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README.md

@@ -1,3 +1,49 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- fertility
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Fertility
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- encoding
+- fertility
 ---
+# Fertility
+The [Fertility dataset](https://archive.ics.uci.edu/ml/datasets/Fertility) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Classify fertility abnormalities of patients.
+
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**                          |
+|-------------------|---------------------------|------------------------------------------|
+| encoding          |                           | Encoding dictionary                      |
+| fertility         | Binary classification     | Does the patient have fertility issues?  |
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/fertility", "fertility")["train"]
+```
+
+# Features
+|**Feature**                             |**Type**          |
+|----------------------------------------|------------------|
+| season_of_sampling                     | `[string]`       |  
+| age_at_time_of_sampling                | `[int8]`         |         
+| has_had_childhood_diseases             | `[bool]`         |              
+| has_had_serious_trauma                 | `[bool]`         |         
+| has_had_surgical_interventions         | `[bool]`         |                
+| has_had_high_fevers_in_the_past_year   | `[string]`       |                      
+| frequency_of_alcohol_consumption       | `[float16]`      |                  
+| smoking_frequency                      | `[string]`       |  
+| number_of_sitting_hours_per_day        | `[float16]`      |          
+

fertility.py

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+"""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" : {
+        "yes": True,
+        "no": False
+    },
+    "has_had_serious_trauma" : {
+        "yes": True,
+        "no": False
+    },
+    "has_had_surgical_interventions" : {
+        "yes": True,
+        "no": 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("float16"),
+        "smoking_frequency": datasets.Value("string"),
+        "number_of_sitting_hours_per_day": datasets.Value("float16"),
+        "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 = 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_Diagnosis.txt

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+-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
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+1,0.69,1,0,1,-1,1,-1,0.44,O
+1,0.56,1,0,1,0,1,-1,0.63,N
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+1,0.67,1,0,1,0,0.6,-1,0.38,O
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+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
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