inzynieria-uczenia-maszynowego/eval.py

# Import dependencies

import numpy as np
import pandas as pd
import torch

from torch import nn
from torch.utils.data import DataLoader, TensorDataset


# Load data

test_data  = pd.read_csv('spambase.data.test.csv')


# X, Y

x_test = np.array(test_data.drop('is_spam', axis=1).values)
y_test = np.array(test_data['is_spam'].values)

test = TensorDataset(torch.Tensor(x_test), torch.LongTensor(y_test))
test_loader = DataLoader(test, batch_size=64)


# Load model

model = torch.jit.load("model.pth")


# Evaluate

def test_loop(dataloader, model, loss_fn):
    open("results.txt", "w").close() # Create empty file

    model.eval()

    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    test_loss, correct = 0, 0

    f = open("results.txt", "a")

    with torch.no_grad():
        for X, y in dataloader:
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()

            for val in pred:
                f.write(str(torch.argmax(val)) + "\n")

    test_loss /= num_batches
    correct /= size

    print(f"Avg loss: {test_loss:>8f}")


loss_fn = nn.CrossEntropyLoss()

test_loop(test_loader, model, loss_fn)