notebooks/random_forest_48k_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have mentioned in a previous post, random forest is very similar to bagging. As in bagging, many different trees are also grown on bootstrap samples in random forest. The difference between the two methods is the number of variable considered while doing a split in the tree. In bagging, to make a split, all predictor variables are considered and the best one is selected with a suitable value. But in random forest only a random sample of few variables are chosen for each split. If training data have $p$ features, usually $\\sqrt{p}$ feature are chosen at random at each split and out of those best one is selected. This helps in reducing correlation between trees. Due to this, usually random forest produces better results than bagging. \n",
    "\n",
    "In this post, we will use random forest to classify different bearing faults. The dataset has 8 variables corresponding to wavelet energy value in each packet. In random forest we will use 4 random features at the time of split.\n",
    "\n",
    "## Description of data\n",
    "\n",
    "[Detailed discussion of how to prepare the data and its source can be found in this post.](https://github.com/biswajitsahoo1111/cbm_codes_open/blob/master/notebooks/SVM_wavelet_energy_multiclass_cwru_python.ipynb) Here we will only mention about different classes of the data. There are 10 classes and data for each class are taken at a load of 1hp. The classes are:\n",
    "\n",
    "* C1  : Ball defect (0.007 inch)\n",
    "* C2  : Ball defect (0.014 inch)\n",
    "* C3  : Ball defect (0.021 inch)\n",
    "* C4  : Inner race fault (0.007 inch)\n",
    "* C5  : Inner race fault (0.014 inch)\n",
    "* C6  : Inner race fault (0.021 inch)\n",
    "* C7  : Normal\n",
    "* C8  : Outer race fault (0.007 inch, data collected from 6 O'clock position)\n",
    "* C9  : Outer race fault (0.014 inch, 6 O'clock)\n",
    "* C10 : Outer race fault (0.021 inch, 6 O'clock)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to get data?\n",
    "\n",
    "Readers can download the `.csv` file used in this notebook from [here](https://github.com/biswajitsahoo1111/cbm_codes_open/blob/master/notebooks/data/feature_wav_energy8_48k_2048_load_1.csv). Another convenient way is to [download the whole repository](https://codeload.github.com/biswajitsahoo1111/cbm_codes_open/legacy.zip/master) and run the downloaded notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_wav_energy = pd.read_csv(\"./data/feature_wav_energy8_48k_2048_load_1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(data_wav_energy, test_size = 750, stratify = data_wav_energy['fault'],\n",
    "                                         random_state = 132)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_clf = RandomForestClassifier(n_estimators= 150, max_features = 4, n_jobs = -1, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features=4,\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=150,\n",
       "                       n_jobs=-1, oob_score=False, random_state=123, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.fit(train_data.iloc[:,:-1], train_data['fault'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions = rf_clf.predict(train_data.iloc[:,:-1])\n",
    "test_predictions = rf_clf.predict(test_data.iloc[:,:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall train accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "overall_train_accuracy = accuracy_score(train_data['fault'], train_predictions)\n",
    "print(\"Overall train accuracy: \", overall_train_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_confu_matrix = confusion_matrix(train_data['fault'], train_predictions)\n",
    "test_confu_matrix = confusion_matrix(test_data['fault'], test_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fault_type = ['C1','C2','C3','C4','C5','C6','C7','C8','C9','C10']\n",
    "plt.figure(1,figsize=(18,8))\n",
    "plt.subplot(121)\n",
    "sns.heatmap(train_confu_matrix, annot= True,fmt = \"d\",\n",
    "xticklabels=fault_type, yticklabels=fault_type, cmap = \"Blues\", cbar = False)\n",
    "plt.title('Training Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.subplot(122)\n",
    "sns.heatmap(train_confu_matrix/155, annot= True,\n",
    "xticklabels=fault_type, yticklabels=fault_type, cmap = \"Blues\", cbar = False)\n",
    "plt.title('Training Confusion Matrix (in %age)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(2,figsize=(18,8))\n",
    "plt.subplot(121)\n",
    "sns.heatmap(test_confu_matrix, annot = True,\n",
    "xticklabels=fault_type, yticklabels=fault_type, cmap = \"Blues\", cbar = False)\n",
    "plt.title('Test Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.subplot(122)\n",
    "sns.heatmap(test_confu_matrix/75, annot = True,\n",
    "xticklabels=fault_type, yticklabels=fault_type, cmap = \"Blues\", cbar = False)\n",
    "plt.title('Test Confusion Matrix (in %age)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall test accuracy:  0.9813333333333333\n"
     ]
    }
   ],
   "source": [
    "overall_test_accuracy = accuracy_score(test_data['fault'], test_predictions)\n",
    "print(\"Overall test accuracy: \", overall_test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this post, we have taken `max_features = 4`. Readers can try different values and select the best one.\n",
    "\n",
    "[To see results of other techniques applied to public condition monitoring datasets, visit this page](https://biswajitsahoo1111.github.io/cbm_codes_open/)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow_gpu_master",
   "language": "python",
   "name": "tensorflow_gpu_master"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}