Source code for gtda.pipeline

"""The module :mod:`gtda.pipeline` extends scikit-learn's module by defining
Pipelines that include TransformerResamplers."""
# License: GNU AGPLv3

from sklearn import pipeline
from sklearn.base import clone
from sklearn.utils.metaestimators import if_delegate_has_method
from sklearn.utils.validation import check_memory

__all__ = ['Pipeline', 'make_pipeline']


[docs]class Pipeline(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) 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. See also -------- make_pipeline : helper function to make pipeline. Examples -------- >>> import numpy as np >>> import gtda.time_series as ts >>> import gtda.homology as hl >>> import gtda.diagrams as diag >>> from gtda.pipeline import Pipeline >>> import sklearn.preprocessing as skprep >>> >>> X = np.random.rand(600, 1) >>> n_train, n_test = 400, 200 >>> >>> labeller = ts.Labeller(width=5, percentiles=[80], >>> n_steps_future=1) >>> X_train = X[:n_train] >>> y_train = X_train >>> X_train, y_train = labeller.fit_transform_resample(X_train, y_train) >>> >>> print(X_train.shape, y_train.shape) (395, 1) (395,) >>> steps = [ >>> ('embedding', ts.TakensEmbedding()), >>> ('window', ts.SlidingWindow(width=5, stride=1)), >>> ('diagram', hl.VietorisRipsPersistence()), >>> ('rescaler', diag.Scaler()), >>> ('filter', diag.Filtering(epsilon=0.1)), >>> ('entropy', diag.PersistenceEntropy()), >>> ('scaling', skprep.MinMaxScaler(copy=True)), >>> ] >>> pipeline = Pipeline(steps) >>> >>> Xt_train, yr_train = pipeline.\\ >>> fit_transform_resample(X_train, y_train) >>> >>> print(X_train_final.shape, y_train_final.shape) (389, 2) (389,) """ def _fit(self, X, y=None, **fit_params): 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_transform_resample_one_cached = memory.cache( _fit_transform_resample_one) fit_params_steps = {name: {} for name, step in self.steps if step is not None} for pname, pval in fit_params.items(): step, param = pname.split('__', 1) fit_params_steps[step][param] = pval for step_idx, name, transformer in self._iter(with_final=False): if hasattr(memory, 'location') and (memory.location is None): # joblib >= 0.12. 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, "resample") or \ hasattr(cloned_transformer, "fit_transform_resample"): if y is None: X, fitted_transformer = fit_transform_one_cached( cloned_transformer, None, X, y, **fit_params_steps[name]) else: X, y, fitted_transformer = \ fit_transform_resample_one_cached( cloned_transformer, None, X, y, **fit_params_steps[name]) else: X, fitted_transformer = fit_transform_one_cached( cloned_transformer, None, X, y, **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 == 'passthrough': return X, y, {} return X, y, 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 or None, default: ``None`` Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the :meth:`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, yr, fit_params = self._fit(X, y, **fit_params) if self._final_estimator != 'passthrough': self._final_estimator.fit(Xt, yr, **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 :meth:`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, yr, fit_params = self._fit(X, y, **fit_params) if last_step == 'passthrough': return Xt elif hasattr(last_step, 'fit_transform'): return last_step.fit_transform(Xt, yr, **fit_params) else: return last_step.fit(Xt, yr, **fit_params).transform(Xt)
[docs] def fit_transform_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 :meth:`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. yr : array-like, shape (n_samples, n_transformed_features) Transformed target. """ last_step = self._final_estimator Xt, yr, fit_params = self._fit(X, y, **fit_params) if last_step == 'passthrough': return Xt, yr elif hasattr(last_step, 'fit_transform_resample'): return last_step.fit_transform_resample(Xt, yr, **fit_params) elif hasattr(last_step, 'fit_transform'): return last_step.fit_transform(Xt, yr, **fit_params), yr
[docs] @if_delegate_has_method(delegate='_final_estimator') 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 or None, default: ``None`` Training targets. Must fulfill label requirements for all steps of the pipeline. **fit_params : dict of string -> object Parameters passed to the :meth:`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, yr, fit_params = self._fit(X, y, **fit_params) return self.steps[-1][-1].fit_predict(Xt, yr, **fit_params)
@property def resample(self): """Apply transformers/transformer_resamplers, and transform with the final estimator. This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- y : array-like, shape = (n_samples,) Data to resample. Must fulfill input requirements of first step of the pipeline. Returns ------- yr : array-like, shape = (n_samples_new,) """ # _final_estimator is None or has transform, otherwise attribute error if self._final_estimator != 'passthrough': self._final_estimator.resample return self._resample def _resample(self, X, y=None): yr = y for _, _, transform in self._iter(): yr = transform.resample(yr) return yr @property def transform_resample(self): """Apply transformers/transformer_resamplers, 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_new, n_transformed_features) yr : array-like, shape = (n_samples_new,) """ # _final_estimator is None or has transform, otherwise attribute error final_estimator = self._final_estimator if final_estimator != 'passthrough': if hasattr(final_estimator, 'transform_resample'): final_estimator.transform_resample else: final_estimator.transform return self._transform_resample def _transform_resample(self, X, y): Xt, yr = X, y for _, _, transform in self._iter(): if hasattr(transform, 'transform_resample'): Xt, yr = transform.transform_resample(Xt, yr) else: Xt = transform.transform(Xt) return Xt, yr @property def transform(self): """Apply transformers/transformer_resamplers, 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 != 'passthrough': self._final_estimator.transform return self._transform def _transform(self, X, y=None): Xt = X for _, _, transform in self._iter(): 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 _, _, transform in self._iter(): transform.inverse_transform return self._inverse_transform def _inverse_transform(self, X, y=None): Xt, yr = X, y reverse_iter = reversed(list(self._iter())) for _, _, transform in reverse_iter: Xt = transform.inverse_transform(Xt, yr) return Xt
[docs] @if_delegate_has_method(delegate='_final_estimator') 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 or None, default: ``None`` Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. sample_weight : array-like or None, 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, yr = X, y for _, _, transform in self._iter(with_final=False): if (hasattr(transform, "transform_resample")): Xt, yr = transform.transform_resample(Xt, yr) 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, yr, **score_params)
def _fit_transform_one(transformer, weight, X, y, **fit_params): if hasattr(transformer, 'fit_transform'): X_res = transformer.fit_transform(X, y, **fit_params) else: X_res = transformer.fit(X, y, **fit_params).transform(X) # if we have a weight for this transformer, multiply output if weight is None: return X_res, transformer return X_res * weight, transformer def _fit_transform_resample_one(transformer_resampler, weight, X, y, **fit_params): if hasattr(transformer_resampler, 'fit_transform_resample'): X_res, y_res = transformer_resampler.fit_transform_resample( X, y, **fit_params) else: X_res, y_res = transformer_resampler.fit( X, y, **fit_params).transform_resample( X, y) if weight is None: return X_res, y_res, transformer_resampler return X_res * weight, y_res, transformer_resampler
[docs]def make_pipeline(*steps, **kwargs): """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. memory : None, str or object with the joblib.Memory interface, optional 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. Returns ------- p : Pipeline 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)) ... # doctest: +NORMALIZE_WHITESPACE Pipeline(memory=None, steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('gaussiannb', GaussianNB(priors=None, var_smoothing=1e-09))], verbose=False) """ memory = kwargs.pop('memory', None) if kwargs: raise TypeError( f'Unknown keyword arguments: "{list(kwargs.keys())[0]}"') return Pipeline(pipeline._name_estimators(steps), memory=memory)