# -*- coding: utf-8 -*- """DurhamTrees Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv """ # -*- coding: utf-8 -*- """DurhamTrees Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1czig7JIbqTKp9wNUIRcdMEDF3pFgtxKv """ import pyarrow.parquet as pq 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 import base64 import matplotlib.pyplot as plt import matplotlib.image as mpimg import io # 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(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=("image", "label"), 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, }, ), ] def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split): 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 df = pd.DataFrame(examples) for id_, example in enumerate(examples): if not isinstance(example, dict): # Convert the example to a dictionary if it's not 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_, 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 durham_trees_dataset = DurhamTrees(name='class1_domain1') # Build the dataset durham_trees_dataset.download_and_prepare() # Access the dataset dataset = durham_trees_dataset.as_dataset() # Create an instance of the DurhamTrees class for another configuration durham_trees_dataset_another = DurhamTrees(name='class2_domain1') # Build the dataset for the new instance durham_trees_dataset_another.download_and_prepare() # Access the dataset for the new instance dataset_another = durham_trees_dataset_another.as_dataset()