def supervised_training(self, x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax):
settings = self.settings
print 'new deep learning using split network'
# Autoencoder A
train_x = x_train_minmax[:, :x_train_minmax.shape[1]/2]
print "original shape for A", train_x.shape
cfg = self.settings.copy()
batch_size = settings['batch_size']
cfg['epoch_number'] = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
cfg['n_ins'] = train_x.shape[1]
sdafA = Sda_factory(cfg)
self.sdafA = sdafA
a_MAE_A = sdafA.sda
a_MAE_A.pretraining(train_x = train_x)
new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])
self.a_MAE_A = a_MAE_A
# Autoencoder B
train_x = x_train_minmax[:, x_train_minmax.shape[1]/2:]
sdafB = Sda_factory(cfg)
self.sdafB = sdafB
a_MAE_B = sdafB.sda
a_MAE_B.pretraining(train_x = train_x)
print "original shape for B", train_x.shape
new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])
self.a_MAE_B = a_MAE_B
new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])
new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])
new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))
new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))
# train sda with seperately transformed data
train_x = new_x_train_minmax_whole
cfg = settings.copy()
cfg['epoch_number'] = cal_epochs(settings['pretraining_interations'], new_x_train_minmax_whole, batch_size = batch_size)
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sdaf.sda.pretraining(train_x = train_x)
sdaf.dnn.finetuning((new_x_train_minmax_whole, y_train_minmax), (new_x_validationt_minmax_whole, y_validation_minmax))
self.sdaf = sdaf
self.sda_transformed = sdaf.dnn
def run_models(settings = None):
analysis_scr = []
with_auc_score = settings['with_auc_score']
f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
X_train,y_train = train_set
X_valid,y_valid = valid_set
X_test,y_test = test_set
n_outs = settings['n_outs']
for subset_no in xrange(1,settings['number_iterations']+1):
print("Subset:", subset_no)
#(train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = (X_train[:1000],y_train[:1000]),(X_train[:1000],y_train[:1000]), (X_test[:1000],y_test[:1000])
X_train,y_train = train_set
X_valid,y_valid = valid_set
X_test,y_test = test_set
(train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = (X_train,y_train),(X_train,y_train), (X_test,y_test)
standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
fisher_mode = settings['fisher_mode']
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, train_y_reduced)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
min_max_scaler = Preprocessing_Scaler_with_mean_point5()
X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)
X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)
x_test_minmax = min_max_scaler.transform(test_X)
x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax,
train_y_reduced
, test_size=0.4, random_state=42)
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'], x_train_minmax, batch_size = batch_size)
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
hidden_layers_sizes= settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['epoch_number'] = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
# deep xy autoencoders
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 100
print 'DL_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 10
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax; train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x = train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_test, test_predicted).values()))
#### new prepresentation
x = X_train_pre_validation_minmax
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
print 'SAE followed by SVM RBF'
x = X_train_pre_validation_minmax
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_COMBO']:
print 'SAE followed by SVM with combo feature'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_combo, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF_COMBO']:
print 'SAE followed by SVM RBF with combo feature'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_combo, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['DL']:
print "direct deep learning"
sda = train_a_Sda(x_train_minmax, pretrain_lr, finetune_lr,
y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs =n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_U']:
# deep learning using unlabeled data for pretraining
print 'deep learning with unlabel data'
pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
sda_unlabel = trainSda(x_train_minmax, y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
pretraining_X_minmax = pretraining_X_minmax,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs =n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_unlabel.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_unlabel.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S']:
# deep learning using split network
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1]/2]
print "original shape for A", x.shape
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])
x = x_train_minmax[:, x_train_minmax.shape[1]/2:]
print "original shape for B", x.shape
a_MAE_B = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])
new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])
new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])
new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])
new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))
sda_transformed = train_a_Sda(new_x_train_minmax_whole, pretrain_lr, finetune_lr,
y_train_minmax,
new_x_validationt_minmax_whole, y_validation_minmax ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_transformed.predict(new_x_train_minmax_whole)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_transformed.predict(new_x_test_minmax_whole)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.01
cfg['sparsity_weight'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] =0.01
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] =0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.5
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
report_name = 'Hand_classification_' + '_'.join(map(str, hidden_layers_sizes)) + '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(settings['training_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)
def get_ten_fold_crossvalid_perfermance(self, settings=None):
fisher_mode = settings['fisher_mode']
analysis_scr = []
with_auc_score = settings['with_auc_score']
reduce_ratio = settings['reduce_ratio']
#for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):
#subset_size = math.floor(self.ddi_obj.total_number_of_sequences / 10.0)
kf = KFold(self.ddi_obj.total_number_of_sequences,
n_folds=10,
shuffle=True)
#for subset_no in range(1, 11):
for ((train_index, test_index), subset_no) in izip(kf, range(1, 11)):
#for train_index, test_index in kf;
print("Subset:", subset_no)
print("Train index: ", train_index)
print("Test index: ", test_index)
#logger.info('subset number: ' + str(subset_no))
(train_X_10fold,
train_y_10fold), (train_X_reduced, train_y_reduced), (
test_X,
test_y) = self.ddi_obj.get_ten_fold_crossvalid_one_subset(
train_index,
test_index,
fisher_mode=fisher_mode,
reduce_ratio=reduce_ratio)
standard_scaler = preprocessing.StandardScaler().fit(
train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, train_y_reduced)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(
scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(
scaled_train_X)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
min_max_scaler = Preprocessing_Scaler_with_mean_point5()
X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)
X_train_pre_validation_minmax = min_max_scaler.transform(
train_X_reduced)
x_test_minmax = min_max_scaler.transform(test_X)
x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(
X_train_pre_validation_minmax,
train_y_reduced,
test_size=0.4,
random_state=42)
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'],
x_train_minmax,
batch_size=batch_size)
hidden_layers_sizes = settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['epoch_number'] = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
# deep xy autoencoders
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 1
print 'DL_xy'
train_x = x_train_minmax
train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append((
self.ddi, subset_no, fisher_mode, 'DL_xy', isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_xy', isTest) +
tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 10
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax
train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append((
self.ddi, subset_no, fisher_mode, 'Sda_xy_with_first',
isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'Sda_xy_with_first',
isTest) +
tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax
train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x=train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append((
self.ddi, subset_no, fisher_mode, 'Sda_new', isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'Sda_new', isTest) +
tuple(performance_score(y_test, test_predicted).values()))
#### new prepresentation
x = X_train_pre_validation_minmax
a_MAE_A = pretrain_a_Sda_with_estop(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=hidden_layers_sizes,
corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(
X_train_pre_validation_minmax)
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(
new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(
new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
print 'SAE followed by SVM RBF'
x = X_train_pre_validation_minmax
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_SVM_COMBO']:
print 'SAE followed by SVM with combo feature'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_combo, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_combo)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest)
+ tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_combo)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO',
isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_SVM_RBF_COMBO']:
print 'SAE followed by SVM RBF with combo feature'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
new_x_train_combo, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_combo)
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO',
isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_combo)
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO',
isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['DL']:
print "direct deep learning"
sda = train_a_Sda(x_train_minmax, pretrain_lr, finetune_lr,
y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append((
self.ddi, subset_no, fisher_mode, 'DL', isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL', isTest) +
tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_old']:
print "direct deep learning old without early stop"
sda = trainSda(x_train_minmax, y_train,
x_validation_minmax, y_validation_minmax,
x_test_minmax, y_test,pretrain_lr, finetune_lr,
pretraining_X_minmax=None,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append((
self.ddi, subset_no, fisher_mode, 'DL_old', isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_old', isTest) +
tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_U']:
# deep learning using unlabeled data for pretraining
print 'deep learning with unlabel data'
pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)
pretraining_epochs = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
sda_unlabel = trainSda(x_train_minmax, y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
pretraining_X_minmax = pretraining_X_minmax,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_unlabel.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
test_predicted = sda_unlabel.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S']:
# deep learning using split network
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1] / 2]
print "original shape for A", x.shape
a_MAE_A = pretrain_a_Sda_with_estop(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=hidden_layers_sizes,
corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(
x_train_minmax[:, :x_train_minmax.shape[1] / 2])
x = x_train_minmax[:, x_train_minmax.shape[1] / 2:]
print "original shape for B", x.shape
a_MAE_B = pretrain_a_Sda_with_estop(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=hidden_layers_sizes,
corruption_levels=corruption_levels)
new_x_train_minmax_B = a_MAE_B.transform(
x_train_minmax[:, x_train_minmax.shape[1] / 2:])
new_x_test_minmax_A = a_MAE_A.transform(
x_test_minmax[:, :x_test_minmax.shape[1] / 2])
new_x_test_minmax_B = a_MAE_B.transform(
x_test_minmax[:, x_test_minmax.shape[1] / 2:])
new_x_validation_minmax_A = a_MAE_A.transform(
x_validation_minmax[:, :x_validation_minmax.shape[1] / 2])
new_x_validation_minmax_B = a_MAE_B.transform(
x_validation_minmax[:, x_validation_minmax.shape[1] / 2:])
new_x_train_minmax_whole = np.hstack(
(new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack(
(new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack(
(new_x_validation_minmax_A, new_x_validation_minmax_B))
sda_transformed = train_a_Sda(new_x_train_minmax_whole, pretrain_lr, finetune_lr,
y_train_minmax,
new_x_validationt_minmax_whole, y_validation_minmax ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_transformed.predict(
new_x_train_minmax_whole)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
test_predicted = sda_transformed.predict(
new_x_test_minmax_whole)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new', isTest) +
tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new', isTest) +
tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.0001
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_contraction',
isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_contraction',
isTest) + tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.1
cfg['sparsity_weight'] = 0.0001
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_sparsity',
isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_sparsity',
isTest) + tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] = 0.0001
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'l2_reg', isTest) +
tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'l2_reg', isTest) +
tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] = 0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'l1_reg', isTest) +
tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'l1_reg', isTest) +
tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.5
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_Drop_out',
isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(self.ddi, subset_no, fisher_mode, 'DL_S_new_Drop_out',
isTest) + tuple(
performance_score(y_test, test_predicted,
with_auc_score).values()))
report_name = filename + '_' + '_newDL_'.join(
map(str, hidden_layers_sizes)) + '_' + str(
pretrain_lr) + '_' + str(finetune_lr) + '_' + str(
settings['training_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name,
performance_score(test_y, predicted_test_y, with_auc_score),
analysis_scr)
def run_models(settings=None):
analysis_scr = []
with_auc_score = settings['with_auc_score']
n_outs = settings['n_outs']
for subset_no in xrange(1, settings['number_iterations'] + 1):
print("Subset:", subset_no)
################## generate data ###################
array_A = []
array_B = []
for i in range(100000):
array_A.append(np.random.random_integers(0, 59999))
array_B.append(np.random.random_integers(0, 59999))
pos_index = []
neg_index = []
for index in xrange(100000):
if y_total[array_A[index]] - y_total[array_B[index]] == 1:
pos_index.append(index)
else:
neg_index.append(index)
print 'number of positive examples', len(pos_index)
selected_neg_index = neg_index[:len(pos_index)]
array_A = np.array(array_A)
array_B = np.array(array_B)
index_for_positive_image_A = array_A[pos_index]
index_for_positive_image_B = array_B[pos_index]
index_for_neg_image_A = array_A[selected_neg_index]
index_for_neg_image_B = array_B[selected_neg_index]
X_pos_A = X_total[index_for_positive_image_A]
X_pos_B = X_total[index_for_positive_image_B]
X_pos_whole = np.hstack((X_pos_A, X_pos_B))
X_neg_A = X_total[index_for_neg_image_A]
X_neg_B = X_total[index_for_neg_image_B]
X_neg_whole = np.hstack((X_neg_A, X_neg_B))
print X_pos_A.shape, X_pos_B.shape, X_pos_whole.shape
print X_neg_A.shape, X_neg_B.shape, X_neg_whole.shape
X_whole = np.vstack((X_pos_whole, X_neg_whole))
print X_whole.shape
y_pos = np.ones(X_pos_whole.shape[0])
y_neg = np.zeros(X_neg_whole.shape[0])
y_whole = np.concatenate([y_pos, y_neg])
print y_whole.shape
x_train_pre_validation, x_test, y_train_pre_validation, y_test = train_test_split(
X_whole, y_whole, test_size=0.2, random_state=211)
for number_of_training in settings['number_of_training']:
x_train, x_validation, y_train, y_validation = train_test_split(x_train_pre_validation[:number_of_training],
y_train_pre_validation[:number_of_training],\
test_size=0.2, random_state=21)
'''
x_train, x_validation, y_train, y_validation = train_test_split(x_train_pre_validation[:],
y_train_pre_validation[:],\
test_size=0.4, random_state=21)
'''
print x_train.shape, y_train.shape, x_validation.shape, \
y_validation.shape, x_test.shape, y_test.shape
x_train_minmax, x_validation_minmax, x_test_minmax = x_train, x_validation, x_test
train_X_reduced = x_train
train_y_reduced = y_train
test_X = x_test
test_y = y_test
y_train_minmax = y_train
y_validation_minmax = y_validation
y_test_minmax = y_test
###original data###
################ end of data ####################
standard_scaler = preprocessing.StandardScaler().fit(
train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, y_train)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SVM', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SVM', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
scaled_train_X, y_train)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SVM_RBF', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SVM_RBF', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(
scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SVM_POLY', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(
scaled_train_X)
isTest = False
#new
analysis_scr.append((
subset_no, number_of_training, 'SVM_POLY', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['Log']:
print "Log"
log_clf_l2 = sklearn.linear_model.LogisticRegression(
C=1, penalty='l2')
log_clf_l2.fit(scaled_train_X, train_y_reduced)
predicted_test_y = log_clf_l2.predict(scaled_test_X)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'Log', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = log_clf_l2.predict(scaled_train_X)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'Log', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
# direct deep learning
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'],
x_train_minmax,
batch_size=batch_size)
hidden_layers_sizes = settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
if settings['DL']:
print "direct deep learning"
sda = trainSda(x_train_minmax, y_train,
x_validation_minmax, y_validation,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs = n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
test_predicted = sda.predict(x_test_minmax)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'DL', isTest) +
tuple(performance_score(y_test, test_predicted).values()))
training_predicted = sda.predict(x_train_minmax)
isTest = False
#new
analysis_scr.append((
subset_no, number_of_training, 'DL', isTest
) + tuple(
performance_score(y_train, training_predicted).values()))
####transformed original data####
x = train_X_reduced
a_MAE_original = train_a_MultipleAEs(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=hidden_layers_sizes,
corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_original.transform(train_X_reduced)
new_x_test_minmax_A = a_MAE_original.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(
new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(
new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
# SAE_SVM
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_Log']:
print 'SAE followed by Log'
log_clf_l2 = sklearn.linear_model.LogisticRegression(
C=1, penalty='l2')
log_clf_l2.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = log_clf_l2.predict(new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_Log', isTest) + tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = log_clf_l2.predict(new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_Log', isTest) + tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
# SAE_SVM
print 'SAE followed by SVM RBF'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM_RBF', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM_RBF', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
if settings['SAE_SVM_POLY']:
# SAE_SVM
print 'SAE followed by SVM POLY'
L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(
new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM_POLY', isTest) +
tuple(
performance_score(test_y,
predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_SVM_POLY', isTest) +
tuple(
performance_score(train_y_reduced,
predicted_train_y).values()))
#### separated transformed data ####
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1] / 2]
print "original shape for A", x.shape
a_MAE_A = train_a_MultipleAEs(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=[x / 2 for x in hidden_layers_sizes],
corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(
x_train_minmax[:, :x_train_minmax.shape[1] / 2])
x = x_train_minmax[:, x_train_minmax.shape[1] / 2:]
print "original shape for B", x.shape
a_MAE_B = train_a_MultipleAEs(
x,
pretraining_epochs=pretraining_epochs,
pretrain_lr=pretrain_lr,
batch_size=batch_size,
hidden_layers_sizes=[x / 2 for x in hidden_layers_sizes],
corruption_levels=corruption_levels)
new_x_train_minmax_B = a_MAE_B.transform(
x_train_minmax[:, x_train_minmax.shape[1] / 2:])
new_x_test_minmax_A = a_MAE_A.transform(
x_test_minmax[:, :x_test_minmax.shape[1] / 2])
new_x_test_minmax_B = a_MAE_B.transform(
x_test_minmax[:, x_test_minmax.shape[1] / 2:])
new_x_validation_minmax_A = a_MAE_A.transform(
x_validation_minmax[:, :x_validation_minmax.shape[1] / 2])
new_x_validation_minmax_B = a_MAE_B.transform(
x_validation_minmax[:, x_validation_minmax.shape[1] / 2:])
new_x_train_minmax_whole = np.hstack(
(new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack(
(new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack(
(new_x_validation_minmax_A, new_x_validation_minmax_B))
standard_scaler = preprocessing.StandardScaler().fit(
new_x_train_minmax_whole)
new_x_train_minmax_whole_scaled = standard_scaler.transform(
new_x_train_minmax_whole)
new_x_test_minmax_whole_scaled = standard_scaler.transform(
new_x_test_minmax_whole)
if settings['DL_S']:
# deep learning using split network
sda_transformed = trainSda(new_x_train_minmax_whole, y_train,
new_x_validationt_minmax_whole, y_validation ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
predicted_test_y = sda_transformed.predict(
new_x_test_minmax_whole)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'DL_S', isTest) + tuple(
performance_score(y_test, predicted_test_y,
with_auc_score).values()))
training_predicted = sda_transformed.predict(
new_x_train_minmax_whole)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'DL_S', isTest) + tuple(
performance_score(y_train, training_predicted,
with_auc_score).values()))
if settings['SAE_S_SVM']:
print 'SAE_S followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_minmax_whole_scaled,
train_y_reduced)
predicted_test_y = Linear_SVC.predict(
new_x_test_minmax_whole_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM', isTest) +
tuple(
performance_score(test_y, predicted_test_y,
with_auc_score).values())) #new
predicted_train_y = Linear_SVC.predict(
new_x_train_minmax_whole_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM', isTest) +
tuple(
performance_score(train_y_reduced, predicted_train_y,
with_auc_score).values()))
if settings['SAE_S_SVM_RBF']:
print 'SAE S followed by SVM RBF'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(
new_x_train_minmax_whole_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_minmax_whole_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM_RBF', isTest) +
tuple(
performance_score(test_y, predicted_test_y,
with_auc_score).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_minmax_whole_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM_RBF', isTest) +
tuple(
performance_score(train_y_reduced, predicted_train_y,
with_auc_score).values()))
if settings['SAE_S_SVM_POLY']:
# SAE_SVM
print 'SAE S followed by SVM POLY'
L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(
new_x_train_minmax_whole_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(
new_x_test_minmax_whole_scaled)
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM_POLY', isTest) +
tuple(
performance_score(test_y, predicted_test_y,
with_auc_score).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(
new_x_train_minmax_whole_scaled)
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'SAE_S_SVM_POLY', isTest) +
tuple(
performance_score(train_y_reduced, predicted_train_y,
with_auc_score).values()))
settings['epoch_number'] = cal_epochs(
settings['pretraining_interations'],
x_train_minmax,
batch_size=batch_size)
# deep xy autoencoders
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
print 'DL_xy'
train_x = x_train_minmax
train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'DL_xy', isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'DL_xy', isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax
train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'Sda_xy_with_first',
isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'Sda_xy_with_first',
isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax
train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x=train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),
(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False
#new
analysis_scr.append(
(subset_no, number_of_training, 'Sda_new', isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True
#new
analysis_scr.append(
(subset_no, number_of_training, 'Sda_new', isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((
subset_no, number_of_training, 'DL_S_new', isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((
subset_no, number_of_training, 'DL_S_new', isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_contraction',
isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_contraction',
isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.01
cfg['sparsity_weight'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_sparsity',
isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_sparsity',
isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] = 0.01
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(subset_no, number_of_training, 'l2_reg', isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(subset_no, number_of_training, 'l2_reg', isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(subset_no, number_of_training, 'l1_reg', isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(subset_no, number_of_training, 'l1_reg', isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.5
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax,
y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_Drop_out',
isTest) + tuple(
performance_score(train_y_reduced, training_predicted,
with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(
(subset_no, number_of_training, 'DL_S_new_Drop_out',
isTest) + tuple(
performance_score(test_y, test_predicted,
with_auc_score).values()))
report_name = 'DL_handwritten_digits' + '_size_'.join(map(str, hidden_layers_sizes)) + \
'_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + \
'_' + str(settings['pretraining_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name,
performance_score(test_y, predicted_test_y, with_auc_score),
analysis_scr)
return sda, a_MAE_original, a_MAE_A, a_MAE_B, analysis_scr
def get_ten_fold_crossvalid_perfermance(self, settings = None):
fisher_mode = settings['fisher_mode']
analysis_scr = []
with_auc_score = settings['with_auc_score']
reduce_ratio = settings['reduce_ratio']
#for seq_no in range(1, self.ddi_obj.total_number_of_sequences+1):
#subset_size = math.floor(self.ddi_obj.total_number_of_sequences / 10.0)
kf = KFold(self.ddi_obj.total_number_of_sequences, n_folds = 10, shuffle = True)
#for subset_no in range(1, 11):
for ((train_index, test_index),subset_no) in izip(kf,range(1,11)):
#for train_index, test_index in kf;
print("Subset:", subset_no)
print("Train index: ", train_index)
print("Test index: ", test_index)
#logger.info('subset number: ' + str(subset_no))
(train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = self.ddi_obj.get_ten_fold_crossvalid_one_subset(train_index, test_index, fisher_mode = fisher_mode, reduce_ratio = reduce_ratio)
standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, train_y_reduced)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
min_max_scaler = Preprocessing_Scaler_with_mean_point5()
X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)
X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)
x_test_minmax = min_max_scaler.transform(test_X)
x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax,
train_y_reduced
, test_size=0.4, random_state=42)
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'], x_train_minmax, batch_size = batch_size)
hidden_layers_sizes= settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['epoch_number'] = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
# deep xy autoencoders
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
print 'DL_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax; train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x = train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_test, test_predicted).values()))
#### new prepresentation
x = X_train_pre_validation_minmax
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
print 'SAE followed by SVM RBF'
x = X_train_pre_validation_minmax
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_COMBO']:
print 'SAE followed by SVM with combo feature'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_combo, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF_COMBO']:
print 'SAE followed by SVM RBF with combo feature'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_combo, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['DL']:
print "direct deep learning"
sda = train_a_Sda(x_train_minmax, pretrain_lr, finetune_lr,
y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_old']:
print "direct deep learning old without early stop"
sda = trainSda(x_train_minmax, y_train,
x_validation_minmax, y_validation_minmax,
x_test_minmax, y_test,pretrain_lr, finetune_lr,
pretraining_X_minmax=None,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_old', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_old', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_U']:
# deep learning using unlabeled data for pretraining
print 'deep learning with unlabel data'
pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
sda_unlabel = trainSda(x_train_minmax, y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
pretraining_X_minmax = pretraining_X_minmax,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_unlabel.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_unlabel.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S']:
# deep learning using split network
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1]/2]
print "original shape for A", x.shape
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])
x = x_train_minmax[:, x_train_minmax.shape[1]/2:]
print "original shape for B", x.shape
a_MAE_B = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)
new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])
new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])
new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])
new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])
new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))
sda_transformed = train_a_Sda(new_x_train_minmax_whole, pretrain_lr, finetune_lr,
y_train_minmax,
new_x_validationt_minmax_whole, y_validation_minmax ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = settings['n_outs']
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_transformed.predict(new_x_train_minmax_whole)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_transformed.predict(new_x_test_minmax_whole)
y_test = test_y
isTest = True; #new
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.1
cfg['sparsity_weight'] = 0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] =0.1
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] =0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.3
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((self.ddi, subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
report_name = filename + '_' + '_newDL_'.join(map(str, hidden_layers_sizes)) + '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(settings['training_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)
def run_models(settings = None):
analysis_scr = []
with_auc_score = settings['with_auc_score']
n_outs = settings['settings']
for subset_no in xrange(1,settings['number_iterations']+1):
print("Subset:", subset_no)
################## generate data ###################
array_A =[]
array_B =[]
for i in range(100000):
array_A.append(np.random.random_integers(0, 59999))
array_B.append(np.random.random_integers(0, 59999))
pos_index = []
neg_index = []
for index in xrange(100000):
if y_total[array_A[index]] - y_total[array_B[index]] == 1:
pos_index.append(index)
else:
neg_index.append(index)
print 'number of positive examples', len(pos_index)
selected_neg_index= neg_index[ : len(pos_index)]
array_A = np.array(array_A)
array_B = np.array(array_B)
index_for_positive_image_A = array_A[pos_index]
index_for_positive_image_B = array_B[pos_index]
index_for_neg_image_A = array_A[selected_neg_index]
index_for_neg_image_B = array_B[selected_neg_index]
X_pos_A = X_total[index_for_positive_image_A]
X_pos_B = X_total[index_for_positive_image_B]
X_pos_whole = np.hstack((X_pos_A,X_pos_B))
X_neg_A = X_total[index_for_neg_image_A]
X_neg_B = X_total[index_for_neg_image_B]
X_neg_whole = np.hstack((X_neg_A, X_neg_B))
print X_pos_A.shape, X_pos_B.shape, X_pos_whole.shape
print X_neg_A.shape, X_neg_B.shape, X_neg_whole.shape
X_whole = np.vstack((X_pos_whole, X_neg_whole))
print X_whole.shape
y_pos = np.ones(X_pos_whole.shape[0])
y_neg = np.zeros(X_neg_whole.shape[0])
y_whole = np.concatenate([y_pos,y_neg])
print y_whole.shape
x_train_pre_validation, x_test, y_train_pre_validation, y_test = train_test_split(X_whole,y_whole, test_size=0.2, random_state=211)
for number_of_training in settings['number_of_training']:
x_train, x_validation, y_train, y_validation = train_test_split(x_train_pre_validation[:number_of_training],
y_train_pre_validation[:number_of_training],\
test_size=0.2, random_state=21)
'''
x_train, x_validation, y_train, y_validation = train_test_split(x_train_pre_validation[:],
y_train_pre_validation[:],\
test_size=0.4, random_state=21)
'''
print x_train.shape, y_train.shape, x_validation.shape, \
y_validation.shape, x_test.shape, y_test.shape
x_train_minmax, x_validation_minmax, x_test_minmax = x_train, x_validation, x_test
train_X_reduced = x_train
train_y_reduced = y_train
test_X = x_test
test_y = y_test
y_train_minmax = y_train
y_validation_minmax = y_validation
y_test_minmax = y_test
###original data###
################ end of data ####################
standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, y_train)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no,number_of_training, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, y_train)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((subset_no,number_of_training, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, number_of_training,'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training,'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['Log']:
print "Log"
log_clf_l2 = sklearn.linear_model.LogisticRegression(C=1, penalty='l2')
log_clf_l2.fit(scaled_train_X, train_y_reduced)
predicted_test_y = log_clf_l2.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append((subset_no,number_of_training, 'Log', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = log_clf_l2.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training,'Log', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
# direct deep learning
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'], x_train_minmax, batch_size = batch_size)
hidden_layers_sizes = settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
if settings['DL']:
print "direct deep learning"
sda = trainSda(x_train_minmax, y_train,
x_validation_minmax, y_validation,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs = n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
test_predicted = sda.predict(x_test_minmax)
isTest = True; #new
analysis_scr.append((subset_no,number_of_training, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))
training_predicted = sda.predict(x_train_minmax)
isTest = False; #new
analysis_scr.append((subset_no,number_of_training, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))
####transformed original data####
x = train_X_reduced
a_MAE_original = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_original.transform(train_X_reduced)
new_x_test_minmax_A = a_MAE_original.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
# SAE_SVM
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, number_of_training,'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append(( subset_no, number_of_training,'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_Log']:
print 'SAE followed by Log'
log_clf_l2 = sklearn.linear_model.LogisticRegression(C=1, penalty='l2')
log_clf_l2.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = log_clf_l2.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append((subset_no,number_of_training, 'SAE_Log', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = log_clf_l2.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training,'SAE_Log', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
# SAE_SVM
print 'SAE followed by SVM RBF'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_POLY']:
# SAE_SVM
print 'SAE followed by SVM POLY'
L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training,'SAE_SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training, 'SAE_SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
#### separated transformed data ####
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1]/2]
print "original shape for A", x.shape
a_MAE_A = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes = [x/2 for x in hidden_layers_sizes], corruption_levels=corruption_levels)
new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])
x = x_train_minmax[:, x_train_minmax.shape[1]/2:]
print "original shape for B", x.shape
a_MAE_B = train_a_MultipleAEs(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes = [x/2 for x in hidden_layers_sizes], corruption_levels=corruption_levels)
new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])
new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])
new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])
new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])
new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))
standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_whole)
new_x_train_minmax_whole_scaled = standard_scaler.transform(new_x_train_minmax_whole)
new_x_test_minmax_whole_scaled = standard_scaler.transform(new_x_test_minmax_whole)
if settings['DL_S']:
# deep learning using split network
sda_transformed = trainSda(new_x_train_minmax_whole, y_train,
new_x_validationt_minmax_whole, y_validation ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
predicted_test_y = sda_transformed.predict(new_x_test_minmax_whole)
y_test = test_y
isTest = True; #new
analysis_scr.append((subset_no, number_of_training,'DL_S', isTest) + tuple(performance_score(y_test, predicted_test_y, with_auc_score).values()))
training_predicted = sda_transformed.predict(new_x_train_minmax_whole)
isTest = False; #new
analysis_scr.append((subset_no,number_of_training, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
if settings['SAE_S_SVM']:
print 'SAE_S followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_minmax_whole_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_minmax_whole_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, number_of_training,'SAE_S_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_minmax_whole_scaled)
isTest = False; #new
analysis_scr.append(( subset_no,number_of_training, 'SAE_S_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))
if settings['SAE_S_SVM_RBF']:
print 'SAE S followed by SVM RBF'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_minmax_whole_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_minmax_whole_scaled)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'SAE_S_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_minmax_whole_scaled)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))
if settings['SAE_S_SVM_POLY']:
# SAE_SVM
print 'SAE S followed by SVM POLY'
L1_SVC_RBF_Selector = SVC(C=1, kernel='poly').fit(new_x_train_minmax_whole_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_minmax_whole_scaled)
isTest = True; #new
analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y, with_auc_score).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_minmax_whole_scaled)
isTest = False; #new
analysis_scr.append((subset_no, number_of_training,'SAE_S_SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y, with_auc_score).values()))
settings['epoch_number'] = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
# deep xy autoencoders
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
print 'DL_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((subset_no, number_of_training, 'DL_xy', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'DL_xy', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 0.1
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((subset_no, number_of_training, 'Sda_xy_with_first', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'Sda_xy_with_first', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax; train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x = train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append((subset_no, number_of_training, 'Sda_new', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append((subset_no, number_of_training, 'Sda_new', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'DL_S_new', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'DL_S_new', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_contraction', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_contraction', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.01
cfg['sparsity_weight'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_sparsity', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_sparsity', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] =0.01
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'l2_reg', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'l2_reg', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] =0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'l1_reg', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'l1_reg', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.5
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(train_y_reduced, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append((subset_no, number_of_training, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(test_y, test_predicted, with_auc_score).values()))
report_name = 'DL_handwritten_digits' + '_size_'.join(map(str, hidden_layers_sizes)) + \
'_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + \
'_' + str(settings['pretraining_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)
return sda, a_MAE_original, a_MAE_A, a_MAE_B, analysis_scr
def run_models(settings = None):
analysis_scr = []
with_auc_score = settings['with_auc_score']
f = gzip.open('mnist.pkl.gz', 'rb')
train_set, valid_set, test_set = cPickle.load(f)
X_train,y_train = train_set
X_valid,y_valid = valid_set
X_test,y_test = test_set
n_outs = settings['n_outs']
for subset_no in xrange(1,settings['number_iterations']+1):
print("Subset:", subset_no)
#(train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = (X_train[:1000],y_train[:1000]),(X_train[:1000],y_train[:1000]), (X_test[:1000],y_test[:1000])
(train_X_10fold, train_y_10fold),(train_X_reduced, train_y_reduced), (test_X, test_y) = (X_train,y_train),(X_train,y_train), (X_test,y_test)
standard_scaler = preprocessing.StandardScaler().fit(train_X_reduced)
scaled_train_X = standard_scaler.transform(train_X_reduced)
scaled_test_X = standard_scaler.transform(test_X)
fisher_mode = settings['fisher_mode']
if settings['SVM']:
print "SVM"
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(scaled_train_X, train_y_reduced)
predicted_test_y = Linear_SVC.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_RBF']:
print "SVM_RBF"
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SVM_POLY']:
print "SVM_POLY"
L1_SVC_POLY_Selector = SVC(C=1, kernel='poly').fit(scaled_train_X, train_y_reduced)
predicted_test_y = L1_SVC_POLY_Selector.predict(scaled_test_X)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_POLY_Selector.predict(scaled_train_X)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SVM_POLY', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
min_max_scaler = Preprocessing_Scaler_with_mean_point5()
X_train_pre_validation_minmax = min_max_scaler.fit(train_X_reduced)
X_train_pre_validation_minmax = min_max_scaler.transform(train_X_reduced)
x_test_minmax = min_max_scaler.transform(test_X)
x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax = train_test_split(X_train_pre_validation_minmax,
train_y_reduced
, test_size=0.4, random_state=42)
finetune_lr = settings['finetune_lr']
batch_size = settings['batch_size']
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
#pretrain_lr=0.001
pretrain_lr = settings['pretrain_lr']
training_epochs = cal_epochs(settings['training_interations'], x_train_minmax, batch_size = batch_size)
settings['lrate'] = settings['lrate_pre'] + str(training_epochs)
hidden_layers_sizes= settings['hidden_layers_sizes']
corruption_levels = settings['corruption_levels']
settings['epoch_number'] = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
# deep xy autoencoders
settings['n_ins'] = x_train_minmax.shape[1]
if settings['DL_xy']:
cfg = settings.copy()
cfg['weight_y'] = 0.01
print 'DL_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_xy', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_xy_with_first']:
cfg = settings.copy()
cfg['weight_y'] = 1
cfg['firstlayer_xy'] = 1
print 'firstlayer_xy'
train_x = x_train_minmax; train_y = y_train_minmax
sdaf = Sda_xy_factory(cfg)
sdaf.sda.pretraining(train_x, train_y)
dnnf = DNN_factory(cfg)
dnnf.dnn.load_pretrain_from_Sda(sdaf.sda)
dnnf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = dnnf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = dnnf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_xy_with_first', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['Sda_new']:
print 'Sda_new'
cfg = settings.copy()
train_x = x_train_minmax; train_y = y_train_minmax
cfg['n_ins'] = train_x.shape[1]
sdaf = Sda_factory(cfg)
sda = sdaf.sda.pretraining(train_x = train_x)
sdaf.dnn.finetuning((x_train_minmax, y_train_minmax),(x_validation_minmax, y_validation_minmax))
training_predicted = sdaf.dnn.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sdaf.dnn.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'Sda_new', isTest) + tuple(performance_score(y_test, test_predicted).values()))
#### new prepresentation
x = X_train_pre_validation_minmax
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_A = a_MAE_A.transform(X_train_pre_validation_minmax)
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax)
standard_scaler = preprocessing.StandardScaler().fit(new_x_train_minmax_A)
new_x_train_scaled = standard_scaler.transform(new_x_train_minmax_A)
new_x_test_scaled = standard_scaler.transform(new_x_test_minmax_A)
new_x_train_combo = np.hstack((scaled_train_X, new_x_train_scaled))
new_x_test_combo = np.hstack((scaled_test_X, new_x_test_scaled))
if settings['SAE_SVM']:
print 'SAE followed by SVM'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF']:
print 'SAE followed by SVM RBF'
x = X_train_pre_validation_minmax
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_scaled, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_scaled)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_scaled)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_COMBO']:
print 'SAE followed by SVM with combo feature'
Linear_SVC = LinearSVC(C=1, penalty="l2")
Linear_SVC.fit(new_x_train_combo, train_y_reduced)
predicted_test_y = Linear_SVC.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = Linear_SVC.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['SAE_SVM_RBF_COMBO']:
print 'SAE followed by SVM RBF with combo feature'
L1_SVC_RBF_Selector = SVC(C=1, gamma=0.01, kernel='rbf').fit(new_x_train_combo, train_y_reduced)
predicted_test_y = L1_SVC_RBF_Selector.predict(new_x_test_combo)
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(test_y, predicted_test_y).values())) #new
predicted_train_y = L1_SVC_RBF_Selector.predict(new_x_train_combo)
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'SAE_SVM_RBF_COMBO', isTest) + tuple(performance_score(train_y_reduced, predicted_train_y).values()))
if settings['DL']:
print "direct deep learning"
sda = train_a_Sda(x_train_minmax, pretrain_lr, finetune_lr,
y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs =n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_train, training_predicted).values()))
test_predicted = sda.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL', isTest) + tuple(performance_score(y_test, test_predicted).values()))
if settings['DL_U']:
# deep learning using unlabeled data for pretraining
print 'deep learning with unlabel data'
pretraining_X_minmax = min_max_scaler.transform(train_X_10fold)
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
sda_unlabel = trainSda(x_train_minmax, y_train_minmax,
x_validation_minmax, y_validation_minmax ,
x_test_minmax, test_y,
pretraining_X_minmax = pretraining_X_minmax,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs,
pretrain_lr = pretrain_lr, finetune_lr=finetune_lr, n_outs =n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_unlabel.predict(x_train_minmax)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_unlabel.predict(x_test_minmax)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_U', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S']:
# deep learning using split network
y_test = test_y
print 'deep learning using split network'
# get the new representation for A set. first 784-D
pretraining_epochs = cal_epochs(settings['pretraining_interations'], x_train_minmax, batch_size = batch_size)
x = x_train_minmax[:, :x_train_minmax.shape[1]/2]
print "original shape for A", x.shape
a_MAE_A = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_A = a_MAE_A.transform(x_train_minmax[:, :x_train_minmax.shape[1]/2])
x = x_train_minmax[:, x_train_minmax.shape[1]/2:]
print "original shape for B", x.shape
a_MAE_B = pretrain_a_Sda_with_estop(x, pretraining_epochs=pretraining_epochs, pretrain_lr=pretrain_lr, batch_size=batch_size,
hidden_layers_sizes =hidden_layers_sizes, corruption_levels=corruption_levels, n_outs = n_outs)
new_x_train_minmax_B = a_MAE_B.transform(x_train_minmax[:, x_train_minmax.shape[1]/2:])
new_x_test_minmax_A = a_MAE_A.transform(x_test_minmax[:, :x_test_minmax.shape[1]/2])
new_x_test_minmax_B = a_MAE_B.transform(x_test_minmax[:, x_test_minmax.shape[1]/2:])
new_x_validation_minmax_A = a_MAE_A.transform(x_validation_minmax[:, :x_validation_minmax.shape[1]/2])
new_x_validation_minmax_B = a_MAE_B.transform(x_validation_minmax[:, x_validation_minmax.shape[1]/2:])
new_x_train_minmax_whole = np.hstack((new_x_train_minmax_A, new_x_train_minmax_B))
new_x_test_minmax_whole = np.hstack((new_x_test_minmax_A, new_x_test_minmax_B))
new_x_validationt_minmax_whole = np.hstack((new_x_validation_minmax_A, new_x_validation_minmax_B))
sda_transformed = train_a_Sda(new_x_train_minmax_whole, pretrain_lr, finetune_lr,
y_train_minmax,
new_x_validationt_minmax_whole, y_validation_minmax ,
new_x_test_minmax_whole, y_test,
hidden_layers_sizes = hidden_layers_sizes, corruption_levels = corruption_levels, batch_size = batch_size , \
training_epochs = training_epochs, pretraining_epochs = pretraining_epochs, n_outs = n_outs
)
print 'hidden_layers_sizes:', hidden_layers_sizes
print 'corruption_levels:', corruption_levels
training_predicted = sda_transformed.predict(new_x_train_minmax_whole)
y_train = y_train_minmax
isTest = False; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
test_predicted = sda_transformed.predict(new_x_test_minmax_whole)
y_test = test_y
isTest = True; #new
analysis_scr.append(( subset_no, fisher_mode, 'DL_S', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new']:
# deep learning using split network
print 'new deep learning using split network'
cfg = settings.copy()
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_contraction']:
print 'DL_S_new_contraction'
cfg = settings.copy()
cfg['contraction_level'] = 0.1
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_contraction', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_sparsity'] == 1:
print 'DL_S_new_sparsity'
cfg = settings.copy()
cfg['sparsity'] = 0.01
cfg['sparsity_weight'] = 0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_sparsity', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 2:
cfg = settings.copy()
cfg['l2_reg'] =0.01
print 'l2_reg'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'l2_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_weight_decay'] == 1:
print 'l1_reg'
cfg = settings.copy()
cfg['l1_reg'] =0.01
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'l1_reg', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
if settings['DL_S_new_Drop_out'] == 1:
cfg = settings.copy()
cfg['dropout_factor'] = 0.5
print 'DL_S_new_Drop_out'
p_sda = Parellel_Sda_factory(cfg)
p_sda.supervised_training(x_train_minmax, x_validation_minmax, y_train_minmax, y_validation_minmax)
isTest = False #new
training_predicted = p_sda.predict(x_train_minmax)
y_train = y_train_minmax
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_train, training_predicted, with_auc_score).values()))
isTest = True #new
y_test = test_y
test_predicted = p_sda.predict(x_test_minmax)
analysis_scr.append(( subset_no, fisher_mode, 'DL_S_new_Drop_out', isTest) + tuple(performance_score(y_test, test_predicted, with_auc_score).values()))
report_name = 'Hand_classification_' + '_'.join(map(str, hidden_layers_sizes)) + '_' + str(pretrain_lr) + '_' + str(finetune_lr) + '_' + str(settings['training_interations']) + '_' + current_date
saveAsCsv(with_auc_score, report_name, performance_score(test_y, predicted_test_y, with_auc_score), analysis_scr)
