for i in range(0, num_blocks):
x_train_i, x_test_i = get_current_data(XTrain, XTest, blocks_generated[i,:])
new_loss,beta,corr = compute_mse(linearRegression, x_train_i, YTrain,x_test_i, YTest)
losses = np.append(losses, new_loss)
if len(betas)==0:
betas = beta
corrs = corr
else:
betas = np.append(betas, beta, axis =1)
corrs = np.append(corrs, corr,axis = 1)
ordered_losses = np.argsort(losses)
orderd_losses_ = np.sort(losses)
ordered_loss_ten = extract_losses(ordered_losses)
weights_indexes = get_common_indexes(ordered_loss_ten,blocks_generated,n_features,betas)
ordered_weights_indexes = np.argsort(weights_indexes)[::-1]
ordered_weights_indexes_values = np.sort(weights_indexes)[::-1]
saved_indexes = extracte_chosen_indexes(saved_indexes, ordered_weights_indexes, ordered_weights_indexes_values, chosen_indexes)
x_train_saved, x_test_saved = get_current_data(XTrain, XTest, saved_indexes)
mse_saved,_,_ = compute_mse(linearRegression, x_train_saved, YTrain,x_test_saved, YTest)
if count==0:
mse_last = mse_saved
len_last = len(saved_indexes)
while mse_saved>mse_last:
saved_indexes = np.delete(saved_indexes, len(saved_indexes)-1)
x_train_saved, x_test_saved = get_current_data(XTrain, XTest, saved_indexes)
mse_saved,_,_ = compute_mse(linearRegression, x_train_saved, YTrain,x_test_saved, YTest)
mse_last = mse_saved
mses.append(mse_last)
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]
#print("loss scelte", losses_to_select)
first_loss = orderd_losses_[0]
chosen_losses = len(orderd_losses_[orderd_losses_<=first_loss*4./3])
if chosen_losses<5:
chosen_losses=5
print("losses scelte", chosen_losses)
index_chosen_losses = ordered_losses[:chosen_losses]
weights_indexes,beta_sign = get_common_indexes(weights_indexes, index_chosen_losses,blocks_generated,betas,n_features,deleted_indexes,saved_indexes)
ordered_weights_indexes = np.argsort(weights_indexes)[::-1]
ordered_weights_indexes_values = np.sort(weights_indexes)[::-1]
chosen_indexes = r3.choice(np.arange(1,6), 1, replace=False)[0]
if chosen_indexes+len(saved_indexes)>final_active_set:
chosen_indexes = final_active_set-len(saved_indexes)
print("chosen indexes", chosen_indexes)
saved_indexes,del_indexes = extract_chosen_indexes_from_start(saved_indexes, ordered_weights_indexes, chosen_indexes,del_indexes)
deleted_indexes = np.where(del_indexes>index)[0]
assert(len(deleted_indexes)+len(saved_indexes)<=n_features)
x_train_saved, x_test_saved = get_current_data(XTrain, XTest, saved_indexes)
x_test_saved = x_test_saved[rand_vect,:]
YTest_saved = YTest[rand_vect]
mse_saved,_,_ = compute_mse(linearRegression, x_train_saved, YTrain,x_test_saved, YTest_saved)
mses.append(mse_saved)
ordered_losses = np.argsort(losses)
orderd_losses_ = np.array(losses)[ordered_losses]
standard_deviation = np.std(orderd_losses_)
mean_weights = np.mean(orderd_losses_)
chosen_losses = len(orderd_losses_[orderd_losses_ + standard_deviation <= mean_weights])
if chosen_losses > num_blocks / 3:
chosen_losses = num_blocks / 3
print("losses scelte", chosen_losses)
index_chosen_losses = ordered_losses[:chosen_losses]
betas = np.hstack(betas)
if flag_linear == 0:
weights_indexes = get_common_indexes(weights_indexes, index_chosen_losses, blocks_generated, betas,
n_features_transf)
weights_abs = np.abs(weights_indexes)
else:
weights_indexes_copy = get_common_indexes(weights_indexes.copy(), index_chosen_losses, blocks_generated, betas,
n_features_transf)
weights_abs = np.abs(weights_indexes_copy)
ordered_weights_indexes = np.argsort(weights_abs)[::-1]
ordered_weights_indexes_values = weights_abs[ordered_weights_indexes]
chosen_indexes = r3.choice(np.arange(2, 4), 1, replace=False)[0]
if chosen_indexes + len(saved_indexes) > max_active_set:
chosen_indexes = max_active_set - len(saved_indexes)
print("chosen indexes", chosen_indexes)
saved_indexes = extract_chosen_indexes_from_start(saved_indexes, ordered_weights_indexes, chosen_indexes)
x_train_i, x_test_i = get_current_data(XTrain, XTest, blocks_generated[i, :])
new_loss, beta, corr = compute_mse(linearRegression, x_train_i, YTrain, x_test_i, YTest)
losses = np.append(losses, new_loss)
if len(betas) == 0:
betas = beta
corrs = corr
else:
betas = np.append(betas, beta, axis=1)
corrs = np.append(corrs, corr, axis=1)
ordered_losses = np.argsort(losses)
orderd_losses_ = np.sort(losses)
ordered_loss_ten = ordered_losses[:10]
weights_indexes, beta_sign = get_common_indexes(
weights_indexes, ordered_loss_ten, blocks_generated, betas, n_features
)
ordered_weights_indexes = np.argsort(weights_indexes)[::-1]
ordered_weights_indexes_values = np.sort(weights_indexes)[::-1]
saved_indexes = extract_chosen_indexes_from_start(
saved_indexes, ordered_weights_indexes, ordered_weights_indexes_values, chosen_indexes
)
x_train_saved, x_test_saved = get_current_data(XTrain, XTest, saved_indexes)
mse_saved, _, _ = compute_mse(linearRegression, x_train_saved, YTrain, x_test_saved, YTest)
mses.append(mse_saved)
num_informative = [f for f in saved_indexes if f <= 99]
print("num_inf", len(num_informative), "su", len(saved_indexes), "mse", mse_saved)
saved_indexes_list.append(saved_indexes)
