Chapter4-3 Naive_Bayes_CWRU_Fault_Diagnosis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b9cc898",
   "metadata": {},
   "source": [
    "# CWRU 轴承故障诊断数据集\n",
    "## 振动信号\n",
    "## 正常工况、内圈故障、外圈故障、滚动体故障\n",
    "- 训练集 测试集：400x4 8:2\n",
    "- 样本大小：1x1024x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdf9137b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2f47718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1600, 1024)\n"
     ]
    }
   ],
   "source": [
    "data = np.loadtxt(\"./fault_data/CSWU_4Class.txt\")\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0b8f7ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1280, 1024) (320, 1024)\n",
      "(1280,) (320,)\n"
     ]
    }
   ],
   "source": [
    "# create training set and testing set\n",
    "train_data = np.vstack((data[0: 320, :], data[400: 720, :], data[800: 1120, :], data[1200: 1520, :]))\n",
    "test_data = np.vstack((data[320: 400, :], data[720: 800, :], data[1120: 1200, :], data[1520: 1600, :]))\n",
    "\n",
    "print(train_data.shape, test_data.shape)\n",
    "\n",
    "# make label\n",
    "train_label = np.vstack((np.zeros((320, 1), dtype = int), np.full((320, 1), 1), np.full((320, 1), 2), np.full((320, 1), 3)))\n",
    "test_label = np.vstack((np.zeros((80, 1), dtype = int), np.full((80, 1), 1), np.full((80, 1), 2), np.full((80, 1), 3)))\n",
    "\n",
    "train_label = train_label.reshape(-1)\n",
    "test_label = test_label.reshape(-1)\n",
    "\n",
    "print(train_label.shape, test_label.shape)\n",
    "\n",
    "# 打乱样本\n",
    "train_data, train_label = sklearn.utils.shuffle(train_data, train_label, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5539f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = train_label.tolist()\n",
    "test_label = test_label.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0af7c8",
   "metadata": {},
   "source": [
    "# Naive Bayes scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4fe1f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4f3e489b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "clf.fit(train_data, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d3366283",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "# 平均准确率\n",
    "acc = clf.score(test_data, test_label)\n",
    "\n",
    "# 预测标签\n",
    "y_pred = clf.predict(test_data)\n",
    "\n",
    "\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f311a5d1",
   "metadata": {},
   "source": [
    "# Confusion Matrix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "50b573b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "# import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3dc42d4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(true_label, pred_label, class_name):\n",
    "    y_true = true_label\n",
    "    y_pred = pred_label\n",
    "    confusion_mat = confusion_matrix(y_true, y_pred)\n",
    "    print(confusion_mat)\n",
    "    \n",
    "    plt.rcParams['xtick.direction'] = 'in'\n",
    "    plt.rcParams['ytick.direction'] = 'in'\n",
    "    \n",
    "    plt.figure(figsize=(4, 3), dpi = 300)\n",
    "    plt.imshow(confusion_mat, cmap = plt.cm.get_cmap(\"Blues\"))\n",
    "    plt.colorbar()\n",
    "    \n",
    "    indices = range(len(confusion_mat))\n",
    "    \n",
    "    plt.xticks(indices, class_name, rotation = 0)\n",
    "    plt.yticks(indices, class_name)\n",
    "    \n",
    "    plt.title(\"confusion matrix\", fontdict={'fontsize': 12})\n",
    "    plt.xlabel(\"Predcited label\", fontdict={'fontsize': 12})\n",
    "    plt.ylabel(\"True label\", fontdict= {'fontsize': 12})\n",
    "    \n",
    "    for first_index in range(len(confusion_mat)):\n",
    "        for second_index in range(len(confusion_mat[first_index])):\n",
    "            if first_index == second_index:\n",
    "                plt.text(first_index, second_index, confusion_mat[first_index][second_index],\n",
    "                        color = 'white', fontsize = 12, va = 'center', ha = 'center')\n",
    "            else:\n",
    "                plt.text(first_index, second_index, confusion_mat[first_index][second_index],\n",
    "                        color = 'black', fontsize = 12, va = 'center', ha = 'center')\n",
    "                \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bdb3388c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[80  0  0  0]\n",
      " [ 0 80  0  0]\n",
      " [ 0  0 80  0]\n",
      " [ 0  0  0 80]]\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_true = test_label\n",
    "y_pre = y_pred\n",
    "\n",
    "class_name = [\"Health\", \"Inner\", \"Outer\", \"Ball\"]\n",
    "plot_confusion_matrix(y_true, y_pre, class_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec8930c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}