datasets/amazon_review_polarity/__init__.py

@@ -0,0 +1,53 @@
+#! /usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2019 Uber Technologies, Inc.
+#
+# 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.
+# ==============================================================================
+from ludwig.datasets.base_dataset import BaseDataset, DEFAULT_CACHE_LOCATION
+from ludwig.datasets.mixins.download import  TarDownloadMixin
+from ludwig.datasets.mixins.load import CSVLoadMixin
+from ludwig.datasets.mixins.process import *
+
+def load(cache_dir=DEFAULT_CACHE_LOCATION, split=True):
+    dataset = AmazonPolarity(cache_dir=cache_dir)
+    return dataset.load(split=split)
+
+class AmazonPolarity(TarDownloadMixin, MultifileJoinProcessMixin,
+                 CSVLoadMixin, BaseDataset):
+    """
+        The Amazon Reviews Polarity dataset
+        Details:
+            34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 \
+            products, from the Stanford Network Analysis Project (SNAP). \
+            This dataset contains 600,000 training samples and 130,000 \
+            testing samples in each class. 
+        Dataset source: 
+            Character-level Convolutional Networks for Text Classification
+            Xiang Zhang et al., 2015
+    """
+    def __init__(self, cache_dir=DEFAULT_CACHE_LOCATION):
+        super().__init__(dataset_name="amazon_review_polarity", cache_dir=cache_dir)
+
+    def process_downloaded_dataset(self):
+        super(AmazonPolarity, self).process_downloaded_dataset(header=None)
+        processed_df = pd.read_csv(os.path.join(self.processed_dataset_path,
+                                                self.csv_filename))
+        processed_df.columns = ['label', 'review_tile', 'review_text', 'split']
+        processed_df.to_csv(
+            os.path.join(self.processed_dataset_path, self.csv_filename),
+            index=False
+        )
+
+
+

ludwig/datasets/amazon_review_polarity/config.yaml

@@ -0,0 +1,8 @@
+version: 1.0
+download_urls:
+  - "https://s3.amazonaws.com/fast-ai-nlp/amazon_review_polarity_csv.tgz"
+split_filenames:
+  train_file: train.csv
+  test_file: test.csv
+download_file_type: csv
+csv_filename: amazon_review_polarity.csv

ludwig/datasets/amazon_reviews/__init__.py

@@ -0,0 +1,53 @@
+#! /usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2019 Uber Technologies, Inc.
+#
+# 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.
+# ==============================================================================
+from ludwig.datasets.base_dataset import BaseDataset, DEFAULT_CACHE_LOCATION
+from ludwig.datasets.mixins.download import  TarDownloadMixin
+from ludwig.datasets.mixins.load import CSVLoadMixin
+from ludwig.datasets.mixins.process import *
+
+def load(cache_dir=DEFAULT_CACHE_LOCATION, split=True):
+    dataset = AmazonReviews(cache_dir=cache_dir)
+    return dataset.load(split=split)
+
+class AmazonReviews(TarDownloadMixin, MultifileJoinProcessMixin,
+                 CSVLoadMixin, BaseDataset):
+    """
+        The Amazon Reviews dataset
+        Details:
+            34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 \
+            products, from the Stanford Network Analysis Project (SNAP). \
+            This dataset contains 600,000 training samples and 130,000 \
+            testing samples in each class. 
+        Dataset source: 
+            Character-level Convolutional Networks for Text Classification
+            Xiang Zhang et al., 2015
+    """
+    def __init__(self, cache_dir=DEFAULT_CACHE_LOCATION):
+        super().__init__(dataset_name="amazon_reviews", cache_dir=cache_dir)
+
+    def process_downloaded_dataset(self):
+        super(AmazonReviews, self).process_downloaded_dataset(header=None)
+        processed_df = pd.read_csv(os.path.join(self.processed_dataset_path,
+                                                self.csv_filename))
+        processed_df.columns = ['label', 'review_tile', 'review_text', 'split']
+        processed_df.to_csv(
+            os.path.join(self.processed_dataset_path, self.csv_filename),
+            index=False
+        )
+
+
+

ludwig/datasets/amazon_reviews/config.yaml

@@ -0,0 +1,8 @@
+version: 1.0
+download_urls:
+  - "https://s3.amazonaws.com/fast-ai-nlp/amazon_review_full_csv.tgz"
+split_filenames:
+  train_file: train.csv
+  test_file: test.csv
+download_file_type: csv
+csv_filename: amazon_reviews.csv

ludwig/datasets/dbpedia/__init__.py

@@ -0,0 +1,51 @@
+#! /usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2019 Uber Technologies, Inc.
+#
+# 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.
+# ==============================================================================
+from ludwig.datasets.base_dataset import BaseDataset, DEFAULT_CACHE_LOCATION
+from ludwig.datasets.mixins.download import  TarDownloadMixin
+from ludwig.datasets.mixins.load import CSVLoadMixin
+from ludwig.datasets.mixins.process import *
+
+def load(cache_dir=DEFAULT_CACHE_LOCATION, split=True):
+    dataset = DBPedia(cache_dir=cache_dir)
+    return dataset.load(split=split)
+
+class DBPedia(TarDownloadMixin, MultifileJoinProcessMixin,
+                 CSVLoadMixin, BaseDataset):
+    """
+        The DBPedia Ontology dataset. 
+        Details:
+            40,000 training samples and 5,000 testing samples from 14 \
+            nonoverlapping classes from DBpedia 2014. 
+        Dataset source: 
+            Character-level Convolutional Networks for Text Classification
+            Xiang Zhang et al., 2015
+    """
+    def __init__(self, cache_dir=DEFAULT_CACHE_LOCATION):
+        super().__init__(dataset_name="dbpedia", cache_dir=cache_dir)
+
+    def process_downloaded_dataset(self):
+        super(DBPedia, self).process_downloaded_dataset(header=None)
+        processed_df = pd.read_csv(os.path.join(self.processed_dataset_path,
+                                                self.csv_filename))
+        processed_df.columns = ['label', 'title', 'content', 'split']
+        processed_df.to_csv(
+            os.path.join(self.processed_dataset_path, self.csv_filename),
+            index=False
+        )
+
+
+

ludwig/datasets/dbpedia/config.yaml

@@ -0,0 +1,8 @@
+version: 1.0
+download_urls:
+  - "https://s3.amazonaws.com/fast-ai-nlp/dbpedia_csv.tgz"
+split_filenames:
+  train_file: train.csv
+  test_file: test.csv
+download_file_type: csv
+csv_filename: dbpedia.csv

ludwig/datasets/ethos_binary/__init__.py

@@ -0,0 +1,42 @@
+#! /usr/bin/env python
+# coding=utf-8
+# Copyright (c) 2019 Uber Technologies, Inc.
+#
+# 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.
+# ==============================================================================
+from ludwig.datasets.base_dataset import BaseDataset, DEFAULT_CACHE_LOCATION
+from ludwig.datasets.mixins.download import UncompressedFileDownloadMixin
+from ludwig.datasets.mixins.load import CSVLoadMixin
+from ludwig.datasets.mixins.process import *
+
+def load(cache_dir=DEFAULT_CACHE_LOCATION, split=False):
+    dataset = EthosBinary(cache_dir=cache_dir)
+    return dataset.load(split=split)
+
+class EthosBinary(UncompressedFileDownloadMixin, IdentityProcessMixin,
+            CSVLoadMixin, BaseDataset):
+    """The Ethos Hate Speech Dataset.
+
+    Source Paper: 
+        ETHOS: an Online Hate Speech Detection Dataset
+            Ioannis Mollas and Zoe Chrysopoulou and Stamatis Karlos and
+            Grigorios Tsoumakas
+    """
+    def __init__(self, cache_dir=DEFAULT_CACHE_LOCATION):
+        super().__init__(dataset_name="ethos_binary", cache_dir=cache_dir)
+
+    def load_processed_dataset(self, split):
+        dataset_csv = os.path.join(self.processed_dataset_path,
+                                   self.csv_filename)
+        data_df = pd.read_csv(dataset_csv, sep=';')
+        return data_df

ludwig/datasets/ethos_binary/config.yaml

@@ -0,0 +1,5 @@
+version: 1.0
+download_urls:
+  - https://raw.githubusercontent.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset/master/ethos/ethos_data/Ethos_Dataset_Binary.csv
+download_file_type: csv
+csv_filename: Ethos_Dataset_Binary.csv

ludwig/datasets/mixins/download.py

@@ -17,14 +17,14 @@
 import gzip
 import os
 import shutil
+import tarfile
 import urllib.request
 from io import BytesIO
 from urllib.request import urlopen
 from zipfile import ZipFile
 
 from tqdm import tqdm
 
-
 class TqdmUpTo(tqdm):
     """Provides progress bar for `urlretrieve`.
 
@@ -68,6 +68,43 @@ def download_raw_dataset(self):
     def download_urls(self):
         return self.config["download_urls"]
 
+class TarDownloadMixin:
+    """Downloads the compressed tar file containing the training data and extracts the contents."""
+
+    config: dict
+    raw_dataset_path: str
+    raw_temp_path: str
+
+    def download_raw_dataset(self):
+        """
+        Download the raw dataset and extract the contents of the tar file and
+        store that in the cache location.
+        """
+        os.makedirs(self.raw_temp_path, exist_ok=True)
+        for url in self.download_urls:
+            filename = url.split('/')[-1]
+            with TqdmUpTo(unit='B', unit_scale=True, unit_divisor=1024,
+                          miniters=1, desc=filename) as t:
+                urllib.request.urlretrieve(
+                    url,
+                    os.path.join(self.raw_temp_path, filename),
+                    t.update_to
+                )
+
+            download_folder_name = url.split('/')[-1].split('.')[0]
+            file_path = os.path.join(self.raw_temp_path, filename)
+            with tarfile.open(file_path) as tar_file:
+                tar_file.extractall(path=self.raw_temp_path)
+
+            for f in os.scandir(os.path.join(self.raw_temp_path, 
+                                             download_folder_name)):
+                shutil.copyfile(f, os.path.join(self.raw_temp_path, f.name))
+
+        os.rename(self.raw_temp_path, self.raw_dataset_path)
+
+    @property
+    def download_urls(self):
+        return self.config['download_urls']
 
 class GZipDownloadMixin:
     """Downloads the gzip archive file containing the training data and extracts the contents."""

ludwig/datasets/mixins/process.py

@@ -36,7 +36,7 @@ class MultifileJoinProcessMixin:
     raw_dataset_path: str
     processed_dataset_path: str
 
-    def read_file(self, filetype, filename):
+    def read_file(self, filetype, filename, header):
         if filetype == 'json':
             file_df = pd.read_json(
                 os.path.join(self.raw_dataset_path, filename))
@@ -48,17 +48,21 @@ def read_file(self, filetype, filename):
                 os.path.join(self.raw_dataset_path, filename))
         elif filetype == 'csv':
             file_df = pd.read_csv(
-                os.path.join(self.raw_dataset_path, filename))
+                os.path.join(self.raw_dataset_path, filename), header=header)
         else:
             raise ValueError(f'Unsupported file type: {filetype}')
         return file_df
 
-    def process_downloaded_dataset(self):
+    def process_downloaded_dataset(self, header=0):
+        """Processes dataset
+
+        :param header: indicates whether raw  data files contain headers
+        """
         downloaded_files = self.download_filenames
         filetype = self.download_file_type
         all_files = []
         for split_name, filename in downloaded_files.items():
-            file_df = self.read_file(filetype, filename)
+            file_df = self.read_file(filetype, filename, header)
             if split_name == 'train_file':
                 file_df['split'] = 0
             elif split_name == 'val_file':

ludwig/datasets/sst2/__init__.py

@@ -19,9 +19,12 @@
 
 
 def load(cache_dir=DEFAULT_CACHE_LOCATION, split=False,
-         include_subtrees=False, convert_parentheses=True):
+         include_subtrees=False, convert_parentheses=True, 
+         remove_duplicates=False):
+    print("loaddding")
     dataset = SST2(cache_dir=cache_dir, include_subtrees=include_subtrees,
-                   convert_parentheses=convert_parentheses)
+                   convert_parentheses=onvert_parentheses, 
+                   remove_duplicates=remove_duplicates)
     return dataset.load(split=split)
 
 
@@ -54,7 +57,7 @@ def __init__(self, cache_dir=DEFAULT_CACHE_LOCATION,
                          include_subtrees=include_subtrees,
                          discard_neutral=True,
                          convert_parentheses=convert_parentheses,
-                         remove_duplicates=False)
+                         remove_duplicates=remove_duplicates)
 
     def get_sentiment_label(self, id2sent, phrase_id):
         sentiment = id2sent[phrase_id]