Datasets:
File size: 6,504 Bytes
a2a3d92 |
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 |
# -*- coding: utf-8 -*-
"""DurhamTrees
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1W5gDhKokcuqoA8AK4a6JR7PIeCUdmrTU
"""
import datasets
import pandas as pd
import geopandas as gpd
from datasets import DatasetBuilder, DownloadManager, DatasetInfo, SplitGenerator, Split
from datasets.features import Features, Value, ClassLabel
import matplotlib.pyplot as plt
import csv
import json
import os
from typing import List
# 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(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
# Combine BUILDER_CONFIGS from both classes, ensuring they have unique names
BUILDER_CONFIGS = [
# Configurations from DatasetClass1
datasets.BuilderConfig(name="class1_domain1", description="this is combined of csv and geojson"),
# Configurations from DatasetClass2
datasets.BuilderConfig(name="class2_domain1", description="this is csv file"),
# Add other configurations as necessary
]
def _info(self):
# Combine the features from both classes into one Features dictionary
return datasets.DatasetInfo(
description="This dataset combines information from both classes.",
features=datasets.Features({
# Features from DatasetClass1
"feature1_from_class1": Value("string"),
"geometry": Value("string"),
"OBJECTID": Value("int64"),
"X": Value("float64"),
"Y": Value("float64"),
# Features from DatasetClass2
"feature1_from_class2": Value("string"),
"geometry": Value("string"),
"OBJECTID": Value("int64"),
"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"),
"contractwork": 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"),
# ... add other features as needed
}),
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)
# Combine the split generators from both classes
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
# Arguments for DatasetClass1
"class1_data_file": downloaded_files["first_domain1"]["csv_file"],
"class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
# Arguments for DatasetClass2
"class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
# Additional arguments if needed
"split": "train",
},
),
# Add other splits as necessary, each with its own generator
]
def _generate_examples(self, class1_data_file, class1_geojson_file, class2_data_file, split):
# Generate examples from the first dataset class
for example in self._generate_examples_from_class1(class1_data_file, class1_geojson_file):
yield example
# Generate examples from the second dataset class
for example in self._generate_examples_from_class2(class2_data_file):
yield example
def _generate_examples_from_class1(self, csv_filepath, geojson_filepath):
columns_to_extract = ["geometry", "OBJECTID", "X", "Y"]
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'])
merged_data = gdf.merge(csv_data, on='OBJECTID')
final_data = merged_data[columns_to_extract]
for id_, row in final_data.iterrows():
example = row.to_dict()
yield id_, example
def _generate_examples_from_class2(self, csv_filepath2):
columns_to_extract = ["geometry", "OBJECTID", "streetaddress", "city", "facilityid", "present", "genus", "species", "commonname", "diameterin", "condition", "contractwork", "neighborhood", "program", "plantingw", "plantingcond", "underpwerlins", "GlobalID", "created_user", "last_edited_user", "isoprene", "monoterpene", "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", "X", "Y"]
# Load the CSV data into a pandas DataFrame
csv_data2 = pd.read_csv(csv_filepath2)
# Filter the DataFrame to only include the specified columns
final_data = csv_data2[columns_to_extract]
# Iterate over the rows of the final DataFrame
for id_, row in final_data.iterrows():
# Convert the row to a dictionary
example = row.to_dict()
# Yield the example with its index as the identifier
yield id_, example |