Upload durhamtrees.py

durhamtrees.py

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+# -*- coding: utf-8 -*-
+"""DurhamTrees
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Hvt3Y131OjTl7oGQGS55S_v7-aYu1Yj8
+"""
+
+from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
+from datasets.features import Features, Value, ClassLabel
+import pandas as pd
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import csv
+import json
+import os
+from typing import List
+import datasets
+
+
+class DurhamTrees(DatasetBuilder):
+    _URLS = {
+        "csv": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
+        "geojson": "https://drive.google.com/uc?export=download&id=1jpFVanNGy7L5tVO-Z_nltbBXKvrcAoDo"
+    }
+
+    VERSION = datasets.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({
+                "geometry": Value("string"),  # Geometry feature, usually spatial data (GeoJSON format)
+                "OBJECTID": Value("int64"),  # 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("int64"),  # Zip code of the tree planting site
+                "facilityid": Value("int64"),  # Identifier for the facility
+                "present": Value("string"),  # Presence status, assumed to be string
+                "genus": Value("string"),  # Genus of the tree
+                "species": Value("string"),  # Species of the tree
+                "commonname": Value("string"),  # Common name of the tree
+                "diameterin": Value("float64"),  # Diameter in inches
+                "heightft": Value("float64"),  # Height in feet (changed to "float64")
+                "condition": Value("string"),  # Condition of the tree
+                "contractwork": Value("string"),  # Contract work information
+                "neighborhood": Value("string"),  # Neighborhood where the tree is located
+                "program": Value("string"),  # Program under which the tree was planted
+                "plantingw": Value("string"),  # Width available for planting
+                "plantingcond": Value("string"),  # Planting condition
+                "underpwerlins": Value("string"),  # Whether the tree is under power lines
+                "GlobalID": Value("string"),  # Global identifier
+                "created_user": Value("string"),  # User who created the record
+                "last_edited_user": Value("string"),  # User who last edited the record
+                "isoprene": Value("float64"),  # Isoprene emission rate
+                "monoterpene": Value("float64"),
+                "coremoved_ozperyr": Value("float64"),  # Carbon monoxide removed, in ounces per year
+                "coremoved_dolperyr": Value("float64"),  # Monetary value of carbon monoxide removal per year
+                "o3removed_ozperyr": Value("float64"),  # Ozone removed, in ounces per year
+                "o3removed_dolperyr": Value("float64"),  # Monetary value of ozone removal per year
+                "no2removed_ozperyr": Value("float64"),  # Nitrogen dioxide removed, in ounces per year
+                "no2removed_dolperyr": Value("float64"),  # Monetary value of nitrogen dioxide removal per year
+                "so2removed_ozperyr": Value("float64"),  # Sulfur dioxide removed, in ounces per year
+                "so2removed_dolperyr": Value("float64"),  # Monetary value of sulfur dioxide removal per year
+                "pm10removed_ozperyr": Value("float64"),  # Particulate matter (10 micrometers or less) removed, in ounces per year
+                "pm10removed_dolperyr": Value("float64"),  # Monetary value of particulate matter removal per year
+                "pm25removed_ozperyr": Value("float64"),  # Particulate matter (2.5 micrometers or less) removed, in ounces per year
+                "o2production_lbperyr": Value("float64"),  # Oxygen production, in pounds per year
+                "replacevalue_dol": Value("float64"),  # Replacement value in dollars
+                "carbonstorage_lb": Value("float64"),  # Carbon storage, in pounds
+                "carbonstorage_dol": Value("float64"),  # Monetary value of carbon storage
+                "grosscarseq_lbperyr": Value("float64"),  # Gross carbon sequestration, in pounds per year
+                "X": Value("float64"),  # X coordinate (longitude if geographic)
+                "Y": Value("float64"),  # Y coordinate (latitude if geographic)
+            }),
+            supervised_keys=None,
+            homepage="https://github.com/AuraMa111?tab=repositories",
+            citation="Citation for the dataset",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+
+        urls_to_download = self._URLS  # This should now work as _URLS is defined
+
+        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, define them here
+        ]
+
+    def _generate_examples(self, csv_path, geojson_path):
+        # Log the information about the CSV file being processed
+
+        # Load the CSV data into a pandas DataFrame
+        csv_data = pd.read_csv(csv_path)
+
+        # Load the GeoJSON data into a GeoDataFrame
+        geojson_data = gpd.read_file(geojson_path)
+
+        # Merge the CSV data with the GeoJSON data on the 'OBJECTID' column
+        merged_data = geojson_data.merge(csv_data, on='OBJECTID')
+
+        # Drop columns with suffix '_y' that might have been created during the merge
+        merged_data.drop(columns=[col for col in merged_data if col.endswith('_y')], inplace=True)
+
+        # Rename columns to remove suffix '_x'
+        merged_data.rename(columns=lambda x: x.rstrip('_x'), inplace=True)
+
+        # Select the desired columns
+        columns_to_extract = [  "geometry",  # Geometry feature, usually spatial data (GeoJSON format)
+                "OBJECTID",  # Unique identifier for each record
+                "streetaddress",  # Street address of the tree planting site
+                "city",  # City where the tree planting site is located
+                "zipcode",  # Zip code of the tree planting site (changed to string)
+                "facilityid",  # Identifier for the facility
+                "present",  # Presence status, assumed to be string
+                "genus",  # Genus of the tree
+                "species",  # Species of the tree
+                "commonname",  # Common name of the tree
+                "diameterin",  # Diameter in inches
+                "heightft",  # Height in feet (changed to "float64")
+                "condition",  # Condition of the tree
+                "contractwork",  # Contract work information
+                "neighborhood",  # Neighborhood where the tree is located
+                "program",  # Program under which the tree was planted
+                "plantingw",  # Width available for planting
+                "plantingcond",  # Planting condition
+                "underpwerlins",  # Whether the tree is under power lines
+                "GlobalID",  # Global identifier
+                "created_user",  # User who created the record
+                "last_edited_user",  # User who last edited the record
+                "isoprene",  # Isoprene emission rate
+                "monoterpene",
+                "coremoved_ozperyr",  # Carbon monoxide removed, in ounces per year
+                "coremoved_dolperyr",  # Monetary value of carbon monoxide removal per year
+                "o3removed_ozperyr",  # Ozone removed, in ounces per year
+                "o3removed_dolperyr",  # Monetary value of ozone removal per year
+                "no2removed_ozperyr",  # Nitrogen dioxide removed, in ounces per year
+                "no2removed_dolperyr",  # Monetary value of nitrogen dioxide removal per year
+                "so2removed_ozperyr",  # Sulfur dioxide removed, in ounces per year
+                "so2removed_dolperyr",  # Monetary value of sulfur dioxide removal per year
+                "pm10removed_ozperyr",  # Particulate matter (10 micrometers or less) removed, in ounces per year
+                "pm10removed_dolperyr",  # Monetary value of particulate matter removal per year
+                "pm25removed_ozperyr",  # Particulate matter (2.5 micrometers or less) removed, in ounces per year
+                "o2production_lbperyr",  # Oxygen production, in pounds per year
+                "replacevalue_dol",  # Replacement value in dollars
+                "carbonstorage_lb",  # Carbon storage, in pounds
+                "carbonstorage_dol",  # Monetary value of carbon storage
+                "grosscarseq_lbperyr",  # Gross carbon sequestration, in pounds per year
+                "X",  # X coordinate (longitude if geographic)
+                "Y"]
+
+        # Create the final DataFrame with the selected columns
+        df = merged_data[columns_to_extract]
+
+        # Iterate over each row in the DataFrame and yield it
+        for index, row in df.iterrows():
+            # Convert the row to a dictionary, it's more convenient for yielding
+            yield index, row.to_dict()
+