ium_464863/predict.py

import torch
import os
import pandas as pd
import numpy as np

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

import matplotlib.pyplot as plt
import seaborn as sns

from NeuralNetwork import NeuralNetwork

# Load model if it exists
if os.path.exists('./models/model.pth'):
    # Create model
    model = torch.load('./models/model.pth')

    # Load test data
    test = pd.read_csv('./datasets/test.csv')

    # Split data
    X_test = test.drop(columns=['id', 'diagnosis']).values
    y_test = test['diagnosis'].values

    # Convert data to PyTorch tensors
    X_test = torch.FloatTensor(X_test)
    y_test = torch.FloatTensor(y_test).view(-1, 1)

    # Predict
    with torch.no_grad():
        y_pred = model(X_test)
        y_pred = np.where(y_pred >= 0.5, 1, 0)

        # Save predictions to CSV
        pd.DataFrame(y_pred, columns=['Prediction']).to_csv('predictions.csv', index=False)

        # Calculate metrics
        accuracy = accuracy_score(y_test, y_pred)
        precision = precision_score(y_test, y_pred)
        recall = recall_score(y_test, y_pred)
        f1 = f1_score(y_test, y_pred)

        # Save metrics to CSV (append mode, if file exists, if not, create it)
        if not os.path.exists('metrics.csv'):
            pd.DataFrame([[accuracy, precision, recall, f1]], columns=['Accuracy', 'Precision', 'Recall', 'F1']).to_csv('metrics.csv', index=False)
        else:
            # without header
            metrics = pd.read_csv('metrics.csv')
            metrics = metrics._append({'Accuracy': accuracy, 'Precision': precision, 'Recall': recall, 'F1': f1}, ignore_index=True)
            metrics.to_csv('metrics.csv', index=False, mode='a', header=False)

            # Plot metrics line chart
            sns.set(style='whitegrid')
            plt.figure(figsize=(8, 6))
            sns.lineplot(data=metrics)
            plt.title('Metrics history')
            plt.xlabel('History number')
            plt.ylabel('Value')
            plt.legend()
            plt.savefig('metrics.png')
else:
    raise FileNotFoundError('Model not found')