1_validate_models.ipynb

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          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "ec15f0e60a6f4e1b9e85c9feb2edc5dc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-k976iTikGqm",
        "outputId": "0fa7c556-bac5-4502-a47b-a426c98ce459"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'gpHSP'...\n",
            "remote: Enumerating objects: 115, done.\u001b[K\n",
            "remote: Counting objects: 100% (77/77), done.\u001b[K\n",
            "remote: Compressing objects: 100% (53/53), done.\u001b[K\n",
            "remote: Total 115 (delta 44), reused 57 (delta 24), pack-reused 38\u001b[K\n",
            "Receiving objects: 100% (115/115), 32.19 MiB | 24.35 MiB/s, done.\n",
            "Resolving deltas: 100% (58/58), done.\n"
          ]
        }
      ],
      "source": [
        "! rm -rf gpHSP\n",
        "!git clone https://github.com/aspuru-guzik-group/gpHSP"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assuming in a colab enviroment"
      ],
      "metadata": {
        "id": "p54WoW6RkOdK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    print('In colab!')\n",
        "    sys.path.insert(0,'gpHSP')\n",
        "    !pip install rdkit-pypi mordred ml_collections ngboost gpflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQyCA64AkN6w",
        "outputId": "bc13b6d9-1999-4b42-9628-e256339ee317"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In colab!\n",
            "Requirement already satisfied: rdkit-pypi in /usr/local/lib/python3.7/dist-packages (2021.9.4)\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0->lifelines>=0.25->ngboost) (3.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0->lifelines>=0.25->ngboost) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0->lifelines>=0.25->ngboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23.0->lifelines>=0.25->ngboost) (2018.9)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21->ngboost) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21->ngboost) (1.1.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from gpflow) (21.3)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.7/dist-packages (from gpflow) (0.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from gpflow) (57.4.0)\n",
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            "Requirement already satisfied: deprecated in /usr/local/lib/python3.7/dist-packages (from gpflow) (1.2.13)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->gpflow) (3.3.6)\n",
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            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard~=2.6->tensorflow>=2.2.0->gpflow) (3.7.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow>=2.2.0->gpflow) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.6->tensorflow>=2.2.0->gpflow) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=2.2.0->gpflow) (3.2.0)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>0.10.0->gpflow) (0.1.6)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability>0.10.0->gpflow) (4.4.2)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm.auto import tqdm\n",
        "import scipy.stats as stats\n",
        "\n",
        "import sklearn.model_selection\n",
        "import sklearn.preprocessing\n",
        "import ngboost\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import gpflow as gpf\n",
        "import gphsp\n",
        "from gphsp import cast_1d_array\n",
        "\n",
        "gphsp.notebook_context()\n",
        "gphsp.print_modules([ngboost, tf , gpf])"
      ],
      "metadata": {
        "id": "IO5wJqsskd1t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50bbc141-c967-4397-a8c2-4c60b0c6b937"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ngboost    = 0.3.12\n",
            "tensorflow = 2.7.0\n",
            "gpflow     = 2.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Utilities"
      ],
      "metadata": {
        "id": "HVDbXSZAliP3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "at_data_dir = lambda x: os.path.join('gpHSP/data', x)\n",
        "at_model_dir = lambda x: os.path.join('gpHSP/models', x)"
      ],
      "metadata": {
        "id": "wseewPpTCGHD"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load data and get all smiles"
      ],
      "metadata": {
        "id": "_wvvTIsMldgt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(at_data_dir('Solvents_exp.csv'))\n",
        "df['smiles'] = df['smiles'].apply(gphsp.get_isomeric_smiles)\n",
        "gphsp.peek_df(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "YYDEEwkVk9BW",
        "outputId": "6cd38e64-ae2a-40de-cd93-e44376ccee6c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['key', 'Type', 'δd', 'δp', 'δh', 'smiles', 'ID_type', 'ID', 'Ref',\n",
            "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'label',\n",
            "       'finished', 'job_name', 'homo', 'lumo', 'gap', 'd-moments', 'dipole',\n",
            "       'polar', 'run_time', 'n_cores', 'compute_time', 'Area', 'Hba', 'Hbd',\n",
            "       'Volume', 'sigma_mom_0', 'sigma_mom_1', 'sigma_mom_2', 'sigma_mom_3',\n",
            "       'sigma_mom_4', 'sigma_mom_5', 'sigma_norm', 'sigma_profile', 'drug'],\n",
            "      dtype='object')\n",
            "(193, 39)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d7b76b84-aeaf-47ec-aa00-5a5f44321c3b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .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>key</th>\n",
              "      <th>Type</th>\n",
              "      <th>δd</th>\n",
              "      <th>δp</th>\n",
              "      <th>δh</th>\n",
              "      <th>smiles</th>\n",
              "      <th>ID_type</th>\n",
              "      <th>ID</th>\n",
              "      <th>Ref</th>\n",
              "      <th>organic</th>\n",
              "      <th>n_electrons</th>\n",
              "      <th>n_atoms</th>\n",
              "      <th>charge</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>label</th>\n",
              "      <th>finished</th>\n",
              "      <th>job_name</th>\n",
              "      <th>homo</th>\n",
              "      <th>lumo</th>\n",
              "      <th>gap</th>\n",
              "      <th>d-moments</th>\n",
              "      <th>dipole</th>\n",
              "      <th>polar</th>\n",
              "      <th>run_time</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>compute_time</th>\n",
              "      <th>Area</th>\n",
              "      <th>Hba</th>\n",
              "      <th>Hbd</th>\n",
              "      <th>Volume</th>\n",
              "      <th>sigma_mom_0</th>\n",
              "      <th>sigma_mom_1</th>\n",
              "      <th>sigma_mom_2</th>\n",
              "      <th>sigma_mom_3</th>\n",
              "      <th>sigma_mom_4</th>\n",
              "      <th>sigma_mom_5</th>\n",
              "      <th>sigma_norm</th>\n",
              "      <th>sigma_profile</th>\n",
              "      <th>drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1,1,1-Trichloroethane</td>\n",
              "      <td>Solvent</td>\n",
              "      <td>16.8</td>\n",
              "      <td>4.3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CC(Cl)(Cl)Cl</td>\n",
              "      <td>CAS</td>\n",
              "      <td>71-55-6</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>32</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>133.405</td>\n",
              "      <td>Exp-0</td>\n",
              "      <td>True</td>\n",
              "      <td>SOLSPE_Exp-0</td>\n",
              "      <td>-7.097</td>\n",
              "      <td>-1.108</td>\n",
              "      <td>5.989</td>\n",
              "      <td>[-2.37335554 -0.11768285  0.07442233]</td>\n",
              "      <td>2.377</td>\n",
              "      <td>64.374</td>\n",
              "      <td>94.214</td>\n",
              "      <td>4</td>\n",
              "      <td>376.856</td>\n",
              "      <td>1.326</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.126</td>\n",
              "      <td>0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>-3.836</td>\n",
              "      <td>4.24</td>\n",
              "      <td>-2.021</td>\n",
              "      <td>1.54</td>\n",
              "      <td>41.428</td>\n",
              "      <td>[ 0.          0.          0.          0.      ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-d7b76b84-aeaf-47ec-aa00-5a5f44321c3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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            "text/plain": [
              "                     key  ...   drug\n",
              "0  1,1,1-Trichloroethane  ...  False\n",
              "\n",
              "[1 rows x 39 columns]"
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "poly_df = pd.read_csv(at_data_dir('Polymers_exp.csv'))\n",
        "poly_df['smiles'] = poly_df['smiles'].apply(gphsp.get_isomeric_smiles)\n",
        "gphsp.peek_df(poly_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "izpVJD2Ws1g_",
        "outputId": "97ea188a-0793-4f26-8f40-241521d2cb73"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['label', 'δd', 'δp', 'δh', 'smiles', 'test', 'poly_label', 'n_copies',\n",
            "       'organic', 'n_electrons', 'n_atoms', 'charge', 'MolWt', 'n_frags',\n",
            "       'largest', 'finished', 'job_name', 'poly_smiles', 'homo', 'lumo', 'gap',\n",
            "       'd-moments', 'dipole', 'polar', 'run_time', 'n_cores', 'compute_time',\n",
            "       'Area', 'Hba', 'Hbd', 'Volume', 'sigma_mom_0', 'sigma_mom_1',\n",
            "       'sigma_mom_2', 'sigma_mom_3', 'sigma_mom_4', 'sigma_mom_5',\n",
            "       'sigma_norm', 'sigma_profile'],\n",
            "      dtype='object')\n",
            "(31, 39)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>δd</th>\n",
              "      <th>δp</th>\n",
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              "      <th>smiles</th>\n",
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              "      <th>MolWt</th>\n",
              "      <th>n_frags</th>\n",
              "      <th>largest</th>\n",
              "      <th>finished</th>\n",
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              "      <th>poly_smiles</th>\n",
              "      <th>homo</th>\n",
              "      <th>lumo</th>\n",
              "      <th>gap</th>\n",
              "      <th>d-moments</th>\n",
              "      <th>dipole</th>\n",
              "      <th>polar</th>\n",
              "      <th>run_time</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>compute_time</th>\n",
              "      <th>Area</th>\n",
              "      <th>Hba</th>\n",
              "      <th>Hbd</th>\n",
              "      <th>Volume</th>\n",
              "      <th>sigma_mom_0</th>\n",
              "      <th>sigma_mom_1</th>\n",
              "      <th>sigma_mom_2</th>\n",
              "      <th>sigma_mom_3</th>\n",
              "      <th>sigma_mom_4</th>\n",
              "      <th>sigma_mom_5</th>\n",
              "      <th>sigma_norm</th>\n",
              "      <th>sigma_profile</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Polyacrylonitrile-n5</td>\n",
              "      <td>20.0</td>\n",
              "      <td>15.1</td>\n",
              "      <td>7.9</td>\n",
              "      <td>CCC#N</td>\n",
              "      <td>True</td>\n",
              "      <td>Polyacrylonitrile</td>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>110</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>275.4</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>SOLSPE2_Polyacrylonitrile-n5</td>\n",
              "      <td>[C@@H](C#N)(CCC#N)C[C@@H](C#N)C[C@H](C#N)CCC#N</td>\n",
              "      <td>-7.879</td>\n",
              "      <td>-1.12</td>\n",
              "      <td>6.759</td>\n",
              "      <td>[ 0.58302575 -8.49403297 -1.86749721]</td>\n",
              "      <td>8.716</td>\n",
              "      <td>211.624</td>\n",
              "      <td>468.596</td>\n",
              "      <td>4</td>\n",
              "      <td>1874.384</td>\n",
              "      <td>3.047</td>\n",
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              "      <td>0.22</td>\n",
              "      <td>0.343</td>\n",
              "      <td>0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>26.1</td>\n",
              "      <td>154.0</td>\n",
              "      <td>51.4</td>\n",
              "      <td>165.0</td>\n",
              "      <td>66.77</td>\n",
              "      <td>[ 0.          0.          0.          0.      ...</td>\n",
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              "          const element = document.querySelector('#df-cd571c2d-58dd-4289-8241-3a5b62c2907e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "                  label  ...                                      sigma_profile\n",
              "0  Polyacrylonitrile-n5  ...  [ 0.          0.          0.          0.      ...\n",
              "\n",
              "[1 rows x 39 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load features"
      ],
      "metadata": {
        "id": "w1s-j6DgtpgV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = gphsp.SmilesMap(at_data_dir('mordred_features.npz'))"
      ],
      "metadata": {
        "id": "jq0x70z9l-_G"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train rfHSP model on single molecules"
      ],
      "metadata": {
        "id": "UJrOUs-hoHOE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "x = features(df['smiles'].to_numpy(str)).astype(np.float64)\n",
        "n_splits = 5\n",
        "indices = np.arange(len(x))\n",
        "names = gphsp.Y_COLS\n",
        "for name in tqdm(names, total=len(names)):\n",
        "    y = df[name].to_numpy(np.float64).reshape(-1,1)\n",
        "    spliter = sklearn.model_selection.KFold(n_splits, shuffle=True, random_state=42)\n",
        "    y_mean_rf, y_mean_gp = np.zeros_like(indices), np.zeros_like(indices)\n",
        "    y_std_rf, y_std_gp = np.zeros_like(indices), np.zeros_like(indices)\n",
        "    for train_index, test_index in tqdm(spliter.split(indices), total=n_splits):\n",
        "        x_train, x_test = x[train_index], x[test_index]\n",
        "        y_train, y_test = y[train_index], y[test_index]\n",
        "        model = ngboost.NGBRegressor().fit(x_train, y_train.ravel())\n",
        "        y_dists = model.pred_dist(x_test)\n",
        "        y_mean_rf[test_index] = y_dists.mean().ravel()\n",
        "        y_std_rf[test_index] = y_dists.std().ravel()\n",
        "\n",
        "        # Standarize target\n",
        "        preproc = sklearn.preprocessing.StandardScaler()\n",
        "        y_train = preproc.fit_transform(y_train)     \n",
        "\n",
        "        model = gphsp.make_gp(x_train, y_train, False, None)\n",
        "        y_mu, y_std = model.predict_f(x_test)\n",
        "        y_mean_gp[test_index] = cast_1d_array(preproc.inverse_transform(y_mu.numpy()))\n",
        "        y_std_gp[test_index] = cast_1d_array(y_std)*preproc.scale_\n",
        "\n",
        "    results.append(gphsp.evaluate(y, y_mean_rf, y_std_rf, {'name':name, 'method':'ngboost'}))\n",
        "    results.append(gphsp.evaluate(y, y_mean_gp, y_std_gp, {'name':name, 'method':'gp'}))\n",
        "    display(pd.DataFrame(results[-2:]))\n",
        "\n",
        "results_df = pd.DataFrame(results)\n",
        "results_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "[iter 0] loss=2.0688 val_loss=0.0000 scale=1.0000 norm=1.6247\n",
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            "[iter 0] loss=3.0900 val_loss=0.0000 scale=1.0000 norm=4.2681\n",
            "[iter 100] loss=2.4715 val_loss=0.0000 scale=1.0000 norm=2.2474\n",
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            "[iter 400] loss=1.0292 val_loss=0.0000 scale=1.0000 norm=0.7235\n",
            "[iter 0] loss=3.0941 val_loss=0.0000 scale=1.0000 norm=4.1960\n",
            "[iter 100] loss=2.4755 val_loss=0.0000 scale=1.0000 norm=2.2681\n",
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            "[iter 100] loss=2.4962 val_loss=0.0000 scale=2.0000 norm=4.6117\n",
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            "[iter 0] loss=3.2078 val_loss=0.0000 scale=1.0000 norm=4.7793\n",
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            "[iter 300] loss=0.8680 val_loss=0.0000 scale=2.0000 norm=1.4474\n",
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            "[iter 0] loss=3.1454 val_loss=0.0000 scale=1.0000 norm=4.6482\n",
            "[iter 100] loss=2.2995 val_loss=0.0000 scale=2.0000 norm=3.2669\n",
            "[iter 200] loss=1.4898 val_loss=0.0000 scale=2.0000 norm=1.6703\n",
            "[iter 300] loss=0.7864 val_loss=0.0000 scale=2.0000 norm=1.1698\n",
            "[iter 400] loss=0.3486 val_loss=0.0000 scale=1.0000 norm=0.4818\n",
            "[iter 0] loss=3.1566 val_loss=0.0000 scale=1.0000 norm=4.6127\n",
            "[iter 100] loss=2.3468 val_loss=0.0000 scale=2.0000 norm=3.5918\n",
            "[iter 200] loss=1.5463 val_loss=0.0000 scale=2.0000 norm=1.9141\n",
            "[iter 300] loss=0.8886 val_loss=0.0000 scale=2.0000 norm=1.4523\n",
            "[iter 400] loss=0.4311 val_loss=0.0000 scale=1.0000 norm=0.6058\n",
            "[iter 0] loss=3.1856 val_loss=0.0000 scale=1.0000 norm=4.7159\n",
            "[iter 100] loss=2.3898 val_loss=0.0000 scale=2.0000 norm=3.6810\n",
            "[iter 200] loss=1.5900 val_loss=0.0000 scale=2.0000 norm=1.9509\n",
            "[iter 300] loss=0.9435 val_loss=0.0000 scale=2.0000 norm=1.4921\n",
            "[iter 400] loss=0.5150 val_loss=0.0000 scale=1.0000 norm=0.6362\n",
            "[iter 0] loss=3.1842 val_loss=0.0000 scale=1.0000 norm=4.6656\n",
            "[iter 100] loss=2.3395 val_loss=0.0000 scale=2.0000 norm=3.4417\n",
            "[iter 200] loss=1.5490 val_loss=0.0000 scale=2.0000 norm=1.7687\n",
            "[iter 300] loss=0.8944 val_loss=0.0000 scale=2.0000 norm=1.2608\n",
            "[iter 400] loss=0.5083 val_loss=0.0000 scale=1.0000 norm=0.5225\n"
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              "  name   method     R2    MAE    tau  uncertainty_tau\n",
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              "1   δh       gp  0.005  4.616  0.119           -0.023"
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              "  "
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            "text/plain": [
              "  name   method     R2    MAE    tau  uncertainty_tau\n",
              "0   δd  ngboost  0.303  1.046  0.583              NaN\n",
              "1   δd       gp -0.012  1.412 -0.064              NaN\n",
              "2   δp  ngboost  0.503  2.530  0.610            0.138\n",
              "3   δp       gp -0.024  4.126 -0.069           -0.015\n",
              "4   δh  ngboost  0.691  2.037  0.735            0.052\n",
              "5   δh       gp  0.005  4.616  0.119           -0.023"
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8308ff3d-c8d5-48d7-8ca3-25c0253fdbe3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8308ff3d-c8d5-48d7-8ca3-25c0253fdbe3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  name   method     R2    MAE    tau  uncertainty_tau\n",
              "0   δd  ngboost  0.303  1.046  0.583              NaN\n",
              "1   δd       gp -0.012  1.412 -0.064              NaN\n",
              "2   δp  ngboost  0.503  2.530  0.610            0.138\n",
              "3   δp       gp -0.024  4.126 -0.069           -0.015\n",
              "4   δh  ngboost  0.691  2.037  0.735            0.052\n",
              "5   δh       gp  0.005  4.616  0.119           -0.023"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='name', y='R2', hue='method', data=results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "sRlA7P2qi1tZ",
        "outputId": "8664795b-838b-46e1-e0b9-2210a982e835"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0d7da4cc50>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train gpHSP model on polymers"
      ],
      "metadata": {
        "id": "vgARDFB55pUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predictions_as_features(x, model_dict, pred_fn=None):\n",
        "    default_pred_fn = lambda model, inputs: model.pred_dist(inputs)\n",
        "    pred_fn = pred_fn or default_pred_fn\n",
        "    new_x = np.zeros((len(x), 6), dtype=np.float64)\n",
        "    for index, name in enumerate(names):\n",
        "        y_mol_dist = pred_fn(models[name], x)\n",
        "        new_x[:,index] = y_mol_dist.mean()\n",
        "        new_x[:,index+3] = y_mol_dist.mean()\n",
        "    return new_x\n"
      ],
      "metadata": {
        "id": "BlaqTkyzjJl6"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_suffix = 'HSP_ngboost_mol.pkl'\n",
        "names = gphsp.Y_COLS\n",
        "models = {name: gphsp.load_model(at_model_dir(f\"{name}_{model_suffix}\")) for name in names}\n",
        "mol_x = features(poly_df['smiles'].to_numpy(str)).astype(np.float64)\n",
        "x = predictions_as_features(mol_x, models)"
      ],
      "metadata": {
        "id": "uT3_wZsF-nSL"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "from sklearn.exceptions import ConvergenceWarning\n",
        "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n",
        "\n",
        "results = []\n",
        "indices = np.arange(len(x))\n",
        "n_splits = len(indices)\n",
        "names = gphsp.Y_COLS\n",
        "gp_parts = {'mean':[0,1, 2], 'std':[3, 4, 5]}\n",
        "\n",
        "for index, name in tqdm(enumerate(names), total=len(names)):\n",
        "    y = poly_df[name].to_numpy(np.float64).reshape(-1,1)\n",
        "    spliter = sklearn.model_selection.LeaveOneOut()\n",
        "    y_mean_rf, y_std_rf = np.zeros_like(indices), np.zeros_like(indices)\n",
        "    y_mean_mol, y_std_mol = np.zeros_like(indices), np.zeros_like(indices)\n",
        "    y_mean_gp, y_std_gp = np.zeros_like(indices), np.zeros_like(indices)\n",
        "    y_mean_lin = np.zeros_like(indices)\n",
        "\n",
        "    for train_index, test_index in tqdm(spliter.split(indices), total=n_splits):\n",
        "        x_train, x_test = x[train_index], x[test_index]\n",
        "        y_train, y_test = y[train_index], y[test_index]\n",
        "        \n",
        "        #RF model\n",
        "        model = ngboost.NGBRegressor().fit(x_train, y_train.ravel())\n",
        "        y_dists = model.pred_dist(x_test)\n",
        "        y_mean_rf[test_index] = y_dists.mean().ravel()\n",
        "        y_std_rf[test_index] = y_dists.std().ravel()\n",
        "\n",
        "        # Linear model\n",
        "        x_train, x_test = mol_x[train_index], mol_x[test_index]\n",
        "        model = sklearn.linear_model.LassoCV()\n",
        "        model.fit(x_train, y_train.ravel())\n",
        "        y_mu = model.predict(x_test)\n",
        "        y_mean_lin[test_index] = cast_1d_array(y_mu)\n",
        "\n",
        "        # Standarize target\n",
        "        #preproc = sklearn.preprocessing.StandardScaler()\n",
        "        #y_train = preproc.fit_transform(y_train)     \n",
        "\n",
        "        model = gphsp.make_gp(x_train, y_train, True, gp_parts)\n",
        "        y_mu, y_std = model.predict_f(x_test)\n",
        "        y_mean_gp[test_index] = cast_1d_array(y_mu)\n",
        "        y_std_gp[test_index] = cast_1d_array(y_std)\n",
        "\n",
        "        x_train, x_test = mol_x[train_index], mol_x[test_index]\n",
        "        model = gphsp.make_gp(x_train, y_train, False, None)\n",
        "        y_mu, y_std = model.predict_f(x_test)\n",
        "        y_mean_mol[test_index] = cast_1d_array(y_mu)\n",
        "        y_std_mol[test_index] = cast_1d_array(y_std)\n",
        "\n",
        "\n",
        "    results.append(gphsp.evaluate(y, y_mean_rf, y_std_rf, {'name':name, 'method':'ngboost', 'feature':'HSP'}))\n",
        "    results.append(gphsp.evaluate(y, y_mean_gp, y_std_gp, {'name':name, 'method':'gp', 'feature':'HSP'}))\n",
        "    results.append(gphsp.evaluate(y, y_mean_mol, y_std_mol, {'name':name, 'method':'gp', 'feature':'mol'}))\n",
        "    results.append(gphsp.evaluate(y, y_mean_lin, info={'name':name, 'method':'linear', 'feature':'mol'}))\n",
        "    display(pd.DataFrame(results[-4:]))\n",
        "results_df = pd.DataFrame(results)\n",
        "gphsp.peek_df(results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "jXwf6B1WdLld",
        "outputId": "ac630180-2977-4044-b1ba-4533f25f2506"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0f440f4fc4d14bbeb5f05e511c2cf272",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ]
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            "[iter 0] loss=1.2740 val_loss=0.0000 scale=1.0000 norm=0.8988\n",
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            "[iter 0] loss=1.3039 val_loss=0.0000 scale=1.0000 norm=0.9156\n",
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              "2   δd       gp     mol  0.040  0.709  0.256           -0.204\n",
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            "[iter 0] loss=2.8541 val_loss=0.0000 scale=1.0000 norm=3.5589\n",
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            "[iter 0] loss=2.8618 val_loss=0.0000 scale=1.0000 norm=3.6081\n",
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            "[iter 0] loss=2.8540 val_loss=0.0000 scale=1.0000 norm=3.5928\n",
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            "[iter 0] loss=2.8612 val_loss=0.0000 scale=1.0000 norm=3.6271\n",
            "[iter 100] loss=1.8736 val_loss=0.0000 scale=2.0000 norm=2.1950\n",
            "[iter 200] loss=1.0886 val_loss=0.0000 scale=2.0000 norm=1.3392\n",
            "[iter 300] loss=0.4203 val_loss=0.0000 scale=2.0000 norm=1.0809\n",
            "[iter 400] loss=-0.1243 val_loss=0.0000 scale=2.0000 norm=0.9735\n",
            "[iter 0] loss=2.8399 val_loss=0.0000 scale=1.0000 norm=3.4979\n",
            "[iter 100] loss=1.8883 val_loss=0.0000 scale=2.0000 norm=2.3414\n",
            "[iter 200] loss=1.1872 val_loss=0.0000 scale=2.0000 norm=1.4819\n",
            "[iter 300] loss=0.5655 val_loss=0.0000 scale=2.0000 norm=1.1428\n",
            "[iter 400] loss=0.0347 val_loss=0.0000 scale=1.0000 norm=0.4903\n",
            "[iter 0] loss=2.8575 val_loss=0.0000 scale=1.0000 norm=3.5779\n",
            "[iter 100] loss=1.8689 val_loss=0.0000 scale=2.0000 norm=2.1866\n",
            "[iter 200] loss=1.0654 val_loss=0.0000 scale=2.0000 norm=1.3179\n",
            "[iter 300] loss=0.3466 val_loss=0.0000 scale=2.0000 norm=1.0641\n",
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            "[iter 0] loss=2.8621 val_loss=0.0000 scale=1.0000 norm=3.6328\n",
            "[iter 100] loss=1.8917 val_loss=0.0000 scale=2.0000 norm=2.2427\n",
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            "[iter 0] loss=2.8355 val_loss=0.0000 scale=1.0000 norm=3.4825\n",
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              "  name   method feature     R2    MAE    tau  uncertainty_tau\n",
              "0   δp  ngboost     HSP  0.241  2.718  0.435            0.221\n",
              "1   δp       gp     HSP -0.146  3.462 -0.033           -0.333\n",
              "2   δp       gp     mol  0.031  3.269  0.266           -0.428\n",
              "3   δp   linear     mol  0.055  3.067  0.250              NaN"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d330b05ddf154e37a4be4b135e504db4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/31 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[iter 0] loss=2.9031 val_loss=0.0000 scale=1.0000 norm=3.3796\n",
            "[iter 100] loss=1.9001 val_loss=0.0000 scale=2.0000 norm=2.4607\n",
            "[iter 200] loss=1.1900 val_loss=0.0000 scale=2.0000 norm=1.6279\n",
            "[iter 300] loss=0.6060 val_loss=0.0000 scale=1.0000 norm=0.6476\n",
            "[iter 400] loss=0.0485 val_loss=0.0000 scale=1.0000 norm=0.5590\n",
            "[iter 0] loss=2.8897 val_loss=0.0000 scale=1.0000 norm=3.3140\n",
            "[iter 100] loss=1.8960 val_loss=0.0000 scale=2.0000 norm=2.4401\n",
            "[iter 200] loss=1.1307 val_loss=0.0000 scale=2.0000 norm=1.5204\n",
            "[iter 300] loss=0.4013 val_loss=0.0000 scale=2.0000 norm=1.2133\n",
            "[iter 400] loss=-0.2637 val_loss=0.0000 scale=2.0000 norm=1.0550\n",
            "[iter 0] loss=2.8733 val_loss=0.0000 scale=1.0000 norm=3.2658\n",
            "[iter 100] loss=1.8860 val_loss=0.0000 scale=2.0000 norm=2.4230\n",
            "[iter 200] loss=1.1298 val_loss=0.0000 scale=2.0000 norm=1.5331\n",
            "[iter 300] loss=0.4132 val_loss=0.0000 scale=2.0000 norm=1.2198\n",
            "[iter 400] loss=-0.2538 val_loss=0.0000 scale=2.0000 norm=1.0641\n",
            "[iter 0] loss=2.8943 val_loss=0.0000 scale=1.0000 norm=3.3328\n",
            "[iter 100] loss=1.9188 val_loss=0.0000 scale=2.0000 norm=2.4868\n",
            "[iter 200] loss=1.1762 val_loss=0.0000 scale=2.0000 norm=1.5658\n",
            "[iter 300] loss=0.4905 val_loss=0.0000 scale=2.0000 norm=1.2219\n",
            "[iter 400] loss=-0.1766 val_loss=0.0000 scale=2.0000 norm=1.0775\n",
            "[iter 0] loss=2.9039 val_loss=0.0000 scale=1.0000 norm=3.4047\n",
            "[iter 100] loss=1.9116 val_loss=0.0000 scale=2.0000 norm=2.4588\n",
            "[iter 200] loss=1.1530 val_loss=0.0000 scale=2.0000 norm=1.5676\n",
            "[iter 300] loss=0.4798 val_loss=0.0000 scale=2.0000 norm=1.2517\n",
            "[iter 400] loss=-0.0328 val_loss=0.0000 scale=2.0000 norm=1.1066\n",
            "[iter 0] loss=2.9026 val_loss=0.0000 scale=1.0000 norm=3.3860\n",
            "[iter 100] loss=1.9082 val_loss=0.0000 scale=2.0000 norm=2.4055\n",
            "[iter 200] loss=1.1352 val_loss=0.0000 scale=2.0000 norm=1.5094\n",
            "[iter 300] loss=0.3760 val_loss=0.0000 scale=2.0000 norm=1.2155\n",
            "[iter 400] loss=-0.2672 val_loss=0.0000 scale=2.0000 norm=1.0772\n",
            "[iter 0] loss=2.8832 val_loss=0.0000 scale=1.0000 norm=3.2926\n",
            "[iter 100] loss=1.8868 val_loss=0.0000 scale=2.0000 norm=2.3714\n",
            "[iter 200] loss=1.1183 val_loss=0.0000 scale=2.0000 norm=1.5375\n",
            "[iter 300] loss=0.4047 val_loss=0.0000 scale=2.0000 norm=1.2226\n",
            "[iter 400] loss=-0.1428 val_loss=0.0000 scale=2.0000 norm=1.0914\n",
            "[iter 0] loss=2.9020 val_loss=0.0000 scale=1.0000 norm=3.3803\n",
            "[iter 100] loss=1.9277 val_loss=0.0000 scale=2.0000 norm=2.5068\n",
            "[iter 200] loss=1.1753 val_loss=0.0000 scale=2.0000 norm=1.5582\n",
            "[iter 300] loss=0.4808 val_loss=0.0000 scale=2.0000 norm=1.2267\n",
            "[iter 400] loss=-0.2221 val_loss=0.0000 scale=2.0000 norm=1.0877\n",
            "[iter 0] loss=2.9024 val_loss=0.0000 scale=1.0000 norm=3.3844\n",
            "[iter 100] loss=1.9154 val_loss=0.0000 scale=2.0000 norm=2.4632\n",
            "[iter 200] loss=1.1677 val_loss=0.0000 scale=2.0000 norm=1.5612\n",
            "[iter 300] loss=0.4661 val_loss=0.0000 scale=2.0000 norm=1.2258\n",
            "[iter 400] loss=-0.1019 val_loss=0.0000 scale=2.0000 norm=1.0560\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-8c54c7ee587d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;31m#y_train = preproc.fit_transform(y_train)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgphsp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_gp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgp_parts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0my_mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_std\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0my_mean_gp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast_1d_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/gpHSP/gphsp.py\u001b[0m in \u001b[0;36mmake_gp\u001b[0;34m(x, y, use_ard, parts, const_mean)\u001b[0m\n\u001b[1;32m     88\u001b[0m     opt_logs = opt.minimize(model.training_loss,\n\u001b[1;32m     89\u001b[0m                         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m                         options=dict(maxiter=1000))\n\u001b[0m\u001b[1;32m     91\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(self, closure, variables, method, step_callback, compile, allow_unused_variables, **scipy_kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         return scipy.optimize.minimize(\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mscipy_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m         )\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/optimize/_minimize.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'l-bfgs-b'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m         return _minimize_lbfgsb(fun, x0, args, jac, bounds,\n\u001b[0;32m--> 610\u001b[0;31m                                 callback=callback, **options)\n\u001b[0m\u001b[1;32m    611\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tnc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    612\u001b[0m         return _minimize_tnc(fun, x0, args, jac, bounds, callback=callback,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[0;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0;31m# until the completion of the current minimization iteration.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;31m# Overwrite f and g:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m             \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mtask_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb'NEW_X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m             \u001b[0;31m# new iteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/optimize/lbfgsb.py\u001b[0m in \u001b[0;36mfunc_and_grad\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mfunc_and_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m             \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m             \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjac\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0mfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjac\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\u001b[0m in \u001b[0;36m_eval\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_tf_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 59\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m:  Input matrix is not invertible.\n\t [[node triangular_solve/MatrixTriangularSolve\n (defined at /usr/local/lib/python3.7/dist-packages/gpflow/logdensities.py:97)\n]] [Op:__inference__tf_eval_352328]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node triangular_solve/MatrixTriangularSolve:\nIn[0] Cholesky (defined at /usr/local/lib/python3.7/dist-packages/gpflow/models/gpr.py:87)\t\nIn[1] sub_1 (defined at /usr/local/lib/python3.7/dist-packages/gpflow/logdensities.py:96)\n\nOperation defined at: (most recent call last)\n>>>   File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n>>>     \"__main__\", mod_spec)\n>>> \n>>>   File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n>>>     exec(code, run_globals)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n>>>     app.launch_new_instance()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n>>>     app.start()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 499, in start\n>>>     self.io_loop.start()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n>>>     self.asyncio_loop.run_forever()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n>>>     self._run_once()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n>>>     handle._run()\n>>> \n>>>   File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n>>>     self._context.run(self._callback, *self._args)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n>>>     handler_func(fileobj, events)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 452, in _handle_events\n>>>     self._handle_recv()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 481, in _handle_recv\n>>>     self._run_callback(callback, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py\", line 431, in _run_callback\n>>>     callback(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n>>>     return fn(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n>>>     return self.dispatch_shell(stream, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n>>>     handler(stream, idents, msg)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n>>>     user_expressions, allow_stdin)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n>>>     res = shell.run_cell(code, store_history=store_history, silent=silent)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n>>>     return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2718, in run_cell\n>>>     interactivity=interactivity, compiler=compiler, result=result)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2822, in run_ast_nodes\n>>>     if self.run_code(code, result):\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n>>>     exec(code_obj, self.user_global_ns, self.user_ns)\n>>> \n>>>   File \"<ipython-input-35-8c54c7ee587d>\", line 40, in <module>\n>>>     model = gphsp.make_gp(x_train, y_train, True, gp_parts)\n>>> \n>>>   File \"gpHSP/gphsp.py\", line 90, in make_gp\n>>>     options=dict(maxiter=1000))\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\", line 96, in minimize\n>>>     func, initial_params, jac=True, method=method, **scipy_kwargs\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/scipy/optimize/_minimize.py\", line 610, in minimize\n>>>     callback=callback, **options)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/scipy/optimize/lbfgsb.py\", line 345, in _minimize_lbfgsb\n>>>     f, g = func_and_grad(x)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/scipy/optimize/lbfgsb.py\", line 295, in func_and_grad\n>>>     f = fun(x, *args)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py\", line 327, in function_wrapper\n>>>     return function(*(wrapper_args + args))\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py\", line 65, in __call__\n>>>     fg = self.fun(x, *args)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\", line 137, in _eval\n>>>     loss, grad = _tf_eval(tf.convert_to_tensor(x))\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\", line 119, in _tf_eval\n>>>     if first_call:\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\", line 121, in _tf_eval\n>>>     loss, grads = _compute_loss_and_gradients(\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/optimizers/scipy.py\", line 224, in _compute_loss_and_gradients\n>>>     loss = loss_closure()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/models/training_mixins.py\", line 63, in training_loss\n>>>     return self._training_loss()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/models/model.py\", line 56, in _training_loss\n>>>     return -(self.maximum_log_likelihood_objective(*args, **kwargs) + self.log_prior_density())\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/models/gpr.py\", line 67, in maximum_log_likelihood_objective\n>>>     return self.log_marginal_likelihood()\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/models/gpr.py\", line 91, in log_marginal_likelihood\n>>>     log_prob = multivariate_normal(Y, m, L)\n>>> \n>>>   File \"/usr/local/lib/python3.7/dist-packages/gpflow/logdensities.py\", line 97, in multivariate_normal\n>>>     alpha = tf.linalg.triangular_solve(L, d, lower=True)\n>>> "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VoyUVlNGuNQg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}