Upload treesplantingsitesdataset.py

treesplantingsitesdataset.py

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+# -*- coding: utf-8 -*-
+"""TreesPlantingSitesDataset
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Hvt3Y131OjTl7oGQGS55S_v7-aYu1Yj8
+"""
+
+!pip install datasets
+from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
+from datasets.features import Features, Value, Sequence, ClassLabel
+import pandas as pd
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from datasets import Features, Value, ClassLabel
+
+class TreesPlantingSitesDataset(DatasetBuilder):
+    VERSION = "1.0.0"
+
+    def _info(self):
+        # Specifies the dataset's features
+        return DatasetInfo(
+            description="This dataset contains information about tree planting sites from CSV and GeoJSON files.",
+            features=Features({
+                    "OBJECTID": Value("int32"),  # Unique identifier for each record
+                    "streetaddress": Value("string"),  # Street address of the tree planting site
+                    "city": Value("string"),  # City where the tree planting site is located
+                    "zipcode": Value("int32"),  # Zip code of the tree planting site
+                    "facilityid": Value("int32"),  # Identifier for the facility
+                    "present": ClassLabel(names=["False", "True"]),  # Indicates if the tree is present (assuming boolean represented as string)
+                    "neighborhood": Value("string"),  # Neighborhood where the tree planting site is located
+                    "plantingwidth": Value("string"),  # Width available for planting
+                    "plantingcondition": Value("string"),  # Condition of the planting site
+                    "underpowerlines": ClassLabel(names=["False", "True"]),  # Indicates if the site is under power lines
+                    "matureheight": Value("string"),  # Expected mature height of the tree
+                    "GlobalID": Value("string"),  # Global unique identifier
+                    "created_user": Value("string"),  # User who created the record
+                    "created_date": Value("string"),  # Date when the record was created
+                    "last_edited_user": Value("string"),  # User who last edited the record
+                    "last_edited_date": Value("string"),  # Date when the record was last edited
+                    "geometry": Value("string")  # Geometry feature from GeoJSON
+                }),
+            supervised_keys=None,  # Provide if the dataset is for supervised learning
+            homepage="https://example.com/dataset-homepage",  # Replace with the actual URL
+            citation="Citation for the dataset",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        # Downloads the data and defines the splits
+        urls_to_download = {
+            "csv": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
+            "geojson": "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo"
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            SplitGenerator(name=Split.TRAIN, gen_kwargs={
+                "csv_path": downloaded_files["csv"],
+                "geojson_path": downloaded_files["geojson"]
+            }),
+            # If you have additional splits, add them here
+        ]
+
+    # ... (previous code)
+
+    def _generate_examples(self, csv_path, geojson_path):
+
+        # Load the data into DataFrame and GeoDataFrame
+        csv_data = pd.read_csv(csv_path)
+        geojson_data = gpd.read_file(geojson_path)
+
+        # Merge the CSV data with the GeoJSON data on the 'OBJECTID' column
+        gdf = geojson_data.merge(csv_data, on='OBJECTID')
+        columns_to_extract = [
+            "OBJECTID", "streetaddress", "city", "zipcode", "facilityid", "present",
+            "neighborhood", "plantingwidth", "plantingcondition", "underpowerlines",
+            "matureheight", "GlobalID", "created_user", "created_date",
+            "last_edited_user", "last_edited_date", "geometry"
+        ]
+
+        # Extract the specified columns
+        extracted_gdf = gdf[columns_to_extract]
+        # Basic statistics: Count the number of planting sites
+        number_of_planting_sites = gdf['present'].value_counts()
+        print("Number of planting sites:", number_of_planting_sites)
+
+        # Spatial analysis: Group by neighborhood to see the distribution of features
+        neighborhood_analysis = gdf.groupby('neighborhood').size()
+        print("Distribution by neighborhood:", neighborhood_analysis)
+
+        # Visual analysis: Plot the points on a map
+        gdf.plot(marker='*', color='green', markersize=5)
+        plt.title('TreesPlantingSitesDataset')
+        for id_, row in gdf.iterrows():
+          yield id_, {
+              "OBJECTID": row["OBJECTID"],
+              "neighborhood": row["neighborhood"],  # Assuming 'neighborhood' is a column name in your data
+              "present": row["present"],  # Assuming 'present' indicates if a tree is present
+              "geometry": row["geometry"],  # Geometry information from GeoJSON
+              # Include other fields from your data
+          }