def cross_validation_mse_rdkit(param):
param["num_epochs"] = int(np.round(param["num_epochs"]))
MSE = []
for train_index, test_index in kf5.split(train_RDKIT, train_Y):
model = build_model(input_dim=2048,
learning_rate=param["learning_rate"],
decay=param["decay"],
momentum=param["momentum"],
l2_parameter=param["l2_parameter"],
hidden_layer_size1=param["hidden_layer_size1"],
hidden_layer_size2=param["hidden_layer_size2"])
model.fit(np.array(train_RDKIT[train_index]),
np.array(train_Y[train_index]),
epochs=param["num_epochs"],
batch_size=param["batch_size"],
verbose=0)
MSE.append(
np.mean(
abs(
model.predict(np.array(train_RDKIT[test_index])) -
np.array(train_Y[test_index]))**2))
return (np.mean(MSE))
def cross_validation_mse_gradient_boosting_maccs(param):
num_round = param["num_rounds"]
del param["num_rounds"]
param["max_depth"] = int(np.round(param["max_depth"]))
MSE = []
for i in range(5):
train_index, test_index = CV_indices_train[i], CV_indices_test[i]
train_index = [
True if ind in train_index else False
for ind in list(brenda_train.index)
]
test_index = [
True if ind in test_index else False
for ind in list(brenda_train.index)
]
dtrain = xgb.DMatrix(train_MACCS[train_index],
label=train_Y[train_index])
dvalid = xgb.DMatrix(train_MACCS[test_index])
bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)
y_valid_pred = bst.predict(dvalid)
MSE.append(
np.mean(
abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))
return (np.mean(MSE))
