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BPSAD.py

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+# 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.
+"""Brazilian Portuguese Sentiment Analysis Datasets (BPSAD)"""
+
+import csv
+import re
+import pandas as pd
+import json
+import os
+import datasets
+
+# Functions
+def get_text(text):
+    preproc_text = []
+    for sentence in text:
+        preproc_sentence = re.findall("'([^']*)'", sentence)
+        preproc_sentence = ' '.join(preproc_sentence)
+        preproc_text.append(preproc_sentence)
+    return preproc_text
+
+
+def get_kfold(text, label, kfold_ref, kfolds):
+    output_dictionary = {}
+    boolean_vec = [kfold_ref[i] 
+        in kfolds for i in range(len(kfold_ref))]
+    output_dictionary['text'] = [text[i] for i in range(len(text)) if boolean_vec[i]]
+    output_dictionary['label'] = [int(label[i]) for i in range(len(label)) if boolean_vec[i]]
+    output_dictionary['kfold'] = [kfold_ref[i] for i in range(len(text)) if boolean_vec[i]]
+    return output_dictionary 
+
+
+def load_bpsad_p(address):
+    table =  pd.read_csv(address, low_memory = False)
+    
+    # We'll get 'review_text_tokenized' and 'polarity'
+    text = table['review_text_tokenized'].to_list()
+    text = get_text(text)
+    label = table['polarity'].to_list()
+    # label = [int(i) for i in table['polarity'].to_list()]
+    kfold = table['kfold_polarity'].to_list()
+    # Removing nan instances from polarity
+    data_train = get_kfold(text, label, kfold, [1,2,3,4,5,6,7,8])
+    data_dev = get_kfold(text, label, kfold, [9])
+    data_test = get_kfold(text, label, kfold, [10])
+    return data_train, data_dev, data_test
+
+
+def load_bpsad_r(address):
+    table =  pd.read_csv(address, low_memory = False)
+    
+    # We'll get 'review_text_tokenized' and 'polarity'
+    text = table['review_text_tokenized'].to_list()
+    text = get_text(text)
+    label = table['rating'].to_list()
+    # label = [int(i) for i in table['rating'].to_list()]
+    kfold = table['kfold_polarity'].to_list()
+    # Removing nan instances from polarity
+    data_train = get_kfold(text, label, kfold, [1,2,3,4,5,6,7,8])
+    data_dev = get_kfold(text, label, kfold, [9])
+    data_test = get_kfold(text, label, kfold, [10])
+    return data_train, data_dev, data_test
+
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets"
+
+_DESCRIPTION = """
+The Brazilian Portuguese Sentiment Analysis Dataset (BPSAD) is composed by the
+concatenation of 5 differents sources (Olist, B2W Digital, Buscapé, UTLC-Apps and
+UTLC-Movies), each one is composed by evaluation sentences classified according
+to the polarity (0: negative; 1: positive) and ratings (1, 2, 3, 4 and 5 stars).
+"""
+
+_CITATION = r"""
+@misc{corpusCarolinaV1.1,
+    title={
+        Brazilian Portuguese Sentiment Analysis Datasets},
+    author={
+        Dias, Frederico},
+    howpublished={
+        \url{https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets}},
+    year={
+        2021},
+    note={Version 1},
+}
+"""
+
+_LICENSE = """
+"""
+
+_MANUAL_DOWNLOAD_INSTRUCTIONS = """
+data = datasets.load_dataset(
+    path = 'BPSAD.py',
+    name = 'Polarity'/'Rating',
+    data_dir = 'path to concatenated.csv')
+"""
+
+class BPSADPolarity(datasets.GeneratorBasedBuilder):
+    """BPSAD: Polarity classification task for BPSAD dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # 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="Polarity", version=VERSION, description="Polarity classification of the Brazilian Portuguese Sentiment Analysis Datasets (BPSAD)"),
+        datasets.BuilderConfig(name="Rating", version=VERSION, description="Rating classification of the Brazilian Portuguese Sentiment Analysis Datasets (BPSAD)"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "Polarity"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.Value("int8"),
+                "kfold": datasets.Value("int8")
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+
+        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=("sentence", "label"),
+            # 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):
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        # check if manual folder exists
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('bpsad', data_dir=...)`. Manual download instructions: {_MANUAL_DOWNLOAD_INSTRUCTIONS})"
+            )
+        
+        data_file = os.path.join(data_dir, "concatenated.csv")
+        # check if dataset file exists
+        if not os.path.exists(data_file):
+            raise FileNotFoundError(
+                f"{data_file} does not exist. Make sure you the downloaded data is inside the manual dir passed via `datasetts.load_dataset('bpsad', data_dir=...)`. Manual download instructions: {_MANUAL_DOWNLOAD_INSTRUCTIONS})"
+            )
+        if self.config.name == "Polarity":
+            data_train, data_dev, data_test = load_bpsad_p(data_file)
+        else:
+            data_train, data_dev, data_test = load_bpsad_r(data_file)
+
+        pd.DataFrame(data_train).to_csv(
+            os.path.join(data_dir, "train.csv"), index=False, header=False)
+        pd.DataFrame(data_dev).to_csv(
+            os.path.join(data_dir, "dev.csv"), index=False, header=False)
+        pd.DataFrame(data_test).to_csv(
+            os.path.join(data_dir, "test.csv"), index=False, header=False)
+
+        # with open(os.path.join(data_dir, "train.jsonl"),"w") as fname:
+        #     json.dump(data_train, fname)
+
+        # with open(os.path.join(data_dir, "dev.jsonl"), "w") as fname:
+        #     json.dump(data_dev, fname)
+
+        # with open(os.path.join(data_dir, "test.jsonl"), "w") as fname:
+        #     json.dump(data_test, fname)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "dev.csv"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.csv"),
+                    "split": "test"
+                },
+            ),
+        ]
+
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        with open(filepath, "r") as f:
+            reader = csv.reader(f)
+            # for key, row in enumerate(f):
+            for key, row in enumerate(reader):
+                # data = json.loads(row)
+                
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "text": row[0],
+                    "label": row[1],
+                    "kfold": row[2],
+                }