ad_features_isolation_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target 1 shape: (5049, 25)\n",
      "Target 0 shape: (4951, 25)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Read and standarize the dataset\n",
    "df = pd.read_csv('data/selected_data1.csv')\n",
    "#df = pd.read_csv('Z:\\\\Descargas\\\\resampled_ts_group_scalled.csv')\n",
    "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]\n",
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "df = pd.DataFrame(X)\n",
    "df['target'] = y\n",
    "\n",
    "# Divide dataframe depending on target\n",
    "df_correct = df.loc[df['target'] == 1]\n",
    "df_incorrect = df.loc[df['target'] == 0]\n",
    "\n",
    "# Print each target shape\n",
    "print('Target 1 shape: ' + str(df_correct.shape))\n",
    "print('Target 0 shape: ' + str(df_incorrect.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test_correct = train_test_split(df_correct, test_size=0.2, random_state=0)\n",
    "df_test_incorrect = df_incorrect\n",
    "\n",
    "X_train = df_train.iloc[:,:-1]\n",
    "X_test_correct = df_test_correct.iloc[:,:-1]\n",
    "X_test_incorrect = df_test_incorrect.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "cols = X.columns.values\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test_correct = scaler.transform(X_test_correct)\n",
    "X_test_incorrect = scaler.transform(X_test_incorrect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=0.97).fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test_correct = pca.transform(X_test_correct)\n",
    "X_test_incorrect = pca.transform(X_test_incorrect)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### isolation forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "from sklearn.ensemble import IsolationForest\n",
    "\n",
    "clf = IsolationForest(contamination=0.2, random_state=0, n_jobs=-1).fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 3231, -1: 808})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(clf.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 805, -1: 205})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(clf.predict(X_test_correct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({-1: 3181, 1: 1770})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(clf.predict(X_test_incorrect))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### skop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_skopt = X_test_incorrect\n",
    "Y_skopt = df_test_incorrect.iloc[:,-1].replace(0, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skopt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from skopt.utils import use_named_args\n",
    "\n",
    "\n",
    "# The list of hyper-parameters we want to optimize. For each one we define the\n",
    "# bounds, the corresponding scikit-learn parameter name, as well as how to\n",
    "# sample values from that dimension (`'log-uniform'` for the learning rate)\n",
    "space  = [\n",
    "    skopt.space.Real(0.1, 0.9, name='contamination')\n",
    "]\n",
    "\n",
    "# this decorator allows your objective function to receive a the parameters as\n",
    "# keyword arguments. This is particularly convenient when you want to set\n",
    "# scikit-learn estimator parameters\n",
    "@use_named_args(space)\n",
    "def objective(**params):\n",
    "    clf.set_params(**params)\n",
    "    return -accuracy_score(Y_skopt, clf.predict(X_skopt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n",
      "Z:\\Programacion\\Anaconda\\envs\\AI\\lib\\site-packages\\skopt\\optimizer\\optimizer.py:409: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    }
   ],
   "source": [
    "from skopt import gp_minimize\n",
    "\n",
    "res_gp = gp_minimize(objective, space, n_calls=50, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Best score=-0.6425'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Best score=%.4f\" % res_gp.fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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