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/1W5gDhKokcuqoA8AK4a6JR7PIeCUdmrTU
+"""
+
+import datasets
+import pandas as pd
+import geopandas as gpd
+from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
+from datasets.features import Features, Value, ClassLabel
+import matplotlib.pyplot as plt
+import csv
+import json
+import os
+from typing import List
+# URL definitions
+_URLS = {
+    "first_domain1": {
+        "csv_file": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
+        "geojson_file": "https://drive.google.com/uc?export=download&id=1cbn7EY7RofXN7c6Ph0eIGFIZowPZ5lKE",
+    },
+    "first_domain2": {
+        "csv_file2": "https://drive.google.com/uc?export=download&id=1RVdaI5dSTPStjhOHO40ypDv2cAQZpi_Y",
+    },
+}
+
+# Combined Dataset Class
+class DurhamTrees(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    # Combine BUILDER_CONFIGS from both classes, ensuring they have unique names
+    BUILDER_CONFIGS = [
+        # Configurations from DatasetClass1
+        datasets.BuilderConfig(name="class1_domain1", description="this is combined of csv and geojson"),
+        # Configurations from DatasetClass2
+        datasets.BuilderConfig(name="class2_domain1", description="this is csv file"),
+        # Add other configurations as necessary
+    ]
+
+    def _info(self):
+        # Combine the features from both classes into one Features dictionary
+        return datasets.DatasetInfo(
+            description="This dataset combines information from both classes.",
+            features=datasets.Features({
+                # Features from DatasetClass1
+                "feature1_from_class1": Value("string"),
+                "geometry": Value("string"),
+                "OBJECTID": Value("int64"),
+                "X": Value("float64"),
+                "Y": Value("float64"),
+
+                # Features from DatasetClass2
+                "feature1_from_class2": Value("string"),
+                "geometry": Value("string"),
+                "OBJECTID": Value("int64"),
+                "streetaddress": Value("string"),
+                "city": Value("string"),
+                "facilityid": Value("int64"),
+                "present": Value("string"),
+                "genus": Value("string"),
+                "species": Value("string"),
+                "commonname": Value("string"),
+                "diameterin": Value("float64"),
+                "condition": Value("string"),
+                "contractwork": Value("string"),
+                "neighborhood": Value("string"),
+                "program": Value("string"),
+                "plantingw": Value("string"),
+                "plantingcond": Value("string"),
+                "underpwerlins": Value("string"),
+                "GlobalID": Value("string"),
+                "created_user": Value("string"),
+                "last_edited_user": Value("string"),
+                "isoprene": Value("float64"),
+                "monoterpene": Value("float64"),
+                # ... add other features as needed
+            }),
+            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)
+
+        # Combine the split generators from both classes
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    # Arguments for DatasetClass1
+                    "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
+                    "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
+
+                    # Arguments for DatasetClass2
+                    "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
+
+                    # Additional arguments if needed
+                    "split": "train",
+                },
+            ),
+            # Add other splits as necessary, each with its own generator
+        ]
+
+    def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split):
+        # Generate examples from the first dataset class
+        for example in self._generate_examples_from_class1(class1_data_file, class1_geojson_file):
+            yield example
+
+        # Generate examples from the second dataset class
+        for example in self._generate_examples_from_class2(class2_data_file):
+            yield example
+
+    def _generate_examples_from_class1(self, csv_filepath, geojson_filepath):
+        columns_to_extract = ["geometry", "OBJECTID", "X", "Y"]
+        csv_data = pd.read_csv(csv_filepath)
+
+        with open(geojson_filepath, 'r') as file:
+            geojson_dict = json.load(file)
+        gdf = gpd.GeoDataFrame.from_features(geojson_dict['features'])
+        merged_data = gdf.merge(csv_data, on='OBJECTID')
+        final_data = merged_data[columns_to_extract]
+        for id_, row in final_data.iterrows():
+            example = row.to_dict()
+            yield id_, example
+
+    def _generate_examples_from_class2(self, csv_filepath2):
+        columns_to_extract = ["geometry", "OBJECTID", "streetaddress", "city", "facilityid", "present", "genus", "species", "commonname", "diameterin", "condition", "contractwork", "neighborhood", "program", "plantingw", "plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene", "coremoved_ozperyr", "coremoved_dolperyr", "o3removed_ozperyr", "o3removed_dolperyr", "no2removed_ozperyr", "no2removed_dolperyr", "so2removed_ozperyr", "so2removed_dolperyr", "pm10removed_ozperyr", "pm10removed_dolperyr", "pm25removed_ozperyr", "o2production_lbperyr", "replacevalue_dol", "carbonstorage_lb", "carbonstorage_dol", "grosscarseq_lbperyr", "X", "Y"]
+
+        # Load the CSV data into a pandas DataFrame
+        csv_data2 = pd.read_csv(csv_filepath2)
+
+        # Filter the DataFrame to only include the specified columns
+        final_data = csv_data2[columns_to_extract]
+
+        # Iterate over the rows of the final DataFrame
+        for id_, row in final_data.iterrows():
+            # Convert the row to a dictionary
+            example = row.to_dict()
+
+            # Yield the example with its index as the identifier
+            yield id_, example