def test_w2v_custom_emb(option, function, set_params):
set_params.update({
'embedding_types': [dn.EmbeddingType.WORD2VEC_CUSTOM],
'embedding_custom_files': [
'SMHD-Skipgram-AllUsers-300.bin', 'SMHD-CBOW-AllUsers-300.bin',
'SMHD-Skipgram-A-D-ADUsers-300.bin',
'SMHD-CBOW-A-D-ADUsers-300.bin'
],
'use_embeddings': [dn.UseEmbedding.STATIC, dn.UseEmbedding.NON_STATIC]
})
if option == '1':
print('Initializer experiment '+option+' (model SMHD_cnn_gl_1040_A_D)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 diffs')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=1040,
subdirectory="only_disorders/A_D")
generate_model(exp, 't' + option + '_SMHD_cnn_gl_1040_A_D', '_glorot',
set_params, function)
elif option == '2':
print('Initializer experiment '+option+' (model SMHD_cnn_gl_880_A_AD)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 diffs')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="only_disorders/A_AD")
generate_model(exp, 't' + option + '_SMHD_cnn_gl_880_A_AD', '_glorot',
set_params, function)
elif option == '3':
print('Initializer experiment '+option+' (model SMHD_ml_gl_880_D_AD)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 diffs')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="only_disorders/D_AD")
generate_model(exp, 't' + option + '_SMHD_ml_gl_880_D_AD', '_glorot',
set_params, function)
def test_w2v_custom_emb(option, function):
set_params = dict({'filters_by_layer': [100, 250],
'kernels_size': [3, 4, 5],
'epochs': [10],
'batch_sizes': [20],
'dropouts': [0.2, 0.5],
'embedding_types': [dn.EmbeddingType.WORD2VEC_CUSTOM],
'embedding_custom_files': ['SMHD-Skipgram-AllUsers-300.bin', 'SMHD-CBOW-AllUsers-300.bin',
'SMHD-Skipgram-A-D-ADUsers-300.bin', 'SMHD-CBOW-A-D-ADUsers-300.bin'],
'use_embeddings': [dn.UseEmbedding.STATIC, dn.UseEmbedding.NON_STATIC]})
if option == '1':
print('Initializer experiment ' + option + ' (model SMHD_cnn_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model(exp, 't' + option + '_wc_SMHD_cnn_gl_880', '_glorot', set_params, function)
elif option == '2':
print('Initializer experiment ' + option + ' (model SMHD_cnn_gl_1040)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 multi-label')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
generate_model(exp, 't' + option + '_wc_SMHD_cnn_gl_1040', '_glorot', set_params, function)
elif option == '3':
print('Initializer experiment ' + option + ' (model SMHD_cnn_gl_2160)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2160, subdirectory="depression")
generate_model(exp, 't' + option + '_wc_SMHD_cnn_gl_2160', '_glorot', set_params, function)
elif option == '4':
print('Initializer experiment ' + option + ' (model SMHD_cnn_gl_2640)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label')
exp = ExperimentProcesses('t' + option + '_cnn_L1')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2640, subdirectory="anx_dep_multilabel")
generate_model(exp, 't' + option + '_wc_SMHD_cnn_gl_2460', '_glorot', set_params, function)
def generate_model(set_params, train_mode=True):
from utils.experiment_processes import ExperimentProcesses
import utils.definition_network as dn
exp = ExperimentProcesses(set_params['function'])
exp.pp_data.vocabulary_size = 5000
exp.pp_data.embedding_size = 300
exp.pp_data.max_posts = 1750
exp.pp_data.max_terms_by_post = 300
exp.pp_data.binary_classifier = True
exp.pp_data.format_input_data = dn.InputData.POSTS_ONLY_TEXT
exp.pp_data.remove_stopwords = False
exp.pp_data.delete_low_tfid = False
exp.pp_data.min_df = 0
exp.pp_data.min_tf = 0
exp.pp_data.random_posts = False
exp.pp_data.random_users = True
exp.pp_data.tokenizing_type = 'WE'
exp.pp_data.type_prediction_label = dn.TypePredictionLabel.MULTI_LABEL_CATEGORICAL
exp.use_custom_metrics = False
exp.use_valid_set_for_train = True
exp.valid_split_from_train_set = 0.0
exp.imbalanced_classes = False
exp.pp_data.embedding_type = set_params['embedding_type']
exp.pp_data.word_embedding_custom_file = set_params['custom_file']
exp.pp_data.use_embedding = set_params['use_embedding']
if train_mode:
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
else:
exp.pp_data.load_dataset_type = dn.LoadDataset.TEST_DATA_MODEL
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=set_params['total_registers'],
subdirectory=set_params['subdirectory'])
return exp
def test_diff_we_function(function, option):
if function == 'gloveTwitter':
set_params = dict({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [32],
'embedding_types': [dn.EmbeddingType.GLOVE_TWITTER],
'embedding_custom_files': ['']
})
if option == '1':
set_params.update({'use_embeddings': [dn.UseEmbedding.STATIC]})
else:
set_params.update({'use_embeddings': [dn.UseEmbedding.NON_STATIC]})
model_name = 't' + option + '_gt_SMHD_ml_gl_2640_var_hl'
elif function == 'googleNews':
set_params = dict({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [32],
'embedding_types': [dn.EmbeddingType.WORD2VEC],
'embedding_custom_files': ['']
})
if option == '1':
set_params.update({'use_embeddings': [dn.UseEmbedding.STATIC]})
else:
set_params.update({'use_embeddings': [dn.UseEmbedding.NON_STATIC]})
model_name = 't' + option + '_gn_SMHD_ml_gl_2640_var_hl'
elif function == 'gloveCustom':
set_params = dict({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [32],
'embedding_types': [dn.EmbeddingType.GLOVE_CUSTOM],
'embedding_custom_files':
['SMHD-glove-AllUsers-300.pkl', 'SMHD-glove-A-D-ADUsers-300.pkl']
})
if option == '1':
set_params.update({'use_embeddings': [dn.UseEmbedding.STATIC]})
else:
set_params.update({'use_embeddings': [dn.UseEmbedding.NON_STATIC]})
model_name = 't' + option + '_gc_SMHD_ml_gl_2640_var_hl'
else: # 'w2vCustom'
set_params = dict({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [32],
'embedding_types': [dn.EmbeddingType.WORD2VEC_CUSTOM],
'embedding_custom_files': [
'SMHD-Skipgram-AllUsers-300.bin', 'SMHD-CBOW-AllUsers-300.bin',
'SMHD-Skipgram-A-D-ADUsers-300.bin',
'SMHD-CBOW-A-D-ADUsers-300.bin'
]
})
if option == '1':
set_params.update({'use_embeddings': [dn.UseEmbedding.STATIC]})
else:
set_params.update({'use_embeddings': [dn.UseEmbedding.NON_STATIC]})
model_name = 't' + option + '_wc_SMHD_ml_gl_2640_var_hl'
print('Initializer experiment multclass_lstm_model '+function+', option '+option+'\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, model_name, '_glorot', set_params, function)
def main():
for arg in sys.argv[1]:
if arg == '1':
print('Initializer experiment 1 - model SMHD_anx_1')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'anxiety'],
total_registers=1040)
generate_model_1(exp, 'SMHD_anx_1')
elif arg == '2':
print('Initializer experiment 1 - model SMHD_dep_1')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'depression'],
total_registers=2120)
generate_model_1(exp, 'SMHD_dep_1')
elif arg == '3':
print('Initializer experiment 1 - model SMHD_anx_dep_1')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880)
generate_model_1(exp, 'SMHD_anx_dep_1')
elif arg == '4':
print('Initializer experiment 1 - model SMHD_anx_2')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'anxiety'],
total_registers=1040)
generate_model_2(exp, 'SMHD_anx_2')
elif arg == '5':
print('Initializer experiment 1 - model SMHD_dep_2')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'depression'],
total_registers=2120)
generate_model_2(exp, 'SMHD_dep_2')
elif arg == '6':
print('Initializer experiment 1 - model SMHD_anx_dep_2')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880)
generate_model_2(exp, 'SMHD_anx_dep_2')
elif arg == '7':
print('Initializer experiment 1 - model SMHD_anx_3')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'anxiety'],
total_registers=1040)
generate_model_3(exp, 'SMHD_anx_3')
elif arg == '8':
print('Initializer experiment 1 - model SMHD_dep_3')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD',
label_set=['control', 'depression'],
total_registers=2120)
generate_model_4(exp, 'SMHD_dep_3')
else: #if arg == '9':
print('Initializer experiment 1 - model SMHD_anx_dep_3')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880)
generate_model_5(exp, 'SMHD_anx_dep_3')
def test_none_emb(option, function, dataset):
name_model = 't' + option + '_' + function
set_params = dict({
'embedding_types': [dn.EmbeddingType.NONE],
'embedding_custom_files': [''],
'use_embeddings': [dn.UseEmbedding.RAND]
})
if dataset == 'anx':
if option == '1':
set_params.update({
'filters_by_layer': [128],
'kernels_size': [4],
'dropouts': [0.2],
'neuronios_by_lstm_layer': [64],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
else:
set_params.update({
'filters_by_layer': [128],
'kernels_size': [5],
'dropouts': [0.2],
'neuronios_by_lstm_layer': [64],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
print('Initializer experiment ' + option + ' (model SMHD_cnn_lstm_gl_1040)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 multi-label')
exp = ExperimentProcesses(name_model + '_cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=1040,
subdirectory="anxiety")
generate_model(exp, name_model + '_SMHD_cnn_lstm_gl_1040', '_glorot',
set_params, function)
elif dataset == 'dep':
if option == '1':
set_params.update({
'filters_by_layer': [128],
'kernels_size': [4],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [128],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
else:
set_params.update({
'filters_by_layer': [128],
'kernels_size': [5],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [128],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
print('Initializer experiment ' + option + ' (model SMHD_cnn_lstm_gl_2160)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses(name_model + '_cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2160,
subdirectory="depression")
generate_model(exp, name_model + '_SMHD_cnn_lstm_gl_2160', '_glorot',
set_params, function)
elif dataset == 'anx_dep':
if option == '1':
set_params.update({
'filters_by_layer': [64],
'kernels_size': [5],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [256],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
else:
set_params.update({
'filters_by_layer': [64],
'kernels_size': [4],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [256],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
print('Initializer experiment ' + option + ' (model SMHD_cnn_lstm_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses(name_model + '_cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
generate_model(exp, name_model + '_SMHD_cnn_lstm_gl_880', '_glorot',
set_params, function)
else: # multi
if option == '1':
set_params.update({
'filters_by_layer': [64],
'kernels_size': [4],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [64],
'dropouts_lstm': [0.2],
'epochs': [50],
'batch_sizes': [20]
})
else:
set_params.update({
'filters_by_layer': [128],
'kernels_size': [5],
'dropouts': [0.5],
'neuronios_by_lstm_layer': [256],
'dropouts_lstm': [0.5],
'epochs': [50],
'batch_sizes': [20]
})
print('Initializer experiment ' + option + ' (model SMHD_cnn_lstm_gl_2640)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label')
exp = ExperimentProcesses(name_model + '_cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, name_model + '_SMHD_cnn_lstm_gl_2640', '_glorot',
set_params, function)
def main(arg):
set_params = dict()
if arg == '1':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [40],
'dropouts': [0.1, 0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '2':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.1, 0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '3':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '4':
set_params.update({'neuronios_by_layer': [32],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.1, 0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '5':
set_params.update({'neuronios_by_layer': [100],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '6':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_1040_A_D_glove_a-d-aduser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
load_submodel_anx(exp, 't'+arg+'_SMHD_ml_gl_1040_A_D_glove_a-d-aduser', '_glorot', set_params)
elif arg == '7':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_1040_A_D_cbow_a-d-aduser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
load_submodel_dep(exp, 't'+arg+'_SMHD_ml_gl_1040_A_D_cbow_a-d-aduser', '_glorot', set_params)
elif arg == '8':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [40],
'dropouts': [0.1, 0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_880_A_AD)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_880 only_disorders/A_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/A_AD")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_880_A_AD', set_params)
elif arg == '9':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_880_A_AD_glove_a-d-aduser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 only_disorders/A_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/A_AD")
load_submodel_anx(exp, 't'+arg+'_SMHD_ml_gl_880_A_AD_glove_a-d-aduser', '_glorot', set_params)
elif arg == '10':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_880_A_AD_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 only_disorders/A_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/A_AD")
load_submodel_anx_dep(exp, 't'+arg+'_SMHD_ml_gl_880_A_AD_cbow_alluser', '_glorot', set_params)
elif arg == '11':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [40],
'dropouts': [0.1, 0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_880_D_AD)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_880 only_disorders/D_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/D_AD")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_880_D_AD', set_params)
elif arg == '12':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_880_D_AD_cbow_a-d-aduser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 only_disorders/D_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/D_AD")
load_submodel_dep(exp, 't'+arg+'_SMHD_ml_gl_880_D_AD_cbow_a-d-aduser', '_glorot', set_params)
elif arg == '13':
set_params.update({'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_gl_880_D_AD_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 only_disorders/D_AD')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="only_disorders/D_AD")
load_submodel_anx_dep(exp, 't'+arg+'_SMHD_ml_gl_880_D_AD_cbow_alluser', '_glorot', set_params)
elif arg == '14':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [10],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '15':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [10],
'dropouts': [0.15]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '16':
set_params.update({'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [10],
'dropouts': [0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '17':
set_params.update({'neuronios_by_layer': [32],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '18':
set_params.update({'neuronios_by_layer': [32],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.15]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '19':
set_params.update({'neuronios_by_layer': [32],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
generate_model_ml_le(exp, 't'+arg+'_SMHD_ml_le_1040_A_D', set_params)
elif arg == '20':
set_params.update({'neuronios_by_layer': [16],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.1]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
load_submodel_anx_dep(exp, 't'+arg+'_SMHD_ml_gl_1040_A_D_cbow_alluser', '_glorot', set_params)
elif arg == '21':
set_params.update({'neuronios_by_layer': [16],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.15]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
load_submodel_anx_dep(exp, 't'+arg+'_SMHD_ml_gl_1040_A_D_cbow_alluser', '_glorot', set_params)
elif arg == '22':
set_params.update({'neuronios_by_layer': [16],
'epochs': [64],
'batch_sizes': [10],
'dropouts': [0.2]})
print('Initializer experiment '+arg+' (model SMHD_ml_le_1040_A_D)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 only_disorders/A_D')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="only_disorders/A_D")
load_submodel_anx_dep(exp, 't'+arg+'_SMHD_ml_gl_1040_A_D_cbow_alluser', '_glorot', set_params)
def main(arg):
if arg == '1':
print('Initializer experiment 1 (model SMHD_ml_gl_1040_gloveadad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=1040,
subdirectory="anxiety")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx(exp, 'SMHD_ml_gl_1040_gloveadad', '_glorot',
set_params)
elif arg == '2':
print('Initializer experiment 2 (model SMHD_ml_gl_1040_glove-a-d-ad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=1040,
subdirectory="anxiety")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx(exp, 'SMHD_ml_gl_1040_gloveadad', '_glorot',
set_params)
elif arg == '3':
print('Initializer experiment 3 (model SMHD_ml_gl_1040_glove-a-d-ad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=1040,
subdirectory="anxiety")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx(exp, 'SMHD_ml_gl_1040_gloveadad', '_glorot',
set_params)
elif arg == '4':
print('Initializer experiment 4 (model SMHD_ml_gl_2160_cbow_adad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2160,
subdirectory="depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_dep(exp, 'SMHD_ml_gl_2160_cbow_adad', '_glorot',
set_params)
elif arg == '5':
print('Initializer experiment 5 (model SMHD_ml_gl_2160_cbow_adad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2160,
subdirectory="depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_dep(exp, 'SMHD_ml_gl_2160_cbow_adad', '_glorot',
set_params)
elif arg == '6':
print('Initializer experiment 6 (model SMHD_ml_gl_2160_cbow_adad)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2160,
subdirectory="depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_dep(exp, 'SMHD_ml_gl_2160_cbow_adad', '_glorot',
set_params)
"""
Test Model using RSDD Dataset
"""
import utils.definition_network as dn
from utils.experiment_processes import ExperimentProcesses
import datetime
# LAYERS
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Embedding, TimeDistributed, Input
from keras.layers import LSTM
from network_model.model_class import ModelClass
if __name__ == '__main__':
exp = ExperimentProcesses('lstm_exp9_var_L2')
exp.pp_data.vocabulary_size = 5000
exp.pp_data.embedding_size = 300
exp.pp_data.max_posts = 1750
exp.pp_data.max_terms_by_post = 300
exp.pp_data.binary_classifier = True
exp.pp_data.format_input_data = dn.InputData.POSTS_ONLY_TEXT
exp.pp_data.remove_stopwords = False
exp.pp_data.delete_low_tfid = False
exp.pp_data.min_df = 0
exp.pp_data.min_tf = 0
exp.pp_data.random_posts = False
exp.pp_data.random_users = False
exp.pp_data.tokenizing_type = 'WE'
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
def test_optimizer_function(option):
set_params = dict()
# change optimizer function
if option == '1':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [16],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 1\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' +\
'Optimizer function variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_of', '_glorot', set_params)
elif option == '2':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAMAX.value],
'neuronios_by_layer': [16],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 2\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Optimizer function variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_of', '_glorot', set_params)
elif option == '3':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.NADAM.value],
'neuronios_by_layer': [16],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 3\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Optimizer function variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_of', '_glorot', set_params)
elif option == '4':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADADELTA.value],
'neuronios_by_layer': [16],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 4\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Optimizer function variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_of', '_glorot', set_params)
elif option == '5':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAGRAD.value],
'neuronios_by_layer': [16],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 5\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Optimizer function variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_of', '_glorot', set_params)
# change neuronios by layers
elif option == '6':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 6\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_nl', '_glorot', set_params)
elif option == '7':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [64],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 7\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_nl', '_glorot', set_params)
elif option == '8':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [96],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 8\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_nl', '_glorot', set_params)
elif option == '9':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [128],
'hidden_layers': [3],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 9\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_nl', '_glorot', set_params)
# change hidden layers
elif option == '10':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 10\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Neurons by layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_nl', '_glorot', set_params)
elif option == '11':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [5],
'batch_sizes': [40],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 11\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Hidden layer variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_hl', '_glorot', set_params)
# change batch_size values
elif option == '12':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [5],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 12\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Batch size variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_bs', '_glorot', set_params)
elif option == '13':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [10],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 13\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Epochs variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_ep', '_glorot', set_params)
elif option == '14':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [20],
'epochs': [32]
})
print('Initializer experiment multclass_lstm_model 14\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Epochs variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_mx', '_glorot', set_params)
# change epochs values
elif option == '15':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [64]
})
print('Initializer experiment multclass_lstm_model 15\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Epochs variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_mx', '_glorot', set_params)
elif option == '16':
set_params.update({
'optimizer_function': [dn.OptimizerFunction.ADAM.value],
'neuronios_by_layer': [32],
'hidden_layers': [4],
'batch_sizes': [40],
'epochs': [96]
})
print('Initializer experiment multclass_lstm_model 16\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label\n' + \
'Epochs variation')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=2640,
subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_ml_gl_2640_var_mx', '_glorot', set_params)
"""
Test Model using RSDD Dataset
"""
import utils.definition_network as dn
import numpy as np
from utils.experiment_processes import ExperimentProcesses
import datetime
# LAYERS
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from network_model.model_class import ModelClass
if __name__ == '__main__':
exp = ExperimentProcesses('lstm_exp14_L3')
exp.pp_data.vocabulary_size = 5000
exp.pp_data.embedding_size = 300
exp.pp_data.max_posts = 1750
exp.pp_data.max_terms_by_post = 300
exp.pp_data.binary_classifier = True
exp.pp_data.format_input_data = dn.InputData.POSTS_LIST
exp.pp_data.remove_stopwords = False
exp.pp_data.delete_low_tfid = False
exp.pp_data.min_df = 0
exp.pp_data.min_tf = 0
exp.pp_data.random_posts = False
exp.pp_data.random_users = False
exp.pp_data.tokenizing_type = 'WE'
"""
Test Model using RSDD Dataset
"""
import utils.definition_network as dn
from utils.experiment_processes import ExperimentProcesses
import datetime
# LAYERS
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Embedding, TimeDistributed, Input
from keras.layers import LSTM
from network_model.model_class import ModelClass
if __name__ == '__main__':
exp = ExperimentProcesses('lstm_exp13')
exp.pp_data.vocabulary_size = 5000
exp.pp_data.embedding_size = 300
exp.pp_data.max_posts = 2000
exp.pp_data.max_terms_by_post = 400
exp.pp_data.binary_classifier = True
exp.pp_data.format_input_data = dn.InputData.POSTS_LIST
exp.pp_data.remove_stopwords = False
exp.pp_data.delete_low_tfid = False
exp.pp_data.min_df = 0
exp.pp_data.min_tf = 0
exp.pp_data.random_posts = False
exp.pp_data.random_users = False
exp.pp_data.tokenizing_type = 'WE'
def test(option):
set_params = dict()
if option == '1':
print('Initializer experiment 1 (model SMHD_cnn_lstm_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model(exp, 'SMHD_cnn_lstm_gl_880', '_glorot', set_params)
elif option == '2':
print('Initializer experiment 2 (model SMHD_cnn_lstm_gl_1040)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 multi-label')
exp = ExperimentProcesses('cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
generate_model(exp, 'SMHD_cnn_lstm_gl_1040', '_glorot', set_params)
elif option == '3':
print('Initializer experiment 3 (model SMHD_cnn_lstm_gl_2160)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses('cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2160, subdirectory="depression")
generate_model(exp, 'SMHD_cnn_lstm_gl_2160', '_glorot', set_params)
else: # division test 4 to run in PCAD, with total time experiment not excceding 24h
if option == '1.1.1':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '1.1.2':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '1.1.3':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '1.1.4':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '1.2.1':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '1.2.2':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '1.2.3':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '1.2.4':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '1.3.1':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '1.3.2':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '1.3.3':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '1.3.4':
set_params.update({'filters_by_layer': [32],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '2.1.1':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '2.1.2':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '2.1.3':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '2.1.4':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [64],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '2.2.1':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '2.2.2':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '2.2.3':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '2.2.4':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '2.3.1':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '2.3.2':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '2.3.3':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '2.3.4':
set_params.update({'filters_by_layer': [64],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '3.1.1':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '3.1.2':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '3.1.3':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '3.1.4':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [128],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '3.2.1':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [192],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '3.2.2':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [192],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '3.2.3':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [192],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '3.2.4':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [192],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
elif option == '3.3.1':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.2]})
elif option == '3.3.2':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.2],
'dropouts_lstm': [0.5]})
elif option == '3.3.3':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.2]})
elif option == '3.3.4':
set_params.update({'filters_by_layer': [128],
'neuronios_by_lstm_layer': [256],
'dropouts': [0.5],
'dropouts_lstm': [0.5]})
print('Initializer experiment ' + option + ' (model SMHD_cnn_lstm_gl_2640)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label')
exp = ExperimentProcesses('cnn_L1_lstm_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2640, subdirectory="anx_dep_multilabel")
generate_model(exp, 'SMHD_cnn_lstm_gl_2640', '_glorot', set_params)
def main(arg):
if arg == '1':
print('Initializer experiment 1 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '2':
print('Initializer experiment 2 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '3':
print('Initializer experiment 3 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '4':
print('Initializer experiment 4 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '5':
print('Initializer experiment 5 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '6':
print('Initializer experiment 6 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [8],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '7':
print('Initializer experiment 7 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '8':
print('Initializer experiment 8 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [32],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '9':
print('Initializer experiment 9 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '10':
print('Initializer experiment 10 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [64],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '11':
print('Initializer experiment 11 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.1]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '12':
print('Initializer experiment 12 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [16],
'epochs': [96],
'batch_sizes': [20],
'dropouts': [0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '13':
print('Initializer experiment 13 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [32],
'epochs': [64, 96],
'batch_sizes': [20],
'dropouts': [0.1, 0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
elif arg == '14':
print('Initializer experiment 13 (model SMHD_ml_gl_880_cbow_alluser)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=880,
subdirectory="anxiety,depression")
set_params = dict({
'neuronios_by_layer': [64],
'epochs': [96, 128],
'batch_sizes': [20],
'dropouts': [0.1, 0.2]
})
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot',
set_params)
def main(arg):
if arg == '1':
print('Initializer experiment 1 (model SMHD_ml_gl_1560)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1560 single-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1560, subdirectory="anx_dep_multilabel")
generate_model_ml_gl(exp, 'SMHD_ml_gl_1560')
elif arg == '2':
print('Initializer experiment 2 (model SMHD_ml_le_1560)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1560 single-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1560, subdirectory="anx_dep_multilabel")
generate_model_ml_le(exp, 'SMHD_ml_le_1560')
elif arg == '3':
print('Initializer experiment 3 (model SMHD_ml_gl_2640)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2640 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2640, subdirectory="anx_dep_multilabel")
generate_model_ml_gl(exp, 'SMHD_ml_gl_2640')
elif arg == '4':
print('Initializer experiment 4 (model SMHD_ml_le_2640)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_2640 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2640, subdirectory="anx_dep_multilabel")
generate_model_ml_le(exp, 'SMHD_ml_le_2640')
elif arg == '5':
print('Initializer experiment 5 (model SMHD_ml_gl_1040)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
generate_model_ml_gl(exp, 'SMHD_ml_gl_1040')
elif arg == '6':
print('Initializer experiment 6 (model SMHD_ml_le_1040)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_1040 single-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
generate_model_ml_le(exp, 'SMHD_ml_le_1040')
elif arg == '7':
print('Initializer experiment 7 (model SMHD_ml_gl_2160)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2160, subdirectory="depression")
generate_model_ml_gl(exp, 'SMHD_ml_gl_2160')
elif arg == '8':
print('Initializer experiment 8 (model SMHD_ml_le_2160)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2160, subdirectory="depression")
generate_model_ml_le(exp, 'SMHD_ml_le_2160')
elif arg == '9':
print('Initializer experiment 9 (model SMHD_ml_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model_ml_gl(exp, 'SMHD_ml_gl_880')
elif arg == '10':
print('Initializer experiment 10 (model SMHD_ml_le_880)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model_ml_le(exp, 'SMHD_ml_le_880')
elif arg == '11':
print('Initializer experiment 11 (model SMHD_ml_gl_1040)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_1040 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
load_submodel_anx(exp, 'SMHD_ml_gl_1040_glove_a-d-aduser', '_glorot')
elif arg == '12':
print('Initializer experiment 12 (model SMHD_ml_gl_2160)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_2160 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2160, subdirectory="depression")
load_submodel_dep(exp, 'SMHD_ml_gl_2160_cbow_a-d-aduser', '_glorot')
elif arg == '13':
print('Initializer experiment 13 (model SMHD_ml_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
load_submodel_anx_dep(exp, 'SMHD_ml_gl_880_cbow_alluser', '_glorot')
elif arg == '14':
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=2640, subdirectory="anx_dep_multilabel")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-CBOW-A-D-ADUsers-300.bin'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_glove6B300d_glorot'
load_best_lstm_mult_label_multi_class(exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_2640',
we_file_name)
elif arg == '15':
mode_matrix_encoding = ["binary", "count", "tfidf", "freq"]
for mode in mode_matrix_encoding:
print('Initializer experiment multclass_lstm_model, text matrix enconding mode = ' + mode + '\n' + \
'Set: kernel_initializer=lecun, dataset=SMHD_1040 multi-label\n')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.text_matrix_enconding = True
exp.mode_text_matrix_enconding = mode
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=1040, subdirectory="anxiety")
generate_model_ml_le(exp, 'SMHD_ml_le_1040_tme_'+mode)
elif arg == '16':
print('Initializer experiment 16 (model SMHD_ml_le_880)\n' + \
'Set: kernel_initializer=lecun_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model_ml_le(exp, 'SMHD_ml_le_880')
elif arg == '17':
print('Initializer experiment 9 (model SMHD_ml_gl_880)\n' + \
'Set: kernel_initializer=glorot_uniform=xavier_uniform, dataset=SMHD_880 multi-label')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(dataset_name='SMHD', label_set=['control', 'anxiety', 'depression'],
total_registers=880, subdirectory="anxiety,depression")
generate_model_ml_gl(exp, 'SMHD_ml_gl_880')
def main():
for arg in sys.argv[1]:
if arg == '1':
print(
'Initializer experiment 1 - model SMHD_anx_dep_multilabel_nv_1'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_nv_1(exp, 'SMHD_anx_dep_multilabel_nv_1')
elif arg == '2':
print(
'Initializer experiment 2 - model SMHD_anx_dep_multilabel_nv_2'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_nv_2(exp, 'SMHD_anx_dep_multilabel_nv_2')
elif arg == '3':
print(
'Initializer experiment 1 - model SMHD_anx_dep_multilabel_wv_1'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_wv_1(exp, 'SMHD_anx_dep_multilabel_wv_1')
elif arg == '4':
print(
'Initializer experiment 2 - model SMHD_anx_dep_multilabel_wv_2'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_wv_2(exp, 'SMHD_anx_dep_multilabel_wv_2')
elif arg == '5':
print(
'Initializer experiment 1 - model SMHD_anx_dep_singlelabel_wv_1'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_sl_wv_1(exp, 'SMHD_anx_dep_singlelabel_wv_1')
elif arg == '6':
print(
'Initializer experiment 1 - model SMHD_anx_dep_singlelabel_wv_2'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
generate_model_sl_wv_2(exp, 'SMHD_anx_dep_singlelabel_wv_2')
elif arg == '7':
print(
'Initializer experiment 1 - model SMHD_anx_dep_singlelabel_wv_4'
)
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety', 'depression'],
total_registers=3160,
subdirectory="anx_dep_multilabel")
SMHD_anx_dep_singlelabel_wv_4(exp, 'SMHD_anx_dep_singlelabel_wv_4')
"""
Test Model using RSDD Dataset
"""
import utils.definition_network as dn
from utils.experiment_processes import ExperimentProcesses
import datetime
# LAYERS
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Embedding, TimeDistributed, Input
from keras.layers import LSTM
from network_model.model_class import ModelClass
if __name__ == '__main__':
exp = ExperimentProcesses('lstm_exp7_2')
exp.pp_data.vocabulary_size = 5000
exp.pp_data.embedding_size = 300
exp.pp_data.max_posts = 1750
exp.pp_data.max_terms_by_post = 300
exp.pp_data.binary_classifier = True
exp.pp_data.format_input_data = dn.InputData.POSTS_ONLY_TEXT
exp.pp_data.remove_stopwords = False
exp.pp_data.delete_low_tfid = False
exp.pp_data.min_df = 0
exp.pp_data.min_tf = 0
exp.pp_data.random_posts = False
exp.pp_data.random_users = False
exp.pp_data.tokenizing_type = 'WE'
def generate_anx_dep_model(arg):
if arg == '1':
print('Initializer experiment WE anx_dep 1 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-Skipgram-A-D-ADUsers-300.bin Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-Skipgram-A-D-ADUsers-300.bin'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '2':
print('Initializer experiment WE anx_dep 2 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-Skipgram-A-D-ADUsers-300.bin Non-Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.NON_STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-Skipgram-A-D-ADUsers-300.bin'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '3':
print('Initializer experiment WE anx_dep 3 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-CBOW-A-D-ADUsers-300.bin Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-CBOW-A-D-ADUsers-300.bin'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '4':
print('Initializer experiment WE anx_dep 4 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-CBOW-A-D-ADUsers-300.bin Non-Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.NON_STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-CBOW-A-D-ADUsers-300.bin'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '5':
print('Initializer experiment WE anx_dep 5 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-glove-A-D-ADUsers-300.pkl Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.GLOVE_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-glove-A-D-ADUsers-300.pkl'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '6':
print('Initializer experiment WE anx_dep 6 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-glove-A-D-ADUsers-300.pkl Non-Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.GLOVE_CUSTOM
exp.pp_data.use_embedding = dn.UseEmbedding.NON_STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-glove-A-D-ADUsers-300.pkl'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'exp9_' + we_file_name[0:13] + we_file_name[18:30] + '_880',
we_file_name)
elif arg == '7':
print('Initializer experiment WE anx_dep 7 (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label SMHD-CBOW-AllUsers-300.pkl Non-Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.WORD2VEC
exp.pp_data.use_embedding = dn.UseEmbedding.NON_STATIC
exp.pp_data.word_embedding_custom_file = 'SMHD-CBOW-AllUsers-300.pkl'
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_EF_' + exp.pp_data.word_embedding_custom_file.split('.')[0] + '_lecun'
generate_model(
exp, 'SMHD_anx_dep_3_' + we_file_name[0:13] + we_file_name[18:30] +
'_880', we_file_name)
else:
print('Initializer experiment WE anx_dep - (model SMHD_ml_gl_880)\n' + \
'Set: dataset=SMHD_880 single-label Glove6B Static')
exp = ExperimentProcesses('lstm_exp9_var_L3')
exp.pp_data.set_dataset_source(
dataset_name='SMHD',
label_set=['control', 'anxiety,depression'],
total_registers=880,
subdirectory="anxiety,depression")
exp.pp_data.embedding_type = dn.EmbeddingType.GLOVE_6B
exp.pp_data.use_embedding = dn.UseEmbedding.STATIC
exp.pp_data.word_embedding_custom_file = ''
exp.pp_data.load_dataset_type = dn.LoadDataset.TRAIN_DATA_MODEL
we_file_name = 'ET_' + str(exp.pp_data.embedding_type.value) + '_UE_' + str(exp.pp_data.use_embedding.value) + \
'_lecun'
generate_model(exp, 'exp9_' + 'glove6B' + '_880', we_file_name)