Mercurial > repos > bgruening > sklearn_sample_generator

annotate preprocessors.py @ 24:97b467e06354 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 18:07:39 -0400
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24
97b467e06354 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 Z_RandomOverSampler
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3 """
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5 import imblearn
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6 import numpy as np
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8 from collections import Counter
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9 from imblearn.over_sampling.base import BaseOverSampler
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10 from imblearn.over_sampling import RandomOverSampler
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11 from imblearn.pipeline import Pipeline as imbPipeline
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12 from imblearn.utils import check_target_type
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13 from scipy import sparse
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14 from sklearn.base import BaseEstimator, TransformerMixin
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15 from sklearn.preprocessing.data import _handle_zeros_in_scale
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16 from sklearn.utils import check_array, safe_indexing
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17 from sklearn.utils.fixes import nanpercentile
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18 from sklearn.utils.validation import (check_is_fitted, check_X_y,
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19 FLOAT_DTYPES)
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22 class Z_RandomOverSampler(BaseOverSampler):
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24 def __init__(self, sampling_strategy='auto',
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25 return_indices=False,
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26 random_state=None,
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27 ratio=None,
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28 negative_thres=0,
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29 positive_thres=-1):
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30 super(Z_RandomOverSampler, self).__init__(
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31 sampling_strategy=sampling_strategy, ratio=ratio)
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32 self.random_state = random_state
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33 self.return_indices = return_indices
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34 self.negative_thres = negative_thres
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35 self.positive_thres = positive_thres
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37 @staticmethod
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38 def _check_X_y(X, y):
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39 y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
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40 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'], dtype=None)
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41 return X, y, binarize_y
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43 def _fit_resample(self, X, y):
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44 n_samples = X.shape[0]
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46 # convert y to z_score
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47 y_z = (y - y.mean()) / y.std()
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49 index0 = np.arange(n_samples)
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50 index_negative = index0[y_z > self.negative_thres]
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51 index_positive = index0[y_z <= self.positive_thres]
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52 index_unclassified = [x for x in index0
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53 if x not in index_negative
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54 and x not in index_positive]
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56 y_z[index_negative] = 0
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57 y_z[index_positive] = 1
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58 y_z[index_unclassified] = -1
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59
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60 ros = RandomOverSampler(
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61 sampling_strategy=self.sampling_strategy,
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62 random_state=self.random_state,
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63 ratio=self.ratio)
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64 _, _ = ros.fit_resample(X, y_z)
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65 sample_indices = ros.sample_indices_
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66
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67 print("Before sampler: %s. Total after: %s"
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68 % (Counter(y_z), sample_indices.shape))
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70 self.sample_indices_ = np.array(sample_indices)
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72 if self.return_indices:
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73 return (safe_indexing(X, sample_indices),
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74 safe_indexing(y, sample_indices),
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75 sample_indices)
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76 return (safe_indexing(X, sample_indices),
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77 safe_indexing(y, sample_indices))
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78
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80 def _get_quantiles(X, quantile_range):
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81 """
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82 Calculate column percentiles for 2d array
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83
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84 Parameters
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85 ----------
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86 X : array-like, shape [n_samples, n_features]
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87 """
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88 quantiles = []
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89 for feature_idx in range(X.shape[1]):
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90 if sparse.issparse(X):
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91 column_nnz_data = X.data[
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92 X.indptr[feature_idx]: X.indptr[feature_idx + 1]]
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93 column_data = np.zeros(shape=X.shape[0], dtype=X.dtype)
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94 column_data[:len(column_nnz_data)] = column_nnz_data
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95 else:
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96 column_data = X[:, feature_idx]
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97 quantiles.append(nanpercentile(column_data, quantile_range))
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98
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99 quantiles = np.transpose(quantiles)
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100
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101 return quantiles
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102
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103
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104 class TDMScaler(BaseEstimator, TransformerMixin):
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105 """
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106 Scale features using Training Distribution Matching (TDM) algorithm
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107
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108 References
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109 ----------
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110 .. [1] Thompson JA, Tan J and Greene CS (2016) Cross-platform
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111 normalization of microarray and RNA-seq data for machine
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112 learning applications. PeerJ 4, e1621.
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113 """
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114
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115 def __init__(self, q_lower=25.0, q_upper=75.0, ):
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116 self.q_lower = q_lower
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117 self.q_upper = q_upper
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118
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119 def fit(self, X, y=None):
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120 """
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121 Parameters
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122 ----------
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123 X : array-like, shape [n_samples, n_features]
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124 """
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125 X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES,
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126 force_all_finite=True)
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127
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128 if not 0 <= self.q_lower <= self.q_upper <= 100:
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129 raise ValueError("Invalid quantile parameter values: "
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130 "q_lower %s, q_upper: %s"
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131 % (str(self.q_lower), str(self.q_upper)))
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132
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133 # TODO sparse data
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134 quantiles = nanpercentile(X, (self.q_lower, self.q_upper))
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135 iqr = quantiles[1] - quantiles[0]
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136
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137 self.q_lower_ = quantiles[0]
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138 self.q_upper_ = quantiles[1]
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139 self.iqr_ = _handle_zeros_in_scale(iqr, copy=False)
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140
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141 self.max_ = np.nanmax(X)
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142 self.min_ = np.nanmin(X)
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143
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144 return self
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145
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146 def transform(self, X):
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147 """
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148 Parameters
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149 ----------
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150 X : {array-like, sparse matrix}
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151 The data used to scale along the specified axis.
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152 """
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153 check_is_fitted(self, 'iqr_', 'max_')
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154 X = check_array(X, copy=True, estimator=self, dtype=FLOAT_DTYPES,
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155 force_all_finite=True)
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156
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157 # TODO sparse data
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158 train_upper_scale = (self.max_ - self.q_upper_) / self.iqr_
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159 train_lower_scale = (self.q_lower_ - self.min_) / self.iqr_
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160
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161 test_quantiles = nanpercentile(X, (self.q_lower, self.q_upper))
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162 test_iqr = _handle_zeros_in_scale(
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163 test_quantiles[1] - test_quantiles[0], copy=False)
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164
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165 test_upper_bound = test_quantiles[1] + train_upper_scale * test_iqr
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166 test_lower_bound = test_quantiles[0] - train_lower_scale * test_iqr
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167
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168 test_min = np.nanmin(X)
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169 if test_lower_bound < test_min:
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170 test_lower_bound = test_min
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171
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172 X[X > test_upper_bound] = test_upper_bound
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173 X[X < test_lower_bound] = test_lower_bound
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174
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175 X = (X - test_lower_bound) / (test_upper_bound - test_lower_bound)\
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176 * (self.max_ - self.min_) + self.min_
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177
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178 return X
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179
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180 def inverse_transform(self, X):
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181 """
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182 Scale the data back to the original state
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183 """
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184 raise NotImplementedError("Inverse transformation is not implemented!")