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1336f19 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import os
import pandas as pd
import joblib
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import seaborn as sns
import matplotlib.pyplot as plt
def evaluate_best_model(data_dir, model_path):
print("Loading test dataset...")
X_test = pd.read_csv(os.path.join(data_dir, "X_test.csv"))
y_test = pd.read_csv(os.path.join(data_dir, "y_test.csv")).values.ravel()
print(f"Loading best model from {model_path}...")
if not os.path.exists(model_path):
print(f"Model not found at {model_path}. Please train the model first.")
return
model = joblib.load(model_path)
# Check if label encoder exists
le_path = os.path.join(data_dir, "label_encoder.pkl")
target_names = None
if os.path.exists(le_path):
le = joblib.load(le_path)
target_names = [str(c) for c in le.classes_]
print("Generating predictions...")
y_pred = model.predict(X_test)
print("\n--- Model Evaluation ---")
acc = accuracy_score(y_test, y_pred)
print(f"Test Accuracy: {acc:.4f}\n")
print("Classification Report:")
print(classification_report(y_test, y_pred, target_names=target_names if target_names else None, zero_division=0))
# Generates a confusion matrix (optional visual)
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=target_names if target_names else "auto",
yticklabels=target_names if target_names else "auto")
plt.title('Confusion Matrix')
plt.ylabel('Actual Class')
plt.xlabel('Predicted Class')
cm_path = os.path.join(os.path.dirname(model_path), "confusion_matrix.png")
plt.savefig(cm_path)
print(f"Saved confusion matrix matrix chart to {cm_path}")
print("Evaluation complete.")
if __name__ == "__main__":
datasets_directory = r"C:\Users\KAUSTAV\OneDrive\Desktop\NetWokie\NetWokie-AI\datasets"
model_filepath = r"C:\Users\KAUSTAV\OneDrive\Desktop\NetWokie\NetWokie-AI\ml_models\device_classifier.pkl"
evaluate_best_model(datasets_directory, model_filepath)
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