saved_indexes = []
active_set = 0
countIter = 4
if len(saved_indexes) > 0:
if countIter == 5:
lasso_cv.fit(x_train_saved, YTrain_)
best_alpha = lasso_cv.alpha_
print(best_alpha)
countIter = 0
model = linear_model.Lasso(fit_intercept=False, alpha=best_alpha)
flag_linear = 0
else:
model = linear_model.LinearRegression(fit_intercept=False)
flag_linear = 1
blocks_generated, active_set, num_blocks = generate_samples_dynamic_set(num_blocks, n_features_transf, r,
saved_indexes, r1, min_set, max_set,
active_set, max_active_set)
for i in range(0, num_blocks):
x_train_i, x_val_i = get_current_data(XTrain_, XVal_, blocks_generated[i, :])
rand_vect = r1.choice(n_samples_val, active_set_samples, replace=False)
x_val_i = x_val_i[rand_vect, :]
YVal_i = YVal_[rand_vect]
new_loss, beta = compute_mse(model, x_train_i, YTrain_, x_val_i, YVal_i, score, values_TM)
losses.append(new_loss)
betas.append(beta)
ordered_losses = np.argsort(losses)
orderd_losses_ = np.array(losses)[ordered_losses]
standard_deviation = np.std(orderd_losses_)
linearRegression = linear_model.LinearRegression(fit_intercept=False, n_jobs = -1)
mse,_,_ = compute_mse(linearRegression, XTrain, YTrain,XTest,YTest)
print("start_mse", mse)
weights_indexes = np.zeros(n_features)
index = 1000
deleted_indexes = np.where(del_indexes>index)[0]
iter = 0
r3 = np.random.RandomState(14)
while len(saved_indexes)<=final_active_set-len(deleted_indexes):
losses = np.array([])
betas = np.array([])
coeffs = np.array([])
corrs = np.array([])
blocks_generated = generate_samples_dynamic_set(num_blocks, n_features, r,saved_indexes,r1, deleted_indexes)
for i in range(0, num_blocks):
x_train_i, x_test_i = get_current_data(XTrain, XTest, blocks_generated[i,:])
rand_vect = r1.choice(n_samples_test,active_set_samples, replace = False)
x_test_i = x_test_i[rand_vect,:]
YTest_i = YTest[rand_vect]
new_loss,beta,_ = compute_mse(linearRegression, x_train_i, YTrain,x_test_i, YTest_i)
losses = np.append(losses, new_loss)
if len(betas)==0:
betas = beta
else:
betas = np.append(betas, beta, axis =1)
ordered_losses = np.argsort(losses)
orderd_losses_ = np.sort(losses)
#losses_to_select = r3.choice(np.arange(100,200), 1, replace=False)[0]
