12: FLAML_Class_Reg_TimeSeries.ipynb

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  "metadata": {
    "colab": {
      "name": "12: FLAML_Class_Reg_TimeSeries.ipynb",
      "provenance": [],
      "toc_visible": true,
      "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/gulabpatel/AutoML/blob/main/12%3A%20FLAML_Class_Reg_TimeSeries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wbA740Dfx_aV"
      },
      "source": [
        "## FLAML: A Fast and Lightweight AutoML Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhC_ulqGvYSq"
      },
      "source": [
        "!pip install flaml"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00xUXNvfwyXV"
      },
      "source": [
        "# Classification Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDEkaxcDvJuB",
        "outputId": "a1d925b4-1234-40ee-e590-6e529be5181e"
      },
      "source": [
        "from flaml import AutoML\n",
        "from sklearn.datasets import load_iris\n",
        "# Initialize an AutoML instance\n",
        "automl = AutoML()\n",
        "# Specify automl goal and constraint\n",
        "automl_settings = {\n",
        "    \"time_budget\": 100,  # in seconds\n",
        "    \"metric\": 'accuracy',\n",
        "    \"task\": 'classification',\n",
        "    \"log_file_name\": \"iris.log\",\n",
        "}\n",
        "X_train, y_train = load_iris(return_X_y=True)\n",
        "# Train with labeled input data\n",
        "automl.fit(X_train=X_train, y_train=y_train,\n",
        "           **automl_settings)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[flaml.automl: 10-02 10:38:47] {1432} INFO - Evaluation method: cv\n",
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            "[flaml.automl: 10-02 10:38:47] {1515} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']\n",
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            "[flaml.automl: 10-02 10:38:47] {1866} INFO - Estimated sufficient time budget=428s. Estimated necessary time budget=8s.\n",
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            "No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:47] {1944} INFO -  at 59.9s,\testimator lrl1's best error=0.0200,\tbest estimator lrl1's best error=0.0200\n",
            "[flaml.automl: 10-02 10:39:47] {1748} INFO - iteration 150, current learner rf\n",
            "[flaml.automl: 10-02 10:39:48] {1944} INFO -  at 61.1s,\testimator rf's best error=0.0333,\tbest estimator lrl1's best error=0.0200\n",
            "[flaml.automl: 10-02 10:39:48] {1748} INFO - iteration 151, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:39:49] {1944} INFO -  at 62.2s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0200\n",
            "[flaml.automl: 10-02 10:39:49] {1748} INFO - iteration 152, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:50] {1944} INFO -  at 62.7s,\testimator lrl1's best error=0.0200,\tbest estimator lrl1's best error=0.0200\n",
            "[flaml.automl: 10-02 10:39:50] {1748} INFO - iteration 153, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:50] {1944} INFO -  at 63.3s,\testimator lrl1's best error=0.0200,\tbest estimator lrl1's best error=0.0200\n",
            "[flaml.automl: 10-02 10:39:50] {1748} INFO - iteration 154, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:51] {1944} INFO -  at 63.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:51] {1748} INFO - iteration 155, current learner lgbm\n",
            "[flaml.automl: 10-02 10:39:51] {1944} INFO -  at 63.8s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:51] {1748} INFO - iteration 156, current learner lgbm\n",
            "[flaml.automl: 10-02 10:39:51] {1944} INFO -  at 63.9s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:51] {1748} INFO - iteration 157, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:52] {1944} INFO -  at 64.4s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:52] {1748} INFO - iteration 158, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:52] {1944} INFO -  at 64.9s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:52] {1748} INFO - iteration 159, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:39:53] {1944} INFO -  at 66.1s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:53] {1748} INFO - iteration 160, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:54] {1944} INFO -  at 66.6s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:54] {1748} INFO - iteration 161, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:54] {1944} INFO -  at 67.1s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:54] {1748} INFO - iteration 162, current learner xgboost\n",
            "[flaml.automl: 10-02 10:39:54] {1944} INFO -  at 67.1s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:54] {1748} INFO - iteration 163, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:55] {1944} INFO -  at 67.7s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:55] {1748} INFO - iteration 164, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:55] {1944} INFO -  at 68.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:55] {1748} INFO - iteration 165, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:56] {1944} INFO -  at 68.7s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:56] {1748} INFO - iteration 166, current learner xgboost\n",
            "[flaml.automl: 10-02 10:39:56] {1944} INFO -  at 68.8s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:56] {1748} INFO - iteration 167, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:56] {1944} INFO -  at 69.3s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:56] {1748} INFO - iteration 168, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:57] {1944} INFO -  at 69.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:57] {1748} INFO - iteration 169, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:57] {1944} INFO -  at 70.3s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:58] {1748} INFO - iteration 170, current learner lgbm\n",
            "[flaml.automl: 10-02 10:39:58] {1944} INFO -  at 70.4s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:58] {1748} INFO - iteration 171, current learner lgbm\n",
            "[flaml.automl: 10-02 10:39:58] {1944} INFO -  at 70.4s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:58] {1748} INFO - iteration 172, current learner lgbm\n",
            "[flaml.automl: 10-02 10:39:58] {1944} INFO -  at 70.4s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:58] {1748} INFO - iteration 173, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:58] {1944} INFO -  at 71.0s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:58] {1748} INFO - iteration 174, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:59] {1944} INFO -  at 71.5s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:59] {1748} INFO - iteration 175, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:39:59] {1944} INFO -  at 72.0s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:59] {1748} INFO - iteration 176, current learner xgboost\n",
            "[flaml.automl: 10-02 10:39:59] {1944} INFO -  at 72.1s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:39:59] {1748} INFO - iteration 177, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:00] {1944} INFO -  at 72.6s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:00] {1748} INFO - iteration 178, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:00] {1944} INFO -  at 73.1s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:00] {1748} INFO - iteration 179, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:00] {1944} INFO -  at 73.2s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:00] {1748} INFO - iteration 180, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:40:01] {1944} INFO -  at 74.3s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:01] {1748} INFO - iteration 181, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:02] {1944} INFO -  at 74.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:02] {1748} INFO - iteration 182, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:03] {1944} INFO -  at 75.3s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:03] {1748} INFO - iteration 183, current learner rf\n",
            "[flaml.automl: 10-02 10:40:04] {1944} INFO -  at 76.6s,\testimator rf's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:04] {1748} INFO - iteration 184, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:04] {1944} INFO -  at 76.6s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:04] {1748} INFO - iteration 185, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:04] {1944} INFO -  at 76.7s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:04] {1748} INFO - iteration 186, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:04] {1944} INFO -  at 77.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:04] {1748} INFO - iteration 187, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:04] {1944} INFO -  at 77.2s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:04] {1748} INFO - iteration 188, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:05] {1944} INFO -  at 77.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:05] {1748} INFO - iteration 189, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:05] {1944} INFO -  at 77.8s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:05] {1748} INFO - iteration 190, current learner rf\n",
            "[flaml.automl: 10-02 10:40:06] {1944} INFO -  at 79.1s,\testimator rf's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:06] {1748} INFO - iteration 191, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:07] {1944} INFO -  at 79.6s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:07] {1748} INFO - iteration 192, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:07] {1944} INFO -  at 79.6s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:07] {1748} INFO - iteration 193, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:40:08] {1944} INFO -  at 80.9s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:08] {1748} INFO - iteration 194, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:08] {1944} INFO -  at 81.0s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:08] {1748} INFO - iteration 195, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:09] {1944} INFO -  at 81.5s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:09] {1748} INFO - iteration 196, current learner rf\n",
            "[flaml.automl: 10-02 10:40:10] {1944} INFO -  at 82.7s,\testimator rf's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:10] {1748} INFO - iteration 197, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:10] {1944} INFO -  at 82.8s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:10] {1748} INFO - iteration 198, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:10] {1944} INFO -  at 83.3s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:10] {1748} INFO - iteration 199, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:11] {1944} INFO -  at 83.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:11] {1748} INFO - iteration 200, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:12] {1944} INFO -  at 84.3s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:12] {1748} INFO - iteration 201, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:12] {1944} INFO -  at 84.9s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:12] {1748} INFO - iteration 202, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:12] {1944} INFO -  at 84.9s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:12] {1748} INFO - iteration 203, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:12] {1944} INFO -  at 85.0s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:12] {1748} INFO - iteration 204, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:40:13] {1944} INFO -  at 86.1s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:13] {1748} INFO - iteration 205, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:14] {1944} INFO -  at 86.6s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:14] {1748} INFO - iteration 206, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:14] {1944} INFO -  at 87.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:14] {1748} INFO - iteration 207, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:14] {1944} INFO -  at 87.2s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:14] {1748} INFO - iteration 208, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:15] {1944} INFO -  at 87.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:15] {1748} INFO - iteration 209, current learner extra_tree\n",
            "[flaml.automl: 10-02 10:40:16] {1944} INFO -  at 88.9s,\testimator extra_tree's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:16] {1748} INFO - iteration 210, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:16] {1944} INFO -  at 89.0s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:16] {1748} INFO - iteration 211, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:16] {1944} INFO -  at 89.0s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:16] {1748} INFO - iteration 212, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:16] {1944} INFO -  at 89.1s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:16] {1748} INFO - iteration 213, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:16] {1944} INFO -  at 89.1s,\testimator xgboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:16] {1748} INFO - iteration 214, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:17] {1944} INFO -  at 89.6s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:17] {1748} INFO - iteration 215, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:17] {1944} INFO -  at 90.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:17] {1748} INFO - iteration 216, current learner catboost\n",
            "[flaml.automl: 10-02 10:40:18] {1944} INFO -  at 90.9s,\testimator catboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:18] {1748} INFO - iteration 217, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:19] {1944} INFO -  at 91.4s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:19] {1748} INFO - iteration 218, current learner lgbm\n",
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            "[flaml.automl: 10-02 10:40:19] {1748} INFO - iteration 221, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:19] {1944} INFO -  at 91.6s,\testimator xgboost's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:19] {1748} INFO - iteration 222, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:19] {1944} INFO -  at 91.6s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:19] {1748} INFO - iteration 223, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:19] {1944} INFO -  at 92.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:19] {1748} INFO - iteration 224, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:20] {1944} INFO -  at 92.7s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:20] {1748} INFO - iteration 225, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:20] {1944} INFO -  at 92.7s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
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            "[flaml.automl: 10-02 10:40:20] {1944} INFO -  at 92.8s,\testimator lgbm's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:20] {1748} INFO - iteration 227, current learner catboost\n",
            "[flaml.automl: 10-02 10:40:20] {1944} INFO -  at 93.3s,\testimator catboost's best error=0.0400,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:20] {1748} INFO - iteration 228, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:21] {1944} INFO -  at 93.3s,\testimator lgbm's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:21] {1748} INFO - iteration 229, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:21] {1944} INFO -  at 93.9s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
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            "[flaml.automl: 10-02 10:40:22] {1944} INFO -  at 95.1s,\testimator rf's best error=0.0267,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:22] {1748} INFO - iteration 231, current learner lrl1\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:23] {1944} INFO -  at 95.7s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
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            "[flaml.automl: 10-02 10:40:23] {1944} INFO -  at 95.7s,\testimator xgboost's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
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            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:23] {1944} INFO -  at 96.2s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
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            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:24] {1944} INFO -  at 96.8s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
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            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
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            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
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            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:26] {1944} INFO -  at 98.7s,\testimator lrl1's best error=0.0133,\tbest estimator lrl1's best error=0.0133\n",
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            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 99.9s,\testimator xgboost's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 248, current learner xgboost\n",
            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 99.9s,\testimator xgboost's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 249, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 100.0s,\testimator lgbm's best error=0.0333,\tbest estimator lrl1's best error=0.0133\n",
            "[flaml.automl: 10-02 10:40:27] {2043} INFO - selected model: LogisticRegression(C=0.5000000000000001, n_jobs=-1, penalty='l1', solver='saga')\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "[flaml.automl: 10-02 10:40:27] {2106} INFO - retrain lrl1 for 0.1s\n",
            "[flaml.automl: 10-02 10:40:27] {2110} INFO - retrained model: LogisticRegression(C=0.5000000000000001, n_jobs=-1, penalty='l1', solver='saga')\n",
            "[flaml.automl: 10-02 10:40:27] {1539} INFO - fit succeeded\n",
            "[flaml.automl: 10-02 10:40:27] {1541} INFO - Time taken to find the best model: 63.79490876197815\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-3p9iLJvWwz",
        "outputId": "4caec3c4-639e-4b49-9aac-7586479fdb47"
      },
      "source": [
        "# Predict\n",
        "print(automl.predict_proba(X_train))\n",
        "# Export the best model\n",
        "print(automl.model)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9.80483031e-01 1.95166436e-02 3.25820402e-07]\n",
            " [9.47464579e-01 5.25334752e-02 1.94577368e-06]\n",
            " [9.71402187e-01 2.85968930e-02 9.19799977e-07]\n",
            " [9.46285943e-01 5.37104282e-02 3.62834019e-06]\n",
            " [9.84023449e-01 1.59762700e-02 2.81071461e-07]\n",
            " [9.83055447e-01 1.69441412e-02 4.12157653e-07]\n",
            " [9.76064339e-01 2.39344615e-02 1.19945266e-06]\n",
            " [9.70281426e-01 2.97177590e-02 8.14790506e-07]\n",
            " [9.36056940e-01 6.39374898e-02 5.57011601e-06]\n",
            " [9.46409549e-01 5.35886492e-02 1.80168146e-06]\n",
            " [9.83741286e-01 1.62585328e-02 1.80922164e-07]\n",
            " [9.62981807e-01 3.70164363e-02 1.75650537e-06]\n",
            " [9.47424899e-01 5.25733259e-02 1.77497095e-06]\n",
            " [9.72460526e-01 2.75385265e-02 9.47173391e-07]\n",
            " [9.95487370e-01 4.51262067e-03 9.74463388e-09]\n",
            " [9.96077569e-01 3.92240806e-03 2.26034078e-08]\n",
            " [9.93056685e-01 6.94325207e-03 6.31443381e-08]\n",
            " [9.80482942e-01 1.95166418e-02 4.15826420e-07]\n",
            " [9.79337642e-01 2.06620820e-02 2.76127039e-07]\n",
            " [9.86677541e-01 1.33221896e-02 2.69832951e-07]\n",
            " [9.54256400e-01 4.57424801e-02 1.11988128e-06]\n",
            " [9.83702568e-01 1.62969670e-02 4.65045273e-07]\n",
            " [9.93436242e-01 6.56367893e-03 7.90733527e-08]\n",
            " [9.44320225e-01 5.56748510e-02 4.92445802e-06]\n",
            " [9.29685458e-01 7.03075641e-02 6.97768198e-06]\n",
            " [9.19970775e-01 8.00250578e-02 4.16691753e-06]\n",
            " [9.63038470e-01 3.69594193e-02 2.11076949e-06]\n",
            " [9.75683687e-01 2.43158663e-02 4.46249277e-07]\n",
            " [9.76177045e-01 2.38225774e-02 3.77392856e-07]\n",
            " [9.45249876e-01 5.47464412e-02 3.68285551e-06]\n",
            " [9.33696305e-01 6.62994629e-02 4.23224286e-06]\n",
            " [9.70373668e-01 2.96256100e-02 7.22355697e-07]\n",
            " [9.92749497e-01 7.25044538e-03 5.76112415e-08]\n",
            " [9.95283439e-01 4.71653952e-03 2.15197002e-08]\n",
            " [9.46409078e-01 5.35886225e-02 2.29938470e-06]\n",
            " [9.77098735e-01 2.29008990e-02 3.65846288e-07]\n",
            " [9.84411949e-01 1.55879399e-02 1.11104078e-07]\n",
            " [9.84010889e-01 1.59888548e-02 2.56406082e-07]\n",
            " [9.57460236e-01 4.25371394e-02 2.62496567e-06]\n",
            " [9.70304602e-01 2.96946985e-02 6.99850659e-07]\n",
            " [9.84350248e-01 1.56494490e-02 3.03313265e-07]\n",
            " [8.43248214e-01 1.56730588e-01 2.11975723e-05]\n",
            " [9.71334911e-01 2.86636374e-02 1.45149715e-06]\n",
            " [9.69668169e-01 3.03292748e-02 2.55570162e-06]\n",
            " [9.67784727e-01 3.22130454e-02 2.22719029e-06]\n",
            " [9.47423842e-01 5.25732672e-02 2.89106896e-06]\n",
            " [9.83362578e-01 1.66370839e-02 3.37660936e-07]\n",
            " [9.64395380e-01 3.56029159e-02 1.70365495e-06]\n",
            " [9.83728454e-01 1.62713353e-02 2.10638166e-07]\n",
            " [9.70881155e-01 2.91181556e-02 6.89145486e-07]\n",
            " [1.47480537e-02 9.12870803e-01 7.23811435e-02]\n",
            " [2.14188256e-02 8.48455354e-01 1.30125820e-01]\n",
            " [6.86316967e-03 8.19060148e-01 1.74076682e-01]\n",
            " [9.64196114e-03 7.85044056e-01 2.05313983e-01]\n",
            " [7.06417786e-03 7.94110761e-01 1.98825061e-01]\n",
            " [7.83116504e-03 7.07070551e-01 2.85098284e-01]\n",
            " [1.45257201e-02 7.36889269e-01 2.48585010e-01]\n",
            " [6.47588401e-02 8.90282861e-01 4.49582987e-02]\n",
            " [9.66170303e-03 8.84469813e-01 1.05868484e-01]\n",
            " [2.65334966e-02 7.62486295e-01 2.10980209e-01]\n",
            " [1.81688796e-02 8.88191020e-01 9.36401008e-02]\n",
            " [2.68605783e-02 8.17489134e-01 1.55650288e-01]\n",
            " [9.34680739e-03 9.30030115e-01 6.06230775e-02]\n",
            " [6.09362777e-03 7.01788045e-01 2.92118327e-01]\n",
            " [8.99264338e-02 8.69681366e-01 4.03921998e-02]\n",
            " [2.36708765e-02 9.15977070e-01 6.03520539e-02]\n",
            " [1.01368368e-02 6.08416670e-01 3.81446493e-01]\n",
            " [2.04108998e-02 9.16517949e-01 6.30711517e-02]\n",
            " [2.08747279e-03 6.40582897e-01 3.57329630e-01]\n",
            " [2.08573267e-02 8.99479754e-01 7.96629191e-02]\n",
            " [5.25180762e-03 4.10931270e-01 5.83816922e-01]\n",
            " [3.12389834e-02 9.10180248e-01 5.85807689e-02]\n",
            " [1.24951917e-03 5.11298757e-01 4.87451724e-01]\n",
            " [5.48922618e-03 7.75682129e-01 2.18828645e-01]\n",
            " [1.95255477e-02 9.09993069e-01 7.04813829e-02]\n",
            " [1.90334838e-02 9.04437734e-01 7.65287824e-02]\n",
            " [4.69571825e-03 8.26809170e-01 1.68495112e-01]\n",
            " [3.26451322e-03 5.99943415e-01 3.96792072e-01]\n",
            " [9.78959769e-03 7.18647752e-01 2.71562650e-01]\n",
            " [6.43760999e-02 9.17095641e-01 1.85282588e-02]\n",
            " [2.12667745e-02 8.98796426e-01 7.99367998e-02]\n",
            " [2.73475124e-02 9.22389550e-01 5.02629381e-02]\n",
            " [3.16864927e-02 9.06205445e-01 6.21080619e-02]\n",
            " [6.54238224e-04 2.79866782e-01 7.19478980e-01]\n",
            " [8.91870622e-03 5.36161382e-01 4.54919912e-01]\n",
            " [2.84404797e-02 7.50714953e-01 2.20844567e-01]\n",
            " [1.10472936e-02 8.41038058e-01 1.47914649e-01]\n",
            " [4.13183590e-03 8.24032408e-01 1.71835756e-01]\n",
            " [3.46610552e-02 8.43893762e-01 1.21445182e-01]\n",
            " [1.49953731e-02 8.10962359e-01 1.74042268e-01]\n",
            " [6.49662565e-03 7.05896946e-01 2.87606428e-01]\n",
            " [1.02047415e-02 7.64409848e-01 2.25385410e-01]\n",
            " [2.03150746e-02 8.93257551e-01 8.64273741e-02]\n",
            " [5.35834047e-02 9.03138514e-01 4.32780817e-02]\n",
            " [1.40236461e-02 7.90310862e-01 1.95665492e-01]\n",
            " [2.83592109e-02 8.64481679e-01 1.07159110e-01]\n",
            " [2.22544565e-02 8.32379276e-01 1.45366267e-01]\n",
            " [1.90212168e-02 8.87908422e-01 9.30703616e-02]\n",
            " [1.46989363e-01 8.35771836e-01 1.72388012e-02]\n",
            " [2.31766698e-02 8.48857423e-01 1.27965907e-01]\n",
            " [1.39726278e-05 1.33064982e-02 9.86679529e-01]\n",
            " [2.83430345e-04 1.21438615e-01 8.78277955e-01]\n",
            " [7.34072519e-05 1.02402193e-01 8.97524399e-01]\n",
            " [1.34895792e-04 1.18114142e-01 8.81750962e-01]\n",
            " [3.94125104e-05 4.40885979e-02 9.55871990e-01]\n",
            " [6.18018944e-06 4.16463242e-02 9.58347496e-01]\n",
            " [1.02128677e-03 1.71432044e-01 8.27546669e-01]\n",
            " [2.32737196e-05 9.80474585e-02 9.01929268e-01]\n",
            " [2.95376354e-05 9.17457536e-02 9.08224709e-01]\n",
            " [8.17854779e-05 5.24529129e-02 9.47465302e-01]\n",
            " [2.20584269e-03 3.37939806e-01 6.59854352e-01]\n",
            " [2.58712649e-04 1.73208575e-01 8.26532712e-01]\n",
            " [2.86152394e-04 1.62314587e-01 8.37399261e-01]\n",
            " [1.72776190e-04 8.90151507e-02 9.10812073e-01]\n",
            " [1.23514947e-04 4.31307849e-02 9.56745700e-01]\n",
            " [4.76015086e-04 1.14593337e-01 8.84930648e-01]\n",
            " [3.58255974e-04 2.03702397e-01 7.95939347e-01]\n",
            " [3.89334636e-05 6.39400037e-02 9.36021063e-01]\n",
            " [3.29525051e-07 9.89408797e-03 9.90105583e-01]\n",
            " [2.93938409e-04 2.79075489e-01 7.20630572e-01]\n",
            " [1.58174185e-04 9.34952093e-02 9.06346617e-01]\n",
            " [5.66560062e-04 1.26206724e-01 8.73226716e-01]\n",
            " [3.12973540e-06 3.97542344e-02 9.60242636e-01]\n",
            " [1.39897184e-03 3.80238033e-01 6.18362996e-01]\n",
            " [2.49744203e-04 1.20504031e-01 8.79246225e-01]\n",
            " [1.79418222e-04 2.08145614e-01 7.91674968e-01]\n",
            " [2.40951189e-03 4.26302017e-01 5.71288471e-01]\n",
            " [2.56756060e-03 3.78385477e-01 6.19046963e-01]\n",
            " [5.92330874e-05 6.35862148e-02 9.36354552e-01]\n",
            " [3.27981425e-04 3.64845393e-01 6.34826625e-01]\n",
            " [3.89125260e-05 1.28005534e-01 8.71955554e-01]\n",
            " [2.87291577e-04 2.39435201e-01 7.60277508e-01]\n",
            " [4.70603350e-05 5.05188688e-02 9.49434071e-01]\n",
            " [1.32995513e-03 4.62559159e-01 5.36110886e-01]\n",
            " [1.00801777e-04 1.63303146e-01 8.36596052e-01]\n",
            " [4.40280431e-05 9.59712038e-02 9.03984768e-01]\n",
            " [1.53583258e-04 4.83542103e-02 9.51492206e-01]\n",
            " [4.21149971e-04 1.95318944e-01 8.04259906e-01]\n",
            " [3.40273716e-03 4.00522120e-01 5.96075142e-01]\n",
            " [6.54583933e-04 2.41308278e-01 7.58037138e-01]\n",
            " [1.11610734e-04 6.47044561e-02 9.35183933e-01]\n",
            " [1.54611550e-03 2.89707222e-01 7.08746663e-01]\n",
            " [2.83430345e-04 1.21438615e-01 8.78277955e-01]\n",
            " [5.53697741e-05 5.14280838e-02 9.48516546e-01]\n",
            " [1.01795810e-04 4.91174783e-02 9.50780726e-01]\n",
            " [6.10414248e-04 1.76133859e-01 8.23255727e-01]\n",
            " [4.60363079e-04 2.36045358e-01 7.63494279e-01]\n",
            " [8.55096340e-04 2.47131585e-01 7.52013319e-01]\n",
            " [4.16204869e-04 8.35254374e-02 9.16058358e-01]\n",
            " [9.32869135e-04 2.16200069e-01 7.82867062e-01]]\n",
            "<flaml.model.LRL1Classifier object at 0x7f707bf9d210>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXE4twtlwk1x"
      },
      "source": [
        "# Regression Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENQ6nHCMwEFi",
        "outputId": "ae26b8b4-8ff1-4977-b802-3e56db0cb617"
      },
      "source": [
        "## Regression Problem\n",
        "from flaml import AutoML\n",
        "from sklearn.datasets import load_boston\n",
        "# Initialize an AutoML instance\n",
        "automl = AutoML()\n",
        "# Specify automl goal and constraint\n",
        "automl_settings = {\n",
        "    \"time_budget\": 100,  # in seconds\n",
        "    \"metric\": 'r2',\n",
        "    \"task\": 'regression',\n",
        "    \"log_file_name\": \"boston.log\",\n",
        "}\n",
        "X_train, y_train = load_boston(return_X_y=True)\n",
        "# Train with labeled input data\n",
        "automl.fit(X_train=X_train, y_train=y_train,\n",
        "           **automl_settings)\n",
        "# Predict\n",
        "print(automl.predict(X_train))\n",
        "# Export the best model\n",
        "print(automl.model)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
            "\n",
            "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
            "    the documentation of this function for further details.\n",
            "\n",
            "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
            "    dataset unless the purpose of the code is to study and educate about\n",
            "    ethical issues in data science and machine learning.\n",
            "\n",
            "    In this case special case, you can fetch the dataset from the original\n",
            "    source::\n",
            "\n",
            "        import pandas as pd\n",
            "        import numpy as np\n",
            "\n",
            "\n",
            "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
            "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
            "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
            "        target = raw_df.values[1::2, 2]\n",
            "\n",
            "    Alternative datasets include the California housing dataset (i.e.\n",
            "    func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
            "    dataset. You can load the datasets as follows:\n",
            "\n",
            "        from sklearn.datasets import fetch_california_housing\n",
            "        housing = fetch_california_housing()\n",
            "\n",
            "    for the California housing dataset and:\n",
            "\n",
            "        from sklearn.datasets import fetch_openml\n",
            "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
            "\n",
            "    for the Ames housing dataset.\n",
            "    \n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "[flaml.automl: 10-02 10:40:27] {1432} INFO - Evaluation method: cv\n",
            "[flaml.automl: 10-02 10:40:27] {1478} INFO - Minimizing error metric: 1-r2\n",
            "[flaml.automl: 10-02 10:40:27] {1515} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree']\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 0, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:27] {1866} INFO - Estimated sufficient time budget=610s. Estimated necessary time budget=1s.\n",
            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 0.1s,\testimator lgbm's best error=0.6257,\tbest estimator lgbm's best error=0.6257\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 1, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 0.1s,\testimator lgbm's best error=0.6257,\tbest estimator lgbm's best error=0.6257\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 2, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:27] {1944} INFO -  at 0.2s,\testimator lgbm's best error=0.3498,\tbest estimator lgbm's best error=0.3498\n",
            "[flaml.automl: 10-02 10:40:27] {1748} INFO - iteration 3, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.2s,\testimator lgbm's best error=0.2466,\tbest estimator lgbm's best error=0.2466\n",
            "[flaml.automl: 10-02 10:40:28] {1748} INFO - iteration 4, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.2s,\testimator lgbm's best error=0.2466,\tbest estimator lgbm's best error=0.2466\n",
            "[flaml.automl: 10-02 10:40:28] {1748} INFO - iteration 5, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.3s,\testimator lgbm's best error=0.2087,\tbest estimator lgbm's best error=0.2087\n",
            "[flaml.automl: 10-02 10:40:28] {1748} INFO - iteration 6, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.3s,\testimator lgbm's best error=0.2087,\tbest estimator lgbm's best error=0.2087\n",
            "[flaml.automl: 10-02 10:40:28] {1748} INFO - iteration 7, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.4s,\testimator lgbm's best error=0.2087,\tbest estimator lgbm's best error=0.2087\n",
            "[flaml.automl: 10-02 10:40:28] {1748} INFO - iteration 8, current learner lgbm\n",
            "[flaml.automl: 10-02 10:40:28] {1944} INFO -  at 0.4s,\testimator lgbm's best error=0.1682,\tbest estimator lgbm's best error=0.1682\n",
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            "[flaml.automl: 10-02 10:40:37] {1748} INFO - iteration 46, current learner catboost\n",
            "[flaml.automl: 10-02 10:40:37] {1944} INFO -  at 10.0s,\testimator catboost's best error=0.9086,\tbest estimator lgbm's best error=0.1356\n",
            "[flaml.automl: 10-02 10:40:37] {2043} INFO - selected model: LGBMRegressor(colsample_bytree=0.6649148062238498,\n",
            "              learning_rate=0.17402065726724145, max_bin=128,\n",
            "              min_child_samples=3, n_estimators=32, num_leaves=9,\n",
            "              objective='regression', reg_alpha=0.0009765625,\n",
            "              reg_lambda=0.006761362450996487, verbose=-1)\n",
            "[flaml.automl: 10-02 10:40:37] {2106} INFO - retrain lgbm for 0.0s\n",
            "[flaml.automl: 10-02 10:40:37] {2110} INFO - retrained model: LGBMRegressor(colsample_bytree=0.6649148062238498,\n",
            "              learning_rate=0.17402065726724145, max_bin=128,\n",
            "              min_child_samples=3, n_estimators=32, num_leaves=9,\n",
            "              objective='regression', reg_alpha=0.0009765625,\n",
            "              reg_lambda=0.006761362450996487, verbose=-1)\n",
            "[flaml.automl: 10-02 10:40:37] {1539} INFO - fit succeeded\n",
            "[flaml.automl: 10-02 10:40:37] {1541} INFO - Time taken to find the best model: 0.7118842601776123\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[25.85426879 22.17737651 33.79509964 33.47189557 35.61127167 26.6926382\n",
            " 21.53656446 20.99173684 16.18673463 18.75433976 18.20961633 20.48286627\n",
            " 20.20745331 19.5131759  18.82401423 19.81864373 21.91420674 17.53721008\n",
            " 18.69698259 19.07769813 14.3021372  18.32367106 16.63348583 15.34809335\n",
            " 16.27978499 15.13176242 16.94561436 15.20221211 18.67737191 20.75076542\n",
            " 13.88486622 17.70376242 13.89318927 14.90744983 14.32837615 21.02020963\n",
            " 20.89410764 21.52937191 23.19568492 30.3824402  34.45306735 29.43834549\n",
            " 24.62262048 24.62262048 22.21884844 20.87353145 20.36481607 18.09945043\n",
            " 15.55273032 18.67322033 20.4058421  21.53510487 24.1301244  21.73345138\n",
            " 17.74586736 34.48285914 23.17178682 31.55632404 23.26235097 20.39233374\n",
            " 18.98627393 17.89811291 22.71262836 25.23349363 32.76974079 25.04432847\n",
            " 19.83367435 20.94710592 19.92700235 21.23614492 23.62275307 21.08771163\n",
            " 22.31713619 23.46816704 24.39319272 22.93449232 21.05889974 21.25605159\n",
            " 21.17094097 21.4104049  26.32531539 24.84165632 23.41875466 22.8120377\n",
            " 23.12681643 26.07314549 21.34032521 22.59488629 26.81601064 30.10072674\n",
            " 23.47133879 22.62450376 23.40848205 25.20150116 20.74313622 26.63582822\n",
            " 22.56394496 40.98684448 43.03722064 33.01766391 24.84190574 25.43455857\n",
            " 18.18875737 20.49979008 20.57478854 18.13734457 17.91303198 20.49979008\n",
            " 20.57478854 18.8322359  21.11833105 22.96406162 18.57150096 18.83508569\n",
            " 20.89066811 18.58619509 20.95795201 20.4381487  18.58619509 20.72664127\n",
            " 21.62721157 21.09344795 19.51757484 17.53599996 19.51757484 19.78584094\n",
            " 17.36958717 16.39253423 16.86856532 15.853506   19.71749942 19.35197602\n",
            " 20.07005987 17.39965131 15.92640822 17.14455857 16.51379168 18.47224498\n",
            " 14.38445978 16.3202417  14.36742357 13.09473563 14.46737843 14.73422787\n",
            " 13.79400434 15.41208419 17.4578433  13.90794056 14.77126156 15.18245573\n",
            " 20.57464694 19.22984984 18.55647575 18.00616692 18.32648919 16.20917324\n",
            " 15.94048355 40.44304575 24.93995665 24.55706495 26.17126032 47.56299085\n",
            " 48.57509584 48.92296041 21.89045992 23.47663172 48.88830552 21.21745586\n",
            " 22.24469801 22.12855254 21.21745586 21.21745586 20.71319839 23.99651065\n",
            " 22.20628325 27.61661251 22.24779919 24.35641132 28.82910017 37.4336891\n",
            " 41.82612485 31.40530134 37.2617559  31.63960058 24.06803666 27.95722996\n",
            " 48.31846952 28.85327104 30.14916251 34.44402572 32.43461899 29.21282212\n",
            " 34.58982726 29.81477151 29.09606486 48.42621104 33.91839935 30.81677594\n",
            " 32.76161643 33.0058017  33.0058017  23.35364085 43.33574902 48.15111119\n",
            " 48.42621104 23.7319089  23.12852809 20.08360727 22.292716   18.3152002\n",
            " 20.15144641 18.32878493 21.24073814 25.61968555 20.32169665 23.99488849\n",
            " 22.29871705 25.40375278 20.33285354 23.37310689 27.79745637 20.83190671\n",
            " 26.36215877 26.63632325 45.15579507 45.69665731 43.92509937 30.92171422\n",
            " 45.71273114 31.55492563 22.4680608  32.11251767 44.32137075 45.54983148\n",
            " 27.42782572 22.9617272  25.92533441 32.46382356 23.75605744 25.56397894\n",
            " 24.60210386 20.62465265 21.53758885 25.22345796 19.06214513 17.84017293\n",
            " 21.69626278 20.93442255 22.86047593 26.53529504 23.71037189 26.40330856\n",
            " 32.45002858 40.12441971 21.62770163 20.16906432 43.22019612 48.96580343\n",
            " 36.19046627 31.35388466 34.9431359  44.12722649 46.8156711  32.19902229\n",
            " 34.9431359  22.86211519 29.53873565 47.98290935 46.00178483 21.93765745\n",
            " 21.54793337 25.23142491 24.33883673 35.61319402 31.31696508 32.04267127\n",
            " 33.35308931 31.2559175  25.95200717 34.35510039 46.48696004 35.2163881\n",
            " 45.64080794 49.0327787  30.55331866 22.33257136 21.69757503 23.53652066\n",
            " 23.08245711 24.79937913 31.21276685 34.29118996 28.11258611 23.85939846\n",
            " 21.98861298 27.07522156 25.03558428 19.24341933 24.20354062 30.25026064\n",
            " 26.3510736  24.97967893 24.33108926 32.31816475 34.95139768 27.72254529\n",
            " 34.27356754 28.58064288 26.28401898 20.25949225 19.15765455 23.40595016\n",
            " 19.9069318  22.22895448 23.43789368 19.7967241  17.99129269 18.86799165\n",
            " 21.90295702 21.44864267 23.74221793 23.74221793 21.77357441 19.88044313\n",
            " 23.74861493 26.188891   24.33547333 21.31173366 20.3949194  23.03935223\n",
            " 22.0186464  19.24261422 19.64398129 21.95831249 21.85754325 20.6350395\n",
            " 20.01568444 19.85894712 20.79851822 20.16200017 20.51536951 31.99762077\n",
            " 20.68060523 25.59062004 30.87902463 19.41753345 18.59475908 23.18796837\n",
            " 25.72871802 28.3234078  22.31812575 24.88336613 21.31087817 31.0527335\n",
            " 19.34403778 20.75173519 15.87445808 20.5265433  20.70117246 20.5265433\n",
            " 23.32606114 19.68233643 20.00496907 17.88381107 28.83843559 24.00526687\n",
            " 19.09817287 21.32938439 50.07163944 48.82160629 48.20203737 47.2777543\n",
            " 46.84319485 14.36205747 13.53142624 17.48924424 11.79294408 12.03751809\n",
            " 10.7690999  11.77270178 13.30284038 10.53147486 11.61391857 11.4278251\n",
            "  9.02188405  8.92022632  9.50462006  8.55668465  9.66190862 11.96240478\n",
            " 14.65412353 17.1613234  10.55147356 14.45336107 12.65116497 14.27056086\n",
            " 13.89813053 11.94012717  7.77595912  9.18480867  8.37379402 10.28701903\n",
            " 11.98974767  9.49769797  7.70573926  7.36799143 13.36327035 26.14624567\n",
            " 15.17003545 20.94325541 16.51890218 16.88756044 14.57744105 14.89023868\n",
            "  8.24976464  8.63264333  9.50804859  8.93571718  7.75831071  9.03788871\n",
            " 15.02396264 15.06486095 19.58003642 12.86473687 13.39712744  8.56134775\n",
            " 13.56904967 10.98178079 10.67788405 10.28130892 14.4042725  15.46887706\n",
            " 17.90238886 15.34185827 12.90173312 11.40186526 10.99905848  9.72482922\n",
            "  9.45412739 11.70236191  9.99097952 13.44394369 16.75333771 14.22574262\n",
            " 10.75976886 10.18568291 16.33703165 14.63372832 15.14455645 15.09377379\n",
            " 14.00999287 16.72939436 17.1613234  16.21455097 12.35874932 14.39549294\n",
            " 14.28247556 12.85554461 15.09377379 18.58227987 16.47031642 18.94780328\n",
            " 19.79664167 20.4529397  21.22275643 20.45709742 15.75204166 16.60776586\n",
            " 17.55418796 19.39206231 18.10615643 20.45030404 20.66821931 28.06922267\n",
            " 16.67357885 16.26120202 17.72975505 13.19839222 17.20168726 20.15508873\n",
            " 20.90931887 24.17717967 27.62733557 20.78673312 20.75924242 21.3133487\n",
            " 18.99382477 20.93387158 15.10676681 11.06340755 10.26917289 15.17721649\n",
            " 17.95736363 21.10085132 21.34700608 21.00568378 17.69219141 20.52223098\n",
            " 20.76838574 18.76022157 20.23462213 22.18273078 19.05073131 26.01075867\n",
            " 22.94032743 16.83667178]\n",
            "<flaml.model.LGBMEstimator object at 0x7f707d010f50>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5eRkuWMxUcr",
        "outputId": "895b60e7-7edd-4527-a181-3dfd39ddb91b"
      },
      "source": [
        "# Predict\n",
        "print(automl.predict(X_train))\n",
        "# Export the best model\n",
        "print(automl.model)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[25.85426879 22.17737651 33.79509964 33.47189557 35.61127167 26.6926382\n",
            " 21.53656446 20.99173684 16.18673463 18.75433976 18.20961633 20.48286627\n",
            " 20.20745331 19.5131759  18.82401423 19.81864373 21.91420674 17.53721008\n",
            " 18.69698259 19.07769813 14.3021372  18.32367106 16.63348583 15.34809335\n",
            " 16.27978499 15.13176242 16.94561436 15.20221211 18.67737191 20.75076542\n",
            " 13.88486622 17.70376242 13.89318927 14.90744983 14.32837615 21.02020963\n",
            " 20.89410764 21.52937191 23.19568492 30.3824402  34.45306735 29.43834549\n",
            " 24.62262048 24.62262048 22.21884844 20.87353145 20.36481607 18.09945043\n",
            " 15.55273032 18.67322033 20.4058421  21.53510487 24.1301244  21.73345138\n",
            " 17.74586736 34.48285914 23.17178682 31.55632404 23.26235097 20.39233374\n",
            " 18.98627393 17.89811291 22.71262836 25.23349363 32.76974079 25.04432847\n",
            " 19.83367435 20.94710592 19.92700235 21.23614492 23.62275307 21.08771163\n",
            " 22.31713619 23.46816704 24.39319272 22.93449232 21.05889974 21.25605159\n",
            " 21.17094097 21.4104049  26.32531539 24.84165632 23.41875466 22.8120377\n",
            " 23.12681643 26.07314549 21.34032521 22.59488629 26.81601064 30.10072674\n",
            " 23.47133879 22.62450376 23.40848205 25.20150116 20.74313622 26.63582822\n",
            " 22.56394496 40.98684448 43.03722064 33.01766391 24.84190574 25.43455857\n",
            " 18.18875737 20.49979008 20.57478854 18.13734457 17.91303198 20.49979008\n",
            " 20.57478854 18.8322359  21.11833105 22.96406162 18.57150096 18.83508569\n",
            " 20.89066811 18.58619509 20.95795201 20.4381487  18.58619509 20.72664127\n",
            " 21.62721157 21.09344795 19.51757484 17.53599996 19.51757484 19.78584094\n",
            " 17.36958717 16.39253423 16.86856532 15.853506   19.71749942 19.35197602\n",
            " 20.07005987 17.39965131 15.92640822 17.14455857 16.51379168 18.47224498\n",
            " 14.38445978 16.3202417  14.36742357 13.09473563 14.46737843 14.73422787\n",
            " 13.79400434 15.41208419 17.4578433  13.90794056 14.77126156 15.18245573\n",
            " 20.57464694 19.22984984 18.55647575 18.00616692 18.32648919 16.20917324\n",
            " 15.94048355 40.44304575 24.93995665 24.55706495 26.17126032 47.56299085\n",
            " 48.57509584 48.92296041 21.89045992 23.47663172 48.88830552 21.21745586\n",
            " 22.24469801 22.12855254 21.21745586 21.21745586 20.71319839 23.99651065\n",
            " 22.20628325 27.61661251 22.24779919 24.35641132 28.82910017 37.4336891\n",
            " 41.82612485 31.40530134 37.2617559  31.63960058 24.06803666 27.95722996\n",
            " 48.31846952 28.85327104 30.14916251 34.44402572 32.43461899 29.21282212\n",
            " 34.58982726 29.81477151 29.09606486 48.42621104 33.91839935 30.81677594\n",
            " 32.76161643 33.0058017  33.0058017  23.35364085 43.33574902 48.15111119\n",
            " 48.42621104 23.7319089  23.12852809 20.08360727 22.292716   18.3152002\n",
            " 20.15144641 18.32878493 21.24073814 25.61968555 20.32169665 23.99488849\n",
            " 22.29871705 25.40375278 20.33285354 23.37310689 27.79745637 20.83190671\n",
            " 26.36215877 26.63632325 45.15579507 45.69665731 43.92509937 30.92171422\n",
            " 45.71273114 31.55492563 22.4680608  32.11251767 44.32137075 45.54983148\n",
            " 27.42782572 22.9617272  25.92533441 32.46382356 23.75605744 25.56397894\n",
            " 24.60210386 20.62465265 21.53758885 25.22345796 19.06214513 17.84017293\n",
            " 21.69626278 20.93442255 22.86047593 26.53529504 23.71037189 26.40330856\n",
            " 32.45002858 40.12441971 21.62770163 20.16906432 43.22019612 48.96580343\n",
            " 36.19046627 31.35388466 34.9431359  44.12722649 46.8156711  32.19902229\n",
            " 34.9431359  22.86211519 29.53873565 47.98290935 46.00178483 21.93765745\n",
            " 21.54793337 25.23142491 24.33883673 35.61319402 31.31696508 32.04267127\n",
            " 33.35308931 31.2559175  25.95200717 34.35510039 46.48696004 35.2163881\n",
            " 45.64080794 49.0327787  30.55331866 22.33257136 21.69757503 23.53652066\n",
            " 23.08245711 24.79937913 31.21276685 34.29118996 28.11258611 23.85939846\n",
            " 21.98861298 27.07522156 25.03558428 19.24341933 24.20354062 30.25026064\n",
            " 26.3510736  24.97967893 24.33108926 32.31816475 34.95139768 27.72254529\n",
            " 34.27356754 28.58064288 26.28401898 20.25949225 19.15765455 23.40595016\n",
            " 19.9069318  22.22895448 23.43789368 19.7967241  17.99129269 18.86799165\n",
            " 21.90295702 21.44864267 23.74221793 23.74221793 21.77357441 19.88044313\n",
            " 23.74861493 26.188891   24.33547333 21.31173366 20.3949194  23.03935223\n",
            " 22.0186464  19.24261422 19.64398129 21.95831249 21.85754325 20.6350395\n",
            " 20.01568444 19.85894712 20.79851822 20.16200017 20.51536951 31.99762077\n",
            " 20.68060523 25.59062004 30.87902463 19.41753345 18.59475908 23.18796837\n",
            " 25.72871802 28.3234078  22.31812575 24.88336613 21.31087817 31.0527335\n",
            " 19.34403778 20.75173519 15.87445808 20.5265433  20.70117246 20.5265433\n",
            " 23.32606114 19.68233643 20.00496907 17.88381107 28.83843559 24.00526687\n",
            " 19.09817287 21.32938439 50.07163944 48.82160629 48.20203737 47.2777543\n",
            " 46.84319485 14.36205747 13.53142624 17.48924424 11.79294408 12.03751809\n",
            " 10.7690999  11.77270178 13.30284038 10.53147486 11.61391857 11.4278251\n",
            "  9.02188405  8.92022632  9.50462006  8.55668465  9.66190862 11.96240478\n",
            " 14.65412353 17.1613234  10.55147356 14.45336107 12.65116497 14.27056086\n",
            " 13.89813053 11.94012717  7.77595912  9.18480867  8.37379402 10.28701903\n",
            " 11.98974767  9.49769797  7.70573926  7.36799143 13.36327035 26.14624567\n",
            " 15.17003545 20.94325541 16.51890218 16.88756044 14.57744105 14.89023868\n",
            "  8.24976464  8.63264333  9.50804859  8.93571718  7.75831071  9.03788871\n",
            " 15.02396264 15.06486095 19.58003642 12.86473687 13.39712744  8.56134775\n",
            " 13.56904967 10.98178079 10.67788405 10.28130892 14.4042725  15.46887706\n",
            " 17.90238886 15.34185827 12.90173312 11.40186526 10.99905848  9.72482922\n",
            "  9.45412739 11.70236191  9.99097952 13.44394369 16.75333771 14.22574262\n",
            " 10.75976886 10.18568291 16.33703165 14.63372832 15.14455645 15.09377379\n",
            " 14.00999287 16.72939436 17.1613234  16.21455097 12.35874932 14.39549294\n",
            " 14.28247556 12.85554461 15.09377379 18.58227987 16.47031642 18.94780328\n",
            " 19.79664167 20.4529397  21.22275643 20.45709742 15.75204166 16.60776586\n",
            " 17.55418796 19.39206231 18.10615643 20.45030404 20.66821931 28.06922267\n",
            " 16.67357885 16.26120202 17.72975505 13.19839222 17.20168726 20.15508873\n",
            " 20.90931887 24.17717967 27.62733557 20.78673312 20.75924242 21.3133487\n",
            " 18.99382477 20.93387158 15.10676681 11.06340755 10.26917289 15.17721649\n",
            " 17.95736363 21.10085132 21.34700608 21.00568378 17.69219141 20.52223098\n",
            " 20.76838574 18.76022157 20.23462213 22.18273078 19.05073131 26.01075867\n",
            " 22.94032743 16.83667178]\n",
            "<flaml.model.LGBMEstimator object at 0x7f707d010f50>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAbh8_ZFm2qD"
      },
      "source": [
        "# Time Series Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3HESygUoWFT"
      },
      "source": [
        "pip install flaml[forecast]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08bYLH_2nY4L",
        "outputId": "c3094ce5-7d83-4e26-e764-2cf58d1f6306",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "from flaml import AutoML\n",
        "X_train = np.arange('2014-01', '2021-01', dtype='datetime64[M]')\n",
        "y_train = np.random.random(size=72)\n",
        "automl = AutoML()\n",
        "automl.fit(X_train=X_train[:72],  # a single column of timestamp\n",
        "           y_train=y_train,  # value for each timestamp\n",
        "           period=12,  # time horizon to forecast, e.g., 12 months\n",
        "           task='forecast', time_budget=100,  # time budget in seconds\n",
        "           log_file_name=\"forecast.log\",\n",
        "          )\n",
        "print(automl.predict(X_train[72:]))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[flaml.automl: 10-02 10:54:21] {1432} INFO - Evaluation method: cv\n",
            "[flaml.automl: 10-02 10:54:21] {1478} INFO - Minimizing error metric: mape\n",
            "[flaml.automl: 10-02 10:54:21] {1515} INFO - List of ML learners in AutoML Run: ['prophet', 'arima', 'sarimax']\n",
            "[flaml.automl: 10-02 10:54:21] {1748} INFO - iteration 0, current learner prophet\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 8.\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 18.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "[flaml.automl: 10-02 10:54:33] {1866} INFO - Estimated sufficient time budget=112159s. Estimated necessary time budget=112s.\n",
            "INFO:flaml.automl:Estimated sufficient time budget=112159s. Estimated necessary time budget=112s.\n",
            "[flaml.automl: 10-02 10:54:33] {1944} INFO -  at 11.6s,\testimator prophet's best error=1.4420,\tbest estimator prophet's best error=1.4420\n",
            "INFO:flaml.automl: at 11.6s,\testimator prophet's best error=1.4420,\tbest estimator prophet's best error=1.4420\n",
            "[flaml.automl: 10-02 10:54:33] {1748} INFO - iteration 1, current learner prophet\n",
            "INFO:flaml.automl:iteration 1, current learner prophet\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 8.\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 18.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "[flaml.automl: 10-02 10:54:42] {1944} INFO -  at 21.5s,\testimator prophet's best error=1.4420,\tbest estimator prophet's best error=1.4420\n",
            "INFO:flaml.automl: at 21.5s,\testimator prophet's best error=1.4420,\tbest estimator prophet's best error=1.4420\n",
            "[flaml.automl: 10-02 10:54:42] {1748} INFO - iteration 2, current learner prophet\n",
            "INFO:flaml.automl:iteration 2, current learner prophet\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 8.\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 18.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "[flaml.automl: 10-02 10:54:52] {1944} INFO -  at 31.2s,\testimator prophet's best error=1.4338,\tbest estimator prophet's best error=1.4338\n",
            "INFO:flaml.automl: at 31.2s,\testimator prophet's best error=1.4338,\tbest estimator prophet's best error=1.4338\n",
            "[flaml.automl: 10-02 10:54:52] {1748} INFO - iteration 3, current learner arima\n",
            "INFO:flaml.automl:iteration 3, current learner arima\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:539: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency MS will be used.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:539: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency MS will be used.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:539: ValueWarning:\n",
            "\n",
            "No frequency information was provided, so inferred frequency MS will be used.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/statsmodels/base/model.py:606: ConvergenceWarning:\n",
            "\n",
            "Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "\n",
            "[flaml.automl: 10-02 10:54:53] {1944} INFO -  at 31.8s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 31.8s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:54:53] {1748} INFO - iteration 4, current learner prophet\n",
            "INFO:flaml.automl:iteration 4, current learner prophet\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 8.\n",
            "INFO:prophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:n_changepoints greater than number of observations. Using 18.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "[flaml.automl: 10-02 10:55:03] {1944} INFO -  at 41.6s,\testimator prophet's best error=1.4338,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 41.6s,\testimator prophet's best error=1.4338,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:03] {1748} INFO - iteration 5, current learner arima\n",
            "INFO:flaml.automl:iteration 5, current learner arima\n",
            "[flaml.automl: 10-02 10:55:03] {1944} INFO -  at 42.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 42.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:03] {1748} INFO - iteration 6, current learner arima\n",
            "INFO:flaml.automl:iteration 6, current learner arima\n",
            "[flaml.automl: 10-02 10:55:03] {1944} INFO -  at 42.5s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 42.5s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:03] {1748} INFO - iteration 7, current learner arima\n",
            "INFO:flaml.automl:iteration 7, current learner arima\n",
            "[flaml.automl: 10-02 10:55:04] {1944} INFO -  at 42.7s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 42.7s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:04] {1748} INFO - iteration 8, current learner arima\n",
            "INFO:flaml.automl:iteration 8, current learner arima\n",
            "[flaml.automl: 10-02 10:55:04] {1944} INFO -  at 43.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 43.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:04] {1748} INFO - iteration 9, current learner arima\n",
            "INFO:flaml.automl:iteration 9, current learner arima\n",
            "[flaml.automl: 10-02 10:55:05] {1944} INFO -  at 44.0s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 44.0s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:05] {1748} INFO - iteration 10, current learner arima\n",
            "INFO:flaml.automl:iteration 10, current learner arima\n",
            "[flaml.automl: 10-02 10:55:05] {1944} INFO -  at 44.1s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 44.1s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:05] {1748} INFO - iteration 11, current learner arima\n",
            "INFO:flaml.automl:iteration 11, current learner arima\n",
            "[flaml.automl: 10-02 10:55:05] {1944} INFO -  at 44.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 44.3s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:05] {1748} INFO - iteration 12, current learner arima\n",
            "INFO:flaml.automl:iteration 12, current learner arima\n",
            "[flaml.automl: 10-02 10:55:05] {1944} INFO -  at 44.5s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "INFO:flaml.automl: at 44.5s,\testimator arima's best error=1.0210,\tbest estimator arima's best error=1.0210\n",
            "[flaml.automl: 10-02 10:55:05] {1748} INFO - iteration 13, current learner arima\n",
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            "[flaml.automl: 10-02 10:56:01] {2043} INFO - selected model: <statsmodels.tsa.arima.model.ARIMAResultsWrapper object at 0x7f7075089d90>\n",
            "INFO:flaml.automl:selected model: <statsmodels.tsa.arima.model.ARIMAResultsWrapper object at 0x7f7075089d90>\n",
            "[flaml.automl: 10-02 10:56:01] {2106} INFO - retrain arima for 0.0s\n",
            "INFO:flaml.automl:retrain arima for 0.0s\n",
            "[flaml.automl: 10-02 10:56:01] {2110} INFO - retrained model: <statsmodels.tsa.arima.model.ARIMAResultsWrapper object at 0x7f706ccd8dd0>\n",
            "INFO:flaml.automl:retrained model: <statsmodels.tsa.arima.model.ARIMAResultsWrapper object at 0x7f706ccd8dd0>\n",
            "[flaml.automl: 10-02 10:56:01] {1539} INFO - fit succeeded\n",
            "INFO:flaml.automl:fit succeeded\n",
            "[flaml.automl: 10-02 10:56:01] {1541} INFO - Time taken to find the best model: 44.674925565719604\n",
            "INFO:flaml.automl:Time taken to find the best model: 44.674925565719604\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020-01-01    0.705688\n",
            "2020-02-01    0.705688\n",
            "2020-03-01    0.705688\n",
            "2020-04-01    0.705688\n",
            "2020-05-01    0.705688\n",
            "2020-06-01    0.705688\n",
            "2020-07-01    0.705688\n",
            "2020-08-01    0.705688\n",
            "2020-09-01    0.705688\n",
            "2020-10-01    0.705688\n",
            "2020-11-01    0.705688\n",
            "2020-12-01    0.705688\n",
            "Freq: MS, Name: predicted_mean, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVONcruEpvYu",
        "outputId": "eb118130-7e53-48b5-b0e8-8af107b713c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Export the best model\n",
        "print(automl.model)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<flaml.model.ARIMA object at 0x7f706ceed6d0>\n"
          ]
        }
      ]
    }
  ]
}