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"""DurhamTrees |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1C4L9gZ_hkydWda4rUgNuU-GNJa9fBV-b |
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""" |
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"""DurhamTrees |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv |
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""" |
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import pandas as pd |
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import geopandas as gpd |
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from datasets import ( |
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GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split, |
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Features, Value, BuilderConfig, DatasetInfo |
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) |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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import csv |
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import json |
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from shapely.geometry import Point |
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_URLS = { |
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"first_domain1": { |
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"csv_file": "https://drive.google.com/uc?export=download&id=1P61XDtW9fkRYKj6ULhxJyOHG7PqFhZ3s", |
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"geojson_file": "https://drive.google.com/uc?export=download&id=1St986GN9m8r1_xwyWWTJBmZG7iadYHgW", |
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}, |
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"first_domain2": { |
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"csv_file2": "https://drive.google.com/uc?export=download&id=1QyTJZltvqxiZBDm1V6XcSeykBreY43tj", |
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}, |
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} |
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class DurhamTrees(GeneratorBasedBuilder): |
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VERSION = Version("1.0.0") |
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def _info(self): |
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return DatasetInfo( |
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description="This dataset combines information from both classes, with additional processing for csv_file2.", |
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features=Features({ |
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"feature1_from_class1": Value("string"), |
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"geometry":Value("string"), |
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"OBJECTID": Value("int64"), |
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"X": Value("float64"), |
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"Y": Value("float64"), |
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"feature1_from_class2": Value("string"), |
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"streetaddress": Value("string"), |
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"city": Value("string"), |
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"facilityid": Value("int64"), |
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"present": Value("string"), |
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"genus": Value("string"), |
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"species": Value("string"), |
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"commonname": Value("string"), |
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"diameterin": Value("float64"), |
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"condition": Value("string"), |
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"neighborhood": Value("string"), |
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"program": Value("string"), |
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"plantingw": Value("string"), |
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"plantingcond": Value("string"), |
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"underpwerlins": Value("string"), |
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"GlobalID": Value("string"), |
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"created_user": Value("string"), |
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"last_edited_user": Value("string"), |
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"isoprene": Value("float64"), |
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"monoterpene": Value("float64"), |
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"monoterpene_class2": Value("float64"), |
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"vocs": Value("float64"), |
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"coremoved_ozperyr": Value("float64"), |
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"coremoved_dolperyr": Value("float64"), |
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"o3removed_ozperyr": Value("float64"), |
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"o3removed_dolperyr": Value("float64"), |
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"no2removed_ozperyr": Value("float64"), |
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"no2removed_dolperyr": Value("float64"), |
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"so2removed_ozperyr": Value("float64"), |
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"so2removed_dolperyr": Value("float64"), |
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"pm10removed_ozperyr": Value("float64"), |
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"pm10removed_dolperyr": Value("float64"), |
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"pm25removed_ozperyr": Value("float64"), |
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"o2production_lbperyr": Value("float64"), |
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"replacevalue_dol": Value("float64"), |
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"carbonstorage_lb": Value("float64"), |
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"carbonstorage_dol": Value("float64"), |
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"grosscarseq_lbperyr": Value("float64"), |
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"grosscarseq_dolperyr": Value("float64"), |
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"avoidrunoff_ft2peryr": Value("float64"), |
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"avoidrunoff_dol2peryr": Value("float64"), |
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"polremoved_ozperyr": Value("float64"), |
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"polremoved_dolperyr": Value("float64"), |
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"totannbenefits_dolperyr": Value("float64"), |
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"leafarea_sqft": Value("float64"), |
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"potevapotran_cuftperyr": Value("float64"), |
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"evaporation_cuftperyr": Value("float64"), |
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"transpiration_cuftperyr": Value("float64"), |
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"h2ointercept_cuftperyr": Value("float64"), |
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"carbonavoid_lbperyr": Value("float64"), |
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"carbonavoid_dolperyr": Value("float64"), |
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"heating_mbtuperyr": Value("float64"), |
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"heating_dolperyrmbtu": Value("float64"), |
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"heating_kwhperyr": Value("float64"), |
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"heating_dolperyrmwh": Value("float64"), |
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"cooling_kwhperyr": Value("float64"), |
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"cooling_dolperyr": Value("float64"), |
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"totalenerg_dolperyr": Value("float64"), |
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}), |
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supervised_keys=None, |
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homepage="https://github.com/AuraMa111?tab=repositories", |
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citation="Citation for the combined dataset", |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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SplitGenerator( |
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name=Split.TRAIN, |
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gen_kwargs={ |
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"class1_data_file": downloaded_files["first_domain1"]["csv_file"], |
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"class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"], |
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"class2_data_file": downloaded_files["first_domain2"]["csv_file2"], |
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"split": Split.TRAIN, |
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}, |
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), |
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SplitGenerator( |
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name=Split.VALIDATION, |
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gen_kwargs={ |
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"class1_data_file": downloaded_files["first_domain1"]["csv_file"], |
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"class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"], |
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"class2_data_file": downloaded_files["first_domain2"]["csv_file2"], |
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"split": Split.VALIDATION, |
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}, |
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), |
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SplitGenerator( |
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name=Split.TEST, |
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gen_kwargs={ |
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"class1_data_file": downloaded_files["first_domain1"]["csv_file"], |
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"class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"], |
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"class2_data_file": downloaded_files["first_domain2"]["csv_file2"], |
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"split": Split.TEST, |
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}, |
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), |
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] |
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def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split): |
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if split == Split.TRAIN: |
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class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file)) |
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class2_examples = list(self._generate_examples_from_class2(class2_data_file)) |
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examples = class1_examples + class2_examples |
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elif split == Split.VALIDATION: |
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class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file)) |
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examples = class1_examples |
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elif split == Split.TEST: |
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class2_examples = list(self._generate_examples_from_class2(class2_data_file)) |
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examples = class2_examples |
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df = pd.DataFrame(examples) |
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for id_, example in enumerate(examples): |
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if not isinstance(example, dict): |
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example = {"example": example} |
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yield id_, example |
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def _generate_examples_from_class1(self, csv_filepath, geojson_filepath): |
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columns_to_extract = ["OBJECTID", "X", "Y"] |
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csv_data = pd.read_csv(csv_filepath) |
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with open(geojson_filepath, 'r') as file: |
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geojson_dict = json.load(file) |
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gdf = gpd.GeoDataFrame.from_features(geojson_dict['features'], crs="EPSG:4326") |
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merged_data = gdf.merge(csv_data, on='OBJECTID') |
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final_data = merged_data[columns_to_extract + ['geometry']] |
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for id_, row in final_data.iterrows(): |
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example = row.to_dict() |
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yield id_, example |
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def _generate_examples_from_class2(self, csv_filepath2): |
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csv_data2 = pd.read_csv(csv_filepath2) |
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columns_to_extract = [ |
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"streetaddress", "city", "facilityid", "present", "genus", "species", |
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"commonname", "diameterin", "condition", "neighborhood", "program", "plantingw", |
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"plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene", |
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"monoterpene", "vocs", "coremoved_ozperyr", "coremoved_dolperyr", |
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"o3removed_ozperyr", "o3removed_dolperyr", "no2removed_ozperyr", "no2removed_dolperyr", |
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"so2removed_ozperyr", "so2removed_dolperyr", "pm10removed_ozperyr", "pm10removed_dolperyr", |
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"pm25removed_ozperyr", "o2production_lbperyr", "replacevalue_dol", "carbonstorage_lb", |
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"carbonstorage_dol", "grosscarseq_lbperyr", "grosscarseq_dolperyr", "polremoved_ozperyr", "polremoved_dolperyr", |
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"totannbenefits_dolperyr", "leafarea_sqft", "potevapotran_cuftperyr", "evaporation_cuftperyr", |
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"transpiration_cuftperyr", "h2ointercept_cuftperyr", |
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"carbonavoid_lbperyr", "carbonavoid_dolperyr", "heating_mbtuperyr", |
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"heating_dolperyrmbtu", "heating_kwhperyr", "heating_dolperyrmwh", "cooling_kwhperyr", |
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"cooling_dolperyr", "totalenerg_dolperyr", |
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] |
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final_data = csv_data2[columns_to_extract] |
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for id_, row in final_data.iterrows(): |
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example = row.to_dict() |
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non_empty_example = {key: value for key, value in example.items() if pd.notna(value)} |
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yield id_, non_empty_example |
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def _correlation_analysis(self, df): |
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correlation_matrix = df.corr() |
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sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5) |
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plt.title("Correlation Analysis") |
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plt.show() |
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durham_trees_dataset_train = DurhamTrees(split=Split.TRAIN) |
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durham_trees_dataset_train.download_and_prepare() |
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dataset_train = durham_trees_dataset_train.as_dataset() |
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durham_trees_dataset_val = DurhamTrees(split=Split.VALIDATION) |
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durham_trees_dataset_val.download_and_prepare() |
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dataset_val = durham_trees_dataset_val.as_dataset() |
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durham_trees_dataset_test = DurhamTrees(split=Split.TEST) |
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durham_trees_dataset_test.download_and_prepare() |
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dataset_test = durham_trees_dataset_test.as_dataset() |
