Datasets:
File size: 9,762 Bytes
ec658a9 d33477c ec658a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 |
# -*- 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, parquet_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()
|