51 lines
1.4 KiB
Python
51 lines
1.4 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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from os import path
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import torch.nn.functional as F
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from torch import nn
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from torch.autograd import Variable
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import torchvision.transforms as transforms
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import pandas as pd
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import classification_report
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import matplotlib.pyplot as plt
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class LogisticRegressionModel(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LogisticRegressionModel, self).__init__()
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self.linear = nn.Linear(input_dim, output_dim)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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out = self.linear(x)
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return self.sigmoid(out)
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np.set_printoptions(suppress=False)
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data_test = pd.read_csv("data_test.csv")
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FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
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x_test = data_test[FEATURES].astype(np.float32)
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y_test = data_test['stroke'].astype(np.float32)
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fTest= torch.from_numpy(x_test.values)
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tTest = torch.from_numpy(y_test.values)
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model = LogisticRegressionModel(6,1)
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model.load_state_dict(torch.load('stroke.pth'))
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y_pred = model(fTest)
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rmse = mean_squared_error(tTest, y_pred.detach().numpy())
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acc = accuracy_score(tTest, np.argmax(y_pred.detach().numpy(), axis=1))
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print('-' * 60)
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print(classification_report(tTest, y_pred.detach().numpy().round()))
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print(f" RMSE: {rmse}")
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print(f" Accuracy: {acc}")
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print('-' * 60)
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