04.iForest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f12ec631-0d57-4a6d-a0ec-7940abac6968",
   "metadata": {},
   "source": [
    "## 4. Isolation Forest (iForest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9021294f-5f48-4f53-8b36-773852531a1f",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.396090</td>\n",
       "      <td>2.092611</td>\n",
       "      <td>2.073392</td>\n",
       "      <td>1.988262</td>\n",
       "      <td>1.953473</td>\n",
       "      <td>2.450997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.631040</td>\n",
       "      <td>1.746182</td>\n",
       "      <td>1.898050</td>\n",
       "      <td>2.380148</td>\n",
       "      <td>1.967332</td>\n",
       "      <td>1.858916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.824683</td>\n",
       "      <td>2.131412</td>\n",
       "      <td>2.028829</td>\n",
       "      <td>1.703454</td>\n",
       "      <td>2.502966</td>\n",
       "      <td>2.119108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.106098</td>\n",
       "      <td>2.165173</td>\n",
       "      <td>2.340826</td>\n",
       "      <td>2.170109</td>\n",
       "      <td>1.749139</td>\n",
       "      <td>1.678661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.829647</td>\n",
       "      <td>1.775596</td>\n",
       "      <td>1.829438</td>\n",
       "      <td>2.054768</td>\n",
       "      <td>1.577190</td>\n",
       "      <td>1.594549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5\n",
       "0  2.396090  2.092611  2.073392  1.988262  1.953473  2.450997\n",
       "1  1.631040  1.746182  1.898050  2.380148  1.967332  1.858916\n",
       "2  1.824683  2.131412  2.028829  1.703454  2.502966  2.119108\n",
       "3  2.106098  2.165173  2.340826  2.170109  1.749139  1.678661\n",
       "4  1.829647  1.775596  1.829438  2.054768  1.577190  1.594549"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "X_train_pd = pd.DataFrame(X_train)\n",
    "X_train_pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05bcc7a0-bafc-425e-af0a-e48a584cbf24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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": "8453e735-4c51-4ab3-93e0-a1b83fd15a42",
   "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 threshold for the defined contamination rate: -3.986394547794703e-15\n",
      "The training data: {0: 475, 1: 25}\n",
      "The training data: {0: 470, 1: 30}\n"
     ]
    }
   ],
   "source": [
    "from pyod.models.iforest import IForest\n",
    "isft = IForest(contamination=0.05, max_samples=40, behaviour='new') \n",
    "isft.fit(X_train)\n",
    "\n",
    "# Training data\n",
    "y_train_scores = isft.decision_function(X_train)\n",
    "y_train_pred = isft.predict(X_train)\n",
    "\n",
    "# Test data\n",
    "y_test_scores = isft.decision_function(X_test)\n",
    "y_test_pred = isft.predict(X_test) # outlier labels (0 or 1)\n",
    "\n",
    "# Threshold for the defined comtanimation rate\n",
    "print(\"The threshold for the defined contamination rate:\" , isft.threshold_)\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a150d79-9337-4890-ae82-bb312d166a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'behaviour': 'new',\n",
       " 'bootstrap': False,\n",
       " 'contamination': 0.05,\n",
       " 'max_features': 1.0,\n",
       " 'max_samples': 40,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': 1,\n",
       " 'random_state': None,\n",
       " 'verbose': 0}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isft.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "697875b2-d0a3-4d0d-827d-2bddbaa76700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17325335, 0.13573292, 0.17200832, 0.17157248, 0.17091259,\n",
       "       0.17652033])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isft_vi = isft.feature_importances_\n",
    "isft_vi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35eacbf7-7440-4168-8d8b-ffa2c55b2a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "from matplotlib import pyplot as plt\n",
    "for_plot = pd.DataFrame({'x_axis':X_train_pd.columns, 'y_axis':isft_vi}).sort_values(by='y_axis',ascending=True)\n",
    "for_plot['y_axis'].plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1943f6f-327d-497b-9a4a-5ef69864ddc1",
   "metadata": {},
   "source": [
    "### Step 2: Determine the threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9015e76e-d382-4bfb-867f-96eccef1d5c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEICAYAAAC+iFRkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATF0lEQVR4nO3dfbCmd13f8ffHQEQimF1ydrM8JCs1gxNbCXgKKB2muMTJA3W3zsSBUVw0dclMdWQGahdsZ2SssjBtFS1F1xA8goIRSbOF8rDdFm0HTDmLAYIbXGCWJGbdPeTBiDgI9Ns/7iv0zHLOnut+PPfu7/2aOXM9/q7re64989nf/buv+7pTVUiSzm/fstkFSJKmz7CXpAYY9pLUAMNekhpg2EtSAwx7SWqAYa/zQpKdSSrJY7rl9yfZu9l1SfPCsNemSPLyJJ9K8uUkf5XkLUkuHqL9iSQvWm97VV1bVUsTKVY6Dxj2mrkkrwLeAPwr4DuA5wGXA4eTXLjJtT2mhXOqPYa9ZirJE4HXAT9bVR+oqq9W1QngRxkE/o93+/1Okn+3qt0/TXJfN/924DLgvyb5UpKfX+M8H07yL1Yt/1SSY0keSvLBJJev2lZJ/mWS48DxNY71uCTvSPJAkoeTfCzJ9m7b1iRvS3J/d+z/sqrdTyf5bJIHkxxK8uSznTPJi5Pc2Z3jI0m+d6SLLK3BsNes/QDwOOA9q1dW1ZeA9wNXb3SAqnoZcA/wz6rq26vqjWfbP8ke4LXAjwALwP8C3nnGbnuA5wJXrnGIvQxegTwNeBJwE/B33ba3A48HvgfYBvxqd84fBF7P4D+xHcAXgHetd84kzwZuAV7RneO3gENJvvVsv5vUl2GvWbsE+GJVfW2NbSe77ZP2CuD1VXWsO++vAFet7t132x+sqr9bo/1XGQTwd1XV16vqaFU9kmQHcC1wU1U91L1K+eOuzY8Bt1TVx6vqK8BrgO9PsnOdc/408FtVdUd3jiXgKwyGuKSxGfaatS8Cl6wzTr2j2z5plwNv6oZHHgYeBAI8ZdU+956l/duBDwLv6oZr3pjksQx6+g9W1UNrtHkyg9488I1XLg+c5ZyXA696tMauzqd1x5HGZthr1j7KoMf6I6tXJrmIQS/5SLfqbxkMjzzq0jOOM8zjWu8FXlFVF6/6+baq+kif43U99tdV1ZUMhqFeDPxEd9yt69xFdD+DAAe+8fs9CfjLdc55L/DLZ9T4+Ko6c7hJGolhr5mqqr9m8AbtbyS5Jslju6GNPwTuY9CLBrgTuK57A/RS4JVnHOoU8PSep/1N4DVJvgcgyXckuaFvzUlemOQfJbkAeITBsM7Xq+okg/cZ/nOSLd3v8oKu2e8DP5nkqm7c/VeAO7o3o9fy28BNSZ6bgYuSXJ/kCX3rlM7GsNfMdW+ovhb49wzC8w4GPdtd3fg2DEL/E8AJ4EPAH5xxmNcD/6Yb8nj1Bue7jcGtnu9K8ghwF4NXEX1dCry7q/UY8MfAO7ptL2MQ/ncDp+n+U6qqI8C/Bf6IwXsR/wB4yVlqXGYwbv+fgIeAzwIvH6JG6azil5dI0vnPnr0kNcCwl6QGGPaS1ADDXpIaMNMHMF1yySW1c+fOWZ5Sks55R48e/WJVLYxzjJmG/c6dO1leXp7lKSXpnJfkCxvvdXYO40hSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgNm+gna883O/e/bcJ8TB66fQSWSdHb27CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQEbhn2SZyS5c9XPI0lemWRrksNJjnfTLbMoWJI0vA3Dvqo+U1VXVdVVwPcBXwZuA/YDR6rqCuBItyxJmkPDDuPsAj5XVV8AdgNL3folYM8E65IkTdCwYf8S4J3d/PaqOgnQTbdNsjBJ0uT0DvskFwI/DPzhMCdIsi/JcpLllZWVYeuTJE3AMD37a4GPV9WpbvlUkh0A3fT0Wo2q6mBVLVbV4sLCwnjVSpJGMkzYv5T/P4QDcAjY283vBW6fVFGSpMnqFfZJHg9cDbxn1eoDwNVJjnfbDky+PEnSJPT6pqqq+jLwpDPWPcDg7hxJ0pzzE7SS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQG9HnGs+bBz//s23OfEgetnUImkc409e0lqgGEvSQ0w7CWpAYa9JDWg7xeOX5zk3UnuTnIsyfcn2ZrkcJLj3XTLtIuVJI2mb8/+TcAHquq7gWcCx4D9wJGqugI40i1LkubQhmGf5InAC4C3AlTV31fVw8BuYKnbbQnYM50SJUnj6tOzfzqwArwtyZ8luTnJRcD2qjoJ0E23rdU4yb4ky0mWV1ZWJla4JKm/PmH/GODZwFuq6lnA3zLEkE1VHayqxapaXFhYGLFMSdI4+oT9fcB9VXVHt/xuBuF/KskOgG56ejolSpLGteHjEqrqr5Lcm+QZVfUZYBfw593PXuBAN719qpXOWJ9HE0jSuaLvs3F+Fvi9JBcCnwd+ksGrgluT3AjcA9wwnRIlSePqFfZVdSewuMamXROtRpI0FX6CVpIaYNhLUgN8nv2U9X2j1+fQS5ome/aS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGuDjEuaEz8+XNE327CWpAYa9JDXAsJekBhj2ktQAw16SGtDrbpwkJ4C/Ab4OfK2qFpNsBf4A2AmcAH60qh6aTpmSpHEM07N/YVVdVVWPfvH4fuBIVV0BHOmWJUlzaJxhnN3AUje/BOwZuxpJ0lT0DfsCPpTkaJJ93brtVXUSoJtuW6thkn1JlpMsr6ysjF+xJGlofT9B+/yquj/JNuBwkrv7nqCqDgIHARYXF2uEGiVJY+rVs6+q+7vpaeA24DnAqSQ7ALrp6WkVKUkaz4Zhn+SiJE94dB74IeAu4BCwt9ttL3D7tIqUJI2nzzDOduC2JI/u//tV9YEkHwNuTXIjcA9ww/TKlCSNY8Owr6rPA89cY/0DwK5pFCVJmiw/QStJDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQG9wz7JBUn+LMl7u+WtSQ4nOd5Nt0yvTEnSOIbp2f8ccGzV8n7gSFVdARzpliVJc6hX2Cd5KnA9cPOq1buBpW5+Cdgz0cokSRPTt2f/a8DPA/931brtVXUSoJtuW6thkn1JlpMsr6ysjFOrJGlEG4Z9khcDp6vq6CgnqKqDVbVYVYsLCwujHEKSNKbH9Njn+cAPJ7kOeBzwxCTvAE4l2VFVJ5PsAE5Ps1BJ0ug27NlX1Wuq6qlVtRN4CfA/qurHgUPA3m63vcDtU6tSkjSWce6zPwBcneQ4cHW3LEmaQ32Gcb6hqj4MfLibfwDYNfmSJEmT5idoJakBhr0kNcCwl6QGDDVmf77Yuf99m12CJM2UPXtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNaDJB6Gdz/o85O3EgetnUImkeWLPXpIaYNhLUgM2DPskj0vyf5J8Ismnk7yuW781yeEkx7vplumXK0kaRZ+e/VeAH6yqZwJXAdckeR6wHzhSVVcAR7plSdIc2jDsa+BL3eJju58CdgNL3folYM80CpQkja/X3ThJLgCOAt8FvLmq7kiyvapOAlTVySTb1mm7D9gHcNlll02m6nX4dYOStLZeb9BW1der6irgqcBzkvzDvieoqoNVtVhViwsLCyOWKUkax1B341TVw8CHgWuAU0l2AHTT05MuTpI0GX3uxllIcnE3/23Ai4C7gUPA3m63vcDtU6pRkjSmPmP2O4Clbtz+W4Bbq+q9ST4K3JrkRuAe4IYp1ilJGsOGYV9VnwSetcb6B4Bd0yhKkjRZfoJWkhpg2EtSAwx7SWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqgGEvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1ID+nzh+NOS/M8kx5J8OsnPdeu3Jjmc5Hg33TL9ciVJo+jzheNfA15VVR9P8gTgaJLDwMuBI1V1IMl+YD/wr6dXqiZl5/73bbjPiQPXz6ASSbOyYc++qk5W1ce7+b8BjgFPAXYDS91uS8CeKdUoSRrTUGP2SXYCzwLuALZX1UkY/IcAbJt4dZKkiegd9km+Hfgj4JVV9cgQ7fYlWU6yvLKyMkqNkqQx9Qr7JI9lEPS/V1Xv6VafSrKj274DOL1W26o6WFWLVbW4sLAwiZolSUPqczdOgLcCx6rqP67adAjY283vBW6ffHmSpEnoczfO84GXAZ9Kcme37rXAAeDWJDcC9wA3TKVCSdLYNgz7qvrfQNbZvGuy5UiSpqFPz14N6nMvPng/vnSu8HEJktQAw16SGmDYS1IDDHtJaoBhL0kNMOwlqQGGvSQ1wLCXpAYY9pLUAMNekhpwzjwuoe/H9yVJ38yevSQ1wLCXpAYY9pLUAMNekhpg2EtSAwx7SWqAYS9JDdgw7JPckuR0krtWrdua5HCS4910y3TLlCSNo0/P/neAa85Ytx84UlVXAEe6ZUnSnNow7KvqT4AHz1i9G1jq5peAPZMtS5I0SaOO2W+vqpMA3XTbejsm2ZdkOcnyysrKiKeTJI1j6m/QVtXBqlqsqsWFhYVpn06StIZRw/5Ukh0A3fT05EqSJE3aqE+9PATsBQ5009snVpHOKX2eRnriwPUzqETS2fS59fKdwEeBZyS5L8mNDEL+6iTHgau7ZUnSnNqwZ19VL11n064J1yJJmhI/QStJDTDsJakBhr0kNcCwl6QGGPaS1ADDXpIaYNhLUgMMe0lqwKiPS5B685EKmrVJ/c2dT3+79uwlqQGGvSQ1wGEcSeeMPsMqWps9e0lqgGEvSQ1wGEfS1M3jXS2tDQnZs5ekBtiz11yYx56fZqu1nvas2bOXpAYY9pLUgLGGcZJcA7wJuAC4uar84nFpiuZxuKv14Ze+v/9mD0OO3LNPcgHwZuBa4ErgpUmunFRhkqTJGWcY5znAZ6vq81X198C7gN2TKUuSNEnjDOM8Bbh31fJ9wHPP3CnJPmBft/ilJJ8Z45zjuAT44iad+2ysq6e8AZjDujpzU1d3nR41N3WtMo81wZTrOuPfZRiXAJePe/5xwj5rrKtvWlF1EDg4xnkmIslyVS1udh1nsq7hWNdw5rGueawJ5r6uneMeZ5xhnPuAp61afipw/3jlSJKmYZyw/xhwRZLvTHIh8BLg0GTKkiRN0sjDOFX1tSQ/A3yQwa2Xt1TVpydW2eRt+lDSOqxrONY1nHmsax5rgvO8rlR90zC7JOk84ydoJakBhr0kNeC8CvskW5McTnK8m25ZZ79bkpxOctcZ638xyV8mubP7uW5O6urVfop1XZPkM0k+m2T/qvUTu17rnWPV9iT59W77J5M8u2/bcYxZ14kkn+quzfKM6/ruJB9N8pUkrx6m7SbWtZnX68e6f79PJvlIkmf2bbuJdQ13varqvPkB3gjs7+b3A29YZ78XAM8G7jpj/S8Cr57Dunq1n0ZdDN58/xzwdOBC4BPAlZO8Xmc7x6p9rgPez+DzHc8D7ujbdjPq6radAC6Zwt9Tn7q2Af8Y+OXV/0ZzcL3WrGsOrtcPAFu6+Wvn6O9rzbpGuV7nVc+eweMalrr5JWDPWjtV1Z8AD86oJhi/rl7tp1TXLB6L0eccu4HfrYE/BS5OsmPK9Y1T1zRtWFdVna6qjwFfHbbtJtU1TX3q+khVPdQt/imDzw31artJdQ3tfAv77VV1EqCbbhvhGD/TvWS6ZVLDJROoaxK/16jHXeuxGE9ZtTyJ67XROc62T5+2oxqnLhh8ovxDSY5m8NiQSRnnd97s63U283K9bmTwam2UtrOqC4a8XufcN1Ul+e/ApWts+oUJHP4twC8xuIi/BPwH4KfmoK6RTaCusz0WY+TrNcQ5Ntqn12M7RjROXQDPr6r7k2wDDie5u3v1Nou6ptF22sfe9OuV5IUMQvWfDNt2xnXBkNfrnAv7qnrRetuSnEqyo6pOdi+lTw957FOrjvXbwHvnoS5g5PYTqGvdx2KMc736nqPHPhf2aDuqceqiqh6dnk5yG4OX7ZMIr3EeVTLNx5yMdezNvl5Jvhe4Gbi2qh4Ypu0m1DX09TrfhnEOAXu7+b3A7cM0PmOs9Z8Dd6237yzrmkD7cY677mMxJni9+jx64xDwE93dL88D/robeprmYztGrivJRUmeAJDkIuCHmNzf0zi/82ZfrzVt9vVKchnwHuBlVfUXw7TdjLpGul6TeFd5Xn6AJwFHgOPddGu3/snAf1u13zuBkwzeJLoPuLFb/3bgU8Anu4u+Y07qWrP9DOu6DvgLBncO/MKq9RO7XmudA7gJuKmbD4Mvy/lcd87Fjeqb0DUaqS4Gd1h8ovv59CbUdWn3N/QI8HA3/8Q5uF5r1jUH1+tm4CHgzu5neU7+vtasa5Tr5eMSJKkB59swjiRpDYa9JDXAsJekBhj2ktQAw16SGmDYS1IDDHtJasD/A+yQd2/2FhJ2AAAAAElFTkSuQmCC\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(\"Outlier score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33284af5-5a22-465a-ad83-78bb81e3cd9e",
   "metadata": {},
   "source": [
    "### Step 3: Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82b6f4a1-7215-4874-a32a-450a70ff1af6",
   "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>-0.10</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>0.18</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          -0.10\n",
       "1  Outlier     25      5.0  0.45 -0.21 -0.47 -0.23 -0.03 -0.06           0.18"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = isft.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": 9,
   "id": "b9a42470-3753-4067-a35d-9832310d5379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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></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>470</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outlier</td>\n",
       "      <td>30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</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    470     94.0  2.02  2.02  2.01  1.99  2.01  2.00          -0.10\n",
       "1  Outlier     30      6.0  0.35 -0.14  0.19  0.26  0.30  0.45           0.15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_test,y_test_scores, threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bf7362d-5fd3-4151-a61e-b148a7607c98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def confusion_matrix(actual,score, threshold):\n",
    "    Actual_pred = pd.DataFrame({'Actual': actual, 'Pred': score})\n",
    "    Actual_pred['Pred'] = np.where(Actual_pred['Pred']<=threshold,0,1)\n",
    "    cm = pd.crosstab(Actual_pred['Actual'],Actual_pred['Pred'])\n",
    "    return (cm)\n",
    "confusion_matrix(y_train,y_train_scores,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de5405d9-690b-43ad-a4a1-6c32156ac9b4",
   "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>470</td>\n",
       "      <td>5</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     470   5\n",
       "1.0       0  25"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_test_scores,threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc91fcbc-aa09-4c08-b0c7-d77310ccc904",
   "metadata": {},
   "source": [
    "### Step 4: Averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73d2bb66-b6ef-44fc-9c9f-0dbfb4b484a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyod.models.combination import aom, moa, average, maximization\n",
    "from pyod.utils.utility import standardizer\n",
    "from pyod.models.iforest import IForest\n",
    "\n",
    "# Standardize data\n",
    "X_train_norm, X_test_norm = standardizer(X_train, X_test)\n",
    "\n",
    "# Test a range of maximum samples \n",
    "k_list = [20, 30, 40, 50, 60]\n",
    "k_list = [100, 200, 300, 400, 500]\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",
    "\n",
    "# Modeling\n",
    "for i in range(n_clf):\n",
    "    k = k_list[i]\n",
    "    #isft = IForest(contamination=0.05, max_samples=k) \n",
    "    isft = IForest(contamination=0.05, n_estimators=k) \n",
    "    isft.fit(X_train_norm)\n",
    "    \n",
    "    # Store the results in each column:\n",
    "    train_scores[:, i] = isft.decision_function(X_train_norm) \n",
    "    test_scores[:, i] = isft.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": 18,
   "id": "2ec9923e-c4df-432e-96dd-e92e5b66497c",
   "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. 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": 19,
   "id": "37c2b2e2-ebe3-490c-8008-c9984613a60e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>-0.21</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>4.07</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          -0.21\n",
       "1  Outlier     25      5.0  0.45 -0.21 -0.47 -0.23 -0.03 -0.06           4.07"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_train,y_train_by_average, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6a23053-4d1a-4d3e-992a-416439aa5ec7",
   "metadata": {},
   "outputs": [
    {
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       "        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>472</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.01</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>28</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.33</td>\n",
       "      <td>3.82</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    472     94.4  2.01  2.01  2.01  1.99  2.01  2.00          -0.20\n",
       "1  Outlier     28      5.6  0.24 -0.26  0.02  0.17  0.17  0.33           3.82"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptive_stat_threshold(X_test,y_test_by_average, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19589ca4-8802-4014-9d14-3c378f31c39b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31f86490-1377-4b2c-8fad-21c622a33e0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a196290e-37dd-4328-a264-25b0d1b75436",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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