updated to datasets 4.*

README.md

@@ -1,21 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: fertility
+  data_files:
+  - path: fertility/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Fertility
+size_categories: 1M<n<10M
 tags:
-- fertility
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: Fertility
-size_categories:
-- n<1K
 task_categories:
 - tabular-classification
-configs:
-- encoding
-- fertility
-license: cc
 ---
 # Fertility
 The [Fertility dataset](https://archive.ics.uci.edu/ml/datasets/Fertility) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

fertility.py

@@ -1,171 +0,0 @@
-"""Fertility"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_BASE_FEATURE_NAMES = [
-    "season_of_sampling",
-    "age_at_time_of_sampling",
-    "has_had_childhood_diseases",
-    "has_had_serious_trauma",
-    "has_had_surgical_interventions",
-    "has_had_high_fevers_in_the_past_year",
-    "frequency_of_alcohol_consumption",
-    "smoking_frequency",
-    "number_of_sitting_hours_per_day",
-    "has_fertility_issues"
-]
-
-_ENCODING_DICS = {
-    "season_of_sampling" : {
-        -1: "winter",
-        -0.33: "spring",
-        +0.33: "summer",
-        +1: "fall",
-    },
-    "has_had_childhood_diseases" : {
-        1: True,
-        0: False
-    },
-    "has_had_serious_trauma" : {
-        1: True,
-        0: False
-    },
-    "has_had_surgical_interventions" : {
-        1: True,
-        0: False
-    },
-    "has_had_high_fevers_in_the_past_year" : {
-        1: "no",
-        0: "more than three months ago",
-        -1: "less than three months ago"
-    },
-    "smoking_frequency" : {
-        1: "daily",
-        0: "occasionally",
-        -1: "never"
-    },
-    "has_fertility_issues": {
-        "N": 0,
-        "O": 1
-    }
-}
-
-DESCRIPTION = "Fertility dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Fertility"
-_URLS = ("https://archive.ics.uci.edu/ml/datasets/Fertility")
-_CITATION = """
-@misc{misc_fertility_244,
-  author       = {Gil,David & Girela,Jose},
-  title        = {{Fertility}},
-  year         = {2013},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5Z01Z}}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/fertility/raw/main/fertility_Diagnosis.txt"
-}
-features_types_per_config = {
-    "encoding": {
-        "feature": datasets.Value("string"),
-        "original_value": datasets.Value("string"),
-        "encoded_value": datasets.Value("int64"),
-    },
-    "fertility": {
-        "season_of_sampling": datasets.Value("string"),
-        "age_at_time_of_sampling": datasets.Value("int8"),
-        "has_had_childhood_diseases": datasets.Value("bool"),
-        "has_had_serious_trauma": datasets.Value("bool"),
-        "has_had_surgical_interventions": datasets.Value("bool"),
-        "has_had_high_fevers_in_the_past_year": datasets.Value("string"),
-        "frequency_of_alcohol_consumption": datasets.Value("float64"),
-        "smoking_frequency": datasets.Value("string"),
-        "number_of_sitting_hours_per_day": datasets.Value("float64"),
-        "has_fertility_issues": 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 FertilityConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(FertilityConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Fertility(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "fertility"
-    BUILDER_CONFIGS = [
-        FertilityConfig(name="encoding",
-                    description="Encoding dictionaries for discrete features."),
-        FertilityConfig(name="fertility",
-                    description="Fertility 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):
-        if self.config.name == "encoding":
-            data = self.encodings()
-
-            for row_id, row in data.iterrows():
-                data_row = dict(row)
-
-                yield row_id, data_row
-
-        else:               
-            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 encodings(self):
-        data = [pandas.DataFrame([(feature, original_value, encoded_value)
-                                   for original_value, encoded_value in d.items()],
-                                 columns=["feature", "original_value", "encoded_value"])
-                for feature, d in _ENCODING_DICS.items()]
-
-        return data
-
-
-    def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.columns = _BASE_FEATURE_NAMES
-        
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-        data.loc[:, "age_at_time_of_sampling"] = data["age_at_time_of_sampling"].apply(lambda x: 18 + x * 18)
-
-        data = data[list(features_types_per_config[config].keys())]
-
-        return data
-
-    
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")

fertility/train.csv

@@ -0,0 +1,100 @@
+season_of_sampling,age_at_time_of_sampling,has_had_childhood_diseases,has_had_serious_trauma,has_had_surgical_interventions,has_had_high_fevers_in_the_past_year,frequency_of_alcohol_consumption,smoking_frequency,number_of_sitting_hours_per_day,has_fertility_issues
+spring,34,True,False,True,more than three months ago,0.8,daily,0.31,1
+spring,27,True,False,False,more than three months ago,1.0,never,0.5,0
+spring,31,False,True,True,more than three months ago,1.0,never,0.38,0
+spring,30,True,True,False,more than three months ago,0.8,never,0.5,1
+spring,30,True,False,True,more than three months ago,0.8,occasionally,0.5,0
+spring,30,False,False,False,less than three months ago,0.8,never,0.44,0
+spring,36,True,True,True,more than three months ago,0.6,never,0.38,0
+fall,29,False,False,True,more than three months ago,0.8,never,0.25,0
+fall,28,True,False,False,more than three months ago,1.0,never,0.25,0
+fall,30,True,True,False,less than three months ago,0.8,occasionally,0.31,0
+fall,32,True,True,True,more than three months ago,0.6,occasionally,0.13,0
+fall,31,True,True,True,more than three months ago,0.8,daily,0.25,0
+fall,32,True,False,False,more than three months ago,1.0,never,0.38,0
+fall,34,True,True,True,more than three months ago,0.2,never,0.25,0
+fall,32,True,True,False,more than three months ago,1.0,daily,0.5,0
+fall,29,True,False,True,more than three months ago,1.0,never,0.38,0
+fall,30,True,False,True,more than three months ago,0.8,never,0.25,1
+fall,31,True,True,True,more than three months ago,1.0,daily,0.25,0
+fall,30,True,False,False,more than three months ago,0.8,daily,0.38,1
+fall,30,False,False,True,more than three months ago,0.8,never,0.25,0
+fall,31,True,False,False,more than three months ago,0.6,occasionally,0.25,0
+fall,30,True,True,False,more than three months ago,0.8,never,0.25,0
+fall,30,True,False,True,less than three months ago,1.0,never,0.44,1
+fall,28,True,False,True,more than three months ago,1.0,never,0.63,0
+fall,30,True,False,False,more than three months ago,1.0,never,0.25,0
+fall,30,True,False,True,more than three months ago,0.6,never,0.38,1
+fall,32,True,True,False,no,0.6,never,0.38,1
+fall,28,False,False,True,more than three months ago,1.0,never,0.19,0
+fall,30,False,False,True,more than three months ago,0.6,occasionally,0.5,1
+fall,28,True,False,True,more than three months ago,1.0,never,0.63,0
+fall,28,True,False,False,more than three months ago,1.0,never,0.44,0
+fall,29,False,False,False,more than three months ago,1.0,never,0.63,0
+fall,28,True,True,True,more than three months ago,0.8,occasionally,0.44,0
+fall,28,True,True,True,more than three months ago,1.0,never,0.63,0
+winter,32,True,True,False,no,0.6,never,0.38,0
+winter,32,True,False,True,more than three months ago,1.0,never,0.25,0
+winter,28,True,False,True,more than three months ago,1.0,never,0.63,0
+winter,30,False,False,True,more than three months ago,0.6,occasionally,0.5,1
+winter,30,True,False,False,more than three months ago,1.0,never,0.31,0
+winter,27,True,True,True,more than three months ago,0.8,daily,0.5,0
+winter,28,True,True,False,more than three months ago,0.8,daily,0.5,0
+winter,28,True,False,True,less than three months ago,0.8,daily,0.5,0
+winter,28,True,False,False,more than three months ago,1.0,never,0.44,0
+winter,27,True,True,False,no,1.0,occasionally,0.31,0
+winter,27,True,False,False,no,1.0,occasionally,0.44,0
+spring,28,True,False,False,more than three months ago,1.0,never,0.63,0
+spring,30,True,True,False,more than three months ago,0.6,daily,0.19,0
+spring,29,True,True,True,more than three months ago,0.8,never,0.31,0
+spring,31,True,True,True,more than three months ago,0.6,never,0.19,0
+spring,30,True,False,True,more than three months ago,0.8,never,0.19,0
+spring,27,True,True,False,no,1.0,never,0.75,0
+spring,27,True,True,False,more than three months ago,0.8,occasionally,0.5,0
+spring,28,True,True,True,less than three months ago,0.8,occasionally,0.19,0
+spring,28,True,False,True,more than three months ago,1.0,never,0.63,0
+spring,28,True,False,True,more than three months ago,0.8,daily,0.19,0
+spring,27,True,True,False,more than three months ago,0.8,occasionally,0.75,0
+spring,30,True,True,True,less than three months ago,1.0,never,0.75,0
+spring,28,True,True,False,more than three months ago,0.4,daily,0.63,0
+fall,28,False,False,False,no,0.8,daily,0.44,0
+fall,28,False,False,False,no,0.8,occasionally,1.0,0
+winter,29,True,False,False,no,1.0,daily,0.25,0
+winter,28,True,True,True,more than three months ago,0.6,never,0.38,0
+winter,28,True,False,False,no,1.0,never,0.5,0
+winter,27,True,False,False,no,0.8,never,0.31,0
+spring,28,False,False,True,more than three months ago,1.0,never,0.56,0
+spring,27,True,True,False,less than three months ago,0.8,occasionally,0.88,0
+spring,27,True,False,False,no,1.0,never,0.47,0
+spring,27,True,False,False,no,0.8,occasionally,0.31,0
+spring,27,True,False,True,less than three months ago,0.8,never,0.5,0
+spring,27,True,True,False,less than three months ago,0.8,occasionally,0.88,1
+summer,30,True,False,False,no,1.0,never,0.31,0
+fall,28,True,False,False,no,0.6,occasionally,0.5,0
+winter,27,True,False,False,no,0.8,never,0.44,0
+winter,27,True,False,False,no,0.8,never,0.63,0
+winter,32,True,False,True,no,1.0,daily,0.25,0
+winter,31,True,False,True,no,0.6,occasionally,0.56,0
+winter,30,True,True,True,no,0.8,never,0.19,0
+winter,27,True,True,False,no,0.8,never,0.38,0
+winter,36,True,False,True,no,0.6,occasionally,0.25,0
+spring,34,True,True,False,no,1.0,never,0.63,0
+winter,32,True,True,True,no,0.8,occasionally,0.19,0
+spring,34,True,False,False,no,0.6,never,0.19,0
+spring,33,True,True,True,no,1.0,never,0.25,0
+spring,32,True,False,False,no,1.0,daily,0.06,1
+spring,34,True,True,False,more than three months ago,0.6,daily,0.31,0
+spring,31,True,True,True,more than three months ago,0.6,daily,0.25,0
+spring,31,True,True,True,no,0.8,daily,0.25,0
+spring,32,True,True,True,more than three months ago,1.0,never,0.31,0
+spring,32,True,True,True,more than three months ago,1.0,daily,0.38,0
+spring,32,True,True,True,no,0.8,never,0.38,0
+summer,32,True,False,False,more than three months ago,1.0,daily,0.06,0
+summer,31,True,True,False,more than three months ago,0.8,never,0.38,0
+summer,31,True,False,True,more than three months ago,0.8,never,0.44,1
+fall,28,True,False,False,more than three months ago,0.6,daily,0.5,0
+winter,30,True,False,False,more than three months ago,1.0,never,0.5,0
+winter,28,True,False,False,more than three months ago,0.8,occasionally,0.5,0
+winter,30,True,True,True,more than three months ago,1.0,never,0.31,0
+winter,29,True,False,True,more than three months ago,1.0,occasionally,0.19,0
+winter,30,False,True,True,more than three months ago,0.6,never,0.19,0

fertility_Diagnosis.txt

@@ -1,100 +0,0 @@
--0.33,0.69,0,1,1,0,0.8,0,0.88,N
--0.33,0.94,1,0,1,0,0.8,1,0.31,O
--0.33,0.5,1,0,0,0,1,-1,0.5,N
--0.33,0.75,0,1,1,0,1,-1,0.38,N
--0.33,0.67,1,1,0,0,0.8,-1,0.5,O
--0.33,0.67,1,0,1,0,0.8,0,0.5,N
--0.33,0.67,0,0,0,-1,0.8,-1,0.44,N
--0.33,1,1,1,1,0,0.6,-1,0.38,N
-1,0.64,0,0,1,0,0.8,-1,0.25,N
-1,0.61,1,0,0,0,1,-1,0.25,N
-1,0.67,1,1,0,-1,0.8,0,0.31,N
-1,0.78,1,1,1,0,0.6,0,0.13,N
-1,0.75,1,1,1,0,0.8,1,0.25,N
-1,0.81,1,0,0,0,1,-1,0.38,N
-1,0.94,1,1,1,0,0.2,-1,0.25,N
-1,0.81,1,1,0,0,1,1,0.5,N
-1,0.64,1,0,1,0,1,-1,0.38,N
-1,0.69,1,0,1,0,0.8,-1,0.25,O
-1,0.75,1,1,1,0,1,1,0.25,N
-1,0.67,1,0,0,0,0.8,1,0.38,O
-1,0.67,0,0,1,0,0.8,-1,0.25,N
-1,0.75,1,0,0,0,0.6,0,0.25,N
-1,0.67,1,1,0,0,0.8,-1,0.25,N
-1,0.69,1,0,1,-1,1,-1,0.44,O
-1,0.56,1,0,1,0,1,-1,0.63,N
-1,0.67,1,0,0,0,1,-1,0.25,N
-1,0.67,1,0,1,0,0.6,-1,0.38,O
-1,0.78,1,1,0,1,0.6,-1,0.38,O
-1,0.58,0,0,1,0,1,-1,0.19,N
-1,0.67,0,0,1,0,0.6,0,0.5,O
-1,0.61,1,0,1,0,1,-1,0.63,N
-1,0.56,1,0,0,0,1,-1,0.44,N
-1,0.64,0,0,0,0,1,-1,0.63,N
-1,0.58,1,1,1,0,0.8,0,0.44,N
-1,0.56,1,1,1,0,1,-1,0.63,N
--1,0.78,1,1,0,1,0.6,-1,0.38,N
--1,0.78,1,0,1,0,1,-1,0.25,N
--1,0.56,1,0,1,0,1,-1,0.63,N
--1,0.67,0,0,1,0,0.6,0,0.5,O
--1,0.69,1,0,0,0,1,-1,0.31,N
--1,0.53,1,1,1,0,0.8,1,0.5,N
--1,0.56,1,1,0,0,0.8,1,0.5,N
--1,0.58,1,0,1,-1,0.8,1,0.5,N
--1,0.56,1,0,0,0,1,-1,0.44,N
--1,0.53,1,1,0,1,1,0,0.31,N
--1,0.53,1,0,0,1,1,0,0.44,N
--0.33,0.56,1,0,0,0,1,-1,0.63,N
--0.33,0.72,1,1,0,0,0.6,1,0.19,N
--0.33,0.64,1,1,1,0,0.8,-1,0.31,N
--0.33,0.75,1,1,1,0,0.6,-1,0.19,N
--0.33,0.67,1,0,1,0,0.8,-1,0.19,N
--0.33,0.53,1,1,0,1,1,-1,0.75,N
--0.33,0.53,1,1,0,0,0.8,0,0.5,N
--0.33,0.58,1,1,1,-1,0.8,0,0.19,N
--0.33,0.61,1,0,1,0,1,-1,0.63,N
--0.33,0.58,1,0,1,0,0.8,1,0.19,N
--0.33,0.53,1,1,0,0,0.8,0,0.75,N
--0.33,0.69,1,1,1,-1,1,-1,0.75,N
--0.33,0.56,1,1,0,0,0.4,1,0.63,N
-1,0.58,0,0,0,1,0.8,1,0.44,N
-1,0.56,0,0,0,1,0.8,0,1,N
--1,0.64,1,0,0,1,1,1,0.25,N
--1,0.61,1,1,1,0,0.6,-1,0.38,N
--1,0.56,1,0,0,1,1,-1,0.5,N
--1,0.53,1,0,0,1,0.8,-1,0.31,N
--0.33,0.56,0,0,1,0,1,-1,0.56,N
--0.33,0.5,1,1,0,-1,0.8,0,0.88,N
--0.33,0.5,1,0,0,1,1,-1,0.47,N
--0.33,0.5,1,0,0,1,0.8,0,0.31,N
--0.33,0.5,1,0,1,-1,0.8,-1,0.5,N
--0.33,0.5,1,1,0,-1,0.8,0,0.88,O
-0.33,0.69,1,0,0,1,1,-1,0.31,N
-1,0.56,1,0,0,1,0.6,0,0.5,N
--1,0.5,1,0,0,1,0.8,-1,0.44,N
--1,0.53,1,0,0,1,0.8,-1,0.63,N
--1,0.78,1,0,1,1,1,1,0.25,N
--1,0.75,1,0,1,1,0.6,0,0.56,N
--1,0.72,1,1,1,1,0.8,-1,0.19,N
--1,0.53,1,1,0,1,0.8,-1,0.38,N
--1,1,1,0,1,1,0.6,0,0.25,N
--0.33,0.92,1,1,0,1,1,-1,0.63,N
--1,0.81,1,1,1,1,0.8,0,0.19,N
--0.33,0.92,1,0,0,1,0.6,-1,0.19,N
--0.33,0.86,1,1,1,1,1,-1,0.25,N
--0.33,0.78,1,0,0,1,1,1,0.06,O
--0.33,0.89,1,1,0,0,0.6,1,0.31,N
--0.33,0.75,1,1,1,0,0.6,1,0.25,N
--0.33,0.75,1,1,1,1,0.8,1,0.25,N
--0.33,0.83,1,1,1,0,1,-1,0.31,N
--0.33,0.81,1,1,1,0,1,1,0.38,N
--0.33,0.81,1,1,1,1,0.8,-1,0.38,N
-0.33,0.78,1,0,0,0,1,1,0.06,N
-0.33,0.75,1,1,0,0,0.8,-1,0.38,N
-0.33,0.75,1,0,1,0,0.8,-1,0.44,O
-1,0.58,1,0,0,0,0.6,1,0.5,N
--1,0.67,1,0,0,0,1,-1,0.5,N
--1,0.61,1,0,0,0,0.8,0,0.5,N
--1,0.67,1,1,1,0,1,-1,0.31,N
--1,0.64,1,0,1,0,1,0,0.19,N
--1,0.69,0,1,1,0,0.6,-1,0.19,N