def run_supervised_transfer_cv(seed,
dataset,
fold=3,
val_size=0,
k=-1,
batch_size=32,
groups=('WHITE', 'BLACK'),
learning_rate=0.01,
lr_decay=0.0,
dropout=0.5,
tune_epoch=200,
tune_lr=0.002,
train_epoch=1000,
L1_reg=0.001,
L2_reg=0.001,
hiddenLayers=[128, 64],
tune_batch=10):
X, Y, R, y_sub, y_strat = dataset
idx = R == groups[1]
X_b, y_b, R_b, y_strat_b = X[idx], Y[idx], R[idx], y_strat[idx]
idx = R == groups[0]
X_w, y_w, R_w, y_strat_w = X[idx], Y[idx], R[idx], y_strat[idx]
pretrain_set = (X_w, y_w)
df = pd.DataFrame(columns=['scr', 'R', 'Y'])
kf = StratifiedKFold(n_splits=fold, shuffle=True, random_state=seed)
for train_index, test_index in kf.split(X_b, y_strat_b):
X_train, X_test = X_b[train_index], X_b[test_index]
Y_train, Y_test = y_b[train_index], y_b[test_index]
R_train, R_test = R_b[train_index], R_b[test_index]
strat_train, strat_test = y_strat_b[train_index], y_strat_b[test_index]
if k > 0:
k_best = SelectKBest(f_classif, k=k)
k_best.fit(X_train, Y_train)
X_train, X_test = k_best.transform(X_train), k_best.transform(
X_test)
X_base = k_best.transform(X_w)
pretrain_set = (X_base, y_w)
valid_data = None
if val_size:
X_train, X_val, Y_train, Y_val = train_test_split(
X_train,
Y_train,
test_size=val_size,
random_state=0,
stratify=strat_train)
valid_data = (X_val, Y_val)
train_data = (X_train, Y_train)
n_in = X_train.shape[1]
classifier = MLP(n_in=n_in,
learning_rate=learning_rate,
lr_decay=lr_decay,
dropout=dropout,
L1_reg=L1_reg,
L2_reg=L2_reg,
hidden_layers_sizes=hiddenLayers)
classifier.train(pretrain_set,
n_epochs=train_epoch,
batch_size=batch_size)
classifier.learning_rate = tune_lr
classifier.tune(train_data,
valid_data=valid_data,
batch_size=tune_batch,
n_epochs=tune_epoch)
scr = classifier.get_score(X_test)
array = np.column_stack((scr[:, 1], R_test, Y_test))
df_temp = pd.DataFrame(array,
index=list(test_index),
columns=['scr', 'R', 'Y'])
df = df.append(df_temp)
y_test, y_scr = list(df['Y'].values), list(df['scr'].values)
A_CI = roc_auc_score(y_test, y_scr)
res = {'folds': fold, 'TL_Auc': A_CI}
df = pd.DataFrame(res, index=[seed])
return df
def run_cv(seed,
fold,
X,
Y,
R,
y_strat,
val_size=0,
pretrain_set=None,
batch_size=32,
k=-1,
learning_rate=0.01,
lr_decay=0.0,
dropout=0.5,
n_epochs=100,
momentum=0.9,
L1_reg=0.001,
L2_reg=0.001,
hiddenLayers=[128, 64]):
X_w = pretrain_set.get_value(
borrow=True) if k > 0 and pretrain_set else None
m = X.shape[1] if k < 0 else k
columns = list(range(m))
columns.extend(['scr', 'R', 'Y'])
df = pd.DataFrame(columns=columns)
kf = StratifiedKFold(n_splits=fold, shuffle=True, random_state=seed)
for train_index, test_index in kf.split(X, y_strat):
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
R_train, R_test = R[train_index], R[test_index]
strat_train, strat_test = y_strat[train_index], y_strat[test_index]
if k > 0:
k_best = SelectKBest(f_classif, k=k)
k_best.fit(X_train, Y_train)
X_train, X_test = k_best.transform(X_train), k_best.transform(
X_test)
if pretrain_set:
X_base = k_best.transform(X_w)
pretrain_set = theano.shared(X_base,
name='pretrain_set',
borrow=True)
valid_data = None
if val_size:
X_train, X_val, Y_train, Y_val = train_test_split(
X_train,
Y_train,
test_size=val_size,
random_state=0,
stratify=strat_train)
valid_data = (X_val, Y_val)
train_data = (X_train, Y_train)
n_in = X_train.shape[1]
classifier = MLP(n_in=n_in,
learning_rate=learning_rate,
lr_decay=lr_decay,
dropout=dropout,
L1_reg=L1_reg,
L2_reg=L2_reg,
hidden_layers_sizes=hiddenLayers,
momentum=momentum)
if pretrain_set:
pretrain_config = {
'pt_batchsize': 32,
'pt_lr': 0.01,
'pt_epochs': 500,
'corruption_level': 0.3
}
classifier.pretrain(pretrain_set=pretrain_set,
pretrain_config=pretrain_config)
classifier.tune(train_data,
valid_data=valid_data,
batch_size=batch_size,
n_epochs=n_epochs)
else:
classifier.train(train_data,
valid_data=valid_data,
batch_size=batch_size,
n_epochs=n_epochs)
X_scr = classifier.get_score(X_test)
array1 = np.column_stack((X_test, X_scr[:, 1], R_test, Y_test))
df_temp1 = pd.DataFrame(array1,
index=list(test_index),
columns=columns)
df = df.append(df_temp1)
return df
