Mercurial > repos > bgruening > sklearn_discriminant_classifier

annotate keras_deep_learning.py @ 47:6adef419e041 draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57a0433defa3cbc37ab34fbb0ebcfaeb680db8d5
author bgruening
date Sun, 05 Nov 2023 15:28:12 +0000
parents d769d83ec796
children
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1 import argparse
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2 import json
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3 import warnings
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4 from ast import literal_eval
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5
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6 import six
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7 from galaxy_ml.model_persist import dump_model_to_h5
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8 from galaxy_ml.utils import SafeEval, try_get_attr
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9 from tensorflow import keras
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10 from tensorflow.keras.models import Model, Sequential
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12 safe_eval = SafeEval()
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15 def _handle_shape(literal):
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16 """Eval integer or list/tuple of integers from string
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18 Parameters:
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19 -----------
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20 literal : str.
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21 """
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22 literal = literal.strip()
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23 if not literal:
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24 return None
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25 try:
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26 return literal_eval(literal)
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27 except NameError as e:
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28 print(e)
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29 return literal
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32 def _handle_regularizer(literal):
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33 """Construct regularizer from string literal
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35 Parameters
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36 ----------
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37 literal : str. E.g. '(0.1, 0)'
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38 """
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39 literal = literal.strip()
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40 if not literal:
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41 return None
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43 l1, l2 = literal_eval(literal)
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45 if not l1 and not l2:
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46 return None
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48 if l1 is None:
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49 l1 = 0.0
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50 if l2 is None:
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51 l2 = 0.0
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53 return keras.regularizers.l1_l2(l1=l1, l2=l2)
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56 def _handle_constraint(config):
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57 """Construct constraint from galaxy tool parameters.
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58 Suppose correct dictionary format
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60 Parameters
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61 ----------
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62 config : dict. E.g.
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63 "bias_constraint":
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64 {"constraint_options":
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65 {"max_value":1.0,
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66 "min_value":0.0,
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67 "axis":"[0, 1, 2]"
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68 },
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69 "constraint_type":
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70 "MinMaxNorm"
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71 }
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72 """
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73 constraint_type = config["constraint_type"]
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74 if constraint_type in ("None", ""):
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75 return None
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77 klass = getattr(keras.constraints, constraint_type)
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78 options = config.get("constraint_options", {})
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79 if "axis" in options:
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80 options["axis"] = literal_eval(options["axis"])
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82 return klass(**options)
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85 def _handle_lambda(literal):
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86 return None
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89 def _handle_layer_parameters(params):
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90 """Access to handle all kinds of parameters"""
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91 for key, value in six.iteritems(params):
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92 if value in ("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] or (
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97 type(value) is str and value.isalpha()
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98 ):
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99 continue
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100
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101 if key in [
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102 "input_shape",
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103 "noise_shape",
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104 "shape",
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105 "batch_shape",
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106 "target_shape",
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107 "dims",
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108 "kernel_size",
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109 "strides",
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110 "dilation_rate",
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111 "output_padding",
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112 "cropping",
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113 "size",
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114 "padding",
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115 "pool_size",
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116 "axis",
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117 "shared_axes",
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118 ] and isinstance(value, str):
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119 params[key] = _handle_shape(value)
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120
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121 elif key.endswith("_regularizer") and isinstance(value, dict):
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122 params[key] = _handle_regularizer(value)
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123
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124 elif key.endswith("_constraint") and isinstance(value, dict):
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125 params[key] = _handle_constraint(value)
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126
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127 elif key == "function": # No support for lambda/function eval
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128 params.pop(key)
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129
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130 return params
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131
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132
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133 def get_sequential_model(config):
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134 """Construct keras Sequential model from Galaxy tool parameters
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135
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136 Parameters:
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137 -----------
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138 config : dictionary, galaxy tool parameters loaded by JSON
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139 """
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140 model = Sequential()
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141 input_shape = _handle_shape(config["input_shape"])
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142 layers = config["layers"]
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143 for layer in layers:
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144 options = layer["layer_selection"]
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145 layer_type = options.pop("layer_type")
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146 klass = getattr(keras.layers, layer_type)
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147 kwargs = options.pop("kwargs", "")
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148
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149 # parameters needs special care
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150 options = _handle_layer_parameters(options)
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151
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152 if kwargs:
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153 kwargs = safe_eval("dict(" + kwargs + ")")
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154 options.update(kwargs)
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155
26
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156 # add input_shape to the first layer only
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157 if not model.get_config()["layers"] and input_shape is not None:
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158 options["input_shape"] = input_shape
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159
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160 model.add(klass(**options))
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161
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162 return model
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163
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164
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165 def get_functional_model(config):
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166 """Construct keras functional model from Galaxy tool parameters
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167
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168 Parameters
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169 -----------
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170 config : dictionary, galaxy tool parameters loaded by JSON
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171 """
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172 layers = config["layers"]
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173 all_layers = []
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174 for layer in layers:
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175 options = layer["layer_selection"]
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176 layer_type = options.pop("layer_type")
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177 klass = getattr(keras.layers, layer_type)
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178 inbound_nodes = options.pop("inbound_nodes", None)
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179 kwargs = options.pop("kwargs", "")
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180
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181 # parameters needs special care
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182 options = _handle_layer_parameters(options)
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183
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184 if kwargs:
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185 kwargs = safe_eval("dict(" + kwargs + ")")
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186 options.update(kwargs)
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187
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188 # merge layers
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189 if "merging_layers" in options:
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190 idxs = literal_eval(options.pop("merging_layers"))
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191 merging_layers = [all_layers[i - 1] for i in idxs]
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192 new_layer = klass(**options)(merging_layers)
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193 # non-input layers
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194 elif inbound_nodes is not None:
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195 new_layer = klass(**options)(all_layers[inbound_nodes - 1])
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196 # input layers
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197 else:
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198 new_layer = klass(**options)
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199
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200 all_layers.append(new_layer)
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201
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202 input_indexes = _handle_shape(config["input_layers"])
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203 input_layers = [all_layers[i - 1] for i in input_indexes]
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204
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205 output_indexes = _handle_shape(config["output_layers"])
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206 output_layers = [all_layers[i - 1] for i in output_indexes]
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207
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208 return Model(inputs=input_layers, outputs=output_layers)
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209
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210
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211 def get_batch_generator(config):
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212 """Construct keras online data generator from Galaxy tool parameters
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213
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214 Parameters
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215 -----------
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216 config : dictionary, galaxy tool parameters loaded by JSON
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217 """
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218 generator_type = config.pop("generator_type")
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219 if generator_type == "none":
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220 return None
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221
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222 klass = try_get_attr("galaxy_ml.preprocessors", generator_type)
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223
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224 if generator_type == "GenomicIntervalBatchGenerator":
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225 config["ref_genome_path"] = "to_be_determined"
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226 config["intervals_path"] = "to_be_determined"
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227 config["target_path"] = "to_be_determined"
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228 config["features"] = "to_be_determined"
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229 else:
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230 config["fasta_path"] = "to_be_determined"
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231
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232 return klass(**config)
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233
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234
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235 def config_keras_model(inputs, outfile):
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236 """config keras model layers and output JSON
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237
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238 Parameters
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239 ----------
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240 inputs : dict
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241 loaded galaxy tool parameters from `keras_model_config`
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242 tool.
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243 outfile : str
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244 Path to galaxy dataset containing keras model JSON.
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245 """
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246 model_type = inputs["model_selection"]["model_type"]
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247 layers_config = inputs["model_selection"]
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248
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249 if model_type == "sequential":
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250 model = get_sequential_model(layers_config)
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251 else:
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252 model = get_functional_model(layers_config)
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253
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254 json_string = model.to_json()
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255
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256 with open(outfile, "w") as f:
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257 json.dump(json.loads(json_string), f, indent=2)
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258
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259
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260 def build_keras_model(inputs, outfile, model_json, batch_mode=False):
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261 """for `keras_model_builder` tool
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262
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263 Parameters
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264 ----------
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265 inputs : dict
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266 loaded galaxy tool parameters from `keras_model_builder` tool.
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267 outfile : str
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268 Path to galaxy dataset containing the keras_galaxy model output.
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269 model_json : str
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270 Path to dataset containing keras model JSON.
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271 batch_mode : bool, default=False
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272 Whether to build online batch classifier.
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273 """
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274 with open(model_json, "r") as f:
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275 json_model = json.load(f)
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276
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277 config = json_model["config"]
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278
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279 options = {}
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280
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281 if json_model["class_name"] == "Sequential":
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282 options["model_type"] = "sequential"
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283 klass = Sequential
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284 elif json_model["class_name"] == "Functional":
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285 options["model_type"] = "functional"
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286 klass = Model
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287 else:
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288 raise ValueError("Unknow Keras model class: %s" % json_model["class_name"])
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289
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290 # load prefitted model
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291 if inputs["mode_selection"]["mode_type"] == "prefitted":
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292 # estimator = klass.from_config(config)
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293 # estimator.load_weights(infile_weights)
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294 raise Exception("Prefitted was deprecated!")
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295 # build train model
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296 else:
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297 cls_name = inputs["mode_selection"]["learning_type"]
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298 klass = try_get_attr("galaxy_ml.keras_galaxy_models", cls_name)
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299
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300 options["loss"] = inputs["mode_selection"]["compile_params"]["loss"]
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301 options["optimizer"] = (
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302 inputs["mode_selection"]["compile_params"]["optimizer_selection"][
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303 "optimizer_type"
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304 ]
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305 ).lower()
26
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306
37
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307 options.update(
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308 (
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309 inputs["mode_selection"]["compile_params"]["optimizer_selection"][
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310 "optimizer_options"
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311 ]
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312 )
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313 )
26
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314
37
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315 train_metrics = inputs["mode_selection"]["compile_params"]["metrics"]
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316 if not isinstance(train_metrics, list): # for older galaxy
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317 train_metrics = train_metrics.split(",")
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318 if train_metrics[-1] == "none":
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319 train_metrics.pop()
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320 options["metrics"] = train_metrics
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321
37
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322 options.update(inputs["mode_selection"]["fit_params"])
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323 options["seed"] = inputs["mode_selection"]["random_seed"]
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324
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325 if batch_mode:
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326 generator = get_batch_generator(
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327 inputs["mode_selection"]["generator_selection"]
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328 )
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329 options["data_batch_generator"] = generator
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330 options["prediction_steps"] = inputs["mode_selection"]["prediction_steps"]
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331 options["class_positive_factor"] = inputs["mode_selection"][
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332 "class_positive_factor"
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333 ]
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334 estimator = klass(config, **options)
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335
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336 print(repr(estimator))
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337 # save model
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338 dump_model_to_h5(estimator, outfile, verbose=1)
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339
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340
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341 if __name__ == "__main__":
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342 warnings.simplefilter("ignore")
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343
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344 aparser = argparse.ArgumentParser()
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345 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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346 aparser.add_argument("-m", "--model_json", dest="model_json")
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347 aparser.add_argument("-t", "--tool_id", dest="tool_id")
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348 aparser.add_argument("-o", "--outfile", dest="outfile")
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349 args = aparser.parse_args()
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350
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351 input_json_path = args.inputs
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352 with open(input_json_path, "r") as param_handler:
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353 inputs = json.load(param_handler)
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354
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355 tool_id = args.tool_id
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356 outfile = args.outfile
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357 model_json = args.model_json
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358
9bb505eafac9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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359 # for keras_model_config tool
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360 if tool_id == "keras_model_config":
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361 config_keras_model(inputs, outfile)
9bb505eafac9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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362
9bb505eafac9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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363 # for keras_model_builder tool
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364 else:
9bb505eafac9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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365 batch_mode = False
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366 if tool_id == "keras_batch_models":
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367 batch_mode = True
9bb505eafac9 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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368
37
e76f6dfea5c9 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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369 build_keras_model(
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d769d83ec796 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb
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370 inputs=inputs, model_json=model_json, batch_mode=batch_mode, outfile=outfile
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371 )