08. KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f12ec631-0d57-4a6d-a0ec-7940abac6968",
   "metadata": {},
   "source": [
    "## 8. K-Nearest Neighbor (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "77c93202-287d-4fef-af23-92afa2ddf098",
   "metadata": {},
   "source": [
    "### Step 1: Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d222b0f-ae32-4a57-9762-eaf0c5ea6568",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train kNN detector\n",
    "from pyod.models.knn import KNN\n",
    "knn = KNN()\n",
    "knn.fit(X_train)\n",
    "# If you want to see the predictions of the training data, you can use this way:\n",
    "y_train_scores = knn.decision_scores_"
   ]
  },
  {
   "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: 0.7566127656515499\n"
     ]
    }
   ],
   "source": [
    "from pyod.models.knn import KNN\n",
    "knn = KNN(contamination=0.05) \n",
    "knn.fit(X_train)\n",
    "\n",
    "# Training data\n",
    "y_train_scores = knn.decision_function(X_train)\n",
    "y_train_pred = knn.predict(X_train)\n",
    "\n",
    "# Test data\n",
    "y_test_scores = knn.decision_function(X_test)\n",
    "y_test_pred = knn.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:\" , knn.threshold_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba21f02c-d927-4d4d-8351-d2ebea70b6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'contamination': 0.05,\n",
       " 'leaf_size': 30,\n",
       " 'method': 'largest',\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': 1,\n",
       " 'n_neighbors': 5,\n",
       " 'p': 2,\n",
       " 'radius': 1.0}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a9c2da-4ff5-4fe9-ab78-a3f0aba6f8d5",
   "metadata": {},
   "source": [
    "### Step 2: Determine the threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6def6357-3ff3-47c2-b4bb-1b46fb26a2b0",
   "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('KNN outlier score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d1667a7-720c-45fc-9602-03382e182a1d",
   "metadata": {},
   "source": [
    "### Step 3: Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa2b7a05-2dd5-4a0f-a954-0475a1313332",
   "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>0.32</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>2.84</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           0.32\n",
       "1  Outlier     25      5.0  0.45 -0.21 -0.47 -0.23 -0.03 -0.06           2.84"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = knn.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": 7,
   "id": "dfed1dba-cbae-4e63-bd9e-f7a2e6754010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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": 7,
     "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": "2c043832-6926-4492-b4df-442cda1a1477",
   "metadata": {},
   "source": [
    "### Averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9829a99a-67d2-44b3-8fe0-18b17a91d32f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyod.models.combination import aom, moa, average, maximization\n",
    "from pyod.utils.utility import standardizer\n",
    "# Standardize data\n",
    "X_train_norm, X_test_norm = standardizer(X_train, X_test)\n",
    "# Test a range of k-neighbors from 10 to 200. There will be 20 k-NN models.\n",
    "k_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, \n",
    " 120, 130, 140, 150, 160, 170, 180, 190, 200]\n",
    "n_clf = len(k_list)\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",
    "    clf = KNN(n_neighbors=k, method='largest')\n",
    "    clf.fit(X_train_norm)\n",
    "\n",
    "    # Store the results in each column:\n",
    "    train_scores[:, i] = clf.decision_scores_\n",
    "    test_scores[:, i] = clf.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": 16,
   "id": "a5342dc2-fbd4-4b8f-b2df-2038b3cba28b",
   "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 10. The \"average\" function will take the average of the 10 columns. \n",
    "# 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": 17,
   "id": "39343665-6537-4742-a99e-29a851084a6b",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_train,y_train_by_average, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9002ca36-a274-4d52-b579-6c46ec1bdcff",
   "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>-0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outlier</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</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>4.31</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          -0.23\n",
       "1  Outlier     25      5.0  0.00 -0.54 -0.23 -0.05 -0.06  0.12           4.31"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_test,y_test_by_average, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2915de00-e5ad-4940-90eb-04a5b6b8d31c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63626c8d-579d-4b81-9ea5-396a51f5ed66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e351c306-0416-4119-b6f7-1757839fd404",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46afa07b-6743-4336-bcb5-334d76edd8ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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