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import numpy as np
import constants as cst
import os
from torch.utils import data
import torch
def fi_2010_load(path, seq_size, horizon, all_features):
dec_data = np.loadtxt(path + "/Train_Dst_NoAuction_ZScore_CF_7.txt")
full_train = dec_data[:, :int(dec_data.shape[1] * cst.SPLIT_RATES[0])]
full_val = dec_data[:, int(dec_data.shape[1] * cst.SPLIT_RATES[0]):]
dec_test1 = np.loadtxt(path + '/Test_Dst_NoAuction_ZScore_CF_7.txt')
dec_test2 = np.loadtxt(path + '/Test_Dst_NoAuction_ZScore_CF_8.txt')
dec_test3 = np.loadtxt(path + '/Test_Dst_NoAuction_ZScore_CF_9.txt')
full_test = np.hstack((dec_test1, dec_test2, dec_test3))
if horizon == 1:
tmp = 5
elif horizon == 2:
tmp = 4
elif horizon == 3:
tmp = 3
elif horizon == 5:
tmp = 2
elif horizon == 10:
tmp = 1
else:
raise ValueError("Horizon not found")
train_labels = full_train[-tmp, :].flatten()
val_labels = full_val[-tmp, :].flatten()
test_labels = full_test[-tmp, :].flatten()
train_labels = train_labels[seq_size-1:] - 1
val_labels = val_labels[seq_size-1:] - 1
test_labels = test_labels[seq_size-1:] - 1
if all_features:
train_input = full_train[:144, :].T
val_input = full_val[:144, :].T
test_input = full_test[:144, :].T
else:
train_input = full_train[:40, :].T
val_input = full_val[:40, :].T
test_input = full_test[:40, :].T
train_input = torch.from_numpy(train_input).float()
train_labels = torch.from_numpy(train_labels).long()
val_input = torch.from_numpy(val_input).float()
val_labels = torch.from_numpy(val_labels).long()
test_input = torch.from_numpy(test_input).float()
test_labels = torch.from_numpy(test_labels).long()
return train_input, train_labels, val_input, val_labels, test_input, test_labels
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