53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import torch.nn as nn
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import torch
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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df = pd.read_csv('evaluate_data_dvc.csv')
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X, y = df[['age']], df['income_if_<=50k']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=37)
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n_samples, n_features = X.shape
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X_train = np.array(X_train).reshape(-1,1)
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X_test = np.array(X_test).reshape(-1,1)
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y_train = np.array(y_train).reshape(-1,1)
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y_test = np.array(y_test).reshape(-1,1)
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.fit_transform(X_test)
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torch.from_file
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X_train = torch.from_numpy(X_train.astype(np.float32))
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X_test = torch.from_numpy(X_test.astype(np.float32))
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y_train = torch.from_numpy(y_train.astype(np.float32))
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y_test = torch.from_numpy(y_test.astype(np.float32))
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y_train = y_train.view(y_train.shape[0], 1)
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y_test= y_test.view(y_test.shape[0], 1)
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class LogisticRegresion(nn.Module):
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def __init__(self, n_input_featuers):
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super(LogisticRegresion, self).__init__()
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self.linear = nn.Linear(n_input_featuers, 1)
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def forward(self, x):
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y_predicted = torch.sigmoid(self.linear(x))
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return y_predicted
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model = LogisticRegresion(n_features)
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criterion = nn.BCELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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num_epochs = 1500
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for epoch in range(num_epochs):
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y_predicted = model(X_train)
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loss = criterion(y_predicted,y_train)
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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with torch.no_grad():
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y_predicted = model(X_test)
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y_predicted_cls = y_predicted.round()
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acc = y_predicted_cls.eq(y_test).sum()/float(y_test.shape[0])
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result = open("result_pytorch.txt",'w')
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result.write(f'acc:{acc:.4f}') |