Upload sarcasm.py

sarcasm.py

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+# -*- coding: utf-8 -*-
+"""Sarcasm
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/15_wDQ9RJXwyxbomu2F1k0pK9H7XZ1cuT
+"""
+
+import pandas as pd
+import geopandas as gpd
+from datasets import (
+    GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split,
+    Features, Value, BuilderConfig, DatasetInfo
+)
+import matplotlib.pyplot as plt
+import seaborn as sns
+import csv
+import json
+from shapely.geometry import Point
+
+# URL definitions
+_URLS = {
+    "csv_file": "https://drive.google.com/uc?export=download&id=1WcPqVZasDy1nmGcildLS-uw_-04I9Max",
+}
+
+class Sarcasm(GeneratorBasedBuilder):
+    VERSION = Version("1.0.0")
+
+    def _info(self):
+        return DatasetInfo(
+            description="This dataset combines information from sarcasm",
+            features=Features({
+                "comments": Value("string"),
+                "contains_slash_s": Value("int64"),
+            }),
+            supervised_keys=None,
+            homepage="https://github.com/AuraMa111?tab=repositories",
+            citation="Citation for the combined dataset",
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        data_file_path = downloaded_files["combined_data.csv"]
+
+        num_examples = pd.read_csv(data_file_path).shape[0]
+        train_size = int(0.6 * num_examples)
+        val_size = int(0.2 * num_examples)
+        test_size = num_examples - train_size - val_size
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"data_file_path": data_file_path, "split": Split.TRAIN, "size": train_size}
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={"data_file_path": data_file_path, "split": Split.VALIDATION, "size": val_size}
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={"data_file_path": data_file_path, "split": Split.TEST, "size": test_size}
+            ),
+        ]
+
+    def _generate_examples(self, data_file_path, split, size):
+        data = pd.read_csv(data_file_path)
+        if split == Split.TRAIN:
+            subset_data = data[:size]
+        elif split == Split.VALIDATION:
+            subset_data = data[size:size*2]
+        elif split == Split.TEST:
+            subset_data = data[size*2:]
+
+        for index, row in subset_data.iterrows():
+            example = {
+                "comments": row["comments"],
+                "contains_slash_s": row["contains_slash_s"]
+            }
+            yield index, example
+
+# Instantiate your dataset class
+sarcasm = Sarcasm()
+
+# Build the datasets
+sarcasm.download_and_prepare()
+
+# Access the datasets for training, validation, and testing
+dataset_train = sarcasm.as_dataset(split='train')
+dataset_validation = sarcasm.as_dataset(split='validation')
+dataset_test = sarcasm.as_dataset(split='test')