""" The :mod:`sklearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms and estimators. """ # Author: Edouard Duchesnay # Gael Varoquaux # Virgile Fritsch # Alexandre Gramfort # Lars Buitinck # License: BSD from collections import defaultdict from itertools import islice import numpy as np from scipy import sparse from .base import clone, TransformerMixin from .preprocessing import FunctionTransformer from .utils._estimator_html_repr import _VisualBlock from .utils.metaestimators import available_if from .utils import ( Bunch, _print_elapsed_time, ) from .utils._tags import _safe_tags from .utils.validation import check_memory from .utils.validation import check_is_fitted from .utils import check_pandas_support from .utils._set_output import _safe_set_output, _get_output_config from .utils.parallel import delayed, Parallel from .exceptions import NotFittedError from .utils.metaestimators import _BaseComposition __all__ = ["Pipeline", "FeatureUnion", "make_pipeline", "make_union"] def _final_estimator_has(attr): """Check that final_estimator has `attr`. Used together with `avaliable_if` in `Pipeline`.""" def check(self): # raise original `AttributeError` if `attr` does not exist getattr(self._final_estimator, attr) return True return check class Pipeline(_BaseComposition): """ Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final estimator only needs to implement `fit`. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a `'__'`, as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to `'passthrough'` or `None`. Read more in the :ref:`User Guide `. .. versionadded:: 0.5 Parameters ---------- steps : list of tuple List of (name, transform) tuples (implementing `fit`/`transform`) that are chained in sequential order. The last transform must be an estimator. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Attributes ---------- named_steps : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters. classes_ : ndarray of shape (n_classes,) The classes labels. Only exist if the last step of the pipeline is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 See Also -------- make_pipeline : Convenience function for simplified pipeline construction. Examples -------- >>> from sklearn.svm import SVC >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.pipeline import Pipeline >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... random_state=0) >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())]) >>> # The pipeline can be used as any other estimator >>> # and avoids leaking the test set into the train set >>> pipe.fit(X_train, y_train) Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())]) >>> pipe.score(X_test, y_test) 0.88 """ # BaseEstimator interface _required_parameters = ["steps"] def __init__(self, steps, *, memory=None, verbose=False): self.steps = steps self.memory = memory self.verbose = verbose def set_output(self, *, transform=None): """Set the output container when `"transform"` and `"fit_transform"` are called. Calling `set_output` will set the output of all estimators in `steps`. Parameters ---------- transform : {"default", "pandas"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. """ for _, _, step in self._iter(): _safe_set_output(step, transform=transform) return self def get_params(self, deep=True): """Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `steps` of the `Pipeline`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return self._get_params("steps", deep=deep) def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `steps`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `steps`. Parameters of the steps may be set using its name and the parameter name separated by a '__'. Returns ------- self : object Pipeline class instance. """ self._set_params("steps", **kwargs) return self def _validate_steps(self): names, estimators = zip(*self.steps) # validate names self._validate_names(names) # validate estimators transformers = estimators[:-1] estimator = estimators[-1] for t in transformers: if t is None or t == "passthrough": continue if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr( t, "transform" ): raise TypeError( "All intermediate steps should be " "transformers and implement fit and transform " "or be the string 'passthrough' " "'%s' (type %s) doesn't" % (t, type(t)) ) # We allow last estimator to be None as an identity transformation if ( estimator is not None and estimator != "passthrough" and not hasattr(estimator, "fit") ): raise TypeError( "Last step of Pipeline should implement fit " "or be the string 'passthrough'. " "'%s' (type %s) doesn't" % (estimator, type(estimator)) ) def _iter(self, with_final=True, filter_passthrough=True): """ Generate (idx, (name, trans)) tuples from self.steps When filter_passthrough is True, 'passthrough' and None transformers are filtered out. """ stop = len(self.steps) if not with_final: stop -= 1 for idx, (name, trans) in enumerate(islice(self.steps, 0, stop)): if not filter_passthrough: yield idx, name, trans elif trans is not None and trans != "passthrough": yield idx, name, trans def __len__(self): """ Returns the length of the Pipeline """ return len(self.steps) def __getitem__(self, ind): """Returns a sub-pipeline or a single estimator in the pipeline Indexing with an integer will return an estimator; using a slice returns another Pipeline instance which copies a slice of this Pipeline. This copy is shallow: modifying (or fitting) estimators in the sub-pipeline will affect the larger pipeline and vice-versa. However, replacing a value in `step` will not affect a copy. """ if isinstance(ind, slice): if ind.step not in (1, None): raise ValueError("Pipeline slicing only supports a step of 1") return self.__class__( self.steps[ind], memory=self.memory, verbose=self.verbose ) try: name, est = self.steps[ind] except TypeError: # Not an int, try get step by name return self.named_steps[ind] return est @property def _estimator_type(self): return self.steps[-1][1]._estimator_type @property def named_steps(self): """Access the steps by name. Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.""" # Use Bunch object to improve autocomplete return Bunch(**dict(self.steps)) @property def _final_estimator(self): estimator = self.steps[-1][1] return "passthrough" if estimator is None else estimator def _log_message(self, step_idx): if not self.verbose: return None name, _ = self.steps[step_idx] return "(step %d of %d) Processing %s" % (step_idx + 1, len(self.steps), name) def _check_fit_params(self, **fit_params): fit_params_steps = {name: {} for name, step in self.steps if step is not None} for pname, pval in fit_params.items(): if "__" not in pname: raise ValueError( "Pipeline.fit does not accept the {} parameter. " "You can pass parameters to specific steps of your " "pipeline using the stepname__parameter format, e.g. " "`Pipeline.fit(X, y, logisticregression__sample_weight" "=sample_weight)`.".format(pname) ) step, param = pname.split("__", 1) fit_params_steps[step][param] = pval return fit_params_steps # Estimator interface def _fit(self, X, y=None, **fit_params_steps): # shallow copy of steps - this should really be steps_ self.steps = list(self.steps) self._validate_steps() # Setup the memory memory = check_memory(self.memory) fit_transform_one_cached = memory.cache(_fit_transform_one) for step_idx, name, transformer in self._iter( with_final=False, filter_passthrough=False ): if transformer is None or transformer == "passthrough": with _print_elapsed_time("Pipeline", self._log_message(step_idx)): continue if hasattr(memory, "location") and memory.location is None: # we do not clone when caching is disabled to # preserve backward compatibility cloned_transformer = transformer else: cloned_transformer = clone(transformer) # Fit or load from cache the current transformer X, fitted_transformer = fit_transform_one_cached( cloned_transformer, X, y, None, message_clsname="Pipeline", message=self._log_message(step_idx), **fit_params_steps[name], ) # Replace the transformer of the step with the fitted # transformer. This is necessary when loading the transformer # from the cache. self.steps[step_idx] = (name, fitted_transformer) return X def fit(self, X, y=None, **fit_params): """Fit the model. Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- self : object Pipeline with fitted steps. """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if self._final_estimator != "passthrough": fit_params_last_step = fit_params_steps[self.steps[-1][0]] self._final_estimator.fit(Xt, y, **fit_params_last_step) return self def fit_transform(self, X, y=None, **fit_params): """Fit the model and transform with the final estimator. Fits all the transformers one after the other and transform the data. Then uses `fit_transform` on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed samples. """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) last_step = self._final_estimator with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if last_step == "passthrough": return Xt fit_params_last_step = fit_params_steps[self.steps[-1][0]] if hasattr(last_step, "fit_transform"): return last_step.fit_transform(Xt, y, **fit_params_last_step) else: return last_step.fit(Xt, y, **fit_params_last_step).transform(Xt) @available_if(_final_estimator_has("predict")) def predict(self, X, **predict_params): """Transform the data, and apply `predict` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict` method. Only valid if the final estimator implements `predict`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **predict_params : dict of string -> object Parameters to the ``predict`` called at the end of all transformations in the pipeline. Note that while this may be used to return uncertainties from some models with return_std or return_cov, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. .. versionadded:: 0.20 Returns ------- y_pred : ndarray Result of calling `predict` on the final estimator. """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict(Xt, **predict_params) @available_if(_final_estimator_has("fit_predict")) def fit_predict(self, X, y=None, **fit_params): """Transform the data, and apply `fit_predict` with the final estimator. Call `fit_transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `fit_predict` method. Only valid if the final estimator implements `fit_predict`. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. Returns ------- y_pred : ndarray Result of calling `fit_predict` on the final estimator. """ fit_params_steps = self._check_fit_params(**fit_params) Xt = self._fit(X, y, **fit_params_steps) fit_params_last_step = fit_params_steps[self.steps[-1][0]] with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): y_pred = self.steps[-1][1].fit_predict(Xt, y, **fit_params_last_step) return y_pred @available_if(_final_estimator_has("predict_proba")) def predict_proba(self, X, **predict_proba_params): """Transform the data, and apply `predict_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_proba` method. Only valid if the final estimator implements `predict_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **predict_proba_params : dict of string -> object Parameters to the `predict_proba` called at the end of all transformations in the pipeline. Returns ------- y_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_proba` on the final estimator. """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_proba(Xt, **predict_proba_params) @available_if(_final_estimator_has("decision_function")) def decision_function(self, X): """Transform the data, and apply `decision_function` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `decision_function` method. Only valid if the final estimator implements `decision_function`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].decision_function(Xt) @available_if(_final_estimator_has("score_samples")) def score_samples(self, X): """Transform the data, and apply `score_samples` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score_samples` method. Only valid if the final estimator implements `score_samples`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : ndarray of shape (n_samples,) Result of calling `score_samples` on the final estimator. """ Xt = X for _, _, transformer in self._iter(with_final=False): Xt = transformer.transform(Xt) return self.steps[-1][1].score_samples(Xt) @available_if(_final_estimator_has("predict_log_proba")) def predict_log_proba(self, X, **predict_log_proba_params): """Transform the data, and apply `predict_log_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_log_proba` method. Only valid if the final estimator implements `predict_log_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **predict_log_proba_params : dict of string -> object Parameters to the ``predict_log_proba`` called at the end of all transformations in the pipeline. Returns ------- y_log_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_log_proba` on the final estimator. """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_log_proba(Xt, **predict_log_proba_params) def _can_transform(self): return self._final_estimator == "passthrough" or hasattr( self._final_estimator, "transform" ) @available_if(_can_transform) def transform(self, X): """Transform the data, and apply `transform` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `transform` method. Only valid if the final estimator implements `transform`. This also works where final estimator is `None` in which case all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data. """ Xt = X for _, _, transform in self._iter(): Xt = transform.transform(Xt) return Xt def _can_inverse_transform(self): return all(hasattr(t, "inverse_transform") for _, _, t in self._iter()) @available_if(_can_inverse_transform) def inverse_transform(self, Xt): """Apply `inverse_transform` for each step in a reverse order. All estimators in the pipeline must support `inverse_transform`. Parameters ---------- Xt : array-like of shape (n_samples, n_transformed_features) Data samples, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. Must fulfill input requirements of last step of pipeline's ``inverse_transform`` method. Returns ------- Xt : ndarray of shape (n_samples, n_features) Inverse transformed data, that is, data in the original feature space. """ reverse_iter = reversed(list(self._iter())) for _, _, transform in reverse_iter: Xt = transform.inverse_transform(Xt) return Xt @available_if(_final_estimator_has("score")) def score(self, X, y=None, sample_weight=None): """Transform the data, and apply `score` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score` method. Only valid if the final estimator implements `score`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. sample_weight : array-like, default=None If not None, this argument is passed as ``sample_weight`` keyword argument to the ``score`` method of the final estimator. Returns ------- score : float Result of calling `score` on the final estimator. """ Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) score_params = {} if sample_weight is not None: score_params["sample_weight"] = sample_weight return self.steps[-1][1].score(Xt, y, **score_params) @property def classes_(self): """The classes labels. Only exist if the last step is a classifier.""" return self.steps[-1][1].classes_ def _more_tags(self): # check if first estimator expects pairwise input return {"pairwise": _safe_tags(self.steps[0][1], "pairwise")} def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Transform input features using the pipeline. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ feature_names_out = input_features for _, name, transform in self._iter(): if not hasattr(transform, "get_feature_names_out"): raise AttributeError( "Estimator {} does not provide get_feature_names_out. " "Did you mean to call pipeline[:-1].get_feature_names_out" "()?".format(name) ) feature_names_out = transform.get_feature_names_out(feature_names_out) return feature_names_out @property def n_features_in_(self): """Number of features seen during first step `fit` method.""" # delegate to first step (which will call _check_is_fitted) return self.steps[0][1].n_features_in_ @property def feature_names_in_(self): """Names of features seen during first step `fit` method.""" # delegate to first step (which will call _check_is_fitted) return self.steps[0][1].feature_names_in_ def __sklearn_is_fitted__(self): """Indicate whether pipeline has been fit.""" try: # check if the last step of the pipeline is fitted # we only check the last step since if the last step is fit, it # means the previous steps should also be fit. This is faster than # checking if every step of the pipeline is fit. check_is_fitted(self.steps[-1][1]) return True except NotFittedError: return False def _sk_visual_block_(self): _, estimators = zip(*self.steps) def _get_name(name, est): if est is None or est == "passthrough": return f"{name}: passthrough" # Is an estimator return f"{name}: {est.__class__.__name__}" names = [_get_name(name, est) for name, est in self.steps] name_details = [str(est) for est in estimators] return _VisualBlock( "serial", estimators, names=names, name_details=name_details, dash_wrapped=False, ) def _name_estimators(estimators): """Generate names for estimators.""" names = [ estimator if isinstance(estimator, str) else type(estimator).__name__.lower() for estimator in estimators ] namecount = defaultdict(int) for est, name in zip(estimators, names): namecount[name] += 1 for k, v in list(namecount.items()): if v == 1: del namecount[k] for i in reversed(range(len(estimators))): name = names[i] if name in namecount: names[i] += "-%d" % namecount[name] namecount[name] -= 1 return list(zip(names, estimators)) def make_pipeline(*steps, memory=None, verbose=False): """Construct a :class:`Pipeline` from the given estimators. This is a shorthand for the :class:`Pipeline` constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of Estimator objects List of the scikit-learn estimators that are chained together. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Returns ------- p : Pipeline Returns a scikit-learn :class:`Pipeline` object. See Also -------- Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.pipeline import make_pipeline >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) """ return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose) def _transform_one(transformer, X, y, weight, **fit_params): res = transformer.transform(X) # if we have a weight for this transformer, multiply output if weight is None: return res return res * weight def _fit_transform_one( transformer, X, y, weight, message_clsname="", message=None, **fit_params ): """ Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. """ with _print_elapsed_time(message_clsname, message): if hasattr(transformer, "fit_transform"): res = transformer.fit_transform(X, y, **fit_params) else: res = transformer.fit(X, y, **fit_params).transform(X) if weight is None: return res, transformer return res * weight, transformer def _fit_one(transformer, X, y, weight, message_clsname="", message=None, **fit_params): """ Fits ``transformer`` to ``X`` and ``y``. """ with _print_elapsed_time(message_clsname, message): return transformer.fit(X, y, **fit_params) class FeatureUnion(TransformerMixin, _BaseComposition): """Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, removed by setting to 'drop' or disabled by setting to 'passthrough' (features are passed without transformation). Read more in the :ref:`User Guide `. .. versionadded:: 0.13 Parameters ---------- transformer_list : list of (str, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. The transformer can be 'drop' for it to be ignored or can be 'passthrough' for features to be passed unchanged. .. versionadded:: 1.1 Added the option `"passthrough"`. .. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights. Raises ValueError if key not present in ``transformer_list``. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Attributes ---------- named_transformers : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. Read-only attribute to access any transformer parameter by user given name. Keys are transformer names and values are transformer parameters. .. versionadded:: 1.2 n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first transformer in `transformer_list` exposes such an attribute when fit. .. versionadded:: 0.24 See Also -------- make_union : Convenience function for simplified feature union construction. Examples -------- >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA, TruncatedSVD >>> union = FeatureUnion([("pca", PCA(n_components=1)), ... ("svd", TruncatedSVD(n_components=2))]) >>> X = [[0., 1., 3], [2., 2., 5]] >>> union.fit_transform(X) array([[ 1.5 , 3.0..., 0.8...], [-1.5 , 5.7..., -0.4...]]) """ _required_parameters = ["transformer_list"] def __init__( self, transformer_list, *, n_jobs=None, transformer_weights=None, verbose=False ): self.transformer_list = transformer_list self.n_jobs = n_jobs self.transformer_weights = transformer_weights self.verbose = verbose def set_output(self, *, transform=None): """Set the output container when `"transform"` and `"fit_transform"` are called. `set_output` will set the output of all estimators in `transformer_list`. Parameters ---------- transform : {"default", "pandas"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. """ super().set_output(transform=transform) for _, step, _ in self._iter(): _safe_set_output(step, transform=transform) return self @property def named_transformers(self): # Use Bunch object to improve autocomplete return Bunch(**dict(self.transformer_list)) def get_params(self, deep=True): """Get parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `transformer_list` of the `FeatureUnion`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. """ return self._get_params("transformer_list", deep=deep) def set_params(self, **kwargs): """Set the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `transformer_list`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `transform_list`. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. Returns ------- self : object FeatureUnion class instance. """ self._set_params("transformer_list", **kwargs) return self def _validate_transformers(self): names, transformers = zip(*self.transformer_list) # validate names self._validate_names(names) # validate estimators for t in transformers: if t in ("drop", "passthrough"): continue if not (hasattr(t, "fit") or hasattr(t, "fit_transform")) or not hasattr( t, "transform" ): raise TypeError( "All estimators should implement fit and " "transform. '%s' (type %s) doesn't" % (t, type(t)) ) def _validate_transformer_weights(self): if not self.transformer_weights: return transformer_names = set(name for name, _ in self.transformer_list) for name in self.transformer_weights: if name not in transformer_names: raise ValueError( f'Attempting to weight transformer "{name}", ' "but it is not present in transformer_list." ) def _iter(self): """ Generate (name, trans, weight) tuples excluding None and 'drop' transformers. """ get_weight = (self.transformer_weights or {}).get for name, trans in self.transformer_list: if trans == "drop": continue if trans == "passthrough": trans = FunctionTransformer(feature_names_out="one-to-one") yield (name, trans, get_weight(name)) def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ feature_names = [] for name, trans, _ in self._iter(): if not hasattr(trans, "get_feature_names_out"): raise AttributeError( "Transformer %s (type %s) does not provide get_feature_names_out." % (str(name), type(trans).__name__) ) feature_names.extend( [f"{name}__{f}" for f in trans.get_feature_names_out(input_features)] ) return np.asarray(feature_names, dtype=object) def fit(self, X, y=None, **fit_params): """Fit all transformers using X. Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **fit_params : dict, default=None Parameters to pass to the fit method of the estimator. Returns ------- self : object FeatureUnion class instance. """ transformers = self._parallel_func(X, y, fit_params, _fit_one) if not transformers: # All transformers are None return self self._update_transformer_list(transformers) return self def fit_transform(self, X, y=None, **fit_params): """Fit all transformers, transform the data and concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **fit_params : dict, default=None Parameters to pass to the fit method of the estimator. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. """ results = self._parallel_func(X, y, fit_params, _fit_transform_one) if not results: # All transformers are None return np.zeros((X.shape[0], 0)) Xs, transformers = zip(*results) self._update_transformer_list(transformers) return self._hstack(Xs) def _log_message(self, name, idx, total): if not self.verbose: return None return "(step %d of %d) Processing %s" % (idx, total, name) def _parallel_func(self, X, y, fit_params, func): """Runs func in parallel on X and y""" self.transformer_list = list(self.transformer_list) self._validate_transformers() self._validate_transformer_weights() transformers = list(self._iter()) return Parallel(n_jobs=self.n_jobs)( delayed(func)( transformer, X, y, weight, message_clsname="FeatureUnion", message=self._log_message(name, idx, len(transformers)), **fit_params, ) for idx, (name, transformer, weight) in enumerate(transformers, 1) ) def transform(self, X): """Transform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. Returns ------- X_t : array-like or sparse matrix of \ shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. """ Xs = Parallel(n_jobs=self.n_jobs)( delayed(_transform_one)(trans, X, None, weight) for name, trans, weight in self._iter() ) if not Xs: # All transformers are None return np.zeros((X.shape[0], 0)) return self._hstack(Xs) def _hstack(self, Xs): config = _get_output_config("transform", self) if config["dense"] == "pandas" and all(hasattr(X, "iloc") for X in Xs): pd = check_pandas_support("transform") return pd.concat(Xs, axis=1) if any(sparse.issparse(f) for f in Xs): Xs = sparse.hstack(Xs).tocsr() else: Xs = np.hstack(Xs) return Xs def _update_transformer_list(self, transformers): transformers = iter(transformers) self.transformer_list[:] = [ (name, old if old == "drop" else next(transformers)) for name, old in self.transformer_list ] @property def n_features_in_(self): """Number of features seen during :term:`fit`.""" # X is passed to all transformers so we just delegate to the first one return self.transformer_list[0][1].n_features_in_ def __sklearn_is_fitted__(self): # Delegate whether feature union was fitted for _, transformer, _ in self._iter(): check_is_fitted(transformer) return True def _sk_visual_block_(self): names, transformers = zip(*self.transformer_list) return _VisualBlock("parallel", transformers, names=names) def make_union(*transformers, n_jobs=None, verbose=False): """Construct a FeatureUnion from the given transformers. This is a shorthand for the FeatureUnion constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting. Parameters ---------- *transformers : list of estimators One or more estimators. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Returns ------- f : FeatureUnion A :class:`FeatureUnion` object for concatenating the results of multiple transformer objects. See Also -------- FeatureUnion : Class for concatenating the results of multiple transformer objects. Examples -------- >>> from sklearn.decomposition import PCA, TruncatedSVD >>> from sklearn.pipeline import make_union >>> make_union(PCA(), TruncatedSVD()) FeatureUnion(transformer_list=[('pca', PCA()), ('truncatedsvd', TruncatedSVD())]) """ return FeatureUnion(_name_estimators(transformers), n_jobs=n_jobs, verbose=verbose)