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هذا لكود تحليلي للبيانات: import warnings warnings.filterwarnings('ignore') import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline sns.set_context('notebook') sns.set_style('white') import dtreeviz # Data Preparation # 1.Download Data and some preprocessing training = pd.read_csv(r'C:\Users\Dell\Downloads\dataa\UNSW_NB15_training-set.csv') testing = pd.read_csv(r'C:\Users\Dell\Downloads\dataa\UNSW_NB15_testing-set.csv') print("training ",training.shape) print("testing ",testing.shape) df = pd.concat([training,testing]).drop('id',axis=1) df = df.reset_index(drop=True) df.head() df.attack_cat.unique() # Categorical data for col in ['proto', 'service', 'state']: df[col] = df[col].astype('category').cat.codes df['attack_cat'] = df['attack_cat'].astype('category') # Visualizing attacks categories validAttacks = df[df['label']==1]['attack_cat'].value_counts() print(validAttacks) plt.figure(figsize = (15,8)) plt.pie(validAttacks,labels = validAttacks.index, autopct = '%1.1f%%',explode = [0,0,0,0,0,0.2,0.2,0.2,0.2,1.2]) plt.show() from sklearn.model_selection import train_test_split X = df.drop(columns = ['attack_cat', 'label']) y = df['label'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=11) feature_names = list(X.columns) print("X_train shape: ", X_train.shape) print("y_train shape: ", y_train.shape) print("X_test shape: ", X_test.shape) print("y_test shape: ", y_test.shape) from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor def evaluate_anomaly_detection_models(X_train, X_test, y_test): # Initialize models isolation_forest = IsolationForest(contamination=0.1) # Adjust contamination based on your dataset lof = LocalOutlierFactor(contamination=0.1) # Adjust contamination based on your dataset # Fit models on training data isolation_forest.fit(X_train) lof.fit(X_train) # Predict anomalies if_preds = isolation_forest.predict(X_test) lof_preds = lof.fit_predict(X_test) المشكل هنا رغم اني استعملت افرايج f1_score,recall_score... # Evaluate Isolation Forest if_accuracy = accuracy_score(y_test, if_preds) if_precision = precision_score(y_test, if_preds) if_recall = recall_score(y_test, if_preds) if_f1 = f1_score(y_test, if_preds,average='micro') # Evaluate LOF lof_accuracy = accuracy_score(y_test, lof_preds) lof_precision = precision_score(y_test, lof_preds) lof_recall = recall_score(y_test, lof_preds) lof_f1 = f1_score(y_test, lof_preds,average='micro') # Create a dictionary to store the results results = { 'Isolation Forest': { 'Accuracy': if_accuracy, 'Precision': if_precision, 'Recall': if_recall, 'F1 Score': if_f1 }, 'Local Outlier Factor': { 'Accuracy': lof_accuracy, 'Precision': lof_precision, 'Recall': lof_recall, 'F1 Score': lof_f1 } } return results # Example usage: # Assuming you have X_train, X_test, and y_test as your training and test data results = evaluate_anomaly_detection_models(X_train, X_test, y_test) print(results) الجواب: ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].