SARIMA_multi_quarterly.ipynb

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  "metadata": {
    "colab": {
      "name": "SARIMA_multi_quarterly.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "geqvAJuoFNLe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlvRyEB52WmW"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJcpLt1G863g"
      },
      "source": [
        "data = pd.read_csv('/content/clean_quarterly_df_human.csv').dropna()\n",
        "data.time=pd.to_datetime(data.time,dayfirst=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9qGp6yKGuVm",
        "outputId": "6bd397b3-6bca-42ca-96b3-10387daeb3fb"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'MIN_TEMPERATURE_C', 'max_clients_quarter',\n",
              "       'total_Holiday_Food_Related', 'total_consommation',\n",
              "       'pdl_max_total_daily_load', 'pdl_max_daily_load'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrRDeouB866S"
      },
      "source": [
        "df_input=data[['MIN_TEMPERATURE_C', 'max_clients_quarter',\n",
        "       'total_Holiday_Food_Related', 'total_consommation',\n",
        "       'pdl_max_total_daily_load', 'pdl_max_daily_load']]\n",
        "\n",
        "train,test=df_input[:-2],df_input[-2:]\n",
        "X_train,y_train=np.array(train.drop(['pdl_max_daily_load'],axis=1)),np.array(train.pdl_max_daily_load)\n",
        "X_test,y_test=np.array(test.drop(['pdl_max_daily_load'],axis=1)),np.array(test.pdl_max_daily_load)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rv024CQR85as",
        "outputId": "6108e512-fa1f-42e7-80c8-b54f22e733eb"
      },
      "source": [
        "!pip install pmdarima"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.7/dist-packages (1.8.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.1)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.4.0)\n",
            "Requirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.29.24)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.12.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)\n",
            "Requirement already satisfied: numpy~=1.19.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 788
        },
        "id": "w_bcUwZ386N9",
        "outputId": "2f00fa35-ed3f-4bf3-d553-16dacf544b7a"
      },
      "source": [
        "from pmdarima import auto_arima\n",
        "\n",
        "stepwise_model = auto_arima(y=y_train,X=X_train,start_p=1, start_q=1,max_p=7, max_q=7,\n",
        "                            max_P=7, max_Q=7,max_order=None,m=4,stepwise=True,\n",
        "                            seasonal=True,stationary=True,suppress_warnings=True,\n",
        "                            trace=True,)\n",
        "stepwise_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(1,0,1)(1,0,1)[4] intercept   : AIC=255.774, Time=0.21 sec\n",
            " ARIMA(0,0,0)(0,0,0)[4] intercept   : AIC=247.390, Time=0.09 sec\n",
            " ARIMA(1,0,0)(1,0,0)[4] intercept   : AIC=249.474, Time=0.18 sec\n",
            " ARIMA(0,0,1)(0,0,1)[4] intercept   : AIC=249.979, Time=0.41 sec\n",
            " ARIMA(0,0,0)(0,0,0)[4]             : AIC=286.345, Time=0.13 sec\n",
            " ARIMA(0,0,0)(1,0,0)[4] intercept   : AIC=247.431, Time=0.13 sec\n",
            " ARIMA(0,0,0)(0,0,1)[4] intercept   : AIC=248.722, Time=0.16 sec\n",
            " ARIMA(0,0,0)(1,0,1)[4] intercept   : AIC=251.390, Time=0.18 sec\n",
            " ARIMA(1,0,0)(0,0,0)[4] intercept   : AIC=249.967, Time=0.23 sec\n",
            " ARIMA(0,0,1)(0,0,0)[4] intercept   : AIC=249.632, Time=0.11 sec\n",
            " ARIMA(1,0,1)(0,0,0)[4] intercept   : AIC=251.774, Time=0.27 sec\n",
            "\n",
            "Best model:  ARIMA(0,0,0)(0,0,0)[4] intercept\n",
            "Total fit time: 2.118 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>20</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                <td>SARIMAX</td>     <th>  Log Likelihood     </th> <td>-116.695</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Fri, 27 Aug 2021</td> <th>  AIC                </th>  <td>247.390</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>00:28:33</td>     <th>  BIC                </th>  <td>254.360</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>248.750</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                       <td> - 20</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>intercept</th> <td>    3.6027</td> <td>  220.351</td> <td>    0.016</td> <td> 0.987</td> <td> -428.277</td> <td>  435.482</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x1</th>        <td>   -1.6022</td> <td>   23.056</td> <td>   -0.069</td> <td> 0.945</td> <td>  -46.791</td> <td>   43.586</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x2</th>        <td>    0.0768</td> <td>    0.144</td> <td>    0.532</td> <td> 0.595</td> <td>   -0.206</td> <td>    0.360</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x3</th>        <td>    7.9383</td> <td>    3.370</td> <td>    2.356</td> <td> 0.018</td> <td>    1.334</td> <td>   14.543</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x4</th>        <td>    0.0004</td> <td>    0.000</td> <td>    0.963</td> <td> 0.336</td> <td>   -0.000</td> <td>    0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x5</th>        <td>    0.0344</td> <td>    0.023</td> <td>    1.522</td> <td> 0.128</td> <td>   -0.010</td> <td>    0.079</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>    <td> 6848.0898</td> <td>   19.932</td> <td>  343.573</td> <td> 0.000</td> <td> 6809.024</td> <td> 6887.156</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.03</td> <th>  Jarque-Bera (JB):  </th> <td>5.38</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.87</td> <th>  Prob(JB):          </th> <td>0.07</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>1.49</td> <th>  Skew:              </th> <td>-0.83</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.61</td> <th>  Kurtosis:          </th> <td>4.92</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 3.19e+17. Standard errors may be unstable."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                               SARIMAX Results                                \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                   20\n",
              "Model:                        SARIMAX   Log Likelihood                -116.695\n",
              "Date:                Fri, 27 Aug 2021   AIC                            247.390\n",
              "Time:                        00:28:33   BIC                            254.360\n",
              "Sample:                             0   HQIC                           248.750\n",
              "                                 - 20                                         \n",
              "Covariance Type:                  opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "intercept      3.6027    220.351      0.016      0.987    -428.277     435.482\n",
              "x1            -1.6022     23.056     -0.069      0.945     -46.791      43.586\n",
              "x2             0.0768      0.144      0.532      0.595      -0.206       0.360\n",
              "x3             7.9383      3.370      2.356      0.018       1.334      14.543\n",
              "x4             0.0004      0.000      0.963      0.336      -0.000       0.001\n",
              "x5             0.0344      0.023      1.522      0.128      -0.010       0.079\n",
              "sigma2      6848.0898     19.932    343.573      0.000    6809.024    6887.156\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):                 5.38\n",
              "Prob(Q):                              0.87   Prob(JB):                         0.07\n",
              "Heteroskedasticity (H):               1.49   Skew:                            -0.83\n",
              "Prob(H) (two-sided):                  0.61   Kurtosis:                         4.92\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "[2] Covariance matrix is singular or near-singular, with condition number 3.19e+17. Standard errors may be unstable.\n",
              "\"\"\""
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKUV_Vyg9b9P"
      },
      "source": [
        "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
        "  def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "  def smape(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return 2/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum((np.abs(y_true)+np.abs(y_pred)))*100\n",
        "  def mase(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    naive_pred=y_true[1:]\n",
        "    return 1/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum(np.abs(y_true[:len(y_true)-1]-naive_pred))*(y_true.shape[0]-1)\n",
        "  #def owe(y_true,y_pred):\n",
        "   # return (mase(y_true,y_pred)+smape(y_true,y_pred))/2\n",
        "\n",
        "  print('Evaluation metric results:-')\n",
        "  print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
        "  print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
        "  print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
        "  print(f'MAPE is : {mean_absolute_percentage_error(y_true, y_pred)} %')\n",
        "  #mape.append(mean_absolute_percentage_error(y_true, y_pred))\n",
        "  #rmse.append(np.sqrt(metrics.mean_squared_error(y_true, y_pred)))\n",
        "  print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')\n",
        "  print('SMAPE is : '+str(smape(y_true,y_pred))+'\\n')\n",
        "  print('MASE is : '+str(mase(y_true,y_pred)))\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XQLv8RSPooa"
      },
      "source": [
        "y_test=pd.DataFrame(data=y_test,index=np.arange(20,22),columns=['actual'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXMU0v0I9cAI",
        "outputId": "d4359d90-6efa-4dcb-fc70-2861c946681d"
      },
      "source": [
        "forecast,conf_int = stepwise_model.predict(n_periods=2, X=X_test, return_conf_int=True)\n",
        "#alpha=confidence_interval=0.05 (previous method)\n",
        "forecast = pd.DataFrame(forecast,columns=['PDL_pred'])\n",
        "df_conf = pd.DataFrame(conf_int,columns= ['Upper_bound','Lower_bound'])\n",
        "df_conf[\"new_index\"] = range(20, 22)\n",
        "df_conf = df_conf.set_index(\"new_index\")\n",
        "timeseries_evaluation_metrics_func(y_test.actual, forecast.PDL_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 2580.842075967353\n",
            "MAE is : 42.12872835443818\n",
            "RMSE is : 50.80198889775235\n",
            "MAPE is : 2.645432020437749 %\n",
            "R2 is : -5.787224394391461\n",
            "\n",
            "SMAPE is : 1.345703653517067\n",
            "\n",
            "MASE is : 1.0802238039599534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oT1rpoUj9cCr"
      },
      "source": [
        "forecast[\"new_index\"] = range(20, 22)\n",
        "forecast = forecast.set_index(\"new_index\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "EPrvKKOp9cEv",
        "outputId": "33b9e6d5-2aec-4432-f707-f66529bb2118"
      },
      "source": [
        "forecast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PDL_pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_index</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1573.738372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1528.480915</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              PDL_pred\n",
              "new_index             \n",
              "20         1573.738372\n",
              "21         1528.480915"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "9EUKZF4U_r_d",
        "outputId": "f247dc7a-abcb-4917-864d-69ca6137c893"
      },
      "source": [
        "get_ipython().run_line_magic('matplotlib', 'inline')\n",
        "plt.rcParams[\"figure.figsize\"] = [15,7]\n",
        "plt.plot(y_train, label='Train ')\n",
        "plt.plot(y_test.actual, label='Test ')\n",
        "plt.plot(forecast['PDL_pred'], label='Predicted ')\n",
        "plt.plot(df_conf['Upper_bound'], label='Confidence Interval Upper bound ')\n",
        "plt.plot(df_conf['Lower_bound'], label='Confidence Interval Lower bound ')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "YuZHCV9i_Muz",
        "outputId": "74812588-348e-4650-eb4f-df86ea391b17"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.time[-2:]\n",
        "\n",
        "plt.plot(time,y_test.actual,label='actual')\n",
        "plt.plot(time,forecast['PDL_pred'],label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iB0zI574N5jv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boWo2CvQGkkd"
      },
      "source": [
        "# peak period"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hIgVR90Glba",
        "outputId": "d0bb556b-5915-49c8-d772-60ae8aefcaca"
      },
      "source": [
        "timeseries_evaluation_metrics_func(y_test.actual[:1], forecast.PDL_pred[:1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 188.74287040112915\n",
            "MAE is : 13.738372188914127\n",
            "RMSE is : 13.738372188914127\n",
            "MAPE is : 0.8806648839047517 %\n",
            "R2 is : nan\n",
            "\n",
            "SMAPE is : 0.8768040313025802\n",
            "\n",
            "MASE is : nan\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py:582: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
            "  warnings.warn(msg, UndefinedMetricWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tC7k5ckrHzYL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}