Mercurial > repos > bgruening > create_tool_recommendation_model

annotate main.py @ 2:76251d1ccdcc draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 6fa2a0294d615c9f267b766337dca0b2d3637219"
author bgruening
date Fri, 11 Oct 2019 18:24:54 -0400
parents 12764915e1c5
children 5b3c08710e47
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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1 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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2 Predict next tools in the Galaxy workflows
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3 using machine learning (recurrent neural network)
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4 """
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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6 import numpy as np
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7 import argparse
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8 import time
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10 # machine learning library
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11 import tensorflow as tf
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12 from keras import backend as K
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13 import keras.callbacks as callbacks
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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15 import extract_workflow_connections
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16 import prepare_data
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17 import optimise_hyperparameters
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18 import utils
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21 class PredictTool:
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23 @classmethod
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24 def __init__(self, num_cpus):
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25 """ Init method. """
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26 # set the number of cpus
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27 cpu_config = tf.ConfigProto(
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28 device_count={"CPU": num_cpus},
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29 intra_op_parallelism_threads=num_cpus,
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30 inter_op_parallelism_threads=num_cpus,
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31 allow_soft_placement=True
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32 )
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33 K.set_session(tf.Session(config=cpu_config))
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35 @classmethod
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36 def find_train_best_network(self, network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, n_epochs, class_weights, usage_pred, compatible_next_tools):
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37 """
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38 Define recurrent neural network and train sequential data
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39 """
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40 print("Start hyperparameter optimisation...")
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41 hyper_opt = optimise_hyperparameters.HyperparameterOptimisation()
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42 best_params, best_model = hyper_opt.train_model(network_config, reverse_dictionary, train_data, train_labels, class_weights)
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44 # define callbacks
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45 early_stopping = callbacks.EarlyStopping(monitor='loss', mode='min', verbose=1, min_delta=1e-4, restore_best_weights=True)
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46 predict_callback_test = PredictCallback(test_data, test_labels, reverse_dictionary, n_epochs, compatible_next_tools, usage_pred)
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48 callbacks_list = [predict_callback_test, early_stopping]
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49
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50 print("Start training on the best model...")
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51 train_performance = dict()
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52 if len(test_data) > 0:
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53 trained_model = best_model.fit(
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54 train_data,
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55 train_labels,
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56 batch_size=int(best_params["batch_size"]),
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57 epochs=n_epochs,
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58 verbose=2,
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59 callbacks=callbacks_list,
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60 shuffle="batch",
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61 validation_data=(test_data, test_labels)
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62 )
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63 train_performance["validation_loss"] = np.array(trained_model.history["val_loss"])
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64 train_performance["precision"] = predict_callback_test.precision
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65 train_performance["usage_weights"] = predict_callback_test.usage_weights
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66 else:
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67 trained_model = best_model.fit(
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68 train_data,
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69 train_labels,
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70 batch_size=int(best_params["batch_size"]),
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71 epochs=n_epochs,
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72 verbose=2,
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73 callbacks=callbacks_list,
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74 shuffle="batch"
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75 )
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76 train_performance["train_loss"] = np.array(trained_model.history["loss"])
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77 train_performance["model"] = best_model
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78 train_performance["best_parameters"] = best_params
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79 return train_performance
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82 class PredictCallback(callbacks.Callback):
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83 def __init__(self, test_data, test_labels, reverse_data_dictionary, n_epochs, next_compatible_tools, usg_scores):
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84 self.test_data = test_data
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85 self.test_labels = test_labels
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86 self.reverse_data_dictionary = reverse_data_dictionary
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87 self.precision = list()
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88 self.usage_weights = list()
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89 self.n_epochs = n_epochs
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90 self.next_compatible_tools = next_compatible_tools
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91 self.pred_usage_scores = usg_scores
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92
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93 def on_epoch_end(self, epoch, logs={}):
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94 """
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95 Compute absolute and compatible precision for test data
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96 """
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97 if len(self.test_data) > 0:
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98 precision, usage_weights = utils.verify_model(self.model, self.test_data, self.test_labels, self.reverse_data_dictionary, self.next_compatible_tools, self.pred_usage_scores)
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99 self.precision.append(precision)
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100 self.usage_weights.append(usage_weights)
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101 print("Epoch %d precision: %s" % (epoch + 1, precision))
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102 print("Epoch %d usage weights: %s" % (epoch + 1, usage_weights))
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103
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104
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105 if __name__ == "__main__":
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106 start_time = time.time()
2
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107
0
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108 arg_parser = argparse.ArgumentParser()
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109 arg_parser.add_argument("-wf", "--workflow_file", required=True, help="workflows tabular file")
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110 arg_parser.add_argument("-tu", "--tool_usage_file", required=True, help="tool usage file")
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111 arg_parser.add_argument("-om", "--output_model", required=True, help="trained model file")
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112 # data parameters
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113 arg_parser.add_argument("-cd", "--cutoff_date", required=True, help="earliest date for taking tool usage")
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114 arg_parser.add_argument("-pl", "--maximum_path_length", required=True, help="maximum length of tool path")
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115 arg_parser.add_argument("-ep", "--n_epochs", required=True, help="number of iterations to run to create model")
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116 arg_parser.add_argument("-oe", "--optimize_n_epochs", required=True, help="number of iterations to run to find best model parameters")
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117 arg_parser.add_argument("-me", "--max_evals", required=True, help="maximum number of configuration evaluations")
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118 arg_parser.add_argument("-ts", "--test_share", required=True, help="share of data to be used for testing")
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119 arg_parser.add_argument("-vs", "--validation_share", required=True, help="share of data to be used for validation")
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120 # neural network parameters
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121 arg_parser.add_argument("-bs", "--batch_size", required=True, help="size of the tranining batch i.e. the number of samples per batch")
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122 arg_parser.add_argument("-ut", "--units", required=True, help="number of hidden recurrent units")
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123 arg_parser.add_argument("-es", "--embedding_size", required=True, help="size of the fixed vector learned for each tool")
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124 arg_parser.add_argument("-dt", "--dropout", required=True, help="percentage of neurons to be dropped")
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125 arg_parser.add_argument("-sd", "--spatial_dropout", required=True, help="1d dropout used for embedding layer")
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126 arg_parser.add_argument("-rd", "--recurrent_dropout", required=True, help="dropout for the recurrent layers")
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127 arg_parser.add_argument("-lr", "--learning_rate", required=True, help="learning rate")
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128 arg_parser.add_argument("-ar", "--activation_recurrent", required=True, help="activation function for recurrent layers")
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129 arg_parser.add_argument("-ao", "--activation_output", required=True, help="activation function for output layers")
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130
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131 # get argument values
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132 args = vars(arg_parser.parse_args())
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133 tool_usage_path = args["tool_usage_file"]
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134 workflows_path = args["workflow_file"]
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135 cutoff_date = args["cutoff_date"]
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136 maximum_path_length = int(args["maximum_path_length"])
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137 trained_model_path = args["output_model"]
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138 n_epochs = int(args["n_epochs"])
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139 optimize_n_epochs = int(args["optimize_n_epochs"])
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140 max_evals = int(args["max_evals"])
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141 test_share = float(args["test_share"])
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142 validation_share = float(args["validation_share"])
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143 batch_size = args["batch_size"]
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144 units = args["units"]
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145 embedding_size = args["embedding_size"]
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146 dropout = args["dropout"]
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147 spatial_dropout = args["spatial_dropout"]
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148 recurrent_dropout = args["recurrent_dropout"]
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149 learning_rate = args["learning_rate"]
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150 activation_recurrent = args["activation_recurrent"]
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151 activation_output = args["activation_output"]
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152 num_cpus = 16
0
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153
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154 config = {
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155 'cutoff_date': cutoff_date,
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156 'maximum_path_length': maximum_path_length,
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157 'n_epochs': n_epochs,
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158 'optimize_n_epochs': optimize_n_epochs,
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159 'max_evals': max_evals,
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160 'test_share': test_share,
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161 'validation_share': validation_share,
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162 'batch_size': batch_size,
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163 'units': units,
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164 'embedding_size': embedding_size,
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165 'dropout': dropout,
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166 'spatial_dropout': spatial_dropout,
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167 'recurrent_dropout': recurrent_dropout,
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168 'learning_rate': learning_rate,
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169 'activation_recurrent': activation_recurrent,
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170 'activation_output': activation_output
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171 }
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172
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173 # Extract and process workflows
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174 connections = extract_workflow_connections.ExtractWorkflowConnections()
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175 workflow_paths, compatible_next_tools = connections.read_tabular_file(workflows_path)
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176 # Process the paths from workflows
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177 print("Dividing data...")
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178 data = prepare_data.PrepareData(maximum_path_length, test_share)
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179 train_data, train_labels, test_data, test_labels, data_dictionary, reverse_dictionary, class_weights, usage_pred = data.get_data_labels_matrices(workflow_paths, tool_usage_path, cutoff_date, compatible_next_tools)
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180 # find the best model and start training
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181 predict_tool = PredictTool(num_cpus)
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182 # start training with weighted classes
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183 print("Training with weighted classes and samples ...")
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184 results_weighted = predict_tool.find_train_best_network(config, reverse_dictionary, train_data, train_labels, test_data, test_labels, n_epochs, class_weights, usage_pred, compatible_next_tools)
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185 print()
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186 print("Best parameters \n")
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187 print(results_weighted["best_parameters"])
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188 print()
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189 utils.save_model(results_weighted, data_dictionary, compatible_next_tools, trained_model_path, class_weights)
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190 end_time = time.time()
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191 print()
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192 print("Program finished in %s seconds" % str(end_time - start_time))