Note
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Column Transformer with Mixed Types¶
This example illustrates how to apply different preprocessing and feature
extraction pipelines to different subsets of features, using
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. The
categorical data is one-hot encoded via OneHotEncoder
, which
creates a new category for missing values.
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 Pipeline
, together with a simple classification
model.
# Author: Pedro Morales <part.morales@gmail.com>
#
# 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))
model score: 0.790
HTML representation of Pipeline
(display diagram)
When the Pipeline
is printed out in a jupyter notebook an HTML
representation of the estimator is displayed:
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.
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()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1047 entries, 1118 to 684
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 embarked 1045 non-null category
1 sex 1047 non-null category
2 pclass 1047 non-null float64
3 age 841 non-null float64
4 fare 1046 non-null float64
dtypes: category(2), float64(3)
memory usage: 35.0 KB
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))
clf
model score: 0.794
The resulting score is not exactly the same as the one from the previous
pipeline because the dtype-based selector treats the pclass
column as
a numeric feature instead of a categorical feature as previously:
selector(dtype_exclude="category")(X_train)
['pclass', 'age', 'fare']
selector(dtype_include="category")(X_train)
['embarked', 'sex']
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
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("Best params:")
print(grid_search.best_params_)
Best params:
{'classifier__C': 0.1, 'preprocessor__num__imputer__strategy': 'mean'}
The internal cross-validation scores obtained by those parameters is:
print(f"Internal CV score: {grid_search.best_score_:.3f}")
Internal CV score: 0.784
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 hyperparameter tuning.
print(
(
"best logistic regression from grid search: %.3f"
% grid_search.score(X_test, y_test)
)
)
best logistic regression from grid search: 0.794
Total running time of the script: ( 0 minutes 1.311 seconds)