Mercurial > repos > bgruening > create_tool_recommendation_model

annotate utils.py @ 0:9bf25dbe00ad draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
author bgruening
date Wed, 28 Aug 2019 07:19:38 -0400
parents
children 76251d1ccdcc
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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1 import os
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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2 import numpy as np
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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3 import json
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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4 import h5py
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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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 from keras.models import model_from_json, Sequential
9bf25dbe00ad "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.layers import Dense, GRU, Dropout
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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8 from keras.layers.embeddings import Embedding
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9 from keras.layers.core import SpatialDropout1D
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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10 from keras.optimizers import RMSprop
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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11 from keras import backend as K
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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14 def read_file(file_path):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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15 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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16 Read a file
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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17 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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18 with open(file_path, "r") as json_file:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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19 file_content = json.loads(json_file.read())
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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20 return file_content
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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23 def write_file(file_path, content):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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24 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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25 Write a file
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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26 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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27 remove_file(file_path)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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28 with open(file_path, "w") as json_file:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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29 json_file.write(json.dumps(content))
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32 def save_processed_workflows(file_path, unique_paths):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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33 workflow_paths_unique = ""
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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34 for path in unique_paths:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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35 workflow_paths_unique += path + "\n"
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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36 with open(file_path, "w") as workflows_file:
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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37 workflows_file.write(workflow_paths_unique)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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40 def load_saved_model(model_config, model_weights):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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41 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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42 Load the saved trained model using the saved network and its weights
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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43 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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44 # load the network
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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45 loaded_model = model_from_json(model_config)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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46 # load the saved weights into the model
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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47 loaded_model.set_weights(model_weights)
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48 return loaded_model
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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49
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50
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51 def format_tool_id(tool_link):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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52 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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53 Extract tool id from tool link
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54 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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55 tool_id_split = tool_link.split("/")
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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56 tool_id = tool_id_split[-2] if len(tool_id_split) > 1 else tool_link
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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57 return tool_id
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60 def get_HDF5(hf, d_key):
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61 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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62 Read h5 file to get train and test data
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63 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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64 return hf.get(d_key).value
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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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66
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67 def save_HDF5(hf_file, d_key, data, d_type=""):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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68 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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69 Save datasets as h5 file
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70 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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71 if (d_type == 'json'):
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72 data = json.dumps(data)
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73 hf_file.create_dataset(d_key, data=data)
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75
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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76 def set_trained_model(dump_file, model_values):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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77 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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78 Create an h5 file with the trained weights and associated dicts
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79 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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80 hf_file = h5py.File(dump_file, 'w')
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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81 for key in model_values:
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82 value = model_values[key]
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83 if key == 'model_weights':
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84 for idx, item in enumerate(value):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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85 w_key = "weight_" + str(idx)
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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86 if w_key in hf_file:
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87 hf_file.modify(w_key, item)
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88 else:
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89 hf_file.create_dataset(w_key, data=item)
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90 else:
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91 if key in hf_file:
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92 hf_file.modify(key, json.dumps(value))
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93 else:
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94 hf_file.create_dataset(key, data=json.dumps(value))
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95 hf_file.close()
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96
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97
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98 def remove_file(file_path):
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99 if os.path.exists(file_path):
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100 os.remove(file_path)
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101
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102
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103 def extract_configuration(config_object):
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104 config_loss = dict()
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105 for index, item in enumerate(config_object):
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106 config_loss[index] = list()
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107 d_config = dict()
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108 d_config['loss'] = item['result']['loss']
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109 d_config['params_config'] = item['misc']['vals']
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110 config_loss[index].append(d_config)
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111 return config_loss
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112
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113
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114 def get_best_parameters(mdl_dict):
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115 """
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116 Get param values (defaults as well)
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117 """
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118 lr = float(mdl_dict.get("learning_rate", "0.001"))
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119 embedding_size = int(mdl_dict.get("embedding_size", "512"))
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120 dropout = float(mdl_dict.get("dropout", "0.2"))
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121 recurrent_dropout = float(mdl_dict.get("recurrent_dropout", "0.2"))
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122 spatial_dropout = float(mdl_dict.get("spatial_dropout", "0.2"))
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123 units = int(mdl_dict.get("units", "512"))
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124 batch_size = int(mdl_dict.get("batch_size", "512"))
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125 activation_recurrent = mdl_dict.get("activation_recurrent", "elu")
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126 activation_output = mdl_dict.get("activation_output", "sigmoid")
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127
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128 return {
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129 "lr": lr,
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130 "embedding_size": embedding_size,
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131 "dropout": dropout,
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132 "recurrent_dropout": recurrent_dropout,
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133 "spatial_dropout": spatial_dropout,
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134 "units": units,
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135 "batch_size": batch_size,
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136 "activation_recurrent": activation_recurrent,
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137 "activation_output": activation_output,
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138 }
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139
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140
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141 def weighted_loss(class_weights):
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142 """
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143 Create a weighted loss function. Penalise the misclassification
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144 of classes more with the higher usage
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145 """
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146 weight_values = list(class_weights.values())
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147
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148 def weighted_binary_crossentropy(y_true, y_pred):
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149 # add another dimension to compute dot product
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150 expanded_weights = K.expand_dims(weight_values, axis=-1)
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151 return K.dot(K.binary_crossentropy(y_true, y_pred), expanded_weights)
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152 return weighted_binary_crossentropy
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153
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154
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155 def set_recurrent_network(mdl_dict, reverse_dictionary, class_weights):
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156 """
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157 Create a RNN network and set its parameters
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158 """
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159 dimensions = len(reverse_dictionary) + 1
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160 model_params = get_best_parameters(mdl_dict)
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161
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162 # define the architecture of the neural network
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163 model = Sequential()
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164 model.add(Embedding(dimensions, model_params["embedding_size"], mask_zero=True))
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165 model.add(SpatialDropout1D(model_params["spatial_dropout"]))
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166 model.add(GRU(model_params["units"], dropout=model_params["spatial_dropout"], recurrent_dropout=model_params["recurrent_dropout"], activation=model_params["activation_recurrent"], return_sequences=True))
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167 model.add(Dropout(model_params["dropout"]))
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168 model.add(GRU(model_params["units"], dropout=model_params["spatial_dropout"], recurrent_dropout=model_params["recurrent_dropout"], activation=model_params["activation_recurrent"], return_sequences=False))
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169 model.add(Dropout(model_params["dropout"]))
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170 model.add(Dense(dimensions, activation=model_params["activation_output"]))
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171 optimizer = RMSprop(lr=model_params["lr"])
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172 model.compile(loss=weighted_loss(class_weights), optimizer=optimizer)
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173 return model, model_params
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174
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175
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176 def compute_precision(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, topk):
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 Compute absolute and compatible precision
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179 """
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180 absolute_precision = 0.0
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181 test_sample = np.reshape(x, (1, len(x)))
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182
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183 # predict next tools for a test path
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184 prediction = model.predict(test_sample, verbose=0)
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185
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186 nw_dimension = prediction.shape[1]
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187
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188 # remove the 0th position as there is no tool at this index
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189 prediction = np.reshape(prediction, (nw_dimension,))
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190
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191 prediction_pos = np.argsort(prediction, axis=-1)
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192 topk_prediction_pos = prediction_pos[-topk:]
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193
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194 # remove the wrong tool position from the predicted list of tool positions
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195 topk_prediction_pos = [x for x in topk_prediction_pos if x > 0]
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196
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197 # read tool names using reverse dictionary
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198 actual_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in actual_classes_pos]
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199 top_predicted_next_tool_names = [reverse_data_dictionary[int(tool_pos)] for tool_pos in topk_prediction_pos]
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200
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201 # compute the class weights of predicted tools
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202 mean_usg_score = 0
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203 usg_wt_scores = list()
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204 for t_id in topk_prediction_pos:
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205 t_name = reverse_data_dictionary[int(t_id)]
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206 if t_id in usage_scores and t_name in actual_next_tool_names:
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207 usg_wt_scores.append(np.log(usage_scores[t_id] + 1.0))
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208 if len(usg_wt_scores) > 0:
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209 mean_usg_score = np.sum(usg_wt_scores) / float(topk)
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210 false_positives = [tool_name for tool_name in top_predicted_next_tool_names if tool_name not in actual_next_tool_names]
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211 absolute_precision = 1 - (len(false_positives) / float(topk))
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212 return mean_usg_score, absolute_precision
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213
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214
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215 def verify_model(model, x, y, reverse_data_dictionary, next_compatible_tools, usage_scores, topk_list=[1, 2, 3]):
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216 """
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217 Verify the model on test data
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218 """
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219 print("Evaluating performance on test data...")
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220 print("Test data size: %d" % len(y))
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221 size = y.shape[0]
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222 precision = np.zeros([len(y), len(topk_list)])
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223 usage_weights = np.zeros([len(y), len(topk_list)])
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224 # loop over all the test samples and find prediction precision
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225 for i in range(size):
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226 actual_classes_pos = np.where(y[i] > 0)[0]
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227 for index, abs_topk in enumerate(topk_list):
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228 abs_mean_usg_score, absolute_precision = compute_precision(model, x[i, :], y, reverse_data_dictionary, next_compatible_tools, usage_scores, actual_classes_pos, abs_topk)
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229 precision[i][index] = absolute_precision
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230 usage_weights[i][index] = abs_mean_usg_score
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231 mean_precision = np.mean(precision, axis=0)
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232 mean_usage = np.mean(usage_weights, axis=0)
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233 return mean_precision, mean_usage
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234
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235
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236 def save_model(results, data_dictionary, compatible_next_tools, trained_model_path, class_weights):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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237 # save files
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238 trained_model = results["model"]
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239 best_model_parameters = results["best_parameters"]
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240 model_config = trained_model.to_json()
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241 model_weights = trained_model.get_weights()
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242
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243 model_values = {
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244 'data_dictionary': data_dictionary,
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245 'model_config': model_config,
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246 'best_parameters': best_model_parameters,
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247 'model_weights': model_weights,
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248 "compatible_tools": compatible_next_tools,
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249 "class_weights": class_weights
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250 }
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251 set_trained_model(trained_model_path, model_values)