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)