def update_hyper_pars(hyper_pars, bandwidth= 0.1):
v = copy.deepcopy(hyper_pars.varirate)
v['meta_neurons'] = gen_candidate( v['meta_neurons'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['meta_dropout'] = gen_candidate( v['meta_dropout'] , bandwidth = bandwidth , type = '' , min_value = 0, max_value=0.99)
v['meta_layer'] = gen_candidate( v['meta_layer'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['lstm1_max_len'] = gen_candidate( v['lstm1_max_len'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['lstm1_neurons'] = gen_candidate( v['lstm1_neurons'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['lstm1_dropout'] = gen_candidate( v['lstm1_dropout'] , bandwidth = bandwidth , type = '' , min_value = 0, max_value=0.99)
v['lstm1_layer'] = gen_candidate( v['lstm1_layer'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
# v['lstm2_max_len'] = gen_candidate( v['lstm2_max_len'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
# v['lstm2_neurons'] = gen_candidate( v['lstm2_neurons'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
# v['lstm2_dropout'] = gen_candidate( v['lstm2_dropout'] , bandwidth = bandwidth , type = '' , min_value = 0, max_value=0.99)
# v['lstm2_layer'] = gen_candidate( v['lstm2_layer'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['fc_neurons'] = gen_candidate( v['fc_neurons'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['fc_dropout'] = gen_candidate( v['fc_dropout'] , bandwidth = bandwidth , type = '' , min_value = 0, max_value=0.99)
v['fc_layer'] = gen_candidate( v['fc_layer'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['max_words'] = gen_candidate( v['max_words'] , bandwidth = bandwidth , type = 'int' , min_value = 1)
v['lr'] = gen_candidate( v['lr'] , bandwidth = bandwidth , type = '' , min_value = 0.000001)
v['n_embedding'] = gen_candidate( v['n_embedding'] , bandwidth = bandwidth , type = 'int' , min_value = 1, max_value = 300)
v['decay'] = gen_candidate( v['decay'] , bandwidth = bandwidth , type = '' , min_value = 0)
v['w'] = gen_candidate( v['w'] , bandwidth = bandwidth , type = '' , min_value = 0)
print(v)
f = copy.deepcopy(hyper_pars.fixed)
f['mod_id'] = str(round((time())))
return hyper_parameters(v, f)
def gen_hyper_pars_10_years(year_target, qt_target, root):
x = hyper_parameters(
varirate ={
'meta_layer' : 2,
'meta_neurons' : 50,
'meta_dropout' : 0.4,
'lstm1_max_len' : 100,
'lstm1_neurons' : 80 ,
'lstm1_dropout' : 0.1 ,
'lstm1_layer' : 5,
# 'lstm2_max_len' : 58,
# 'lstm2_neurons' : 32,
# 'lstm2_dropout' : 0.1,
# 'lstm2_layer' : 2,
'fc_neurons' : 40,
'fc_dropout' : 0.3,
'fc_layer' : 2,
'max_words' : 10000,
'lr' : 0.002,
'n_embedding' : 150,
'decay': 0.0001,
'w': 0.3
},
fixed = get_fixed_10_years(year_target, qt_target, root)
)
return x
def gen_hyper_pars(root="./"):
x = hyper_parameters(varirate={
'lstm1_max_len': 30,
'lstm1_neurons': 30,
'lstm1_dropout': 0.1,
'lstm1_layer': 5,
'fc_neurons': 30,
'fc_dropout': 0.3,
'fc_layer': 2,
'max_words': 10000,
'lr': 0.002,
'n_embedding': 150,
'decay': 0.0001,
'w': 0.3
},
fixed=get_fixed(root))
return x
def gen_hyper_pars_testing(year_target, mt_target, root):
x = hyper_parameters(varirate={
'meta_layer': 2,
'meta_neurons': 10,
'meta_dropout': 0.1,
'lstm1_max_len': 10,
'lstm1_neurons': 10,
'lstm1_dropout': 0.1,
'lstm1_layer': 2,
'fc_neurons': 10,
'fc_dropout': 0.1,
'fc_layer': 2,
'max_words': 10000,
'lr': 0.002,
'n_embedding': 150,
'decay': 0.0001,
'w': 1.2
},
fixed=get_fixed_2_years_quarterly(
year_target, mt_target, root))
return x
