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README.md

@@ -14,4 +14,76 @@ 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.
+The [Speed dating dataset](https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536) from OpenML.
+
+
+# Configurations and tasks
+- `dating` Predict the success of speed dating.
+
+# Features
+|**Features**                                       |**Type** |
+|---------------------------------------------------|---------|
+|`is_dater_male`                                    |`int8`   |
+|`dater_age`                                        |`int8`   |
+|`dated_age`                                        |`int8`   |
+|`age_difference`                                   |`int8`   |
+|`dater_race`                                       |`string` |
+|`dated_race`                                       |`string` |
+|`are_same_race`                                    |`int8`   |
+|`same_race_importance_for_dater`                   |`float64`|
+|`same_religion_importance_for_dater`               |`float64`|
+|`attractiveness_importance_for_dated`              |`float64`|
+|`sincerity_importance_for_dated`                   |`float64`|
+|`intelligence_importance_for_dated`                |`float64`|
+|`humor_importance_for_dated`                       |`float64`|
+|`ambition_importance_for_dated`                    |`float64`|
+|`shared_interests_importance_for_dated`            |`float64`|
+|`attractiveness_score_of_dater_from_dated`         |`float64`|
+|`sincerity_score_of_dater_from_dated`              |`float64`|
+|`intelligence_score_of_dater_from_dated`           |`float64`|
+|`humor_score_of_dater_from_dated`                  |`float64`|
+|`ambition_score_of_dater_from_dated`               |`float64`|
+|`shared_interests_score_of_dater_from_dated`       |`float64`|
+|`attractiveness_importance_for_dater`              |`float64`|
+|`sincerity_importance_for_dater`                   |`float64`|
+|`intelligence_importance_for_dater`                |`float64`|
+|`humor_importance_for_dater`                       |`float64`|
+|`ambition_importance_for_dater`                    |`float64`|
+|`shared_interests_importance_for_dater`            |`float64`|
+|`self_reported_attractiveness_of_dater`            |`float64`|
+|`self_reported_sincerity_of_dater`                 |`float64`|
+|`self_reported_intelligence_of_dater`              |`float64`|
+|`self_reported_humor_of_dater`                     |`float64`|
+|`self_reported_ambition_of_dater`                  |`float64`|
+|`reported_attractiveness_of_dated_from_dater`      |`float64`|
+|`reported_sincerity_of_dated_from_dater`           |`float64`|
+|`reported_intelligence_of_dated_from_dater`        |`float64`|
+|`reported_humor_of_dated_from_dater`               |`float64`|
+|`reported_ambition_of_dated_from_dater`            |`float64`|
+|`reported_shared_interests_of_dated_from_dater`    |`float64`|
+|`dater_interest_in_sports`                         |`float64`|
+|`dater_interest_in_tvsports`                       |`float64`|
+|`dater_interest_in_exercise`                       |`float64`|
+|`dater_interest_in_dining`                         |`float64`|
+|`dater_interest_in_museums`                        |`float64`|
+|`dater_interest_in_art`                            |`float64`|
+|`dater_interest_in_hiking`                         |`float64`|
+|`dater_interest_in_gaming`                         |`float64`|
+|`dater_interest_in_clubbing`                       |`float64`|
+|`dater_interest_in_reading`                        |`float64`|
+|`dater_interest_in_tv`                             |`float64`|
+|`dater_interest_in_theater`                        |`float64`|
+|`dater_interest_in_movies`                         |`float64`|
+|`dater_interest_in_concerts`                       |`float64`|
+|`dater_interest_in_music`                          |`float64`|
+|`dater_interest_in_shopping`                       |`float64`|
+|`dater_interest_in_yoga`                           |`float64`|
+|`interests_correlation`                            |`float64`|
+|`expected_satisfaction_of_dater`                   |`float64`|
+|`expected_number_of_likes_of_dater_from_20_people` |`int8`   |
+|`expected_number_of_dates_for_dater`               |`int8`   |
+|`dater_liked_dated`                                |`float64`|
+|`probability_dated_wants_to_date`                  |`float64`|
+|`already_met_before`                               |`int8`   |
+|`dater_wants_to_date`                              |`int8`   |
+|`dated_wants_to_date`                              |`int8`   |

speeddating.py

@@ -1,7 +1,6 @@
 """Speeddating Dataset"""
 
 from typing import List
-from functools import partial
 
 import datasets
 
@@ -77,12 +76,6 @@ _BASE_FEATURE_NAMES = [
     "is_match"
 ]
 
-_ENCODING_DICS = {
-    "sex": {
-        "female": 0,
-        "male": 1
-        }
-}
 
 DESCRIPTION = "Speed-dating dataset."
 _HOMEPAGE = "https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536"
@@ -95,7 +88,7 @@ urls_per_split = {
 }
 features_types_per_config = {
     "dating": {
-        "dater_gender": datasets.Value("int8"),
+        "is_dater_male": datasets.Value("int8"),
         "dater_age": datasets.Value("int8"),
         "dated_age": datasets.Value("int8"),
         "age_difference": datasets.Value("int8"),
@@ -198,13 +191,16 @@ class Speeddating(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath)
-        data = self.preprocess(data, config=self.config.name)
+        if self.config.name == "dating":
+            data = pandas.read_csv(filepath)
+            data = self.preprocess(data, config=self.config.name)
 
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
+            for row_id, row in data.iterrows():
+                data_row = dict(row)
 
-            yield row_id, data_row
+                yield row_id, data_row
+        else:
+            raise ValueError(f"Unknown config: {self.config.name}")
 
     def preprocess(self, data: pandas.DataFrame, config: str = "dating") -> pandas.DataFrame:
         data.loc[data.race == "?", "race"] = "unknown"
@@ -220,8 +216,6 @@ class Speeddating(datasets.GeneratorBasedBuilder):
         data.loc[data.race == "Black/African American", "race"] = "african-american"
         data.loc[data.race_o == "Black/African American", "race_o"] = "african-american"
 
-        sex_transform = partial(self.encoding_dics, "sex")
-        data.loc[:, "gender"] = data.gender.apply(sex_transform)
         data = data.rename(columns={"gender": "sex"})
 
         data.drop("has_null", axis="columns", inplace=True)
@@ -322,13 +316,4 @@ class Speeddating(datasets.GeneratorBasedBuilder):
         
         data.columns = _BASE_FEATURE_NAMES
         
-        if config == "dating":
-            return data
-        else:
-            raise ValueError(f"Unknown config: {config}")
-    
-    def encoding_dics(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
-               
+        return data