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parameters_selection.py
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parameters_selection.py
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### This is run when you want to select the parameters from the parameters file
from sklearn.model_selection import ParameterGrid
import json
params_data={
'include_special':False, #True is want to include <url> in place of urls if False will be removed
'bert_tokens':False, #True /False
'type_attention':'softmax', #softmax
'set_decay':0.1,
'majority':2,
'max_length':128,
'variance':5,
'window':4,
'alpha':0.5,
'p_value':0.8,
'method':'additive',
'decay':False,
'normalized':False,
'not_recollect':True,
}
#"birnn","birnnatt","birnnscrat","cnn_gru"
common_hp={
'is_model':True,
'logging':'neptune', ###neptune /local
'learning_rate':2e-5, ### learning rate 2e-5 for bert 0.001 for gru
'epsilon':1e-8,
'batch_size':32,
'to_save':True,
'epochs':20,
'auto_weights':True,
'weights':[1.0,1.0,1.0],
'model_name':'birnn',
'random_seed':42,
'max_length':128,
'num_classes':3,
'att_lambda':1,
'device':'cuda',
'train_att':True
}
params_bert={
'path_files':'bert-base-uncased',
'what_bert':'weighted',
'save_only_bert':False,
'supervised_layer_pos':11,
'num_supervised_heads':1,
'dropout_bert':0.1
}
params_other = {
"vocab_size": 0,
"padding_idx": 0,
"hidden_size":64,
"embed_size":0,
"embeddings":None,
"drop_fc":0.2,
"drop_embed":0.2,
"drop_hidden":0.1,
"train_embed":False,
"seq_model":"gru",
"attention":"softmax"
}
if(params_data['bert_tokens']):
for key in params_other:
params_other[key]='N/A'
else:
for key in params_bert:
params_bert[key]='N/A'
def Merge(dict1, dict2,dict3, dict4):
res = {**dict1, **dict2,**dict3, **dict4}
return res
params = Merge(params_data,common_hp,params_bert,params_other)
if __name__=='__main__':
params_list = []
params_new = {}
for key in params.keys():
params_new[key]=[params[key]]
params_new['model_name']=["birnnscrat"]
params_new['learning_rate']=[0.1,0.01,0.001]
params_new['hidden_size']=[64,128]
params_new['drop_embed'] = [0.1,0.2,0.5]
params_new['drop_fc'] = [0.1,0.2,0.5]
params_new['att_lambda']=[0.001,0.01,0.1,1,10,100]
#params_new['drop_hidden'] = [0.1,0.2,0.5]
params_new['seq_model']=['lstm','gru']
params_new['train_embed']=[True,False]
params_list=list(ParameterGrid(params_new))
print('Total experiments to be done:',len(params_list))
with open('all_params_scrat.json', 'w') as fout:
json.dump(params_list ,fout,indent=4)
# for train_att in [True,False]:
# print(train_att)
# params['train_att']=train_att
# if(train_att):
# for supervised_layer_pos in range(10,12):
# params['supervised_layer_pos'] = supervised_layer_pos
# for num_supervised_heads in range(10,12):
# params['num_supervised_heads']= num_supervised_heads
# for att_lambda in [0.01,0.1,1,10,100]:
# params['att_lambda']=att_lambda
# for dropout_bert in [0.1,0.5]:
# params['dropout_bert']=dropout_bert
# for auto_weights in [True,False]:
# params['auto_weights']=auto_weights
# for learning_rate in [2e-5]:
# params['learning_rate']=learning_rate
# params_list.append(params.copy())
# else:
# for dropout_bert in [0.1,0.5]:
# params['dropout_bert']=dropout_bert
# for auto_weights in [True,False]:
# params['auto_weights']=auto_weights
# for learning_rate in [2e-5]:
# params['learning_rate']=learning_rate
# params_list.append(params.copy())
# #