Update parquet files

README.md

@@ -1,17 +0,0 @@
----
-language:
-- en
-tags:
-- heart failure
-- tabular_classification
-- binary_classification
-pretty_name: Student Performance
-size_categories:
-- 100<n<1K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
-- tabular-classification
-configs:
-- death
----
-# Heart failure
-The [Heart failure dataset](https://www.kaggle.com/datasets/andrewmvd/heart-failure-clinical-data) is cool.

death/heart_failure-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77ae50fd765d050dfb1b20297dca1a41556df509d21e2c40a3fa63fa39944f46
+size 12757

heart_failure.py

@@ -1,105 +0,0 @@
-"""Heart Failure Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_BASE_FEATURE_NAMES = [
-    "age",
-    "has_anaemia",
-    "creatinine_phosphokinase_concentration_in_blood",
-    "has_diabetes",
-    "heart_ejection_fraction",
-    "has_high_blood_pressure",
-    "platelets_concentration_in_blood",
-    "serum_creatinine_concentration_in_blood",
-    "serum_sodium_concentration_in_blood",
-    "sex",
-    "is_smoker",
-    "days_in_study",
-    "is_dead"
-]
-
-DESCRIPTION = "Heart Failure dataset."
-_HOMEPAGE = "https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures"
-_URLS = ("https://www.kaggle.com/datasets/ulrikthygepedersen/heart_failures")
-_CITATION = """"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/heart_failure/raw/main/heart_failure_clinical_records_dataset.csv",
-}
-features_types_per_config = {
-    "death": {
-        "age": datasets.Value("int8"),
-        "has_anaemia": datasets.Value("int8"),
-        "creatinine_phosphokinase_concentration_in_blood": datasets.Value("float64"),
-        "has_diabetes": datasets.Value("int8"),
-        "heart_ejection_fraction": datasets.Value("float64"),
-        "has_high_blood_pressure": datasets.Value("int8"),
-        "platelets_concentration_in_blood": datasets.Value("float64"),
-        "serum_creatinine_concentration_in_blood": datasets.Value("float64"),
-        "serum_sodium_concentration_in_blood": datasets.Value("float64"),
-        "sex": datasets.Value("int8"),
-        "is_smoker": datasets.Value("int8"),
-        "days_in_study": datasets.Value("int64"),
-        "is_dead": 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 HeartFailureConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(HeartFailureConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class HeartFailure(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "death"
-    BUILDER_CONFIGS = [
-        HeartFailureConfig(name="death",
-                           description="Binary classification, predict if the patient dies.")
-    ]
-
-
-    def _info(self):
-        if self.config.name not in features_per_config:
-            raise ValueError(f"Unknown configuration: {self.config.name}")
-        
-        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]:
-        print("downloading...")
-        downloads = dl_manager.download_and_extract(urls_per_split)
-        print("downloaded!")
-
-        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, config=self.config.name)
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row
-
-    def preprocess(self, data: pandas.DataFrame, config: str = "death") -> pandas.DataFrame:
-        data.columns = _BASE_FEATURE_NAMES
-        
-        if config == "death":
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")              

heart_failure_clinical_records_dataset.csv

@@ -1,300 +0,0 @@
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