I want to construct a scikit-learn pipeline in which some columns have values imputed, and then scaling is subsequently applied to some. If I put both operations in the same columntransformer this does not work as they proceed in parallel (and so missing values cause the scaler to fail). If I make two columntransformers and run them in series, however, I run into the issue that I cannot specify column names (as output of the first transformer is a np array). What is the correct way to go about this?
numeric_columns = list(X.select_dtypes('float64').columns)
cat_columns = list(X.select_dtypes('object').columns)+list(X.select_dtypes('int64').columns)
# Imputation
imp_mean = SimpleImputer(strategy='mean')
imp_freq = SimpleImputer(strategy='most_frequent')
imputer = ColumnTransformer(
[('Imput_mean', imp_mean, numeric_columns),
('Imput_freq', imp_freq, cat_columns),
], remainder='passthrough'
)
# Scaling
feature_transformer = ColumnTransformer(
[('num',StandardScaler(),numeric_columns),
], remainder='passthrough'
)
#Hyperparameters
parameters = {'model__n_components':[1,2,3,4,5]}
#Pipeline
pipeline = Pipeline([('imputer', imputer),
('feature_transformer', feature_transformer),
('model', PLSRegression())])
#Cross validation strategy
cv = KFold(n_splits=10, shuffle=True)
#Cross valdiate and evaluate
clf = GridSearchCV(pipeline, parameters, scoring="r2", cv=10)
cross_val_score(clf, X, y, cv=cv, scoring="r2"))
You might nest a Pipeline which takes care of the preprocessing of the numerical columns (performed serially) within the ColumnTransformer instance.
Here's an example:
import numpy as np
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X = pd.DataFrame({'city': ['London', 'London', '', 'Sallisaw'],
'title': ['His Last Bow', 'How Watson Learned the Trick', 'A Moveable Feast', 'The Grapes of Wrath'],
'expert_rating': [5, 3, np.nan, 5],
'user_rating': [4, np.nan, 4, 3]})
numeric_columns = list(X.select_dtypes('float64').columns)
cat_columns = list(X.select_dtypes('object').columns) + list(X.select_dtypes('int64').columns)
imp_mean = SimpleImputer(strategy='mean')
imp_freq = SimpleImputer(missing_values='', strategy='most_frequent')
ct = ColumnTransformer([
('Imput_freq', imp_freq, cat_columns),
('pipe_num', Pipeline([('Imput_mean', imp_mean), ('num', StandardScaler())]), numeric_columns)
], remainder='passthrough'
)
pd.DataFrame(ct.fit_transform(X))
Here's a similar post: How to execute both parallel and serial transformations with sklearn pipeline?.
Related
i am trying make pipeline with scaler, onhotencoder, polynomialfeature, and finally linear regression model
from sklearn.pipeline import Pipeline
pipeline = Pipeline([
('scaler', StandardScaler(), num_cols),
('polynom', PolynomialFeatures(3), num_cols),
('encoder', OneHotEncoder(), cat_cols),
('linear_regression', LinearRegression() )
])
but when i fit the pipeline i have ValueError: too many values to unpack (expected 2)
pipeline.fit(x_train,y_train)
pipeline.score(x_test, y_test)
If I understand correctly, you want to apply some steps of the pipeline to specific columns. Instead of doing it by adding the column names ad the end of the pipeline stage (which is incorrect and causes the error), you have to use a ColumnTransformer. Here you can find another similar example.
In your case, you could do something like this:
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.compose import ColumnTransformer
# Fake data.
train_data = pd.DataFrame({'n1': range(10), 'n2': range(10)})
train_data['c1'] = 0
train_data['c1'][5:] = 1
y_train = [0]*10
y_train[5:] = [1]*5
# Here I assumed you are using a DataFrame. If not, use integer indices instead of column names.
num_cols = ['n1', 'n2']
cat_cols = ['c1']
# Pipeline to transform the numerical features.
numerical_transformer = Pipeline([('scaler', StandardScaler()),
('polynom', PolynomialFeatures(3))
])
# Apply the numerical transformer only on the numerical columns.
# Spearately, apply the OneHotEncoder.
ct = ColumnTransformer([('num_transformer', numerical_transformer, num_cols),
('encoder', OneHotEncoder(), cat_cols)])
# Main pipeline for fitting.
pipeline = Pipeline([
('column_transformer', ct),
('linear_regression', LinearRegression() )
])
pipeline.fit(train_data, y_train)
Schematically, the layout of your pipeline would be like this:
I am using sklearn and mlxtend.regressor.StackingRegressor to build a stacked regression model.
For example, say I want the following small pipeline:
A Stacking Regressor with two regressors:
A pipeline which:
Performs data imputation
1-hot encodes categorical features
Performs linear regression
A pipeline which:
Performs data imputation
Performs regression using a Decision Tree
Unfortunately this is not possible, because StackingRegressor doesn't accept NaN in its input data.
This is even if its regressors know how to handle NaN, as it would be in my case where the regressors are actually pipelines which perform data imputation.
However, this is not a problem: I can just move data imputation outside the stacked regressor.
Now my pipeline looks like this:
Perform data imputation
Apply a Stacking Regressor with two regressors:
A pipeline which:
1-hot encodes categorical features
Standardises numerical features
Performs linear regression
An sklearn.tree.DecisionTreeRegressor.
One might try to implement it as follows (the entire minimal working example in this gist, with comments):
sr_linear = Pipeline(steps=[
('preprocessing', ColumnTransformer(transformers=[
('categorical',
make_pipeline(OneHotEncoder(), StandardScaler()),
make_column_selector(dtype_include='category')),
('numerical',
StandardScaler(),
make_column_selector(dtype_include=np.number))
])),
('model', LinearRegression())
])
sr_tree = DecisionTreeRegressor()
ct_imputation = ColumnTransformer(transformers=[
('categorical',
SimpleImputer(strategy='constant', fill_value='None'),
make_column_selector(dtype_include='category')),
('numerical',
SimpleImputer(strategy='median'),
make_column_selector(dtype_include=np.number))
])
stacked_regressor = Pipeline(steps=[
('imputation', ct_imputation),
('back_to_pandas', FunctionTransformer(
func=lambda values: pd.DataFrame(values, columns=ct_imputation.get_feature_names_out())
)),
('model', StackingRegressor(
regressors=[sr_linear, sr_tree],
meta_regressor=DecisionTreeRegressor(),
use_features_in_secondary=True
))
])
Note that the "outer" ColumnTransformer (in stacked_regressor) returns a numpy matrix.
But the "inner" ColumnTransformer (in sr_linear) expects a pandas.DataFrame, so I had to convert the matrix back to a data frame using step back_to_pandas.
(To use get_feature_names_out I had to use the nightly version of sklearn, because the current stable 1.0.2 version does not support it yet. Fortunately it can be installed with one simple command.)
The above code fails when calling stacked_regressor.fit(), with the following error (the entire stacktrace is again in the gist):
ValueError: make_column_selector can only be applied to pandas dataframes
However, because I added the back_to_pandas step to my outer pipeline, the inner pipelines should be getting a pandas data frame!
In fact, if I only fit_transform() my ct_imputation object, I clearly obtain a pandas data frame.
I cannot understand where and when exactly the data which gets passed around ceases to be a data frame.
Why is my code failing?
Imo the issue has to be ascribed to StackingRegressor. Actually, I am not an expert on its usage and still I have not explored its source code, but I've found this sklearn issue - #16473 which seems implying that << the concatenation [of regressors and meta_regressors] does not preserve dataframe >> (though this is referred to sklearn StackingRegressor instance, rather than on mlxtend one).
Indeed, have a look at what happens once you replace it with your sr_linear pipeline:
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from mlxtend.regressor import StackingRegressor
import numpy as np
import pandas as pd
# We use the Ames house prices dataset for this example
d = fetch_openml('house_prices', as_frame=True).frame
# Small data preprocessing:
for column in d.columns:
if d[column].dtype == object or column == 'MSSubClass':
d[column] = pd.Categorical(d[column])
d.drop(columns='Id', inplace=True)
# Prepare the data for training
label = 'SalePrice'
features = [col for col in d.columns if col != label]
X, y = d[features], d[label]
# Train the stacked regressor
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
sr_linear = Pipeline(steps=[
('preprocessing', ColumnTransformer(transformers=[
('categorical',
make_pipeline(OneHotEncoder(), StandardScaler(with_mean=False)),
make_column_selector(dtype_include='category')),
('numerical',
StandardScaler(),
make_column_selector(dtype_include=np.number))
])),
('model', LinearRegression())
])
ct_imputation = ColumnTransformer(transformers=[
('categorical',
SimpleImputer(missing_values=np.nan, strategy='constant', fill_value='None'),
make_column_selector(dtype_include='category')),
('numerical',
SimpleImputer(strategy='median'),
make_column_selector(dtype_include=np.number))
])
stacked_regressor = Pipeline(steps=[
('imputation', ct_imputation),
('back_to_pandas', FunctionTransformer(
func=lambda values: pd.DataFrame(values, columns=ct_imputation.get_feature_names_out()).astype(types)
)),
('mdl', sr_linear)
])
stacked_regressor.fit(X_train, y_train)
Observe that I had to slightly modify the 'back_to_pandas' step because for some reason pd.DataFrame was changing the dtypes of the columns to 'object' only (from 'category' and 'float64'), therefore clashing with the imputation performed in sr_linear. For this reason, I've applied .astype(types) to the pd.DataFrame constructor, where types is defined as follows (based on the implementation of .get_feature_names_out() method of the SimpleImputer from the dev version of sklearn):
types = {}
for col in d.columns[:-1]:
if d[col].dtype == 'category':
types['categorical__' + col] = str(d[col].dtype)
else:
types['numerical__' + col] = str(d[col].dtype)
The correct thing to do was:
Move from mlxtend's to sklearn's StackingRegressor. I believe the former was creater when sklearn still didn't have a stacking regressor. Now there is no need to use more 'obscure' solutions. sklearn's stacking regressor works pretty well.
Move the 1-hot-encoding step to the outer pipeline, because (surprisingly!) sklearn's DecisionTreeRegressor cannot handle categorical data among the features.
A working version of the code is given below:
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.compose import ColumnTransformer, make_column_selector
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import StackingRegressor
import numpy as np
import pandas as pd
def set_correct_categories(df: pd.DataFrame) -> pd.DataFrame:
for column in df.columns:
if df[column].dtype == object or 'MSSubClass' in column:
df[column] = pd.Categorical(df[column])
return df
d = fetch_openml('house_prices', as_frame=True).frame
d = set_correct_categories(d).drop(columns='Id')
sr_linear = Pipeline(steps=[
('preprocessing', StandardScaler()),
('model', LinearRegression())
])
ct_preprocessing = ColumnTransformer(transformers=[
('categorical',
make_pipeline(
SimpleImputer(strategy='constant', fill_value='None'),
OneHotEncoder(sparse=False, handle_unknown='ignore')
),
make_column_selector(dtype_include='category')),
('numerical',
SimpleImputer(strategy='median'),
make_column_selector(dtype_include=np.number))
])
stacking_regressor = Pipeline(steps=[
('preprocessing', ct_preprocessing),
('model', StackingRegressor(
estimators=[('linear_regression', sr_linear), ('regression_tree', DecisionTreeRegressor())],
final_estimator=DecisionTreeRegressor(),
passthrough=True
))
])
label = 'SalePrice'
features = [col for col in d.columns if col != label]
X, y = d[features], d[label]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
stacking_regressor.fit(X_train, y_train)
Thanks to user amiola for his answer putting me on the right track.
I have downloaded this data, and this is my code:
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.utils.multiclass import unique_labels
import plotly.figure_factory as ff
import pandas as pd
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.impute import SimpleImputer
import numpy as np
from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.compose import make_column_transformer
random_state = 27912
df_train = pd.read_csv("...")
df_test = pd.read_csv("...")
X_train, X_test, y_train, y_test = train_test_split(df_train.drop(["Survived", "Ticket", "Cabin", "Name", "PassengerId"],
axis = 1),
df_train["Survived"], test_size=0.2,
random_state=42)
numeric_col_names = ["Age", "SibSp", "Parch", "Fare"]
ordinal_col_names = ["Pclass"]
one_hot_col_names = ["Embarked", "Sex"]
ct = make_column_transformer(
(SimpleImputer(strategy="median"), numeric_col_names),
(SimpleImputer(strategy="most_frequent"), ordinal_col_names + one_hot_col_names),
(OrdinalEncoder(), ordinal_col_names),
(OneHotEncoder(), one_hot_col_names),
(StandardScaler(), ordinal_col_names + one_hot_col_names + numeric_col_names))
preprocessing_pipeline = Pipeline([("transformers", ct)])
preprocessing_pipeline.fit_transform(X_train)
I'm trying make column_transformer for preprocessing step, however, the OneHotEncoding step is giving me an error, ValueError: Input contains NaN. I don't really know why this is happening, because I'm imputing the values before. Any clues on why this is happening?
Trying something like this doesn't help neither
preprocessing_pipeline = Pipeline([("transformers", ct_first)])
ct_second = make_column_transformer((OneHotEncoder(), one_hot_col_names),(StandardScaler(), ordinal_col_names + one_hot_col_names + numeric_col_names))
pipeline = Pipeline([("transformer1", preprocessing_pipeline), ("transformer2", ct_second)])
pipeline.fit_transform(X_train)
I would like to know why is this happening and why the above code, first and second tries, are not correct.
Thanks
You need to create a pipeline for each column type to make sure that the different steps are applied sequentially (i.e. to make sure that the missing values are imputed prior to encoding and scaling), see also this example in the scikit-learn documentation.
import pandas as pd
from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.compose import make_column_transformer
# Load the data (from https://www.kaggle.com/c/titanic/data)
df_train = pd.read_csv('train.csv')
df_test = pd.read_csv('test.csv')
# Extract the features
X_train = df_train.drop(labels=['Survived', 'Ticket', 'Cabin', 'Name', 'PassengerId'], axis=1)
X_test = df_test.drop(labels=['Ticket', 'Cabin', 'Name', 'PassengerId'], axis=1)
# Map the feature names to the corresponding
# types (numerical, ordinal or categorical)
numeric_col_names = ['Age', 'SibSp', 'Parch', 'Fare']
ordinal_col_names = ['Pclass']
one_hot_col_names = ['Embarked', 'Sex']
# Define the numerical features pipeline
numeric_col_transformer = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
# Define the ordinal features pipeline
ordinal_col_transformer = Pipeline([
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder', OrdinalEncoder()),
('scaler', StandardScaler())
])
# Define the categorical features pipeline
one_hot_col_transformer = Pipeline([
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder', OneHotEncoder(sparse=False)),
('scaler', StandardScaler())
])
# Create the overall preprocessing pipeline
preprocessing_pipeline = make_column_transformer(
(numeric_col_transformer, numeric_col_names),
(ordinal_col_transformer, ordinal_col_names),
(one_hot_col_transformer, one_hot_col_names),
)
# Fit the pipeline to the training data
preprocessing_pipeline.fit(X_train)
# Apply the pipeline to the training and test data
X_train_ = preprocessing_pipeline.transform(X_train)
X_test_ = preprocessing_pipeline.transform(X_test)
I am applying the following code to impute and then encode categorical data in my dataset:
# Encoding categorical data
# Define a Pipeline with an imputing step using SimpleImputer prior to the OneHot encoding
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
# use strategy='constant', fill_value='missing' for imputing to preserve the categories' structure
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('encoder', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, [0])
])
Z = np.array(preprocessor.fit_transform(Z))
print (Z[:,0])
I want to repeat these steps for all columns in the array Z, as Z is comprised of all categorical features from my original dataset.
Is there a more efficient way of doing this rather than listing each column as such:
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, [0,1,2,3,4,5,6,7,8,9,10])
])
Thanks in advance!
If all columns have the same type, I would simply omit the ColumnTransformer and use a simple pipeline in your case:
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
# some sample data
X = pd.DataFrame({
'col1': ['obj1', 'obj2', 'obj3'],
'col2': [np.nan, 'oj3', 'oj1'],
'col3': ['jo3', 'jo1', np.nan]
}).astype('category')
y = pd.Series([0, 1, 1])
pipeline = make_pipeline(
SimpleImputer(missing_values=np.nan, strategy='constant', fill_value='missing'),
OneHotEncoder(handle_unknown='ignore', sparse=False)
)
Z = pipeline.fit_transform(X, y)
The ColumnTransformer is meant to be used for heterogeneous data when columns or column subsets of the input need to be transformed separately (read here). However, since your features are all of the same type and all require the same preprocessing procedure, you can just apply SimpleImputer and OneHotEncoder to the whole dataset as these transformers will automatically detect the columns to transform (which in your case are simply all).
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 ultimately look at the ranked features of importance for the best performing model that GridSearchCV selects. The problem I am encountering is that sklearn pipelines output numpy array objects and lose any column information along the way. Thus when I go to examine the most important coefficients of the model I am left with an unlabeled numpy array.
I have read that building a custom transformer might be a possible solution to this, but I do not have any experience doing so myself. I have also looked into leveraging the sklearn-pandas package, but I am hesitant to try and implement something that might not be updated in parallel with sklearn. Can anyone suggest what they believe is the best path to go about getting around this issue? I am also open to any literature that has hands on application of pandas and sklearn pipelines.
My Pipeline:
# 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())
])
Cross Validation:
kf = KFold(n_splits=4, shuffle=True, random_state=44)
cross_val_score(clf, X_train, y_train, cv=kf).mean()
Grid Search:
param_grid = {
'ridge__alpha': [.001, .1, 1.0, 5, 10, 100]
}
gs = GridSearchCV(clf, param_grid, cv = kf)
gs.fit(X_train, y_train)
Examining Coefficients:
model = gs.best_estimator_
predictions = model.fit(X_train, y_train).predict(X_test)
model.named_steps['ridge'].coef_
Here is the output of the model coefficients as it currently stands when performed on the seaborn "mpg" dataset:
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])
Ideally I would like to preserve the pandas dataframe information and retrieve the derived column names after OneHotEncoder and the other methods are called.
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:
I would use model.named_steps['transform'].get_feature_names_out().
It will return the feature names like this:
array(['num__cylinders', 'num__displacement', 'num__horsepower',
'num__weight', 'num__acceleration', 'num__model_year',
'cat__origin_europe', 'cat__origin_japan', 'cat__origin_usa',...])
Then you can use the feature names to transform the output to a dataframe:
weights_df = pd.DataFrame(model.named_steps['ridge'].coef_,index=model.named_steps['transform'].get_feature_names_out()).T