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"""Speeddating Dataset""" |
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from typing import List |
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from functools import partial |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_BASE_FEATURE_NAMES = [ |
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"dater_gender", |
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"dater_age", |
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"dated_age", |
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"age_difference", |
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"dater_race", |
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"dated_race", |
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"are_same_race", |
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"same_race_importance_for_dater", |
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"same_religion_importance_for_dater", |
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"attractiveness_importance_for_dated", |
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"sincerity_importance_for_dated", |
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"intelligence_importance_for_dated", |
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"humor_importance_for_dated", |
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"ambition_importance_for_dated", |
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"shared_interests_importance_for_dated", |
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"attractiveness_score_of_dater_from_dated", |
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"sincerity_score_of_dater_from_dated", |
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"intelligence_score_of_dater_from_dated", |
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"humor_score_of_dater_from_dated", |
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"ambition_score_of_dater_from_dated", |
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"shared_interests_score_of_dater_from_dated", |
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"attractiveness_importance_for_dater", |
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"sincerity_importance_for_dater", |
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"intelligence_importance_for_dater", |
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"humor_importance_for_dater", |
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"ambition_importance_for_dater", |
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"shared_interests_importance_for_dater", |
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"self_reported_attractiveness_of_dater", |
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"self_reported_sincerity_of_dater", |
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"self_reported_intelligence_of_dater", |
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"self_reported_humor_of_dater", |
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"self_reported_ambition_of_dater", |
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"reported_attractiveness_of_dated_from_dater", |
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"reported_sincerity_of_dated_from_dater", |
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"reported_intelligence_of_dated_from_dater", |
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"reported_humor_of_dated_from_dater", |
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"reported_ambition_of_dated_from_dater", |
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"reported_shared_interests_of_dated_from_dater", |
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"dater_interest_in_sports", |
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"dater_interest_in_tvsports", |
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"dater_interest_in_exercise", |
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"dater_interest_in_dining", |
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"dater_interest_in_museums", |
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"dater_interest_in_art", |
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"dater_interest_in_hiking", |
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"dater_interest_in_gaming", |
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"dater_interest_in_clubbing", |
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"dater_interest_in_reading", |
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"dater_interest_in_tv", |
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"dater_interest_in_theater", |
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"dater_interest_in_movies", |
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"dater_interest_in_concerts", |
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"dater_interest_in_music", |
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"dater_interest_in_shopping", |
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"dater_interest_in_yoga", |
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"interests_correlation", |
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"expected_satisfaction_of_dater", |
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"expected_number_of_likes_of_dater_from_20_people", |
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"expected_number_of_dates_for_dater", |
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"dater_liked_dated", |
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"probability_dated_wants_to_date", |
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"already_met_before", |
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"dater_wants_to_date", |
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"dated_wants_to_date", |
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"is_match" |
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] |
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_ENCODING_DICS = { |
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"sex": { |
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"female": 0, |
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"male": 1 |
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} |
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} |
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DESCRIPTION = "Speed-dating dataset." |
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_HOMEPAGE = "https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536" |
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_URLS = ("https://huggingface.co/datasets/mstz/speeddating/raw/main/speeddating.csv") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/speeddating/raw/main/speeddating.csv", |
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} |
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features_types_per_config = { |
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"dating": { |
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"dater_gender": datasets.Value("int8"), |
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"dater_age": datasets.Value("int8"), |
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"dated_age": datasets.Value("int8"), |
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"age_difference": datasets.Value("int8"), |
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"dater_race": datasets.Value("string"), |
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"dated_race": datasets.Value("string"), |
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"are_same_race": datasets.Value("int8"), |
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"same_race_importance_for_dater": datasets.Value("int8"), |
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"same_religion_importance_for_dater": datasets.Value("int8"), |
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"attractiveness_importance_for_dated": datasets.Value("int8"), |
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"sincerity_importance_for_dated": datasets.Value("int8"), |
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"intelligence_importance_for_dated": datasets.Value("int8"), |
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"humor_importance_for_dated": datasets.Value("int8"), |
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"ambition_importance_for_dated": datasets.Value("int8"), |
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"shared_interests_importance_for_dated": datasets.Value("int8"), |
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"attractiveness_score_of_dater_from_dated": datasets.Value("int8"), |
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"sincerity_score_of_dater_from_dated": datasets.Value("int8"), |
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"intelligence_score_of_dater_from_dated": datasets.Value("int8"), |
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"humor_score_of_dater_from_dated": datasets.Value("int8"), |
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"ambition_score_of_dater_from_dated": datasets.Value("int8"), |
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"shared_interests_score_of_dater_from_dated": datasets.Value("int8"), |
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"attractiveness_importance_for_dater": datasets.Value("int8"), |
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"sincerity_importance_for_dater": datasets.Value("int8"), |
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"intelligence_importance_for_dater": datasets.Value("int8"), |
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"humor_importance_for_dater": datasets.Value("int8"), |
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"ambition_importance_for_dater": datasets.Value("int8"), |
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"shared_interests_importance_for_dater": datasets.Value("int8"), |
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"self_reported_attractiveness_of_dater": datasets.Value("int8"), |
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"self_reported_sincerity_of_dater": datasets.Value("int8"), |
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"self_reported_intelligence_of_dater": datasets.Value("int8"), |
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"self_reported_humor_of_dater": datasets.Value("int8"), |
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"self_reported_ambition_of_dater": datasets.Value("int8"), |
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"reported_attractiveness_of_dated_from_dater": datasets.Value("int8"), |
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"reported_sincerity_of_dated_from_dater": datasets.Value("int8"), |
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"reported_intelligence_of_dated_from_dater": datasets.Value("int8"), |
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"reported_humor_of_dated_from_dater": datasets.Value("int8"), |
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"reported_ambition_of_dated_from_dater": datasets.Value("int8"), |
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"reported_shared_interests_of_dated_from_dater": datasets.Value("int8"), |
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"dater_interest_in_sports": datasets.Value("int8"), |
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"dater_interest_in_tvsports": datasets.Value("int8"), |
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"dater_interest_in_exercise": datasets.Value("int8"), |
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"dater_interest_in_dining": datasets.Value("int8"), |
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"dater_interest_in_museums": datasets.Value("int8"), |
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"dater_interest_in_art": datasets.Value("int8"), |
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"dater_interest_in_hiking": datasets.Value("int8"), |
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"dater_interest_in_gaming": datasets.Value("int8"), |
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"dater_interest_in_clubbing": datasets.Value("int8"), |
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"dater_interest_in_reading": datasets.Value("int8"), |
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"dater_interest_in_tv": datasets.Value("int8"), |
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"dater_interest_in_theater": datasets.Value("int8"), |
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"dater_interest_in_movies": datasets.Value("int8"), |
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"dater_interest_in_concerts": datasets.Value("int8"), |
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"dater_interest_in_music": datasets.Value("int8"), |
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"dater_interest_in_shopping": datasets.Value("int8"), |
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"dater_interest_in_yoga": datasets.Value("int8"), |
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"interests_correlation": datasets.Value("float16"), |
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"expected_satisfaction_of_dater": datasets.Value("int8"), |
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"expected_number_of_likes_of_dater_from_20_people": datasets.Value("int8"), |
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"expected_number_of_dates_for_dater": datasets.Value("int8"), |
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"dater_liked_dated": datasets.Value("int8"), |
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"probability_dated_wants_to_date": datasets.Value("int8"), |
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"already_met_before": datasets.Value("int8"), |
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"dater_wants_to_date": datasets.Value("int8"), |
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"dated_wants_to_date": datasets.Value("int8"), |
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"is_match": datasets.ClassLabel(num_classes=2, names=("no", "yes")) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class SpeeddatingConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(SpeeddatingConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Speeddating(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "dating" |
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BUILDER_CONFIGS = [ |
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SpeeddatingConfig(name="dating", |
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description="Binary classification."), |
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] |
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def _info(self): |
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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath) |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data: pandas.DataFrame, config: str = "dating") -> pandas.DataFrame: |
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data.loc[data.race == "?", "race"] = "unknown" |
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data.loc[data.race == "Asian/Pacific Islander/Asian-American", "race"] = "asian" |
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data.loc[data.race == "European/Caucasian-American", "race"] = "caucasian" |
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data.loc[data.race == "Other", "race"] = "other" |
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data.loc[data.race == "Latino/Hispanic American", "race"] = "hispanic" |
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data.loc[data.race == "Black/African American", "race"] = "african-american" |
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sex_transform = partial(self.encoding_dics, "sex") |
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data.loc[:, "gender"] = data.gender.apply(sex_transform) |
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data = data.rename(columns={"gender": "sex"}) |
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data.drop("has_null", axis="columns", inplace=True) |
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data.drop("field", axis="columns", inplace=True) |
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data.drop("wave", axis="columns", inplace=True) |
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data.drop("d_d_age", axis="columns", inplace=True) |
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data.drop("d_importance_same_race", axis="columns", inplace=True) |
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data.drop("d_importance_same_religion", axis="columns", inplace=True) |
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data.drop("d_pref_o_attractive", axis="columns", inplace=True) |
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data.drop("d_pref_o_sincere", axis="columns", inplace=True) |
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data.drop("d_pref_o_intelligence", axis="columns", inplace=True) |
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data.drop("d_pref_o_funny", axis="columns", inplace=True) |
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data.drop("d_pref_o_ambitious", axis="columns", inplace=True) |
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data.drop("d_pref_o_shared_interests", axis="columns", inplace=True) |
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data.drop("d_attractive_o", axis="columns", inplace=True) |
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data.drop("d_sinsere_o", axis="columns", inplace=True) |
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data.drop("d_intelligence_o", axis="columns", inplace=True) |
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data.drop("d_funny_o", axis="columns", inplace=True) |
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data.drop("d_ambitous_o", axis="columns", inplace=True) |
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data.drop("d_shared_interests_o", axis="columns", inplace=True) |
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data.drop("d_attractive_important", axis="columns", inplace=True) |
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data.drop("d_sincere_important", axis="columns", inplace=True) |
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data.drop("d_intellicence_important", axis="columns", inplace=True) |
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data.drop("d_funny_important", axis="columns", inplace=True) |
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data.drop("d_ambtition_important", axis="columns", inplace=True) |
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data.drop("d_shared_interests_important", axis="columns", inplace=True) |
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data.drop("d_attractive", axis="columns", inplace=True) |
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data.drop("d_sincere", axis="columns", inplace=True) |
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data.drop("d_intelligence", axis="columns", inplace=True) |
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data.drop("d_funny", axis="columns", inplace=True) |
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data.drop("d_ambition", axis="columns", inplace=True) |
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data.drop("d_attractive_partner", axis="columns", inplace=True) |
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data.drop("d_sincere_partner", axis="columns", inplace=True) |
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data.drop("d_intelligence_partner", axis="columns", inplace=True) |
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data.drop("d_funny_partner", axis="columns", inplace=True) |
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data.drop("d_ambition_partner", axis="columns", inplace=True) |
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data.drop("d_shared_interests_partner", axis="columns", inplace=True) |
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data.drop("d_sports", axis="columns", inplace=True) |
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data.drop("d_tvsports", axis="columns", inplace=True) |
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data.drop("d_exercise", axis="columns", inplace=True) |
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data.drop("d_dining", axis="columns", inplace=True) |
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data.drop("d_museums", axis="columns", inplace=True) |
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data.drop("d_art", axis="columns", inplace=True) |
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data.drop("d_hiking", axis="columns", inplace=True) |
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data.drop("d_gaming", axis="columns", inplace=True) |
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data.drop("d_clubbing", axis="columns", inplace=True) |
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data.drop("d_reading", axis="columns", inplace=True) |
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data.drop("d_tv", axis="columns", inplace=True) |
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data.drop("d_theater", axis="columns", inplace=True) |
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data.drop("d_movies", axis="columns", inplace=True) |
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data.drop("d_concerts", axis="columns", inplace=True) |
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data.drop("d_music", axis="columns", inplace=True) |
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data.drop("d_shopping", axis="columns", inplace=True) |
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data.drop("d_yoga", axis="columns", inplace=True) |
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data.drop("d_interests_correlate", axis="columns", inplace=True) |
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data.drop("d_expected_happy_with_sd_people", axis="columns", inplace=True) |
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data.drop("d_expected_num_interested_in_me", axis="columns", inplace=True) |
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data.drop("d_expected_num_matches", axis="columns", inplace=True) |
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data.drop("d_like", axis="columns", inplace=True) |
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data.drop("d_guess_prob_liked", axis="columns", inplace=True) |
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data = data[data.age != "?"] |
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data = data[data.importance_same_race != "?"] |
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data = data[data.pref_o_attractive != "?"] |
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data = data[data.pref_o_sincere != "?"] |
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data = data[data.interests_correlate != "?"] |
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print(data.columns) |
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print(data.head()) |
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data.columns = _BASE_FEATURE_NAMES |
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if config == "dating": |
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return data |
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else: |
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raise ValueError(f"Unknown config: {config}") |
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def encoding_dics(self, feature, value): |
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if feature in _ENCODING_DICS: |
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return _ENCODING_DICS[feature][value] |
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raise ValueError(f"Unknown feature: {feature}") |
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