Mercurial > repos > bgruening > create_tool_recommendation_model

annotate main.py @ 2:50753817983a draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
author bgruening
date Sat, 09 May 2020 09:38:04 +0000
parents 275e98795e99
children 98bc44d17561
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1 """
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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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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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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 def __init__(self, num_cpus):
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24 """ Init method. """
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25 # set the number of cpus
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26 cpu_config = tf.ConfigProto(
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27 device_count={"CPU": num_cpus},
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28 intra_op_parallelism_threads=num_cpus,
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29 inter_op_parallelism_threads=num_cpus,
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30 allow_soft_placement=True
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31 )
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32 K.set_session(tf.Session(config=cpu_config))
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34 def find_train_best_network(self, network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, n_epochs, class_weights, usage_pred, standard_connections, l_tool_freq, l_tool_tr_samples):
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35 """
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36 Define recurrent neural network and train sequential data
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37 """
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38 # get tools with lowest representation
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39 lowest_tool_ids = utils.get_lowest_tools(l_tool_freq)
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41 print("Start hyperparameter optimisation...")
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42 hyper_opt = optimise_hyperparameters.HyperparameterOptimisation()
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43 best_params, best_model = hyper_opt.train_model(network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, l_tool_tr_samples, class_weights)
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45 # define callbacks
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46 early_stopping = callbacks.EarlyStopping(monitor='loss', mode='min', verbose=1, min_delta=1e-1, restore_best_weights=True)
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47 predict_callback_test = PredictCallback(test_data, test_labels, reverse_dictionary, n_epochs, usage_pred, standard_connections, lowest_tool_ids)
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49 callbacks_list = [predict_callback_test, early_stopping]
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51 batch_size = int(best_params["batch_size"])
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53 print("Start training on the best model...")
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54 train_performance = dict()
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55 trained_model = best_model.fit_generator(
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56 utils.balanced_sample_generator(
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57 train_data,
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58 train_labels,
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59 batch_size,
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60 l_tool_tr_samples
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61 ),
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62 steps_per_epoch=len(train_data) // batch_size,
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63 epochs=n_epochs,
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64 callbacks=callbacks_list,
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65 validation_data=(test_data, test_labels),
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66 verbose=2,
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67 shuffle=True
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68 )
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69 train_performance["validation_loss"] = np.array(trained_model.history["val_loss"])
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70 train_performance["precision"] = predict_callback_test.precision
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71 train_performance["usage_weights"] = predict_callback_test.usage_weights
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72 train_performance["published_precision"] = predict_callback_test.published_precision
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73 train_performance["lowest_pub_precision"] = predict_callback_test.lowest_pub_precision
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74 train_performance["lowest_norm_precision"] = predict_callback_test.lowest_norm_precision
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75 train_performance["train_loss"] = np.array(trained_model.history["loss"])
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76 train_performance["model"] = best_model
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77 train_performance["best_parameters"] = best_params
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78 return train_performance
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81 class PredictCallback(callbacks.Callback):
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82 def __init__(self, test_data, test_labels, reverse_data_dictionary, n_epochs, usg_scores, standard_connections, lowest_tool_ids):
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83 self.test_data = test_data
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84 self.test_labels = test_labels
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85 self.reverse_data_dictionary = reverse_data_dictionary
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86 self.precision = list()
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87 self.usage_weights = list()
2
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88 self.published_precision = list()
0
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89 self.n_epochs = n_epochs
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90 self.pred_usage_scores = usg_scores
2
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91 self.standard_connections = standard_connections
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92 self.lowest_tool_ids = lowest_tool_ids
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93 self.lowest_pub_precision = list()
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94 self.lowest_norm_precision = list()
0
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95
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96 def on_epoch_end(self, epoch, logs={}):
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97 """
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98 Compute absolute and compatible precision for test data
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99 """
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100 if len(self.test_data) > 0:
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101 usage_weights, precision, precision_pub, low_pub_prec, low_norm_prec, low_num = utils.verify_model(self.model, self.test_data, self.test_labels, self.reverse_data_dictionary, self.pred_usage_scores, self.standard_connections, self.lowest_tool_ids)
0
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102 self.precision.append(precision)
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103 self.usage_weights.append(usage_weights)
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104 self.published_precision.append(precision_pub)
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105 self.lowest_pub_precision.append(low_pub_prec)
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106 self.lowest_norm_precision.append(low_norm_prec)
0
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107 print("Epoch %d usage weights: %s" % (epoch + 1, usage_weights))
2
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108 print("Epoch %d normal precision: %s" % (epoch + 1, precision))
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109 print("Epoch %d published precision: %s" % (epoch + 1, precision_pub))
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110 print("Epoch %d lowest published precision: %s" % (epoch + 1, low_pub_prec))
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111 print("Epoch %d lowest normal precision: %s" % (epoch + 1, low_norm_prec))
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112 print("Epoch %d number of test samples with lowest tool ids: %s" % (epoch + 1, low_num))
0
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113
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114
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115 if __name__ == "__main__":
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116 start_time = time.time()
2
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117
0
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118 arg_parser = argparse.ArgumentParser()
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119 arg_parser.add_argument("-wf", "--workflow_file", required=True, help="workflows tabular file")
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120 arg_parser.add_argument("-tu", "--tool_usage_file", required=True, help="tool usage file")
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121 arg_parser.add_argument("-om", "--output_model", required=True, help="trained model file")
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122 # data parameters
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123 arg_parser.add_argument("-cd", "--cutoff_date", required=True, help="earliest date for taking tool usage")
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124 arg_parser.add_argument("-pl", "--maximum_path_length", required=True, help="maximum length of tool path")
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125 arg_parser.add_argument("-ep", "--n_epochs", required=True, help="number of iterations to run to create model")
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126 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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127 arg_parser.add_argument("-me", "--max_evals", required=True, help="maximum number of configuration evaluations")
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128 arg_parser.add_argument("-ts", "--test_share", required=True, help="share of data to be used for testing")
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129 # neural network parameters
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130 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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131 arg_parser.add_argument("-ut", "--units", required=True, help="number of hidden recurrent units")
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132 arg_parser.add_argument("-es", "--embedding_size", required=True, help="size of the fixed vector learned for each tool")
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133 arg_parser.add_argument("-dt", "--dropout", required=True, help="percentage of neurons to be dropped")
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134 arg_parser.add_argument("-sd", "--spatial_dropout", required=True, help="1d dropout used for embedding layer")
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135 arg_parser.add_argument("-rd", "--recurrent_dropout", required=True, help="dropout for the recurrent layers")
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136 arg_parser.add_argument("-lr", "--learning_rate", required=True, help="learning rate")
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137
0
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138 # get argument values
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139 args = vars(arg_parser.parse_args())
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140 tool_usage_path = args["tool_usage_file"]
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141 workflows_path = args["workflow_file"]
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142 cutoff_date = args["cutoff_date"]
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143 maximum_path_length = int(args["maximum_path_length"])
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144 trained_model_path = args["output_model"]
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145 n_epochs = int(args["n_epochs"])
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146 optimize_n_epochs = int(args["optimize_n_epochs"])
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147 max_evals = int(args["max_evals"])
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148 test_share = float(args["test_share"])
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149 batch_size = args["batch_size"]
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150 units = args["units"]
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151 embedding_size = args["embedding_size"]
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152 dropout = args["dropout"]
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153 spatial_dropout = args["spatial_dropout"]
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154 recurrent_dropout = args["recurrent_dropout"]
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155 learning_rate = args["learning_rate"]
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156 num_cpus = 16
0
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157
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158 config = {
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159 'cutoff_date': cutoff_date,
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160 'maximum_path_length': maximum_path_length,
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161 'n_epochs': n_epochs,
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162 'optimize_n_epochs': optimize_n_epochs,
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163 'max_evals': max_evals,
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164 'test_share': test_share,
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165 'batch_size': batch_size,
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166 'units': units,
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167 'embedding_size': embedding_size,
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168 'dropout': dropout,
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169 'spatial_dropout': spatial_dropout,
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170 'recurrent_dropout': recurrent_dropout,
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171 'learning_rate': learning_rate
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172 }
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173
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174 # Extract and process workflows
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175 connections = extract_workflow_connections.ExtractWorkflowConnections()
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176 workflow_paths, compatible_next_tools, standard_connections = connections.read_tabular_file(workflows_path)
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177 # Process the paths from workflows
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178 print("Dividing data...")
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179 data = prepare_data.PrepareData(maximum_path_length, test_share)
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180 train_data, train_labels, test_data, test_labels, data_dictionary, reverse_dictionary, class_weights, usage_pred, l_tool_freq, l_tool_tr_samples = data.get_data_labels_matrices(workflow_paths, tool_usage_path, cutoff_date, compatible_next_tools, standard_connections)
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181 # find the best model and start training
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182 predict_tool = PredictTool(num_cpus)
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183 # start training with weighted classes
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184 print("Training with weighted classes and samples ...")
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185 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, standard_connections, l_tool_freq, l_tool_tr_samples)
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186 utils.save_model(results_weighted, data_dictionary, compatible_next_tools, trained_model_path, class_weights, standard_connections)
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187 end_time = time.time()
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188 print()
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189 print("Program finished in %s seconds" % str(end_time - start_time))