Mercurial > repos > bgruening > create_tool_recommendation_model

annotate utils.py @ 3:98bc44d17561 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
author bgruening
date Tue, 07 Jul 2020 07:24:21 +0000
parents 50753817983a
children f0da532be419
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22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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1 import numpy as np
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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2 import json
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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3 import h5py
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50753817983a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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4 import random
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5 from numpy.random import choice
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22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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7 from keras import backend as K
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10 def read_file(file_path):
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11 """
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12 Read a file
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13 """
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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14 with open(file_path, "r") as json_file:
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15 file_content = json.loads(json_file.read())
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16 return file_content
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19 def format_tool_id(tool_link):
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20 """
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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21 Extract tool id from tool link
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22 """
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23 tool_id_split = tool_link.split("/")
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24 tool_id = tool_id_split[-2] if len(tool_id_split) > 1 else tool_link
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25 return tool_id
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28 def set_trained_model(dump_file, model_values):
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29 """
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30 Create an h5 file with the trained weights and associated dicts
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31 """
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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32 hf_file = h5py.File(dump_file, 'w')
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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33 for key in model_values:
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34 value = model_values[key]
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35 if key == 'model_weights':
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36 for idx, item in enumerate(value):
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37 w_key = "weight_" + str(idx)
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38 if w_key in hf_file:
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39 hf_file.modify(w_key, item)
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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40 else:
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41 hf_file.create_dataset(w_key, data=item)
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42 else:
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43 if key in hf_file:
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44 hf_file.modify(key, json.dumps(value))
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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45 else:
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46 hf_file.create_dataset(key, data=json.dumps(value))
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47 hf_file.close()
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49
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50 def weighted_loss(class_weights):
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51 """
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52 Create a weighted loss function. Penalise the misclassification
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53 of classes more with the higher usage
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54 """
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55 weight_values = list(class_weights.values())
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56 weight_values.extend(weight_values)
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57 def weighted_binary_crossentropy(y_true, y_pred):
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58 # add another dimension to compute dot product
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59 expanded_weights = K.expand_dims(weight_values, axis=-1)
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60 return K.dot(K.binary_crossentropy(y_true, y_pred), expanded_weights)
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61 return weighted_binary_crossentropy
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62
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63
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64 def balanced_sample_generator(train_data, train_labels, batch_size, l_tool_tr_samples, reverse_dictionary):
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65 while True:
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66 dimension = train_data.shape[1]
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67 n_classes = train_labels.shape[1]
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68 tool_ids = list(l_tool_tr_samples.keys())
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69 random.shuffle(tool_ids)
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70 generator_batch_data = np.zeros([batch_size, dimension])
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71 generator_batch_labels = np.zeros([batch_size, n_classes])
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72 generated_tool_ids = choice(tool_ids, batch_size)
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73 for i in range(batch_size):
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74 random_toolid = generated_tool_ids[i]
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75 sample_indices = l_tool_tr_samples[str(random_toolid)]
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76 random_index = random.sample(range(0, len(sample_indices)), 1)[0]
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77 random_tr_index = sample_indices[random_index]
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78 generator_batch_data[i] = train_data[random_tr_index]
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79 generator_batch_labels[i] = train_labels[random_tr_index]
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80 yield generator_batch_data, generator_batch_labels
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82
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83 def compute_precision(model, x, y, reverse_data_dictionary, usage_scores, actual_classes_pos, topk, standard_conn, last_tool_id, lowest_tool_ids):
0
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84 """
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85 Compute absolute and compatible precision
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86 """
2
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87 pred_t_name = ""
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88 top_precision = 0.0
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89 mean_usage = 0.0
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90 usage_wt_score = list()
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91 pub_precision = 0.0
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92 lowest_pub_prec = 0.0
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93 lowest_norm_prec = 0.0
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94 pub_tools = list()
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95 actual_next_tool_names = list()
0
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96 test_sample = np.reshape(x, (1, len(x)))
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97
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98 # predict next tools for a test path
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99 prediction = model.predict(test_sample, verbose=0)
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100
2
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101 # divide the predicted vector into two halves - one for published and
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102 # another for normal workflows
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103 nw_dimension = prediction.shape[1]
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104 half_len = int(nw_dimension / 2)
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105
2
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106 # predict tools
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107 prediction = np.reshape(prediction, (nw_dimension,))
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108 # get predictions of tools from published workflows
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109 standard_pred = prediction[:half_len]
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110 # get predictions of tools from normal workflows
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111 normal_pred = prediction[half_len:]
0
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112
2
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113 standard_prediction_pos = np.argsort(standard_pred, axis=-1)
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114 standard_topk_prediction_pos = standard_prediction_pos[-topk]
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115
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116 normal_prediction_pos = np.argsort(normal_pred, axis=-1)
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117 normal_topk_prediction_pos = normal_prediction_pos[-topk]
0
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118
2
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119 # get true tools names
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120 for a_t_pos in actual_classes_pos:
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121 if a_t_pos > half_len:
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122 t_name = reverse_data_dictionary[int(a_t_pos - half_len)]
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123 else:
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124 t_name = reverse_data_dictionary[int(a_t_pos)]
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125 actual_next_tool_names.append(t_name)
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126 last_tool_name = reverse_data_dictionary[x[-1]]
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127 # compute scores for published recommendations
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128 if standard_topk_prediction_pos in reverse_data_dictionary:
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129 pred_t_name = reverse_data_dictionary[int(standard_topk_prediction_pos)]
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130 if last_tool_name in standard_conn:
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131 pub_tools = standard_conn[last_tool_name]
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132 if pred_t_name in pub_tools:
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133 pub_precision = 1.0
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134 # count precision only when there is actually true published tools
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135 if last_tool_id in lowest_tool_ids:
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136 lowest_pub_prec = 1.0
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137 else:
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138 lowest_pub_prec = np.nan
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139 if standard_topk_prediction_pos in usage_scores:
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140 usage_wt_score.append(np.log(usage_scores[standard_topk_prediction_pos] + 1.0))
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141 else:
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142 # count precision only when there is actually true published tools
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143 # else set to np.nan. Set to 0 only when there is wrong prediction
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144 pub_precision = np.nan
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145 lowest_pub_prec = np.nan
2
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146 # compute scores for normal recommendations
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147 if normal_topk_prediction_pos in reverse_data_dictionary:
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148 pred_t_name = reverse_data_dictionary[int(normal_topk_prediction_pos)]
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149 if pred_t_name in actual_next_tool_names:
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150 if normal_topk_prediction_pos in usage_scores:
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151 usage_wt_score.append(np.log(usage_scores[normal_topk_prediction_pos] + 1.0))
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152 top_precision = 1.0
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153 if last_tool_id in lowest_tool_ids:
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154 lowest_norm_prec = 1.0
3
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155 else:
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156 lowest_norm_prec = np.nan
2
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157 if len(usage_wt_score) > 0:
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158 mean_usage = np.mean(usage_wt_score)
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159 return mean_usage, top_precision, pub_precision, lowest_pub_prec, lowest_norm_prec
0
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160
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161
2
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162 def get_lowest_tools(l_tool_freq, fraction=0.25):
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163 l_tool_freq = dict(sorted(l_tool_freq.items(), key=lambda kv: kv[1], reverse=True))
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164 tool_ids = list(l_tool_freq.keys())
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165 lowest_ids = tool_ids[-int(len(tool_ids) * fraction):]
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166 return lowest_ids
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167
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168
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169 def verify_model(model, x, y, reverse_data_dictionary, usage_scores, standard_conn, lowest_tool_ids, topk_list=[1, 2, 3]):
0
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170 """
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171 Verify the model on test data
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172 """
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173 print("Evaluating performance on test data...")
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174 print("Test data size: %d" % len(y))
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175 size = y.shape[0]
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176 precision = np.zeros([len(y), len(topk_list)])
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177 usage_weights = np.zeros([len(y), len(topk_list)])
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178 epo_pub_prec = np.zeros([len(y), len(topk_list)])
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179 epo_lowest_tools_pub_prec = list()
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180 epo_lowest_tools_norm_prec = list()
3
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181 lowest_counter = 0
0
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182 # loop over all the test samples and find prediction precision
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183 for i in range(size):
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184 lowest_pub_topk = list()
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185 lowest_norm_topk = list()
0
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186 actual_classes_pos = np.where(y[i] > 0)[0]
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187 test_sample = x[i, :]
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188 last_tool_id = str(int(test_sample[-1]))
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189 for index, abs_topk in enumerate(topk_list):
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190 usg_wt_score, absolute_precision, pub_prec, lowest_p_prec, lowest_n_prec = compute_precision(model, test_sample, y, reverse_data_dictionary, usage_scores, actual_classes_pos, abs_topk, standard_conn, last_tool_id, lowest_tool_ids)
0
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191 precision[i][index] = absolute_precision
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192 usage_weights[i][index] = usg_wt_score
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193 epo_pub_prec[i][index] = pub_prec
3
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194 lowest_pub_topk.append(lowest_p_prec)
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195 lowest_norm_topk.append(lowest_n_prec)
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196 epo_lowest_tools_pub_prec.append(lowest_pub_topk)
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197 epo_lowest_tools_norm_prec.append(lowest_norm_topk)
2
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198 if last_tool_id in lowest_tool_ids:
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199 lowest_counter += 1
0
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200 mean_precision = np.mean(precision, axis=0)
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201 mean_usage = np.mean(usage_weights, axis=0)
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202 mean_pub_prec = np.nanmean(epo_pub_prec, axis=0)
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203 mean_lowest_pub_prec = np.nanmean(epo_lowest_tools_pub_prec, axis=0)
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204 mean_lowest_norm_prec = np.nanmean(epo_lowest_tools_norm_prec, axis=0)
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205 return mean_usage, mean_precision, mean_pub_prec, mean_lowest_pub_prec, mean_lowest_norm_prec, lowest_counter
0
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206
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207
2
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208 def save_model(results, data_dictionary, compatible_next_tools, trained_model_path, class_weights, standard_connections):
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209 # save files
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210 trained_model = results["model"]
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211 best_model_parameters = results["best_parameters"]
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212 model_config = trained_model.to_json()
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213 model_weights = trained_model.get_weights()
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214 model_values = {
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215 'data_dictionary': data_dictionary,
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216 'model_config': model_config,
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217 'best_parameters': best_model_parameters,
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218 'model_weights': model_weights,
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219 "compatible_tools": compatible_next_tools,
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220 "class_weights": class_weights,
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221 "standard_connections": standard_connections
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222 }
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223 set_trained_model(trained_model_path, model_values)