Duplicate from mstz/heart

Co-authored-by: Mattia <mstz@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+tags:
+- heart
+- tabular_classification
+- binary_classification
+- UCI
+pretty_name: Heart
+size_categories:
+- n<1K
+task_categories:
+- tabular-classification
+configs:
+- cleveland
+- va
+- switzerland
+- hungary
+license: cc
+---
+# Heart
+The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Does the patient have heart disease?
+
+# Configurations and tasks
+| **Configuration** | **Task**                  |
+|-------------------|---------------------------|
+| hungary           | Binary classification     |
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/heart", "hungary")["train"]
+```

heart-disease.names

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+Publication Request: 
+   >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+   This file describes the contents of the heart-disease directory.
+
+   This directory contains 4 databases concerning heart disease diagnosis.
+   All attributes are numeric-valued.  The data was collected from the
+   four following locations:
+
+     1. Cleveland Clinic Foundation (cleveland.data)
+     2. Hungarian Institute of Cardiology, Budapest (hungarian.data)
+     3. V.A. Medical Center, Long Beach, CA (long-beach-va.data)
+     4. University Hospital, Zurich, Switzerland (switzerland.data)
+
+   Each database has the same instance format.  While the databases have 76
+   raw attributes, only 14 of them are actually used.  Thus I've taken the
+   liberty of making 2 copies of each database: one with all the attributes
+   and 1 with the 14 attributes actually used in past experiments.
+
+   The authors of the databases have requested:
+
+      ...that any publications resulting from the use of the data include the 
+      names of the principal investigator responsible for the data collection
+      at each institution.  They would be:
+
+       1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
+       2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
+       3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
+       4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
+	  Robert Detrano, M.D., Ph.D.
+
+   Thanks in advance for abiding by this request.
+
+   David Aha
+   July 22, 1988
+   >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+
+1. Title: Heart Disease Databases
+
+2. Source Information:
+   (a) Creators: 
+       -- 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
+       -- 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
+       -- 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
+       -- 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
+             Robert Detrano, M.D., Ph.D.
+   (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779   
+   (c) Date: July, 1988
+
+3. Past Usage:
+    1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J.,
+       Sandhu,~S., Guppy,~K., Lee,~S., \& Froelicher,~V. (1989).  {\it 
+       International application of a new probability algorithm for the 
+       diagnosis of coronary artery disease.}  {\it American Journal of 
+       Cardiology}, {\it 64},304--310.
+       -- International Probability Analysis 
+       -- Address: Robert Detrano, M.D.
+                   Cardiology 111-C
+                   V.A. Medical Center
+                   5901 E. 7th Street
+                   Long Beach, CA 90028
+       -- Results in percent accuracy: (for 0.5 probability threshold)
+             Data Name:  CDF    CADENZA
+          -- Hungarian   77     74
+             Long beach  79     77
+             Swiss       81     81
+          -- Approximately a 77% correct classification accuracy with a
+             logistic-regression-derived discriminant function
+    2. David W. Aha & Dennis Kibler
+       -- 
+          
+          
+          -- Instance-based prediction of heart-disease presence with the 
+             Cleveland database
+             -- NTgrowth: 77.0% accuracy
+             --       C4: 74.8% accuracy
+    3. John Gennari
+       -- Gennari, J.~H., Langley, P, \& Fisher, D. (1989). Models of
+          incremental concept formation. {\it Artificial Intelligence, 40},
+          11--61.
+       -- Results: 
+          -- The CLASSIT conceptual clustering system achieved a 78.9% accuracy
+             on the Cleveland database.
+
+4. Relevant Information:
+     This database contains 76 attributes, but all published experiments
+     refer to using a subset of 14 of them.  In particular, the Cleveland
+     database is the only one that has been used by ML researchers to 
+     this date.  The "goal" field refers to the presence of heart disease
+     in the patient.  It is integer valued from 0 (no presence) to 4.
+     Experiments with the Cleveland database have concentrated on simply
+     attempting to distinguish presence (values 1,2,3,4) from absence (value
+     0).  
+   
+     The names and social security numbers of the patients were recently 
+     removed from the database, replaced with dummy values.
+
+     One file has been "processed", that one containing the Cleveland 
+     database.  All four unprocessed files also exist in this directory.
+    
+5. Number of Instances: 
+        Database:    # of instances:
+          Cleveland: 303
+          Hungarian: 294
+        Switzerland: 123
+      Long Beach VA: 200
+
+6. Number of Attributes: 76 (including the predicted attribute)
+
+7. Attribute Information:
+   -- Only 14 used
+      -- 1. #3  (age)       
+      -- 2. #4  (sex)       
+      -- 3. #9  (cp)        
+      -- 4. #10 (trestbps)  
+      -- 5. #12 (chol)      
+      -- 6. #16 (fbs)       
+      -- 7. #19 (restecg)   
+      -- 8. #32 (thalach)   
+      -- 9. #38 (exang)     
+      -- 10. #40 (oldpeak)   
+      -- 11. #41 (slope)     
+      -- 12. #44 (ca)        
+      -- 13. #51 (thal)      
+      -- 14. #58 (num)       (the predicted attribute)
+
+   -- Complete attribute documentation:
+      1 id: patient identification number
+      2 ccf: social security number (I replaced this with a dummy value of 0)
+      3 age: age in years
+      4 sex: sex (1 = male; 0 = female)
+      5 painloc: chest pain location (1 = substernal; 0 = otherwise)
+      6 painexer (1 = provoked by exertion; 0 = otherwise)
+      7 relrest (1 = relieved after rest; 0 = otherwise)
+      8 pncaden (sum of 5, 6, and 7)
+      9 cp: chest pain type
+        -- Value 1: typical angina
+        -- Value 2: atypical angina
+        -- Value 3: non-anginal pain
+        -- Value 4: asymptomatic
+     10 trestbps: resting blood pressure (in mm Hg on admission to the 
+        hospital)
+     11 htn
+     12 chol: serum cholestoral in mg/dl
+     13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker)
+     14 cigs (cigarettes per day)
+     15 years (number of years as a smoker)
+     16 fbs: (fasting blood sugar > 120 mg/dl)  (1 = true; 0 = false)
+     17 dm (1 = history of diabetes; 0 = no such history)
+     18 famhist: family history of coronary artery disease (1 = yes; 0 = no)
+     19 restecg: resting electrocardiographic results
+        -- Value 0: normal
+        -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST 
+                    elevation or depression of > 0.05 mV)
+        -- Value 2: showing probable or definite left ventricular hypertrophy
+                    by Estes' criteria
+     20 ekgmo (month of exercise ECG reading)
+     21 ekgday(day of exercise ECG reading)
+     22 ekgyr (year of exercise ECG reading)
+     23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no)
+     24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no)
+     25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no)
+     26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no)
+     27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no)
+     28 proto: exercise protocol
+          1 = Bruce     
+          2 = Kottus
+          3 = McHenry
+          4 = fast Balke
+          5 = Balke
+          6 = Noughton 
+          7 = bike 150 kpa min/min  (Not sure if "kpa min/min" is what was 
+              written!)
+          8 = bike 125 kpa min/min  
+          9 = bike 100 kpa min/min
+         10 = bike 75 kpa min/min
+         11 = bike 50 kpa min/min
+         12 = arm ergometer
+     29 thaldur: duration of exercise test in minutes
+     30 thaltime: time when ST measure depression was noted
+     31 met: mets achieved
+     32 thalach: maximum heart rate achieved
+     33 thalrest: resting heart rate
+     34 tpeakbps: peak exercise blood pressure (first of 2 parts)
+     35 tpeakbpd: peak exercise blood pressure (second of 2 parts)
+     36 dummy
+     37 trestbpd: resting blood pressure
+     38 exang: exercise induced angina (1 = yes; 0 = no)
+     39 xhypo: (1 = yes; 0 = no)
+     40 oldpeak = ST depression induced by exercise relative to rest
+     41 slope: the slope of the peak exercise ST segment
+        -- Value 1: upsloping
+        -- Value 2: flat
+        -- Value 3: downsloping
+     42 rldv5: height at rest
+     43 rldv5e: height at peak exercise
+     44 ca: number of major vessels (0-3) colored by flourosopy
+     45 restckm: irrelevant
+     46 exerckm: irrelevant
+     47 restef: rest raidonuclid (sp?) ejection fraction
+     48 restwm: rest wall (sp?) motion abnormality
+        0 = none
+        1 = mild or moderate
+        2 = moderate or severe
+        3 = akinesis or dyskmem (sp?)
+     49 exeref: exercise radinalid (sp?) ejection fraction
+     50 exerwm: exercise wall (sp?) motion 
+     51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
+     52 thalsev: not used
+     53 thalpul: not used
+     54 earlobe: not used
+     55 cmo: month of cardiac cath (sp?)  (perhaps "call")
+     56 cday: day of cardiac cath (sp?)
+     57 cyr: year of cardiac cath (sp?)
+     58 num: diagnosis of heart disease (angiographic disease status)
+        -- Value 0: < 50% diameter narrowing
+        -- Value 1: > 50% diameter narrowing
+        (in any major vessel: attributes 59 through 68 are vessels)
+     59 lmt
+     60 ladprox
+     61 laddist
+     62 diag
+     63 cxmain
+     64 ramus
+     65 om1
+     66 om2
+     67 rcaprox
+     68 rcadist
+     69 lvx1: not used
+     70 lvx2: not used
+     71 lvx3: not used
+     72 lvx4: not used
+     73 lvf: not used
+     74 cathef: not used
+     75 junk: not used
+     76 name: last name of patient 
+	(I replaced this with the dummy string "name")
+
+9. Missing Attribute Values: Several.  Distinguished with value -9.0.
+
+10. Class Distribution:
+        Database:      0   1   2   3   4 Total
+          Cleveland: 164  55  36  35  13   303
+          Hungarian: 188  37  26  28  15   294
+        Switzerland:   8  48  32  30   5   123
+      Long Beach VA:  51  56  41  42  10   200

heart.py

@@ -0,0 +1,138 @@
+"""Heart"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "age",
+    "is_male",
+    "type_of_chest_pain",
+    "resting_blood_pressure",
+    "serum_cholesterol",
+    "fasting_blood_sugar",
+    "rest_electrocardiographic_type",
+    "maximum_heart_rate",
+    "has_exercise_induced_angina",
+    "depression_induced_by_exercise",
+    "slope_of_peak_exercise",
+    "number_of_major_vessels_colored_by_flourosopy",
+    "thal",
+    "has_hearth_disease"
+]
+
+DESCRIPTION = "Heart dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Heart"
+_URLS = ("https://huggingface.co/datasets/mstz/heart/raw/heart.csv")
+_CITATION = """
+@misc{misc_heart_disease_45,
+  author       = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.},
+  title        = {{Heart Disease}},
+  year         = {1988},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C52P4X}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"},
+}
+features_types_per_config = {
+    "hungary": {
+        "age": datasets.Value("int8"),
+        "is_male": datasets.Value("bool"),
+        "type_of_chest_pain": datasets.Value("string"),
+        "resting_blood_pressure": datasets.Value("float32"),
+        "serum_cholesterol": datasets.Value("float32"),
+        "fasting_blood_sugar": datasets.Value("float32"),
+        "rest_electrocardiographic_type": datasets.Value("string"),
+        "maximum_heart_rate": datasets.Value("float32"),
+        "has_exercise_induced_angina": datasets.Value("bool"),
+        "depression_induced_by_exercise": datasets.Value("float32"),
+        "has_hearth_disease": 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}
+
+_ENCODING_DICS = {
+    "type_of_chest_pain": {
+        1: "typical angina",
+        2: "atypical angina",
+        3: "non-anginal pain",
+        4: "asymptomatic"
+    }
+}
+
+class HeartConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(HeartConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Heart(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "hungary"
+    BUILDER_CONFIGS = [
+        HeartConfig(name="hungary",
+                    description="Heart for binary classification, hungary dataset.")
+    ]
+
+
+    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[self.config.name]["train"]})
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        data = pandas.read_csv(filepath, header=None)
+        data.columns = _BASE_FEATURE_NAMES
+        data = self.preprocess(data, self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row
+
+    def preprocess(self, data, config):
+        for feature in _ENCODING_DICS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
+
+        data[["age"]].applymap(int)
+
+        data.drop("slope_of_peak_exercise", axis="columns", inplace=True)
+        data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True)
+        data.drop("thal", axis="columns", inplace=True)
+        data = data[data.serum_cholesterol != "?"]
+
+        data = data.infer_objects()
+
+        data = data[data.resting_blood_pressure != "?"]
+        data = data[data.fasting_blood_sugar != "?"]
+        data = data[data.rest_electrocardiographic_type != "?"]
+        data = data[data.maximum_heart_rate != "?"]
+        data = data[data.has_exercise_induced_angina != "?"]
+
+        data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool,
+                            "serum_cholesterol": float, "maximum_heart_rate": float,
+                            "resting_blood_pressure": float, "fasting_blood_sugar": float})       
+
+        return data
+
+    def encode(self, feature, value):
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")

processed.hungarian.data

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+43,0,2,120,201,0,0,165,0,0,?,?,?,0
+43,0,2,120,215,0,1,175,0,0,?,?,?,0
+43,0,2,120,249,0,1,176,0,0,?,?,?,0
+43,0,2,120,266,0,0,118,0,0,?,?,?,0
+43,0,2,150,186,0,0,154,0,0,?,?,?,0
+43,0,3,150,?,0,0,175,0,0,?,?,3,0
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+44,0,4,120,218,0,1,115,0,0,?,?,?,0
+44,1,2,120,184,0,0,142,0,1,2,?,?,0
+44,1,2,130,215,0,0,135,0,0,?,?,?,0
+44,1,4,150,412,0,0,170,0,0,?,?,?,0
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+45,1,2,140,224,1,0,122,0,0,?,?,?,0
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+45,1,4,140,224,0,0,144,0,0,?,?,?,0
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