updated to datasets 4.*

README.md

@@ -1,17 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: dating
+  data_files:
+  - path: dating/train.csv
+    split: train
+  default: true
+language: en
+license: unknown
+pretty_name: Speeddating
+size_categories: 1M<n<10M
 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
+- multiclass_classification
+task_categories:
 - 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) from OpenML.

speeddating.py

@@ -1,317 +0,0 @@
-"""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