"""Speeddating Dataset""" from typing import List import datasets import pandas VERSION = datasets.Version("1.0.0") _BASE_FEATURE_NAMES = [ "is_dater_male", "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": { "is_dater_male": datasets.Value("bool"), "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("bool"), "same_race_importance_for_dater": datasets.Value("float64"), "same_religion_importance_for_dater": datasets.Value("float64"), "attractiveness_importance_for_dated": datasets.Value("float64"), "sincerity_importance_for_dated": datasets.Value("float64"), "intelligence_importance_for_dated": datasets.Value("float64"), "humor_importance_for_dated": datasets.Value("float64"), "ambition_importance_for_dated": datasets.Value("float64"), "shared_interests_importance_for_dated": datasets.Value("float64"), "attractiveness_score_of_dater_from_dated": datasets.Value("float64"), "sincerity_score_of_dater_from_dated": datasets.Value("float64"), "intelligence_score_of_dater_from_dated": datasets.Value("float64"), "humor_score_of_dater_from_dated": datasets.Value("float64"), "ambition_score_of_dater_from_dated": datasets.Value("float64"), "shared_interests_score_of_dater_from_dated": datasets.Value("float64"), "attractiveness_importance_for_dater": datasets.Value("float64"), "sincerity_importance_for_dater": datasets.Value("float64"), "intelligence_importance_for_dater": datasets.Value("float64"), "humor_importance_for_dater": datasets.Value("float64"), "ambition_importance_for_dater": datasets.Value("float64"), "shared_interests_importance_for_dater": datasets.Value("float64"), "self_reported_attractiveness_of_dater": datasets.Value("float64"), "self_reported_sincerity_of_dater": datasets.Value("float64"), "self_reported_intelligence_of_dater": datasets.Value("float64"), "self_reported_humor_of_dater": datasets.Value("float64"), "self_reported_ambition_of_dater": datasets.Value("float64"), "reported_attractiveness_of_dated_from_dater": datasets.Value("float64"), "reported_sincerity_of_dated_from_dater": datasets.Value("float64"), "reported_intelligence_of_dated_from_dater": datasets.Value("float64"), "reported_humor_of_dated_from_dater": datasets.Value("float64"), "reported_ambition_of_dated_from_dater": datasets.Value("float64"), "reported_shared_interests_of_dated_from_dater": datasets.Value("float64"), "dater_interest_in_sports": datasets.Value("float64"), "dater_interest_in_tvsports": datasets.Value("float64"), "dater_interest_in_exercise": datasets.Value("float64"), "dater_interest_in_dining": datasets.Value("float64"), "dater_interest_in_museums": datasets.Value("float64"), "dater_interest_in_art": datasets.Value("float64"), "dater_interest_in_hiking": datasets.Value("float64"), "dater_interest_in_gaming": datasets.Value("float64"), "dater_interest_in_clubbing": datasets.Value("float64"), "dater_interest_in_reading": datasets.Value("float64"), "dater_interest_in_tv": datasets.Value("float64"), "dater_interest_in_theater": datasets.Value("float64"), "dater_interest_in_movies": datasets.Value("float64"), "dater_interest_in_concerts": datasets.Value("float64"), "dater_interest_in_music": datasets.Value("float64"), "dater_interest_in_shopping": datasets.Value("float64"), "dater_interest_in_yoga": datasets.Value("float64"), "interests_correlation": datasets.Value("float64"), "expected_satisfaction_of_dater": datasets.Value("float64"), "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("float64"), "probability_dated_wants_to_date": datasets.Value("float64"), "already_met_before": datasets.Value("bool"), "dater_wants_to_date": datasets.Value("bool"), "dated_wants_to_date": datasets.Value("bool"), "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): 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_o == "?", "race_o"] = "unknown" data.loc[data.race == "Asian/Pacific Islander/Asian-American", "race"] = "asian" data.loc[data.race_o == "Asian/Pacific Islander/Asian-American", "race_o"] = "asian" data.loc[data.race == "European/Caucasian-American", "race"] = "caucasian" data.loc[data.race_o == "European/Caucasian-American", "race_o"] = "caucasian" data.loc[data.race == "Other", "race"] = "other" data.loc[data.race_o == "Other", "race_o"] = "other" data.loc[data.race == "Latino/Hispanic American", "race"] = "hispanic" data.loc[data.race_o == "Latino/Hispanic American", "race_o"] = "hispanic" data.loc[data.race == "Black/African American", "race"] = "african-american" data.loc[data.race_o == "Black/African American", "race_o"] = "african-american" data = data.rename(columns={"gender": "is_dater_male"}) data.loc[:, "is_dater_male"] = data.is_dater_male.apply(lambda x: 1 if x == "male" else 0) data.drop("has_null", axis="columns", inplace=True) data.drop("field", axis="columns", inplace=True) data.drop("wave", axis="columns", inplace=True) # data.drop("d_age", axis="columns", inplace=True) data.drop("d_d_age", axis="columns", inplace=True) data.drop("d_importance_same_race", axis="columns", inplace=True) data.drop("d_importance_same_religion", axis="columns", inplace=True) data.drop("d_pref_o_attractive", axis="columns", inplace=True) data.drop("d_pref_o_sincere", axis="columns", inplace=True) data.drop("d_pref_o_intelligence", axis="columns", inplace=True) data.drop("d_pref_o_funny", axis="columns", inplace=True) data.drop("d_pref_o_ambitious", axis="columns", inplace=True) data.drop("d_pref_o_shared_interests", axis="columns", inplace=True) data.drop("d_attractive_o", axis="columns", inplace=True) data.drop("d_sinsere_o", axis="columns", inplace=True) data.drop("d_intelligence_o", axis="columns", inplace=True) data.drop("d_funny_o", axis="columns", inplace=True) data.drop("d_ambitous_o", axis="columns", inplace=True) data.drop("d_shared_interests_o", axis="columns", inplace=True) data.drop("d_attractive_important", axis="columns", inplace=True) data.drop("d_sincere_important", axis="columns", inplace=True) data.drop("d_intellicence_important", axis="columns", inplace=True) data.drop("d_funny_important", axis="columns", inplace=True) data.drop("d_ambtition_important", axis="columns", inplace=True) data.drop("d_shared_interests_important", axis="columns", inplace=True) data.drop("d_attractive", axis="columns", inplace=True) data.drop("d_sincere", axis="columns", inplace=True) data.drop("d_intelligence", axis="columns", inplace=True) data.drop("d_funny", axis="columns", inplace=True) data.drop("d_ambition", axis="columns", inplace=True) data.drop("d_attractive_partner", axis="columns", inplace=True) data.drop("d_sincere_partner", axis="columns", inplace=True) data.drop("d_intelligence_partner", axis="columns", inplace=True) data.drop("d_funny_partner", axis="columns", inplace=True) data.drop("d_ambition_partner", axis="columns", inplace=True) data.drop("d_shared_interests_partner", axis="columns", inplace=True) data.drop("d_sports", axis="columns", inplace=True) data.drop("d_tvsports", axis="columns", inplace=True) data.drop("d_exercise", axis="columns", inplace=True) data.drop("d_dining", axis="columns", inplace=True) data.drop("d_museums", axis="columns", inplace=True) data.drop("d_art", axis="columns", inplace=True) data.drop("d_hiking", axis="columns", inplace=True) data.drop("d_gaming", axis="columns", inplace=True) data.drop("d_clubbing", axis="columns", inplace=True) data.drop("d_reading", axis="columns", inplace=True) data.drop("d_tv", axis="columns", inplace=True) data.drop("d_theater", axis="columns", inplace=True) data.drop("d_movies", axis="columns", inplace=True) data.drop("d_concerts", axis="columns", inplace=True) data.drop("d_music", axis="columns", inplace=True) data.drop("d_shopping", axis="columns", inplace=True) data.drop("d_yoga", axis="columns", inplace=True) data.drop("d_interests_correlate", axis="columns", inplace=True) data.drop("d_expected_happy_with_sd_people", axis="columns", inplace=True) data.drop("d_expected_num_interested_in_me", axis="columns", inplace=True) data.drop("d_expected_num_matches", axis="columns", inplace=True) data.drop("d_like", axis="columns", inplace=True) data.drop("d_guess_prob_liked", axis="columns", inplace=True) if "Unnamed: 123" in data.columns: data.drop("Unnamed: 123", axis="columns", inplace=True) data = data[data.age != "?"] data = data[data.age_o != "?"] data = data[data.importance_same_race != "?"] data = data[data.pref_o_attractive != "?"] data = data[data.pref_o_sincere != "?"] data = data[data.interests_correlate != "?"] data = data[data.pref_o_funny != "?"] data = data[data.pref_o_ambitious != "?"] data = data[data.pref_o_shared_interests != "?"] data = data[data.attractive_o != "?"] data = data[data.sinsere_o != "?"] data = data[data.intelligence_o != "?"] data = data[data.funny_o != "?"] data = data[data.ambitous_o != "?"] data = data[data.shared_interests_o != "?"] data = data[data.funny_important != "?"] data = data[data.ambtition_important != "?"] data = data[data.shared_interests_important != "?"] data = data[data.attractive != "?"] data = data[data.sincere != "?"] data = data[data.intelligence != "?"] data = data[data.funny != "?"] data = data[data.ambition != "?"] data = data[data.attractive_partner != "?"] data = data[data.sincere_partner != "?"] data = data[data.intelligence_partner != "?"] data = data[data.funny_partner != "?"] data = data[data.ambition_partner != "?"] data = data[data.shared_interests_partner != "?"] data = data[data.expected_num_interested_in_me != "?"] data = data[data.expected_num_matches != "?"] data = data[data.like != "?"] data = data[data.guess_prob_liked != "?"] data = data[data.met != "?"] data.columns = _BASE_FEATURE_NAMES data = data.astype({"is_dater_male": "bool", "are_same_race": "bool", "already_met_before": "bool", "dater_wants_to_date": "bool", "dated_wants_to_date": "bool"}) return data