Mercurial > repos > bgruening > create_tool_recommendation_model

comparison main.py @ 1:12764915e1c5 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit edeb85d311990eabd65f3c4576fbeabc6d9165c9"
author bgruening
date Wed, 25 Sep 2019 06:42:40 -0400
parents 9bf25dbe00ad
children 76251d1ccdcc
comparison
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0:9bf25dbe00ad 1:12764915e1c5
110 arg_parser.add_argument("-sd", "--spatial_dropout", required=True, help="1d dropout used for embedding layer") 110 arg_parser.add_argument("-sd", "--spatial_dropout", required=True, help="1d dropout used for embedding layer")
111 arg_parser.add_argument("-rd", "--recurrent_dropout", required=True, help="dropout for the recurrent layers") 111 arg_parser.add_argument("-rd", "--recurrent_dropout", required=True, help="dropout for the recurrent layers")
112 arg_parser.add_argument("-lr", "--learning_rate", required=True, help="learning rate") 112 arg_parser.add_argument("-lr", "--learning_rate", required=True, help="learning rate")
113 arg_parser.add_argument("-ar", "--activation_recurrent", required=True, help="activation function for recurrent layers") 113 arg_parser.add_argument("-ar", "--activation_recurrent", required=True, help="activation function for recurrent layers")
114 arg_parser.add_argument("-ao", "--activation_output", required=True, help="activation function for output layers") 114 arg_parser.add_argument("-ao", "--activation_output", required=True, help="activation function for output layers")
115 arg_parser.add_argument("-lt", "--loss_type", required=True, help="type of the loss/error function")
116 # get argument values 115 # get argument values
117 args = vars(arg_parser.parse_args()) 116 args = vars(arg_parser.parse_args())
118 tool_usage_path = args["tool_usage_file"] 117 tool_usage_path = args["tool_usage_file"]
119 workflows_path = args["workflow_file"] 118 workflows_path = args["workflow_file"]
120 cutoff_date = args["cutoff_date"] 119 cutoff_date = args["cutoff_date"]
132 spatial_dropout = args["spatial_dropout"] 131 spatial_dropout = args["spatial_dropout"]
133 recurrent_dropout = args["recurrent_dropout"] 132 recurrent_dropout = args["recurrent_dropout"]
134 learning_rate = args["learning_rate"] 133 learning_rate = args["learning_rate"]
135 activation_recurrent = args["activation_recurrent"] 134 activation_recurrent = args["activation_recurrent"]
136 activation_output = args["activation_output"] 135 activation_output = args["activation_output"]
137 loss_type = args["loss_type"]
138 136
139 config = { 137 config = {
140 'cutoff_date': cutoff_date, 138 'cutoff_date': cutoff_date,
141 'maximum_path_length': maximum_path_length, 139 'maximum_path_length': maximum_path_length,
142 'n_epochs': n_epochs, 140 'n_epochs': n_epochs,
150 'dropout': dropout, 148 'dropout': dropout,
151 'spatial_dropout': spatial_dropout, 149 'spatial_dropout': spatial_dropout,
152 'recurrent_dropout': recurrent_dropout, 150 'recurrent_dropout': recurrent_dropout,
153 'learning_rate': learning_rate, 151 'learning_rate': learning_rate,
154 'activation_recurrent': activation_recurrent, 152 'activation_recurrent': activation_recurrent,
155 'activation_output': activation_output, 153 'activation_output': activation_output
156 'loss_type': loss_type
157 } 154 }
158 155
159 # Extract and process workflows 156 # Extract and process workflows
160 connections = extract_workflow_connections.ExtractWorkflowConnections() 157 connections = extract_workflow_connections.ExtractWorkflowConnections()
161 workflow_paths, compatible_next_tools = connections.read_tabular_file(workflows_path) 158 workflow_paths, compatible_next_tools = connections.read_tabular_file(workflows_path)