Mercurial > repos > bgruening > create_tool_recommendation_model

annotate prepare_data.py @ 5:4f7e6612906b 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 09:05:18 +0000
parents afec8c595124
children e94dc7945639
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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 Prepare the workflow paths to be used by downstream
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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3 machine learning algorithm. The paths are divided
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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4 into the test and training sets
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5 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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6
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7 import collections
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8 import os
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9 import random
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11 import numpy as np
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12 import predict_tool_usage
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14 main_path = os.getcwd()
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17 class PrepareData:
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18 def __init__(self, max_seq_length, test_data_share):
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19 """ Init method. """
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20 self.max_tool_sequence_len = max_seq_length
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21 self.test_share = test_data_share
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23 def process_workflow_paths(self, workflow_paths):
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24 """
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25 Get all the tools and complete set of individual paths for each workflow
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26 """
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27 tokens = list()
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28 raw_paths = workflow_paths
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29 raw_paths = [x.replace("\n", "") for x in raw_paths]
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30 for item in raw_paths:
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31 split_items = item.split(",")
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32 for token in split_items:
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33 if token != "":
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34 tokens.append(token)
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35 tokens = list(set(tokens))
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36 tokens = np.array(tokens)
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37 tokens = np.reshape(
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38 tokens,
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39 [
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40 -1,
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41 ],
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42 )
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43 return tokens, raw_paths
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45 def create_new_dict(self, new_data_dict):
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46 """
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47 Create new data dictionary
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48 """
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49 reverse_dict = dict((v, k) for k, v in new_data_dict.items())
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50 return new_data_dict, reverse_dict
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52 def assemble_dictionary(self, new_data_dict, old_data_dictionary={}):
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53 """
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54 Create/update tools indices in the forward and backward dictionary
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55 """
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56 new_data_dict, reverse_dict = self.create_new_dict(new_data_dict)
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57 return new_data_dict, reverse_dict
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58
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59 def create_data_dictionary(self, words, old_data_dictionary={}):
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60 """
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61 Create two dictionaries having tools names and their indexes
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62 """
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63 count = collections.Counter(words).most_common()
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64 dictionary = dict()
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65 for word, _ in count:
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66 dictionary[word] = len(dictionary) + 1
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67 word = word.strip()
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68 dictionary, reverse_dictionary = self.assemble_dictionary(
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69 dictionary, old_data_dictionary
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70 )
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71 return dictionary, reverse_dictionary
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72
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73 def decompose_paths(self, paths, dictionary):
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74 """
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75 Decompose the paths to variable length sub-paths keeping the first tool fixed
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76 """
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77 sub_paths_pos = list()
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78 for index, item in enumerate(paths):
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79 tools = item.split(",")
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80 len_tools = len(tools)
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81 if len_tools <= self.max_tool_sequence_len:
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82 for window in range(1, len_tools):
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83 sequence = tools[0: window + 1]
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84 tools_pos = [
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85 str(dictionary[str(tool_item)]) for tool_item in sequence
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86 ]
0
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87 if len(tools_pos) > 1:
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88 sub_paths_pos.append(",".join(tools_pos))
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89 sub_paths_pos = list(set(sub_paths_pos))
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90 return sub_paths_pos
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91
5
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92 def prepare_paths_labels_dictionary(
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93 self, dictionary, reverse_dictionary, paths, compatible_next_tools
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94 ):
0
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95 """
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96 Create a dictionary of sequences with their labels for training and test paths
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97 """
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98 paths_labels = dict()
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99 random.shuffle(paths)
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100 for item in paths:
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101 if item and item not in "":
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102 tools = item.split(",")
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103 label = tools[-1]
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104 train_tools = tools[: len(tools) - 1]
0
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105 last_but_one_name = reverse_dictionary[int(train_tools[-1])]
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106 try:
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107 compatible_tools = compatible_next_tools[last_but_one_name].split(
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108 ","
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109 )
0
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110 except Exception:
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111 continue
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112 if len(compatible_tools) > 0:
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113 compatible_tools_ids = [
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114 str(dictionary[x]) for x in compatible_tools
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115 ]
0
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116 compatible_tools_ids.append(label)
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117 composite_labels = ",".join(compatible_tools_ids)
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118 train_tools = ",".join(train_tools)
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119 if train_tools in paths_labels:
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120 paths_labels[train_tools] += "," + composite_labels
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121 else:
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122 paths_labels[train_tools] = composite_labels
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123 for item in paths_labels:
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124 paths_labels[item] = ",".join(list(set(paths_labels[item].split(","))))
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125 return paths_labels
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126
3
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127 def pad_test_paths(self, paths_dictionary, num_classes):
0
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128 """
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129 Add padding to the tools sequences and create multi-hot encoded labels
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130 """
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131 size_data = len(paths_dictionary)
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132 data_mat = np.zeros([size_data, self.max_tool_sequence_len])
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133 label_mat = np.zeros([size_data, num_classes + 1])
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134 train_counter = 0
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135 for train_seq, train_label in list(paths_dictionary.items()):
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136 positions = train_seq.split(",")
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137 start_pos = self.max_tool_sequence_len - len(positions)
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138 for id_pos, pos in enumerate(positions):
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139 data_mat[train_counter][start_pos + id_pos] = int(pos)
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140 for label_item in train_label.split(","):
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141 label_mat[train_counter][int(label_item)] = 1.0
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142 train_counter += 1
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143 return data_mat, label_mat
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144
5
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145 def pad_paths(
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146 self, paths_dictionary, num_classes, standard_connections, reverse_dictionary
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147 ):
3
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148 """
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149 Add padding to the tools sequences and create multi-hot encoded labels
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150 """
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151 size_data = len(paths_dictionary)
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152 data_mat = np.zeros([size_data, self.max_tool_sequence_len])
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153 label_mat = np.zeros([size_data, 2 * (num_classes + 1)])
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154 pos_flag = 1.0
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155 train_counter = 0
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156 for train_seq, train_label in list(paths_dictionary.items()):
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157 pub_connections = list()
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158 positions = train_seq.split(",")
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159 last_tool_id = positions[-1]
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160 last_tool_name = reverse_dictionary[int(last_tool_id)]
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161 start_pos = self.max_tool_sequence_len - len(positions)
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162 for id_pos, pos in enumerate(positions):
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163 data_mat[train_counter][start_pos + id_pos] = int(pos)
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164 if last_tool_name in standard_connections:
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165 pub_connections = standard_connections[last_tool_name]
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166 for label_item in train_label.split(","):
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167 label_pos = int(label_item)
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168 label_row = label_mat[train_counter]
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169 if reverse_dictionary[label_pos] in pub_connections:
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170 label_row[label_pos] = pos_flag
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171 else:
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172 label_row[label_pos + num_classes + 1] = pos_flag
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173 train_counter += 1
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174 return data_mat, label_mat
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175
0
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176 def split_test_train_data(self, multilabels_paths):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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177 """
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178 Split into test and train data randomly for each run
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179 """
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180 train_dict = dict()
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181 test_dict = dict()
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182 all_paths = multilabels_paths.keys()
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183 random.shuffle(list(all_paths))
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184 split_number = int(self.test_share * len(all_paths))
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185 for index, path in enumerate(list(all_paths)):
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186 if index < split_number:
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187 test_dict[path] = multilabels_paths[path]
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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188 else:
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189 train_dict[path] = multilabels_paths[path]
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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190 return train_dict, test_dict
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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191
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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192 def get_predicted_usage(self, data_dictionary, predicted_usage):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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193 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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194 Get predicted usage for tools
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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195 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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196 usage = dict()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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197 epsilon = 0.0
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198 # index 0 does not belong to any tool
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199 usage[0] = epsilon
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200 for k, v in data_dictionary.items():
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201 try:
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202 usg = predicted_usage[k]
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203 if usg < epsilon:
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204 usg = epsilon
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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205 usage[v] = usg
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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206 except Exception:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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207 usage[v] = epsilon
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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208 continue
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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209 return usage
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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210
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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211 def assign_class_weights(self, n_classes, predicted_usage):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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212 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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213 Compute class weights using usage
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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214 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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215 class_weights = dict()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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216 class_weights[str(0)] = 0.0
3
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217 for key in range(1, n_classes + 1):
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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218 u_score = predicted_usage[key]
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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219 if u_score < 1.0:
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220 u_score += 1.0
3
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221 class_weights[key] = np.round(np.log(u_score), 6)
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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222 return 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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223
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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224 def get_train_last_tool_freq(self, train_paths, reverse_dictionary):
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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225 """
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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226 Get the frequency of last tool of each tool sequence
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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227 to estimate the frequency of tool sequences
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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228 """
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
229 last_tool_freq = dict()
4
afec8c595124 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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230 freq_dict_names = dict()
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
231 for path in train_paths:
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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232 last_tool = path.split(",")[-1]
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
233 if last_tool not in last_tool_freq:
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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234 last_tool_freq[last_tool] = 0
4
afec8c595124 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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235 freq_dict_names[reverse_dictionary[int(last_tool)]] = 0
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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236 last_tool_freq[last_tool] += 1
4
afec8c595124 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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237 freq_dict_names[reverse_dictionary[int(last_tool)]] += 1
afec8c595124 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 65d36f271296a38deeceb0d0e8d471b2898ee8f4"
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238 return last_tool_freq
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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239
3
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
240 def get_toolid_samples(self, train_data, l_tool_freq):
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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241 l_tool_tr_samples = dict()
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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242 for tool_id in l_tool_freq:
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243 for index, tr_sample in enumerate(train_data):
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
244 last_tool_id = str(int(tr_sample[-1]))
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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245 if last_tool_id == tool_id:
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
246 if last_tool_id not in l_tool_tr_samples:
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
247 l_tool_tr_samples[last_tool_id] = list()
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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248 l_tool_tr_samples[last_tool_id].append(index)
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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diff changeset
249 return l_tool_tr_samples
5b3c08710e47 "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit c635df659fe1835679438589ded43136b0e515c6"
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250
5
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diff changeset
251 def get_data_labels_matrices(
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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diff changeset
252 self,
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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253 workflow_paths,
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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254 tool_usage_path,
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255 cutoff_date,
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256 compatible_next_tools,
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257 standard_connections,
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258 old_data_dictionary={},
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259 ):
0
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260 """
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261 Convert the training and test paths into corresponding numpy matrices
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262 """
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263 processed_data, raw_paths = self.process_workflow_paths(workflow_paths)
5
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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264 dictionary, rev_dict = self.create_data_dictionary(
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265 processed_data, old_data_dictionary
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266 )
0
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267 num_classes = len(dictionary)
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268
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269 print("Raw paths: %d" % len(raw_paths))
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270 random.shuffle(raw_paths)
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271
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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272 print("Decomposing paths...")
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273 all_unique_paths = self.decompose_paths(raw_paths, dictionary)
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274 random.shuffle(all_unique_paths)
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275
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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276 print("Creating dictionaries...")
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277 multilabels_paths = self.prepare_paths_labels_dictionary(
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278 dictionary, rev_dict, all_unique_paths, compatible_next_tools
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279 )
0
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280
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281 print("Complete data: %d" % len(multilabels_paths))
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282 train_paths_dict, test_paths_dict = self.split_test_train_data(
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283 multilabels_paths
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284 )
0
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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285
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286 print("Train data: %d" % len(train_paths_dict))
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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287 print("Test data: %d" % len(test_paths_dict))
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288
3
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289 print("Padding train and test data...")
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290 # pad training and test data with leading zeros
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291 test_data, test_labels = self.pad_paths(
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292 test_paths_dict, num_classes, standard_connections, rev_dict
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293 )
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294 train_data, train_labels = self.pad_paths(
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295 train_paths_dict, num_classes, standard_connections, rev_dict
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296 )
3
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297
4
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298 print("Estimating sample frequency...")
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299 l_tool_freq = self.get_train_last_tool_freq(train_paths_dict, rev_dict)
3
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300 l_tool_tr_samples = self.get_toolid_samples(train_data, l_tool_freq)
0
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301
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302 # Predict tools usage
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303 print("Predicting tools' usage...")
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304 usage_pred = predict_tool_usage.ToolPopularity()
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305 usage = usage_pred.extract_tool_usage(tool_usage_path, cutoff_date, dictionary)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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306 tool_usage_prediction = usage_pred.get_pupularity_prediction(usage)
3
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307 t_pred_usage = self.get_predicted_usage(dictionary, tool_usage_prediction)
0
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308
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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309 # get class weights using the predicted usage for each tool
3
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310 class_weights = self.assign_class_weights(num_classes, t_pred_usage)
0
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311
5
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312 return (
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313 train_data,
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314 train_labels,
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315 test_data,
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316 test_labels,
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317 dictionary,
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318 rev_dict,
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319 class_weights,
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320 t_pred_usage,
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321 l_tool_freq,
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322 l_tool_tr_samples,
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323 )