transformer fault with down sampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # pandas is used to load and manipulate data and for one hot encoding\n",
    "import numpy as np # data manipulation\n",
    "import matplotlib.pyplot as plt # matplotlib is for drawing graphs\n",
    "import matplotlib.colors as colors \n",
    "from sklearn.utils import resample # downsample the dataset\n",
    "from sklearn.model_selection import train_test_split # split data into training and testing sets\n",
    "from sklearn.preprocessing import scale # scale and center data\n",
    "from sklearn.svm import SVC # this will make support vector machines for classification\n",
    "from sklearn.model_selection import GridSearchCV # this will do cross validation\n",
    "from sklearn.metrics import confusion_matrix # this will create confusion matrix\n",
    "from sklearn.metrics import plot_confusion_matrix # draws a confusion matrix\n",
    "from sklearn.decomposition import PCA # to perform PCA to plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Overview.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DeviceTimeStamp</th>\n",
       "      <th>OTI</th>\n",
       "      <th>WTI</th>\n",
       "      <th>ATI</th>\n",
       "      <th>OLI</th>\n",
       "      <th>OTI_A</th>\n",
       "      <th>OTI_T</th>\n",
       "      <th>MOG_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-25T13:06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-25T13:09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-27T10:49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-27T10:51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-27T10:52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DeviceTimeStamp  OTI  WTI  ATI  OLI  OTI_A  OTI_T  MOG_A\n",
       "0  2019-06-25T13:06    0    0    0   37      0      0      0\n",
       "1  2019-06-25T13:09    0    0    0   37      0      0      0\n",
       "2  2019-06-27T10:49    0    0    0   38      0      0      0\n",
       "3  2019-06-27T10:51    0    0    0   38      0      0      0\n",
       "4  2019-06-27T10:52    0    0    0   39      0      0      0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>OTI</th>\n",
       "      <th>WTI</th>\n",
       "      <th>ATI</th>\n",
       "      <th>OLI</th>\n",
       "      <th>OTI_A</th>\n",
       "      <th>OTI_T</th>\n",
       "      <th>MOG_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   OTI  WTI  ATI  OLI  OTI_A  OTI_T  MOG_A\n",
       "0    0    0    0   37      0      0      0\n",
       "1    0    0    0   37      0      0      0\n",
       "2    0    0    0   38      0      0      0\n",
       "3    0    0    0   38      0      0      0\n",
       "4    0    0    0   39      0      0      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('DeviceTimeStamp', axis=1, inplace = True) ## set axis = 0 to remove rows, axis = 1 to remove colums\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OTI      int64\n",
       "WTI      int64\n",
       "ATI      int64\n",
       "OLI      int64\n",
       "OTI_A    int64\n",
       "OTI_T    int64\n",
       "MOG_A    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  34,  35,  13,  12,  11,   9,  33,  32,  30,  31,  29,  28,\n",
       "        27,  42,  43,  41,  39,  38,  37,  40,  26,  25,  22,  23,  24,\n",
       "        21,  20,  36,  44,  45,  47, 245, 246,  49,  48,  46,  50,  51,\n",
       "        52,  54, 238, 239, 243, 244, 242, 241, 247, 250, 249, 248,  53,\n",
       "       236,  19,  18,  16,  17,  15,  14], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OTI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['WTI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 34, 35, 33, 32, 29, 30, 28, 31, 26, 25, 24, 27, 23, 36, 37, 38,\n",
       "       40, 41, 42, 39, 43, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 44],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ATI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37,  38,  39,  36,  40,  41,  42,  43,  44,  45,  46,  47,  48,\n",
       "        49,  50,  58,  57,  56,  53,  55,  54,  51,  52,  59,  60,  61,\n",
       "        62,  63,  64,  65,  66,  67,  68,  69,  70,  71,  72,  73,  80,\n",
       "        79,  83,  82,  81,  84,  85,  86,  74,  75,  77,  78,  76,  87,\n",
       "        88,  89,  91,  94,  90,  92,  93,  95,  96,  97,  98,  99, 100],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OLI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20316"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_missing = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20316"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_no_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_no_fault = df_no_missing[df_no_missing['OTI_T'] == 0]\n",
    "df_fault = df_no_missing[df_no_missing['OTI_T'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_fault_downsampled = resample(df_no_fault,\n",
    "                                    replace = False,\n",
    "                                    n_samples=1000,\n",
    "                                    random_state=42)\n",
    "len(df_no_fault_downsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['OTI_T'] == 1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fault_downsampled = resample(df_fault,\n",
    "                                    replace = False,\n",
    "                                    n_samples=47,\n",
    "                                    random_state=42)\n",
    "len(df_fault_downsampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1047"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_downsample = pd.concat([df_no_fault_downsampled, df_fault_downsampled])\n",
    "len(df_downsample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OTI</th>\n",
       "      <th>WTI</th>\n",
       "      <th>ATI</th>\n",
       "      <th>OLI</th>\n",
       "      <th>OTI_A</th>\n",
       "      <th>MOG_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15921</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11994</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18293</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5058</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11863</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OTI  WTI  ATI  OLI  OTI_A  MOG_A\n",
       "15921   25    1   22  100      0      0\n",
       "11994   32    0   36   76      0      0\n",
       "18293   40    1   35  100      0      0\n",
       "5058    34    0   27   41      0      0\n",
       "11863   23    0   22   96      0      0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_downsample.drop('OTI_T', axis=1).copy() # alternatively: X = df_no_missing.iloc[:,:-1].copy\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15921    0\n",
       "11994    0\n",
       "18293    0\n",
       "5058     0\n",
       "11863    0\n",
       "Name: OTI_T, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_downsample['OTI_T'].copy()\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# centering and scalling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# building a preliminary support vector machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_svm = SVC(random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "y_pred = clf_svm.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x218cb82ac70>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf_svm,\n",
    "                     X_test_scaled,\n",
    "                     y_test,\n",
    "                     values_format='d',\n",
    "                     display_labels=[\"no fault\", \"fault\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# precision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision score for SVM rbf kernel: 100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "print(\"precision score for SVM rbf kernel:\", 100*precision_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall for SVM rbf kernels: 100.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "print('Recall for SVM rbf kernels:', 100 * recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# F1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print('F1 score:', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# building, evaluating, drawing, interpreting the final svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, gamma=0.001, random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm = SVC(random_state=42, C = 1, gamma = 0.001)\n",
    "clf_svm.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2130ba90700>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(clf_svm,\n",
    "                     X_test_scaled,\n",
    "                     y_test,\n",
    "                     values_format='d',\n",
    "                     display_labels=[\"no fault\", \"fault\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_downsample.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "pca = PCA() # by default pca centers the data by does not scale it\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)\n",
    "\n",
    "per_var = np.round(pca.explained_variance_ratio_* 100, decimals = 1)\n",
    "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
    "\n",
    "plt.bar(x = range(1, len(per_var)+1), height= per_var)\n",
    "plt.tick_params(\n",
    "axis='x',            # change apply to x axis \n",
    "which='both',        # both major and minor ticks are affected\n",
    "bottom = False,      # ticks along the bottom edges are off\n",
    "top = False,         # ticks along the top edges are off\n",
    "labelbottom = False) # labels along the bottom edges are off\n",
    "plt.ylabel('percentage of explained variance')\n",
    "plt.xlabel('principal components')\n",
    "plt.title('Scree plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}