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Update p3b3.py
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‎Pilot3/P3B3/p3b3.py‎

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from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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from sklearn.metrics import accuracy_score
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import os
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import sys
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import argparse
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try:
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import configparser
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except ImportError:
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import ConfigParser as configparser
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file_path = os.path.dirname(os.path.realpath(__file__))
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lib_path = os.path.abspath(os.path.join(file_path, '..', 'common'))
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sys.path.append(lib_path)
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lib_path2 = os.path.abspath(os.path.join(file_path, '..', '..', 'common'))
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sys.path.append(lib_path2)
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import p3_common
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def common_parser(parser):
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parser.add_argument("--config_file", dest='config_file', type=str,
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default=os.path.join(file_path, 'p3b3_default_model.txt'),
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help="specify model configuration file")
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# Parse has been split between arguments that are common with the default neon parser
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# and all the other options
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parser = p3_common.get_default_neon_parse(parser)
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parser = p3_common.get_p3_common_parser(parser)
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# Arguments that are applicable just to p3b1
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parser = p3b3_parser(parser)
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return parser
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def p3b3_parser(parser):
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### Hyperparameters and model save path
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# these are leftover from other models but don't conflict so leave for now
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parser.add_argument("--train", action="store_true",dest="train_bool",default=True,help="Invoke training")
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parser.add_argument("--evaluate", action="store_true",dest="eval_bool",default=False,help="Use model for inference")
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parser.add_argument("--home-dir",help="Home Directory",dest="home_dir",type=str,default='.')
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parser.add_argument("--save-dir",help="Save Directory",dest="save_path",type=str,default=None)
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parser.add_argument("--config-file",help="Config File",dest="config_file",type=str,default=os.path.join(file_path, 'p3b3_default_model.txt'))
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parser.add_argument("--memo",help="Memo",dest="base_memo",type=str,default=None)
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parser.add_argument("--seed", action="store_true",dest="seed",default=False,help="Random Seed")
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parser.add_argument("--case",help="[Full, Center, CenterZ]",dest="case",type=str,default='CenterZ')
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parser.add_argument("--fig", action="store_true",dest="fig_bool",default=False,help="Generate Prediction Figure")
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# MTL_run params start here
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parser.add_argument("-v", "--verbose", action="store_true",
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default= True,
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help="increase output verbosity")
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parser.add_argument("--dropout", action="store",
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default=argparse.SUPPRESS, # DROPOUT, type=float,
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help="ratio of dropout used in fully connected layers")
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parser.add_argument("--learning_rate", action='store',
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default=argparse.SUPPRESS, # LEARNING_RATE, type=float,
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help='learning rate')
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parser.add_argument("--train_features", action="store",
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default='data/train_X.npy',
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help='training feature data filenames')
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parser.add_argument("--train_truths", action="store",
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default='data/train_Y.npy',
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help='training truth data filenames')
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parser.add_argument("--valid_features", action="store",
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default='data/test_X.npy',
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help='validation feature data filenames')
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parser.add_argument("--valid_truths", action="store",
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default='data/test_Y.npy',
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help='validation truth data filenames')
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parser.add_argument("--output_files", action="store",
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default='result.csv',
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help="output filename")
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# parser.add_argument("--shared_nnet_spec", action="store",
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# default=argparse.SUPPRESS, # DEF_SHARED_NNET_SPEC,
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# help='network structure of shared layer')
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# parser.add_argument("--individual_nnet_spec", action="store",
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# default=argparse.SUPPRESS, # DEF_INDIV_NNET_SPEC,
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# help='network structore of task-specific layer')
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return parser
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def read_config_file(File):
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config=configparser.ConfigParser()
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config.read(File)
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section=config.sections()
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Global_Params={}
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Global_Params['learning_rate'] =eval(config.get(section[0],'learning_rate'))
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Global_Params['batch_size'] =eval(config.get(section[0],'batch_size'))
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Global_Params['epochs'] =eval(config.get(section[0],'epochs'))
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Global_Params['dropout'] =eval(config.get(section[0],'dropout'))
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Global_Params['optimizer'] =eval(config.get(section[0],'optimizer'))
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Global_Params['wv_len'] =eval(config.get(section[0],'wv_len'))
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Global_Params['filter_sizes'] =eval(config.get(section[0],'filter_sizes'))
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Global_Params['filter_sets'] =eval(config.get(section[0],'filter_sets'))
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Global_Params['num_filters'] =eval(config.get(section[0],'num_filters'))
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Global_Params['emb_l2'] =eval(config.get(section[0],'emb_l2'))
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Global_Params['w_l2'] =eval(config.get(section[0],'w_l2'))
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import candle_keras as candle
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'''
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additional_definitions = [
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{'name':'train_features',
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'action':'store',
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'default':'data/task0_0_train_feature.csv;data/task1_0_train_feature.csv;data/task2_0_train_feature.csv',
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'help':'training feature data filenames'},
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{'name':'train_truths',
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'action':'store',
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'default':'data/task0_0_train_label.csv;data/task1_0_train_label.csv;data/task2_0_train_label.csv',
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'help':'training truth data filenames'},
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{'name':'valid_features',
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'action':'store',
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'default':'data/task0_0_test_feature.csv;data/task1_0_test_feature.csv;data/task2_0_test_feature.csv',
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'help':'validation feature data filenames'},
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{'name':'valid_truths',
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'action':'store',
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'default':'data/task0_0_test_label.csv;data/task1_0_test_label.csv;data/task2_0_test_label.csv',
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'help':'validation truth data filenames'},
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{'name':'output_files',
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'action':'store',
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'default':'result0_0.csv;result1_0.csv;result2_0.csv',
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'help':'output filename'},
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{'name':'shared_nnet_spec',
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'nargs':'+',
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'type': int,
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'help':'network structure of shared layer'},
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{'name':'ind_nnet_spec',
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'action':'list-of-lists',
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'help':'network structure of task-specific layer'},
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{'name':'case',
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'default':'CenterZ',
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'choices':['Full', 'Center', 'CenterZ'],
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'help':'case classes'},
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{'name':'fig',
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'type': candle.str2bool,
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'default': False,
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'help':'Generate Prediction Figure'},
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{'name':'feature_names',
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'nargs':'+',
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'type': str},
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{'name':'n_fold',
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'action':'store',
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'type':int}
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]
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'''
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required = [
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'learning_rate', 'batch_size', 'epochs', 'dropout', \
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'optimizer', 'wv_len', \
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'filter_sizes', 'filter_sets', 'num_filters', 'emb_l2', 'w_l2']
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class BenchmarkP3B3(candle.Benchmark):
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def set_locals(self):
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"""Functionality to set variables specific for the benchmark
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- required: set of required parameters for the benchmark.
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- additional_definitions: list of dictionaries describing the additional parameters for the
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benchmark.
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"""
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if required is not None:
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self.required = set(required)
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# if additional_definitions is not None:
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# self.additional_definitions = additional_definitions
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# note 'cool' is a boolean
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#Global_Params['cool'] =config.get(section[0],'cool')
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return Global_Params

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