""" The :mod:`imblearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms, samples and estimators. """ # Adapted from scikit-learn # Author: Edouard Duchesnay # Gael Varoquaux # Virgile Fritsch # Alexandre Gramfort # Lars Buitinck # Christos Aridas # Guillaume Lemaitre # License: BSD import sklearn from sklearn import pipeline from sklearn.base import clone from sklearn.utils import Bunch from sklearn.utils.fixes import parse_version from sklearn.utils.metaestimators import available_if from sklearn.utils.validation import check_memory from .base import _ParamsValidationMixin from .utils._metadata_requests import ( METHODS, MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing, ) from .utils._param_validation import HasMethods, validate_params from .utils.fixes import _fit_context METHODS.append("fit_resample") __all__ = ["Pipeline", "make_pipeline"] sklearn_version = parse_version(sklearn.__version__).base_version if parse_version(sklearn_version) < parse_version("1.5"): from sklearn.utils import _print_elapsed_time else: from sklearn.utils._user_interface import _print_elapsed_time class Pipeline(_ParamsValidationMixin, pipeline.Pipeline): """Pipeline of transforms and resamples with a final estimator. Sequentially apply a list of transforms, sampling, and a final estimator. Intermediate steps of the pipeline must be transformers or resamplers, that is, they must implement fit, transform and sample methods. The samplers are only applied during fit. The final estimator only needs to implement fit. The transformers and samplers 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``. Parameters ---------- steps : list List of (name, transform) tuples (implementing fit/transform/fit_resample) that are chained, in the order in which they are chained, with the last object an estimator. memory : Instance of joblib.Memory or str, 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` 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. n_features_in_ : int Number of features seen during first step `fit` method. See Also -------- make_pipeline : Helper function to make pipeline. Notes ----- See :ref:`sphx_glr_auto_examples_pipeline_plot_pipeline_classification.py` .. warning:: A surprising behaviour of the `imbalanced-learn` pipeline is that it breaks the `scikit-learn` contract where one expects `estimmator.fit_transform(X, y)` to be equivalent to `estimator.fit(X, y).transform(X)`. The semantic of `fit_resample` is to be applied only during the fit stage. Therefore, resampling will happen when calling `fit_transform` while it will only happen on the `fit` stage when calling `fit` and `transform` separately. Practically, `fit_transform` will lead to a resampled dataset while `fit` and `transform` will not. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split as tts >>> from sklearn.decomposition import PCA >>> from sklearn.neighbors import KNeighborsClassifier as KNN >>> from sklearn.metrics import classification_report >>> from imblearn.over_sampling import SMOTE >>> from imblearn.pipeline import Pipeline >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) >>> print(f'Original dataset shape {Counter(y)}') Original dataset shape Counter({1: 900, 0: 100}) >>> pca = PCA() >>> smt = SMOTE(random_state=42) >>> knn = KNN() >>> pipeline = Pipeline([('smt', smt), ('pca', pca), ('knn', knn)]) >>> X_train, X_test, y_train, y_test = tts(X, y, random_state=42) >>> pipeline.fit(X_train, y_train) Pipeline(...) >>> y_hat = pipeline.predict(X_test) >>> print(classification_report(y_test, y_hat)) precision recall f1-score support 0 0.87 1.00 0.93 26 1 1.00 0.98 0.99 224 accuracy 0.98 250 macro avg 0.93 0.99 0.96 250 weighted avg 0.99 0.98 0.98 250 """ _parameter_constraints: dict = { "steps": "no_validation", # validated in `_validate_steps` "memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"], } # BaseEstimator interface 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 is_transfomer = hasattr(t, "fit") and hasattr(t, "transform") is_sampler = hasattr(t, "fit_resample") is_not_transfomer_or_sampler = not (is_transfomer or is_sampler) if is_not_transfomer_or_sampler: raise TypeError( "All intermediate steps of the chain should " "be estimators that implement fit and transform or " "fit_resample (but not both) or be a string 'passthrough' " "'%s' (type %s) doesn't)" % (t, type(t)) ) if is_transfomer and is_sampler: raise TypeError( "All intermediate steps of the chain should " "be estimators that implement fit and transform or " "fit_resample." " '%s' implements both)" % (t) ) if isinstance(t, pipeline.Pipeline): raise TypeError( "All intermediate steps of the chain should not be Pipelines" ) # 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, filter_resample=True): """Generate (idx, (name, trans)) tuples from self.steps. When `filter_passthrough` is `True`, 'passthrough' and None transformers are filtered out. When `filter_resample` is `True`, estimator with a method `fit_resample` are filtered out. """ it = super()._iter(with_final, filter_passthrough) if filter_resample: return filter(lambda x: not hasattr(x[-1], "fit_resample"), it) else: return it # Estimator interface # def _fit(self, X, y=None, **fit_params_steps): def _fit(self, X, y=None, routed_params=None): 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) fit_resample_one_cached = memory.cache(_fit_resample_one) for step_idx, name, transformer in self._iter( with_final=False, filter_passthrough=False, filter_resample=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 if hasattr(cloned_transformer, "transform") or hasattr( cloned_transformer, "fit_transform" ): X, fitted_transformer = fit_transform_one_cached( cloned_transformer, X, y, None, message_clsname="Pipeline", message=self._log_message(step_idx), params=routed_params[name], ) elif hasattr(cloned_transformer, "fit_resample"): X, y, fitted_transformer = fit_resample_one_cached( cloned_transformer, X, y, message_clsname="Pipeline", message=self._log_message(step_idx), params=routed_params[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, y # The `fit_*` methods need to be overridden to support the samplers. @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y=None, **params): """Fit the model. Fit all the transforms/samplers one after the other and transform/sample the data, then fit the transformed/sampled 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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): 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``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : Pipeline This estimator. """ routed_params = self._check_method_params(method="fit", props=params) Xt, yt = self._fit(X, y, routed_params) with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if self._final_estimator != "passthrough": last_step_params = routed_params[self.steps[-1][0]] self._final_estimator.fit(Xt, yt, **last_step_params["fit"]) return self def _can_fit_transform(self): return ( self._final_estimator == "passthrough" or hasattr(self._final_estimator, "transform") or hasattr(self._final_estimator, "fit_transform") ) @available_if(_can_fit_transform) @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit_transform(self, X, y=None, **params): """Fit the model and transform with the final estimator. Fits all the transformers/samplers one after the other and transform/sample 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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): 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``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples. """ routed_params = self._check_method_params(method="fit_transform", props=params) Xt, yt = self._fit(X, y, routed_params) last_step = self._final_estimator with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if last_step == "passthrough": return Xt last_step_params = routed_params[self.steps[-1][0]] if hasattr(last_step, "fit_transform"): return last_step.fit_transform( Xt, yt, **last_step_params["fit_transform"] ) else: return last_step.fit(Xt, y, **last_step_params["fit"]).transform( Xt, **last_step_params["transform"] ) @available_if(pipeline._final_estimator_has("predict")) def predict(self, X, **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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide ` for more details. 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. Returns ------- y_pred : ndarray Result of calling `predict` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict(Xt, **routed_params[self.steps[-1][0]].predict) def _can_fit_resample(self): return self._final_estimator == "passthrough" or hasattr( self._final_estimator, "fit_resample" ) @available_if(_can_fit_resample) @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit_resample(self, X, y=None, **params): """Fit the model and sample with the final estimator. Fits all the transformers/samplers one after the other and transform/sample the data, then uses fit_resample 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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): 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``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples. yt : array-like of shape (n_samples, n_transformed_features) Transformed target. """ routed_params = self._check_method_params(method="fit_resample", props=params) Xt, yt = self._fit(X, y, routed_params) last_step = self._final_estimator with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): if last_step == "passthrough": return Xt last_step_params = routed_params[self.steps[-1][0]] if hasattr(last_step, "fit_resample"): return last_step.fit_resample( Xt, yt, **last_step_params["fit_resample"] ) @available_if(pipeline._final_estimator_has("fit_predict")) @_fit_context( # estimators in Pipeline.steps are not validated yet prefer_skip_nested_validation=False ) def fit_predict(self, X, y=None, **params): """Apply `fit_predict` of last step in pipeline after transforms. Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only 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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. 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. Returns ------- y_pred : ndarray of shape (n_samples,) The predicted target. """ routed_params = self._check_method_params(method="fit_predict", props=params) Xt, yt = self._fit(X, y, routed_params) params_last_step = routed_params[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, yt, **params_last_step.get("fit_predict", {}) ) return y_pred # TODO: remove the following methods when the minimum scikit-learn >= 1.4 # They do not depend on resampling but we need to redefine them for the # compatibility with the metadata routing framework. @available_if(pipeline._final_estimator_has("predict_proba")) def predict_proba(self, X, **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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_proba` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_proba(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict_proba", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict_proba( Xt, **routed_params[self.steps[-1][0]].predict_proba ) @available_if(pipeline._final_estimator_has("decision_function")) def decision_function(self, X, **params): """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. **params : dict of string -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. """ _raise_for_params(params, self, "decision_function") # not branching here since params is only available if # enable_metadata_routing=True routed_params = process_routing(self, "decision_function", **params) Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform( Xt, **routed_params.get(name, {}).get("transform", {}) ) return self.steps[-1][1].decision_function( Xt, **routed_params.get(self.steps[-1][0], {}).get("decision_function", {}) ) @available_if(pipeline._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(pipeline._final_estimator_has("predict_log_proba")) def predict_log_proba(self, X, **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. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_log_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_log_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_log_proba` on the final estimator. """ Xt = X if not _routing_enabled(): for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt) return self.steps[-1][1].predict_log_proba(Xt, **params) # metadata routing enabled routed_params = process_routing(self, "predict_log_proba", **params) for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].predict_log_proba( Xt, **routed_params[self.steps[-1][0]].predict_log_proba ) def _can_transform(self): return self._final_estimator == "passthrough" or hasattr( self._final_estimator, "transform" ) @available_if(_can_transform) def transform(self, X, **params): """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. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data. """ _raise_for_params(params, self, "transform") # not branching here since params is only available if # enable_metadata_routing=True routed_params = process_routing(self, "transform", **params) Xt = X for _, name, transform in self._iter(): Xt = transform.transform(Xt, **routed_params[name].transform) 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, **params): """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. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_features) Inverse transformed data, that is, data in the original feature space. """ _raise_for_params(params, self, "inverse_transform") # we don't have to branch here, since params is only non-empty if # enable_metadata_routing=True. routed_params = process_routing(self, "inverse_transform", **params) reverse_iter = reversed(list(self._iter())) for _, name, transform in reverse_iter: Xt = transform.inverse_transform( Xt, **routed_params[name].inverse_transform ) return Xt @available_if(pipeline._final_estimator_has("score")) def score(self, X, y=None, sample_weight=None, **params): """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. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- score : float Result of calling `score` on the final estimator. """ Xt = X if not _routing_enabled(): 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) # metadata routing is enabled. routed_params = process_routing( self, "score", sample_weight=sample_weight, **params ) Xt = X for _, name, transform in self._iter(with_final=False): Xt = transform.transform(Xt, **routed_params[name].transform) return self.steps[-1][1].score(Xt, y, **routed_params[self.steps[-1][0]].score) # TODO: once scikit-learn >= 1.4, the following function should be simplified by # calling `super().get_metadata_routing()` def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__) # first we add all steps except the last one for _, name, trans in self._iter(with_final=False, filter_passthrough=True): method_mapping = MethodMapping() # fit, fit_predict, and fit_transform call fit_transform if it # exists, or else fit and transform if hasattr(trans, "fit_transform"): ( method_mapping.add(caller="fit", callee="fit_transform") .add(caller="fit_transform", callee="fit_transform") .add(caller="fit_predict", callee="fit_transform") .add(caller="fit_resample", callee="fit_transform") ) else: ( method_mapping.add(caller="fit", callee="fit") .add(caller="fit", callee="transform") .add(caller="fit_transform", callee="fit") .add(caller="fit_transform", callee="transform") .add(caller="fit_predict", callee="fit") .add(caller="fit_predict", callee="transform") .add(caller="fit_resample", callee="fit") .add(caller="fit_resample", callee="transform") ) ( method_mapping.add(caller="predict", callee="transform") .add(caller="predict", callee="transform") .add(caller="predict_proba", callee="transform") .add(caller="decision_function", callee="transform") .add(caller="predict_log_proba", callee="transform") .add(caller="transform", callee="transform") .add(caller="inverse_transform", callee="inverse_transform") .add(caller="score", callee="transform") .add(caller="fit_resample", callee="transform") ) router.add(method_mapping=method_mapping, **{name: trans}) final_name, final_est = self.steps[-1] if final_est is None or final_est == "passthrough": return router # then we add the last step method_mapping = MethodMapping() if hasattr(final_est, "fit_transform"): ( method_mapping.add(caller="fit_transform", callee="fit_transform").add( caller="fit_resample", callee="fit_transform" ) ) else: ( method_mapping.add(caller="fit", callee="fit") .add(caller="fit", callee="transform") .add(caller="fit_resample", callee="fit") .add(caller="fit_resample", callee="transform") ) ( method_mapping.add(caller="fit", callee="fit") .add(caller="predict", callee="predict") .add(caller="fit_predict", callee="fit_predict") .add(caller="predict_proba", callee="predict_proba") .add(caller="decision_function", callee="decision_function") .add(caller="predict_log_proba", callee="predict_log_proba") .add(caller="transform", callee="transform") .add(caller="inverse_transform", callee="inverse_transform") .add(caller="score", callee="score") .add(caller="fit_resample", callee="fit_resample") ) router.add(method_mapping=method_mapping, **{final_name: final_est}) return router def _check_method_params(self, method, props, **kwargs): if _routing_enabled(): routed_params = process_routing(self, method, **props, **kwargs) return routed_params else: fit_params_steps = Bunch( **{ name: Bunch(**{method: {} for method in METHODS}) for name, step in self.steps if step is not None } ) for pname, pval in props.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]["fit"][param] = pval # without metadata routing, fit_transform and fit_predict # get all the same params and pass it to the last fit. fit_params_steps[step]["fit_transform"][param] = pval fit_params_steps[step]["fit_predict"][param] = pval return fit_params_steps def _fit_resample_one(sampler, X, y, message_clsname="", message=None, params=None): with _print_elapsed_time(message_clsname, message): X_res, y_res = sampler.fit_resample(X, y, **params.get("fit_resample", {})) return X_res, y_res, sampler def _transform_one(transformer, X, y, weight, params): """Call transform and apply weight to output. Parameters ---------- transformer : estimator Estimator to be used for transformation. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input data to be transformed. y : ndarray of shape (n_samples,) Ignored. weight : float Weight to be applied to the output of the transformation. params : dict Parameters to be passed to the transformer's ``transform`` method. This should be of the form ``process_routing()["step_name"]``. """ res = transformer.transform(X, **params.transform) # 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, params=None ): """ 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``. ``params`` needs to be of the form ``process_routing()["step_name"]``. """ params = params or {} with _print_elapsed_time(message_clsname, message): if hasattr(transformer, "fit_transform"): res = transformer.fit_transform(X, y, **params.get("fit_transform", {})) else: res = transformer.fit(X, y, **params.get("fit", {})).transform( X, **params.get("transform", {}) ) if weight is None: return res, transformer return res * weight, transformer @validate_params( {"memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"]}, prefer_skip_nested_validation=True, ) def make_pipeline(*steps, memory=None, verbose=False): """Construct a Pipeline from the given estimators. This is a shorthand for the 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 estimators A list of estimators. memory : None, 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 an imbalanced-learn `Pipeline` instance that handles samplers. See Also -------- imblearn.pipeline.Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) """ return Pipeline(pipeline._name_estimators(steps), memory=memory, verbose=verbose)