mlbox_tabular.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mlbox-tabular.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMIm2BJ4fpZ2L6FFZSC9H1K",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tmtong/ai-library-exploration/blob/main/mlbox_tabular.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yapbKgF656Y",
        "outputId": "07d220d0-a463-440a-d42f-dc98c560be51"
      },
      "source": [
        "!pip install git+https://github.com/fastai/fastai.git\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/fastai/fastai.git\n",
            "  Cloning https://github.com/fastai/fastai.git to /tmp/pip-req-build-42p8e3th\n",
            "  Running command git clone -q https://github.com/fastai/fastai.git /tmp/pip-req-build-42p8e3th\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (19.3.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (20.9)\n",
            "Collecting fastcore<1.4,>=1.3.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d8/b0/f1fbf554e0bf3c76e1bdc3b82eedfe41fcf656479586be38c64421082b1b/fastcore-1.3.20-py3-none-any.whl (53kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 2.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: torchvision>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.10.0+cu102)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (3.0.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.25.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (2.23.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (3.13)\n",
            "Requirement already satisfied: fastprogress>=0.2.4 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.0.0)\n",
            "Requirement already satisfied: pillow>6.0.0 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (7.1.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.22.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.4.1)\n",
            "Requirement already satisfied: spacy<4 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (2.2.4)\n",
            "Requirement already satisfied: torch<1.10,>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.9.0+cu102)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->fastai==2.4.1) (2.4.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.8.2->fastai==2.4.1) (1.18.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (1.3.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->fastai==2.4.1) (2018.9)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (2021.5.30)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->fastai==2.4.1) (0.14.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (4.41.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (2.0.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (57.0.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (0.4.1)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (7.4.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (0.8.2)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (3.0.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch<1.10,>=1.7.0->fastai==2.4.1) (3.7.4.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->fastai==2.4.1) (1.15.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<4->fastai==2.4.1) (4.6.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<4->fastai==2.4.1) (3.4.1)\n",
            "Building wheels for collected packages: fastai\n",
            "  Building wheel for fastai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastai: filename=fastai-2.4.1-cp37-none-any.whl size=188188 sha256=e40d72c03587fa8b980b9f18c790342506c55e0deb894c18ed15151c2803066b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-fhd_jim5/wheels/cf/46/39/b2d08762125ed2376861976ab2c4ac30c029b86e375735d9b8\n",
            "Successfully built fastai\n",
            "Installing collected packages: fastcore, fastai\n",
            "  Found existing installation: fastai 1.0.61\n",
            "    Uninstalling fastai-1.0.61:\n",
            "      Successfully uninstalled fastai-1.0.61\n",
            "Successfully installed fastai-2.4.1 fastcore-1.3.20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msxoTL9x7KhZ",
        "outputId": "012a60ef-6859-4566-8dae-471dd9ab2465"
      },
      "source": [
        " !pip install mlbox"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlbox in /usr/local/lib/python3.7/dist-packages (0.8.5)\n",
            "Requirement already satisfied: joblib==0.14.1 in /usr/local/lib/python3.7/dist-packages (from mlbox) (0.14.1)\n",
            "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.7/dist-packages (from mlbox) (1.4.1)\n",
            "Requirement already satisfied: tables==3.5.2 in /usr/local/lib/python3.7/dist-packages (from mlbox) (3.5.2)\n",
            "Requirement already satisfied: lightgbm==2.3.1 in /usr/local/lib/python3.7/dist-packages (from mlbox) (2.3.1)\n",
            "Requirement already satisfied: pandas==0.25.3 in /usr/local/lib/python3.7/dist-packages (from mlbox) (0.25.3)\n",
            "Requirement already satisfied: matplotlib==3.0.3 in /usr/local/lib/python3.7/dist-packages (from mlbox) (3.0.3)\n",
            "Requirement already satisfied: hyperopt==0.2.3 in /usr/local/lib/python3.7/dist-packages (from mlbox) (0.2.3)\n",
            "Requirement already satisfied: tensorflow==2.0.0 in /usr/local/lib/python3.7/dist-packages (from mlbox) (2.0.0)\n",
            "Requirement already satisfied: numpy==1.18.2 in /usr/local/lib/python3.7/dist-packages (from mlbox) (1.18.2)\n",
            "Requirement already satisfied: xlrd==1.2.0 in /usr/local/lib/python3.7/dist-packages (from mlbox) (1.2.0)\n",
            "Requirement already satisfied: scikit-learn==0.22.1 in /usr/local/lib/python3.7/dist-packages (from mlbox) (0.22.1)\n",
            "Requirement already satisfied: mock>=2.0 in /usr/local/lib/python3.7/dist-packages (from tables==3.5.2->mlbox) (4.0.3)\n",
            "Requirement already satisfied: numexpr>=2.6.2 in /usr/local/lib/python3.7/dist-packages (from tables==3.5.2->mlbox) (2.7.3)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from tables==3.5.2->mlbox) (1.15.0)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from pandas==0.25.3->mlbox) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==0.25.3->mlbox) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.0.3->mlbox) (0.10.0)\n",
            "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==3.0.3->mlbox) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib==3.0.3->mlbox) (1.3.1)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3->mlbox) (1.3.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3->mlbox) (0.16.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3->mlbox) (4.41.1)\n",
            "Requirement already satisfied: networkx==2.2 in /usr/local/lib/python3.7/dist-packages (from hyperopt==0.2.3->mlbox) (2.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (0.2.0)\n",
            "Requirement already satisfied: tensorboard<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (2.0.2)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (1.12.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (0.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (3.17.3)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (1.34.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (1.1.2)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (1.0.8)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (1.1.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (3.3.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (2.0.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (0.8.1)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.0.0->mlbox) (0.36.2)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx==2.2->hyperopt==0.2.3->mlbox) (4.4.2)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (1.32.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (0.4.4)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (57.0.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (3.3.4)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.8->tensorflow==2.0.0->mlbox) (3.1.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (4.2.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (1.3.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (4.6.0)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.8->tensorflow==2.0.0->mlbox) (1.5.2)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (0.4.8)\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<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (3.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.1.0,>=2.0.0->tensorflow==2.0.0->mlbox) (3.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_0FgpHHi7bDo",
        "outputId": "66237f56-e173-4585-f803-e5f9bcdaf479"
      },
      "source": [
        "!pip install git+https://github.com/fastai/fastai.git\n",
        "from fastai.tabular.all import *\n",
        "path = untar_data(URLs.ADULT_SAMPLE)\n",
        "path.ls()\n",
        "\n",
        "from mlbox.preprocessing import *\n",
        "from mlbox.optimisation import *\n",
        "from mlbox.prediction import *\n",
        "data_path = '/root/.fastai/data/adult_sample/adult.csv'\n",
        "paths = [data_path] #to modify\n",
        "target_name = \"salary\" #to modify\n",
        "data = Reader(sep=\",\").train_test_split(paths, target_name)  #reading\n",
        "data = Drift_thresholder().fit_transform(data)  #deleting non-stable variables"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/fastai/fastai.git\n",
            "  Cloning https://github.com/fastai/fastai.git to /tmp/pip-req-build-04ilh9ya\n",
            "  Running command git clone -q https://github.com/fastai/fastai.git /tmp/pip-req-build-04ilh9ya\n",
            "Requirement already satisfied (use --upgrade to upgrade): fastai==2.4.1 from git+https://github.com/fastai/fastai.git in /usr/local/lib/python3.7/dist-packages\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (19.3.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (20.9)\n",
            "Requirement already satisfied: fastcore<1.4,>=1.3.8 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.3.20)\n",
            "Requirement already satisfied: torchvision>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.10.0+cu102)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (3.0.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.25.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (2.23.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (3.13)\n",
            "Requirement already satisfied: fastprogress>=0.2.4 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.0.0)\n",
            "Requirement already satisfied: pillow>6.0.0 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (7.1.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (0.22.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.4.1)\n",
            "Requirement already satisfied: spacy<4 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (2.2.4)\n",
            "Requirement already satisfied: torch<1.10,>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from fastai==2.4.1) (1.9.0+cu102)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->fastai==2.4.1) (2.4.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision>=0.8.2->fastai==2.4.1) (1.18.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->fastai==2.4.1) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->fastai==2.4.1) (2018.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fastai==2.4.1) (3.0.4)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->fastai==2.4.1) (0.14.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (0.8.2)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.1.3)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (4.41.1)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (7.4.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (2.0.5)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (1.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (3.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<4->fastai==2.4.1) (57.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch<1.10,>=1.7.0->fastai==2.4.1) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->fastai==2.4.1) (1.15.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<4->fastai==2.4.1) (4.6.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<4->fastai==2.4.1) (3.4.1)\n",
            "Building wheels for collected packages: fastai\n",
            "  Building wheel for fastai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fastai: filename=fastai-2.4.1-cp37-none-any.whl size=188188 sha256=3687c16c79407ac0978e20a62e98c6703dbe2c1ee36cd09f10458613548c0ea6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-5lu2ig3m/wheels/cf/46/39/b2d08762125ed2376861976ab2c4ac30c029b86e375735d9b8\n",
            "Successfully built fastai\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "reading csv : adult.csv ...\n",
            "cleaning data ...\n",
            "CPU time: 4.834441184997559 seconds\n",
            "\n",
            "You have no test dataset !\n",
            "\n",
            "> Number of common features : 14\n",
            "\n",
            "gathering and crunching for train and test datasets ...\n",
            "reindexing for train and test datasets ...\n",
            "dropping training duplicates ...\n",
            "dropping constant variables on training set ...\n",
            "\n",
            "> Number of categorical features: 8\n",
            "> Number of numerical features: 6\n",
            "> Number of training samples : 32540\n",
            "> Number of test samples : 0\n",
            "\n",
            "> Top sparse features (% missing values on train set):\n",
            "occupation       1.6\n",
            "education-num    1.5\n",
            "dtype: float64\n",
            "\n",
            "> Task : classification\n",
            "<50k     24701\n",
            ">=50k     7839\n",
            "Name: salary, dtype: int64\n",
            "\n",
            "encoding target ...\n",
            "\n",
            "You have no test dataset...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNrv-Nei8FQ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0866f1a1-4a33-4a2a-86d9-d11c51af0963"
      },
      "source": [
        "opt = Optimiser()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlbox/optimisation/optimiser.py:74: UserWarning: Optimiser will save all your fitted models into directory 'save/joblib'. Please clear it regularly.\n",
            "  +str(self.to_path)+\"/joblib'. Please clear it regularly.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKp-feiv-lst",
        "outputId": "99497450-7327-4d5d-9981-f65d5aac1b7e"
      },
      "source": [
        "Optimiser().evaluate(None, data)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlbox/optimisation/optimiser.py:74: UserWarning: Optimiser will save all your fitted models into directory 'save/joblib'. Please clear it regularly.\n",
            "  +str(self.to_path)+\"/joblib'. Please clear it regularly.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "No parameters set. Default configuration is tested\n",
            "\n",
            "##################################################### testing hyper-parameters... #####################################################\n",
            "\n",
            ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': '<NULL>'}\n",
            "\n",
            ">>> CA ENCODER :{'strategy': 'label_encoding'}\n",
            "\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'max_depth': -1, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample': 0.9, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            "\n",
            "\n",
            "MEAN SCORE : neg_log_loss = -0.2880682040042626\n",
            "VARIANCE : 0.0036415354924750065 (fold 1 = -0.28442666851178755, fold 2 = -0.29170973949673756)\n",
            "CPU time: 3.512822389602661 seconds\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.2880682040042626"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9yGv8rBAN_N",
        "outputId": "da76f620-e950-4d91-d83a-2206f43b0994"
      },
      "source": [
        "space = {\n",
        "\n",
        "        'ne__numerical_strategy' : {\"space\" : [0, 'mean']},\n",
        "\n",
        "        'ce__strategy' : {\"space\" : [\"label_encoding\", \"random_projection\", \"entity_embedding\"]},\n",
        "\n",
        "        'fs__strategy' : {\"space\" : [\"variance\", \"rf_feature_importance\"]},\n",
        "        'fs__threshold': {\"search\" : \"choice\", \"space\" : [0.1, 0.2, 0.3]},\n",
        "\n",
        "        'est__strategy' : {\"space\" : [\"LightGBM\"]},\n",
        "        'est__max_depth' : {\"search\" : \"choice\", \"space\" : [5,6]},\n",
        "        'est__subsample' : {\"search\" : \"uniform\", \"space\" : [0.6,0.9]}\n",
        "\n",
        "        }\n",
        "\n",
        "best = opt.optimise(space, data, max_evals = 5)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "##################################################### testing hyper-parameters... #####################################################\n",
            ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': '<NULL>'}\n",
            ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
            ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.3}\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'subsample': 0.7104076877139489, 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            "MEAN SCORE : neg_log_loss = -0.3382878587486122\n",
            "VARIANCE : 0.001458106892452099 (fold 1 = -0.33682975185616015, fold 2 = -0.33974596564106435)\n",
            "CPU time: 3.3157284259796143 seconds\n",
            "##################################################### testing hyper-parameters... #####################################################\n",
            ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': '<NULL>'}\n",
            ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
            ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.1}\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 6, 'subsample': 0.7223672465625196, 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            " 20%|██        | 1/5 [00:03<00:13,  3.35s/trial, best loss: 0.3382878587486122]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.50s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.51s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MEAN SCORE : neg_log_loss = -0.2845650759783326\n",
            "VARIANCE : 0.003592959703563686 (fold 1 = -0.28097211627476887, fold 2 = -0.28815803568189624)\n",
            "CPU time: 36.48231816291809 seconds\n",
            "##################################################### testing hyper-parameters... #####################################################\n",
            ">>> NA ENCODER :{'numerical_strategy': 0, 'categorical_strategy': '<NULL>'}\n",
            ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
            ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.3}\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'subsample': 0.6904651083623674, 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            " 40%|████      | 2/5 [00:39<00:39, 13.31s/trial, best loss: 0.2845650759783326]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.51s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.50s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MEAN SCORE : neg_log_loss = -0.28340166681353074\n",
            "VARIANCE : 0.0035570858548371336 (fold 1 = -0.27984458095869363, fold 2 = -0.2869587526683679)\n",
            "CPU time: 12.746681928634644 seconds\n",
            "##################################################### testing hyper-parameters... #####################################################\n",
            ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': '<NULL>'}\n",
            ">>> CA ENCODER :{'strategy': 'entity_embedding'}\n",
            ">>> FEATURE SELECTOR :{'strategy': 'variance', 'threshold': 0.2}\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'subsample': 0.6222404628769318, 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            "MEAN SCORE : neg_log_loss = -0.2820602260473074\n",
            "VARIANCE : 0.0033658579448214987 (fold 1 = -0.27869436810248593, fold 2 = -0.28542608399212893)\n",
            "CPU time: 6.23528528213501 seconds\n",
            "##################################################### testing hyper-parameters... #####################################################\n",
            ">>> NA ENCODER :{'numerical_strategy': 'mean', 'categorical_strategy': '<NULL>'}\n",
            ">>> CA ENCODER :{'strategy': 'random_projection'}\n",
            ">>> FEATURE SELECTOR :{'strategy': 'rf_feature_importance', 'threshold': 0.1}\n",
            ">>> ESTIMATOR :{'strategy': 'LightGBM', 'max_depth': 5, 'subsample': 0.8768745729512117, 'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 0.8, 'importance_type': 'split', 'learning_rate': 0.05, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 500, 'n_jobs': -1, 'num_leaves': 31, 'objective': None, 'random_state': None, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'silent': True, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'nthread': -1, 'seed': 0}\n",
            " 80%|████████  | 4/5 [00:59<00:11, 11.09s/trial, best loss: 0.2820602260473074]"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.50s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MEAN SCORE : neg_log_loss = -0.2817777726013707\n",
            "VARIANCE : 0.0030283006329343276 (fold 1 = -0.27874947196843636, fold 2 = -0.284806073234305)\n",
            "CPU time: 9.902539491653442 seconds\n",
            "100%|██████████| 5/5 [01:08<00:00, 13.78s/trial, best loss: 0.2817777726013707]\n",
            "\n",
            "\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BEST HYPER-PARAMETERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "\n",
            "{'ce__strategy': 'random_projection', 'est__max_depth': 5, 'est__strategy': 'LightGBM', 'est__subsample': 0.8768745729512117, 'fs__strategy': 'rf_feature_importance', 'fs__threshold': 0.1, 'ne__numerical_strategy': 'mean'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "1Rh9JlyMCUyS",
        "outputId": "9d57ea75-d973-4f98-e578-0cd17876b926"
      },
      "source": [
        "Predictor().fit_predict(best, data)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "fitting the pipeline ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.99s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/pipeline.py:315: UserWarning: Persisting input arguments took 0.96s to run.\n",
            "If this happens often in your code, it can cause performance problems \n",
            "(results will be correct in all cases). \n",
            "The reason for this is probably some large input arguments for a wrapped\n",
            " function (e.g. large strings).\n",
            "THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an\n",
            " example so that they can fix the problem.\n",
            "  **fit_params_steps[name])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CPU time: 10.388745546340942 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "> Feature importances dumped into directory : save\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlbox/prediction/predictor.py:405: UserWarning: You have no test dataset. Cannot predict !\n",
            "  warnings.warn(\"You have no test dataset. Cannot predict !\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<mlbox.prediction.predictor.Predictor at 0x7f56ee947a10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    }
  ]
}