sklearn 自定義轉換器

sklearn已經提供了很多轉換器，如果想自定義轉換器，可以定義一個新的類並且實現其fit(),transform(),fit_transform()三個方法。

添加TransformerMixin作為基類，會直接得到fit_transform()方法；

添加BaseEstimator作為基類，可以獲得兩個自動調整超參數的方法：get_params()和set_params()

 

#自定義轉換器,添加新的屬性
from sklearn.base import BaseEstimator,TransformerMixin
rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6
class CombinedAttributesAdder(BaseEstimator,TransformerMixin):
    def __init__(self,add_bedrooms_per_room=True):
        self.add_bedrooms_per_room=add_bedrooms_per_room
    def fit(self,X,y=None):
        return delf
    def transform(self,X,y=None):
        rooms_per_household=X[:,rooms_ix]/X[:,household_ix]
        population_per_household=X[:,population_ix]/X[:,household_ix]
        if self.add_bedrooms_per_room:
            bedrooms_per_room=X[:,bedrooms_ix]/X[:,rooms_ix]
            return np.c_[X,rooms_per_household,population_per_household,bedrooms_per_room]
        else:
            return np.c_[X,rooms_per_household,population_per_household]
attr_adder=CombinedAttributesAdder(add_bedrooms_per_room=True)
housing_extra_attribs=attr_adder.transform(housing.values)
pd.DataFrame(housing_extra_attribs,columns=['longitude', 'latitude', 'housing_median_age', 'total_rooms',
       'total_bedrooms', 'population', 'households', 'median_income',
       'ocean_proximity','rooms_per_household','population_per_household','bedrooms_per_room']).head()

 

 輸出為：

原來的訓練集為：

多了三個屬性：rooms_per_household,population_per_household,bedrooms_per_room