Mercurial > repos > bgruening > create_tool_recommendation_model

annotate utils.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 9bf25dbe00ad
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 import os
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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2 import numpy as np
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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3 import json
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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4 import h5py
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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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 from keras import backend as K
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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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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9 def read_file(file_path):
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10 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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11 Read a file
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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12 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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13 with open(file_path, "r") as json_file:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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14 file_content = json.loads(json_file.read())
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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15 return file_content
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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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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18 def write_file(file_path, content):
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19 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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20 Write a file
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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21 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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22 remove_file(file_path)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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23 with open(file_path, "w") as json_file:
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24 json_file.write(json.dumps(content))
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27 def save_processed_workflows(file_path, unique_paths):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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28 workflow_paths_unique = ""
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29 for path in unique_paths:
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30 workflow_paths_unique += path + "\n"
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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31 with open(file_path, "w") as workflows_file:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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32 workflows_file.write(workflow_paths_unique)
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35 def format_tool_id(tool_link):
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36 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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37 Extract tool id from tool link
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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38 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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39 tool_id_split = tool_link.split("/")
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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40 tool_id = tool_id_split[-2] if len(tool_id_split) > 1 else tool_link
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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41 return tool_id
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44 def set_trained_model(dump_file, model_values):
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45 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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46 Create an h5 file with the trained weights and associated dicts
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47 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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48 hf_file = h5py.File(dump_file, 'w')
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49 for key in model_values:
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50 value = model_values[key]
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51 if key == 'model_weights':
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52 for idx, item in enumerate(value):
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53 w_key = "weight_" + str(idx)
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54 if w_key in hf_file:
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55 hf_file.modify(w_key, item)
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56 else:
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57 hf_file.create_dataset(w_key, data=item)
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58 else:
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59 if key in hf_file:
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60 hf_file.modify(key, json.dumps(value))
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61 else:
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62 hf_file.create_dataset(key, data=json.dumps(value))
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63 hf_file.close()
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64
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66 def remove_file(file_path):
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67 if os.path.exists(file_path):
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68 os.remove(file_path)
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71 def weighted_loss(class_weights):
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72 """
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73 Create a weighted loss function. Penalise the misclassification
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74 of classes more with the higher usage
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75 """
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76 weight_values = list(class_weights.values())
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78 def weighted_binary_crossentropy(y_true, y_pred):
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79 # add another dimension to compute dot product
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80 expanded_weights = K.expand_dims(weight_values, axis=-1)
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81 return K.dot(K.binary_crossentropy(y_true, y_pred), expanded_weights)
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82 return weighted_binary_crossentropy
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85 def compute_precision(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, topk):
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86 """
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87 Compute absolute and compatible precision
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88 """
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89 absolute_precision = 0.0
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90 test_sample = np.reshape(x, (1, len(x)))
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91
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92 # predict next tools for a test path
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93 prediction = model.predict(test_sample, verbose=0)
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94
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95 nw_dimension = prediction.shape[1]
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96
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97 # remove the 0th position as there is no tool at this index
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98 prediction = np.reshape(prediction, (nw_dimension,))
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99
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100 prediction_pos = np.argsort(prediction, axis=-1)
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101 topk_prediction_pos = prediction_pos[-topk:]
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102
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103 # remove the wrong tool position from the predicted list of tool positions
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104 topk_prediction_pos = [x for x in topk_prediction_pos if x > 0]
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105
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106 # read tool names using reverse dictionary
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107 actual_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in actual_classes_pos]
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108 top_predicted_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in topk_prediction_pos]
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109
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110 # compute the class weights of predicted tools
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111 mean_usg_score = 0
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112 usg_wt_scores = list()
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113 for t_id in topk_prediction_pos:
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114 t_name = reverse_data_dictionary[int(t_id)]
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115 if t_id in usage_scores and t_name in actual_next_tool_names:
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116 usg_wt_scores.append(np.log(usage_scores[t_id] + 1.0))
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117 if len(usg_wt_scores) > 0:
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118 mean_usg_score = np.sum(usg_wt_scores) / float(topk)
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119 false_positives = [tool_name for tool_name in top_predicted_next_tool_names if tool_name not in actual_next_tool_names]
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120 absolute_precision = 1 - (len(false_positives) / float(topk))
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121 return mean_usg_score, absolute_precision
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122
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123
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124 def verify_model(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, topk_list=[1, 2, 3]):
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125 """
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126 Verify the model on test data
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127 """
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128 print("Evaluating performance on test data...")
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129 print("Test data size: %d" % len(y))
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130 size = y.shape[0]
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131 precision = np.zeros([len(y), len(topk_list)])
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132 usage_weights = np.zeros([len(y), len(topk_list)])
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133 # loop over all the test samples and find prediction precision
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134 for i in range(size):
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135 actual_classes_pos = np.where(y[i] > 0)[0]
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136 for index, abs_topk in enumerate(topk_list):
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137 abs_mean_usg_score, absolute_precision = compute_precision(model, x[i, :], y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, abs_topk)
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138 precision[i][index] = absolute_precision
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139 usage_weights[i][index] = abs_mean_usg_score
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140 mean_precision = np.mean(precision, axis=0)
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141 mean_usage = np.mean(usage_weights, axis=0)
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142 return mean_precision, mean_usage
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143
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144
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145 def save_model(results, data_dictionary, compatible_next_tools, trained_model_path, class_weights):
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146 # save files
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147 trained_model = results["model"]
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148 best_model_parameters = results["best_parameters"]
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149 model_config = trained_model.to_json()
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150 model_weights = trained_model.get_weights()
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151
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152 model_values = {
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153 'data_dictionary': data_dictionary,
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154 'model_config': model_config,
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155 'best_parameters': best_model_parameters,
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156 'model_weights': model_weights,
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157 "compatible_tools": compatible_next_tools,
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158 "class_weights": class_weights
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159 }
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160 set_trained_model(trained_model_path, model_values)