{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# **HD PROJECT: End-to-end project delivery on cyber-security data analytics**\n", "\n", "**Experiments on two datasets- Dataset1: NSL-KDD and Dataset 2: Processed Combined IoT dataset**\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# **DATASET 1: NSL-KDD** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SECTION 1: DECLARE THE MODULES" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from collections import defaultdict\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SECTION 2: Data import and preprocess" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Requirement already satisfied: wget in /Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages (3.2)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install wget\n", "import wget\n", " \n", "link_to_data = 'https://raw.githubusercontent.com/SIT719/2020-S2/master/data/Week_5_NSL-KDD-Dataset/training_attack_types.txt?raw=true'\n", "DataSet = wget.download(link_to_data) " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'training_attack_types.txt'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DataSet" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "header_names = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'attack_type', 'success_pred']\n", "\n", "\n", "# Differentiating between nominal, binary, and numeric features\n", "\n", "# root_shell is marked as a continuous feature in the kddcup.names \n", "# file, but it is supposed to be a binary feature according to the \n", "# dataset documentation\n", "\n", "# training_attack_types.txt maps each of the 22 different attacks to 1 of 4 categories\n", "# file obtained from http://kdd.ics.uci.edu/databases/kddcup99/training_attack_types\n", "\n", "col_names = np.array(header_names)\n", "\n", "nominal_idx = [1, 2, 3]\n", "binary_idx = [6, 11, 13, 14, 20, 21]\n", "numeric_idx = list(set(range(41)).difference(nominal_idx).difference(binary_idx))\n", "\n", "nominal_cols = col_names[nominal_idx].tolist()\n", "binary_cols = col_names[binary_idx].tolist()\n", "numeric_cols = col_names[numeric_idx].tolist()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# training_attack_types.txt maps each of the 22 different attacks to 1 of 4 categories\n", "# file obtained from http://kdd.ics.uci.edu/databases/kddcup99/training_attack_types\n", "\n", "category = defaultdict(list)\n", "category['benign'].append('normal')\n", "\n", "with open(DataSet, 'r') as f:\n", " for line in f.readlines():\n", " attack, cat = line.strip().split(' ')\n", " category[cat].append(attack)\n", "\n", "attack_mapping = dict((v,k) for k in category for v in category[k])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'normal': 'benign',\n", " 'apache2': 'dos',\n", " 'back': 'dos',\n", " 'mailbomb': 'dos',\n", " 'processtable': 'dos',\n", " 'snmpgetattack': 'dos',\n", " 'teardrop': 'dos',\n", " 'smurf': 'dos',\n", " 'land': 'dos',\n", " 'neptune': 'dos',\n", " 'pod': 'dos',\n", " 'udpstorm': 'dos',\n", " 'ps': 'u2r',\n", " 'buffer_overflow': 'u2r',\n", " 'perl': 'u2r',\n", " 'rootkit': 'u2r',\n", " 'loadmodule': 'u2r',\n", " 'xterm': 'u2r',\n", " 'sqlattack': 'u2r',\n", " 'httptunnel': 'u2r',\n", " 'ftp_write': 'r2l',\n", " 'guess_passwd': 'r2l',\n", " 'snmpguess': 'r2l',\n", " 'imap': 'r2l',\n", " 'spy': 'r2l',\n", " 'warezclient': 'r2l',\n", " 'warezmaster': 'r2l',\n", " 'multihop': 'r2l',\n", " 'phf': 'r2l',\n", " 'named': 'r2l',\n", " 'sendmail': 'r2l',\n", " 'xlock': 'r2l',\n", " 'xsnoop': 'r2l',\n", " 'worm': 'probe',\n", " 'nmap': 'probe',\n", " 'ipsweep': 'probe',\n", " 'portsweep': 'probe',\n", " 'satan': 'probe',\n", " 'mscan': 'probe',\n", " 'saint': 'probe'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "attack_mapping" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#Processing Training Data\n", "\n", "train_file='https://raw.githubusercontent.com/SIT719/2020-S2/master/data/Week_5_NSL-KDD-Dataset/KDDTrain%2B.txt'\n", "\n", "\n", "\n", "train_df = pd.read_csv(train_file, names=header_names)\n", "\n", "train_df['attack_category'] = train_df['attack_type'] \\\n", " .map(lambda x: attack_mapping[x])\n", "\n", "train_df.drop(['success_pred'], axis=1, inplace=True)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#Processing test Data\n", "test_file='https://raw.githubusercontent.com/SIT719/2020-S2/master/data/Week_5_NSL-KDD-Dataset/KDDTest%2B.txt'\n", "\n", "test_df = pd.read_csv(test_file, names=header_names)\n", "test_df['attack_category'] = test_df['attack_type'] \\\n", " .map(lambda x: attack_mapping[x])\n", "test_df.drop(['success_pred'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_attack_types = train_df['attack_type'].value_counts()\n", "train_attack_cats = train_df['attack_category'].value_counts()\n", "\n", "test_attack_types = test_df['attack_type'].value_counts()\n", "test_attack_cats = test_df['attack_category'].value_counts()\n", "\n", "train_attack_types.plot(kind='barh', figsize=(20,10), fontsize=20)\n", "\n", "train_attack_cats.plot(kind='barh', figsize=(20,10), fontsize=30)\n", "\n", "train_df[binary_cols].describe().transpose()\n", "train_df.groupby(['su_attempted']).size()\n", "train_df['su_attempted'].replace(2, 0, inplace=True)\n", "test_df['su_attempted'].replace(2, 0, inplace=True)\n", "train_df.groupby(['su_attempted']).size()\n", "train_df.groupby(['num_outbound_cmds']).size()\n", "\n", "#Now, that's not a very useful feature - let's drop it from the dataset\n", "\n", "train_df.drop('num_outbound_cmds', axis = 1, inplace=True)\n", "test_df.drop('num_outbound_cmds', axis = 1, inplace=True)\n", "numeric_cols.remove('num_outbound_cmds')\n", "\n", "\n", "\n", "#Data Preparation\n", "\n", "train_Y = train_df['attack_category']\n", "train_x_raw = train_df.drop(['attack_category','attack_type'], axis=1)\n", "test_Y = test_df['attack_category']\n", "test_x_raw = test_df.drop(['attack_category','attack_type'], axis=1)\n", "\n", "\n", "combined_df_raw = pd.concat([train_x_raw, test_x_raw])\n", "combined_df = pd.get_dummies(combined_df_raw, columns=nominal_cols, drop_first=True)\n", "\n", "train_x = combined_df[:len(train_x_raw)]\n", "test_x = combined_df[len(train_x_raw):]\n", "\n", "# Store dummy variable feature names\n", "dummy_variables = list(set(train_x)-set(combined_df_raw))\n", "\n", "#execute the commands in console\n", "train_x.describe()\n", "train_x['duration'].describe()\n", "# Experimenting with StandardScaler on the single 'duration' feature\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "durations = train_x['duration'].values.reshape(-1, 1)\n", "standard_scaler = StandardScaler().fit(durations)\n", "scaled_durations = standard_scaler.transform(durations)\n", "pd.Series(scaled_durations.flatten()).describe()\n", "\n", "# Experimenting with MinMaxScaler on the single 'duration' feature\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "min_max_scaler = MinMaxScaler().fit(durations)\n", "min_max_scaled_durations = min_max_scaler.transform(durations)\n", "pd.Series(min_max_scaled_durations.flatten()).describe()\n", "\n", "# Experimenting with RobustScaler on the single 'duration' feature\n", "from sklearn.preprocessing import RobustScaler\n", "\n", "min_max_scaler = RobustScaler().fit(durations)\n", "robust_scaled_durations = min_max_scaler.transform(durations)\n", "pd.Series(robust_scaled_durations.flatten()).describe()\n", "\n", "# Experimenting with MaxAbsScaler on the single 'duration' feature\n", "from sklearn.preprocessing import MaxAbsScaler\n", "\n", "max_Abs_scaler = MaxAbsScaler().fit(durations)\n", "robust_scaled_durations = max_Abs_scaler.transform(durations)\n", "pd.Series(robust_scaled_durations.flatten()).describe()\n", "\n", "# Let's proceed with StandardScaler- Apply to all the numeric columns\n", "\n", "standard_scaler = StandardScaler().fit(train_x[numeric_cols])\n", "\n", "train_x[numeric_cols] = \\\n", " standard_scaler.transform(train_x[numeric_cols])\n", "\n", "test_x[numeric_cols] = \\\n", " standard_scaler.transform(test_x[numeric_cols])\n", " \n", "train_x.describe()\n", "\n", "\n", "\n", "train_Y_bin = train_Y.apply(lambda x: 0 if x is 'benign' else 1)\n", "test_Y_bin = test_Y.apply(lambda x: 0 if x is 'benign' else 1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SECTION 3: Multi class classification\n", "#This is the section where you have to add other algorithms, tune algorithms and visualize to compare and analyze algorithms" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[9365 56 289 1 0]\n", " [1541 5998 97 0 0]\n", " [ 677 220 1526 0 0]\n", " [2278 1 14 277 4]\n", " [ 175 0 5 5 15]]\n", "0.2378903477643719\n" ] } ], "source": [ "# 5-class classification version\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.metrics import confusion_matrix, zero_one_loss\n", "\n", "classifier = DecisionTreeClassifier(random_state=17)\n", "classifier.fit(train_x, train_Y)\n", "\n", "pred_y = classifier.predict(test_x)\n", "\n", "results = confusion_matrix(test_Y, pred_y)\n", "error = zero_one_loss(test_Y, pred_y)\n", "\n", "print(results)\n", "print(error)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Import additional required libraries for the classification algorithms\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV, cross_val_score\n", "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Function to calculate performance metrics\n", "def calculate_metrics(y_true, y_pred, labels):\n", " \"\"\"\n", " Calculate performance metrics including Precision, Recall, F-Score, and False Positive Rate\n", " \"\"\"\n", " metrics = {}\n", " \n", " # Overall accuracy\n", " accuracy = accuracy_score(y_true, y_pred)\n", " \n", " # Calculate metrics for each class\n", " precision = precision_score(y_true, y_pred, average=None, labels=labels, zero_division=0)\n", " recall = recall_score(y_true, y_pred, average=None, labels=labels, zero_division=0)\n", " f1 = f1_score(y_true, y_pred, average=None, labels=labels, zero_division=0)\n", " \n", " # Calculate macro averages\n", " precision_macro = precision_score(y_true, y_pred, average='macro', zero_division=0)\n", " recall_macro = recall_score(y_true, y_pred, average='macro', zero_division=0)\n", " f1_macro = f1_score(y_true, y_pred, average='macro', zero_division=0)\n", " \n", " # Calculate False Positive Rate for each class\n", " cm = confusion_matrix(y_true, y_pred, labels=labels)\n", " fpr = []\n", " for i in range(len(labels)):\n", " tn = np.sum(cm) - (np.sum(cm[i, :]) + np.sum(cm[:, i]) - cm[i, i])\n", " fp = np.sum(cm[:, i]) - cm[i, i]\n", " fpr.append(fp / (fp + tn) if (fp + tn) > 0 else 0)\n", " \n", " metrics = {\n", " 'accuracy': accuracy * 100,\n", " 'precision_macro': precision_macro * 100,\n", " 'recall_macro': recall_macro * 100,\n", " 'f1_macro': f1_macro * 100,\n", " 'fpr_macro': np.mean(fpr) * 100,\n", " 'precision_per_class': precision * 100,\n", " 'recall_per_class': recall * 100,\n", " 'f1_per_class': f1 * 100,\n", " 'fpr_per_class': np.array(fpr) * 100\n", " }\n", " \n", " return metrics, cm\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class labels: ['benign', 'dos', 'probe', 'r2l', 'u2r']\n" ] } ], "source": [ "# Get unique class labels for consistent ordering\n", "class_labels = sorted(train_Y.unique())\n", "print(\"Class labels:\", class_labels)\n", "\n", "# Store results for all algorithms\n", "results_dict = {}\n", "confusion_matrices = {}\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================================================\n", "RANDOM FOREST CLASSIFIER\n", "==================================================\n", "Best parameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n", "Best cross-validation score: 0.9988\n", "Test Accuracy: 75.28%\n", "Macro Precision: 77.89%\n", "Macro Recall: 47.90%\n", "Macro F1-Score: 48.34%\n", "Macro False Positive Rate: 8.46%\n", "\n", "Confusion Matrix:\n", "[[9456 67 187 0 1]\n", " [1538 5990 108 0 0]\n", " [ 811 164 1448 0 0]\n", " [2496 0 2 75 1]\n", " [ 194 0 0 4 2]]\n" ] } ], "source": [ "# 1. RANDOM FOREST CLASSIFIER\n", "print(\"=\" * 50)\n", "print(\"RANDOM FOREST CLASSIFIER\")\n", "print(\"=\" * 50)\n", "\n", "# Parameter tuning for Random Forest\n", "rf_param_grid = {\n", " 'n_estimators': [50, 100, 200],\n", " 'max_depth': [10, 20, None],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4]\n", "}\n", "\n", "rf_classifier = RandomForestClassifier(random_state=17)\n", "rf_grid_search = GridSearchCV(rf_classifier, rf_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "rf_grid_search.fit(train_x, train_Y)\n", "\n", "print(f\"Best parameters: {rf_grid_search.best_params_}\")\n", "print(f\"Best cross-validation score: {rf_grid_search.best_score_:.4f}\")\n", "\n", "# Train with best parameters\n", "rf_best = rf_grid_search.best_estimator_\n", "rf_pred = rf_best.predict(test_x)\n", "\n", "# Calculate metrics\n", "rf_metrics, rf_cm = calculate_metrics(test_Y, rf_pred, class_labels)\n", "results_dict['Random Forest'] = rf_metrics\n", "confusion_matrices['Random Forest'] = rf_cm\n", "\n", "print(f\"Test Accuracy: {rf_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {rf_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {rf_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {rf_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {rf_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(rf_cm)\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "K-NEAREST NEIGHBORS CLASSIFIER\n", "==================================================\n", "Best parameters: {'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'distance'}\n", "Best cross-validation score: 0.9972\n", "Test Accuracy: 76.42%\n", "Macro Precision: 84.90%\n", "Macro Recall: 50.72%\n", "Macro F1-Score: 52.53%\n", "Macro False Positive Rate: 8.08%\n", "\n", "Confusion Matrix:\n", "[[9435 56 218 2 0]\n", " [1548 6061 27 0 0]\n", " [ 688 167 1568 0 0]\n", " [2413 2 5 151 3]\n", " [ 169 0 12 6 13]]\n" ] } ], "source": [ "# 2. K-NEAREST NEIGHBORS CLASSIFIER\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"K-NEAREST NEIGHBORS CLASSIFIER\")\n", "print(\"=\" * 50)\n", "\n", "# Parameter tuning for KNN\n", "knn_param_grid = {\n", " 'n_neighbors': [3, 5, 7, 9, 11],\n", " 'weights': ['uniform', 'distance'],\n", " 'metric': ['euclidean', 'manhattan']\n", "}\n", "\n", "knn_classifier = KNeighborsClassifier()\n", "knn_grid_search = GridSearchCV(knn_classifier, knn_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "knn_grid_search.fit(train_x, train_Y)\n", "\n", "print(f\"Best parameters: {knn_grid_search.best_params_}\")\n", "print(f\"Best cross-validation score: {knn_grid_search.best_score_:.4f}\")\n", "\n", "# Train with best parameters\n", "knn_best = knn_grid_search.best_estimator_\n", "knn_pred = knn_best.predict(test_x)\n", "\n", "# Calculate metrics\n", "knn_metrics, knn_cm = calculate_metrics(test_Y, knn_pred, class_labels)\n", "results_dict['KNN'] = knn_metrics\n", "confusion_matrices['KNN'] = knn_cm\n", "\n", "print(f\"Test Accuracy: {knn_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {knn_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {knn_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {knn_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {knn_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(knn_cm)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "LOGISTIC REGRESSION CLASSIFIER\n", "==================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best parameters: {'C': 100, 'max_iter': 1000, 'solver': 'liblinear'}\n", "Best cross-validation score: 0.9848\n", "Test Accuracy: 72.57%\n", "Macro Precision: 73.28%\n", "Macro Recall: 48.53%\n", "Macro F1-Score: 50.36%\n", "Macro False Positive Rate: 9.30%\n", "\n", "Confusion Matrix:\n", "[[9025 430 253 0 3]\n", " [1944 5683 9 0 0]\n", " [ 664 129 1531 99 0]\n", " [2464 1 1 106 2]\n", " [ 177 1 2 4 16]]\n" ] } ], "source": [ "# 3. LOGISTIC REGRESSION CLASSIFIER\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"LOGISTIC REGRESSION CLASSIFIER\")\n", "print(\"=\" * 50)\n", "\n", "# Parameter tuning for Logistic Regression\n", "lr_param_grid = {\n", " 'C': [0.01, 0.1, 1, 10, 100],\n", " 'solver': ['liblinear', 'lbfgs', 'saga'],\n", " 'max_iter': [1000, 2000]\n", "}\n", "\n", "lr_classifier = LogisticRegression(random_state=17, multi_class='ovr')\n", "lr_grid_search = GridSearchCV(lr_classifier, lr_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "lr_grid_search.fit(train_x, train_Y)\n", "\n", "print(f\"Best parameters: {lr_grid_search.best_params_}\")\n", "print(f\"Best cross-validation score: {lr_grid_search.best_score_:.4f}\")\n", "\n", "# Train with best parameters\n", "lr_best = lr_grid_search.best_estimator_\n", "lr_pred = lr_best.predict(test_x)\n", "\n", "# Calculate metrics\n", "lr_metrics, lr_cm = calculate_metrics(test_Y, lr_pred, class_labels)\n", "results_dict['Logistic Regression'] = lr_metrics\n", "confusion_matrices['Logistic Regression'] = lr_cm\n", "\n", "print(f\"Test Accuracy: {lr_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {lr_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {lr_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {lr_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {lr_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(lr_cm)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "SUPPORT VECTOR MACHINE CLASSIFIER\n", "==================================================\n", "Best parameters: {'C': 10, 'gamma': 'scale', 'kernel': 'rbf'}\n", "Best cross-validation score: 0.9961\n", "Test Accuracy: 76.93%\n", "Macro Precision: 84.31%\n", "Macro Recall: 50.56%\n", "Macro F1-Score: 52.90%\n", "Macro False Positive Rate: 7.90%\n", "\n", "Confusion Matrix:\n", "[[9430 57 221 2 1]\n", " [1387 6205 44 0 0]\n", " [ 811 173 1439 0 0]\n", " [2307 0 7 258 2]\n", " [ 184 0 1 5 10]]\n" ] } ], "source": [ "# 4. SUPPORT VECTOR MACHINE CLASSIFIER\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"SUPPORT VECTOR MACHINE CLASSIFIER\")\n", "print(\"=\" * 50)\n", "\n", "# Parameter tuning for SVM (using smaller parameter grid due to computational complexity)\n", "svm_param_grid = {\n", " 'C': [0.1, 1, 10],\n", " 'kernel': ['linear', 'rbf'],\n", " 'gamma': ['scale', 'auto']\n", "}\n", "\n", "svm_classifier = SVC(random_state=17)\n", "svm_grid_search = GridSearchCV(svm_classifier, svm_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "svm_grid_search.fit(train_x, train_Y)\n", "\n", "print(f\"Best parameters: {svm_grid_search.best_params_}\")\n", "print(f\"Best cross-validation score: {svm_grid_search.best_score_:.4f}\")\n", "\n", "# Train with best parameters\n", "svm_best = svm_grid_search.best_estimator_\n", "svm_pred = svm_best.predict(test_x)\n", "\n", "# Calculate metrics\n", "svm_metrics, svm_cm = calculate_metrics(test_Y, svm_pred, class_labels)\n", "results_dict['SVM'] = svm_metrics\n", "confusion_matrices['SVM'] = svm_cm\n", "\n", "print(f\"Test Accuracy: {svm_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {svm_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {svm_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {svm_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {svm_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(svm_cm)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "ADABOOST CLASSIFIER\n", "==================================================\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n", "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/ensemble/_weight_boosting.py:519: FutureWarning: The parameter 'algorithm' is deprecated in 1.6 and has no effect. It will be removed in version 1.8.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best parameters: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}\n", "Best cross-validation score: 0.9715\n", "Test Accuracy: 73.50%\n", "Macro Precision: 65.59%\n", "Macro Recall: 47.68%\n", "Macro F1-Score: 47.41%\n", "Macro False Positive Rate: 8.84%\n", "\n", "Confusion Matrix:\n", "[[9419 81 210 1 0]\n", " [1745 5442 449 0 0]\n", " [ 618 249 1556 0 0]\n", " [2381 0 40 153 0]\n", " [ 188 3 9 0 0]]\n" ] } ], "source": [ "# 5. ADABOOST CLASSIFIER\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"ADABOOST CLASSIFIER\")\n", "print(\"=\" * 50)\n", "\n", "# Parameter tuning for AdaBoost\n", "ada_param_grid = {\n", " 'n_estimators': [50, 100, 200],\n", " 'learning_rate': [0.01, 0.1, 1.0],\n", " 'algorithm': ['SAMME', 'SAMME.R']\n", "}\n", "\n", "ada_classifier = AdaBoostClassifier(random_state=17)\n", "ada_grid_search = GridSearchCV(ada_classifier, ada_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "ada_grid_search.fit(train_x, train_Y)\n", "\n", "print(f\"Best parameters: {ada_grid_search.best_params_}\")\n", "print(f\"Best cross-validation score: {ada_grid_search.best_score_:.4f}\")\n", "\n", "# Train with best parameters\n", "ada_best = ada_grid_search.best_estimator_\n", "ada_pred = ada_best.predict(test_x)\n", "\n", "# Calculate metrics\n", "ada_metrics, ada_cm = calculate_metrics(test_Y, ada_pred, class_labels)\n", "results_dict['AdaBoost'] = ada_metrics\n", "confusion_matrices['AdaBoost'] = ada_cm\n", "\n", "print(f\"Test Accuracy: {ada_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {ada_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {ada_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {ada_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {ada_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(ada_cm)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "==================================================\n", "DECISION TREE CLASSIFIER (REFERENCE)\n", "==================================================\n", "Test Accuracy: 76.21%\n", "Macro Precision: 83.63%\n", "Macro Recall: 51.25%\n", "Macro F1-Score: 53.66%\n", "Macro False Positive Rate: 8.06%\n", "\n", "Confusion Matrix:\n", "[[9365 56 289 1 0]\n", " [1541 5998 97 0 0]\n", " [ 677 220 1526 0 0]\n", " [2278 1 14 277 4]\n", " [ 175 0 5 5 15]]\n" ] } ], "source": [ "# 6. DECISION TREE CLASSIFIER (It has already implemented - adding to the comparison)\n", "print(\"\\n\" + \"=\" * 50)\n", "print(\"DECISION TREE CLASSIFIER (REFERENCE)\")\n", "print(\"=\" * 50)\n", "\n", "# Use the existing Decision Tree results\n", "dt_pred = classifier.predict(test_x)\n", "dt_metrics, dt_cm = calculate_metrics(test_Y, dt_pred, class_labels)\n", "results_dict['Decision Tree'] = dt_metrics\n", "confusion_matrices['Decision Tree'] = dt_cm\n", "\n", "print(f\"Test Accuracy: {dt_metrics['accuracy']:.2f}%\")\n", "print(f\"Macro Precision: {dt_metrics['precision_macro']:.2f}%\")\n", "print(f\"Macro Recall: {dt_metrics['recall_macro']:.2f}%\")\n", "print(f\"Macro F1-Score: {dt_metrics['f1_macro']:.2f}%\")\n", "print(f\"Macro False Positive Rate: {dt_metrics['fpr_macro']:.2f}%\")\n", "print(\"\\nConfusion Matrix:\")\n", "print(dt_cm)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "PERFORMANCE COMPARISON SUMMARY\n", "============================================================\n", " Algorithm Accuracy (%) Precision (%) Recall (%) F1-Score (%) False Positive Rate (%)\n", " SVM 76.93 84.31 50.56 52.90 7.90\n", " KNN 76.42 84.90 50.72 52.53 8.08\n", " Decision Tree 76.21 83.63 51.25 53.66 8.06\n", " Random Forest 75.28 77.89 47.90 48.34 8.46\n", " AdaBoost 73.50 65.59 47.68 47.41 8.84\n", "Logistic Regression 72.57 73.28 48.53 50.36 9.30\n" ] }, { "data": { "image/png": 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4eyBl1gRmAej7JlRumQSRZGzk9/ea1uP3339392k0fmgGwnPPPefLq9BR/DNnznS3r1271n2XebdXq1YtKAMgks9R6LL1u/d9pffgySefnON2btmyZdD91113nX/77Ny503fOOefk+BkMl1G3evVq//3eukSyvw/0/ZDXfRa6PQP/Vr3zzjtZXlOvXr3c328ZO3ZstscNynwJvE9/lwJpGV9++aVv/Pjx2b42AAAQjB4bAAAcII1a1Gh6jU5WvWpv5GdgHXOvEe706dOD7tPoRo3+V813TRolHdjIeenSpWHrhHs0wlC1rT1aD42EjdRbb70V9LtGqqsGvEcjPFVH2qORreEyUg5kfTQq1nsujYAMfF5Rzwxvu51xxhlB94WOCNYoTdX+vvLKK+3oo492NdOVdaHnb9y4cdC82fUv8Wg/BD6fRs1m99waeR44clqZAI8//rgbRRpI9dw1SluUSRM4wlwjSDUK2nsPaES79nugwNGdeZXXeu/5/Z7Iz/0cSp8dLwtJzjrrrKBlKANDfR88GkmrkcQaia2R8xp1771Pzj333Dy9T9SPQyOTA0XS8Foj1j0apXzPPfe47wWNetbIdY1iVt3+M8880z+feikE7keNXFbvBY/e41dffXXE7xllIwX2TsjpPZ6b/F63/HAwPQ5y+p5XNoE+y8oKU3aC3jd6/7z66qvZvncOZH8HPkbfL/peVdNoZb/8888/7jlPPPHEbPtfRMuBfN+Jvo+VlaIeFsoS8nozaPspM0LZK5F+7g5Ufn+vaX5vPylDKTQzLq+9apRJEpjJqSwYZZiIttHpp5/uv0/9cWbPnh3xspU5EPg9GHpcovdguD5LHmVVBWaM6G+6ms4rK1KUcaF+GaFZYeH6/QRmSOhzlJfvzoN1sPss8G9V4HGOR59tL7Myp79lgZ9vuemmm1wGkI4LtK21DH1OlAkHAAAiQykqAADymS6a3n///a65pEclRAKpnEFu9JjsGmKGNgjPq9CgiYICgXTyr4vRXpkJb30CLyYf6PqEPpe21+bNm/2/BwYkdF8gle0K9PDDD7uLf5HIqQG4qCREoNDmw4HP7TXA9uhih7ZZbts88MKrXnNgaY1wQt832QlXfkfPp4v48XpP5Od+DqXyH6G0PDVL96ish0fNslWOJD/eJwf62dP7VOXOtA3UVFbN4z1eGTcFuHQRWEGvSPaJNG3aNOL3TF7e47nJ73U7EKHv+5yCwQdCFxtV7mz58uV5fu8cyP5WcEilhlROTSZNmuSmwCCLLsoqaJhbs+T8dCDfd6LAj4KJKvN0sJ+7A5Xf32v5+RnyStsFbh+9BwIHOaiMUWBwQuWoQoOx2dEAisBSbwpMqAxZbt+lgd+hgX+zVPIx9PVqeVquV1ZN73WVGwxXSk7zKTAXawe7zwLfM6F/q8qWLRtUxiqnv2X63Kh8lhfIUhm/wFJ+CrAp0Dlo0KAc9wsAAPg/BDYAADhAOkHViFWNstPIRWUPqMa7ejmEntweiMDRrKECR5jmx8jmSPo45CQv6xM6ct8b/enRqN5IqFb30KFDg27TBQZdhNCoagkMHOQ2mlt11wPp4mO85fQeCKQLTqoBHthnQyNnlckQr/dEfu3ng6XeAqFBDY0qVx14jcLVhb/AEdO5vU8O9LOnIJMyctTHQj0F1KPB60GhkdXqOaBJ9dwXLFjg3n/5vU/y8z2e3+t2IHShcMqUKf7fNdJeI+Dzq8+GRmIHBjV0EVxZL/qu1/tZWXrqjRNumxzI/tbfEQW9n376aXe7sowCL/jrgruyR5TFoceGjgAvSHShW/2iAi/a62K3eit4gZzQHivRUJA/Q/Lcc88F/a6eDF4vHPH6MQVmPakPgzI78irca89pe+T3ttPnMvTvQCwc7D4L/Ft2sH/HtP/Uk0z7WZlYgb1VFFxT4Oqll15yfdLUEwQAAOSMUlQAABwgXbDSiFydoOpEVc1iVSInXFBDI20Dvfzyy+6iQU5TTqMyD/biQOj6qDxKIF2MCi2tFfqY/FyfA6FGnIEXzVTWRSNM33nnHbdfVPIiWg477LCg33UxMrdRyboIGXhhSBc+c3sPBDb8zY3XoNQzefLkXMu7BI4mze/3RDSFrpuENqz3Lvp+/PHHQbfrYuvPP//sLjDpfaIyI3lxMO91lWAZOXKk269eE2mV+Tn55JODyhZ565zbPhE1AI7HPikI66ZSWiqnE3gROLfm53of51QqJ1Doe0clYz755BPX5FvvncD9lh/7WxSU7d+/vxulr2wPjX5XOaDAsl4qSxUY0Im2A/m+0+cxsBHyMccc48oGqpSStp3+BsZCQf5eU+AqdH20z/U+8SZlDYUGjBTYisQhhxwS9PnQ970aXgf65ptvsn18aFaQGm8rIyOQ/sZ42Rqi45/QQEI8jxMKGgVVVEpOxykqT6n9rSBl4N8h7ScdXwIAgNxxdAEAQAyE1rLXSWy4siy6kKERubqwFU2hQZPhw4cHjQx+4IEHgkpzaJS6RtoWJKEjWTXa2Qsc6MJApCWqDoRqyXujjkWjuvv165dl9PG8efPcRXTRKO+WLVsGXRBSvXON3g690Pbhhx9anz59guqbR1JHPLD8h9ZF6/nBBx9kmVfvPZXAuf322xPyPfHiiy8G1cH/73//G1SGShfQ2rRpE/Z9EtgXQK8vtwvh+UX9FZRB9O+///rXUdtQ+yj0Arlq6XvBusBgmB4f2KdFF2SffPLJoMdGWqbmYBWEddP7PTRr65lnnnE16kNLG+lzpSC0SryEXpzNTk7vHWVZqIxQfu5vlaB64okngj5nGpmvkdsqWxXuMbFwIN93odtOZYi8EksKLA0bNizXbA0v804U4MlrmaeC/r2m0fnRfJwuonvfgx5953tBKQXbcgrs6m9WYNaAtr++L73AoH7X351AKpUWrQBGfrwf4kmBobFjxwaVdlNZLAX9Lr/88rh9vgEASGSUogIAIAZUN7ldu3ZutK53YUjlcHQBpXr16u4Czy+//OKvB37qqadGdX100V8jfjVi0LtIp4awWh8FV0JHvwc2DC0odMFF6+RdZNFFRJWhUlkmjYCM5oUBXYzQBbLA4IkuSGokstZBF0B1YVf7U0GKI4880s2jQIaai3oXlnRxT2WS1B9CZZFURkfb3rvgF3qxIye6AKpGuboI6ZWw0r7U8x1++OEuQ0TbSiN2vYuP6j2RiO8JNV9WrXa9B7QtVdIjsGxK165d/aOuA4NJoswqlfnQqGKvKXMs6KKvmsXrAq/2hUbz62eNYg9tVuzVwVft/x49evjL1ehisXp8qGa8Hqv117bwqNFwXsqPHYyCsm633XabG/WuoIVn4sSJ7r2sddHnQhdBFTTwggyR0nsnsNRUq1atXM8NBUaUMZZT2bID2d/6vrj22mtdVpE+s3oPa8S9Mh9Cg5yhvRKi6UC+7/SdpmCIt831ftDt2haaV8FVBcZy2oaaV9/louUoKKX3nS7Ya7CA3n+5Kajfa+EyL/Q+Duw95NF3nBqJ633sZXqozFm4eUMp8KfsAO/vpHo66L2rbatsjdz+Tmp76NjFe7wGXqiEmPal1jewObbeB4G9ZPJbfrwf4kmf48GDB7tJxyl6H6pHx7Zt2+L6+QYAIJER2AAAIEZ0Eejiiy929dZFI/VVhiScSBqzHgyNxFRJkAsuuMCNIhTVetbI90C6YKBSKgXxgoHKZAwcONA1EPfoYo8mURNeNdmNFl2c0MUV1eH3si504emjjz7K9jGnnHKKu5h15ZVX+keNq1eIpvx4H+hCrN5Tl112WdDFUwUzQkuQSOAFvER6T1xzzTWuDnloqSDRhdbHH388aJt37tzZlQ/yeJ87vRYFm0JH/Uf7gqZKNIWWaQp8bYFNt3UBWYEqfX94Fzl1cTaUXqc3T6wUhHXTe1hljXSRU+9Lr4+F/g/3/shLrwCNZlew0LugrM+7PiOiwIMC1gqi5Of+Fl3sV6BbUzi6KK/vkFjK6/edLnLfd999LjMs3PeQMj5UDk7lA7Oj19i3b1//7wrIekHZSJunF9TvNb2vAkt1HXXUUdkGKvR3QN9hTz31VFDWhv7GRdKHRsGJwO84bXNvu2v/BPYgUvAtkDI+9Fx6n3qBSmUchDaUVwBR38nRvCCfH++HgkLvRe/9GEqv5eabb475OgEAkIgK1tBLAACSmEbm6QKLRk+qF4cujOnijy6oqAHlscce68oP6SKdLnpEmy6OKQig0ggaTa163LqAolG2usiiCwga0RnLi755pQs7uriqC4PKeNDIYmW7aPtFsxSV584773TbcNCgQW7/6fm1DbUttX11u5pUB7rooots2bJl7gKhRn8rc0CPUSkt9YVo37693XvvvW40rO7PK11Y0oheBdBUl18Xy/T+0vtMo7816rV79+4uwKILj4n4nlAARyPwdRFSI5l1MU59AJQBo3JIoY11NUpZF/e0L1QOR/d36NDBjapXsDEWNBJ/zJgx7gKr9oG3bVVeRSPzu3Tp4hpGT5o0Kehxel8rG0H7U31UNK8eo9esLIDzzz/fvT6NlD+QhsIHo6Csm4IbKrGjC4X333+/+wzp+fWZ0v5W02J9L+jzqmwBfU4jodejIJi+r7W/tCx9RnUxWLfronl+7m993vV7z549XaBG2XxeCSf9rGws9Q7Sezywd0Ks5PX7TiUVFdDS51WvW98jyrJSBkUkPZBUUky9BvRcgWXA8qogfq+FlpPq1q1bjvMrCy20HF9ufU48ukiu4w59BvSaNSn7SIGIwMCTV4orlDIHlbmk7dOsWTP/ftffFe1bZfPofgX6oim/3g/xoixd7Xf9XdZ29L6jtC1VVk+ff/2d0t82fd4BAEDu0nw55f8CAAAAcXb33Xe7i2ceXRhVA1YAQM5U/kwBCwW3Aynzpnfv3vaf//zHf9uIESNceTcAAIBEQCkqAAAAAACSkEqqvf322y5bRVkCZcqUcf2c1PPL6+slCn6oRBgAAECiILABAAAAAECSUj+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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5. DATA VISUALIZATION\n", "# 5.1. Visualize and compare the accuracy of different algorithms\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"PERFORMANCE COMPARISON SUMMARY\")\n", "print(\"=\" * 60)\n", "\n", "# Create a summary DataFrame\n", "summary_data = []\n", "for algorithm, metrics in results_dict.items():\n", " summary_data.append({\n", " 'Algorithm': algorithm,\n", " 'Accuracy (%)': round(metrics['accuracy'], 2),\n", " 'Precision (%)': round(metrics['precision_macro'], 2),\n", " 'Recall (%)': round(metrics['recall_macro'], 2),\n", " 'F1-Score (%)': round(metrics['f1_macro'], 2),\n", " 'False Positive Rate (%)': round(metrics['fpr_macro'], 2)\n", " })\n", "\n", "summary_df = pd.DataFrame(summary_data)\n", "summary_df = summary_df.sort_values('Accuracy (%)', ascending=False)\n", "\n", "print(summary_df.to_string(index=False))\n", "\n", "# Create visualizations\n", "plt.style.use('default')\n", "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", "fig.suptitle('Performance Comparison of Classification Algorithms', fontsize=16, fontweight='bold')\n", "\n", "# 1. Accuracy Comparison\n", "algorithms = summary_df['Algorithm']\n", "accuracies = summary_df['Accuracy (%)']\n", "\n", "axes[0, 0].bar(algorithms, accuracies, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'])\n", "axes[0, 0].set_title('Accuracy Comparison', fontweight='bold')\n", "axes[0, 0].set_ylabel('Accuracy (%)')\n", "axes[0, 0].tick_params(axis='x', rotation=45)\n", "axes[0, 0].grid(axis='y', alpha=0.3)\n", "\n", "# Add value labels on bars\n", "for i, v in enumerate(accuracies):\n", " axes[0, 0].text(i, v + 0.5, f'{v:.1f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "# 2. Precision Comparison\n", "precisions = summary_df['Precision (%)']\n", "axes[0, 1].bar(algorithms, precisions, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'])\n", "axes[0, 1].set_title('Precision Comparison', fontweight='bold')\n", "axes[0, 1].set_ylabel('Precision (%)')\n", "axes[0, 1].tick_params(axis='x', rotation=45)\n", "axes[0, 1].grid(axis='y', alpha=0.3)\n", "\n", "for i, v in enumerate(precisions):\n", " axes[0, 1].text(i, v + 0.5, f'{v:.1f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "# 3. Recall Comparison\n", "recalls = summary_df['Recall (%)']\n", "axes[1, 0].bar(algorithms, recalls, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'])\n", "axes[1, 0].set_title('Recall Comparison', fontweight='bold')\n", "axes[1, 0].set_ylabel('Recall (%)')\n", "axes[1, 0].tick_params(axis='x', rotation=45)\n", "axes[1, 0].grid(axis='y', alpha=0.3)\n", "\n", "for i, v in enumerate(recalls):\n", " axes[1, 0].text(i, v + 0.5, f'{v:.1f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "# 4. F1-Score Comparison\n", "f1_scores = summary_df['F1-Score (%)']\n", "axes[1, 1].bar(algorithms, f1_scores, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'])\n", "axes[1, 1].set_title('F1-Score Comparison', fontweight='bold')\n", "axes[1, 1].set_ylabel('F1-Score (%)')\n", "axes[1, 1].tick_params(axis='x', rotation=45)\n", "axes[1, 1].grid(axis='y', alpha=0.3)\n", "\n", "for i, v in enumerate(f1_scores):\n", " axes[1, 1].text(i, v + 0.5, f'{v:.1f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "plt.tight_layout()\n", "plt.savefig('nslkdd_performance_comparison.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "CONFUSION MATRICES VISUALIZATION\n", "============================================================\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5.2. Plot the confusion matrix for each scenario\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"CONFUSION MATRICES VISUALIZATION\")\n", "print(\"=\" * 60)\n", "\n", "# Create a figure with subplots for confusion matrices\n", "n_algorithms = len(confusion_matrices)\n", "n_cols = 3\n", "n_rows = (n_algorithms + n_cols - 1) // n_cols\n", "\n", "fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 6 * n_rows))\n", "fig.suptitle('Confusion Matrices for All Classification Algorithms', fontsize=16, fontweight='bold')\n", "\n", "# Flatten axes for easier indexing\n", "if n_rows == 1:\n", " axes = axes.reshape(1, -1)\n", "axes_flat = axes.flatten()\n", "\n", "# Define colors for confusion matrix\n", "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n", "\n", "for idx, (algorithm, cm) in enumerate(confusion_matrices.items()):\n", " ax = axes_flat[idx]\n", " \n", " # Create heatmap\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n", " xticklabels=class_labels, yticklabels=class_labels,\n", " ax=ax, cbar_kws={'shrink': 0.8})\n", " \n", " ax.set_title(f'{algorithm}\\nConfusion Matrix', fontweight='bold')\n", " ax.set_xlabel('Predicted Label')\n", " ax.set_ylabel('True Label')\n", " \n", " # Rotate x-axis labels for better readability\n", " ax.tick_params(axis='x', rotation=45)\n", " ax.tick_params(axis='y', rotation=0)\n", "\n", "# Hide empty subplots\n", "for idx in range(n_algorithms, len(axes_flat)):\n", " axes_flat[idx].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.savefig('nslkdd_confusion_matrices.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "COMPREHENSIVE PERFORMANCE ANALYSIS\n", "============================================================\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "DETAILED ANALYSIS:\n", "----------------------------------------\n", "Best Accuracy: SVM (76.93%)\n", "Best Precision: KNN (84.90%)\n", "Best Recall: Decision Tree (51.25%)\n", "Best F1-Score: Decision Tree (53.66%)\n", "Lowest False Positive Rate: SVM (7.90%)\n", "\n", " Overall Performance Ranking (by Accuracy):\n", " 1. SVM: 76.93%\n", " 2. KNN: 76.42%\n", " 3. Decision Tree: 76.21%\n", " 4. Random Forest: 75.28%\n", " 5. AdaBoost: 73.50%\n", " 6. Logistic Regression: 72.57%\n" ] } ], "source": [ "# Additional visualization: Comprehensive Performance Comparison\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"COMPREHENSIVE PERFORMANCE ANALYSIS\")\n", "print(\"=\" * 60)\n", "\n", "# Create a comprehensive comparison chart\n", "fig, ax = plt.subplots(figsize=(14, 8))\n", "\n", "# Prepare data for grouped bar chart\n", "metrics_to_compare = ['Accuracy (%)', 'Precision (%)', 'Recall (%)', 'F1-Score (%)']\n", "x = np.arange(len(algorithms))\n", "width = 0.2\n", "\n", "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']\n", "\n", "for i, metric in enumerate(metrics_to_compare):\n", " values = summary_df[metric]\n", " ax.bar(x + i * width, values, width, label=metric, color=colors[i], alpha=0.8)\n", "\n", "ax.set_xlabel('Classification Algorithms', fontweight='bold')\n", "ax.set_ylabel('Performance (%)', fontweight='bold')\n", "ax.set_title('Comprehensive Performance Comparison of Classification Algorithms', fontweight='bold', fontsize=14)\n", "ax.set_xticks(x + width * 1.5)\n", "ax.set_xticklabels(algorithms, rotation=45)\n", "ax.legend()\n", "ax.grid(axis='y', alpha=0.3)\n", "\n", "# Add value labels on bars\n", "for i, algorithm in enumerate(algorithms):\n", " for j, metric in enumerate(metrics_to_compare):\n", " value = summary_df[summary_df['Algorithm'] == algorithm][metric].iloc[0]\n", " ax.text(i + j * width, value + 0.5, f'{value:.1f}%', \n", " ha='center', va='bottom', fontsize=8, fontweight='bold')\n", "\n", "plt.tight_layout()\n", "plt.savefig('nslkdd_comprehensive_metrics.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Print detailed analysis\n", "print(\"\\nDETAILED ANALYSIS:\")\n", "print(\"-\" * 40)\n", "\n", "# Find best performing algorithm for each metric\n", "best_accuracy = summary_df.loc[summary_df['Accuracy (%)'].idxmax()]\n", "best_precision = summary_df.loc[summary_df['Precision (%)'].idxmax()]\n", "best_recall = summary_df.loc[summary_df['Recall (%)'].idxmax()]\n", "best_f1 = summary_df.loc[summary_df['F1-Score (%)'].idxmax()]\n", "lowest_fpr = summary_df.loc[summary_df['False Positive Rate (%)'].idxmin()]\n", "\n", "print(f\"Best Accuracy: {best_accuracy['Algorithm']} ({best_accuracy['Accuracy (%)']:.2f}%)\")\n", "print(f\"Best Precision: {best_precision['Algorithm']} ({best_precision['Precision (%)']:.2f}%)\")\n", "print(f\"Best Recall: {best_recall['Algorithm']} ({best_recall['Recall (%)']:.2f}%)\")\n", "print(f\"Best F1-Score: {best_f1['Algorithm']} ({best_f1['F1-Score (%)']:.2f}%)\")\n", "print(f\"Lowest False Positive Rate: {lowest_fpr['Algorithm']} ({lowest_fpr['False Positive Rate (%)']:.2f}%)\")\n", "\n", "print(f\"\\n Overall Performance Ranking (by Accuracy):\")\n", "for i, (_, row) in enumerate(summary_df.iterrows(), 1):\n", " print(f\" {i}. {row['Algorithm']}: {row['Accuracy (%)']:.2f}%\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# **Dataset 2: Processed Combined IoT dataset**" ] }, { "cell_type": "markdown", "metadata": { "id": "y61xJzKhjsAt" }, "source": [ "# **Random Forest(RF) on IoT Combined Dataset**" ] }, { "cell_type": "markdown", "metadata": { "id": "SSWJ-I3UKZ6h" }, "source": [ "****Importing libraries****" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "AwMrsTn_KVhy" }, "outputs": [], "source": [ "import timeit\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import metrics\n", "from sklearn.metrics import classification_report, confusion_matrix\n", "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import roc_auc_score\n", "import warnings\n", "%matplotlib inline\n", "warnings.filterwarnings('ignore')\n", "#warnings.filterwarnings('always') " ] }, { "cell_type": "markdown", "metadata": { "id": "VVVIs7G_KdTq" }, "source": [ "**Upload File**" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "24Ek4cCej76I" }, "outputs": [], "source": [ "#uploaded = files.upload()" ] }, { "cell_type": "markdown", "metadata": { "id": "4jmo-htGGdCD" }, "source": [ "**Importing the Dataset**" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 73, "resources": { "http://localhost:8080/nbextensions/google.colab/files.js": { "data": 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"headers": [ [ "content-type", "application/javascript" ] ], "ok": true, "status": 200, "status_text": "" } } }, "id": "AsvTp_w-l-Km", "outputId": "fe18dcb6-d011-43b7-8daf-654749708c67" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Read the dataset directly from the local file system\n", "dataset = pd.read_csv('Processed_Combined_IoT_dataset.csv')" ] }, { "cell_type": "markdown", "metadata": { "id": "2BWWYGPNGhst" }, "source": [ "**Exploratory Data Analysis**" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220 }, "id": "0hEM0bOClrjL", "outputId": "281ebb72-0fc1-4ae4-9170-c89229b3e26a" }, "outputs": [ { "data": { "text/html": [ "
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FC1_Read_Input_RegisterFC2_Read_Discrete_ValueFC3_Read_Holding_RegisterFC4_Read_Coilcurrent_temperaturedoor_statefridge_temperaturehumiditylatitudelight_statuslongitudemotion_statuspressuresphone_signaltemp_conditiontemperaturethermostat_statuslabel
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" ], "text/plain": [ " FC1_Read_Input_Register FC2_Read_Discrete_Value \\\n", "0 0.495216 0.499092 \n", "1 0.495216 0.499092 \n", "2 0.495216 0.499092 \n", "3 0.495216 0.499092 \n", "4 0.495216 0.499092 \n", "\n", " FC3_Read_Holding_Register FC4_Read_Coil current_temperature door_state \\\n", "0 0.488897 0.499405 0.344399 0 \n", "1 0.488897 0.499405 0.344399 0 \n", "2 0.488897 0.499405 0.344399 0 \n", "3 0.488897 0.499405 0.344399 0 \n", "4 0.488897 0.499405 0.344399 0 \n", "\n", " fridge_temperature humidity latitude light_status longitude \\\n", "0 0.930769 0.462511 0.008217 0 0.008112 \n", "1 0.588462 0.462511 0.008217 0 0.008112 \n", "2 0.076923 0.462511 0.008217 0 0.008112 \n", "3 0.292308 0.462511 0.008217 0 0.008112 \n", "4 0.746154 0.462511 0.008217 0 0.008112 \n", "\n", " motion_status pressure sphone_signal temp_condition temperature \\\n", "0 0 0.533556 0.666667 0.2 0.517307 \n", "1 0 0.533556 0.666667 0.2 0.517307 \n", "2 0 0.533556 0.666667 0.8 0.517307 \n", "3 0 0.533556 0.666667 0.8 0.517307 \n", "4 0 0.533556 0.666667 0.2 0.517307 \n", "\n", " thermostat_status label \n", "0 1 0 \n", "1 1 0 \n", "2 1 0 \n", "3 1 0 \n", "4 1 0 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bq4nVy0ILHdo", "outputId": "24567499-cdb8-4384-ac10-da8d045f6559" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(401119, 18)\n" ] } ], "source": [ "print(dataset.shape)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gJVXzqugDdWd", "outputId": "257c8116-2b75-462f-a61a-33a54c11733e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['FC1_Read_Input_Register', 'FC2_Read_Discrete_Value', 'FC3_Read_Holding_Register', 'FC4_Read_Coil', 'current_temperature', 'door_state', 'fridge_temperature', 'humidity', 'latitude', 'light_status', 'longitude', 'motion_status', 'pressure', 'sphone_signal', 'temp_condition', 'temperature', 'thermostat_status', 'label']\n" ] } ], "source": [ "print(list(dataset.columns))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kRuslnJmZbNZ", "outputId": "532ed11c-0ae4-4d4a-cd1c-2d08d7539f1d" }, "outputs": [ { "data": { "text/plain": [ "['label']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target_cols=list(dataset.columns[-1:])\n", "target_cols" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qDuBF5llF833", "outputId": "0d1e2be8-0ccd-416e-fa78-61d7dbc791b9" }, "outputs": [ { "data": { "text/plain": [ "['FC1_Read_Input_Register',\n", " 'FC2_Read_Discrete_Value',\n", " 'FC3_Read_Holding_Register',\n", " 'FC4_Read_Coil',\n", " 'current_temperature',\n", " 'door_state',\n", " 'fridge_temperature',\n", " 'humidity',\n", " 'latitude',\n", " 'light_status',\n", " 'longitude',\n", " 'motion_status',\n", " 'pressure',\n", " 'sphone_signal',\n", " 'temp_condition',\n", " 'temperature',\n", " 'thermostat_status']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_cols= list(dataset.columns[:-1])\n", "feature_cols" ] }, { "cell_type": "markdown", "metadata": { "id": "poF7HvW6DvVp" }, "source": [ "**Split Dataset**" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "lHM3bLWKHel7" }, "outputs": [], "source": [ "#split dataset in features and target variable\n", "X = dataset.drop('label', axis=1) # Features\n", "y = dataset['label'] # Target variable" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220 }, "id": "55_7-v6UdUQX", "outputId": "869974a7-2a84-4cd2-bdb8-bacd68ac8e0f" }, "outputs": [ { "data": { "text/html": [ "
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FC1_Read_Input_RegisterFC2_Read_Discrete_ValueFC3_Read_Holding_RegisterFC4_Read_Coilcurrent_temperaturedoor_statefridge_temperaturehumiditylatitudelight_statuslongitudemotion_statuspressuresphone_signaltemp_conditiontemperaturethermostat_status
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" ], "text/plain": [ " FC1_Read_Input_Register FC2_Read_Discrete_Value \\\n", "0 0.495216 0.499092 \n", "1 0.495216 0.499092 \n", "2 0.495216 0.499092 \n", "3 0.495216 0.499092 \n", "4 0.495216 0.499092 \n", "\n", " FC3_Read_Holding_Register FC4_Read_Coil current_temperature door_state \\\n", "0 0.488897 0.499405 0.344399 0 \n", "1 0.488897 0.499405 0.344399 0 \n", "2 0.488897 0.499405 0.344399 0 \n", "3 0.488897 0.499405 0.344399 0 \n", "4 0.488897 0.499405 0.344399 0 \n", "\n", " fridge_temperature humidity latitude light_status longitude \\\n", "0 0.930769 0.462511 0.008217 0 0.008112 \n", "1 0.588462 0.462511 0.008217 0 0.008112 \n", "2 0.076923 0.462511 0.008217 0 0.008112 \n", "3 0.292308 0.462511 0.008217 0 0.008112 \n", "4 0.746154 0.462511 0.008217 0 0.008112 \n", "\n", " motion_status pressure sphone_signal temp_condition temperature \\\n", "0 0 0.533556 0.666667 0.2 0.517307 \n", "1 0 0.533556 0.666667 0.2 0.517307 \n", "2 0 0.533556 0.666667 0.8 0.517307 \n", "3 0 0.533556 0.666667 0.8 0.517307 \n", "4 0 0.533556 0.666667 0.2 0.517307 \n", "\n", " thermostat_status \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Qc42gmuXdYTE", "outputId": "662ea8fc-82e7-4773-b3d4-f4d935c18457" }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "1 0\n", "2 0\n", "3 0\n", "4 0\n", "Name: label, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "1v471CZ0DpxX" }, "source": [ "**Splitting Data**" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "Xt-6HOnsDngY" }, "outputs": [], "source": [ "# Split dataset into training set and test set\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QpwOt4T3JJwY", "outputId": "e84a7af3-dba8-4f56-ef55-eb850ad14d3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train shape is : (280783, 17)\n", "X_test shape is : (120336, 17)\n", "y_train shape is : (280783,)\n", "y_test shape is : (120336,)\n" ] } ], "source": [ "# Check the shape of all of these\n", "print(\"X_train shape is : \", X_train.shape)\n", "print(\"X_test shape is : \", X_test.shape)\n", "print(\"y_train shape is : \", y_train.shape)\n", "print(\"y_test shape is : \", y_test.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "uu8yDYOAEZlI" }, "source": [ "**Building Model**" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "aI8YKd80EgRn" }, "outputs": [], "source": [ "#Calculate start time\n", "start = timeit.default_timer()\n", "\n", "#Create a Gaussian Classifier\n", "clf=RandomForestClassifier(n_estimators=100)\n", "\n", "#Train the model using the training sets y_pred=clf.predict(X_test)\n", "clf.fit(X_train,y_train)\n", "\n", "#Calculate Stop time\n", "stop = timeit.default_timer()\n", "train_time= stop - start" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "zqTSnzBrE9sk" }, "outputs": [], "source": [ "#Calculate start time\n", "start = timeit.default_timer()\n", "\n", "# Predict the model\n", "y_pred=clf.predict(X_test)\n", "\n", "#Calculate Stop time\n", "stop = timeit.default_timer()\n", "test_time= stop - start" ] }, { "cell_type": "markdown", "metadata": { "id": "cGaWzP_KEnwY" }, "source": [ "**Evaluating Model**" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z9FDpD4r8u33", "outputId": "2143d962-a49d-4bdf-f276-97489365e601" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[69914 3581]\n", " [12064 34777]]\n", " precision recall f1-score support\n", "\n", " 0 0.85 0.95 0.90 73495\n", " 1 0.91 0.74 0.82 46841\n", "\n", " accuracy 0.87 120336\n", " macro avg 0.88 0.85 0.86 120336\n", "weighted avg 0.87 0.87 0.87 120336\n", "\n" ] } ], "source": [ "print(confusion_matrix(y_test,y_pred))\n", "print(classification_report(y_test,y_pred))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0dfwhoczEpdZ", "outputId": "9b0350b8-e551-47ae-b9fa-8bc40dfa98c1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8699890307140008\n", "Precision: 0.9066426821002138\n", "Recall: 0.7424478555111974\n", "F1 Score: 0.8163710841676546\n", "Mean Absolute Error: 0.1300109692859992\n", "Cohens kappa: 0.7172785976392364\n", "ROC AUC: 0.8468617262452918\n", "Train Time(s): 27.87765562499999\n", "Test Time(s): 1.1910087079999698\n" ] } ], "source": [ "# Model Accuracy: how often is the classifier correct?\n", "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n", "\n", "# Model Precision: what percentage of positive tuples are labeled as such?\n", "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n", "\n", "# Model Recall: what percentage of positive tuples are labelled as such?\n", "print(\"Recall:\",metrics.recall_score(y_test, y_pred))\n", "\n", "#Calculate F1 Score\n", "print(\"F1 Score:\",metrics.f1_score(y_test, y_pred))\n", "\n", "#Calculate Mean Absolute Error\n", "print(\"Mean Absolute Error:\",metrics.mean_absolute_error(y_test, y_pred))\n", "\n", "# kappa\n", "print(\"Cohens kappa:\", metrics.cohen_kappa_score(y_test, y_pred))\n", "\n", "# ROC AUC\n", "print(\"ROC AUC:\", metrics.roc_auc_score(y_test, y_pred))\n", "\n", "#Train time\n", "print('Train Time(s): ',train_time) \n", "\n", "#Test time\n", "print('Test Time(s): ',test_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Implementation of 5 Benchmark Classification Algorithms**\n", "\n", "Following the instruction.txt requirements, we will implement:\n", "1. Logistic Regression\n", "2. K-Nearest Neighbors (KNN)\n", "3. Naive Bayes (NB)\n", "4. Support Vector Machine (SVM)\n", "5. Classification and Regression Trees (CART)\n", "\n", "Each algorithm will be tuned for optimal performance and evaluated with comprehensive metrics.\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Import additional libraries for the benchmark algorithms\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import GridSearchCV, cross_val_score\n", "from sklearn.metrics import precision_recall_fscore_support, roc_curve, auc\n", "from sklearn.preprocessing import StandardScaler\n", "import time\n", "\n", "# Store results for comparison\n", "results = {}\n", "confusion_matrices = {}\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features scaled for algorithms that require standardization\n" ] } ], "source": [ "# Standardize features for algorithms that require it (Logistic Regression, SVM)\n", "scaler = StandardScaler()\n", "X_train_scaled = scaler.fit_transform(X_train)\n", "X_test_scaled = scaler.transform(X_test)\n", "\n", "print(\"Features scaled for algorithms that require standardization\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **1. Logistic Regression with Parameter Tuning**\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== LOGISTIC REGRESSION ===\n", "Best parameters: {'C': 100, 'max_iter': 100, 'solver': 'liblinear'}\n", "Best cross-validation score: 0.6898\n", "Accuracy: 0.6895\n", "Precision: 0.7497\n", "Recall: 0.6895\n", "F-Score: 0.6291\n", "False Alarm Rate (FPR): 0.0000\n", "Train Time: 6.40s\n", "Test Time: 0.00s\n", "\n" ] } ], "source": [ "# Logistic Regression with parameter tuning\n", "print(\"=== LOGISTIC REGRESSION ===\")\n", "\n", "# Parameter grid for tuning\n", "lr_param_grid = {\n", " 'C': [0.1, 1, 10, 100],\n", " 'max_iter': [100, 500, 1000],\n", " 'solver': ['liblinear', 'lbfgs']\n", "}\n", "\n", "# Grid search with cross-validation\n", "lr_grid = GridSearchCV(LogisticRegression(random_state=42), \n", " lr_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "\n", "# Train with timing\n", "start_time = time.time()\n", "lr_grid.fit(X_train_scaled, y_train)\n", "train_time = time.time() - start_time\n", "\n", "print(f\"Best parameters: {lr_grid.best_params_}\")\n", "print(f\"Best cross-validation score: {lr_grid.best_score_:.4f}\")\n", "\n", "# Make predictions\n", "start_time = time.time()\n", "lr_pred = lr_grid.predict(X_test_scaled)\n", "test_time = time.time() - start_time\n", "\n", "# Calculate metrics\n", "lr_accuracy = metrics.accuracy_score(y_test, lr_pred)\n", "lr_precision, lr_recall, lr_f1, _ = precision_recall_fscore_support(y_test, lr_pred, average='weighted')\n", "lr_fpr = metrics.roc_curve(y_test, lr_grid.predict_proba(X_test_scaled)[:, 1])[0][1]\n", "\n", "# Store results\n", "results['Logistic Regression'] = {\n", " 'Accuracy': lr_accuracy,\n", " 'Precision': lr_precision,\n", " 'Recall': lr_recall,\n", " 'F-Score': lr_f1,\n", " 'FPR': lr_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['Logistic Regression'] = confusion_matrix(y_test, lr_pred)\n", "\n", "print(f\"Accuracy: {lr_accuracy:.4f}\")\n", "print(f\"Precision: {lr_precision:.4f}\")\n", "print(f\"Recall: {lr_recall:.4f}\")\n", "print(f\"F-Score: {lr_f1:.4f}\")\n", "print(f\"False Alarm Rate (FPR): {lr_fpr:.4f}\")\n", "print(f\"Train Time: {train_time:.2f}s\")\n", "print(f\"Test Time: {test_time:.2f}s\")\n", "print()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **2. K-Nearest Neighbors (KNN) with Parameter Tuning**\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== K-NEAREST NEIGHBORS ===\n", "Best parameters: {'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'}\n", "Best cross-validation score: 0.8600\n", "Accuracy: 0.8690\n", "Precision: 0.8747\n", "Recall: 0.8690\n", "F-Score: 0.8655\n", "False Alarm Rate (FPR): 0.0001\n", "Train Time: 510.85s\n", "Test Time: 19.05s\n", "\n" ] } ], "source": [ "# K-Nearest Neighbors with parameter tuning\n", "print(\"=== K-NEAREST NEIGHBORS ===\")\n", "\n", "# Parameter grid for tuning\n", "knn_param_grid = {\n", " 'n_neighbors': [3, 5, 7, 9, 11, 15],\n", " 'weights': ['uniform', 'distance'],\n", " 'metric': ['euclidean', 'manhattan']\n", "}\n", "\n", "# Grid search with cross-validation\n", "knn_grid = GridSearchCV(KNeighborsClassifier(), \n", " knn_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "\n", "# Train with timing\n", "start_time = time.time()\n", "knn_grid.fit(X_train, y_train)\n", "train_time = time.time() - start_time\n", "\n", "print(f\"Best parameters: {knn_grid.best_params_}\")\n", "print(f\"Best cross-validation score: {knn_grid.best_score_:.4f}\")\n", "\n", "# Make predictions\n", "start_time = time.time()\n", "knn_pred = knn_grid.predict(X_test)\n", "test_time = time.time() - start_time\n", "\n", "# Calculate metrics\n", "knn_accuracy = metrics.accuracy_score(y_test, knn_pred)\n", "knn_precision, knn_recall, knn_f1, _ = precision_recall_fscore_support(y_test, knn_pred, average='weighted')\n", "\n", "# For KNN, we need to get probabilities for FPR calculation\n", "try:\n", " knn_proba = knn_grid.predict_proba(X_test)[:, 1]\n", " knn_fpr = metrics.roc_curve(y_test, knn_proba)[0][1]\n", "except:\n", " # If predict_proba fails, use a default value\n", " knn_fpr = 0.0\n", "\n", "# Store results\n", "results['K-Nearest Neighbors'] = {\n", " 'Accuracy': knn_accuracy,\n", " 'Precision': knn_precision,\n", " 'Recall': knn_recall,\n", " 'F-Score': knn_f1,\n", " 'FPR': knn_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['K-Nearest Neighbors'] = confusion_matrix(y_test, knn_pred)\n", "\n", "print(f\"Accuracy: {knn_accuracy:.4f}\")\n", "print(f\"Precision: {knn_precision:.4f}\")\n", "print(f\"Recall: {knn_recall:.4f}\")\n", "print(f\"F-Score: {knn_f1:.4f}\")\n", "print(f\"False Alarm Rate (FPR): {knn_fpr:.4f}\")\n", "print(f\"Train Time: {train_time:.2f}s\")\n", "print(f\"Test Time: {test_time:.2f}s\")\n", "print()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **3. Naive Bayes (NB) with Parameter Tuning**\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== NAIVE BAYES ===\n", "Best parameters: {'var_smoothing': 1e-09}\n", "Best cross-validation score: 0.7015\n", "Accuracy: 0.6999\n", "Precision: 0.7172\n", "Recall: 0.6999\n", "F-Score: 0.6634\n", "False Alarm Rate (FPR): 0.0083\n", "Train Time: 0.76s\n", "Test Time: 0.01s\n", "\n" ] } ], "source": [ "# Naive Bayes with parameter tuning\n", "print(\"=== NAIVE BAYES ===\")\n", "\n", "# For GaussianNB, we can tune var_smoothing parameter\n", "nb_param_grid = {\n", " 'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6, 1e-5]\n", "}\n", "\n", "# Grid search with cross-validation\n", "nb_grid = GridSearchCV(GaussianNB(), \n", " nb_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "\n", "# Train with timing\n", "start_time = time.time()\n", "nb_grid.fit(X_train, y_train)\n", "train_time = time.time() - start_time\n", "\n", "print(f\"Best parameters: {nb_grid.best_params_}\")\n", "print(f\"Best cross-validation score: {nb_grid.best_score_:.4f}\")\n", "\n", "# Make predictions\n", "start_time = time.time()\n", "nb_pred = nb_grid.predict(X_test)\n", "test_time = time.time() - start_time\n", "\n", "# Calculate metrics\n", "nb_accuracy = metrics.accuracy_score(y_test, nb_pred)\n", "nb_precision, nb_recall, nb_f1, _ = precision_recall_fscore_support(y_test, nb_pred, average='weighted')\n", "\n", "# Calculate FPR using ROC curve\n", "try:\n", " nb_proba = nb_grid.predict_proba(X_test)[:, 1]\n", " nb_fpr = metrics.roc_curve(y_test, nb_proba)[0][1]\n", "except:\n", " nb_fpr = 0.0\n", "\n", "# Store results\n", "results['Naive Bayes'] = {\n", " 'Accuracy': nb_accuracy,\n", " 'Precision': nb_precision,\n", " 'Recall': nb_recall,\n", " 'F-Score': nb_f1,\n", " 'FPR': nb_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['Naive Bayes'] = confusion_matrix(y_test, nb_pred)\n", "\n", "print(f\"Accuracy: {nb_accuracy:.4f}\")\n", "print(f\"Precision: {nb_precision:.4f}\")\n", "print(f\"Recall: {nb_recall:.4f}\")\n", "print(f\"F-Score: {nb_f1:.4f}\")\n", "print(f\"False Alarm Rate (FPR): {nb_fpr:.4f}\")\n", "print(f\"Train Time: {train_time:.2f}s\")\n", "print(f\"Test Time: {test_time:.2f}s\")\n", "print()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **4. Support Vector Machine (SVM) with Parameter Tuning**\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== SUPPORT VECTOR MACHINE (OPTIMIZED) ===\n", "Using 14039 samples for parameter tuning (5% of training data)\n", "Optimizing Linear SVM (fastest option)...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/sophiengo1811/Library/Python/3.9/lib/python/site-packages/sklearn/svm/_base.py:1249: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Linear SVM best CV score: 0.6857\n", "Best parameters: {'C': 10}\n", "Training final Linear SVM model on full dataset...\n", "Accuracy: 0.6879\n", "Precision: 0.7483\n", "Recall: 0.6879\n", "F-Score: 0.6265\n", "False Alarm Rate (FPR): 0.0000\n", "Train Time: 0.43s\n", "Test Time: 0.02s\n", "\n" ] } ], "source": [ "# OPTIMIZED Support Vector Machine Implementation\n", "print(\"=== SUPPORT VECTOR MACHINE (OPTIMIZED) ===\")\n", "\n", "\n", "# Strategy 1: Use much smaller sample for parameter tuning (5% instead of 10%)\n", "# Use only 5% of data for parameter tuning (much faster)\n", "X_tune, _, y_tune, _ = train_test_split(X_train_scaled, y_train, \n", " test_size=0.95, random_state=42, \n", " stratify=y_train)\n", "\n", "print(f\"Using {len(X_tune)} samples for parameter tuning (5% of training data)\")\n", "\n", "# Strategy 2: Focus on LinearSVC first (much faster than RBF)\n", "print(\"Optimizing Linear SVM (fastest option)...\")\n", "linear_param_grid = {'C': [0.1, 1, 10]}\n", "linear_grid = GridSearchCV(LinearSVC(random_state=42, max_iter=500), \n", " linear_param_grid, cv=2, scoring='accuracy', n_jobs=-1)\n", "linear_grid.fit(X_tune, y_tune)\n", "\n", "print(f\"Linear SVM best CV score: {linear_grid.best_score_:.4f}\")\n", "print(f\"Best parameters: {linear_grid.best_params_}\")\n", "\n", "# Strategy 3: Use LinearSVC for final training (much faster than RBF)\n", "print(\"Training final Linear SVM model on full dataset...\")\n", "best_svm = LinearSVC(C=linear_grid.best_params_['C'], random_state=42, max_iter=1000)\n", "\n", "start_time = time.time()\n", "best_svm.fit(X_train_scaled, y_train)\n", "train_time = time.time() - start_time\n", "\n", "# Make predictions\n", "start_time = time.time()\n", "svm_pred = best_svm.predict(X_test_scaled)\n", "# For LinearSVC, we need to use decision_function for ROC curve\n", "svm_proba = best_svm.decision_function(X_test_scaled)\n", "test_time = time.time() - start_time\n", "\n", "# Calculate metrics\n", "svm_accuracy = metrics.accuracy_score(y_test, svm_pred)\n", "svm_precision, svm_recall, svm_f1, _ = precision_recall_fscore_support(y_test, svm_pred, average='weighted')\n", "\n", "# Calculate FPR\n", "try:\n", " fpr, tpr, _ = metrics.roc_curve(y_test, svm_proba)\n", " svm_fpr = fpr[1] if len(fpr) > 1 else 0.0\n", "except:\n", " svm_fpr = 0.0\n", "\n", "# Store results\n", "results['Support Vector Machine'] = {\n", " 'Accuracy': svm_accuracy,\n", " 'Precision': svm_precision,\n", " 'Recall': svm_recall,\n", " 'F-Score': svm_f1,\n", " 'FPR': svm_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['Support Vector Machine'] = confusion_matrix(y_test, svm_pred)\n", "\n", "print(f\"Accuracy: {svm_accuracy:.4f}\")\n", "print(f\"Precision: {svm_precision:.4f}\")\n", "print(f\"Recall: {svm_recall:.4f}\")\n", "print(f\"F-Score: {svm_f1:.4f}\")\n", "print(f\"False Alarm Rate (FPR): {svm_fpr:.4f}\")\n", "print(f\"Train Time: {train_time:.2f}s\")\n", "print(f\"Test Time: {test_time:.2f}s\")\n", "print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **5. Classification and Regression Trees (CART) with Parameter Tuning**\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== DECISION TREE (CART) ===\n", "Best parameters: {'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n", "Best cross-validation score: 0.8554\n", "Accuracy: 0.8653\n", "Precision: 0.8680\n", "Recall: 0.8653\n", "F-Score: 0.8626\n", "False Alarm Rate (FPR): 0.0565\n", "Train Time: 14.98s\n", "Test Time: 0.02s\n", "\n" ] } ], "source": [ "# Decision Tree (CART) with parameter tuning\n", "print(\"=== DECISION TREE (CART) ===\")\n", "\n", "# Parameter grid for tuning\n", "dt_param_grid = {\n", " 'max_depth': [10, 20, 30, None],\n", " 'min_samples_split': [2, 5, 10],\n", " 'min_samples_leaf': [1, 2, 4],\n", " 'criterion': ['gini', 'entropy']\n", "}\n", "\n", "# Grid search with cross-validation\n", "dt_grid = GridSearchCV(DecisionTreeClassifier(random_state=42), \n", " dt_param_grid, cv=3, scoring='accuracy', n_jobs=-1)\n", "\n", "# Train with timing\n", "start_time = time.time()\n", "dt_grid.fit(X_train, y_train)\n", "train_time = time.time() - start_time\n", "\n", "print(f\"Best parameters: {dt_grid.best_params_}\")\n", "print(f\"Best cross-validation score: {dt_grid.best_score_:.4f}\")\n", "\n", "# Make predictions\n", "start_time = time.time()\n", "dt_pred = dt_grid.predict(X_test)\n", "test_time = time.time() - start_time\n", "\n", "# Calculate metrics\n", "dt_accuracy = metrics.accuracy_score(y_test, dt_pred)\n", "dt_precision, dt_recall, dt_f1, _ = precision_recall_fscore_support(y_test, dt_pred, average='weighted')\n", "\n", "# Calculate FPR using ROC curve\n", "try:\n", " dt_proba = dt_grid.predict_proba(X_test)[:, 1]\n", " dt_fpr = metrics.roc_curve(y_test, dt_proba)[0][1]\n", "except:\n", " dt_fpr = 0.0\n", "\n", "# Store results\n", "results['Decision Tree (CART)'] = {\n", " 'Accuracy': dt_accuracy,\n", " 'Precision': dt_precision,\n", " 'Recall': dt_recall,\n", " 'F-Score': dt_f1,\n", " 'FPR': dt_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['Decision Tree (CART)'] = confusion_matrix(y_test, dt_pred)\n", "\n", "print(f\"Accuracy: {dt_accuracy:.4f}\")\n", "print(f\"Precision: {dt_precision:.4f}\")\n", "print(f\"Recall: {dt_recall:.4f}\")\n", "print(f\"F-Score: {dt_f1:.4f}\")\n", "print(f\"False Alarm Rate (FPR): {dt_fpr:.4f}\")\n", "print(f\"Train Time: {train_time:.2f}s\")\n", "print(f\"Test Time: {test_time:.2f}s\")\n", "print()\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random Forest results added to comparison\n" ] } ], "source": [ "# Add Random Forest results to our comparison\n", "rf_accuracy = metrics.accuracy_score(y_test, y_pred)\n", "rf_precision, rf_recall, rf_f1, _ = precision_recall_fscore_support(y_test, y_pred, average='weighted')\n", "\n", "# Calculate FPR for Random Forest\n", "try:\n", " rf_proba = clf.predict_proba(X_test)[:, 1]\n", " rf_fpr = metrics.roc_curve(y_test, rf_proba)[0][1]\n", "except:\n", " rf_fpr = 0.0\n", "\n", "# Store Random Forest results\n", "results['Random Forest'] = {\n", " 'Accuracy': rf_accuracy,\n", " 'Precision': rf_precision,\n", " 'Recall': rf_recall,\n", " 'F-Score': rf_f1,\n", " 'FPR': rf_fpr,\n", " 'Train Time': train_time,\n", " 'Test Time': test_time\n", "}\n", "\n", "confusion_matrices['Random Forest'] = confusion_matrix(y_test, y_pred)\n", "\n", "print(\"Random Forest results added to comparison\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Performance Comparison Summary**\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== COMPREHENSIVE PERFORMANCE COMPARISON ===\n", "\n", "Performance Metrics Summary:\n", "================================================================================\n", " Accuracy Precision Recall F-Score FPR Train Time Test Time\n", "Logistic Regression 0.6895 0.7497 0.6895 0.6291 0.0000 6.3958 0.0025\n", "K-Nearest Neighbors 0.8690 0.8747 0.8690 0.8655 0.0001 510.8480 19.0496\n", "Naive Bayes 0.6999 0.7172 0.6999 0.6634 0.0083 0.7562 0.0146\n", "Support Vector Machine 0.6879 0.7483 0.6879 0.6265 0.0000 0.4300 0.0190\n", "Decision Tree (CART) 0.8653 0.8680 0.8653 0.8626 0.0565 14.9771 0.0177\n", "Random Forest 0.8700 0.8738 0.8700 0.8671 0.0005 14.9771 0.0177\n", "\n", "=== KEY PERFORMANCE METRICS (%) ===\n", "================================================================================\n", "Logistic Regression:\n", " Accuracy: 68.95%\n", " Precision: 74.97%\n", " Recall: 68.95%\n", " F-Score: 62.91%\n", " False Alarm Rate (FPR): 0.00%\n", "\n", "K-Nearest Neighbors:\n", " Accuracy: 86.90%\n", " Precision: 87.47%\n", " Recall: 86.90%\n", " F-Score: 86.55%\n", " False Alarm Rate (FPR): 0.01%\n", "\n", "Naive Bayes:\n", " Accuracy: 69.99%\n", " Precision: 71.72%\n", " Recall: 69.99%\n", " F-Score: 66.34%\n", " False Alarm Rate (FPR): 0.83%\n", "\n", "Support Vector Machine:\n", " Accuracy: 68.79%\n", " Precision: 74.83%\n", " Recall: 68.79%\n", " F-Score: 62.65%\n", " False Alarm Rate (FPR): 0.00%\n", "\n", "Decision Tree (CART):\n", " Accuracy: 86.53%\n", " Precision: 86.80%\n", " Recall: 86.53%\n", " F-Score: 86.26%\n", " False Alarm Rate (FPR): 5.65%\n", "\n", "Random Forest:\n", " Accuracy: 87.00%\n", " Precision: 87.38%\n", " Recall: 87.00%\n", " F-Score: 86.71%\n", " False Alarm Rate (FPR): 0.05%\n", "\n" ] } ], "source": [ "# Create a comprehensive performance comparison table\n", "import pandas as pd\n", "\n", "# Convert results dictionary to DataFrame for better visualization\n", "results_df = pd.DataFrame(results).T\n", "results_df = results_df.round(4)\n", "\n", "print(\"=== COMPREHENSIVE PERFORMANCE COMPARISON ===\")\n", "print(\"\\nPerformance Metrics Summary:\")\n", "print(\"=\" * 80)\n", "print(results_df.to_string())\n", "\n", "# Display results in percentage format for key metrics\n", "print(\"\\n=== KEY PERFORMANCE METRICS (%) ===\")\n", "print(\"=\" * 80)\n", "for algorithm in results_df.index:\n", " print(f\"{algorithm}:\")\n", " print(f\" Accuracy: {results_df.loc[algorithm, 'Accuracy']*100:.2f}%\")\n", " print(f\" Precision: {results_df.loc[algorithm, 'Precision']*100:.2f}%\")\n", " print(f\" Recall: {results_df.loc[algorithm, 'Recall']*100:.2f}%\")\n", " print(f\" F-Score: {results_df.loc[algorithm, 'F-Score']*100:.2f}%\")\n", " print(f\" False Alarm Rate (FPR): {results_df.loc[algorithm, 'FPR']*100:.2f}%\")\n", " print()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Data Visualization**\n", "\n", "### **1. Accuracy Comparison of Different Algorithms**\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "=== ALGORITHM RANKING BY ACCURACY ===\n", "1. Random Forest: 87.00%\n", "2. K-Nearest Neighbors: 86.90%\n", "3. Decision Tree (CART): 86.53%\n", "4. Naive Bayes: 69.99%\n", "5. Logistic Regression: 68.95%\n", "6. Support Vector Machine: 68.79%\n" ] } ], "source": [ "# Create accuracy comparison visualization\n", "plt.figure(figsize=(12, 8))\n", "\n", "# Extract accuracy values\n", "algorithms = list(results_df.index)\n", "accuracies = [results_df.loc[alg, 'Accuracy'] * 100 for alg in algorithms]\n", "\n", "# Create bar plot\n", "bars = plt.bar(algorithms, accuracies, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD'])\n", "\n", "# Add value labels on bars\n", "for bar, accuracy in zip(bars, accuracies):\n", " plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, \n", " f'{accuracy:.2f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "plt.title('Accuracy Comparison of Classification Algorithms', fontsize=16, fontweight='bold', pad=20)\n", "plt.xlabel('Classification Algorithms', fontsize=12, fontweight='bold')\n", "plt.ylabel('Accuracy (%)', fontsize=12, fontweight='bold')\n", "plt.ylim(0, 100)\n", "plt.grid(axis='y', alpha=0.3)\n", "plt.xticks(rotation=45, ha='right')\n", "\n", "# Highlight the best performing algorithm\n", "best_accuracy = max(accuracies)\n", "best_idx = accuracies.index(best_accuracy)\n", "bars[best_idx].set_color('#FF6B6B')\n", "bars[best_idx].set_edgecolor('black')\n", "bars[best_idx].set_linewidth(2)\n", "\n", "plt.tight_layout()\n", "plt.savefig('iot_accuracy_comparison.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Print ranking\n", "print(\"=== ALGORITHM RANKING BY ACCURACY ===\")\n", "accuracy_ranking = sorted(zip(algorithms, accuracies), key=lambda x: x[1], reverse=True)\n", "for i, (alg, acc) in enumerate(accuracy_ranking, 1):\n", " print(f\"{i}. {alg}: {acc:.2f}%\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **2. Confusion Matrix Plots for Each Algorithm**\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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8J5bSgVBaUlzXD9bR3FGtH6xVDCJbP/j999836wdbHUPu1g8OP1AqsvWDGzVq5LJ+sL8NjCKWAgAgDgQFe+Wyy5Ytc3k9Y8YMEw/pWr7VqlUz6wlPmzbNLPlSq1YtR7WowoULy+bNm82SLitWrDDJvh9//NHMxtNKTkOHDpU+ffrIoEGDTJykCTmN00aPHm3Ooe/XaglabcpK3o0ZM0Y6dOjgWL5F36NJw88//1z69u0b7c/knW9SxNRu10AzMrpejn6pAAAg+rTSk50bfBexFAAAcZO8s/N/0aUjxb/88kvToWStH6ybVbrRef1gLeOkHVHaIRTZ+sG//PKLLF++3O36wUeOHDHrB2tZKS0VpesH60h0i15Dk1YzZ86UAwcOmOSWP64fTCwFAIDvd0yFhoaatXmdN933IJqsU+nTpzePGjvdvn1b6tSp42hTqFAhyZUrl2zatMm81sdixYq5lNHUhJxeU0tkWm2cz2G1sc6hlRL0Ws5tdAkZfW218fmZd7pOTVTy589vAlIAAABERCwFAID/YP3g+EcsBQCA7xs+fLgMHjzYZd+7775rZsJFRqsGdOvWzQzU0Zn2SmManTnnXH5caXyjx6w24eMd6/WD2miCTwddXbx40ZTfdNdGB04liOQdAACwH5PlAAAAYsFLpQdYPxgAAPgHe4s99uvXz1QFcGZVFIiMVh349ddfTTnLhMwrZTO1JntMnDhxIs7uBQAAIKEhlgIAAPAcsRQAAAlD0qRJzfq9zltUyTsdfKSVCHT2vFYYsGTNmtWUtLx06ZJL+zNnzphjVht9Hf64dSyqNnpfyZMnl4wZM5pqCO7aWOfw6eRduXLl5LXXXpNt27ZFWZNUa6zrtMZvvvkmXu8PAIAEK8jmDT6JWAoAAP9a8w7xi1gKAICEseZdTKoYaOLuu+++k9WrV5tS4c7KlCkjISEhsmrVKsc+XftWB/RouXGlj3v37pWzZ8862qxcudIk5nRdYauN8zmsNtY5tDSnXsu5jZbx1NdWG58um7l//355//33pW7duqbsg36Y7Nmzm+daE1SP6wKApUuXlpEjR8qTTz7pjdsEACDBoZMoMBBLAQAQN4K8VDYT8YtYCgCAOOKlWKpz587y1VdfycKFCyV16tSONerSpk1rZsTpY7t27UwJzvTp05uEXNeuXU1CrWLFiqZtvXr1TJLulVdeMb//eo7+/fubc1uz/Tp27CgfffSR9O7dW9q2bWsShfPmzTNrClv0GrrucNmyZaV8+fIybtw4uXbtmrRp0yZGnykoLDqF1eOILuCnH0prjx47dsy81mmFpUqVMoslW4sJxlSP72O28B8A9zpXyu3tWwD8Rv5MyePlOtuPXrH1fGXzprH1fEgYsdTxf/+w/V6BQDTjwCxv3wLgNwaWGxgv19n5z2Zbz1c6w/3OIARWLHXvy1dsv1cgEAVVae/tWwD8RlCe6vFynXv7v7b1fMFFmsZqANb06dOldevW5vnNmzelZ8+eMnv2bAkNDTW/9R9//LFLOUuNBzp16iRr166VlClTmiTciBEjJHHi/+bB6bHu3bubwT5amnPAgAGOa1g0wTdq1CiTACxZsqRMmDBBKlSokHCSd3GF5B1gD5J3QMJL3u34097kXZk8JO8CEck7wB4k74CEl7zb9c8WW89XKkPMOmngH0jeAfYgeQckxOTdt7aeL7hIYwlUXimbCQAA4gaFngAAADxHCXIAAIBYoAS5bYLtOxUAAAAAAAAAAACA2GDmHQAA/oQBTgAAAB6LbL0UAAAARAOxlG1I3gEA4Eco9QQAAOA5YikAAIDYoNij33yTV65cifTY77//Hq/3AgAAkNAQSwEAAHiOWAoAAPgiryfvGjZsKKGhoRH2Hzp0SGrUqOGVewIAICFXJ7Bzg+8jlgIAwN6Zd3b+D76PWAoAABvRMeU/ybtUqVLJc889J3fu3HHsO3DggAmQmjRp4tV7AwAgoQmyeYPvI5YCAMDeNe/s3OD7iKUAAPg/9u4DPIqqa+D4SUghlBB67x1BqgLSe1NBEWkKKIIoHZEiSJNXUAQpClgoFlCKiohKERUQQbogXar03kkj+z3n8s2ymwJJ2LCbzf/nM2Z35u7M3UnZwy3nupCPr2u3FMzta959++23Ur9+fWnfvr18/fXXsnPnTqlXr555PmHCBHdXD//vsQIhZssU5G+en7oaLsv3nZM9Z66b568+lk+KZEnj9Jo/D1+UhdtP25/nDUktzUpmNV9tNpGjl27Kkl1n5cSVmCPcsqT1l361CphyQ37eb9/v6yNSv2hmqZQ3g2RI7Sdnr4Wbc+w5e7segKfbsW2zfDP3M/l37265cP6sDH17gjxWs679uM1mky9nTJOlP3wr169elVJlykn3/m9I7rz57WX0tTOnTZT9e3aKr28qqVarnnTp2V+C0tz5Hdy26S/54tMP5fCBfyV1UJDUa/yEdOzaQ1L53f6zv33LRlk0f47s3f2P3Lh+TXLnySct23WUOg2bPeA7AuB+EUslD9u37JAFn38j+3b/KxfOXZAR7w2VanUeM8ciIyJl1rTPZcMfG+XU8VOSJl1aqVC5nHTu+YJkyZrZlPl703bp//KgWM/9wecTpfhDxUyZb+Yukr3/7JUb129Irny55dnnW0q9pnVifd1vy1bJ22+8I4/VqiIjJwxLwncPuM6+X/bJ/pX75drZa+Z5SJ4QKf1UacldNrdTOY2pfhv3m5zcflJq9qkpeSvltR/b9PkmObvvrFw6dkky5MogTd9u6vTaW+G3ZMOsDXL+0Hm5cuKK5C6fW2r1reVUZt1H6+TgmoMx6pchdwZ5/J3HXfyuASQlYqnk4atNp+XrzWfk+KXbbUhFsgbJqzVzS80iIeb5/C1nZMk/52XXyetyPTxK/nq9ggSndm72fPXrfbLn9A05fz1CgoP8pGrBYOlfL69kSx/g9Pkxa/0pc74Tl8MlYxo/aVsxu3SrkcteZsPhKzJ2xVH59+xNyRkcYI49VTbrA7sXwP3YuGOfzFiwXHbuPyJnL1yWD4a/IvUfK28/Pui9WbJoxTqn11Sv+JB8+nbvGOcKD4+QZ3uPkT0Hj8l3U9+UkoXvxFtrNu2UD75YLPuPnJDAAH+pVLqoDOzaSvLkyGKOnzl/Sd75eKH8s/+wHD1xVp5vXlfeeKV1kr53ILlxe+ddUFCQ/Pjjj2ZE07PPPiurV6+WDh06yLhx49xdNTi4dDNSftx1Vs5eDzczMbTz7MVH88j4VYfk9NVwU2bd4UuydO9Z+2vCb9nsjwNS+UjXKnll56mr8s320+LrK9K4eBazb9SKfyXqTlHTQfdchVxy6PxNKZApyKkeTUtklYp5gmX+36fk9LVwKZEtrbzwaG6ZvOaIHI+lExDwNKE3b0rBIsWkYbMWMnpIvxjHF86ZLYsXzpV+Q96SHDlzyxefTpU3+70q07/8VgICA+X8uTPyRp+XpWa9RvJqv8Gm4+2jyeNkwtvDZMjo98w5Du7fK8Ne7yFtOrwkrw0dLefPnpEP3vufREVFyUs9bl9z9z9/S4HCReWZ9p0kY6bM8tfa1TJ+9JuSJm16qVyt5gO/L3AhBninOMRSyUPozVApVKygNHqyoYx8fbTTsbDQMPl3z7/y3EttpVCxQnL16jWZNm66DOs7UqZ+OdmUKVW2pMxb9qXT62ZP+0K2bvxbipUqap7v3L5bChUpIK07PiMZM2WU9Wv+kneHj5e06dJIlZqVnV576sRp+Xjip1Km/ENJ/t4BV0qTKY2Ua11O0udIL2IT04G2esJqafK/JqYjz7Jn6Z67znoqVKuQnD9wXi4dvRTjmC3KJqkCUkmJRiXk6Iajsb6+4vMVTT3sr7llkx+H/Cj5Hs133+8R7kWqy5SHWCp5yBEcIP3q5pX8mVKLTWzy/d/npMe8/fJNl4ekaLY0cjMiSmoUzmC2Cb8ei/UcjxYIlq7Vc0nWdP5y5mq4vPvLf9J74b/y1Qul7GXeXnZU1h68LAPq55Ni2dLI5dBI0yZmOXYxTLp9vU9aV8wm41oUlvWHr8ibPxwy56xe+M7nEOCpboaGSYlCeaRlo2rSc9S0WMvUqPSQvP1aJ/vzAP/YuxDGzfhGsmUOMZ13jo6dOifdR3wonZ5uIOMGdpar12/KmI/mS6+3psm3H75pyoRHREqmkHTySttm8tl3v7j0PcLdiKWSdedd9MWAfX19Zd68edKgQQOTkuDNN9+0lwkODnZHFRHNrtO3R7Zaft5zTqoVyCgFMgbZO+8ibkXJ1bBbsb4+W7pASRuQSpbuOSeXQm8HPcv3npfX6wRLpjT+cu56hFMH3Zlr4bL/3I0YnXcV8wbLL/vOy+7/n/H35+FLUixLWqldJJPM2XLS5e8bcLVHqlY3W2x0hN+iBXOkTYcuUrXG7VkSrw19S9o9WU/WrflNatVvLBvWrhY/Pz/Tcad/O1WP/kOle8dWcuLYUcmVJ5+s/nWZFCxcVNq98LI5rvtefKWPjBk2QNq9+LKkSZNWWnd4yenaLZ5tL1s3rpM/V62k8y6Zo8EpZSCWSn4erfaI2WKTNn1aeWfq2077egx8VXp06CNnTp6RbDmzib+/v2TKksl+XGfrrVu1Xpq3fsLeQdHuReeRqk+3ayGb12+VP37706nz7tatWzJm6LvS4eXnZMfWnXL9qnOcB3iyPBXyOD0v92w5MxPv3L/n7J13F45ckN0/7ZYmbzWRb3t8G+MclTpUMl+3X9kea+edX2o/efSFR81jnaEXfuP2v3ccBaQJEHFIPPLfpv8k/Hq46RRE8kaqy5SBWCr5qVMso9PzPnXzmpl4fx+/bjrvOlbOYZ8VF5dOVW6XUblDAqXLYzmlx/z9pj3LP5WvHDh705xz8culpWCW2+1ReSTQ6Rx6XF87sMHtwRqFswbJ5qNX5bO/TtN5h2Sh5iNlzHY32lmXNVOGu5ZZvXGHrN28Sya/2U1Wb/zH6dg/+4+YAeR9OjW3t129+ExD6T5iqkRERoq/n5+ZgTfklTbm2DfL1973+4IHIZZK3p13ISEhsQbE2nA9ffp0+eijj8xjLaONC/As+p0rmyu9mU13+MJN+/4KeYLNph142tmnaTUj/n/2naa3vBYWKZXzh8gv+86Jr4+PVM6XQU5dDZMLN+503GnqTT33e6sOy8M508e4tp+vr0Q6TtPTTsOoKCmYyTllJ5AcnTpxXC6ePyflHrnTwJo2XXopXqqMmSmnnXcRERHi5+9vD35UYODtf0zs3L7VdNRFhEdIQIDzPzB01l54uM7s2CUPV4i98fj6tWuSNz8NTkByQCzl/a5fu26+f2nTp4v1+LrV6+XK5atmJt+9zpOv4J30NerLT76SjBlDpEmLRqbzDkiutFHo6F9HJTIsUrIWvZ2uTB+v/XCtPNLpEQkKcR4ImJQOrDogOR7KIemyxP47C8CzEEslb7eibLJ01wW5EREl5fIk7u+uzqb74Z/zUj5vOtNxp37bf0nyhATK7/svSZev9pqlXKoWzCD96+eVkKDbTajbjl8z6TYdVS+cQcYsj32mNpAcbdi+Tx579jUJTp9GqpQtIb07NZeMwXd+185dvCJvTvxCPhj+qqQOvJN21lK6aH7x8fWVb5f/KU81eExuhIbK4l/WS9XyJUzHHYD4cctvy2+//eayc4WFhZnNUWREuPj5x/zDgfuTM32g9KqRX/x8fST8VpTM2njcpK5UW45flos3IuVKaKTkDA6Ux0tllazpAmT2xuPmeNitKJn651GTarNBscz2Dr2P1/9nT5mZxt9X2pbLKXO2nJCwyKhY67D3zDWpVSiTHDh/O0d50axppEyO9CbVJpDcXbxwznzNmPH274glJGMmuXjhvHlctsIj8smU8bJw7mxp3qq9ScM5a/rtlGoXzt9+fcXKVeX7BXPk9xU/S426Dc15587+2KlMdKtXLpN9e3ZKz9eHJul7RNJjgFPKkNSxVFhEmH1gAB688LBw+XTyLKnTqJZJeRmbn79fLhWrVpCs2W+vGRGbVctXy75d+6TPkJ72ff9s3SlLv18m0+d+kCR1Bx6Ei/9dlOUjlsutiFtmlpyuaadrzanNX242HXl5Kzp3WielGxdvyIm/T0i1V6s9sGsi6ZDFIGVI6ljKP+KWBPqnctk1cNu+0zek7axdps0oTUAqmdKqqFn7LiHe++U/mbvptEmzWTZ3WpnWppj92LGLoXLicpgs3X1BxjYvLFFRNhm7/Kj0WbhfZj9f0pQ5dy1csqR1npGUOa2/XAu7JaERUZLa/85AWyA50pSZDauVl9w5ssh/J8/K+7MWSdchk+XriYMkVSpfM7Bh8HuzpE2zWlKmWAGTIjM6nVU34+3e0vd/H8vwSV/KragoKVeykHw8updb3hMeMB/+DibrzrtatZwX+74fY8aMkZEjRzrtq9Kmu1Rt28Nl18BtZ66FmTXuUvv5StlcwdK2fE75cO1R04G3/shle7mTV8PkSlikvPpYPsmcxl/O34gQf18faV0upxy6cFO+2HzCdLbVLpxJXqqcVyauPiwRUTZ5tlxO2XL8ihx0mM0X3Xf/nJFny+aQQXULmRFQ52+Ey4b/LptZfEBKkL9QEek3ZJR8+sF4mf3RFDMDr/kzbc26db7//+FY4dHH5MVX+5p17t4bPdSkWmvbsavs/HuLGfkU3d9bNsr7Y4ZL7wHDzPmRvNHclDIkdSzVZ3BP6ftGzAXJkfQ0HeZbg8aYfxT3Ghx7PHv29DnZvG6LDB07KM7zbNv4t7w38n3pO7S3FCic3+y7cf2GvDPsPek7tJdkyEjshOQrOGewNP1fUwm/GW7WpFv30TppMLSBXD11VU7vOm3Wv3uQdN09TaOZp5JzSk8kT3TepQxJHUsNe6qMDH+6rMuugdsKZEkt33YtbTrKlu26IIMXH5TPO5RMUAde58dySMvyWeTE5XCZuvq4DPr+oExvU8zMstTB5eG3bDK2eSEpmPn2Od96oqA88+lOOXTupj2VJuDNmtW+nTpcFS+Yx2wNOg2RDdv3StXyJeWL73+V6zdDpWvruOOtsxcum5l5LRpUNefT8pM/Xyy935ouM8f2JUW1l+P76zpun6c6a9YsSZcunbRq1cpp/4IFC+TGjRvSsWPHu75+8ODB0q9fP6d9Q1ccTpK6pnSaAdNam+7Y5bOSNyS11CyUURZsPx2j7NGLtzvgsqQNMJ13mk5T17abvOaIritvfLn5hIxuUkweypFOtp24KkWzpJGHsqcznXpKf881vea4x4vLgr9PmU666+G3zIw/nf2na+jpwsGPl8xqZuEByV3GTLdnT1y8eF4yZbmd+kldunhBChW5MxqwTsOmZtPZeKlTB5kPxe/mfSk5cuW2l3m6zfPyVOvn5ML5s5IufbCcPnlCZn80WXI6lFE7tm6SkQN7Sdee/aVekyceyPsE4Pmx1OkI5wXH8eA67kYPGmPWuRs3fUycs+6WLV4uwRnSS9WaVWI9/vfmHfJm35HS7bWu0uDxevb9J46dlFMnTptjFtv/p0Bo9OjjMuubTyRX3pwuf1+Aq6XySyXpc9xOsZ+5YGa5cPCC7Fm6R1IFpJKrZ67Kgq4LnMqvmbRGshbPajr4XE072g+uOigFqxc09QKQ/CRFLOX/TbckqWtKF5DKV/JnSm0eP5Qzrew4eV2+2HBKRjYrGO9zZEzjbzbtnCucJUjqTNpmUmGWz5NesqbzN+1NVsed0jLqxJVw03mXJV2AvW3Mom1S6QJTMesOXilvzqySMUM6OXLijOm8+2vbHtm2+6A8/PirTuWe6fE/ebxuZXnn9Rdk7g+/S/q0QfL6S8/Yj48b0FlqPzdQ/t5zyMzCA5AMOu90hJLmEo8uW7Zs0rVr13sGSZrSKXpaJ1JmPhjah54qllk8KleG28GUzsBTmj9cZ8o5rlZnPdYOOjVpzRGn9Jelc6SXukUyyeQ/jphOOke67p3u0/IP50ov247HvSAxkFxo51vGzFnk700bpHDREmbfjevXZO+uHdKshfM/JJXOtlPLlywS/4AAKf+IcyOuduplzpLNPF71y1LJmi2HFC52O9WH2r5lo4wY2Ete6NZbmjS/E1AhmWOAU4qTFLHUpWukzHRXx93x/07IuI/GSnCI81oqjh0Fy374Reo3qyd+/jFD+b83bZehfUbIS71ekGZPO4+GzVcgr3w8b6rTvtlTP5cbN27Kq/1flqw54k7BCXgy/b2IioySh1s+LEVqO2cR+HHwj1LhuQqSp3zSzIo7s/uMXD19VQrXKpwk58eDx2jxlCcpYqkoUmY+sL//4ZGOLU0JE6UNVSIS8f/nqJA3vURGnZCjF0Il3/93Eh4+H2q+5spwu62xXO50svrfS07n+fPg5USvvQd4ulNnL8qlK9clW6bbmTuGvNpGendqYT9+5vwleemNSTLhjS5StsTtTrmboeH29l6L7/83+mo6Wng7BjJ4Tefd0aNHpWDBmCNk8ufPb47BMzQrmVV2n74mF29GmrSZOpOucJY0Zs06TY2pz/X49fAoyRUcKM1LZ5MD527IySu3877vO3tdniiVVVqWyS5rDl00s+rqFclsAqX9566bMmf+f/08S96QSNPBd+rqnf35QlJLhiA/OX45TDKk9pNGxbOYdupf/73wgO8IkDg3b9yQE8fv/G07ffK4HNi/R9KnzyDZcuSUFq3ay9ef6cyHfJI9Z2754tMPJXPmrFK1Rh37a3745mspWbqspA5KI1s3rpOZUydKp269zAw7i66JV7FyNRMsrV39qyz4cqYMGvWupEqVyp4qc8SAntK8VTupVru+fS08TbGZPphUakByQiyVPNy8cdN0zFl0Bty/ew9IcHB6yZQlk4wa+Lb8u+dfeWviCIm6dUsunLsd26TPkN78bbZs3fi3nDp+Spq0aBRrqsw3+4yQFm2bS4261ezn8PP3NzP1AgIDpGCRAk6vSZv+dkNT9P2Ap9o6b6vkKptL0mZOKxGhEXL4z8NyevdpqTugrgSFBJktOi2bLtudRlVNrxkRFiE3L9+UyPBIuXDk9u+KrptnzZ67fPyy3Iq8JWHXwiQy9E6ZTPlvZwmx/LvqX8lcOLOE5A1J4ncOIKkQSyUPE1b+JzWKZJBcGQLletgtWfLPedlw+Kp80j6XOX72WricuxYhRy7e7mzbd+ampA3wlZwZAiUkyE/+Pn5N/jlxXSrkTSfBqf3kv4thMvn3Y5IvY6C9461qoWAplSONDPnhkAxumM+0SY36+bA8VijYPhuvTcVsZs28cb8clZblssr6w1dk6a4LMr3tnWw5gCfTFJZHT5y1P9c163Yf+E8ypE8jGdKnlQ+/XCINq1eQLBmDzZp34z79RvLlyirVKz5kyufKdnsguSVN6tuDF/LlyiY5smY0j2tXLiOfffeLOVezOo/I9RuhZu28XNkzS6kid9Yl1uuqGzfD5MLlq+a5v18qKZL/9u81kNK5vfNORzJt375dChRwbjD4+++/JXNm5z8GcJ90AamkXYVcEhyYSm5GRplOOe2423f2hoSk9pNiWdJKzUKZJCCVj1y6GSnbT16VFfvO21+vHXMzNhyThsWySO8a+c0svGOXQ805robdinc9/FP5SJMSWU2HoS5QvPvMdZm75aSERkYl0TsHXGv/np0yqFcX+/NPpow3X+s3eUL6DXlLnmnfSUJDb8qUd9+Sa9euykNlysuo8VMlwGEk595d/8iXM6bJzZs3JG++gtLj9aFSr/HjTtfZtH6tzPv8U4kIj5CCRYrJm2MmyiNVq9uPr/x5sYSFhsr8L2aazVKmXEV554MZSXwXkJRYpyXlIZZKHvbt2i/9X76zRt30CZ+Yrw0ery8dXm4v61atN8+7RVu3+b2PxkrZSg/bny9dtExKlS0p+Qre+UevZfmSlRIaGiZfz5pvNsvDFcvI+I/fSZL3BTxoYVfCZN30dXLz0k3x19RneTOajrucZeKf8nX9p+vlzJ4z9uc/D/nZfG3+fnNJl/V2A+5v436T6/8/yNCxTPs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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "=== CONFUSION MATRIX ANALYSIS ===\n", "Format: [TN, FP]\n", " [FN, TP]\n", "==================================================\n", "\n", "Logistic Regression:\n", "True Negatives (TN): 71976\n", "False Positives (FP): 1519\n", "False Negatives (FN): 35849\n", "True Positives (TP): 10992\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.2347\n", "Specificity: 0.9793\n", "False Positive Rate: 0.0207\n", "\n", "K-Nearest Neighbors:\n", "True Negatives (TN): 70452\n", "False Positives (FP): 3043\n", "False Negatives (FN): 12724\n", "True Positives (TP): 34117\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.7284\n", "Specificity: 0.9586\n", "False Positive Rate: 0.0414\n", "\n", "Naive Bayes:\n", "True Negatives (TN): 68740\n", "False Positives (FP): 4755\n", "False Negatives (FN): 31360\n", "True Positives (TP): 15481\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.3305\n", "Specificity: 0.9353\n", "False Positive Rate: 0.0647\n", "\n", "Support Vector Machine:\n", "True Negatives (TN): 71977\n", "False Positives (FP): 1518\n", "False Negatives (FN): 36039\n", "True Positives (TP): 10802\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.2306\n", "Specificity: 0.9793\n", "False Positive Rate: 0.0207\n", "\n", "Decision Tree (CART):\n", "True Negatives (TN): 69342\n", "False Positives (FP): 4153\n", "False Negatives (FN): 12053\n", "True Positives (TP): 34788\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.7427\n", "Specificity: 0.9435\n", "False Positive Rate: 0.0565\n", "\n", "Random Forest:\n", "True Negatives (TN): 69914\n", "False Positives (FP): 3581\n", "False Negatives (FN): 12064\n", "True Positives (TP): 34777\n", "Total Samples: 120336\n", "Sensitivity (Recall): 0.7424\n", "Specificity: 0.9513\n", "False Positive Rate: 0.0487\n" ] } ], "source": [ "# Create confusion matrix plots for each algorithm\n", "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n", "axes = axes.ravel()\n", "\n", "algorithms = list(confusion_matrices.keys())\n", "colors = ['Blues', 'Greens', 'Oranges', 'Reds', 'Purples', 'YlOrRd']\n", "\n", "for i, (algorithm, cm) in enumerate(confusion_matrices.items()):\n", " # Create heatmap\n", " sns.heatmap(cm, annot=True, fmt='d', cmap=colors[i], \n", " xticklabels=['Normal (0)', 'Attack (1)'], \n", " yticklabels=['Normal (0)', 'Attack (1)'],\n", " ax=axes[i], cbar_kws={'shrink': 0.8})\n", " \n", " axes[i].set_title(f'{algorithm}\\nConfusion Matrix', fontsize=12, fontweight='bold')\n", " axes[i].set_xlabel('Predicted Label', fontsize=10)\n", " axes[i].set_ylabel('True Label', fontsize=10)\n", "\n", "# Remove the empty subplot if we have 5 algorithms\n", "if len(algorithms) == 5:\n", " fig.delaxes(axes[5])\n", "\n", "plt.tight_layout()\n", "plt.savefig('iot_confusion_matrices.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Print detailed confusion matrix analysis\n", "print(\"=== CONFUSION MATRIX ANALYSIS ===\")\n", "print(\"Format: [TN, FP]\")\n", "print(\" [FN, TP]\")\n", "print(\"=\" * 50)\n", "\n", "for algorithm, cm in confusion_matrices.items():\n", " tn, fp, fn, tp = cm.ravel()\n", " \n", " print(f\"\\n{algorithm}:\")\n", " print(f\"True Negatives (TN): {tn}\")\n", " print(f\"False Positives (FP): {fp}\")\n", " print(f\"False Negatives (FN): {fn}\")\n", " print(f\"True Positives (TP): {tp}\")\n", " print(f\"Total Samples: {tn + fp + fn + tp}\")\n", " \n", " # Calculate additional metrics\n", " sensitivity = tp / (tp + fn) if (tp + fn) > 0 else 0\n", " specificity = tn / (tn + fp) if (tn + fp) > 0 else 0\n", " fpr = fp / (fp + tn) if (fp + tn) > 0 else 0\n", " \n", " print(f\"Sensitivity (Recall): {sensitivity:.4f}\")\n", " print(f\"Specificity: {specificity:.4f}\")\n", " print(f\"False Positive Rate: {fpr:.4f}\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **3. Comprehensive Performance Metrics Comparison**\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "=== FINAL PERFORMANCE SUMMARY ===\n", "============================================================\n", "Algorithm Rankings:\n", "============================================================\n", "1. Random Forest: 87.02% overall performance\n", "2. K-Nearest Neighbors: 86.95% overall performance\n", "3. Decision Tree (CART): 86.53% overall performance\n", "4. Naive Bayes: 69.51% overall performance\n", "5. Logistic Regression: 68.95% overall performance\n", "6. Support Vector Machine: 68.77% overall performance\n" ] } ], "source": [ "# Create comprehensive performance metrics comparison\n", "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n", "\n", "# 1. Precision, Recall, F-Score comparison\n", "metrics_to_plot = ['Precision', 'Recall', 'F-Score']\n", "x = np.arange(len(algorithms))\n", "width = 0.25\n", "\n", "for i, metric in enumerate(metrics_to_plot):\n", " values = [results_df.loc[alg, metric] * 100 for alg in algorithms]\n", " axes[0, 0].bar(x + i*width, values, width, label=metric, alpha=0.8)\n", "\n", "axes[0, 0].set_xlabel('Algorithms')\n", "axes[0, 0].set_ylabel('Score (%)')\n", "axes[0, 0].set_title('Precision, Recall, and F-Score Comparison')\n", "axes[0, 0].set_xticks(x + width)\n", "axes[0, 0].set_xticklabels(algorithms, rotation=45, ha='right')\n", "axes[0, 0].legend()\n", "axes[0, 0].grid(axis='y', alpha=0.3)\n", "\n", "# 2. False Positive Rate comparison\n", "fpr_values = [results_df.loc[alg, 'FPR'] * 100 for alg in algorithms]\n", "bars = axes[0, 1].bar(algorithms, fpr_values, color='red', alpha=0.7)\n", "axes[0, 1].set_xlabel('Algorithms')\n", "axes[0, 1].set_ylabel('False Positive Rate (%)')\n", "axes[0, 1].set_title('False Positive Rate (FPR) Comparison')\n", "axes[0, 1].tick_params(axis='x', rotation=45)\n", "axes[0, 1].grid(axis='y', alpha=0.3)\n", "\n", "# Add value labels on bars\n", "for bar, fpr in zip(bars, fpr_values):\n", " axes[0, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, \n", " f'{fpr:.2f}%', ha='center', va='bottom')\n", "\n", "# 3. Training and Testing Time comparison\n", "train_times = [results_df.loc[alg, 'Train Time'] for alg in algorithms]\n", "test_times = [results_df.loc[alg, 'Test Time'] for alg in algorithms]\n", "\n", "x = np.arange(len(algorithms))\n", "width = 0.35\n", "\n", "bars1 = axes[1, 0].bar(x - width/2, train_times, width, label='Train Time', alpha=0.8, color='skyblue')\n", "bars2 = axes[1, 0].bar(x + width/2, test_times, width, label='Test Time', alpha=0.8, color='lightcoral')\n", "\n", "axes[1, 0].set_xlabel('Algorithms')\n", "axes[1, 0].set_ylabel('Time (seconds)')\n", "axes[1, 0].set_title('Training and Testing Time Comparison')\n", "axes[1, 0].set_xticks(x)\n", "axes[1, 0].set_xticklabels(algorithms, rotation=45, ha='right')\n", "axes[1, 0].legend()\n", "axes[1, 0].grid(axis='y', alpha=0.3)\n", "\n", "# 4. Overall Performance Radar Chart (simplified to bar chart for better readability)\n", "performance_metrics = ['Accuracy', 'Precision', 'Recall', 'F-Score']\n", "avg_scores = []\n", "\n", "for alg in algorithms:\n", " avg_score = np.mean([results_df.loc[alg, metric] for metric in performance_metrics]) * 100\n", " avg_scores.append(avg_score)\n", "\n", "bars = axes[1, 1].bar(algorithms, avg_scores, color='green', alpha=0.7)\n", "axes[1, 1].set_xlabel('Algorithms')\n", "axes[1, 1].set_ylabel('Average Performance Score (%)')\n", "axes[1, 1].set_title('Overall Performance Ranking')\n", "axes[1, 1].tick_params(axis='x', rotation=45)\n", "axes[1, 1].grid(axis='y', alpha=0.3)\n", "\n", "# Add value labels\n", "for bar, score in zip(bars, avg_scores):\n", " axes[1, 1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1, \n", " f'{score:.1f}%', ha='center', va='bottom', fontweight='bold')\n", "\n", "plt.tight_layout()\n", "plt.savefig('iot_comprehensive_metrics.png', dpi=200, bbox_inches='tight')\n", "plt.show()\n", "\n", "# Print final summary\n", "print(\"\\n=== FINAL PERFORMANCE SUMMARY ===\")\n", "print(\"=\" * 60)\n", "print(\"Algorithm Rankings:\")\n", "print(\"=\" * 60)\n", "\n", "# Rank by overall performance\n", "overall_ranking = sorted(zip(algorithms, avg_scores), key=lambda x: x[1], reverse=True)\n", "for i, (alg, score) in enumerate(overall_ranking, 1):\n", " print(f\"{i}. {alg}: {score:.2f}% overall performance\")\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 4 }