def testing(self, fea2obj):
config = self._config
dsdir = config['dsdir']
devfile = dsdir + '/dev.txt'
testfile = dsdir + '/test.txt'
networkfile = config['net']
batch_size = 10000#int(config['batchsize'])
devMentions = load_ent_ds(devfile)
tstMentions = load_ent_ds(testfile)
logger.info('#dev: %d #test: %d', len(devMentions), len(tstMentions))
main_loop = load(networkfile + '.best.pkl')
logger.info('Model loaded. Building prediction function...')
old_model = main_loop.model
logger.info(old_model.inputs)
sources = [inp.name for inp in old_model.inputs]
# fea2obj = build_input_objs(sources, config)
t2idx = fea2obj['targets'].t2idx
deterministic = str_to_bool(config['use_mean_pred']) if 'use_mean_pred' in config else True
kl_weight = shared_floatx(0.001, 'kl_weight')
entropy_weight= shared_floatx(0.001, 'entropy_weight')
cost, _, y_hat, _, _,_,_ = build_model_new(fea2obj, len(t2idx), self._config, kl_weight, entropy_weight, deterministic=deterministic, test=True)
model = Model(cost)
model.set_parameter_values(old_model.get_parameter_values())
theinputs = []
for fe in fea2obj.keys():
if 'targets' in fe:
continue
for inp in model.inputs:
if inp.name == fe:
theinputs.append(inp)
# theinputs = [inp for inp in model.inputs if inp.name != 'targets']
print "theinputs: ", theinputs
predict = theano.function(theinputs, y_hat)
test_stream, num_samples_test = get_comb_stream(fea2obj, 'test', batch_size, shuffle=False)
dev_stream, num_samples_dev = get_comb_stream(fea2obj, 'dev', batch_size, shuffle=False)
logger.info('sources: %s -- number of test/dev samples: %d/%d', test_stream.sources, num_samples_test, num_samples_dev)
idx2type = {idx:t for t,idx in t2idx.iteritems()}
logger.info('Starting to apply on dev inputs...')
self.applypredict(theinputs, predict, dev_stream, devMentions, num_samples_dev, batch_size, os.path.join(config['exp_dir'], config['matrixdev']), idx2type)
logger.info('...apply on dev data finished')
logger.info('Starting to apply on test inputs...')
self.applypredict(theinputs, predict, test_stream, tstMentions, num_samples_test, batch_size, os.path.join(config['exp_dir'], config['matrixtest']), idx2type)
logger.info('...apply on test data finished')
f.write(onestr.strip() + '\n')
f.close()
logger.info('P@1 is = %f ', goods / num_samples)
if __name__ == '__main__':
logger.info('loading config file: %s', sys.argv[1])
config = cmn.loadConfig(sys.argv[1])
dsdir = config['dsdir']
devfile = dsdir + '/dev.txt'
testfile = dsdir + '/test.txt'
networkfile = config['net']
num_of_hidden_units = int(config['hidden_units'])
targetTypesFile = config['typefile']
batch_size = int(config['batchsize'])
devMentions = load_ent_ds(devfile)
tstMentions = load_ent_ds(testfile)
logger.info('#dev: %d #test: %d', len(devMentions), len(tstMentions))
main_loop = load(networkfile + '.best.pkl')
logger.info('Model loaded. Building prediction function...')
model = main_loop.model
logger.info(model.inputs)
sources = [inp.name for inp in model.inputs]
theinputs = [inp for inp in model.inputs if inp.name != 'targets']
linear_output = [v for v in model.variables
if v.name == 'linear_output'][0]
y_hat = Logistic().apply(linear_output)
predict = theano.function(theinputs, y_hat)