autogen/notebook/flaml_automl.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
    "\n",
    "Licensed under the MIT License.\n",
    "\n",
    "# AutoML with FLAML Library\n",
    "\n",
    "\n",
    "## 1. Introduction\n",
    "\n",
    "FLAML is a Python library (https://github.com/microsoft/FLAML) designed to automatically produce accurate machine learning models \n",
    "with low computational cost. It is fast and cheap. The simple and lightweight design makes it easy \n",
    "to use and extend, such as adding new learners. FLAML can \n",
    "- serve as an economical AutoML engine,\n",
    "- be used as a fast hyperparameter tuning tool, or \n",
    "- be embedded in self-tuning software that requires low latency & resource in repetitive\n",
    "   tuning tasks.\n",
    "\n",
    "In this notebook, we use one real data example (binary classification) to showcase how to use FLAML library.\n",
    "\n",
    "FLAML requires `Python>=3.6`. To run this notebook example, please install flaml with the `notebook` option:\n",
    "```bash\n",
    "pip install flaml[notebook]\n",
    "```"
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!pip install flaml[notebook];"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. Classification Example\n",
    "### Load data and preprocess\n",
    "\n",
    "Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure."
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from flaml.data import load_openml_dataset\n",
    "X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir='./')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "load dataset from ./openml_ds1169.pkl\n",
      "Dataset name: airlines\n",
      "X_train.shape: (404537, 7), y_train.shape: (404537,);\n",
      "X_test.shape: (134846, 7), y_test.shape: (134846,)\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Run FLAML\n",
    "In the FLAML automl run configuration, users can specify the task type, time budget, error metric, learner list, whether to subsample, resampling strategy type, and so on. All these arguments have default values which will be used if users do not provide them. For example, the default ML learners of FLAML are `['lgbm', 'xgboost', 'catboost', 'rf', 'extra_tree', 'lrl1']`. "
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "''' import AutoML class from flaml package '''\n",
    "from flaml import AutoML\n",
    "automl = AutoML()"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "settings = {\n",
    "    \"time_budget\": 240,  # total running time in seconds\n",
    "    \"metric\": 'accuracy',  # can be: 'r2', 'rmse', 'mae', 'mse', 'accuracy', 'roc_auc', 'roc_auc_ovr',\n",
    "                           # 'roc_auc_ovo', 'log_loss', 'mape', 'f1', 'ap', 'ndcg', 'micro_f1', 'macro_f1'\n",
    "    \"task\": 'classification',  # task type    \n",
    "    \"log_file_name\": 'airlines_experiment.log',  # flaml log file\n",
    "    \"seed\": 7654321,    # random seed\n",
    "}"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "'''The main flaml automl API'''\n",
    "automl.fit(X_train=X_train, y_train=y_train, **settings)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
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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",
      "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
      "[flaml.automl: 08-31 00:55:28] {1689} INFO -  at 115.8s,\tbest lrl1's error=0.4338,\tbest lgbm's error=0.3300\n",
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      "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
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      "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
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      "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
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      "/home/dmx/miniconda2/envs/blend/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:328: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n",
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      "[flaml.automl: 08-31 00:57:34] {1766} INFO - selected model: LGBMClassifier(colsample_bytree=0.3841266992710469,\n",
      "               learning_rate=0.04886499949999022, max_bin=512,\n",
      "               min_child_samples=4, n_estimators=220, num_leaves=270,\n",
      "               objective='binary', reg_alpha=0.0009765625,\n",
      "               reg_lambda=0.07539015928723636, verbose=-1)\n",
      "[flaml.automl: 08-31 00:57:43] {1814} INFO - retrain lgbm for 9.1s\n",
      "[flaml.automl: 08-31 00:57:43] {1817} INFO - retrained model: LGBMClassifier(colsample_bytree=0.3841266992710469,\n",
      "               learning_rate=0.04886499949999022, max_bin=512,\n",
      "               min_child_samples=4, n_estimators=220, num_leaves=270,\n",
      "               objective='binary', reg_alpha=0.0009765625,\n",
      "               reg_lambda=0.07539015928723636, verbose=-1)\n",
      "[flaml.automl: 08-31 00:57:43] {1364} INFO - fit succeeded\n",
      "[flaml.automl: 08-31 00:57:43] {1365} INFO - Time taken to find the best model: 241.71747207641602\n",
      "[flaml.automl: 08-31 00:57:43] {1370} WARNING - Time taken to find the best model is 101% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "outputPrepend"
    ]
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Best model and metric"
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "''' retrieve best config and best learner'''\n",
    "print('Best ML leaner:', automl.best_estimator)\n",
    "print('Best hyperparmeter config:', automl.best_config)\n",
    "print('Best accuracy on validation data: {0:.4g}'.format(1-automl.best_loss))\n",
    "print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Best ML leaner: lgbm\n",
      "Best hyperparmeter config: {'n_estimators': 220, 'num_leaves': 270, 'min_child_samples': 4, 'learning_rate': 0.04886499949999022, 'log_max_bin': 10, 'colsample_bytree': 0.3841266992710469, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.07539015928723636, 'FLAML_sample_size': 364083}\n",
      "Best accuracy on validation data: 0.67\n",
      "Training duration of best run: 9.323 s\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "automl.model.estimator"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LGBMClassifier(colsample_bytree=0.3841266992710469,\n",
       "               learning_rate=0.04886499949999022, max_bin=512,\n",
       "               min_child_samples=4, n_estimators=220, num_leaves=270,\n",
       "               objective='binary', reg_alpha=0.0009765625,\n",
       "               reg_lambda=0.07539015928723636, verbose=-1)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "''' pickle and save the automl object '''\n",
    "import pickle\n",
    "with open('automl.pkl', 'wb') as f:\n",
    "    pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "''' compute predictions of testing dataset ''' \n",
    "y_pred = automl.predict(X_test)\n",
    "print('Predicted labels', y_pred)\n",
    "print('True labels', y_test)\n",
    "y_pred_proba = automl.predict_proba(X_test)[:,1]"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Predicted labels ['1' '0' '1' ... '1' '0' '0']\n",
      "True labels 118331    0\n",
      "328182    0\n",
      "335454    0\n",
      "520591    1\n",
      "344651    0\n",
      "         ..\n",
      "367080    0\n",
      "203510    1\n",
      "254894    0\n",
      "296512    1\n",
      "362444    0\n",
      "Name: Delay, Length: 134846, dtype: category\n",
      "Categories (2, object): ['0' < '1']\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "''' compute different metric values on testing dataset'''\n",
    "from flaml.ml import sklearn_metric_loss_score\n",
    "print('accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred, y_test))\n",
    "print('roc_auc', '=', 1 - sklearn_metric_loss_score('roc_auc', y_pred_proba, y_test))\n",
    "print('log_loss', '=', sklearn_metric_loss_score('log_loss', y_pred_proba, y_test))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "accuracy = 0.6729231864497278\n",
      "roc_auc = 0.7261961112785199\n",
      "log_loss = 0.6033707263741326\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "See Section 4 for an accuracy comparison with default LightGBM and XGBoost.\n",
    "\n",
    "### Log history"
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "from flaml.data import get_output_from_log\n",
    "time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
    "    get_output_from_log(filename=settings['log_file_name'], time_budget=240)\n",
    "for config in config_history:\n",
    "    print(config)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 4, 'num_leaves': 4, 'min_child_samples': 20, 'learning_rate': 0.09999999999999995, 'log_max_bin': 8, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0, 'FLAML_sample_size': 10000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 12, 'num_leaves': 4, 'min_child_samples': 15, 'learning_rate': 0.2712162364070373, 'log_max_bin': 10, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.002668211515123386, 'reg_lambda': 0.5215467339232843, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 12, 'num_leaves': 4, 'min_child_samples': 15, 'learning_rate': 0.2712162364070373, 'log_max_bin': 10, 'colsample_bytree': 0.9285002286474459, 'reg_alpha': 0.002668211515123386, 'reg_lambda': 0.5215467339232843, 'FLAML_sample_size': 10000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 14, 'num_leaves': 5, 'min_child_samples': 9, 'learning_rate': 0.2835381908967212, 'log_max_bin': 9, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.0014132988481787994, 'reg_lambda': 0.033183495034912504, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 14, 'num_leaves': 5, 'min_child_samples': 9, 'learning_rate': 0.2835381908967212, 'log_max_bin': 9, 'colsample_bytree': 0.8304072431299575, 'reg_alpha': 0.0014132988481787994, 'reg_lambda': 0.033183495034912504, 'FLAML_sample_size': 10000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 10000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 10000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 10000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 4, 'min_child_samples': 7, 'learning_rate': 0.15662398373030859, 'log_max_bin': 10, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 89, 'num_leaves': 4, 'min_child_samples': 6, 'learning_rate': 0.2915468353191124, 'log_max_bin': 10, 'colsample_bytree': 0.8291836310024803, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.022917008702549507, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 89, 'num_leaves': 4, 'min_child_samples': 6, 'learning_rate': 0.2915468353191124, 'log_max_bin': 10, 'colsample_bytree': 0.8291836310024803, 'reg_alpha': 0.0009765625, 'reg_lambda': 0.022917008702549507, 'FLAML_sample_size': 40000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 40000, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 40000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 53, 'num_leaves': 19, 'min_child_samples': 7, 'learning_rate': 0.15662398373030845, 'log_max_bin': 9, 'colsample_bytree': 0.7610534336273627, 'reg_alpha': 0.006958608037974516, 'reg_lambda': 0.0064258982194552745, 'FLAML_sample_size': 364083}}\n",
      "{'Current Learner': 'catboost', 'Current Sample': 40000, 'Current Hyper-parameters': {'early_stopping_rounds': 11, 'learning_rate': 0.0943289179113066, 'FLAML_sample_size': 40000}, 'Best Learner': 'catboost', 'Best Hyper-parameters': {'early_stopping_rounds': 11, 'learning_rate': 0.0943289179113066, 'FLAML_sample_size': 40000}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 112, 'num_leaves': 42, 'min_child_samples': 6, 'learning_rate': 0.05081630216512539, 'log_max_bin': 8, 'colsample_bytree': 0.6140029119098487, 'reg_alpha': 0.0043659867548350275, 'reg_lambda': 0.00988067243709054, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 112, 'num_leaves': 42, 'min_child_samples': 6, 'learning_rate': 0.05081630216512539, 'log_max_bin': 8, 'colsample_bytree': 0.6140029119098487, 'reg_alpha': 0.0043659867548350275, 'reg_lambda': 0.00988067243709054, 'FLAML_sample_size': 364083}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 224, 'num_leaves': 19, 'min_child_samples': 3, 'learning_rate': 0.06277721620788371, 'log_max_bin': 9, 'colsample_bytree': 0.6641909243388362, 'reg_alpha': 0.02046640007359354, 'reg_lambda': 0.021186644668220945, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 224, 'num_leaves': 19, 'min_child_samples': 3, 'learning_rate': 0.06277721620788371, 'log_max_bin': 9, 'colsample_bytree': 0.6641909243388362, 'reg_alpha': 0.02046640007359354, 'reg_lambda': 0.021186644668220945, 'FLAML_sample_size': 364083}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 159, 'num_leaves': 54, 'min_child_samples': 3, 'learning_rate': 0.15480526013767984, 'log_max_bin': 9, 'colsample_bytree': 0.4565737938156385, 'reg_alpha': 0.014844095616079196, 'reg_lambda': 0.01191495998733094, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 159, 'num_leaves': 54, 'min_child_samples': 3, 'learning_rate': 0.15480526013767984, 'log_max_bin': 9, 'colsample_bytree': 0.4565737938156385, 'reg_alpha': 0.014844095616079196, 'reg_lambda': 0.01191495998733094, 'FLAML_sample_size': 364083}}\n",
      "{'Current Learner': 'lgbm', 'Current Sample': 364083, 'Current Hyper-parameters': {'n_estimators': 100, 'num_leaves': 310, 'min_child_samples': 2, 'learning_rate': 0.0958942820044505, 'log_max_bin': 10, 'colsample_bytree': 0.40118043723920377, 'reg_alpha': 0.006532533034382694, 'reg_lambda': 0.02014962736208268, 'FLAML_sample_size': 364083}, 'Best Learner': 'lgbm', 'Best Hyper-parameters': {'n_estimators': 100, 'num_leaves': 310, 'min_child_samples': 2, 'learning_rate': 0.0958942820044505, 'log_max_bin': 10, 'colsample_bytree': 0.40118043723920377, 'reg_alpha': 0.006532533034382694, 'reg_lambda': 0.02014962736208268, 'FLAML_sample_size': 364083}}\n"
     ]
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.title('Learning Curve')\n",
    "plt.xlabel('Wall Clock Time (s)')\n",
    "plt.ylabel('Validation Accuracy')\n",
    "plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
    "plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3. Comparison with alternatives\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Default LightGBM"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "lgbm = LGBMClassifier()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "lgbm.fit(X_train, y_train)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "y_pred_lgbm = lgbm.predict(X_test)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Default XGBoost"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier()\n",
    "cat_columns = X_train.select_dtypes(include=['category']).columns\n",
    "X = X_train.copy()\n",
    "X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "xgb.fit(X, y_train)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "X = X_test.copy()\n",
    "X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes)\n",
    "y_pred_xgb = xgb.predict(X)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "print('default xgboost accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred_xgb, y_test))\n",
    "print('default lgbm accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred_lgbm, y_test))\n",
    "print('flaml accuracy', '=', 1 - sklearn_metric_loss_score('accuracy', y_pred, y_test))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "default xgboost accuracy = 0.6676060098186078\n",
      "default lgbm accuracy = 0.6602346380315323\n",
      "flaml accuracy = 0.6729231864497278\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 4. Customized Learner"
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Some experienced automl users may have a preferred model to tune or may already have a reasonably by-hand-tuned model before launching the automl experiment. They need to select optimal configurations for the customized model mixed with standard built-in learners. \n",
    "\n",
    "FLAML can easily incorporate customized/new learners (preferably with sklearn API) provided by users in a real-time manner, as demonstrated below."
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example of Regularized Greedy Forest\n",
    "\n",
    "[Regularized Greedy Forest](https://arxiv.org/abs/1109.0887) (RGF) is a machine learning method currently not included in FLAML. The RGF has many tuning parameters, the most critical of which are: `[max_leaf, n_iter, n_tree_search, opt_interval, min_samples_leaf]`. To run a customized/new learner, the user needs to provide the following information:\n",
    "* an implementation of the customized/new learner\n",
    "* a list of hyperparameter names and types\n",
    "* rough ranges of hyperparameters (i.e., upper/lower bounds)\n",
    "* choose initial value corresponding to low cost for cost-related hyperparameters (e.g., initial value for max_leaf and n_iter should be small)\n",
    "\n",
    "In this example, the above information for RGF is wrapped in a python class called *MyRegularizedGreedyForest* that exposes the hyperparameters."
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "''' SKLearnEstimator is the super class for a sklearn learner '''\n",
    "from flaml.model import SKLearnEstimator\n",
    "from flaml import tune\n",
    "from rgf.sklearn import RGFClassifier, RGFRegressor\n",
    "\n",
    "\n",
    "class MyRegularizedGreedyForest(SKLearnEstimator):\n",
    "\n",
    "\n",
    "    def __init__(self, task='binary:logistic', n_jobs=1, **params):\n",
    "        '''Constructor\n",
    "        \n",
    "        Args:\n",
    "            task: A string of the task type, one of\n",
    "                'binary:logistic', 'multi:softmax', 'regression'\n",
    "            n_jobs: An integer of the number of parallel threads\n",
    "            params: A dictionary of the hyperparameter names and values\n",
    "        '''\n",
    "\n",
    "        super().__init__(task, **params)\n",
    "\n",
    "        '''task=regression for RGFRegressor; \n",
    "        binary:logistic and multiclass:softmax for RGFClassifier'''\n",
    "        if 'regression' in task:\n",
    "            self.estimator_class = RGFRegressor\n",
    "        else:\n",
    "            self.estimator_class = RGFClassifier\n",
    "\n",
    "        # convert to int for integer hyperparameters\n",
    "        self.params = {\n",
    "            \"n_jobs\": n_jobs,\n",
    "            'max_leaf': int(params['max_leaf']),\n",
    "            'n_iter': int(params['n_iter']),\n",
    "            'n_tree_search': int(params['n_tree_search']),\n",
    "            'opt_interval': int(params['opt_interval']),\n",
    "            'learning_rate': params['learning_rate'],\n",
    "            'min_samples_leaf': int(params['min_samples_leaf'])\n",
    "        }    \n",
    "\n",
    "    @classmethod\n",
    "    def search_space(cls, data_size, task):\n",
    "        '''[required method] search space\n",
    "\n",
    "        Returns:\n",
    "            A dictionary of the search space. \n",
    "            Each key is the name of a hyperparameter, and value is a dict with\n",
    "                its domain and init_value (optional), cat_hp_cost (optional) \n",
    "                e.g., \n",
    "                {'domain': tune.randint(lower=1, upper=10), 'init_value': 1}\n",
    "        '''\n",
    "        space = {        \n",
    "            'max_leaf': {'domain': tune.lograndint(lower=4, upper=data_size), 'init_value': 4, 'low_cost_init_value': 4},\n",
    "            'n_iter': {'domain': tune.lograndint(lower=1, upper=data_size), 'init_value': 1, 'low_cost_init_value': 1},\n",
    "            'n_tree_search': {'domain': tune.lograndint(lower=1, upper=32768), 'init_value': 1, 'low_cost_init_value': 1},\n",
    "            'opt_interval': {'domain': tune.lograndint(lower=1, upper=10000), 'init_value': 100},\n",
    "            'learning_rate': {'domain': tune.loguniform(lower=0.01, upper=20.0)},\n",
    "            'min_samples_leaf': {'domain': tune.lograndint(lower=1, upper=20), 'init_value': 20},\n",
    "        }\n",
    "        return space\n",
    "\n",
    "    @classmethod\n",
    "    def size(cls, config):\n",
    "        '''[optional method] memory size of the estimator in bytes\n",
    "        \n",
    "        Args:\n",
    "            config - the dict of the hyperparameter config\n",
    "\n",
    "        Returns:\n",
    "            A float of the memory size required by the estimator to train the\n",
    "            given config\n",
    "        '''\n",
    "        max_leaves = int(round(config['max_leaf']))\n",
    "        n_estimators = int(round(config['n_iter']))\n",
    "        return (max_leaves * 3 + (max_leaves - 1) * 4 + 1.0) * n_estimators * 8\n",
    "\n",
    "    @classmethod\n",
    "    def cost_relative2lgbm(cls):\n",
    "        '''[optional method] relative cost compared to lightgbm\n",
    "        '''\n",
    "        return 1.0\n"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Add Customized Learner and Run FLAML AutoML\n",
    "\n",
    "After adding RGF into the list of learners, we run automl by tuning hyperpameters of RGF as well as the default learners. "
   ],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "automl = AutoML()\n",
    "automl.add_learner(learner_name='RGF', learner_class=MyRegularizedGreedyForest)"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "settings = {\n",
    "    \"time_budget\": 10,  # total running time in seconds\n",
    "    \"metric\": 'accuracy', \n",
    "    \"estimator_list\": ['RGF', 'lgbm', 'rf', 'xgboost'],  # list of ML learners\n",
    "    \"task\": 'classification',  # task type    \n",
    "    \"log_file_name\": 'airlines_experiment_custom_learner.log',  # flaml log file \n",
    "    \"log_training_metric\": True,  # whether to log training metric\n",
    "}\n",
    "\n",
    "automl.fit(X_train = X_train, y_train = y_train, **settings)"
   ],
   "outputs": [],
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 5. Customized Metric\n",
    "\n",
    "It's also easy to customize the optimization metric. As an example, we demonstrate with a custom metric function which combines training loss and test loss as the final loss to minimize."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "def custom_metric(X_test, y_test, estimator, labels, X_train, y_train,\n",
    "                  weight_test=None, weight_train=None, config=None,\n",
    "                  groups_test=None, groups_train=None):\n",
    "    from sklearn.metrics import log_loss\n",
    "    import time\n",
    "    start = time.time()\n",
    "    y_pred = estimator.predict_proba(X_test)\n",
    "    pred_time = (time.time() - start) / len(X_test)\n",
    "    test_loss = log_loss(y_test, y_pred, labels=labels,\n",
    "                         sample_weight=weight_test)\n",
    "    y_pred = estimator.predict_proba(X_train)\n",
    "    train_loss = log_loss(y_train, y_pred, labels=labels,\n",
    "                          sample_weight=weight_train)\n",
    "    alpha = 0.5\n",
    "    return test_loss * (1 + alpha) - alpha * train_loss, {\n",
    "        \"test_loss\": test_loss, \"train_loss\": train_loss, \"pred_time\": pred_time\n",
    "    }\n",
    "    # two elements are returned:\n",
    "    # the first element is the metric to minimize as a float number,\n",
    "    # the second element is a dictionary of the metrics to log"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can then pass this custom metric function to automl's `fit` method."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "automl = AutoML()\n",
    "settings = {\n",
    "    \"time_budget\": 10,  # total running time in seconds\n",
    "    \"metric\": custom_metric,  # pass the custom metric funtion here\n",
    "    \"task\": 'classification',  # task type\n",
    "    \"log_file_name\": 'airlines_experiment_custom_metric.log',  # flaml log file\n",
    "}\n",
    "\n",
    "automl.fit(X_train = X_train, y_train = y_train, **settings)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[flaml.automl: 08-31 00:58:32] {1279} INFO - Evaluation method: holdout\n",
      "[flaml.automl: 08-31 00:58:32] {1312} INFO - Minimizing error metric: customized metric\n",
      "[flaml.automl: 08-31 00:58:32] {1338} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']\n",
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      "[flaml.automl: 08-31 00:58:41] {1766} INFO - selected model: LGBMClassifier(colsample_bytree=0.9318698026248824,\n",
      "               learning_rate=0.3731008541260285, max_bin=256,\n",
      "               min_child_samples=18, n_estimators=4, num_leaves=19,\n",
      "               objective='binary', reg_alpha=0.04956056407320672,\n",
      "               reg_lambda=3.319594476328555, verbose=-1)\n",
      "[flaml.automl: 08-31 00:58:41] {1820} INFO - not retraining because the time budget is too small.\n",
      "[flaml.automl: 08-31 00:58:41] {1364} INFO - fit succeeded\n",
      "[flaml.automl: 08-31 00:58:41] {1365} INFO - Time taken to find the best model: 7.795238971710205\n",
      "[flaml.automl: 08-31 00:58:41] {1370} WARNING - Time taken to find the best model is 78% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
     ]
    }
   ],
   "metadata": {}
  }
 ],
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