commit 66e9e674732a457d6f591b59508eb32aa51cdf58
Author: Marius Ciepluch <11855163+norandom@users.noreply.github.com>
Date:   Tue Jan 16 13:51:20 2024 +0100

    Tpot NN Colab test

diff --git a/TPOT_NN.ipynb b/TPOT_NN.ipynb
new file mode 100644
index 0000000..9244f16
--- /dev/null
+++ b/TPOT_NN.ipynb
@@ -0,0 +1,633 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "A100"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "301a7a8edb37404da369a8607c8e7f39": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_550f9e30494a4cedb8265819b10eda9f",
+              "IPY_MODEL_305334ab788d4c939ef090481cd37014",
+              "IPY_MODEL_5e6d831eb4da408db14779d0bfa1c3ce"
+            ],
+            "layout": "IPY_MODEL_59def110c5334bc7865801cc2c2b1201"
+          }
+        },
+        "550f9e30494a4cedb8265819b10eda9f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_30407bbfe0534d839965fda6ff2ad7a6",
+            "placeholder": "​",
+            "style": "IPY_MODEL_7a64c0004a5b4f63a5e3e123a1bedb42",
+            "value": "Optimization Progress: 100%"
+          }
+        },
+        "305334ab788d4c939ef090481cd37014": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "FloatProgressModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "FloatProgressModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "ProgressView",
+            "bar_style": "",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_09b1baa49b8e4261aa12044843037665",
+            "max": 110,
+            "min": 0,
+            "orientation": "horizontal",
+            "style": "IPY_MODEL_b895890e16f04226ae3379d0a2b061b2",
+            "value": 110
+          }
+        },
+        "5e6d831eb4da408db14779d0bfa1c3ce": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_72f3f1a3683e4b7d93549d9bf998f1c7",
+            "placeholder": "​",
+            "style": "IPY_MODEL_b6633832327f415c85a7d0fc13a1aca3",
+            "value": " 110/110 [11:46&lt;00:00,  5.36s/pipeline]"
+          }
+        },
+        "59def110c5334bc7865801cc2c2b1201": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": "hidden",
+            "width": null
+          }
+        },
+        "30407bbfe0534d839965fda6ff2ad7a6": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "7a64c0004a5b4f63a5e3e123a1bedb42": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "09b1baa49b8e4261aa12044843037665": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b895890e16f04226ae3379d0a2b061b2": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "72f3f1a3683e4b7d93549d9bf998f1c7": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "b6633832327f415c85a7d0fc13a1aca3": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install deap update_checker tqdm stopit xgboost\n",
+        "!pip install dask[delayed] dask[dataframe] dask-ml fsspec>=0.3.3 distributed>=2.10.0\n",
+        "!pip install scikit-mdr skrebate\n",
+        "!pip install tpot"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4deY2TpmK4OV",
+        "outputId": "13db84e2-85a0-4280-e984-a3969cf32afe"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting deap\n",
+            "  Downloading deap-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (135 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/135.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.7/135.4 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.4/135.4 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting update_checker\n",
+            "  Downloading update_checker-0.18.0-py3-none-any.whl (7.0 kB)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.1)\n",
+            "Collecting stopit\n",
+            "  Downloading stopit-1.1.2.tar.gz (18 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from deap) (1.23.5)\n",
+            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from update_checker) (2.31.0)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update_checker) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update_checker) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update_checker) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update_checker) (2023.11.17)\n",
+            "Building wheels for collected packages: stopit\n",
+            "  Building wheel for stopit (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for stopit: filename=stopit-1.1.2-py3-none-any.whl size=11937 sha256=4915a58d356a870dbd83a619555155796895ae1868a1f566245606afe7205dcb\n",
+            "  Stored in directory: /root/.cache/pip/wheels/af/f9/87/bf5b3d565c2a007b4dae9d8142dccc85a9f164e517062dd519\n",
+            "Successfully built stopit\n",
+            "Installing collected packages: stopit, deap, update_checker\n",
+            "Successfully installed deap-1.4.1 stopit-1.1.2 update_checker-0.18.0\n",
+            "Collecting scikit-mdr\n",
+            "  Downloading scikit_MDR-0.4.5-py3-none-any.whl (15 kB)\n",
+            "Collecting skrebate\n",
+            "  Downloading skrebate-0.62.tar.gz (19 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from scikit-mdr) (1.23.5)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from scikit-mdr) (1.11.4)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from scikit-mdr) (1.2.2)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from scikit-mdr) (3.7.1)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (1.2.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (4.47.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (1.4.5)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (23.2)\n",
+            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (9.4.0)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->scikit-mdr) (2.8.2)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->scikit-mdr) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->scikit-mdr) (3.2.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->scikit-mdr) (1.16.0)\n",
+            "Building wheels for collected packages: skrebate\n",
+            "  Building wheel for skrebate (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for skrebate: filename=skrebate-0.62-py3-none-any.whl size=29253 sha256=fabaec25d816d5c70e391d46c1a80a11dd7324cce03c4f89989c8007ebfe3fe3\n",
+            "  Stored in directory: /root/.cache/pip/wheels/dd/67/40/683074a684607162bd0e34dcf7ccdfcab5861c3b2a83286f3a\n",
+            "Successfully built skrebate\n",
+            "Installing collected packages: skrebate, scikit-mdr\n",
+            "Successfully installed scikit-mdr-0.4.5 skrebate-0.62\n",
+            "Collecting tpot\n",
+            "  Downloading TPOT-0.12.1-py3-none-any.whl (87 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.4/87.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.11.4)\n",
+            "Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.2.2)\n",
+            "Requirement already satisfied: deap>=1.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.4.1)\n",
+            "Requirement already satisfied: update-checker>=0.16 in /usr/local/lib/python3.10/dist-packages (from tpot) (0.18.0)\n",
+            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (4.66.1)\n",
+            "Requirement already satisfied: stopit>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.1.2)\n",
+            "Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.5.3)\n",
+            "Requirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from tpot) (1.3.2)\n",
+            "Requirement already satisfied: xgboost>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tpot) (2.0.3)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24.2->tpot) (2023.3.post1)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->tpot) (3.2.0)\n",
+            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from update-checker>=0.16->tpot) (2.31.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.24.2->tpot) (1.16.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2023.11.17)\n",
+            "Installing collected packages: tpot\n",
+            "Successfully installed tpot-0.12.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "print(torch.__version__)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5UH_q7gJL3YU",
+        "outputId": "13ebf85c-bc0a-4c4c-9466-e7194d1d6bc8"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.1.0+cu121\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448,
+          "referenced_widgets": [
+            "301a7a8edb37404da369a8607c8e7f39",
+            "550f9e30494a4cedb8265819b10eda9f",
+            "305334ab788d4c939ef090481cd37014",
+            "5e6d831eb4da408db14779d0bfa1c3ce",
+            "59def110c5334bc7865801cc2c2b1201",
+            "30407bbfe0534d839965fda6ff2ad7a6",
+            "7a64c0004a5b4f63a5e3e123a1bedb42",
+            "09b1baa49b8e4261aa12044843037665",
+            "b895890e16f04226ae3379d0a2b061b2",
+            "72f3f1a3683e4b7d93549d9bf998f1c7",
+            "b6633832327f415c85a7d0fc13a1aca3"
+          ]
+        },
+        "id": "luBYuKTlKPlX",
+        "outputId": "b13f1749-e3b4-4152-d48f-cb857d408727"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Optimization Progress:   0%|          | 0/110 [00:00<?, ?pipeline/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "301a7a8edb37404da369a8607c8e7f39"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Generation 1 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 2 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 3 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 4 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 5 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 6 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 7 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 8 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 9 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Generation 10 - Current best internal CV score: 1.0\n",
+            "\n",
+            "Best pipeline: PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, percentile=57)), batch_size=4, learning_rate=1.0, num_epochs=15, weight_decay=0)\n",
+            "1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "from tpot import TPOTClassifier\n",
+        "from sklearn.datasets import make_blobs\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X, y = make_blobs(n_samples=100, centers=2, n_features=3, random_state=42)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)\n",
+        "\n",
+        "clf = TPOTClassifier(config_dict='TPOT NN', template='Selector-Transformer-PytorchLRClassifier',\n",
+        "                     verbosity=2, population_size=10, generations=10)\n",
+        "clf.fit(X_train, y_train)\n",
+        "print(clf.score(X_test, y_test))\n",
+        "clf.export('tpot_nn_demo_pipeline.py')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from deap import creator\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "import numpy as np\n",
+        "\n",
+        "print(clf.score(X_test, y_test))\n",
+        "# print part of the pipeline dictionary\n",
+        "print(dict(list(clf.evaluated_individuals_.items())[0:2]))\n",
+        "# print a pipeline and its values\n",
+        "pipeline_str = list(clf.evaluated_individuals_.keys())[0]\n",
+        "print(pipeline_str)\n",
+        "print(clf.evaluated_individuals_[pipeline_str])\n",
+        "# convert pipeline string to scikit-learn pipeline object\n",
+        "optimized_pipeline = creator.Individual.from_string(pipeline_str, clf._pset)  # deap object\n",
+        "fitted_pipeline = clf._toolbox.compile(expr=optimized_pipeline)  # scikit-learn pipeline object\n",
+        "# print scikit-learn pipeline object\n",
+        "print(fitted_pipeline)\n",
+        "\n",
+        "# Fix random state when the operator allows (optional) just for getting consistent CV score\n",
+        "for step in fitted_pipeline.steps:\n",
+        "    if 'random_state' in step[1].get_params():\n",
+        "        step[1].set_params(random_state=42)\n",
+        "\n",
+        "# CV scores from scikit-learn\n",
+        "scores = cross_val_score(fitted_pipeline, X_train, y_train, cv=5, scoring='accuracy', verbose=0)\n",
+        "print(np.mean(scores))\n",
+        "print(clf.evaluated_individuals_[pipeline_str])\n",
+        ""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mq0lkWNuO1WU",
+        "outputId": "f2615618-f61f-4f75-91da-c6fd50745777"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.0\n",
+            "{'PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, SelectPercentile__percentile=57)), PytorchLRClassifier__batch_size=4, PytorchLRClassifier__learning_rate=1.0, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}, 'PytorchLRClassifier(OneHotEncoder(SelectFwe(input_matrix, SelectFwe__alpha=0.039), OneHotEncoder__minimum_fraction=0.2, OneHotEncoder__sparse=False, OneHotEncoder__threshold=10), PytorchLRClassifier__batch_size=8, PytorchLRClassifier__learning_rate=0.01, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0.001)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}}\n",
+            "PytorchLRClassifier(MaxAbsScaler(SelectPercentile(input_matrix, SelectPercentile__percentile=57)), PytorchLRClassifier__batch_size=4, PytorchLRClassifier__learning_rate=1.0, PytorchLRClassifier__num_epochs=15, PytorchLRClassifier__weight_decay=0)\n",
+            "{'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}\n",
+            "Pipeline(steps=[('selectpercentile', SelectPercentile(percentile=57)),\n",
+            "                ('maxabsscaler', MaxAbsScaler()),\n",
+            "                ('pytorchlrclassifier',\n",
+            "                 PytorchLRClassifier(batch_size=4, learning_rate=1.0,\n",
+            "                                     num_epochs=15, weight_decay=0))])\n",
+            "1.0\n",
+            "{'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 3, 'internal_cv_score': 1.0}\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file