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/1C4L9gZ_hkydWda4rUgNuU-GNJa9fBV-b
+"""
+
+# -*- coding: utf-8 -*-
+"""DurhamTrees
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv
+"""
+
+import pandas as pd
+import geopandas as gpd
+from datasets import (
+    GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split,
+    Features, Value, BuilderConfig, DatasetInfo
+)
+import matplotlib.pyplot as plt
+import seaborn as sns
+import csv
+import json
+from shapely.geometry import Point
+
+# URL definitions
+_URLS = {
+    "first_domain1": {
+        "csv_file": "https://drive.google.com/uc?export=download&id=1P61XDtW9fkRYKj6ULhxJyOHG7PqFhZ3s",
+        "geojson_file": "https://drive.google.com/uc?export=download&id=1St986GN9m8r1_xwyWWTJBmZG7iadYHgW",
+    },
+    "first_domain2": {
+        "csv_file2": "https://drive.google.com/uc?export=download&id=1QyTJZltvqxiZBDm1V6XcSeykBreY43tj",
+    },
+}
+
+class DurhamTrees(GeneratorBasedBuilder):
+    VERSION = Version("1.0.0")
+
+    class MyConfig(BuilderConfig):
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+
+    BUILDER_CONFIGS = [
+        MyConfig(name="class1_domain1", description="this is combined of csv and geojson"),
+        MyConfig(name="class2_domain1", description="this is csv file"),
+    ]
+
+    def _info(self):
+        return DatasetInfo(
+            description="This dataset combines information from both classes, with additional processing for csv_file2.",
+            features=Features({
+                "feature1_from_class1": Value("string"),
+                "geometry":Value("string"),
+                "OBJECTID": Value("int64"),
+                "X": Value("float64"),
+                "Y": Value("float64"),
+                "feature1_from_class2": Value("string"),
+                "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"),
+                "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"),
+                "monoterpene_class2": Value("float64"),
+                "vocs": Value("float64"),
+                "coremoved_ozperyr": Value("float64"),
+                "coremoved_dolperyr": Value("float64"),
+                "o3removed_ozperyr": Value("float64"),
+                "o3removed_dolperyr": Value("float64"),
+                "no2removed_ozperyr": Value("float64"),
+                "no2removed_dolperyr": Value("float64"),
+                "so2removed_ozperyr": Value("float64"),
+                "so2removed_dolperyr": Value("float64"),
+                "pm10removed_ozperyr": Value("float64"),
+                "pm10removed_dolperyr": Value("float64"),
+                "pm25removed_ozperyr": Value("float64"),
+                "o2production_lbperyr": Value("float64"),
+                "replacevalue_dol": Value("float64"),
+                "carbonstorage_lb": Value("float64"),
+                "carbonstorage_dol": Value("float64"),
+                "grosscarseq_lbperyr": Value("float64"),
+                "grosscarseq_dolperyr": Value("float64"),
+                "avoidrunoff_ft2peryr": Value("float64"),
+                "avoidrunoff_dol2peryr": Value("float64"),
+                "polremoved_ozperyr": Value("float64"),
+                "polremoved_dolperyr": Value("float64"),
+                "totannbenefits_dolperyr": Value("float64"),
+                "leafarea_sqft": Value("float64"),
+                "potevapotran_cuftperyr": Value("float64"),
+                "evaporation_cuftperyr": Value("float64"),
+                "transpiration_cuftperyr": Value("float64"),
+                "h2ointercept_cuftperyr": Value("float64"),
+                "carbonavoid_lbperyr": Value("float64"),
+                "carbonavoid_dolperyr": Value("float64"),
+                "heating_mbtuperyr": Value("float64"),
+                "heating_dolperyrmbtu": Value("float64"),
+                "heating_kwhperyr": Value("float64"),
+                "heating_dolperyrmwh": Value("float64"),
+                "cooling_kwhperyr": Value("float64"),
+                "cooling_dolperyr": Value("float64"),
+                "totalenerg_dolperyr": Value("float64"),
+            }),
+            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)
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
+                    "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
+                    "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
+                    "split": Split.TRAIN,
+                },
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={
+                    "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
+                    "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
+                    "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
+                    "split": Split.VALIDATION,
+                },
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={
+                    "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
+                    "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
+                    "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
+                    "split": Split.TEST,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split):
+        if split == Split.TRAIN:
+            class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file))
+            class2_examples = list(self._generate_examples_from_class2(class2_data_file))
+            examples = class1_examples + class2_examples
+        elif split == Split.VALIDATION:
+            # Adjust this logic based on your validation data requirements
+            examples = []
+        elif split == Split.TEST:
+            # Adjust this logic based on your test data requirements
+            examples = []
+
+        df = pd.DataFrame(examples)
+
+        for id_, example in enumerate(examples):
+            if not isinstance(example, dict):
+                example = {"example": example}
+            yield id_, example
+
+    def _generate_examples_from_class1(self, csv_filepath, geojson_filepath):
+        columns_to_extract = ["OBJECTID", "X", "Y"]  # Remove "geometry" from columns_to_extract
+        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'], crs="EPSG:4326")  # Specify the CRS if known
+        merged_data = gdf.merge(csv_data, on='OBJECTID')
+        final_data = merged_data[columns_to_extract + ['geometry']]  # Include 'geometry' in the final_data
+        for id_, row in final_data.iterrows():
+            example = row.to_dict()
+            yield id_, example
+
+    def _generate_examples_from_class2(self, csv_filepath2):
+        csv_data2 = pd.read_csv(csv_filepath2)
+
+        columns_to_extract = [
+            "streetaddress", "city", "facilityid", "present", "genus", "species",
+            "commonname", "diameterin", "condition",  "neighborhood", "program", "plantingw",
+            "plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene",
+            "monoterpene", "vocs", "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", "grosscarseq_dolperyr", "polremoved_ozperyr", "polremoved_dolperyr",
+            "totannbenefits_dolperyr", "leafarea_sqft", "potevapotran_cuftperyr", "evaporation_cuftperyr",
+            "transpiration_cuftperyr", "h2ointercept_cuftperyr",
+            "carbonavoid_lbperyr", "carbonavoid_dolperyr", "heating_mbtuperyr",
+            "heating_dolperyrmbtu", "heating_kwhperyr", "heating_dolperyrmwh", "cooling_kwhperyr",
+            "cooling_dolperyr", "totalenerg_dolperyr",
+        ]
+
+        final_data = csv_data2[columns_to_extract]
+        for id_, row in final_data.iterrows():
+            example = row.to_dict()
+            non_empty_example = {key: value for key, value in example.items() if pd.notna(value)}
+            yield id_, non_empty_example
+
+    def _correlation_analysis(self, df):
+        correlation_matrix = df.corr()
+        sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5)
+        plt.title("Correlation Analysis")
+        plt.show()
+
+# Create an instance of the DurhamTrees class for training
+durham_trees_dataset_train = DurhamTrees(name='class1_domain1', split=Split.TRAIN)
+
+# Build the training dataset
+durham_trees_dataset_train.download_and_prepare()
+dataset_train = durham_trees_dataset_train.as_dataset()
+
+# Create an instance of the DurhamTrees class for validation
+durham_trees_dataset_val = DurhamTrees(name='class1_domain1', split=Split.VALIDATION)
+
+# Build the validation dataset
+durham_trees_dataset_val.download_and_prepare()
+dataset_val = durham_trees_dataset_val.as_dataset()
+
+# Create an instance of the DurhamTrees class for testing
+durham_trees_dataset_test = DurhamTrees(name='class1_domain1', split=Split.TEST)
+
+# Build the test dataset
+durham_trees_dataset_test.download_and_prepare()
+dataset_test = durham_trees_dataset_test.as_dataset()