diff feature_selectors.py @ 30:60d80322e1e9 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/feature_selectors.py	Tue May 14 17:45:57 2019 -0400
@@ -0,0 +1,357 @@
+"""
+DyRFE
+DyRFECV
+MyPipeline
+MyimbPipeline
+check_feature_importances
+"""
+import numpy as np
+
+from imblearn import under_sampling, over_sampling, combine
+from imblearn.pipeline import Pipeline as imbPipeline
+from sklearn import (cluster, compose, decomposition, ensemble,
+                     feature_extraction, feature_selection,
+                     gaussian_process, kernel_approximation,
+                     metrics, model_selection, naive_bayes,
+                     neighbors, pipeline, preprocessing,
+                     svm, linear_model, tree, discriminant_analysis)
+
+from sklearn.base import BaseEstimator
+from sklearn.base import MetaEstimatorMixin, clone, is_classifier
+from sklearn.feature_selection.rfe import _rfe_single_fit, RFE, RFECV
+from sklearn.model_selection import check_cv
+from sklearn.metrics.scorer import check_scoring
+from sklearn.utils import check_X_y, safe_indexing, safe_sqr
+from sklearn.utils._joblib import Parallel, delayed, effective_n_jobs
+
+
+class DyRFE(RFE):
+    """
+    Mainly used with DyRFECV
+
+    Parameters
+    ----------
+    estimator : object
+        A supervised learning estimator with a ``fit`` method that provides
+        information about feature importance either through a ``coef_``
+        attribute or through a ``feature_importances_`` attribute.
+    n_features_to_select : int or None (default=None)
+        The number of features to select. If `None`, half of the features
+        are selected.
+    step : int, float or list, optional (default=1)
+        If greater than or equal to 1, then ``step`` corresponds to the
+        (integer) number of features to remove at each iteration.
+        If within (0.0, 1.0), then ``step`` corresponds to the percentage
+        (rounded down) of features to remove at each iteration.
+        If list, a series of steps of features to remove at each iteration.
+        Iterations stops when steps finish
+    verbose : int, (default=0)
+        Controls verbosity of output.
+
+    """
+    def __init__(self, estimator, n_features_to_select=None, step=1,
+                 verbose=0):
+        super(DyRFE, self).__init__(estimator, n_features_to_select,
+                                    step, verbose)
+
+    def _fit(self, X, y, step_score=None):
+
+        if type(self.step) is not list:
+            return super(DyRFE, self)._fit(X, y, step_score)
+
+        # dynamic step
+        X, y = check_X_y(X, y, "csc")
+        # Initialization
+        n_features = X.shape[1]
+        if self.n_features_to_select is None:
+            n_features_to_select = n_features // 2
+        else:
+            n_features_to_select = self.n_features_to_select
+
+        step = []
+        for s in self.step:
+            if 0.0 < s < 1.0:
+                step.append(int(max(1, s * n_features)))
+            else:
+                step.append(int(s))
+            if s <= 0:
+                raise ValueError("Step must be >0")
+
+        support_ = np.ones(n_features, dtype=np.bool)
+        ranking_ = np.ones(n_features, dtype=np.int)
+
+        if step_score:
+            self.scores_ = []
+
+        step_i = 0
+        # Elimination
+        while np.sum(support_) > n_features_to_select and step_i < len(step):
+
+            # if last step is 1, will keep loop
+            if step_i == len(step) - 1 and step[step_i] != 0:
+                step.append(step[step_i])
+
+            # Remaining features
+            features = np.arange(n_features)[support_]
+
+            # Rank the remaining features
+            estimator = clone(self.estimator)
+            if self.verbose > 0:
+                print("Fitting estimator with %d features." % np.sum(support_))
+
+            estimator.fit(X[:, features], y)
+
+            # Get coefs
+            if hasattr(estimator, 'coef_'):
+                coefs = estimator.coef_
+            else:
+                coefs = getattr(estimator, 'feature_importances_', None)
+            if coefs is None:
+                raise RuntimeError('The classifier does not expose '
+                                   '"coef_" or "feature_importances_" '
+                                   'attributes')
+
+            # Get ranks
+            if coefs.ndim > 1:
+                ranks = np.argsort(safe_sqr(coefs).sum(axis=0))
+            else:
+                ranks = np.argsort(safe_sqr(coefs))
+
+            # for sparse case ranks is matrix
+            ranks = np.ravel(ranks)
+
+            # Eliminate the worse features
+            threshold =\
+                min(step[step_i], np.sum(support_) - n_features_to_select)
+
+            # Compute step score on the previous selection iteration
+            # because 'estimator' must use features
+            # that have not been eliminated yet
+            if step_score:
+                self.scores_.append(step_score(estimator, features))
+            support_[features[ranks][:threshold]] = False
+            ranking_[np.logical_not(support_)] += 1
+
+            step_i += 1
+
+        # Set final attributes
+        features = np.arange(n_features)[support_]
+        self.estimator_ = clone(self.estimator)
+        self.estimator_.fit(X[:, features], y)
+
+        # Compute step score when only n_features_to_select features left
+        if step_score:
+            self.scores_.append(step_score(self.estimator_, features))
+        self.n_features_ = support_.sum()
+        self.support_ = support_
+        self.ranking_ = ranking_
+
+        return self
+
+
+class DyRFECV(RFECV, MetaEstimatorMixin):
+    """
+    Compared with RFECV, DyRFECV offers flexiable `step` to eleminate
+    features, in the format of list, while RFECV supports only fixed number
+    of `step`.
+
+    Parameters
+    ----------
+    estimator : object
+        A supervised learning estimator with a ``fit`` method that provides
+        information about feature importance either through a ``coef_``
+        attribute or through a ``feature_importances_`` attribute.
+    step : int or float, optional (default=1)
+        If greater than or equal to 1, then ``step`` corresponds to the
+        (integer) number of features to remove at each iteration.
+        If within (0.0, 1.0), then ``step`` corresponds to the percentage
+        (rounded down) of features to remove at each iteration.
+        If list, a series of step to remove at each iteration. iteration stopes
+        when finishing all steps
+        Note that the last iteration may remove fewer than ``step`` features in
+        order to reach ``min_features_to_select``.
+    min_features_to_select : int, (default=1)
+        The minimum number of features to be selected. This number of features
+        will always be scored, even if the difference between the original
+        feature count and ``min_features_to_select`` isn't divisible by
+        ``step``.
+    cv : int, cross-validation generator or an iterable, optional
+        Determines the cross-validation splitting strategy.
+        Possible inputs for cv are:
+        - None, to use the default 3-fold cross-validation,
+        - integer, to specify the number of folds.
+        - :term:`CV splitter`,
+        - An iterable yielding (train, test) splits as arrays of indices.
+        For integer/None inputs, if ``y`` is binary or multiclass,
+        :class:`sklearn.model_selection.StratifiedKFold` is used. If the
+        estimator is a classifier or if ``y`` is neither binary nor multiclass,
+        :class:`sklearn.model_selection.KFold` is used.
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+        .. versionchanged:: 0.20
+            ``cv`` default value of None will change from 3-fold to 5-fold
+            in v0.22.
+    scoring : string, callable or None, optional, (default=None)
+        A string (see model evaluation documentation) or
+        a scorer callable object / function with signature
+        ``scorer(estimator, X, y)``.
+    verbose : int, (default=0)
+        Controls verbosity of output.
+    n_jobs : int or None, optional (default=None)
+        Number of cores to run in parallel while fitting across folds.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
+        for more details.
+    """
+    def __init__(self, estimator, step=1, min_features_to_select=1, cv='warn',
+                 scoring=None, verbose=0, n_jobs=None):
+        super(DyRFECV, self).__init__(
+            estimator, step=step,
+            min_features_to_select=min_features_to_select,
+            cv=cv, scoring=scoring, verbose=verbose,
+            n_jobs=n_jobs)
+
+    def fit(self, X, y, groups=None):
+        """Fit the RFE model and automatically tune the number of selected
+           features.
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape = [n_samples, n_features]
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the total number of features.
+        y : array-like, shape = [n_samples]
+            Target values (integers for classification, real numbers for
+            regression).
+        groups : array-like, shape = [n_samples], optional
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+        """
+        if type(self.step) is not list:
+            return super(DyRFECV, self).fit(X, y, groups)
+
+        X, y = check_X_y(X, y, "csr")
+
+        # Initialization
+        cv = check_cv(self.cv, y, is_classifier(self.estimator))
+        scorer = check_scoring(self.estimator, scoring=self.scoring)
+        n_features = X.shape[1]
+
+        step = []
+        for s in self.step:
+            if 0.0 < s < 1.0:
+                step.append(int(max(1, s * n_features)))
+            else:
+                step.append(int(s))
+            if s <= 0:
+                raise ValueError("Step must be >0")
+
+        # Build an RFE object, which will evaluate and score each possible
+        # feature count, down to self.min_features_to_select
+        rfe = DyRFE(estimator=self.estimator,
+                    n_features_to_select=self.min_features_to_select,
+                    step=self.step, verbose=self.verbose)
+
+        # Determine the number of subsets of features by fitting across
+        # the train folds and choosing the "features_to_select" parameter
+        # that gives the least averaged error across all folds.
+
+        # Note that joblib raises a non-picklable error for bound methods
+        # even if n_jobs is set to 1 with the default multiprocessing
+        # backend.
+        # This branching is done so that to
+        # make sure that user code that sets n_jobs to 1
+        # and provides bound methods as scorers is not broken with the
+        # addition of n_jobs parameter in version 0.18.
+
+        if effective_n_jobs(self.n_jobs) == 1:
+            parallel, func = list, _rfe_single_fit
+        else:
+            parallel = Parallel(n_jobs=self.n_jobs)
+            func = delayed(_rfe_single_fit)
+
+        scores = parallel(
+            func(rfe, self.estimator, X, y, train, test, scorer)
+            for train, test in cv.split(X, y, groups))
+
+        scores = np.sum(scores, axis=0)
+        diff = int(scores.shape[0]) - len(step)
+        if diff > 0:
+            step = np.r_[step, [step[-1]] * diff]
+        scores_rev = scores[::-1]
+        argmax_idx = len(scores) - np.argmax(scores_rev) - 1
+        n_features_to_select = max(
+            n_features - sum(step[:argmax_idx]),
+            self.min_features_to_select)
+
+        # Re-execute an elimination with best_k over the whole set
+        rfe = DyRFE(estimator=self.estimator,
+                    n_features_to_select=n_features_to_select, step=self.step,
+                    verbose=self.verbose)
+
+        rfe.fit(X, y)
+
+        # Set final attributes
+        self.support_ = rfe.support_
+        self.n_features_ = rfe.n_features_
+        self.ranking_ = rfe.ranking_
+        self.estimator_ = clone(self.estimator)
+        self.estimator_.fit(self.transform(X), y)
+
+        # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
+        # here, the scores are normalized by get_n_splits(X, y)
+        self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y, groups)
+        return self
+
+
+class MyPipeline(pipeline.Pipeline):
+    """
+    Extend pipeline object to have feature_importances_ attribute
+    """
+    def fit(self, X, y=None, **fit_params):
+        super(MyPipeline, self).fit(X, y, **fit_params)
+        estimator = self.steps[-1][-1]
+        if hasattr(estimator, 'coef_'):
+            coefs = estimator.coef_
+        else:
+            coefs = getattr(estimator, 'feature_importances_', None)
+        if coefs is None:
+            raise RuntimeError('The estimator in the pipeline does not expose '
+                               '"coef_" or "feature_importances_" '
+                               'attributes')
+        self.feature_importances_ = coefs
+        return self
+
+
+class MyimbPipeline(imbPipeline):
+    """
+    Extend imblance pipeline object to have feature_importances_ attribute
+    """
+    def fit(self, X, y=None, **fit_params):
+        super(MyimbPipeline, self).fit(X, y, **fit_params)
+        estimator = self.steps[-1][-1]
+        if hasattr(estimator, 'coef_'):
+            coefs = estimator.coef_
+        else:
+            coefs = getattr(estimator, 'feature_importances_', None)
+        if coefs is None:
+            raise RuntimeError('The estimator in the pipeline does not expose '
+                               '"coef_" or "feature_importances_" '
+                               'attributes')
+        self.feature_importances_ = coefs
+        return self
+
+
+def check_feature_importances(estimator):
+    """
+    For pipeline object which has no feature_importances_ property,
+    this function returns the same comfigured pipeline object with
+    attached the last estimator's feature_importances_.
+    """
+    if estimator.__class__.__module__ == 'sklearn.pipeline':
+        pipeline_steps = estimator.get_params()['steps']
+        estimator = MyPipeline(pipeline_steps)
+    elif estimator.__class__.__module__ == 'imblearn.pipeline':
+        pipeline_steps = estimator.get_params()['steps']
+        estimator = MyimbPipeline(pipeline_steps)
+    else:
+        return estimator