Delete treesplantingsitesdataset.py

treesplantingsitesdataset.py

@@ -1,102 +0,0 @@
-# -*- 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
-          }