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import pandas as pd
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import numpy as np
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import joblib
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import argparse
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from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
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def evaluate(csv_path, model_path, test_size=0.2, random_state=42):
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# Загрузка модели
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data = joblib.load(model_path)
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model = data['model']
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class_names = data['class_names']
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print(f"Loaded model with classes: {class_names}")
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# Загрузка данных
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df = pd.read_csv(csv_path)
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X = df.iloc[:, 1:].values.astype(np.float32)
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y_labels = df.iloc[:, 0].values
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label_to_id = {label: i for i, label in enumerate(class_names)}
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y = np.array([label_to_id[label] for label in y_labels])
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# Разделение (должно совпадать с параметрами обучения)
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from sklearn.model_selection import train_test_split
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_, X_test, _, y_test, _, y_labels_test = train_test_split(
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X, y, y_labels, test_size=test_size, random_state=random_state, stratify=y
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)
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print(f"Test size: {len(X_test)}")
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y_pred = model.predict(X_test)
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acc = accuracy_score(y_test, y_pred)
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report = classification_report(y_test, y_pred, target_names=class_names)
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cm = confusion_matrix(y_test, y_pred)
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print(f"\nAccuracy: {acc:.4f}")
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print("\nClassification Report:")
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print(report)
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print("Confusion Matrix:")
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print(cm)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--csv', required=True, help='CSV с данными')
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parser.add_argument('--model', required=True, help='Путь к модели')
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parser.add_argument('--test_size', type=float, default=0.2)
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parser.add_argument('--random_state', type=int, default=42)
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args = parser.parse_args()
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evaluate(args.csv, args.model, args.test_size, args.random_state)
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