""" 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 joblib from sklearn import pipeline from sklearn.base import clone from sklearn.utils import _print_elapsed_time from sklearn.utils.metaestimators import available_if from .base import _ParamsValidationMixin from .utils._param_validation import HasMethods, validate_params __all__ = ["Pipeline", "make_pipeline"] class Pipeline(pipeline.Pipeline, _ParamsValidationMixin): """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 if not ( hasattr(t, "fit") or hasattr(t, "fit_transform") or hasattr(t, "fit_resample") ) or not (hasattr(t, "transform") or hasattr(t, "fit_resample")): 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 hasattr(t, "fit_resample") and ( hasattr(t, "fit_transform") or hasattr(t, "transform") ): 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): self.steps = list(self.steps) self._validate_steps() # Setup the memory if self.memory is None or isinstance(self.memory, str): memory = joblib.Memory(location=self.memory, verbose=0) else: memory = self.memory fit_transform_one_cached = memory.cache(pipeline._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 try: # joblib >= 0.12 mem = memory.location except AttributeError: mem = memory.cachedir finally: cloned_transformer = clone(transformer) if mem else 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), **fit_params_steps[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), **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, y def fit(self, X, y=None, **fit_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. **fit_params : dict of str -> 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 : Pipeline This estimator. """ self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt, yt = 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, yt, **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/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. **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 : array-like of shape (n_samples, n_transformed_features) Transformed samples. """ self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt, yt = 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, yt, **fit_params_last_step) else: return last_step.fit(Xt, yt, **fit_params_last_step).transform(Xt) def fit_resample(self, X, y=None, **fit_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. **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 : array-like of shape (n_samples, n_transformed_features) Transformed samples. yt : array-like of shape (n_samples, n_transformed_features) Transformed target. """ self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt, yt = 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_resample"): return last_step.fit_resample(Xt, yt, **fit_params_last_step) @available_if(pipeline._final_estimator_has("fit_predict")) def fit_predict(self, X, y=None, **fit_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. **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 of shape (n_samples,) The predicted target. """ self._validate_params() fit_params_steps = self._check_fit_params(**fit_params) Xt, yt = 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, yt, **fit_params_last_step) return y_pred def _fit_resample_one(sampler, X, y, message_clsname="", message=None, **fit_params): with _print_elapsed_time(message_clsname, message): X_res, y_res = sampler.fit_resample(X, y, **fit_params) return X_res, y_res, sampler @validate_params({"memory": [None, str, HasMethods(["cache"])], "verbose": ["boolean"]}) 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)