Datasets:
File size: 5,602 Bytes
41bde4c a86f0e0 41bde4c a86f0e0 41bde4c a86f0e0 41bde4c |
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 |
"""Landsat Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {}
DESCRIPTION = "Landsat dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification")
_CITATION = """
@misc{misc_statlog_(landsat_satellite)_146,
author = {Srinivasan,Ashwin},
title = {{Statlog (Landsat Satellite)}},
year = {1993},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C55887}}
}
"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/landsat/raw/main/landsat.csv"
}
features_types_per_config = {
"landsat": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=6),
},
"landsat_0": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
"landsat_1": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
"landsat_2": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
"landsat_3": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
"landsat_4": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
"landsat_5": {
"f1": datasets.Value("int32"),
"f2": datasets.Value("int32"),
"f3": datasets.Value("int32"),
"f4": datasets.Value("int32"),
"class": datasets.ClassLabel(num_classes=2),
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class LandsatConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(LandsatConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Landsat(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "landsat"
BUILDER_CONFIGS = [
LandsatConfig(name="landsat", description="Landsat for multiclass classification."),
LandsatConfig(name="landsat_0", description="Landsat for binary classification."),
LandsatConfig(name="landsat_1", description="Landsat for binary classification."),
LandsatConfig(name="landsat_2", description="Landsat for binary classification."),
LandsatConfig(name="landsat_3", description="Landsat for binary classification."),
LandsatConfig(name="landsat_4", description="Landsat for binary classification."),
LandsatConfig(name="landsat_5", description="Landsat for binary classification."),
]
def _info(self):
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
features=features_per_config[self.config.name])
return info
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloads = dl_manager.download_and_extract(urls_per_split)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}),
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath)
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
if self.config.name == "landsat_0":
data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
elif self.config.name == "landsat_1":
data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
elif self.config.name == "landsat_2":
data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
elif self.config.name == "landsat_3":
data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
elif self.config.name == "landsat_4":
data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
elif self.config.name == "landsat_5":
data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
return data[list(features_types_per_config[self.config.name].keys())]
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
|