76 lines
1.7 KiB
Python
76 lines
1.7 KiB
Python
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import torch
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import torch.nn as nn
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import numpy as np
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import pandas as pd
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from torch.autograd import Variable
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train = 'train/train.tsv'
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in_file = 'test-A/in.tsv'
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out_file = 'test-A/out.tsv'
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learning_rate = 0.00001
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class linearRegression(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(linearRegression, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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def forward(self, x):
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out = self.linear(x)
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return out
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inputDim = 1
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outputDim = 1
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model = linearRegression(inputDim, outputDim)
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data = pd.read_csv('train/train.tsv',sep='\t',header=None)
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y_values = data[0].tolist()
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x_values = data[8].tolist()
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x_train = np.array(x_values, dtype=np.float32)
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x_train = x_train.reshape(-1, 1)
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y_train = np.array(y_values, dtype=np.float32)
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y_train = y_train.reshape(-1, 1)
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if torch.cuda.is_available():
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model.cuda()
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criterion = nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
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for epoch in range(1000):
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epoch += 1
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if torch.cuda.is_available():
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inputs = Variable(torch.from_numpy(x_train).cuda())
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labels = Variable(torch.from_numpy(y_train).cuda())
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else:
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inputs = Variable(torch.from_numpy(x_train))
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labels = Variable(torch.from_numpy(y_train))
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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result = pd.read_csv('test-A/in.tsv',sep='\t',header=None)
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end_x = result[7].tolist()
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end_x = np.array(end_x, dtype=np.float32)
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end_x = end_x.reshape(-1, 1)
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predictions = model(torch.from_numpy(end_x).requires_grad_()).data.numpy()
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output = open('test-A/out.tsv', 'w')
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for p in predictions:
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output.write(str(p[0])+'\n')
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