Mercurial > repos > bgruening > create_tool_recommendation_model

annotate utils.py @ 4:f0da532be419 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
author bgruening
date Fri, 06 May 2022 09:04:44 +0000
parents 98bc44d17561
children 9ec705bd11cb
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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 json
2
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2 import random
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f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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4 import h5py
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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5 import numpy as np
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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6 import tensorflow as tf
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7 from numpy.random import choice
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f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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8 from tensorflow.keras import backend
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11 def read_file(file_path):
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12 """
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13 Read a file
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14 """
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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15 with open(file_path, "r") as json_file:
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16 file_content = json.loads(json_file.read())
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17 return file_content
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20 def format_tool_id(tool_link):
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21 """
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22 Extract tool id from tool link
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23 """
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24 tool_id_split = tool_link.split("/")
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25 tool_id = tool_id_split[-2] if len(tool_id_split) > 1 else tool_link
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26 return tool_id
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29 def set_trained_model(dump_file, model_values):
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30 """
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31 Create an h5 file with the trained weights and associated dicts
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32 """
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33 hf_file = h5py.File(dump_file, "w")
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34 for key in model_values:
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35 value = model_values[key]
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36 if key == "model_weights":
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37 for idx, item in enumerate(value):
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38 w_key = "weight_" + str(idx)
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39 if w_key in hf_file:
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40 hf_file.modify(w_key, item)
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41 else:
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42 hf_file.create_dataset(w_key, data=item)
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43 else:
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44 if key in hf_file:
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45 hf_file.modify(key, json.dumps(value))
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46 else:
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47 hf_file.create_dataset(key, data=json.dumps(value))
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48 hf_file.close()
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51 def weighted_loss(class_weights):
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52 """
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53 Create a weighted loss function. Penalise the misclassification
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54 of classes more with the higher usage
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55 """
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56 weight_values = list(class_weights.values())
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57 weight_values.extend(weight_values)
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58
0
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59 def weighted_binary_crossentropy(y_true, y_pred):
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60 # add another dimension to compute dot product
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61 expanded_weights = tf.expand_dims(weight_values, axis=-1)
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62 bce = backend.binary_crossentropy(y_true, y_pred)
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63 return backend.dot(bce, expanded_weights)
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64
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65 return weighted_binary_crossentropy
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66
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67
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68 def balanced_sample_generator(
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69 train_data, train_labels, batch_size, l_tool_tr_samples, reverse_dictionary
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70 ):
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71 while True:
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72 dimension = train_data.shape[1]
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73 n_classes = train_labels.shape[1]
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74 tool_ids = list(l_tool_tr_samples.keys())
3
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75 random.shuffle(tool_ids)
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76 generator_batch_data = np.zeros([batch_size, dimension])
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77 generator_batch_labels = np.zeros([batch_size, n_classes])
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78 generated_tool_ids = choice(tool_ids, batch_size)
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79 for i in range(batch_size):
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80 random_toolid = generated_tool_ids[i]
2
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81 sample_indices = l_tool_tr_samples[str(random_toolid)]
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82 random_index = random.sample(range(0, len(sample_indices)), 1)[0]
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83 random_tr_index = sample_indices[random_index]
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84 generator_batch_data[i] = train_data[random_tr_index]
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85 generator_batch_labels[i] = train_labels[random_tr_index]
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86 yield generator_batch_data, generator_batch_labels
0
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87
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88
4
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89 def compute_precision(
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90 model,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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91 x,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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92 y,
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93 reverse_data_dictionary,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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94 usage_scores,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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95 actual_classes_pos,
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96 topk,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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97 standard_conn,
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98 last_tool_id,
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99 lowest_tool_ids,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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100 ):
0
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101 """
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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102 Compute absolute and compatible precision
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103 """
2
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104 pred_t_name = ""
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105 top_precision = 0.0
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106 mean_usage = 0.0
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107 usage_wt_score = list()
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108 pub_precision = 0.0
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109 lowest_pub_prec = 0.0
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110 lowest_norm_prec = 0.0
50753817983a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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111 pub_tools = list()
50753817983a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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112 actual_next_tool_names = list()
0
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113 test_sample = np.reshape(x, (1, len(x)))
22ebbac136c7 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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114
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115 # predict next tools for a test path
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116 prediction = model.predict(test_sample, verbose=0)
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117
2
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118 # divide the predicted vector into two halves - one for published and
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119 # another for normal workflows
0
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120 nw_dimension = prediction.shape[1]
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121 half_len = int(nw_dimension / 2)
0
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122
2
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123 # predict tools
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124 prediction = np.reshape(prediction, (nw_dimension,))
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125 # get predictions of tools from published workflows
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126 standard_pred = prediction[:half_len]
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127 # get predictions of tools from normal workflows
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128 normal_pred = prediction[half_len:]
0
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129
2
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130 standard_prediction_pos = np.argsort(standard_pred, axis=-1)
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131 standard_topk_prediction_pos = standard_prediction_pos[-topk]
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132
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133 normal_prediction_pos = np.argsort(normal_pred, axis=-1)
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134 normal_topk_prediction_pos = normal_prediction_pos[-topk]
0
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135
2
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136 # get true tools names
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137 for a_t_pos in actual_classes_pos:
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138 if a_t_pos > half_len:
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139 t_name = reverse_data_dictionary[int(a_t_pos - half_len)]
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140 else:
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141 t_name = reverse_data_dictionary[int(a_t_pos)]
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142 actual_next_tool_names.append(t_name)
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143 last_tool_name = reverse_data_dictionary[x[-1]]
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144 # compute scores for published recommendations
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145 if standard_topk_prediction_pos in reverse_data_dictionary:
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146 pred_t_name = reverse_data_dictionary[int(standard_topk_prediction_pos)]
50753817983a "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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147 if last_tool_name in standard_conn:
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148 pub_tools = standard_conn[last_tool_name]
3
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149 if pred_t_name in pub_tools:
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150 pub_precision = 1.0
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151 # count precision only when there is actually true published tools
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152 if last_tool_id in lowest_tool_ids:
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153 lowest_pub_prec = 1.0
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154 else:
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155 lowest_pub_prec = np.nan
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156 if standard_topk_prediction_pos in usage_scores:
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157 usage_wt_score.append(
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158 np.log(usage_scores[standard_topk_prediction_pos] + 1.0)
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159 )
3
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160 else:
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161 # count precision only when there is actually true published tools
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162 # else set to np.nan. Set to 0 only when there is wrong prediction
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163 pub_precision = np.nan
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164 lowest_pub_prec = np.nan
2
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165 # compute scores for normal recommendations
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166 if normal_topk_prediction_pos in reverse_data_dictionary:
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167 pred_t_name = reverse_data_dictionary[int(normal_topk_prediction_pos)]
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168 if pred_t_name in actual_next_tool_names:
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169 if normal_topk_prediction_pos in usage_scores:
4
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170 usage_wt_score.append(
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171 np.log(usage_scores[normal_topk_prediction_pos] + 1.0)
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172 )
2
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173 top_precision = 1.0
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174 if last_tool_id in lowest_tool_ids:
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175 lowest_norm_prec = 1.0
3
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176 else:
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177 lowest_norm_prec = np.nan
2
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178 if len(usage_wt_score) > 0:
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179 mean_usage = np.mean(usage_wt_score)
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180 return mean_usage, top_precision, pub_precision, lowest_pub_prec, lowest_norm_prec
0
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181
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182
2
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183 def get_lowest_tools(l_tool_freq, fraction=0.25):
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184 l_tool_freq = dict(sorted(l_tool_freq.items(), key=lambda kv: kv[1], reverse=True))
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185 tool_ids = list(l_tool_freq.keys())
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186 lowest_ids = tool_ids[-int(len(tool_ids) * fraction):]
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187 return lowest_ids
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188
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189
4
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190 def verify_model(
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191 model,
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192 x,
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193 y,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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194 reverse_data_dictionary,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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195 usage_scores,
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196 standard_conn,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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197 lowest_tool_ids,
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198 topk_list=[1, 2, 3],
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199 ):
0
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200 """
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201 Verify the model on test data
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202 """
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203 print("Evaluating performance on test data...")
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204 print("Test data size: %d" % len(y))
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205 size = y.shape[0]
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206 precision = np.zeros([len(y), len(topk_list)])
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207 usage_weights = np.zeros([len(y), len(topk_list)])
2
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208 epo_pub_prec = np.zeros([len(y), len(topk_list)])
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209 epo_lowest_tools_pub_prec = list()
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210 epo_lowest_tools_norm_prec = list()
3
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211 lowest_counter = 0
0
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212 # loop over all the test samples and find prediction precision
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213 for i in range(size):
2
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214 lowest_pub_topk = list()
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215 lowest_norm_topk = list()
0
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216 actual_classes_pos = np.where(y[i] > 0)[0]
2
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217 test_sample = x[i, :]
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218 last_tool_id = str(int(test_sample[-1]))
0
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219 for index, abs_topk in enumerate(topk_list):
4
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220 (
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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221 usg_wt_score,
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222 absolute_precision,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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223 pub_prec,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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224 lowest_p_prec,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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225 lowest_n_prec,
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226 ) = compute_precision(
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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227 model,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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228 test_sample,
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229 y,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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230 reverse_data_dictionary,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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231 usage_scores,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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232 actual_classes_pos,
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233 abs_topk,
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234 standard_conn,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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235 last_tool_id,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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236 lowest_tool_ids,
f0da532be419 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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237 )
0
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238 precision[i][index] = absolute_precision
2
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diff changeset
239 usage_weights[i][index] = usg_wt_score
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240 epo_pub_prec[i][index] = pub_prec
3
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241 lowest_pub_topk.append(lowest_p_prec)
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242 lowest_norm_topk.append(lowest_n_prec)
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243 epo_lowest_tools_pub_prec.append(lowest_pub_topk)
98bc44d17561 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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244 epo_lowest_tools_norm_prec.append(lowest_norm_topk)
2
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245 if last_tool_id in lowest_tool_ids:
3
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246 lowest_counter += 1
0
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247 mean_precision = np.mean(precision, axis=0)
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248 mean_usage = np.mean(usage_weights, axis=0)
3
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249 mean_pub_prec = np.nanmean(epo_pub_prec, axis=0)
98bc44d17561 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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250 mean_lowest_pub_prec = np.nanmean(epo_lowest_tools_pub_prec, axis=0)
98bc44d17561 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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251 mean_lowest_norm_prec = np.nanmean(epo_lowest_tools_norm_prec, axis=0)
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252 return (
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253 mean_usage,
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254 mean_precision,
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255 mean_pub_prec,
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256 mean_lowest_pub_prec,
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257 mean_lowest_norm_prec,
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258 lowest_counter,
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259 )
0
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260
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261
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262 def save_model(
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263 results,
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264 data_dictionary,
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265 compatible_next_tools,
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266 trained_model_path,
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267 class_weights,
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268 standard_connections,
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269 ):
0
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270 # save files
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271 trained_model = results["model"]
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272 best_model_parameters = results["best_parameters"]
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273 model_config = trained_model.to_json()
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274 model_weights = trained_model.get_weights()
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275 model_values = {
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276 "data_dictionary": data_dictionary,
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277 "model_config": model_config,
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278 "best_parameters": best_model_parameters,
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279 "model_weights": model_weights,
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280 "compatible_tools": compatible_next_tools,
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281 "class_weights": class_weights,
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282 "standard_connections": standard_connections,
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283 }
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284 set_trained_model(trained_model_path, model_values)