Datasets:
File size: 2,431 Bytes
2d447eb b3022c9 2d447eb eff9d96 2d447eb 98b0f0d 2d447eb |
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 |
"""Titanic"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Titanic dataset from the UCI ML repository."
_HOMEPAGE = "https://www.kaggle.com/datasets/vinicius150987/titanic3"
_URLS = ("https://www.kaggle.com/datasets/vinicius150987/titanic3")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/titanic/raw/main/titanic.csv"
}
features_types_per_config = {
"survival": {
"passenger_class": datasets.Value("int8"),
"is_male": datasets.Value("bool"),
"age": datasets.Value("float64"),
"sibsp": datasets.Value("float64"),
"parch": datasets.Value("float64"),
"ticket": datasets.Value("string"),
"fare": datasets.Value("float64"),
"cabin": datasets.Value("string"),
"embarked": datasets.Value("string"),
"has_survived": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class TitanicConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(TitanicConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Titanic(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "survival"
BUILDER_CONFIGS = [
TitanicConfig(name="survival",
description="Titanic 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):
data = data.rename(columns={"sex": "is_male"})
data = data[list(features_types_per_config[self.config.name].keys())]
data.loc[:, "is_male"] = data.is_male.apply(lambda x: x == "male")
data.loc[data.age == "?", "age"] = data.age.apply(lambda x: x if x != "?" else -1)
return data
|