Source code for imblearn.pipeline

"""
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 <g.lemaitre58@gmail.com>
# License: BSD

from __future__ import division

from sklearn import pipeline
from sklearn.base import clone
from sklearn.externals import six
from sklearn.externals.joblib import Memory
from sklearn.utils import tosequence
from sklearn.utils.metaestimators import if_delegate_has_method

__all__ = ['Pipeline', 'make_pipeline']


[docs]class Pipeline(pipeline.Pipeline): """Pipeline of transforms and resamples with a final estimator. Sequentially apply a list of transforms, samples 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 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. Parameters ---------- steps : list List of (name, transform) tuples (implementing fit/transform/fit_sample) that are chained, in the order in which they are chained, with the last object an estimator. memory : Instance of joblib.Memory or string, optional (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. Attributes ---------- named_steps : dict Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from sklearn.cross_validation 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 # doctest: +NORMALIZE_WHITESPACE >>> 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('Original dataset shape {}'.format(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) # doctest: +ELLIPSIS Pipeline(...) >>> y_hat = pipeline.predict(X_test) >>> print(classification_report(y_test, y_hat)) precision recall f1-score support <BLANKLINE> 0 0.87 1.00 0.93 26 1 1.00 0.98 0.99 224 <BLANKLINE> avg / total 0.99 0.98 0.98 250 <BLANKLINE> """ # BaseEstimator interface
[docs] def __init__(self, steps, memory=None): # shallow copy of steps self.steps = tosequence(steps) self._validate_steps() self.memory = memory
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: continue if (not (hasattr(t, "fit") or hasattr(t, "fit_transform") or hasattr(t, "fit_sample")) or not (hasattr(t, "transform") or hasattr(t, "sample"))): raise TypeError( "All intermediate steps of the chain should " "be estimators that implement fit and transform or sample " "(but not both) '%s' (type %s) doesn't)" % (t, type(t))) if ((hasattr(t, "fit_sample") and hasattr(t, "fit_transform")) or (hasattr(t, "sample") and hasattr(t, "transform"))): raise TypeError( "All intermediate steps of the chain should " "be estimators that implement fit and transform or sample." " '%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 not hasattr(estimator, "fit"): raise TypeError("Last step of Pipeline should implement fit. " "'%s' (type %s) doesn't" % (estimator, type(estimator))) # Estimator interface def _fit(self, X, y=None, **fit_params): self._validate_steps() # Setup the memory memory = self.memory if memory is None: memory = Memory(cachedir=None, verbose=0) elif isinstance(memory, six.string_types): memory = Memory(cachedir=memory, verbose=0) elif not isinstance(memory, Memory): raise ValueError("'memory' should either be a string or" " a joblib.Memory instance, got" " 'memory={!r}' instead.".format(memory)) fit_transform_one_cached = memory.cache(_fit_transform_one) fit_sample_one_cached = memory.cache(_fit_sample_one) fit_params_steps = dict((name, {}) for name, step in self.steps if step is not None) for pname, pval in six.iteritems(fit_params): step, param = pname.split('__', 1) fit_params_steps[step][param] = pval Xt = X yt = y for step_idx, (name, transformer) in enumerate(self.steps[:-1]): if transformer is None: pass else: if memory.cachedir 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 transfomer if (hasattr(cloned_transformer, "transform") or hasattr(cloned_transformer, "fit_transform")): Xt, fitted_transformer = fit_transform_one_cached( cloned_transformer, None, Xt, yt, **fit_params_steps[name]) elif hasattr(cloned_transformer, "sample"): Xt, yt, fitted_transformer = fit_sample_one_cached( cloned_transformer, Xt, yt, **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) if self._final_estimator is None: return Xt, yt, {} return Xt, yt, fit_params_steps[self.steps[-1][0]]
[docs] 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 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 : Pipeline This estimator """ Xt, yt, fit_params = self._fit(X, y, **fit_params) if self._final_estimator is not None: self._final_estimator.fit(Xt, yt, **fit_params) return self
[docs] 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, shape = [n_samples, n_transformed_features] Transformed samples """ last_step = self._final_estimator Xt, yt, fit_params = self._fit(X, y, **fit_params) if last_step is None: return Xt elif hasattr(last_step, 'fit_transform'): return last_step.fit_transform(Xt, yt, **fit_params) else: return last_step.fit(Xt, yt, **fit_params).transform(Xt)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def fit_sample(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_sample 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, shape = [n_samples, n_transformed_features] Transformed samples yt : array-like, shape = [n_samples, n_transformed_features] Transformed target """ last_step = self._final_estimator Xt, yt, fit_params = self._fit(X, y, **fit_params) if last_step is None: return Xt elif hasattr(last_step, 'fit_sample'): return last_step.fit_sample(Xt, yt, **fit_params)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def sample(self, X, y): """Sample the data with the final estimator Applies transformers/samplers to the data, and the sample method of the final estimator. Valid only if the final estimator implements sample. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. """ Xt = X for name, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): # XXX: Calling sample in pipeline it means that the # last estimator is a sampler. Samplers don't carry # the sampled data. So, call 'fit_sample' in all intermediate # steps to get the sampled data for the last estimator. Xt, y = transform.fit_sample(Xt, y) else: Xt = transform.transform(Xt) return self.steps[-1][-1].fit_sample(Xt, y)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def predict(self, X): """Apply transformers/samplers to the data, and predict with the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_pred : array-like """ Xt = X for _, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.transform(Xt) return self.steps[-1][-1].predict(Xt)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def fit_predict(self, X, y=None, **fit_params): """Applies 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 : array-like """ Xt, yt, fit_params = self._fit(X, y, **fit_params) return self.steps[-1][-1].fit_predict(Xt, yt, **fit_params)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def predict_proba(self, X): """Apply transformers/samplers, and predict_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_proba : array-like, shape = [n_samples, n_classes] """ Xt = X for _, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.transform(Xt) return self.steps[-1][-1].predict_proba(Xt)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def decision_function(self, X): """Apply transformers/samplers, and decision_function of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like, shape = [n_samples, n_classes] """ Xt = X for _, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.transform(Xt) return self.steps[-1][-1].decision_function(Xt)
@if_delegate_has_method(delegate='_final_estimator')
[docs] def predict_log_proba(self, X): """Apply transformers/samplers, and predict_log_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like, shape = [n_samples, n_classes] """ Xt = X for _, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.transform(Xt) return self.steps[-1][-1].predict_log_proba(Xt)
@property def transform(self): """Apply transformers/samplers, and transform with the final estimator This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : array-like, shape = [n_samples, n_transformed_features] """ # _final_estimator is None or has transform, otherwise attribute error if self._final_estimator is not None: self._final_estimator.transform return self._transform def _transform(self, X): Xt = X for name, transform in self.steps: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.transform(Xt) return Xt @property def inverse_transform(self): """Apply inverse transformations in reverse order All estimators in the pipeline must support ``inverse_transform``. Parameters ---------- Xt : array-like, 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 : array-like, shape = [n_samples, n_features] """ # raise AttributeError if necessary for hasattr behaviour for name, transform in self.steps: if transform is not None: transform.inverse_transform return self._inverse_transform def _inverse_transform(self, X): Xt = X for name, transform in self.steps[::-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: Xt = transform.inverse_transform(Xt) return Xt @if_delegate_has_method(delegate='_final_estimator')
[docs] def score(self, X, y=None, sample_weight=None): """Apply transformers/samplers, and score with the final estimator 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 """ Xt = X for _, transform in self.steps[:-1]: if transform is None: continue if hasattr(transform, "fit_sample"): pass else: 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)
def _fit_transform_one(transformer, weight, X, y, **fit_params): if hasattr(transformer, 'fit_transform'): res = transformer.fit_transform(X, y, **fit_params) else: res = transformer.fit(X, y, **fit_params).transform(X) # if we have a weight for this transformer, multiply output if weight is None: return res, transformer return res * weight, transformer def _fit_sample_one(sampler, X, y, **fit_params): X_res, y_res = sampler.fit_sample(X, y, **fit_params) return X_res, y_res, sampler
[docs]def make_pipeline(*steps): """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. Returns ------- p : Pipeline """ return Pipeline(pipeline._name_estimators(steps))