notebooks/boosting_48k_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boosting is slow learning procedure. In boosting, a tree is not allowed to grow fully. Rather, the depth is fixed to a small number. In some cases, a tree is build with only one branch and two nodes. But subsequent trees are built on top of it. Further, contributions from subsequent trees are also shrinked and added to previously built tree. Though an individual tree might preform badly for the classification, combination of many trees perform surprisingly well. For more details regarding the algorithm, readers can refer to [this excellent book](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf). \n",
    "\n",
    "In this post we will apply boosting to classify multiclass bearing fault. \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)"
   ]
  },
  {
   "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 GradientBoostingClassifier\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": [
    "boosting_clf = GradientBoostingClassifier(n_estimators= 200, max_depth= 2, random_state = 321)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=2,\n",
       "                           max_features=None, max_leaf_nodes=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=200,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=321, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boosting_clf.fit(train_data.iloc[:,:-1], train_data['fault'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictions = boosting_clf.predict(train_data.iloc[:,:-1])\n",
    "test_predictions = boosting_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:  0.9906666666666667\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": [
    "[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
}