%matplotlib inline
Column Transformer with Mixed Types¶
.. currentmodule:: sklearn
This example illustrates how to apply different preprocessing and feature
extraction pipelines to different subsets of features, using
:class:~compose.ColumnTransformer
. This is particularly handy for the
case of datasets that contain heterogeneous data types, since we may want to
scale the numeric features and one-hot encode the categorical ones.
In this example, the numeric data is standard-scaled after mean-imputation,
while the categorical data is one-hot encoded after imputing missing values
with a new category ('missing'
).
In addition, we show two different ways to dispatch the columns to the particular pre-processor: by column names and by column data types.
Finally, the preprocessing pipeline is integrated in a full prediction pipeline
using :class:~pipeline.Pipeline
, together with a simple classification
model.
# Author: Pedro Morales <[email protected]>
#
# License: BSD 3 clause
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV
np.random.seed(0)
# Load data from https://www.openml.org/d/40945
X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
# Alternatively X and y can be obtained directly from the frame attribute:
# X = titanic.frame.drop('survived', axis=1)
# y = titanic.frame['survived']
Use ColumnTransformer
by selecting column by names¶
We will train our classifier with the following features:
Numeric Features:
age
: float;fare
: float.Categorical Features:
embarked
: categories encoded as strings{'C', 'S', 'Q'}
;sex
: categories encoded as strings{'female', 'male'}
;pclass
: ordinal integers{1, 2, 3}
.We create the preprocessing pipelines for both numeric and categorical data. Note that
pclass
could either be treated as a categorical or numeric feature.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = OneHotEncoder(handle_unknown='ignore')
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', LogisticRegression())])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=0)
clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))
HTML representation of Pipeline
¶
When the Pipeline
is printed out in a jupyter notebook an HTML
representation of the estimator is displayed as follows:
from sklearn import set_config
set_config(display='diagram')
clf
Use ColumnTransformer
by selecting column by data types¶
When dealing with a cleaned dataset, the preprocessing can be automatic by
using the data types of the column to decide whether to treat a column as a
numerical or categorical feature.
:func:sklearn.compose.make_column_selector
gives this possibility.
First, let's only select a subset of columns to simplify our
example.
subset_feature = ['embarked', 'sex', 'pclass', 'age', 'fare']
X_train, X_test = X_train[subset_feature], X_test[subset_feature]
Then, we introspect the information regarding each column data type.
X_train.info()
We can observe that the embarked
and sex
columns were tagged as
category
columns when loading the data with fetch_openml
. Therefore, we
can use this information to dispatch the categorical columns to the
categorical_transformer
and the remaining columns to the
numerical_transformer
.
Note
In practice, you will have to handle yourself the column data type.
If you want some columns to be considered as `category`, you will have to
convert them into categorical columns. If you are using pandas, you can
refer to their documentation regarding `Categorical data
from sklearn.compose import make_column_selector as selector
preprocessor = ColumnTransformer(transformers=[
('num', numeric_transformer, selector(dtype_exclude="category")),
('cat', categorical_transformer, selector(dtype_include="category"))
])
clf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', LogisticRegression())])
clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))
The resulting score is not exactly the same as the one from the previous
pipeline becase the dtype-based selector treats the pclass
columns as
a numeric features instead of a categorical feature as previously:
selector(dtype_exclude="category")(X_train)
selector(dtype_include="category")(X_train)
Using the prediction pipeline in a grid search¶
Grid search can also be performed on the different preprocessing steps
defined in the ColumnTransformer
object, together with the classifier's
hyperparameters as part of the Pipeline
.
We will search for both the imputer strategy of the numeric preprocessing
and the regularization parameter of the logistic regression using
:class:~sklearn.model_selection.GridSearchCV
.
param_grid = {
'preprocessor__num__imputer__strategy': ['mean', 'median'],
'classifier__C': [0.1, 1.0, 10, 100],
}
grid_search = GridSearchCV(clf, param_grid, cv=10)
grid_search
Calling 'fit' triggers the cross-validated search for the best hyper-parameters combination:
grid_search.fit(X_train, y_train)
print(f"Best params:")
print(grid_search.best_params_)
The internal cross-validation scores obtained by those parameters is:
print(f"Internal CV score: {grid_search.best_score_:.3f}")
We can also introspect the top grid search results as a pandas dataframe:
import pandas as pd
cv_results = pd.DataFrame(grid_search.cv_results_)
cv_results = cv_results.sort_values("mean_test_score", ascending=False)
cv_results[["mean_test_score", "std_test_score",
"param_preprocessor__num__imputer__strategy",
"param_classifier__C"
]].head(5)
The best hyper-parameters have be used to re-fit a final model on the full training set. We can evaluate that final model on held out test data that was not used for hyparameter tuning.
print(("best logistic regression from grid search: %.3f"
% grid_search.score(X_test, y_test)))