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ColumnTransformer(s) in various parts of a pipeline do not play well

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.

Trouble changing imputer strategy in scikit-learn pipeline

I am trying to use GridSearchCV to select the best imputer strategy but I am having trouble doing that.
First, I have a data preparation pipeline for numerical and categorical columns-
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline, make_pipeline
num_pipe = make_pipeline(SimpleImputer(strategy='median'), StandardScaler())
cat_pipe = make_pipeline(SimpleImputer(strategy='constant', fill_value='NA'),
OneHotEncoder(sparse=False, handle_unknown='ignore'))
preprocessing = ColumnTransformer([
("num", num_pipe, num_cols),
("cat", cat_pipe, cat_cols)
])
Next, I have created a pipeline to train a support vector machine model with feature selection.
from sklearn.feature_selection import SelectFromModel
model = Pipeline([
("preprocess", preprocessing),
("feature_select", SelectFromModel(RandomForestRegressor(random_state=42))),
("regressor", SVR(kernel='rbf', C=30000.0, gamma=0.3))
])
Now, I am trying to see which imputer strategy is best for imputing missing values for numerical columns using a GridSearchCV
grid = {"model.named_steps.preprocess.transformers[0][1].named_steps['simpleimputer'].strategy":
['mean','median','most_frequent']}
grid_search = GridSearchCV(model, param_grid = grid, cv=5, scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)
This is where I am getting the error. The full pipeline looks like this -
Pipeline(steps=[('preprocess',
ColumnTransformer(transformers=[('num',
Pipeline(steps=[('simpleimputer',
SimpleImputer(strategy='median')),
('standardscaler',
StandardScaler())]),
['longitude', 'latitude',
'housing_median_age',
'total_rooms',
'total_bedrooms',
'population', 'households',
'median_income']),
('cat',
Pipeline(steps=[('simpleimputer',
SimpleImputer(fill_value='NA',
strategy='constant')),
('onehotencoder',
OneHotEncoder(handle_unknown='ignore',
sparse=False))]),
['ocean_proximity'])])),
('feature_select',
SelectFromModel(estimator=RandomForestRegressor(random_state=42))),
('regressor', SVR(C=30000.0, gamma=0.3))])
Can anyone tell me what I need to change in the grid search to make it work?

The way you specify the parameter is via a dictionary that maps the name of the estimator/transformer and name of the parameter you want to change to the parameters you want to try. If you have a pipeline or a pipeline of pipelines, the name is the names of all its parents combined with a double underscore. So for your case, it looks like
gird = {
"preprocess__num__simpleimputer__strategy":['median']
}
simpleimputer is simply the name that was automatically assigned by make_pipeline.
However, I think there are other issues in your code like fill_value='NA' being incorrect and actually not needed as it is not the falues to be filled but the value needed to filling missing values.

Using Column Transformer in Scikit to preprocess train and test data with target variable

I have problems to preprocess the dataset as a whole with columntransformer - maybe you can help:
First I read in my dataset:
X_train, X_test, y_train, y_test = train_test_split(df, target, test_size=0.2, random_state=seed)
Then I do my preprocessing:
preprocessor = ColumnTransformer(
transformers=
[
("col_drop", "drop",["col1","col2",]),
('enc_1', BinaryEncoder(), ["Bank"]),
('enc_2', OneHotEncoder(), ["Chair"]),
('log', FunctionTransformer(np.log1p, validate=False), log_features),
('log_p', FunctionTransformer(np.log1p, validate=False), ["target_y]),
('pow', PowerTransformer(method="yeo-johnson"), pow_features)
],
remainder='passthrough',n_jobs=-1)
And after that I call a pipeline with my preprocessor:
pipe.fit_transform(X_train, y_train)
This produces the error: A given column is not a column of the dataframe
And this makes in a way sense, because I use the preprocessor to do a nlog1p function on
target_y, which is basically my target feature, which is only present in y_train and y_test.
I assume that this causes the error, because the target is not in X_train.
Question: Is it possible to preprocess X and y at once or is it mandatory to use another columntransformer/pipeline for my y values? Is there any good solution for this?

You cannot preprocess targets in a ColumnTransformer or Pipeline (unless you plan on putting them together with the independent variables and then splitting them out later); however, there is the TransformedTargetRegressor (docs) meant for this use-case.

usually , one do pipeline for the features and other pipeline for the target values .

Names for Feature Selection

I want to know the names of the features within my RF model. I read here that the output from gs.best_estimator_.named_steps["stepname"].feature_importances_ would mirror my columns from my data. However, the length of gs.best_estimator_.... is 10 and I have 13 columns. Some columns were not important. From other answers around (answer1, answer2), I would have to declare something within my pipeline. But I am confused as to what to declare because both answers deal with PCA, not RF.
Here is what I have so far.
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn import datasets
# use iris as example
iris = datasets.load_iris()
X = iris.drop(['sepal_length'],axis=1)
y = iris.sepal_length
cats_feats = ['species']
X_train, X_test, y_train, y_test = \
train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=13)
# Pipeline
categorical_transformer = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore',sparse=False))
])
# Bundle any preprocessing
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, cat_feats)
])
rf = RandomForestRegressor(random_state = 13)
mymodel = Pipeline(steps = [('preprocessor', preprocessor),
('model', rf)
])
# For this example, I used default values. In reality I do use a dictionary of parameters
gs = GridSearchCV(mymodel
,n_jobs = -1
,cv = 5
)
gs.fit(X_train,y_train)

Why the length of the feature list does not match
The length of your features does not match because all non-categorical columns are being discarded when you are using your ColumnTransformer. By default, it only keeps columns for which a transformation was specified. As a result, if you do not want this to happen, you need to do this
preprocessor = ColumnTransformer(transformers=[('cat', OneHotEncoder(), cat_feats)],
remainder='passthrough')
(I removed your categorical pipeline, which is not necessary here)
Also keep in mind that applying the OHE will add features and so the total number of features is going to be larger than what you had in the beginning.
How to get the feature names
Once you have fitted everything, you need to retrieve the feature names for the result of the OHE and the remaining numerical columns.
For the OHE columns:
cat_features = gs.best_estimator_["preprocessor"].named_transformers_["cat"].get_feature_names()
For the numerical columns, you need to declare num_feats where all numerical features are in the same order as in your original dataframe.
Then just do:
feature_names = np.concatenate((cat_features, num_feats))
PS: this is a bit cumbersome, and this might be improved in later sklearn versions, but as of now, this is the procedure

How to Save and Load Machine Learning (One-vs-Rest) Models (PYTHON)

I have here my code wherein it loops through each label or category then creates a model out of it. However, what I want is to create a general model that will be able to accept new predictions that are inputs from a user.
I'm aware that the code below saves the model that is fit for the last category in the loop. How can I fix this so that models for each category will be saved so that when I load those models, i would be able to predict a label for a new text?
vectorizer = TfidfVectorizer(strip_accents='unicode',
stop_words=stop_words, analyzer='word', ngram_range=(1,3), norm='l2')
vectorizer.fit(train_text)
vectorizer.fit(test_text)
x_train = vectorizer.transform(train_text)
y_train = train.drop(labels = ['question_body'], axis=1)
x_test = vectorizer.transform(test_text)
y_test = test.drop(labels = ['question_body'], axis=1)
# Using pipeline for applying linearSVC and one vs rest classifier
SVC_pipeline = Pipeline([
('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
])
for category in categories:
print('... Processing {}'.format(category))
# train the SVC model using X_dtm & y
SVC_pipeline.fit(x_train, train[category])
# compute the testing accuracy of SVC
svc_prediction = SVC_pipeline.predict(x_test)
print("SVC Prediction:")
print(svc_prediction)
print('Test accuracy is {}'.format(f1_score(test[category], svc_prediction)))
print("\n")
#save the model to disk
filename = 'svc_model.sav'
pickle.dump(SVC_pipeline, open(filename, 'wb'))

There are multiple mistakes in your code.
You are fitting your TfidfVectorizer on both train and test:
vectorizer.fit(train_text)
vectorizer.fit(test_text)
This is wrong. Calling fit() is not incremental. It will not learn on both data if called two times. The most recent call to fit() will forget everything from past calls. You never fit (learn) something on test data.
What you need to do is this:
vectorizer.fit(train_text)
The pipeline does not work the way you think:
# Using pipeline for applying linearSVC and one vs rest classifier
SVC_pipeline = Pipeline([
('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
])
See that you are passing LinearSVC inside the OneVsRestClassifier, so it will automatically use that without the need of Pipeline. Pipeline will not do anything here. Pipeline is of use when you sequentially want to pass your data through multiple models. Something like this:
pipe = Pipeline([
('pca', pca),
('logistic', LogisticRegression())
])
What the above pipe will do is pass the data to PCA which will transform it. Then that new data is passed to LogisticRegression and so on..
Correct usage of pipeline in your case can be:
SVC_pipeline = Pipeline([
('vectorizer', vectorizer)
('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
])
See more examples here:
https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#examples-using-sklearn-pipeline-pipeline
You need to describe more about your "categories". Show some examples of your data. You are not using y_train and y_test anywhere. Is the categories different from "question_body"?