"estimator,iter_num,n_selected_var,recall,precision,f1_score,exec_time\n"
)
for data in dataset_names:
data_dir = os.path.join('Simulation_Data', data)
beta = np.load(os.path.join(data_dir, 'beta0.npy'))
for iter_num in range(n_iter):
print(iter_num)
X = np.load(os.path.join(data_dir, f'x_tr{iter_num}.npy'))
y = np.load(os.path.join(data_dir, f'y_tr{iter_num}.npy'))
start_time = time.time()
lasso = glmnet.ElasticNet(alpha=1,
standardize=True,
fit_intercept=False,
n_splits=5,
scoring='mean_squared_error',
n_jobs=-1,
random_state=random_state)
lasso.fit(X, y)
end_time = time.time()
record.record_result(record_path,
method,
beta,
beta_hat=enet.coef_,
exec_time=end_time - start_time)
for iter_num in range(n_iter):
print(iter_num)
X = np.load(os.path.join(data_dir, f'x_tr{iter_num}.npy'))
y = np.load(os.path.join(data_dir, f'y_tr{iter_num}.npy'))
start_time = time.time()
model = PrecisionLasso()
model.setLogisticFlag(False)
model.calculateGamma(X)
min_mse = np.inf
min_lam = 0
for i in range(11):
lam = 10**(i-5)
model.setLambda(lam)
model.setLearningRate(1e-6)
mse = 0
for Xtrain, ytrain, Xtest, ytest in KFold(X, y, 5):
model.fit(Xtrain, ytrain)
pred = model.predict(Xtest)
mse += np.linalg.norm(pred - ytest)
if mse < min_mse:
min_mse = mse
min_lam = lam
model.setLambda(min_lam)
model.fit(X, y)
end_time = time.time()
record.record_result(record_path, method, beta, beta_hat=model.getBeta(), exec_time=end_time-start_time)