Mercurial > repos > bgruening > sklearn_numeric_clustering

annotate feature_selectors.py @ 24:abb5a3f256e3 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
author bgruening
date Tue, 14 May 2019 18:11:02 -0400
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24
abb5a3f256e3 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7
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1 """
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2 DyRFE
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3 DyRFECV
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4 MyPipeline
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5 MyimbPipeline
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6 check_feature_importances
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7 """
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8 import numpy as np
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9
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10 from imblearn import under_sampling, over_sampling, combine
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11 from imblearn.pipeline import Pipeline as imbPipeline
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12 from sklearn import (cluster, compose, decomposition, ensemble,
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13 feature_extraction, feature_selection,
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14 gaussian_process, kernel_approximation,
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15 metrics, model_selection, naive_bayes,
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16 neighbors, pipeline, preprocessing,
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17 svm, linear_model, tree, discriminant_analysis)
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18
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19 from sklearn.base import BaseEstimator
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20 from sklearn.base import MetaEstimatorMixin, clone, is_classifier
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21 from sklearn.feature_selection.rfe import _rfe_single_fit, RFE, RFECV
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22 from sklearn.model_selection import check_cv
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23 from sklearn.metrics.scorer import check_scoring
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24 from sklearn.utils import check_X_y, safe_indexing, safe_sqr
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25 from sklearn.utils._joblib import Parallel, delayed, effective_n_jobs
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26
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27
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28 class DyRFE(RFE):
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29 """
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30 Mainly used with DyRFECV
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31
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32 Parameters
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33 ----------
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34 estimator : object
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35 A supervised learning estimator with a ``fit`` method that provides
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36 information about feature importance either through a ``coef_``
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37 attribute or through a ``feature_importances_`` attribute.
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38 n_features_to_select : int or None (default=None)
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39 The number of features to select. If `None`, half of the features
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40 are selected.
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41 step : int, float or list, optional (default=1)
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42 If greater than or equal to 1, then ``step`` corresponds to the
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43 (integer) number of features to remove at each iteration.
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44 If within (0.0, 1.0), then ``step`` corresponds to the percentage
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45 (rounded down) of features to remove at each iteration.
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46 If list, a series of steps of features to remove at each iteration.
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47 Iterations stops when steps finish
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48 verbose : int, (default=0)
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49 Controls verbosity of output.
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50
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51 """
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52 def __init__(self, estimator, n_features_to_select=None, step=1,
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53 verbose=0):
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54 super(DyRFE, self).__init__(estimator, n_features_to_select,
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55 step, verbose)
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56
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57 def _fit(self, X, y, step_score=None):
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58
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59 if type(self.step) is not list:
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60 return super(DyRFE, self)._fit(X, y, step_score)
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61
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62 # dynamic step
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63 X, y = check_X_y(X, y, "csc")
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64 # Initialization
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65 n_features = X.shape[1]
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66 if self.n_features_to_select is None:
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67 n_features_to_select = n_features // 2
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68 else:
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69 n_features_to_select = self.n_features_to_select
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70
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71 step = []
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72 for s in self.step:
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73 if 0.0 < s < 1.0:
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74 step.append(int(max(1, s * n_features)))
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75 else:
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76 step.append(int(s))
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77 if s <= 0:
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78 raise ValueError("Step must be >0")
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79
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80 support_ = np.ones(n_features, dtype=np.bool)
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81 ranking_ = np.ones(n_features, dtype=np.int)
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82
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83 if step_score:
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84 self.scores_ = []
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85
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86 step_i = 0
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87 # Elimination
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88 while np.sum(support_) > n_features_to_select and step_i < len(step):
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89
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90 # if last step is 1, will keep loop
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91 if step_i == len(step) - 1 and step[step_i] != 0:
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92 step.append(step[step_i])
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93
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94 # Remaining features
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95 features = np.arange(n_features)[support_]
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96
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97 # Rank the remaining features
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98 estimator = clone(self.estimator)
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99 if self.verbose > 0:
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100 print("Fitting estimator with %d features." % np.sum(support_))
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101
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102 estimator.fit(X[:, features], y)
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103
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104 # Get coefs
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105 if hasattr(estimator, 'coef_'):
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106 coefs = estimator.coef_
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107 else:
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108 coefs = getattr(estimator, 'feature_importances_', None)
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109 if coefs is None:
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110 raise RuntimeError('The classifier does not expose '
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111 '"coef_" or "feature_importances_" '
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112 'attributes')
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113
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114 # Get ranks
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115 if coefs.ndim > 1:
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116 ranks = np.argsort(safe_sqr(coefs).sum(axis=0))
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117 else:
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118 ranks = np.argsort(safe_sqr(coefs))
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119
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120 # for sparse case ranks is matrix
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121 ranks = np.ravel(ranks)
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122
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123 # Eliminate the worse features
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124 threshold =\
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125 min(step[step_i], np.sum(support_) - n_features_to_select)
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126
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127 # Compute step score on the previous selection iteration
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128 # because 'estimator' must use features
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129 # that have not been eliminated yet
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130 if step_score:
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131 self.scores_.append(step_score(estimator, features))
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132 support_[features[ranks][:threshold]] = False
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133 ranking_[np.logical_not(support_)] += 1
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134
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135 step_i += 1
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136
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137 # Set final attributes
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138 features = np.arange(n_features)[support_]
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139 self.estimator_ = clone(self.estimator)
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140 self.estimator_.fit(X[:, features], y)
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141
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142 # Compute step score when only n_features_to_select features left
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143 if step_score:
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144 self.scores_.append(step_score(self.estimator_, features))
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145 self.n_features_ = support_.sum()
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146 self.support_ = support_
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147 self.ranking_ = ranking_
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148
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149 return self
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150
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151
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152 class DyRFECV(RFECV, MetaEstimatorMixin):
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153 """
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154 Compared with RFECV, DyRFECV offers flexiable `step` to eleminate
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155 features, in the format of list, while RFECV supports only fixed number
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156 of `step`.
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157
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158 Parameters
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159 ----------
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160 estimator : object
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161 A supervised learning estimator with a ``fit`` method that provides
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162 information about feature importance either through a ``coef_``
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163 attribute or through a ``feature_importances_`` attribute.
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164 step : int or float, optional (default=1)
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165 If greater than or equal to 1, then ``step`` corresponds to the
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166 (integer) number of features to remove at each iteration.
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167 If within (0.0, 1.0), then ``step`` corresponds to the percentage
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168 (rounded down) of features to remove at each iteration.
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169 If list, a series of step to remove at each iteration. iteration stopes
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170 when finishing all steps
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171 Note that the last iteration may remove fewer than ``step`` features in
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172 order to reach ``min_features_to_select``.
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173 min_features_to_select : int, (default=1)
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174 The minimum number of features to be selected. This number of features
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175 will always be scored, even if the difference between the original
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176 feature count and ``min_features_to_select`` isn't divisible by
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177 ``step``.
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178 cv : int, cross-validation generator or an iterable, optional
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179 Determines the cross-validation splitting strategy.
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180 Possible inputs for cv are:
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181 - None, to use the default 3-fold cross-validation,
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182 - integer, to specify the number of folds.
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183 - :term:`CV splitter`,
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184 - An iterable yielding (train, test) splits as arrays of indices.
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185 For integer/None inputs, if ``y`` is binary or multiclass,
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186 :class:`sklearn.model_selection.StratifiedKFold` is used. If the
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187 estimator is a classifier or if ``y`` is neither binary nor multiclass,
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188 :class:`sklearn.model_selection.KFold` is used.
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189 Refer :ref:`User Guide <cross_validation>` for the various
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190 cross-validation strategies that can be used here.
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191 .. versionchanged:: 0.20
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192 ``cv`` default value of None will change from 3-fold to 5-fold
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193 in v0.22.
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194 scoring : string, callable or None, optional, (default=None)
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195 A string (see model evaluation documentation) or
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196 a scorer callable object / function with signature
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197 ``scorer(estimator, X, y)``.
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198 verbose : int, (default=0)
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199 Controls verbosity of output.
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200 n_jobs : int or None, optional (default=None)
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201 Number of cores to run in parallel while fitting across folds.
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202 ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
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203 ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
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204 for more details.
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205 """
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206 def __init__(self, estimator, step=1, min_features_to_select=1, cv='warn',
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207 scoring=None, verbose=0, n_jobs=None):
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208 super(DyRFECV, self).__init__(
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209 estimator, step=step,
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210 min_features_to_select=min_features_to_select,
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211 cv=cv, scoring=scoring, verbose=verbose,
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212 n_jobs=n_jobs)
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213
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214 def fit(self, X, y, groups=None):
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215 """Fit the RFE model and automatically tune the number of selected
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216 features.
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217 Parameters
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218 ----------
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219 X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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220 Training vector, where `n_samples` is the number of samples and
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221 `n_features` is the total number of features.
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222 y : array-like, shape = [n_samples]
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223 Target values (integers for classification, real numbers for
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224 regression).
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225 groups : array-like, shape = [n_samples], optional
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226 Group labels for the samples used while splitting the dataset into
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227 train/test set.
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228 """
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229 if type(self.step) is not list:
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230 return super(DyRFECV, self).fit(X, y, groups)
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231
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232 X, y = check_X_y(X, y, "csr")
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233
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234 # Initialization
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235 cv = check_cv(self.cv, y, is_classifier(self.estimator))
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236 scorer = check_scoring(self.estimator, scoring=self.scoring)
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237 n_features = X.shape[1]
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238
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239 step = []
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240 for s in self.step:
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241 if 0.0 < s < 1.0:
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242 step.append(int(max(1, s * n_features)))
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243 else:
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244 step.append(int(s))
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245 if s <= 0:
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246 raise ValueError("Step must be >0")
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247
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248 # Build an RFE object, which will evaluate and score each possible
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249 # feature count, down to self.min_features_to_select
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250 rfe = DyRFE(estimator=self.estimator,
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251 n_features_to_select=self.min_features_to_select,
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252 step=self.step, verbose=self.verbose)
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253
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254 # Determine the number of subsets of features by fitting across
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255 # the train folds and choosing the "features_to_select" parameter
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256 # that gives the least averaged error across all folds.
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257
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258 # Note that joblib raises a non-picklable error for bound methods
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259 # even if n_jobs is set to 1 with the default multiprocessing
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260 # backend.
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261 # This branching is done so that to
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262 # make sure that user code that sets n_jobs to 1
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263 # and provides bound methods as scorers is not broken with the
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264 # addition of n_jobs parameter in version 0.18.
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265
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266 if effective_n_jobs(self.n_jobs) == 1:
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267 parallel, func = list, _rfe_single_fit
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268 else:
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269 parallel = Parallel(n_jobs=self.n_jobs)
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270 func = delayed(_rfe_single_fit)
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271
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272 scores = parallel(
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273 func(rfe, self.estimator, X, y, train, test, scorer)
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274 for train, test in cv.split(X, y, groups))
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275
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276 scores = np.sum(scores, axis=0)
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277 diff = int(scores.shape[0]) - len(step)
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278 if diff > 0:
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279 step = np.r_[step, [step[-1]] * diff]
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280 scores_rev = scores[::-1]
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281 argmax_idx = len(scores) - np.argmax(scores_rev) - 1
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282 n_features_to_select = max(
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283 n_features - sum(step[:argmax_idx]),
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284 self.min_features_to_select)
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285
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286 # Re-execute an elimination with best_k over the whole set
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287 rfe = DyRFE(estimator=self.estimator,
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288 n_features_to_select=n_features_to_select, step=self.step,
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289 verbose=self.verbose)
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290
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291 rfe.fit(X, y)
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292
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293 # Set final attributes
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294 self.support_ = rfe.support_
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295 self.n_features_ = rfe.n_features_
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296 self.ranking_ = rfe.ranking_
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297 self.estimator_ = clone(self.estimator)
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298 self.estimator_.fit(self.transform(X), y)
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299
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300 # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
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301 # here, the scores are normalized by get_n_splits(X, y)
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302 self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y, groups)
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303 return self
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304
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305
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306 class MyPipeline(pipeline.Pipeline):
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307 """
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308 Extend pipeline object to have feature_importances_ attribute
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309 """
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310 def fit(self, X, y=None, **fit_params):
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311 super(MyPipeline, self).fit(X, y, **fit_params)
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312 estimator = self.steps[-1][-1]
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313 if hasattr(estimator, 'coef_'):
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314 coefs = estimator.coef_
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315 else:
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316 coefs = getattr(estimator, 'feature_importances_', None)
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317 if coefs is None:
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318 raise RuntimeError('The estimator in the pipeline does not expose '
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319 '"coef_" or "feature_importances_" '
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320 'attributes')
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321 self.feature_importances_ = coefs
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322 return self
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323
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324
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325 class MyimbPipeline(imbPipeline):
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326 """
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327 Extend imblance pipeline object to have feature_importances_ attribute
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328 """
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329 def fit(self, X, y=None, **fit_params):
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330 super(MyimbPipeline, self).fit(X, y, **fit_params)
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331 estimator = self.steps[-1][-1]
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332 if hasattr(estimator, 'coef_'):
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333 coefs = estimator.coef_
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334 else:
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335 coefs = getattr(estimator, 'feature_importances_', None)
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336 if coefs is None:
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337 raise RuntimeError('The estimator in the pipeline does not expose '
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338 '"coef_" or "feature_importances_" '
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339 'attributes')
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340 self.feature_importances_ = coefs
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341 return self
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342
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343
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344 def check_feature_importances(estimator):
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345 """
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346 For pipeline object which has no feature_importances_ property,
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347 this function returns the same comfigured pipeline object with
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348 attached the last estimator's feature_importances_.
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349 """
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350 if estimator.__class__.__module__ == 'sklearn.pipeline':
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351 pipeline_steps = estimator.get_params()['steps']
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352 estimator = MyPipeline(pipeline_steps)
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353 elif estimator.__class__.__module__ == 'imblearn.pipeline':
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354 pipeline_steps = estimator.get_params()['steps']
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355 estimator = MyimbPipeline(pipeline_steps)
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356 else:
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357 return estimator