Mercurial > repos > bgruening > create_tool_recommendation_model

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
author bgruening
date Wed, 28 Aug 2019 07:19:38 -0400
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children 12764915e1c5
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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
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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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 keras.callbacks as callbacks
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13 import extract_workflow_connections
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14 import prepare_data
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15 import optimise_hyperparameters
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16 import utils
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19 class PredictTool:
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21 @classmethod
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22 def __init__(self):
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23 """ Init method. """
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25 @classmethod
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26 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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27 """
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28 Define recurrent neural network and train sequential data
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29 """
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30 print("Start hyperparameter optimisation...")
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31 hyper_opt = optimise_hyperparameters.HyperparameterOptimisation()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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32 best_params = hyper_opt.train_model(network_config, reverse_dictionary, train_data, train_labels, test_data, test_labels, class_weights)
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34 # retrieve the model and train on complete dataset without validation set
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35 model, best_params = utils.set_recurrent_network(best_params, reverse_dictionary, class_weights)
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36
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37 # define callbacks
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38 predict_callback_test = PredictCallback(test_data, test_labels, reverse_dictionary, n_epochs, compatible_next_tools, usage_pred)
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39 # tensor_board = callbacks.TensorBoard(log_dir=log_directory, histogram_freq=0, write_graph=True, write_images=True)
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40 callbacks_list = [predict_callback_test]
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41
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42 print("Start training on the best model...")
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43 model_fit = model.fit(
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44 train_data,
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45 train_labels,
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46 batch_size=int(best_params["batch_size"]),
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47 epochs=n_epochs,
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48 verbose=2,
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49 callbacks=callbacks_list,
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50 shuffle="batch",
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51 validation_data=(test_data, test_labels)
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52 )
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53
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54 train_performance = {
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55 "train_loss": np.array(model_fit.history["loss"]),
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56 "model": model,
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57 "best_parameters": best_params
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58 }
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59
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60 # if there is test data, add more information
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61 if len(test_data) > 0:
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62 train_performance["validation_loss"] = np.array(model_fit.history["val_loss"])
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63 train_performance["precision"] = predict_callback_test.precision
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64 train_performance["usage_weights"] = predict_callback_test.usage_weights
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65 return train_performance
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66
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67
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68 class PredictCallback(callbacks.Callback):
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69 def __init__(self, test_data, test_labels, reverse_data_dictionary, n_epochs, next_compatible_tools, usg_scores):
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70 self.test_data = test_data
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71 self.test_labels = test_labels
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72 self.reverse_data_dictionary = reverse_data_dictionary
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73 self.precision = list()
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74 self.usage_weights = list()
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75 self.n_epochs = n_epochs
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76 self.next_compatible_tools = next_compatible_tools
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77 self.pred_usage_scores = usg_scores
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78
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79 def on_epoch_end(self, epoch, logs={}):
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80 """
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81 Compute absolute and compatible precision for test data
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82 """
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83 if len(self.test_data) > 0:
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84 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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85 self.precision.append(precision)
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86 self.usage_weights.append(usage_weights)
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87 print("Epoch %d precision: %s" % (epoch + 1, precision))
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88 print("Epoch %d usage weights: %s" % (epoch + 1, usage_weights))
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89
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90
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91 if __name__ == "__main__":
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92 start_time = time.time()
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93 arg_parser = argparse.ArgumentParser()
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94 arg_parser.add_argument("-wf", "--workflow_file", required=True, help="workflows tabular file")
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95 arg_parser.add_argument("-tu", "--tool_usage_file", required=True, help="tool usage file")
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96 arg_parser.add_argument("-om", "--output_model", required=True, help="trained model file")
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97 # data parameters
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98 arg_parser.add_argument("-cd", "--cutoff_date", required=True, help="earliest date for taking tool usage")
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99 arg_parser.add_argument("-pl", "--maximum_path_length", required=True, help="maximum length of tool path")
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100 arg_parser.add_argument("-ep", "--n_epochs", required=True, help="number of iterations to run to create model")
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101 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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102 arg_parser.add_argument("-me", "--max_evals", required=True, help="maximum number of configuration evaluations")
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103 arg_parser.add_argument("-ts", "--test_share", required=True, help="share of data to be used for testing")
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104 arg_parser.add_argument("-vs", "--validation_share", required=True, help="share of data to be used for validation")
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105 # neural network parameters
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106 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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107 arg_parser.add_argument("-ut", "--units", required=True, help="number of hidden recurrent units")
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108 arg_parser.add_argument("-es", "--embedding_size", required=True, help="size of the fixed vector learned for each tool")
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109 arg_parser.add_argument("-dt", "--dropout", required=True, help="percentage of neurons to be dropped")
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110 arg_parser.add_argument("-sd", "--spatial_dropout", required=True, help="1d dropout used for embedding layer")
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111 arg_parser.add_argument("-rd", "--recurrent_dropout", required=True, help="dropout for the recurrent layers")
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112 arg_parser.add_argument("-lr", "--learning_rate", required=True, help="learning rate")
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113 arg_parser.add_argument("-ar", "--activation_recurrent", required=True, help="activation function for recurrent layers")
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114 arg_parser.add_argument("-ao", "--activation_output", required=True, help="activation function for output layers")
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115 arg_parser.add_argument("-lt", "--loss_type", required=True, help="type of the loss/error function")
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116 # get argument values
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117 args = vars(arg_parser.parse_args())
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118 tool_usage_path = args["tool_usage_file"]
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119 workflows_path = args["workflow_file"]
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120 cutoff_date = args["cutoff_date"]
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121 maximum_path_length = int(args["maximum_path_length"])
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122 trained_model_path = args["output_model"]
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123 n_epochs = int(args["n_epochs"])
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124 optimize_n_epochs = int(args["optimize_n_epochs"])
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125 max_evals = int(args["max_evals"])
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126 test_share = float(args["test_share"])
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127 validation_share = float(args["validation_share"])
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128 batch_size = args["batch_size"]
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129 units = args["units"]
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130 embedding_size = args["embedding_size"]
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131 dropout = args["dropout"]
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132 spatial_dropout = args["spatial_dropout"]
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133 recurrent_dropout = args["recurrent_dropout"]
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134 learning_rate = args["learning_rate"]
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135 activation_recurrent = args["activation_recurrent"]
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136 activation_output = args["activation_output"]
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137 loss_type = args["loss_type"]
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138
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139 config = {
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140 'cutoff_date': cutoff_date,
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141 'maximum_path_length': maximum_path_length,
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142 'n_epochs': n_epochs,
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143 'optimize_n_epochs': optimize_n_epochs,
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144 'max_evals': max_evals,
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145 'test_share': test_share,
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146 'validation_share': validation_share,
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147 'batch_size': batch_size,
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148 'units': units,
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149 'embedding_size': embedding_size,
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150 'dropout': dropout,
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151 'spatial_dropout': spatial_dropout,
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152 'recurrent_dropout': recurrent_dropout,
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153 'learning_rate': learning_rate,
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154 'activation_recurrent': activation_recurrent,
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155 'activation_output': activation_output,
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156 'loss_type': loss_type
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157 }
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158
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159 # Extract and process workflows
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160 connections = extract_workflow_connections.ExtractWorkflowConnections()
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161 workflow_paths, compatible_next_tools = connections.read_tabular_file(workflows_path)
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162 # Process the paths from workflows
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163 print("Dividing data...")
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164 data = prepare_data.PrepareData(maximum_path_length, test_share)
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165 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)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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166 # find the best model and start training
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167 predict_tool = PredictTool()
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168 # start training with weighted classes
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169 print("Training with weighted classes and samples ...")
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170 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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171 print()
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172 print("Best parameters \n")
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173 print(results_weighted["best_parameters"])
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174 print()
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175 utils.save_model(results_weighted, data_dictionary, compatible_next_tools, trained_model_path, class_weights)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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176 end_time = time.time()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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177 print()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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178 print("Program finished in %s seconds" % str(end_time - start_time))