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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")
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:
class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file))
examples = class1_examples
elif split == Split.TEST:
class2_examples = list(self._generate_examples_from_class2(class2_data_file))
examples = class2_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(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(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(split=Split.TEST)
# Build the test dataset
durham_trees_dataset_test.download_and_prepare()
dataset_test = durham_trees_dataset_test.as_dataset() |