10. CBLOF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f12ec631-0d57-4a6d-a0ec-7940abac6968",
   "metadata": {},
   "source": [
    "## 10. Cluster-based Local Outlier Factor (CBLOF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9021294f-5f48-4f53-8b36-773852531a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pyod.utils.data import generate_data\n",
    "contamination = 0.05 # percentage of outliers\n",
    "n_train = 500       # number of training points\n",
    "n_test = 500        # number of testing points\n",
    "n_features = 6      # number of features\n",
    "X_train, X_test, y_train, y_test = generate_data(\n",
    "    n_train=n_train, \n",
    "    n_test=n_test, \n",
    "    n_features= n_features, \n",
    "    contamination=contamination, \n",
    "    random_state=123)\n",
    "\n",
    "# Make the 2d numpy array a pandas dataframe for each manipulation \n",
    "X_train_pd = pd.DataFrame(X_train)\n",
    "    \n",
    "# Plot\n",
    "plt.scatter(X_train_pd[0], X_train_pd[1], c=y_train, alpha=0.8)\n",
    "plt.title('Scatter plot')\n",
    "plt.xlabel('x0')\n",
    "plt.ylabel('x1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af446b0-4119-439c-aacf-477c942fb03b",
   "metadata": {},
   "source": [
    "### Step 1: Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea5b38f5-3d40-43b2-a465-1810e5a0b363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training data: {0: 475, 1: 25}\n",
      "The training data: {0: 475, 1: 25}\n",
      "The threshold for the defined comtanimation rate: 1.0114992923100408\n"
     ]
    }
   ],
   "source": [
    "from pyod.models.cblof import CBLOF\n",
    "cblof = CBLOF(n_clusters=10, contamination = 0.05) \n",
    "cblof.fit(X_train)\n",
    "\n",
    "# Training data\n",
    "y_train_scores = cblof.decision_function(X_train)\n",
    "y_train_pred = cblof.predict(X_train)\n",
    "\n",
    "# Test data\n",
    "y_test_scores = cblof.decision_function(X_test)\n",
    "y_test_pred = cblof.predict(X_test) # outlier labels (0 or 1)\n",
    "\n",
    "def count_stat(vector):\n",
    "    # Because it is '0' and '1', we can run a count statistic. \n",
    "    unique, counts = np.unique(vector, return_counts=True)\n",
    "    return dict(zip(unique, counts))\n",
    "\n",
    "print(\"The training data:\", count_stat(y_train_pred))\n",
    "print(\"The training data:\", count_stat(y_test_pred))\n",
    "# Threshold for the defined comtanimation rate\n",
    "print(\"The threshold for the defined comtanimation rate:\" , cblof.threshold_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba21f02c-d927-4d4d-8351-d2ebea70b6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.9,\n",
       " 'beta': 5,\n",
       " 'check_estimator': False,\n",
       " 'clustering_estimator': None,\n",
       " 'contamination': 0.05,\n",
       " 'n_clusters': 10,\n",
       " 'n_jobs': None,\n",
       " 'random_state': None,\n",
       " 'use_weights': False}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cblof.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2db62b-1bbf-43e9-a39e-4ff8a5c4a948",
   "metadata": {},
   "source": [
    "### Step 2: Determine the threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df278797-c345-45f8-9dce-99b895ae5dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(y_train_scores, bins='auto')  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram with 'auto' bins\")\n",
    "plt.xlabel('CBLOF outlier score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2fbabc-3935-435e-8c27-f4ea42baa419",
   "metadata": {},
   "source": [
    "### Step 3: Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9635f04-f31e-45fe-b4e9-36cb2c9ac29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count %</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>475</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outlier</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>5.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Count  Count %     0     1     2     3     4     5  Anomaly_Score\n",
       "0   Normal    475     95.0  2.00  2.01  2.01  1.99  2.01  1.98           0.47\n",
       "1  Outlier     25      5.0  0.45 -0.21 -0.47 -0.23 -0.03 -0.06           5.70"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = cblof.threshold_ # Or other value from the above histogram\n",
    "\n",
    "def descriptive_stat_threshold(df,pred_score, threshold):\n",
    "    # Let's see how many '0's and '1's.\n",
    "    df = pd.DataFrame(df)\n",
    "    df['Anomaly_Score'] = pred_score\n",
    "    df['Group'] = np.where(df['Anomaly_Score']< threshold, 'Normal', 'Outlier')\n",
    "\n",
    "    # Now let's show the summary statistics:\n",
    "    cnt = df.groupby('Group')['Anomaly_Score'].count().reset_index().rename(columns={'Anomaly_Score':'Count'})\n",
    "    cnt['Count %'] = (cnt['Count'] / cnt['Count'].sum()) * 100 # The count and count %\n",
    "    stat = df.groupby('Group').mean().round(2).reset_index() # The avg.\n",
    "    stat = cnt.merge(stat, left_on='Group',right_on='Group') # Put the count and the avg. together\n",
    "    return (stat)\n",
    "\n",
    "descriptive_stat_threshold(X_train,y_train_scores, threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bf7362d-5fd3-4151-a61e-b148a7607c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pred</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pred      0   1\n",
       "Actual         \n",
       "0.0     475   0\n",
       "1.0       0  25"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Actual_pred = pd.DataFrame({'Actual': y_test, 'Anomaly_Score': y_test_scores})\n",
    "Actual_pred['Pred'] = np.where(Actual_pred['Anomaly_Score']< threshold,0,1)\n",
    "pd.crosstab(Actual_pred['Actual'],Actual_pred['Pred'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a51005a9-bec4-49d7-b786-89312cf6bd07",
   "metadata": {},
   "source": [
    "### Averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0acfd54f-bb57-4e85-9edf-e28372693005",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyod.models.combination import aom, moa, average, maximization\n",
    "from pyod.utils.utility import standardizer\n",
    "from pyod.models.cblof import CBLOF\n",
    "\n",
    "# Standardize data\n",
    "X_train_norm, X_test_norm = standardizer(X_train, X_test)\n",
    "# Test a range of clusters from 10 to 50. There will be 5 models.\n",
    "n_clf = 5\n",
    "k_list = [10, 20, 30, 40, 50]\n",
    "# Just prepare data frames so we can store the model results\n",
    "train_scores = np.zeros([X_train.shape[0], n_clf])\n",
    "test_scores = np.zeros([X_test.shape[0], n_clf])\n",
    "train_scores.shape\n",
    "# Modeling\n",
    "for i in range(n_clf):\n",
    "    k = k_list[i]\n",
    "    cblof = CBLOF(n_clusters = k, contamination=0.05)  \n",
    "    cblof.fit(X_train_norm)\n",
    "\n",
    "    # Store the results in each column:\n",
    "    train_scores[:, i] = cblof.decision_function(X_train_norm) \n",
    "    test_scores[:, i] = cblof.decision_function(X_test_norm) \n",
    "# Decision scores have to be normalized before combination\n",
    "train_scores_norm, test_scores_norm = standardizer(train_scores,test_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89bfd22b-ad58-4f62-93d5-f7a1206dc72f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combination by average\n",
    "# The test_scores_norm is 500 x 5. The \"average\" function will take the average of the 5 columns. The result \"y_by_average\" is a single column: \n",
    "y_train_by_average = average(train_scores_norm)\n",
    "y_test_by_average = average(test_scores_norm)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(y_train_by_average, bins='auto') # arguments are passed to np.histogram\n",
    "plt.title(\"Combination by average\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "10f31f01-7fa8-465e-b8de-be6d147d88c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count %</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>475</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.98</td>\n",
       "      <td>-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outlier</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Count  Count %     0     1     2     3     4     5  Anomaly_Score\n",
       "0   Normal    475     95.0  2.00  2.01  2.01  1.99  2.01  1.98          -0.23\n",
       "1  Outlier     25      5.0  0.45 -0.21 -0.47 -0.23 -0.03 -0.06           4.28"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_train,y_train_by_average, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2915de00-e5ad-4940-90eb-04a5b6b8d31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Count</th>\n",
       "      <th>Count %</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>Anomaly_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Normal</td>\n",
       "      <td>475</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outlier</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Group  Count  Count %     0     1     2     3     4     5  Anomaly_Score\n",
       "0   Normal    475     95.0  2.02  2.01  2.01  1.99  2.01  2.00          -0.20\n",
       "1  Outlier     25      5.0  0.00 -0.54 -0.23 -0.05 -0.06  0.12           4.33"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_test,y_test_by_average, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2dcd7e-8ce1-41e6-8a2b-96430ddc619f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}