80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
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import torch
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from train import MyNeuralNetwork, load_data
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from torch.utils.data import DataLoader
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import csv
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import os
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import matplotlib.pyplot as plt
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from typing import Tuple, List
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def evaluate_model() -> Tuple[List[float], float]:
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model = MyNeuralNetwork()
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model.load_state_dict(torch.load('model.pt'))
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model.eval()
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test_dataset = load_data("gender_classification_test.csv")
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batch_size: int = 32
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test_dataloader: DataLoader = DataLoader(test_dataset, batch_size=batch_size)
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predictions = []
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labels = []
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get_label = lambda pred: 1 if pred >= 0.5 else 0
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total = 0
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correct = 0
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with torch.no_grad():
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for batch_data, batch_labels in test_dataloader:
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batch_predictions = model(batch_data)
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predicted_batch_labels = [get_label(prediction) for prediction in batch_predictions]
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total += len(predicted_batch_labels)
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batch_labels_list = list(map(int,batch_labels.tolist()))
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correct += sum(x == y for x, y in zip(predicted_batch_labels, batch_labels_list))
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predictions.extend(batch_predictions)
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labels.extend(batch_labels)
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accuracy = correct/total
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return predictions, accuracy
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def save_predictions(predictions: list[float]) -> None:
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filename = "results.csv"
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column_name = "predict"
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with open(filename, 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow([column_name])
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for result in predictions:
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loan_decision = 1 if result.item() > 0.5 else 0
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writer.writerow([loan_decision])
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def save_accuracy(accuracy):
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filename = 'results.csv'
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if os.path.exists(filename):
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with open(filename, 'a') as file:
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writer = csv.writer(file)
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writer.writerow([accuracy])
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else:
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with open(filename, 'w') as file:
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writer = csv.writer(file)
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writer.writerow(['accuracy'])
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writer.writerow([accuracy])
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def plot_accuracy():
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filename = 'results.csv'
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accuracy_results = []
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if os.path.exists(filename):
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with open(filename, 'r') as file:
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reader = csv.reader(file)
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for idx, row in enumerate(reader):
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if idx == 0:
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continue
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accuracy_results.append(float(row[0]))
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iterations = list(map(str,range(1, len(accuracy_results)+1)))
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plt.plot(iterations, accuracy_results)
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plt.xlabel('build')
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plt.ylabel('accuracy')
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plt.title("Accuracies over builds.")
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plt.savefig("plot.png")
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def main():
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predictions, accuracy = evaluate_model()
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save_predictions(predictions)
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save_accuracy(accuracy)
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plot_accuracy()
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if __name__ == "__main__":
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main()
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