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73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# numpy for loading dataset
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import numpy as np
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# pytorch for deep learning models
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import torch
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import torch.nn as nn
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import torch.optim as optim
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# Pima indians describes pateitn medical data and whether they had diabetes for last 5 years
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# It is binary classification (they could either have diabetes 1 or not 0)
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# load the file as a matrix of numbers,
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dataset = np.loadtxt('pima-indians-diabetes.csv', delimiter=',')
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# split into input (X) -> in this case everything beside info whether patient had diabetes or not is input
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# We are spliting data into two subsets by using NumPy slie operator : and choose first 8 columns using 0:8 slice
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X = dataset[:,0:8]
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# and output (y) variables -> in this case we are only interested whether patient had diabetes or not as an output
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# you can simplify that y = f(X)
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# We are spliting the data by using slice operator : and choosing last column
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y = dataset[:,8]
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# we need to convert this data to pytorch tensors
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# Pyutoarch usually operates on 32-bit floating point and NumPy by default uses 64 bit floating point
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X = torch.tensor(X, dtype=torch.float32)
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# We can also correct the shape to fit what PyTorch would expect (here we are converting n vectors to n x 1 matrix)
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# This simplifies handling matrix multiplication operations (which are the basis of deep learning models)
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# reshape is converting the output variable y from a 1-dimensional NumPy array to a 2-dimensional PyTorch tensor with a shape of (n, 1), where n is the number of samples in the dataset.
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y = torch.tensor(y, dtype=torch.float32).reshape(-1, 1)
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# define the model
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class PimaClassifier(nn.Module):
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def __init__(self):
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super().__init__()
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self.hidden1 = nn.Linear(8, 12)
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self.act1 = nn.ReLU()
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self.hidden2 = nn.Linear(12, 8)
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self.act2 = nn.ReLU()
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self.output = nn.Linear(8, 1)
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self.act_output = nn.Sigmoid()
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def forward(self, x):
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x = self.act1(self.hidden1(x))
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x = self.act2(self.hidden2(x))
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x = self.act_output(self.output(x))
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return x
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model = PimaClassifier()
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print(model)
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# train the model
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loss_fn = nn.BCELoss() # binary cross entropy
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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n_epochs = 100
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batch_size = 10
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for epoch in range(n_epochs):
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for i in range(0, len(X), batch_size):
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Xbatch = X[i:i+batch_size]
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y_pred = model(Xbatch)
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ybatch = y[i:i+batch_size]
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loss = loss_fn(y_pred, ybatch)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# compute accuracy
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y_pred = model(X)
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accuracy = (y_pred.round() == y).float().mean()
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print(f"Accuracy {accuracy}")
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# make class predictions with the model
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predictions = (model(X) > 0.5).int()
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for i in range(5):
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print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i])) |