How to output Pandas object from sklearn pipeline

Question

I have constructed a pipeline that takes a pandas dataframe that has been split into categorical and numerical columns. I am trying to run GridSearchCV on my results and ultimat

Answer 1

I would actually go for creating column names from the input. If your input is already divided into numerical an categorical you can use pd.get_dummies to get the number of different category for each categorical feature.

then you can just create proper names for the columns as shown in the last part of this working example based on the question with some artificial data.

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)}))

the code above (in the end) prints out the following dataframe which is a map from parameter name to parameter value: