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README.md

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+---
+language:
+- en
+tags:
+- speeddating
+- tabular_classification
+- binary_classification
+pretty_name: Speed dating
+size_categories:
+- 1K<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- dating
+---
+# Speed dating
+The [Speed dating dataset](https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536) is cool.

speeddating.py

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+"""Speeddating Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "dater_gender",
+    "dater_age",
+    "dated_age",
+    "age_difference",
+    "dater_race",
+    "dated_race",
+    "are_same_race",
+    "same_race_importance_for_dater",
+    "same_religion_importance_for_dater",
+    "attractiveness_importance_for_dated",
+    "sincerity_importance_for_dated",
+    "intelligence_importance_for_dated",
+    "humor_importance_for_dated",
+    "ambition_importance_for_dated",
+    "shared_interests_importance_for_dated",
+    "attractiveness_score_of_dater_from_dated",
+    "sincerity_score_of_dater_from_dated",
+    "intelligence_score_of_dater_from_dated",
+    "humor_score_of_dater_from_dated",
+    "ambition_score_of_dater_from_dated",
+    "shared_interests_score_of_dater_from_dated",
+    "attractiveness_importance_for_dater",
+    "sincerity_importance_for_dater",
+    "intelligence_importance_for_dater",
+    "humor_importance_for_dater",
+    "ambition_importance_for_dater",
+    "shared_interests_importance_for_dater",
+    "self_reported_attractiveness_of_dater",
+    "self_reported_sincerity_of_dater",
+    "self_reported_intelligence_of_dater",
+    "self_reported_humor_of_dater",
+    "self_reported_ambition_of_dater",
+    "reported_attractiveness_of_dated_from_dater",
+    "reported_sincerity_of_dated_from_dater",
+    "reported_intelligence_of_dated_from_dater",
+    "reported_humor_of_dated_from_dater",
+    "reported_ambition_of_dated_from_dater",
+    "reported_shared_interests_of_dated_from_dater",
+    "dater_interest_in_sports",
+    "dater_interest_in_tvsports",
+    "dater_interest_in_exercise",
+    "dater_interest_in_dining",
+    "dater_interest_in_museums",
+    "dater_interest_in_art",
+    "dater_interest_in_hiking",
+    "dater_interest_in_gaming",
+    "dater_interest_in_clubbing",
+    "dater_interest_in_reading",
+    "dater_interest_in_tv",
+    "dater_interest_in_theater",
+    "dater_interest_in_movies",
+    "dater_interest_in_concerts",
+    "dater_interest_in_music",
+    "dater_interest_in_shopping",
+    "dater_interest_in_yoga",
+    "interests_correlation",
+    "expected_satisfaction_of_dater",
+    "expected_number_of_likes_of_dater_from_20_people",
+    "expected_number_of_dates_for_dater",
+    "dater_liked_dated",
+    "probability_dated_wants_to_date",
+    "already_met_before",
+    "dater_wants_to_date",
+    "dated_wants_to_date",
+    "is_match"
+]
+
+DESCRIPTION = "Speed-dating dataset."
+_HOMEPAGE = "https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536"
+_URLS = ("https://huggingface.co/datasets/mstz/speeddating/raw/main/speeddating.csv")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/speeddating/raw/main/speeddating.csv",
+}
+features_types_per_config = {
+    "dating": {
+        "dater_gender": datasets.Value("int8"),
+        "dater_age": datasets.Value("int8"),
+        "dated_age": datasets.Value("int8"),
+        "age_difference": datasets.Value("int8"),
+        "dater_race": datasets.Value("string"),
+        "dated_race": datasets.Value("string"),
+        "are_same_race": datasets.Value("int8"),
+        "same_race_importance_for_dater": datasets.Value("int8"),
+        "same_religion_importance_for_dater": datasets.Value("int8"),
+        "attractiveness_importance_for_dated": datasets.Value("int8"),
+        "sincerity_importance_for_dated": datasets.Value("int8"),
+        "intelligence_importance_for_dated": datasets.Value("int8"),
+        "humor_importance_for_dated": datasets.Value("int8"),
+        "ambition_importance_for_dated": datasets.Value("int8"),
+        "shared_interests_importance_for_dated": datasets.Value("int8"),
+        "attractiveness_score_of_dater_from_dated": datasets.Value("int8"),
+        "sincerity_score_of_dater_from_dated": datasets.Value("int8"),
+        "intelligence_score_of_dater_from_dated": datasets.Value("int8"),
+        "humor_score_of_dater_from_dated": datasets.Value("int8"),
+        "ambition_score_of_dater_from_dated": datasets.Value("int8"),
+        "shared_interests_score_of_dater_from_dated": datasets.Value("int8"),
+        "attractiveness_importance_for_dater": datasets.Value("int8"),
+        "sincerity_importance_for_dater": datasets.Value("int8"),
+        "intelligence_importance_for_dater": datasets.Value("int8"),
+        "humor_importance_for_dater": datasets.Value("int8"),
+        "ambition_importance_for_dater": datasets.Value("int8"),
+        "shared_interests_importance_for_dater": datasets.Value("int8"),
+        "self_reported_attractiveness_of_dater": datasets.Value("int8"),
+        "self_reported_sincerity_of_dater": datasets.Value("int8"),
+        "self_reported_intelligence_of_dater": datasets.Value("int8"),
+        "self_reported_humor_of_dater": datasets.Value("int8"),
+        "self_reported_ambition_of_dater": datasets.Value("int8"),
+        "reported_attractiveness_of_dated_from_dater": datasets.Value("int8"),
+        "reported_sincerity_of_dated_from_dater": datasets.Value("int8"),
+        "reported_intelligence_of_dated_from_dater": datasets.Value("int8"),
+        "reported_humor_of_dated_from_dater": datasets.Value("int8"),
+        "reported_ambition_of_dated_from_dater": datasets.Value("int8"),
+        "reported_shared_interests_of_dated_from_dater": datasets.Value("int8"),
+        "dater_interest_in_sports": datasets.Value("int8"),
+        "dater_interest_in_tvsports": datasets.Value("int8"),
+        "dater_interest_in_exercise": datasets.Value("int8"),
+        "dater_interest_in_dining": datasets.Value("int8"),
+        "dater_interest_in_museums": datasets.Value("int8"),
+        "dater_interest_in_art": datasets.Value("int8"),
+        "dater_interest_in_hiking": datasets.Value("int8"),
+        "dater_interest_in_gaming": datasets.Value("int8"),
+        "dater_interest_in_clubbing": datasets.Value("int8"),
+        "dater_interest_in_reading": datasets.Value("int8"),
+        "dater_interest_in_tv": datasets.Value("int8"),
+        "dater_interest_in_theater": datasets.Value("int8"),
+        "dater_interest_in_movies": datasets.Value("int8"),
+        "dater_interest_in_concerts": datasets.Value("int8"),
+        "dater_interest_in_music": datasets.Value("int8"),
+        "dater_interest_in_shopping": datasets.Value("int8"),
+        "dater_interest_in_yoga": datasets.Value("int8"),
+        "interests_correlation": datasets.Value("float16"),
+        "expected_satisfaction_of_dater": datasets.Value("int8"),
+        "expected_number_of_likes_of_dater_from_20_people": datasets.Value("int8"),
+        "expected_number_of_dates_for_dater": datasets.Value("int8"),
+        "dater_liked_dated": datasets.Value("int8"),
+        "probability_dated_wants_to_date": datasets.Value("int8"),
+        "already_met_before": datasets.Value("int8"),
+        "dater_wants_to_date": datasets.Value("int8"),
+        "dated_wants_to_date": datasets.Value("int8"),
+        "is_match": 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 SpeeddatingConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(SpeeddatingConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Speeddating(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "dating"
+    BUILDER_CONFIGS = [
+        SpeeddatingConfig(name="dating",
+                          description="Binary classification."),
+    ]
+
+
+    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]:
+        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, 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 = "dating") -> pandas.DataFrame:
+        data.loc[data.race == "?", "race"] = "unknown"
+        data.loc[data.race == "Asian/Pacific Islander/Asian-American", "race"] = "asian"
+        data.loc[data.race == "European/Caucasian-American", "race"] = "caucasian"
+        data.loc[data.race == "Other", "race"] = "other"
+        data.loc[data.race == "Latino/Hispanic American", "race"] = "hispanic"
+        data.loc[data.race == "Black/African American", "race"] = "african-american"
+        
+        sex_transform = partial(self.encoding_dics, "sex")
+        data.loc[:, "sex"] = data.sex.apply(sex_transform)
+        
+        data.drop("has_null", axis="columns", inplace=True)
+        data.drop("field", axis="columns", inplace=True)
+
+        data = data[data.age != "?"]
+        data = data[data.importance_same_race != "?"]
+        data = data[data.pref_o_attractive != "?"]
+        data = data[data.pref_o_sincere != "?"]
+        data = data[data.interests_correlate != "?"]
+        
+        data.columns = _BASE_FEATURE_NAMES
+        
+        if config == "dating":
+            return data
+        else:
+            raise ValueError(f"Unknown config: {config}")
+    
+    def encoding_dics(feature, value):
+        match feature:
+            case "sex":
+                return {
+                    "female": 0,
+                    "male": 1
+                }
+            case _:
+                raise ValueError(f"Unknown feature: {feature}")