為 sklearn 管道實現自定義 one-hot-encoding function

Question

與One Hot Encoding 中發布的問題相關，請保留 NA 以進行插補，我正在嘗試創建一個自定義 function 來在一個熱編碼分類變量時處理 NA。 該設置應適用於使用sklearn pipeline進行訓練/測試拆分和建模。

我的問題的一個簡單的可重現示例：

#Packages
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.impute import KNNImputer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import Ridge
from sklearn.impute import SimpleImputer


# Make some categorical data X and a response y and split it. 
X = pd.DataFrame(columns=["1","2"],data = [["A",np.nan],["B","A"],[np.nan,"A"],[np.nan,"B"],["B","A"],["A","B"],["C","B"],["D","E"]])
y = pd.DataFrame(data = np.array([1,5,4,6,2,3,9,9]))
X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2,random_state=42)

然后，我創建了一個自定義 function 與 nan 進行 OHE（使用Scikit-learn 中的 OneHotEncoder 和 KNNImpute 之間的循環循環中描述的過程）

class OHE_with_nan(BaseEstimator,TransformerMixin):
    """ OHE with NAN. Not super pretty but works..
    """
    def __init__(self, copy=True):
        self.copy = copy
        
    def fit(self, X, y = None):
        """ This transformer does not use a fit procedure """
        return self
    
    def transform(self, X, y = None):
        """ Return the new object here"""
        # Replace nans with "Missing" such that OneHotEncoder can work.
        enc_missing = SimpleImputer(strategy="constant",fill_value="missing")
        data1 = pd.DataFrame(columns=X.columns,data = enc_missing.fit_transform(X))
        #Perform standard OHE
        OHE = OneHotEncoder(sparse=False,handle_unknown="ignore")
        OHE_fit = OHE.fit_transform(data1)
        #save feature names of the OHE dataframe
        data_OHE = pd.DataFrame(columns=OHE.get_feature_names(data1.columns),data = OHE_fit)
        
        # Initialize
        Column_names = data1.columns
        Final_OHE = pd.DataFrame()
        # Loop over columns to replace 0s with nan the correct places.
        for i in range(len(data1.columns)):
           tmp_data = data_OHE[data_OHE.columns[pd.Series(data_OHE.columns).str.startswith(Column_names[i])]]
           missing_name = tmp_data.iloc[:,-1:].columns
           missing_index = np.where(tmp_data[missing_name]==1)[0]
           tmp_data.loc[missing_index,:] = np.nan
           tmp_data1 = tmp_data.drop(missing_name,axis=1)
           Final_OHE = pd.concat([Final_OHE, tmp_data1], axis=1)
        
        return Final_OHE

然后將其組合成一個使用嶺回歸預測 y 的管道（僅作為示例，隨機選擇 model..）

Estimator = Pipeline([
   ('Ohe_with_NA',OHE_with_nan()),
   ("Imputer",KNNImputer(n_neighbors=1)),
   ('Model',Ridge(alpha = 0.01))
    ])

該程序可以安裝：

pipe_fit = Estimator.fit(X_train,Y_train)

但是對看不見的數據進行測試失敗：

pipe_fit.score(X_test, Y_test)

ValueError: X has 2 features, but KNNImputer is expecting 7 features as input.

這是因為 OHE_with_nan 中OneHotEncoder中的OHE_with_nan handle_unknown = "ignore不再是“活動的”，因為它已被包裝到我的自定義 function 中。

如果一個人只是在管道中直接使用OneHotEncoder(handle_unknown = "ignore") ，那么一切正常（但這不是我的意圖，因為這會從我試圖估算的數據中“刪除”nans。）

我的問題如何在我的自定義 function 中啟用handle_unknown = "ignore"以便它也可以在管道設置中對看不見的數據執行？

希望您了解我的情況 - 任何幫助都非常感謝！

Answer 1

我認為主要問題是您需要在合適的時候保存更多信息（尤其是內部OneHotEncoder ）。 我還使缺失列的識別更加健壯（我想您可能依賴於將其放在最后的排序，但由於字母順序，這僅適用於您的樣本數據？）。 我沒有花太多時間清理東西或尋找效率。

class OHE_with_nan(BaseEstimator, TransformerMixin):
    """One-hot encode, propagating NaNs.

    Requires a dataframe as input!
    """
    def fit(self, X, y=None):
        self.orig_cols_ = X.columns
        self.imputer_ = SimpleImputer(strategy="constant", fill_value="MISSING")
        X_filled = self.imputer_.fit_transform(X)
        self.ohe_ = OneHotEncoder(sparse=False, handle_unknown="ignore")
        self.ohe_.fit(X_filled)

        self.ohe_colnames_ = self.ohe_.get_feature_names(X.columns)
        self.missing_value_columns = np.array(["MISSING" in col for col in self.ohe_colnames_])
        return self

    def transform(self, X, y=None):
        raw_ohe = pd.DataFrame(self.ohe_.transform(self.imputer_.transform(X)), columns=self.ohe_colnames_)
        out_list = []
        # Loop over columns to replace 0s with nan the correct places.
        for orig_col in self.orig_cols_:
            tmp_data = raw_ohe[self.ohe_colnames_[pd.Series(self.ohe_colnames_).str.startswith(orig_col)]]
            missing_name = tmp_data.columns[["MISSING" in col for col in tmp_data.columns]]
            missing_indices = np.where(tmp_data[missing_name]==1)[0]
            tmp_data.loc[missing_indices, :] = np.nan
            tmp_data1 = tmp_data.drop(missing_name, axis=1)
            out_list.append(tmp_data1)
        out = pd.concat(out_list, axis=1)
        return out