Mercurial > repos > bgruening > sklearn_estimator_attributes

annotate keras_deep_learning.py @ 2:c411ff569a26 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
author bgruening
date Fri, 09 Aug 2019 07:24:14 -0400
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children 7a64b9f39a46
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c411ff569a26 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse
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2 import json
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3 import keras
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4 import pandas as pd
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5 import pickle
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6 import six
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7 import warnings
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8
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9 from ast import literal_eval
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10 from keras.models import Sequential, Model
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11 from galaxy_ml.utils import try_get_attr, get_search_params
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12
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13
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14 def _handle_shape(literal):
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15 """Eval integer or list/tuple of integers from string
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16
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17 Parameters:
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18 -----------
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19 literal : str.
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20 """
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21 literal = literal.strip()
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22 if not literal:
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23 return None
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24 try:
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25 return literal_eval(literal)
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26 except NameError as e:
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27 print(e)
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28 return literal
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31 def _handle_regularizer(literal):
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32 """Construct regularizer from string literal
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34 Parameters
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35 ----------
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36 literal : str. E.g. '(0.1, 0)'
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37 """
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38 literal = literal.strip()
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39 if not literal:
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40 return None
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41
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42 l1, l2 = literal_eval(literal)
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43
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44 if not l1 and not l2:
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45 return None
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46
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47 if l1 is None:
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48 l1 = 0.
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49 if l2 is None:
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50 l2 = 0.
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51
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52 return keras.regularizers.l1_l2(l1=l1, l2=l2)
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53
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54
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55 def _handle_constraint(config):
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56 """Construct constraint from galaxy tool parameters.
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57 Suppose correct dictionary format
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58
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59 Parameters
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60 ----------
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61 config : dict. E.g.
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62 "bias_constraint":
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63 {"constraint_options":
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64 {"max_value":1.0,
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65 "min_value":0.0,
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66 "axis":"[0, 1, 2]"
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67 },
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68 "constraint_type":
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69 "MinMaxNorm"
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70 }
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71 """
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72 constraint_type = config['constraint_type']
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73 if constraint_type == 'None':
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74 return None
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75
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76 klass = getattr(keras.constraints, constraint_type)
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77 options = config.get('constraint_options', {})
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78 if 'axis' in options:
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79 options['axis'] = literal_eval(options['axis'])
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80
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81 return klass(**options)
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82
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83
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84 def _handle_lambda(literal):
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85 return None
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86
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87
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88 def _handle_layer_parameters(params):
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89 """Access to handle all kinds of parameters
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90 """
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91 for key, value in six.iteritems(params):
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92 if value == 'None':
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93 params[key] = None
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94 continue
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95
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96 if type(value) in [int, float, bool]\
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97 or (type(value) is str and value.isalpha()):
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98 continue
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99
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100 if key in ['input_shape', 'noise_shape', 'shape', 'batch_shape',
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101 'target_shape', 'dims', 'kernel_size', 'strides',
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102 'dilation_rate', 'output_padding', 'cropping', 'size',
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103 'padding', 'pool_size', 'axis', 'shared_axes']:
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104 params[key] = _handle_shape(value)
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105
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106 elif key.endswith('_regularizer'):
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107 params[key] = _handle_regularizer(value)
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108
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109 elif key.endswith('_constraint'):
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110 params[key] = _handle_constraint(value)
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111
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112 elif key == 'function': # No support for lambda/function eval
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113 params.pop(key)
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114
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115 return params
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116
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117
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118 def get_sequential_model(config):
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119 """Construct keras Sequential model from Galaxy tool parameters
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120
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121 Parameters:
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122 -----------
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123 config : dictionary, galaxy tool parameters loaded by JSON
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124 """
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125 model = Sequential()
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126 input_shape = _handle_shape(config['input_shape'])
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127 layers = config['layers']
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128 for layer in layers:
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129 options = layer['layer_selection']
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130 layer_type = options.pop('layer_type')
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131 klass = getattr(keras.layers, layer_type)
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132 other_options = options.pop('layer_options', {})
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133 options.update(other_options)
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134
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135 # parameters needs special care
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136 options = _handle_layer_parameters(options)
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137
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138 # add input_shape to the first layer only
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139 if not getattr(model, '_layers') and input_shape is not None:
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140 options['input_shape'] = input_shape
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141
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142 model.add(klass(**options))
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143
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144 return model
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145
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146
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147 def get_functional_model(config):
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148 """Construct keras functional model from Galaxy tool parameters
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149
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150 Parameters
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151 -----------
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152 config : dictionary, galaxy tool parameters loaded by JSON
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153 """
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154 layers = config['layers']
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155 all_layers = []
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156 for layer in layers:
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157 options = layer['layer_selection']
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158 layer_type = options.pop('layer_type')
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159 klass = getattr(keras.layers, layer_type)
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160 inbound_nodes = options.pop('inbound_nodes', None)
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161 other_options = options.pop('layer_options', {})
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162 options.update(other_options)
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163
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164 # parameters needs special care
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165 options = _handle_layer_parameters(options)
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166 # merge layers
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167 if 'merging_layers' in options:
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168 idxs = literal_eval(options.pop('merging_layers'))
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169 merging_layers = [all_layers[i-1] for i in idxs]
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170 new_layer = klass(**options)(merging_layers)
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171 # non-input layers
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172 elif inbound_nodes is not None:
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173 new_layer = klass(**options)(all_layers[inbound_nodes-1])
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174 # input layers
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175 else:
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176 new_layer = klass(**options)
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177
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178 all_layers.append(new_layer)
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179
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180 input_indexes = _handle_shape(config['input_layers'])
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181 input_layers = [all_layers[i-1] for i in input_indexes]
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182
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183 output_indexes = _handle_shape(config['output_layers'])
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184 output_layers = [all_layers[i-1] for i in output_indexes]
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185
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186 return Model(inputs=input_layers, outputs=output_layers)
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187
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188
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189 def get_batch_generator(config):
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190 """Construct keras online data generator from Galaxy tool parameters
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191
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192 Parameters
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193 -----------
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194 config : dictionary, galaxy tool parameters loaded by JSON
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195 """
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196 generator_type = config.pop('generator_type')
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197 klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
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198
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199 if generator_type == 'GenomicIntervalBatchGenerator':
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200 config['ref_genome_path'] = 'to_be_determined'
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201 config['intervals_path'] = 'to_be_determined'
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202 config['target_path'] = 'to_be_determined'
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203 config['features'] = 'to_be_determined'
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204 else:
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205 config['fasta_path'] = 'to_be_determined'
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206
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207 return klass(**config)
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208
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209
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210 def config_keras_model(inputs, outfile):
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211 """ config keras model layers and output JSON
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212
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213 Parameters
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214 ----------
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215 inputs : dict
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216 loaded galaxy tool parameters from `keras_model_config`
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217 tool.
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218 outfile : str
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219 Path to galaxy dataset containing keras model JSON.
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220 """
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221 model_type = inputs['model_selection']['model_type']
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222 layers_config = inputs['model_selection']
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223
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224 if model_type == 'sequential':
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225 model = get_sequential_model(layers_config)
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226 else:
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227 model = get_functional_model(layers_config)
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228
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229 json_string = model.to_json()
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230
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231 with open(outfile, 'w') as f:
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232 f.write(json_string)
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233
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234
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235 def build_keras_model(inputs, outfile, model_json, infile_weights=None,
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236 batch_mode=False, outfile_params=None):
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237 """ for `keras_model_builder` tool
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238
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239 Parameters
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240 ----------
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241 inputs : dict
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242 loaded galaxy tool parameters from `keras_model_builder` tool.
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243 outfile : str
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244 Path to galaxy dataset containing the keras_galaxy model output.
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245 model_json : str
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246 Path to dataset containing keras model JSON.
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247 infile_weights : str or None
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248 If string, path to dataset containing model weights.
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249 batch_mode : bool, default=False
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250 Whether to build online batch classifier.
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251 outfile_params : str, default=None
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252 File path to search parameters output.
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253 """
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254 with open(model_json, 'r') as f:
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255 json_model = json.load(f)
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256
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257 config = json_model['config']
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258
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259 options = {}
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260
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261 if json_model['class_name'] == 'Sequential':
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262 options['model_type'] = 'sequential'
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263 klass = Sequential
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264 elif json_model['class_name'] == 'Model':
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265 options['model_type'] = 'functional'
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266 klass = Model
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267 else:
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268 raise ValueError("Unknow Keras model class: %s"
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269 % json_model['class_name'])
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270
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271 # load prefitted model
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272 if inputs['mode_selection']['mode_type'] == 'prefitted':
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273 estimator = klass.from_config(config)
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274 estimator.load_weights(infile_weights)
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275 # build train model
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276 else:
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277 cls_name = inputs['mode_selection']['learning_type']
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278 klass = try_get_attr('galaxy_ml.keras_galaxy_models', cls_name)
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279
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280 options['loss'] = (inputs['mode_selection']
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281 ['compile_params']['loss'])
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282 options['optimizer'] =\
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283 (inputs['mode_selection']['compile_params']
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284 ['optimizer_selection']['optimizer_type']).lower()
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285
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286 options.update((inputs['mode_selection']['compile_params']
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287 ['optimizer_selection']['optimizer_options']))
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288
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289 train_metrics = (inputs['mode_selection']['compile_params']
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290 ['metrics']).split(',')
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291 if train_metrics[-1] == 'none':
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292 train_metrics = train_metrics[:-1]
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293 options['metrics'] = train_metrics
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294
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295 options.update(inputs['mode_selection']['fit_params'])
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296 options['seed'] = inputs['mode_selection']['random_seed']
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297
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298 if batch_mode:
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299 generator = get_batch_generator(inputs['mode_selection']
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300 ['generator_selection'])
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301 options['data_batch_generator'] = generator
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302 options['prediction_steps'] = \
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303 inputs['mode_selection']['prediction_steps']
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304 options['class_positive_factor'] = \
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305 inputs['mode_selection']['class_positive_factor']
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306 estimator = klass(config, **options)
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307 if outfile_params:
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308 hyper_params = get_search_params(estimator)
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309 # TODO: remove this after making `verbose` tunable
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310 for h_param in hyper_params:
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311 if h_param[1].endswith('verbose'):
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312 h_param[0] = '@'
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313 df = pd.DataFrame(hyper_params, columns=['', 'Parameter', 'Value'])
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314 df.to_csv(outfile_params, sep='\t', index=False)
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315
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316 print(repr(estimator))
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317 # save model by pickle
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318 with open(outfile, 'wb') as f:
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319 pickle.dump(estimator, f, pickle.HIGHEST_PROTOCOL)
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320
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321
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322 if __name__ == '__main__':
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323 warnings.simplefilter('ignore')
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324
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325 aparser = argparse.ArgumentParser()
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326 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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327 aparser.add_argument("-m", "--model_json", dest="model_json")
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328 aparser.add_argument("-t", "--tool_id", dest="tool_id")
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329 aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
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330 aparser.add_argument("-o", "--outfile", dest="outfile")
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331 aparser.add_argument("-p", "--outfile_params", dest="outfile_params")
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332 args = aparser.parse_args()
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333
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334 input_json_path = args.inputs
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335 with open(input_json_path, 'r') as param_handler:
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336 inputs = json.load(param_handler)
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337
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338 tool_id = args.tool_id
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339 outfile = args.outfile
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340 outfile_params = args.outfile_params
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341 model_json = args.model_json
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342 infile_weights = args.infile_weights
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343
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344 # for keras_model_config tool
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345 if tool_id == 'keras_model_config':
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346 config_keras_model(inputs, outfile)
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347
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348 # for keras_model_builder tool
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349 else:
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350 batch_mode = False
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351 if tool_id == 'keras_batch_models':
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352 batch_mode = True
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353
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354 build_keras_model(inputs=inputs,
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355 model_json=model_json,
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356 infile_weights=infile_weights,
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357 batch_mode=batch_mode,
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358 outfile=outfile,
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359 outfile_params=outfile_params)