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annotate iraps_classifier.py @ 24:b628de0d101f draft

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author bgruening
date Wed, 15 May 2019 07:40:56 -0400
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24
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1 """
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2 class IRAPSCore
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3 class IRAPSClassifier
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4 class BinarizeTargetClassifier
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5 class BinarizeTargetRegressor
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6 class _BinarizeTargetScorer
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7 class _BinarizeTargetProbaScorer
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8
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9 binarize_auc_scorer
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10 binarize_average_precision_scorer
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12 binarize_accuracy_scorer
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13 binarize_balanced_accuracy_scorer
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14 binarize_precision_scorer
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15 binarize_recall_scorer
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16 """
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19 import numpy as np
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20 import random
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21 import warnings
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23 from abc import ABCMeta
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24 from scipy.stats import ttest_ind
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25 from sklearn import metrics
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26 from sklearn.base import BaseEstimator, clone, RegressorMixin
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27 from sklearn.externals import six
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28 from sklearn.feature_selection.univariate_selection import _BaseFilter
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29 from sklearn.metrics.scorer import _BaseScorer
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30 from sklearn.pipeline import Pipeline
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31 from sklearn.utils import as_float_array, check_X_y
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32 from sklearn.utils._joblib import Parallel, delayed
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33 from sklearn.utils.validation import (check_array, check_is_fitted,
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34 check_memory, column_or_1d)
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37 VERSION = '0.1.1'
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39
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40 class IRAPSCore(six.with_metaclass(ABCMeta, BaseEstimator)):
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41 """
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42 Base class of IRAPSClassifier
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43 From sklearn BaseEstimator:
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44 get_params()
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45 set_params()
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47 Parameters
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48 ----------
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49 n_iter : int
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50 sample count
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51
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52 positive_thres : float
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53 z_score shreshold to discretize positive target values
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54
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55 negative_thres : float
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56 z_score threshold to discretize negative target values
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57
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58 verbose : int
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59 0 or geater, if not 0, print progress
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60
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61 n_jobs : int, default=1
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62 The number of CPUs to use to do the computation.
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63
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64 pre_dispatch : int, or string.
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65 Controls the number of jobs that get dispatched during parallel
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66 execution. Reducing this number can be useful to avoid an
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67 explosion of memory consumption when more jobs get dispatched
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68 than CPUs can process. This parameter can be:
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69 - None, in which case all the jobs are immediately
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70 created and spawned. Use this for lightweight and
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71 fast-running jobs, to avoid delays due to on-demand
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72 spawning of the jobs
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73 - An int, giving the exact number of total jobs that are
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74 spawned
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75 - A string, giving an expression as a function of n_jobs,
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76 as in '2*n_jobs'
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77
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78 random_state : int or None
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79 """
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80
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81 def __init__(self, n_iter=1000, positive_thres=-1, negative_thres=0,
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82 verbose=0, n_jobs=1, pre_dispatch='2*n_jobs',
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83 random_state=None):
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84 """
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85 IRAPS turns towwards general Anomaly Detection
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86 It comapares positive_thres with negative_thres,
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87 and decide which portion is the positive target.
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88 e.g.:
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89 (positive_thres=-1, negative_thres=0)
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90 => positive = Z_score of target < -1
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91 (positive_thres=1, negative_thres=0)
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92 => positive = Z_score of target > 1
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93
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94 Note: The positive targets here is always the
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95 abnormal minority group.
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96 """
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97 self.n_iter = n_iter
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98 self.positive_thres = positive_thres
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99 self.negative_thres = negative_thres
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100 self.verbose = verbose
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101 self.n_jobs = n_jobs
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102 self.pre_dispatch = pre_dispatch
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103 self.random_state = random_state
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104
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105 def fit(self, X, y):
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106 """
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107 X: array-like (n_samples x n_features)
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108 y: 1-d array-like (n_samples)
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109 """
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110 X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=False)
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111
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112 def _stochastic_sampling(X, y, random_state=None, positive_thres=-1,
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113 negative_thres=0):
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114 # each iteration select a random number of random subset of
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115 # training samples. this is somewhat different from the original
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116 # IRAPS method, but effect is almost the same.
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117 SAMPLE_SIZE = [0.25, 0.75]
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118 n_samples = X.shape[0]
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119
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120 if random_state is None:
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121 n_select = random.randint(int(n_samples * SAMPLE_SIZE[0]),
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122 int(n_samples * SAMPLE_SIZE[1]))
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123 index = random.sample(list(range(n_samples)), n_select)
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124 else:
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125 n_select = random.Random(random_state).randint(
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126 int(n_samples * SAMPLE_SIZE[0]),
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127 int(n_samples * SAMPLE_SIZE[1]))
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128 index = random.Random(random_state).sample(
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129 list(range(n_samples)), n_select)
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130
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131 X_selected, y_selected = X[index], y[index]
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132
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133 # Spliting by z_scores.
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134 y_selected = (y_selected - y_selected.mean()) / y_selected.std()
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135 if positive_thres < negative_thres:
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136 X_selected_positive = X_selected[y_selected < positive_thres]
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137 X_selected_negative = X_selected[y_selected > negative_thres]
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138 else:
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139 X_selected_positive = X_selected[y_selected > positive_thres]
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140 X_selected_negative = X_selected[y_selected < negative_thres]
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141
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142 # For every iteration, at least 5 responders are selected
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143 if X_selected_positive.shape[0] < 5:
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144 warnings.warn("Warning: fewer than 5 positives were selected!")
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145 return
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146
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147 # p_values
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148 _, p = ttest_ind(X_selected_positive, X_selected_negative,
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149 axis=0, equal_var=False)
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150
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151 # fold_change == mean change?
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152 # TODO implement other normalization method
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153 positive_mean = X_selected_positive.mean(axis=0)
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154 negative_mean = X_selected_negative.mean(axis=0)
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155 mean_change = positive_mean - negative_mean
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156 # mean_change = np.select(
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157 # [positive_mean > negative_mean,
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158 # positive_mean < negative_mean],
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159 # [positive_mean / negative_mean,
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160 # -negative_mean / positive_mean])
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161 # mean_change could be adjusted by power of 2
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162 # mean_change = 2**mean_change \
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163 # if mean_change>0 else -2**abs(mean_change)
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164
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165 return p, mean_change, negative_mean
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166
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167 parallel = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
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168 pre_dispatch=self.pre_dispatch)
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169 if self.random_state is None:
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170 res = parallel(delayed(_stochastic_sampling)(
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171 X, y, random_state=None,
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172 positive_thres=self.positive_thres,
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173 negative_thres=self.negative_thres)
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174 for i in range(self.n_iter))
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175 else:
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176 res = parallel(delayed(_stochastic_sampling)(
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177 X, y, random_state=seed,
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178 positive_thres=self.positive_thres,
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179 negative_thres=self.negative_thres)
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180 for seed in range(self.random_state,
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181 self.random_state+self.n_iter))
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182 res = [_ for _ in res if _]
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183 if len(res) < 50:
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184 raise ValueError("too few (%d) valid feature lists "
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185 "were generated!" % len(res))
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186 pvalues = np.vstack([x[0] for x in res])
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187 fold_changes = np.vstack([x[1] for x in res])
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188 base_values = np.vstack([x[2] for x in res])
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189
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190 self.pvalues_ = np.asarray(pvalues)
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191 self.fold_changes_ = np.asarray(fold_changes)
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192 self.base_values_ = np.asarray(base_values)
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193
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194 return self
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195
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196
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197 def _iraps_core_fit(iraps_core, X, y):
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198 return iraps_core.fit(X, y)
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199
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200
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201 class IRAPSClassifier(six.with_metaclass(ABCMeta, _BaseFilter,
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202 BaseEstimator, RegressorMixin)):
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203 """
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204 Extend the bases of both sklearn feature_selector and classifier.
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205 From sklearn BaseEstimator:
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206 get_params()
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207 set_params()
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208 From sklearn _BaseFilter:
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209 get_support()
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210 fit_transform(X)
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211 transform(X)
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212 From sklearn RegressorMixin:
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213 score(X, y): R2
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214 New:
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215 predict(X)
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216 predict_label(X)
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217 get_signature()
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218 Properties:
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219 discretize_value
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220
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221 Parameters
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222 ----------
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223 iraps_core: object
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224 p_thres: float, threshold for p_values
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225 fc_thres: float, threshold for fold change or mean difference
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226 occurrence: float, occurrence rate selected by set of p_thres and fc_thres
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227 discretize: float, threshold of z_score to discretize target value
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228 memory: None, str or joblib.Memory object
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229 min_signature_features: int, the mininum number of features in a signature
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230 """
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231
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232 def __init__(self, iraps_core, p_thres=1e-4, fc_thres=0.1,
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233 occurrence=0.8, discretize=-1, memory=None,
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234 min_signature_features=1):
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235 self.iraps_core = iraps_core
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236 self.p_thres = p_thres
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237 self.fc_thres = fc_thres
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238 self.occurrence = occurrence
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239 self.discretize = discretize
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240 self.memory = memory
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241 self.min_signature_features = min_signature_features
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242
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243 def fit(self, X, y):
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244 memory = check_memory(self.memory)
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245 cached_fit = memory.cache(_iraps_core_fit)
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246 iraps_core = clone(self.iraps_core)
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247 # allow pre-fitted iraps_core here
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248 if not hasattr(iraps_core, 'pvalues_'):
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249 iraps_core = cached_fit(iraps_core, X, y)
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250 self.iraps_core_ = iraps_core
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251
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252 pvalues = as_float_array(iraps_core.pvalues_, copy=True)
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253 # why np.nan is here?
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254 pvalues[np.isnan(pvalues)] = np.finfo(pvalues.dtype).max
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255
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256 fold_changes = as_float_array(iraps_core.fold_changes_, copy=True)
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257 fold_changes[np.isnan(fold_changes)] = 0.0
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258
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259 base_values = as_float_array(iraps_core.base_values_, copy=True)
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260
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261 p_thres = self.p_thres
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262 fc_thres = self.fc_thres
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263 occurrence = self.occurrence
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264
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265 mask_0 = np.zeros(pvalues.shape, dtype=np.int32)
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266 # mark p_values less than the threashold
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267 mask_0[pvalues <= p_thres] = 1
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268 # mark fold_changes only when greater than the threashold
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269 mask_0[abs(fold_changes) < fc_thres] = 0
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270
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271 # count the occurrence and mask greater than the threshold
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272 counts = mask_0.sum(axis=0)
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273 occurrence_thres = int(occurrence * iraps_core.n_iter)
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274 mask = np.zeros(counts.shape, dtype=bool)
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275 mask[counts >= occurrence_thres] = 1
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276
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277 # generate signature
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278 fold_changes[mask_0 == 0] = 0.0
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279 signature = fold_changes[:, mask].sum(axis=0) / counts[mask]
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280 signature = np.vstack((signature, base_values[:, mask].mean(axis=0)))
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281 # It's not clearn whether min_size could impact prediction
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282 # performance
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283 if signature is None\
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284 or signature.shape[1] < self.min_signature_features:
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285 raise ValueError("The classifier got None signature or the number "
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286 "of sinature feature is less than minimum!")
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287
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288 self.signature_ = np.asarray(signature)
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289 self.mask_ = mask
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290 # TODO: support other discretize method: fixed value, upper
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291 # third quater, etc.
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292 self.discretize_value = y.mean() + y.std() * self.discretize
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293 if iraps_core.negative_thres > iraps_core.positive_thres:
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294 self.less_is_positive = True
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295 else:
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296 self.less_is_positive = False
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297
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298 return self
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299
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300 def _get_support_mask(self):
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301 """
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302 return mask of feature selection indices
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303 """
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304 check_is_fitted(self, 'mask_')
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305
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306 return self.mask_
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307
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308 def get_signature(self):
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309 """
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310 return signature
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311 """
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312 check_is_fitted(self, 'signature_')
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313
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314 return self.signature_
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315
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316 def predict(self, X):
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317 """
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318 compute the correlation coefficient with irpas signature
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319 """
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320 signature = self.get_signature()
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321
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322 X = as_float_array(X)
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323 X_transformed = self.transform(X) - signature[1]
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324 corrcoef = np.array(
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325 [np.corrcoef(signature[0], e)[0][1] for e in X_transformed])
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326 corrcoef[np.isnan(corrcoef)] = np.finfo(np.float32).min
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327
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328 return corrcoef
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329
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330 def predict_label(self, X, clf_cutoff=0.4):
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331 return self.predict(X) >= clf_cutoff
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332
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333
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334 class BinarizeTargetClassifier(BaseEstimator, RegressorMixin):
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335 """
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336 Convert continuous target to binary labels (True and False)
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337 and apply a classification estimator.
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338
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339 Parameters
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340 ----------
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341 classifier: object
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342 Estimator object such as derived from sklearn `ClassifierMixin`.
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343
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344 z_score: float, default=-1.0
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345 Threshold value based on z_score. Will be ignored when
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346 fixed_value is set
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347
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348 value: float, default=None
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349 Threshold value
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350
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351 less_is_positive: boolean, default=True
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352 When target is less the threshold value, it will be converted
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353 to True, False otherwise.
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354
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355 Attributes
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356 ----------
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357 classifier_: object
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358 Fitted classifier
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359
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360 discretize_value: float
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361 The threshold value used to discretize True and False targets
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362 """
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363
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364 def __init__(self, classifier, z_score=-1, value=None,
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365 less_is_positive=True):
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366 self.classifier = classifier
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367 self.z_score = z_score
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368 self.value = value
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369 self.less_is_positive = less_is_positive
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370
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371 def fit(self, X, y, sample_weight=None):
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372 """
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373 Convert y to True and False labels and then fit the classifier
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374 with X and new y
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375
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
376 Returns
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
377 ------
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
378 self: object
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
379 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
380 y = check_array(y, accept_sparse=False, force_all_finite=True,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
381 ensure_2d=False, dtype='numeric')
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
382 y = column_or_1d(y)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
383
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
384 if self.value is None:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
385 discretize_value = y.mean() + y.std() * self.z_score
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
386 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
387 discretize_value = self.Value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
388 self.discretize_value = discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
389
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
390 if self.less_is_positive:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
391 y_trans = y < discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
392 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
393 y_trans = y > discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
394
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
395 self.classifier_ = clone(self.classifier)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
396
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
397 if sample_weight is not None:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
398 self.classifier_.fit(X, y_trans, sample_weight=sample_weight)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
399 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
400 self.classifier_.fit(X, y_trans)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
401
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
402 if hasattr(self.classifier_, 'feature_importances_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
403 self.feature_importances_ = self.classifier_.feature_importances_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
404 if hasattr(self.classifier_, 'coef_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
405 self.coef_ = self.classifier_.coef_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
406 if hasattr(self.classifier_, 'n_outputs_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
407 self.n_outputs_ = self.classifier_.n_outputs_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
408 if hasattr(self.classifier_, 'n_features_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
409 self.n_features_ = self.classifier_.n_features_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
410
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
411 return self
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
412
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
413 def predict(self, X):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
414 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
415 Predict class probabilities of X.
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
416 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
417 check_is_fitted(self, 'classifier_')
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
418 proba = self.classifier_.predict_proba(X)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
419 return proba[:, 1]
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
420
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
421 def predict_label(self, X):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
422 """Predict class label of X
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
423 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
424 check_is_fitted(self, 'classifier_')
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
425 return self.classifier_.predict(X)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
426
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
427
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
428 class _BinarizeTargetProbaScorer(_BaseScorer):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
429 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
430 base class to make binarized target specific scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
431 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
432
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
433 def __call__(self, clf, X, y, sample_weight=None):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
434 clf_name = clf.__class__.__name__
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
435 # support pipeline object
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
436 if isinstance(clf, Pipeline):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
437 main_estimator = clf.steps[-1][-1]
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
438 # support stacking ensemble estimators
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
439 # TODO support nested pipeline/stacking estimators
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
440 elif clf_name in ['StackingCVClassifier', 'StackingClassifier']:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
441 main_estimator = clf.meta_clf_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
442 elif clf_name in ['StackingCVRegressor', 'StackingRegressor']:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
443 main_estimator = clf.meta_regr_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
444 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
445 main_estimator = clf
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
446
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
447 discretize_value = main_estimator.discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
448 less_is_positive = main_estimator.less_is_positive
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
449
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
450 if less_is_positive:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
451 y_trans = y < discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
452 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
453 y_trans = y > discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
454
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
455 y_pred = clf.predict(X)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
456 if sample_weight is not None:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
457 return self._sign * self._score_func(y_trans, y_pred,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
458 sample_weight=sample_weight,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
459 **self._kwargs)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
460 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
461 return self._sign * self._score_func(y_trans, y_pred,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
462 **self._kwargs)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
463
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
464
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
465 # roc_auc
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
466 binarize_auc_scorer =\
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
467 _BinarizeTargetProbaScorer(metrics.roc_auc_score, 1, {})
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
468
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
469 # average_precision_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
470 binarize_average_precision_scorer =\
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
471 _BinarizeTargetProbaScorer(metrics.average_precision_score, 1, {})
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
472
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
473 # roc_auc_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
474 iraps_auc_scorer = binarize_auc_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
475
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
476 # average_precision_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
477 iraps_average_precision_scorer = binarize_average_precision_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
478
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
479
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
480 class BinarizeTargetRegressor(BaseEstimator, RegressorMixin):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
481 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
482 Extend regression estimator to have discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
483
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
484 Parameters
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
485 ----------
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
486 regressor: object
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
487 Estimator object such as derived from sklearn `RegressionMixin`.
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
488
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
489 z_score: float, default=-1.0
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
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490 Threshold value based on z_score. Will be ignored when
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
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491 fixed_value is set
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
492
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
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diff changeset
493 value: float, default=None
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
494 Threshold value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
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495
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
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496 less_is_positive: boolean, default=True
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
497 When target is less the threshold value, it will be converted
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
498 to True, False otherwise.
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
499
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
500 Attributes
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
501 ----------
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
502 regressor_: object
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
503 Fitted regressor
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
504
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
505 discretize_value: float
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
506 The threshold value used to discretize True and False targets
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
507 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
508
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
509 def __init__(self, regressor, z_score=-1, value=None,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
510 less_is_positive=True):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
511 self.regressor = regressor
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
512 self.z_score = z_score
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
513 self.value = value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
514 self.less_is_positive = less_is_positive
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
515
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
516 def fit(self, X, y, sample_weight=None):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
517 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
518 Calculate the discretize_value fit the regressor with traning data
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
519
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
520 Returns
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
521 ------
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
522 self: object
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
523 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
524 y = check_array(y, accept_sparse=False, force_all_finite=True,
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
525 ensure_2d=False, dtype='numeric')
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
526 y = column_or_1d(y)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
527
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
528 if self.value is None:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
529 discretize_value = y.mean() + y.std() * self.z_score
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
530 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
531 discretize_value = self.Value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
532 self.discretize_value = discretize_value
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
533
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
534 self.regressor_ = clone(self.regressor)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
535
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
536 if sample_weight is not None:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
537 self.regressor_.fit(X, y, sample_weight=sample_weight)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
538 else:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
539 self.regressor_.fit(X, y)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
540
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
541 # attach classifier attributes
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
542 if hasattr(self.regressor_, 'feature_importances_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
543 self.feature_importances_ = self.regressor_.feature_importances_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
544 if hasattr(self.regressor_, 'coef_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
545 self.coef_ = self.regressor_.coef_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
546 if hasattr(self.regressor_, 'n_outputs_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
547 self.n_outputs_ = self.regressor_.n_outputs_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
548 if hasattr(self.regressor_, 'n_features_'):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
549 self.n_features_ = self.regressor_.n_features_
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
550
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
551 return self
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
552
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
553 def predict(self, X):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
554 """Predict target value of X
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
555 """
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
556 check_is_fitted(self, 'regressor_')
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
557 y_pred = self.regressor_.predict(X)
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
558 if not np.all((y_pred >= 0) & (y_pred <= 1)):
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
559 y_pred = (y_pred - y_pred.min()) / (y_pred.max() - y_pred.min())
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
560 if self.less_is_positive:
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
561 y_pred = 1 - y_pred
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
562 return y_pred
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
563
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
564
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
565 # roc_auc_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
566 regression_auc_scorer = binarize_auc_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
567
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
568 # average_precision_scorer
b628de0d101f planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c
bgruening
parents:
diff changeset
569 regression_average_precision_scorer = binarize_average_precision_scorer