AMD_T2L_GaussianProcess_Predictor.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AMD_T2L_GaussianProcess_Predictor.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM29PhWrg9ZPyplmy+80nm1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JRopes/CrystalEnergyPredictionWithInvariants/blob/main/AMD_T2L_GaussianProcess_Predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYazXU3nZxRB"
      },
      "source": [
        "**SETUP**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94kRXaBiZrxr",
        "outputId": "d67775c8-8fa5-4eee-9731-e4fcca2bf72d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKHxZYVLZ3g_"
      },
      "source": [
        "import sys\n",
        "sys.path.insert(0,'/content/drive/MyDrive/Colab_Notebooks/Dissertation/Prediction_Prototyping') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwYUEZHZOXCR",
        "outputId": "3eaab499-1997-4e44-b379-0d685adfb6db"
      },
      "source": [
        "!pip install ipython-autotime"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: ipython-autotime in /usr/local/lib/python3.7/dist-packages (0.3.1)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from ipython-autotime) (5.5.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (1.0.18)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.8.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (2.6.1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (0.8.1)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (5.0.5)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (4.4.2)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->ipython-autotime) (56.1.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (0.2.5)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ipython-autotime) (1.15.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->ipython-autotime) (0.7.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFAyTfLfZ6QF"
      },
      "source": [
        "**IMPORTING LIBRARY DEPENDENCIES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOQTM4jsZ-dV"
      },
      "source": [
        "import numpy as np\n",
        "import pickle\n",
        "import math\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIdm440CaB_L"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.gaussian_process import GaussianProcessRegressor\n",
        "from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RationalQuadratic, RBF, ConstantKernel, Matern, ExpSineSquared"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUV0xUk4Oc5f",
        "outputId": "b040937a-fa7b-4df7-e34f-925a1b6605d5"
      },
      "source": [
        "%load_ext autotime"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 130 µs (started: 2021-05-08 13:43:42 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRqn60z8aPvY"
      },
      "source": [
        "**IMPORTING DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEUkja7zaStV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e45970f7-9bfb-45fa-a3e3-f3b786af6f24"
      },
      "source": [
        "feature_dir_path = '/content/drive/MyDrive/Colab_Notebooks/Dissertation/Data/PublishedData/AMDs_T2_1000.csv'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.33 ms (started: 2021-05-08 13:43:42 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Va4IFR6TaW_d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea23e3cd-60b7-48e3-f7ea-0cae837b7a47"
      },
      "source": [
        "RAW_DATA = pd.read_csv(feature_dir_path)\n",
        "data = RAW_DATA.to_numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.55 s (started: 2021-05-08 13:43:42 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dk7bSMzO-ng0",
        "outputId": "77f98adf-ceac-42df-cdd0-1d8993e41671"
      },
      "source": [
        "label_dir_path = '/content/drive/MyDrive/Colab_Notebooks/Dissertation/Data/AMDs_T2_1000.csv'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.85 ms (started: 2021-05-08 13:43:46 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXoJgjkQ-njr",
        "outputId": "09acf9c0-c641-4f25-d5e8-3949983979d5"
      },
      "source": [
        "RAW_LABEL_DATA = pd.read_csv(label_dir_path)\n",
        "label_data = RAW_LABEL_DATA.to_numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.11 s (started: 2021-05-08 13:43:46 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHZCXYaEaY_j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7554cbbf-09d3-4f70-fd54-1dcc83be91ba"
      },
      "source": [
        "features = data[:,1:]\n",
        "labels = label_data[:,1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.56 ms (started: 2021-05-08 13:43:49 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFQPvWBPafu5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "357fe4ec-584b-4b33-bfcd-5e60c02faadc"
      },
      "source": [
        "pickle.dump(features, open(\"amd_feature_data_forest.p\", \"wb\"))\n",
        "\n",
        "pickle.dump(labels, open(\"amd_label_data_forest.p\",\"wb\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.86 s (started: 2021-05-08 13:43:49 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc7T8Vo4agid",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d420c27-75b3-4cad-8121-b526f1b34853"
      },
      "source": [
        "def data():\n",
        "  feature_data = pickle.load(open(\"amd_feature_data_forest.p\",\"rb\"))\n",
        "  label_data = pickle.load(open(\"amd_label_data_forest.p\",\"rb\"))\n",
        "\n",
        "  feature_data = np.nan_to_num(feature_data)\n",
        "\n",
        "  feature_data = feature_data[:,:100]\n",
        "\n",
        "  ## MinMax Scaler\n",
        "  feature_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
        "  X_scaled = feature_scaler.fit_transform(feature_data)\n",
        "  \n",
        "  label_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))\n",
        "  y_scaled = label_scaler.fit_transform(label_data.reshape(-1,1))\n",
        "\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.1, shuffle=True)\n",
        "\n",
        "  return X_train, y_train, X_test, y_test, label_scaler"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 14.2 ms (started: 2021-05-08 13:43:51 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lujM-nE0bQJs"
      },
      "source": [
        "**ARCHITECTURE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fa3PUXyhbW_g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26a08780-3e6d-4b4e-aadf-5d5d0412cbb2"
      },
      "source": [
        "X_train, y_train, X_test, y_test, label_scaler = data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.87 s (started: 2021-05-08 13:43:51 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_lxN4SZbpOb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77024f38-c8e2-4b94-9977-932fb96377a4"
      },
      "source": [
        "kernel = RationalQuadratic()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 3.73 ms (started: 2021-05-08 13:43:53 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whv-BG1wbiWJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bfb307c-83a1-42c4-d404-c580ba94235e"
      },
      "source": [
        "gpr = GaussianProcessRegressor(kernel=kernel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 6.29 ms (started: 2021-05-08 13:43:53 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4RYyRIfcC27"
      },
      "source": [
        "**TRAINING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4agadkpDcFVT",
        "outputId": "13fada4b-6713-4737-9173-a39172da2bda"
      },
      "source": [
        "gpr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianProcessRegressor(alpha=1e-10, copy_X_train=True,\n",
              "                         kernel=RationalQuadratic(alpha=1, length_scale=1),\n",
              "                         n_restarts_optimizer=0, normalize_y=False,\n",
              "                         optimizer='fmin_l_bfgs_b', random_state=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "stream",
          "text": [
            "time: 6min 46s (started: 2021-05-08 13:43:53 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1SDmVSwclSf"
      },
      "source": [
        "**EVALUATING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7Pq6bWfcpJz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "705559fb-ca12-4f40-f23e-4573cd23d0d4"
      },
      "source": [
        "mean_predictions, std_predictions = gpr.predict(X_test, return_std=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 10.1 s (started: 2021-05-08 13:50:39 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VFuDikYmwhs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90804cab-2561-457e-d594-8b1a5ef21967"
      },
      "source": [
        "std_predictions = std_predictions.reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 1.26 ms (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO7zHCYnl3h0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f25fe3fd-4b48-441d-8ae4-5b52178977b1"
      },
      "source": [
        "scaler = np.divide(std_predictions, mean_predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 2.46 ms (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtCe5QIbeIuq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2bcbfe1-6435-4aec-cf69-e902a1ef65e1"
      },
      "source": [
        "mean_predictions = label_scaler.inverse_transform(mean_predictions)\n",
        "std_predictions = np.multiply(scaler,mean_predictions)\n",
        "y_test = label_scaler.inverse_transform(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 4.56 ms (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0s7t8LaX5XsM",
        "outputId": "290f580b-e0a6-4efd-f9a7-67d93ab25420"
      },
      "source": [
        "average_loss = 0\n",
        "average_loss_percentage = 0\n",
        "average_loss_percentage_rel_range = 0\n",
        "counter = 0\n",
        "rms = 0\n",
        "\n",
        "error_ranges = np.array((0,0,0,0,0,0))\n",
        "\n",
        "max_value = -999999.99\n",
        "min_value = 999999.99\n",
        "\n",
        "for label in y_test:\n",
        "    if(label > max_value):\n",
        "        max_value = label\n",
        "        \n",
        "    if(label < min_value):\n",
        "        min_value = label\n",
        "        \n",
        "label_range = abs(max_value - min_value)\n",
        "\n",
        "\n",
        "for i,prediction in enumerate(mean_predictions):\n",
        "    percentage_difference = abs((abs(prediction - y_test[i]) / y_test[i]) * 100)\n",
        "    percentage_difference2 = abs((abs(prediction - y_test[i]) / label_range) * 100)\n",
        "    loss = abs(prediction - y_test[i])\n",
        "    average_loss += loss\n",
        "\n",
        "    rms += loss**2\n",
        "\n",
        "    if(loss <= 1.0):\n",
        "      error_ranges[0] += 1\n",
        "    elif(loss <= 2.0):\n",
        "      error_ranges[1] += 1\n",
        "    elif(loss <= 4.0):\n",
        "      error_ranges[2] += 1\n",
        "    elif(loss <= 8.0):\n",
        "      error_ranges[3] += 1\n",
        "    elif(loss <= 10.0):\n",
        "      error_ranges[4] += 1\n",
        "    else:\n",
        "      error_ranges[5] += 1\n",
        "\n",
        "    average_loss_percentage += percentage_difference\n",
        "    average_loss_percentage_rel_range += percentage_difference2\n",
        "    counter += 1\n",
        "\n",
        "rms = math.sqrt(rms / counter)\n",
        "\n",
        "print()\n",
        "print(\"SUMMARY:\")\n",
        "print()\n",
        "print(\"Root Mean Squared Error: \" + str(rms))\n",
        "print(\"Mean Absolute Error: \" + str(average_loss / counter))\n",
        "print(\"Mean Absolute Percentage Error: \" + str(average_loss_percentage / counter) + \"%\")\n",
        "print(\"Mean Absolute Percentage Error relative to Label Range: \" + str(average_loss_percentage_rel_range / counter) + \"%\")\n",
        "print(\"Accuracy: \" + str(100 - (average_loss_percentage / counter)) + \"%\")\n",
        "print()\n",
        "print(\"BREAKDOWN:\")\n",
        "print(\"   Error <= 1.0 kJ/mol: \" + str(error_ranges[0]) + \" or \" + str((error_ranges[0] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"   Error <= 2.0 kJ/mol: \" + str(error_ranges[1]) + \" or \" + str((error_ranges[1] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"   Error <= 4.0 kJ/mol: \" + str(error_ranges[2]) + \" or \" + str((error_ranges[2] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"   Error <= 8.0 kJ/mol: \" + str(error_ranges[3]) + \" or \" + str((error_ranges[3] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"   Error <= 10.0.0 kJ/mol: \" + str(error_ranges[4]) + \" or \" + str((error_ranges[4] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"   Error > 10.0 kJ/mol: \" + str(error_ranges[5]) + \" or \" + str((error_ranges[5] / counter) * 100) + \"% of Test Set\")\n",
        "print(\"----------------------------------------------------------------------------------------------\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "SUMMARY:\n",
            "\n",
            "Root Mean Squared Error: 6.561656028698875\n",
            "Mean Absolute Error: [4.79217053]\n",
            "Mean Absolute Percentage Error: [3.41474499]%\n",
            "Mean Absolute Percentage Error relative to Label Range: [4.79383399]%\n",
            "Accuracy: [96.58525501]%\n",
            "\n",
            "BREAKDOWN:\n",
            "   Error <= 1.0 kJ/mol: 90 or 15.845070422535212% of Test Set\n",
            "   Error <= 2.0 kJ/mol: 104 or 18.30985915492958% of Test Set\n",
            "   Error <= 4.0 kJ/mol: 127 or 22.359154929577464% of Test Set\n",
            "   Error <= 8.0 kJ/mol: 138 or 24.295774647887324% of Test Set\n",
            "   Error <= 10.0.0 kJ/mol: 35 or 6.161971830985916% of Test Set\n",
            "   Error > 10.0 kJ/mol: 74 or 13.028169014084506% of Test Set\n",
            "----------------------------------------------------------------------------------------------\n",
            "time: 74.2 ms (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-WOlymg5lTG"
      },
      "source": [
        "**Prediction with Uncertainty**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxlAgaqr5kJT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f205383-5055-41db-cd37-bbfdf9a0595e"
      },
      "source": [
        "instance = 23"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "time: 723 µs (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OA7TTUE1mGv1",
        "outputId": "e1f25cbe-0f76-4bd4-ac31-7834f8461fd2"
      },
      "source": [
        "print(\"Prediction: \" + str(mean_predictions[instance,0]) + \" +- \" + str(-1.96 * std_predictions[instance,0]) + \" with 95% Confidence || True Label: \" + str(y_test[instance]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction: -128.6277235940009 +- 18.063523930730177 with 95% Confidence || True Label: [-125.1405]\n",
            "time: 2.77 ms (started: 2021-05-08 13:50:50 +00:00)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}