Upload NC Crime Dateset.py

NC Crime Dateset.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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+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 = {NC Crime Dataset},
+author={huggingface, Inc.
+},
+year={2024}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+The dataset, compiled from public police incident reports across various cities in North Carolina, covers a period from the early 2000s through to 2024. It is intended to facilitate the study of crime trends and patterns.
+"""
+
+class NCCrimeDataset(datasets.GeneratorBasedBuilder):
+    """Dataset for North Carolina Crime Incidents."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+            {
+                "year": datasets.Value("int64"),
+                "city": datasets.Value("string"),
+                "crime_major_category": datasets.Value("string"),
+                "crime_detail": datasets.Value("string"),
+                "latitude": datasets.Value("float64"),
+                "longitude": datasets.Value("float64"),
+                "occurance_time": datasets.Value("string"),
+                "clear_status": datasets.Value("string"),
+                "incident_address": datasets.Value("string"),
+                "notes": datasets.Value("string"),
+            }),
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        # Download and extract the data file
+        downloaded_file_path = dl_manager.download_and_extract(
+            "https://drive.google.com/uc?id=1Se-B8Y-SdU0caZzGJyX_0YW44TZwaq3l")
+    
+        # Return a list of split generators
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_file_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_file_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        # Read the CSV file
+        df = pd.read_csv(filepath)
+        # Iterate over the rows and yield examples
+        for i, row in df.iterrows():
+            yield i, {
+                "year": int(row["year"]),
+                "city": row["city"],
+                "crime_major_category": row["crime_major_category"],
+                "crime_detail": row["crime_detail"],
+                "latitude": float(row["latitude"]),
+                "longitude": float(row["longitude"]),
+                "occurance_time": row["occurance_time"],
+                "clear_status": row["clear_status"],
+                "incident_address": row["incident_address"],
+                "notes": row["notes"],
+        }