如何从 sklearn 管道输出 Pandas 对象

Question

我构建了一个管道，该管道采用已拆分为分类和数字列的 pandas 数据框。我正在尝试在我的结果上运行 GridSearchCV，并最终查看 GridSearchCV 选择的最佳性能模型的重要性排名特征。我遇到的问题是 sklearn 管道输出 numpy 数组对象并在此过程中丢失任何列信息。因此，当我去检查模型中最重要的系数时，我留下了一个未标记的 numpy 数组。

我已阅读到构建自定义转换器可能是解决此问题的一种可能，但我自己没有任何经验。我也研究过利用 sklearn-pandas 包，但我犹豫是否尝试实现一些可能不会与 sklearn 并行更新的东西。任何人都可以建议他们认为解决此问题的最佳途径吗？我也对任何涉及 pandas 和 sklearn 管道应用的文献持开放态度。

我的管道：

# impute and standardize numeric data 
numeric_transformer = Pipeline([
    ('impute', SimpleImputer(missing_values=np.nan, strategy="mean")),
    ('scale', StandardScaler())
])

# impute and encode dummy variables for categorical data
categorical_transformer = Pipeline([
    ('impute', SimpleImputer(missing_values=np.nan, strategy="most_frequent")),
    ('one_hot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])

preprocessor = ColumnTransformer(transformers=[
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

clf = Pipeline([
    ('transform', preprocessor),
    ('ridge', Ridge())
])

交叉验证：

kf = KFold(n_splits=4, shuffle=True, random_state=44)

cross_val_score(clf, X_train, y_train, cv=kf).mean()

网格搜索：

param_grid = {
    'ridge__alpha': [.001, .1, 1.0, 5, 10, 100]
}

gs = GridSearchCV(clf, param_grid, cv = kf)
gs.fit(X_train, y_train)

检查系数：

model = gs.best_estimator_
predictions = model.fit(X_train, y_train).predict(X_test)
model.named_steps['ridge'].coef_

这是在 seaborn "mpg" 数据集上执行时模型系数的输出：

array([-4.64782052e-01,  1.47805207e+00, -3.28948689e-01, -5.37033173e+00,
        2.80000700e-01,  2.71523808e+00,  6.29170887e-01,  9.51627968e-01,
       ...
       -1.50574860e+00,  1.88477450e+00,  4.57285471e+00, -6.90459868e-01,
        5.49416409e+00])

理想情况下，我想保留 pandas 数据框信息并在调用 OneHotEncoder 和其他方法后检索派生列名。

Answer 1

我实际上会从输入中创建列名。如果您的输入已经分为数字和分类，您可以使用pd.get_dummies 来获取每个分类特征的不同类别的数量。

然后，您可以根据带有一些人工数据的问题为列创建正确的名称，如本工作示例的最后一部分所示。

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import Ridge
from sklearn.model_selection import KFold, cross_val_score, GridSearchCV

# create aritificial data
numeric_features_vals = pd.DataFrame({'x1': [1, 2, 3, 4], 'x2': [0.15, 0.25, 0.5, 0.45]})
numeric_features = ['x1', 'x2']
categorical_features_vals = pd.DataFrame({'cat1': [0, 1, 1, 2], 'cat2': [2, 1, 5, 0] })
categorical_features = ['cat1', 'cat2']

X_train = pd.concat([numeric_features_vals, categorical_features_vals], axis=1)
X_test = pd.DataFrame({'x1':[2,3], 'x2':[0.2, 0.3], 'cat1':[0, 1], 'cat2':[2, 1]})
y_train = pd.DataFrame({'labels': [10, 20, 30, 40]})

# impute and standardize numeric data 
numeric_transformer = Pipeline([
    ('impute', SimpleImputer(missing_values=np.nan, strategy="mean")),
    ('scale', StandardScaler())
])

# impute and encode dummy variables for categorical data
categorical_transformer = Pipeline([
    ('impute', SimpleImputer(missing_values=np.nan, strategy="most_frequent")),
    ('one_hot', OneHotEncoder(sparse=False, handle_unknown='ignore'))
])

preprocessor = ColumnTransformer(transformers=[
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

clf = Pipeline([
    ('transform', preprocessor),
    ('ridge', Ridge())
])


kf = KFold(n_splits=2, shuffle=True, random_state=44)
cross_val_score(clf, X_train, y_train, cv=kf).mean()

param_grid = {
    'ridge__alpha': [.001, .1, 1.0, 5, 10, 100]
}

gs = GridSearchCV(clf, param_grid, cv = kf)
gs.fit(X_train, y_train)

model = gs.best_estimator_
predictions = model.fit(X_train, y_train).predict(X_test)
print('coefficients : ',  model.named_steps['ridge'].coef_, '\n')

# create column names for categorical hot encoded data
columns_names_to_map = list(np.copy(numeric_features))
columns_names_to_map.extend('cat1_' + str(col) for col in pd.get_dummies(X_train['cat1']).columns)
columns_names_to_map.extend('cat2_' + str(col) for col in pd.get_dummies(X_train['cat2']).columns)

print('columns after preprocessing :', columns_names_to_map,  '\n')
print('#'*80)
print( '\n', 'dataframe of rescaled features with custom colum names: \n\n', pd.DataFrame({col:vals for vals, col in zip (preprocessor.fit_transform(X_train).T, columns_names_to_map)}))
print('#'*80)
print( '\n', 'dataframe of ridge coefficients with custom colum names: \n\n', pd.DataFrame({col:vals for vals, col in zip (model.named_steps['ridge'].coef_.T, columns_names_to_map)}))

上面的代码（最后）打印出以下数据帧，它是从参数名称到参数值的映射：