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load.py
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import numpy as np
import pandas as pd
import torch
def read_data(file_name):
data = pd.read_csv(file_name, encoding = "big5")
data = data.values
label_only = True
train_size = 10001
if label_only:
Y = data[:train_size + 1,1]
train_X = data[:train_size + 1,0]
else:
Y = data[:,1]
train_X = data[:,0]
list_Y = []
for y in Y:
y = y.split(' ')
# labels = []
# for label in y:
# labels.append(int(label))
# train_Y.append(torch.tensor(labels))
y = torch.FloatTensor(list(map(int, y)))
y = y.unsqueeze(0)
list_Y.append(y)
train_Y = torch.FloatTensor((train_size, list_Y[0].size()[1]))
torch.cat(list_Y, out = train_Y)
train_X = np.asarray(train_X)
return train_X, train_Y
def read_data_self_(file_name, file_name2):
data = pd.read_csv(file_name, encoding = "big5")
data = data.values
label_only = True
train_size = 10001
if label_only:
Y = data[:train_size + 1,1]
train_X = data[:train_size + 1,0]
else:
Y = data[:,1]
train_X = data[:,0]
list_Y = []
for y in Y:
y = y.split(' ')
# labels = []
# for label in y:
# labels.append(int(label))
# train_Y.append(torch.tensor(labels))
y = torch.FloatTensor(list(map(int, y)))
y = y.unsqueeze(0)
list_Y.append(y)
data2 = pd.read_csv(file_name2, encoding = "big5")
data2 = data2.values
train_Y = torch.FloatTensor((train_size, list_Y[0].size()[1]))
torch.cat(list_Y, out = train_Y)
train_X = np.asarray(train_X)
X2 = data2[:, 0]
Y2 = np.asarray(data2[:, 1:], dtype = np.float32)
train_X = np.concatenate((train_X, X2), axis = 0)
print(train_X.shape)
train_Y = torch.cat((train_Y, torch.from_numpy(Y2)), dim = 0)
print(train_Y.size())
return train_X, train_Y
def read_test(file_name):
data = pd.read_csv(file_name, encoding = "big5")
data = data.values
train_X = data[:,0]
train_X = np.asarray(train_X)
return train_X
def read_feat(file_name1, file_name2):
data = np.load(file_name1)
data2 = np.load(file_name2)
return data, data2
def genLabels_Partition(train_X, train_Y, valid_ratio = 0.1):
data_size = len(train_Y)
labels = {train_X[i] : train_Y[i] for i in range(len(train_Y))}
train_ids = [train_X[i] for i in range(int(data_size*3*valid_ratio))] + [train_X[i] for i in range(int(data_size*(4*valid_ratio)), data_size)]
valid_ids = [train_X[i] for i in range(int(data_size*3*valid_ratio), int(data_size*4*valid_ratio))]
partition = {'train' : train_ids, 'validation' : valid_ids}
return labels, partition
def gen_Partition(train_X, train_Y, valid_ratio = 0.1):
data_size = len(train_Y)
#labels = {train_X[i] : train_Y[i] for i in range(len(train_Y))}
train_ids = [i for i in range(int(data_size*(1-valid_ratio)), data_size)]
valid_ids = [i for i in range(int(data_size*(1-valid_ratio)))]
#train_ids = [i for i in range(int(data_size*(1-valid_ratio)))]
#valid_ids = [i for i in range(int(data_size*(1-valid_ratio)), data_size)]
partition = {'train' : train_ids, 'validation' : valid_ids}
return partition
def genTest(train_X, valid_ratio = 0.9):
data_size = len(train_X)
train_ids = [train_X[i] for i in range(int(data_size))]
valid_ids = [train_X[i] for i in range(int(data_size*(1-valid_ratio)), data_size)]
partition = {'train' : train_ids, 'validation' : valid_ids}
return partition
def read_data_rotate(file_name):
data = pd.read_csv(file_name, encoding = "big5")
data = data.values
Y = np.asarray(data[:,1], dtype = int)
train_X = data[:,0]
train_Y = torch.from_numpy(Y)
return train_X, train_Y