6.1. Pipelines and composite estimators#
To build a composite estimator, transformers are usually combined with other
transformers or with predictors (such as classifiers or regressors).
The most common tool used for composing estimators is a Pipeline. Pipelines require all steps except the last to be a
transformer. The last step can be anything, a transformer, a
predictor, or a clustering estimator which might have or not have a
.predict(...) method. A pipeline exposes all methods provided by the last
estimator: if the last step provides a transform method, then the pipeline
would have a transform method and behave like a transformer. If the last step
provides a predict method, then the pipeline would expose that method, and
given a data X, use all steps except the last to transform the data,
and then give that transformed data to the predict method of the last step of
the pipeline. The class Pipeline is often used in combination with
ColumnTransformer or
FeatureUnion which concatenate the output of transformers
into a composite feature space.
TransformedTargetRegressor
deals with transforming the target (i.e. log-transform y).
6.1.1. Pipeline: chaining estimators#
Pipeline can be used to chain multiple estimators
into one. This is useful as there is often a fixed sequence
of steps in processing the data, for example feature selection, normalization
and classification. Pipeline serves multiple purposes here:
- Convenience and encapsulation
You only have to call fit and predict once on your data to fit a whole sequence of estimators.
- Joint parameter selection
You can grid search over parameters of all estimators in the pipeline at once.
- Safety
Pipelines help avoid leaking statistics from your test data into the trained model in cross-validation, by ensuring that the same samples are used to train the transformers and predictors.
All estimators in a pipeline, except the last one, must be transformers (i.e. must have a transform method). The last estimator may be any type (transformer, classifier, etc.).
Note
Calling fit on the pipeline is the same as calling fit on
each estimator in turn, transform the input and pass it on to the next step.
The pipeline has all the methods that the last estimator in the pipeline has,
i.e. if the last estimator is a classifier, the Pipeline can be used
as a classifier. If the last estimator is a transformer, again, so is the
pipeline.
6.1.1.1. Usage#
6.1.1.1.1. Build a pipeline#
The Pipeline is built using a list of (key, value) pairs, where
the key is a string containing the name you want to give this step and value
is an estimator object:
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [('reduce_dim', PCA()), ('clf', SVC())]
>>> pipe = Pipeline(estimators)
>>> pipe
Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())])
The utility function
Shorthand version using :func:`make_pipeline`
Click for more details
¶
make_pipeline is a shorthand
for constructing pipelines;
it takes a variable number of estimators and returns a pipeline,
filling in the names automatically:>>> from sklearn.pipeline import make_pipeline
>>> make_pipeline(PCA(), SVC())
Pipeline(steps=[('pca', PCA()), ('svc', SVC())])
6.1.1.1.2. Access pipeline steps#
The estimators of a pipeline are stored as a list in the steps attribute.
A sub-pipeline can be extracted using the slicing notation commonly used
for Python Sequences such as lists or strings (although only a step of 1 is
permitted). This is convenient for performing only some of the transformations
(or their inverse):
>>> pipe[:1]
Pipeline(steps=[('reduce_dim', PCA())])
>>> pipe[-1:]
Pipeline(steps=[('clf', SVC())])
A specific step can also be accessed by index or name by indexing (with
Accessing a step by name or position
Click for more details
¶
[idx]) the
pipeline:>>> pipe.steps[0]
('reduce_dim', PCA())
>>> pipe[0]
PCA()
>>> pipe['reduce_dim']
PCA()
Pipeline’s named_steps attribute allows accessing steps by name with tab
completion in interactive environments:>>> pipe.named_steps.reduce_dim is pipe['reduce_dim']
True
6.1.1.1.3. Tracking feature names in a pipeline#
To enable model inspection, Pipeline has a
get_feature_names_out() method, just like all transformers. You can use
pipeline slicing to get the feature names going into each step:
>>> from sklearn.datasets import load_iris
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.feature_selection import SelectKBest
>>> iris = load_iris()
>>> pipe = Pipeline(steps=[
... ('select', SelectKBest(k=2)),
... ('clf', LogisticRegression())])
>>> pipe.fit(iris.data, iris.target)
Pipeline(steps=[('select', SelectKBest(...)), ('clf', LogisticRegression(...))])
>>> pipe[:-1].get_feature_names_out()
array(['x2', 'x3'], ...)
You can also provide custom feature names for the input data using
Customize feature names
Click for more details
¶
get_feature_names_out:>>> pipe[:-1].get_feature_names_out(iris.feature_names)
array(['petal length (cm)', 'petal width (cm)'], ...)
6.1.1.1.4. Access to nested parameters#
It is common to adjust the parameters of an estimator within a pipeline. This parameter
is therefore nested because it belongs to a particular sub-step. Parameters of the
estimators in the pipeline are accessible using the <estimator>__<parameter>
syntax:
>>> pipe = Pipeline(steps=[("reduce_dim", PCA()), ("clf", SVC())])
>>> pipe.set_params(clf__C=10)
Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))])
This is particularly important for doing grid searches: Individual steps may also be replaced as parameters, and non-final steps may be
ignored by setting them to
When does it matter?
Click for more details
¶
>>> from sklearn.model_selection import GridSearchCV
>>> param_grid = dict(reduce_dim__n_components=[2, 5, 10],
... clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
'passthrough':>>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)],
... clf=[SVC(), LogisticRegression()],
... clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
6.1.1.2. Caching transformers: avoid repeated computation#
Fitting transformers may be computationally expensive. With its
memory parameter set, Pipeline will cache each transformer
after calling fit.
This feature is used to avoid computing the fit transformers within a pipeline
if the parameters and input data are identical. A typical example is the case of
a grid search in which the transformers can be fitted only once and reused for
each configuration. The last step will never be cached, even if it is a transformer.
The parameter memory is needed in order to cache the transformers.
memory can be either a string containing the directory where to cache the
transformers or a joblib.Memory
object:
>>> from tempfile import mkdtemp
>>> from shutil import rmtree
>>> from sklearn.decomposition import PCA
>>> from sklearn.svm import SVC
>>> from sklearn.pipeline import Pipeline
>>> estimators = [('reduce_dim', PCA()), ('clf', SVC())]
>>> cachedir = mkdtemp()
>>> pipe = Pipeline(estimators, memory=cachedir)
>>> pipe
Pipeline(memory=...,
steps=[('reduce_dim', PCA()), ('clf', SVC())])
>>> # Clear the cache directory when you don't need it anymore
>>> rmtree(cachedir)
Using a Enabling caching triggers a clone of the transformers before fitting.
Therefore, the transformer instance given to the pipeline cannot be
inspected directly.
In following example, accessing the
Warning: Side effect of caching transformers
Click for more details
¶
Pipeline without cache enabled, it is possible to
inspect the original instance such as:>>> from sklearn.datasets import load_digits
>>> X_digits, y_digits = load_digits(return_X_y=True)
>>> pca1 = PCA()
>>> svm1 = SVC()
>>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)])
>>> pipe.fit(X_digits, y_digits)
Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())])
>>> # The pca instance can be inspected directly
>>> print(pca1.components_)
[[-1.77484909e-19 ... 4.07058917e-18]]
PCA
instance pca2 will raise an AttributeError since pca2 will be an
unfitted transformer.
Instead, use the attribute named_steps to inspect estimators within
the pipeline:>>> cachedir = mkdtemp()
>>> pca2 = PCA()
>>> svm2 = SVC()
>>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)],
... memory=cachedir)
>>> cached_pipe.fit(X_digits, y_digits)
Pipeline(memory=...,
steps=[('reduce_dim', PCA()), ('clf', SVC())])
>>> print(cached_pipe.named_steps['reduce_dim'].components_)
[[-1.77484909e-19 ... 4.07058917e-18]]
>>> # Remove the cache directory
>>> rmtree(cachedir)
6.1.2. Transforming target in regression#
TransformedTargetRegressor transforms the
targets y before fitting a regression model. The predictions are mapped
back to the original space via an inverse transform. It takes as an argument
the regressor that will be used for prediction, and the transformer that will
be applied to the target variable:
>>> import numpy as np
>>> from sklearn.datasets import fetch_california_housing
>>> from sklearn.compose import TransformedTargetRegressor
>>> from sklearn.preprocessing import QuantileTransformer
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.model_selection import train_test_split
>>> X, y = fetch_california_housing(return_X_y=True)
>>> X, y = X[:2000, :], y[:2000] # select a subset of data
>>> transformer = QuantileTransformer(output_distribution='normal')
>>> regressor = LinearRegression()
>>> regr = TransformedTargetRegressor(regressor=regressor,
... transformer=transformer)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
>>> regr.fit(X_train, y_train)
TransformedTargetRegressor(...)
>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
R2 score: 0.61
>>> raw_target_regr = LinearRegression().fit(X_train, y_train)
>>> print('R2 score: {0:.2f}'.format(raw_target_regr.score(X_test, y_test)))
R2 score: 0.59
For simple transformations, instead of a Transformer object, a pair of functions can be passed, defining the transformation and its inverse mapping:
>>> def func(x):
... return np.log(x)
>>> def inverse_func(x):
... return np.exp(x)
Subsequently, the object is created as:
>>> regr = TransformedTargetRegressor(regressor=regressor,
... func=func,
... inverse_func=inverse_func)
>>> regr.fit(X_train, y_train)
TransformedTargetRegressor(...)
>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
R2 score: 0.51
By default, the provided functions are checked at each fit to be the inverse of
each other. However, it is possible to bypass this checking by setting
check_inverse to False:
>>> def inverse_func(x):
... return x
>>> regr = TransformedTargetRegressor(regressor=regressor,
... func=func,
... inverse_func=inverse_func,
... check_inverse=False)
>>> regr.fit(X_train, y_train)
TransformedTargetRegressor(...)
>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
R2 score: -1.57
Note
The transformation can be triggered by setting either transformer or the
pair of functions func and inverse_func. However, setting both
options will raise an error.
6.1.3. FeatureUnion: composite feature spaces#
FeatureUnion combines several transformer objects into a new
transformer that combines their output. A FeatureUnion takes
a list of transformer objects. During fitting, each of these
is fit to the data independently. The transformers are applied in parallel,
and the feature matrices they output are concatenated side-by-side into a
larger matrix.
When you want to apply different transformations to each field of the data,
see the related class ColumnTransformer
(see user guide).
FeatureUnion serves the same purposes as Pipeline -
convenience and joint parameter estimation and validation.
FeatureUnion and Pipeline can be combined to
create complex models.
(A FeatureUnion has no way of checking whether two transformers
might produce identical features. It only produces a union when the
feature sets are disjoint, and making sure they are is the caller’s
responsibility.)
6.1.3.1. Usage#
A FeatureUnion is built using a list of (key, value) pairs,
where the key is the name you want to give to a given transformation
(an arbitrary string; it only serves as an identifier)
and value is an estimator object:
>>> from sklearn.pipeline import FeatureUnion
>>> from sklearn.decomposition import PCA
>>> from sklearn.decomposition import KernelPCA
>>> estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())]
>>> combined = FeatureUnion(estimators)
>>> combined
FeatureUnion(transformer_list=[('linear_pca', PCA()),
('kernel_pca', KernelPCA())])
Like pipelines, feature unions have a shorthand constructor called
make_union that does not require explicit naming of the components.
Like Pipeline, individual steps may be replaced using set_params,
and ignored by setting to 'drop':
>>> combined.set_params(kernel_pca='drop')
FeatureUnion(transformer_list=[('linear_pca', PCA()),
('kernel_pca', 'drop')])
6.1.4. ColumnTransformer for heterogeneous data#
Many datasets contain features of different types, say text, floats, and dates, where each type of feature requires separate preprocessing or feature extraction steps. Often it is easiest to preprocess data before applying scikit-learn methods, for example using pandas. Processing your data before passing it to scikit-learn might be problematic for one of the following reasons:
Incorporating statistics from test data into the preprocessors makes cross-validation scores unreliable (known as data leakage), for example in the case of scalers or imputing missing values.
You may want to include the parameters of the preprocessors in a parameter search.
The ColumnTransformer helps performing different
transformations for different columns of the data, within a
Pipeline that is safe from data leakage and that can
be parametrized. ColumnTransformer works on
arrays, sparse matrices, and
pandas DataFrames.
To each column, a different transformation can be applied, such as preprocessing or a specific feature extraction method:
>>> import pandas as pd
>>> X = pd.DataFrame(
... {'city': ['London', 'London', 'Paris', 'Sallisaw'],
... 'title': ["His Last Bow", "How Watson Learned the Trick",
... "A Moveable Feast", "The Grapes of Wrath"],
... 'expert_rating': [5, 3, 4, 5],
... 'user_rating': [4, 5, 4, 3]})
For this data, we might want to encode the 'city' column as a categorical
variable using OneHotEncoder but apply a
CountVectorizer to the 'title' column.
As we might use multiple feature extraction methods on the same column, we give
each transformer a unique name, say 'city_category' and 'title_bow'.
By default, the remaining rating columns are ignored (remainder='drop'):
>>> from sklearn.compose import ColumnTransformer
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> from sklearn.preprocessing import OneHotEncoder
>>> column_trans = ColumnTransformer(
... [('categories', OneHotEncoder(dtype='int'), ['city']),
... ('title_bow', CountVectorizer(), 'title')],
... remainder='drop', verbose_feature_names_out=False)
>>> column_trans.fit(X)
ColumnTransformer(transformers=[('categories', OneHotEncoder(dtype='int'),
['city']),
('title_bow', CountVectorizer(), 'title')],
verbose_feature_names_out=False)
>>> column_trans.get_feature_names_out()
array(['city_London', 'city_Paris', 'city_Sallisaw', 'bow', 'feast',
'grapes', 'his', 'how', 'last', 'learned', 'moveable', 'of', 'the',
'trick', 'watson', 'wrath'], ...)
>>> column_trans.transform(X).toarray()
array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1]]...)
In the above example, the
CountVectorizer expects a 1D array as
input and therefore the columns were specified as a string ('title').
However, OneHotEncoder
as most of other transformers expects 2D data, therefore in that case you need
to specify the column as a list of strings (['city']).
Apart from a scalar or a single item list, the column selection can be specified
as a list of multiple items, an integer array, a slice, a boolean mask, or
with a make_column_selector. The
make_column_selector is used to select columns based
on data type or column name:
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.compose import make_column_selector
>>> ct = ColumnTransformer([
... ('scale', StandardScaler(),
... make_column_selector(dtype_include=np.number)),
... ('onehot',
... OneHotEncoder(),
... make_column_selector(pattern='city', dtype_include=object))])
>>> ct.fit_transform(X)
array([[ 0.904..., 0. , 1. , 0. , 0. ],
[-1.507..., 1.414..., 1. , 0. , 0. ],
[-0.301..., 0. , 0. , 1. , 0. ],
[ 0.904..., -1.414..., 0. , 0. , 1. ]])
Strings can reference columns if the input is a DataFrame, integers are always interpreted as the positional columns.
We can keep the remaining rating columns by setting
remainder='passthrough'. The values are appended to the end of the
transformation:
>>> column_trans = ColumnTransformer(
... [('city_category', OneHotEncoder(dtype='int'),['city']),
... ('title_bow', CountVectorizer(), 'title')],
... remainder='passthrough')
>>> column_trans.fit_transform(X)
array([[1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 4],
[1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 3, 5],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 4],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 5, 3]]...)
The remainder parameter can be set to an estimator to transform the
remaining rating columns. The transformed values are appended to the end of
the transformation:
>>> from sklearn.preprocessing import MinMaxScaler
>>> column_trans = ColumnTransformer(
... [('city_category', OneHotEncoder(), ['city']),
... ('title_bow', CountVectorizer(), 'title')],
... remainder=MinMaxScaler())
>>> column_trans.fit_transform(X)[:, -2:]
array([[1. , 0.5],
[0. , 1. ],
[0.5, 0.5],
[1. , 0. ]])
The make_column_transformer function is available
to more easily create a ColumnTransformer object.
Specifically, the names will be given automatically. The equivalent for the
above example would be:
>>> from sklearn.compose import make_column_transformer
>>> column_trans = make_column_transformer(
... (OneHotEncoder(), ['city']),
... (CountVectorizer(), 'title'),
... remainder=MinMaxScaler())
>>> column_trans
ColumnTransformer(remainder=MinMaxScaler(),
transformers=[('onehotencoder', OneHotEncoder(), ['city']),
('countvectorizer', CountVectorizer(),
'title')])
If ColumnTransformer is fitted with a dataframe
and the dataframe only has string column names, then transforming a dataframe
will use the column names to select the columns:
>>> ct = ColumnTransformer(
... [("scale", StandardScaler(), ["expert_rating"])]).fit(X)
>>> X_new = pd.DataFrame({"expert_rating": [5, 6, 1],
... "ignored_new_col": [1.2, 0.3, -0.1]})
>>> ct.transform(X_new)
array([[ 0.9...],
[ 2.1...],
[-3.9...]])
6.1.5. Visualizing Composite Estimators#
Estimators are displayed with an HTML representation when shown in a jupyter notebook. This is useful to diagnose or visualize a Pipeline with many estimators. This visualization is activated by default:
>>> column_trans
It can be deactivated by setting the display option in set_config
to ‘text’:
>>> from sklearn import set_config
>>> set_config(display='text')
>>> # displays text representation in a jupyter context
>>> column_trans
An example of the HTML output can be seen in the
HTML representation of Pipeline section of
Column Transformer with Mixed Types.
As an alternative, the HTML can be written to a file using
estimator_html_repr:
>>> from sklearn.utils import estimator_html_repr
>>> with open('my_estimator.html', 'w') as f:
... f.write(estimator_html_repr(clf))