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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Copyright (c) 2020-2021 Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License.\n",
"\n",
"# Tune XGBoost 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 demonstrate how to use FLAML library to tune hyperparameters of XGBoost with a regression example.\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",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install flaml[notebook];"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 2. Regression Example\n",
"### Load data and preprocess\n",
"\n",
"Download [houses dataset](https://www.openml.org/d/537) from OpenML. The task is to predict median price of the house in the region based on demographic composition and a state of housing market in the region."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "load dataset from./openml_ds537.pkl\nDataset name:houses\nX_train.shape: (15480, 8), y_train.shape: (15480,);\nX_test.shape: (5160, 8), y_test.shape: (5160,)\n"
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}
],
"source": [
"from flaml.data import load_openml_dataset\n",
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"X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')"
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]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"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. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"''' import AutoML class from flaml package '''\n",
"from flaml import AutoML\n",
"automl = AutoML()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"settings = {\n",
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" \"time_budget\": 60, # total running time in seconds\n",
" \"metric\": 'r2', # primary metrics for regression can be chosen from: ['mae','mse','r2']\n",
" \"estimator_list\": ['xgboost'], # list of ML learners; we tune xgboost in this example\n",
" \"task\": 'regression', # task type \n",
" \"log_file_name\": 'houses_experiment.log', # flaml log file\n",
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"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
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"text": "[flaml.automl: 04-10 11:34:26] {890} INFO - Evaluation method: cv\n[flaml.automl: 04-10 11:34:26] {606} INFO - Using RepeatedKFold\n[flaml.automl: 04-10 11:34:26] {911} INFO - Minimizing error metric: 1-r2\n[flaml.automl: 04-10 11:34:26] {930} INFO - List of ML learners in AutoML Run: ['xgboost']\n[flaml.automl: 04-10 11:34:26] {994} INFO - iteration 0, current learner xgboost\n[flaml.automl: 04-10 11:34:26] {1147} INFO - at 0.2s,\tbest xgboost's error=2.1267,\tbest xgboost's error=2.1267\n[flaml.automl: 04-10 11:34:26] {994} INFO - iteration 1, current learner xgboost\n[flaml.automl: 04-10 11:34:27] {1147} INFO - at 0.4s,\tbest xgboost's error=2.1267,\tbest xgboost's error=2.1267\n[flaml.automl: 04-10 11:34:27] {994} INFO - iteration 2, current learner xgboost\n[flaml.automl: 04-10 11:34:27] {1147} INFO - at 0.6s,\tbest xgboost's error=0.3799,\tbest xgboost's error=0.3799\n[flaml.automl: 04-10 11:34:27] {994} INFO - iteration 3, current learner xgboost\n[flaml.automl: 04-10 11:34:27] {1147} INFO - at 0.8s,\tbest xgboost's error=0.3799,\tbest xgboost's error=0.3799\n[flaml.automl: 04-10 11:34:27] {994} INFO - iteration 4, current learner xgboost\n[flaml.automl: 04-10 11:34:27] {1147} INFO - at 1.2s,\tbest xgboost's error=0.2664,\tbest xgboost's error=0.2664\n[flaml.automl: 04-10 11:34:27] {994} INFO - iteration 5, current learner xgboost\n[flaml.automl: 04-10 11:34:28] {1147} INFO - at 1.6s,\tbest xgboost's error=0.2281,\tbest xgboost's error=0.2281\n[flaml.automl: 04-10 11:34:28] {994} INFO - iteration 6, current learner xgboost\n[flaml.automl: 04-10 11:34:28] {1147} INFO - at 1.8s,\tbest xgboost's error=0.2281,\tbest xgboost's error=0.2281\n[flaml.automl: 04-10 11:34:28] {994} INFO - iteration 7, current learner xgboost\n[flaml.automl: 04-10 11:34:28] {1147} INFO - at 2.1s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:28] {994} INFO - iteration 8, current learner xgboost\n[flaml.automl: 04-10 11:34:28] {1147} INFO - at 2.3s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:28] {994} INFO - iteration 9, current learner xgboost\n[flaml.automl: 04-10 11:34:29] {1147} INFO - at 2.5s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:29] {994} INFO - iteration 10, current learner xgboost\n[flaml.automl: 04-10 11:34:29] {1147} INFO - at 2.8s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:29] {994} INFO - iteration 11, current learner xgboost\n[flaml.automl: 04-10 11:34:30] {1147} INFO - at 3.5s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:30] {994} INFO - iteration 12, current learner xgboost\n[flaml.automl: 04-10 11:34:30] {1147} INFO - at 3.7s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:30] {994} INFO - iteration 13, current learner xgboost\n[flaml.automl: 04-10 11:34:30] {1147} INFO - at 3.9s,\tbest xgboost's error=0.2234,\tbest xgboost's error=0.2234\n[flaml.automl: 04-10 11:34:30] {994} INFO - iteration 14, current learner xgboost\n[flaml.automl: 04-10 11:34:31] {1147} INFO - at 4.6s,\tbest xgboost's error=0.1820,\tbest xgboost's error=0.1820\n[flaml.automl: 04-10 11:34:31] {994} INFO - iteration 15, current learner xgboost\n[flaml.automl: 04-10 11:34:31] {1147} INFO - at 4.9s,\tbest xgboost's error=0.1820,\tbest xgboost's error=0.1820\n[flaml.automl: 04-10 11:34:31] {994} INFO - iteration 16, current learner xgboost\n[flaml.automl: 04-10 11:34:33] {1147} INFO - at 6.4s,\tbest xgboost's error=0.1820,\tbest xgboost's error=0.1820\n[flaml.automl: 04-10 11:34:33] {994} INFO - iteration 17, current learner xgboost\n[flaml.automl: 04-10 11:34:34] {1147} INFO - at 7.4s,\tbest xgboost's error=0.1820,\tbest xgboost's error=0.1820\n[flaml.automl: 04-10 11:34:34] {994} INFO - iteration 18, current learner xgboost\n[flaml.automl: 04-10 11:34:35] {1147} INFO - at 9.3s,\tbest xgboost's error=0.1820,\tbest xgboost's error=0.1820\n[flaml.automl: 04-10 11:34:35]
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}
],
"source": [
"'''The main flaml automl API'''\n",
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"automl.fit(X_train=X_train, y_train=y_train, **settings)"
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]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Best model and metric"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "Best hyperparmeter config:{'n_estimators': 102.0, 'max_leaves': 65.0, 'min_child_weight': 128.0, 'learning_rate': 0.11244600616797214, 'subsample': 1.0, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.869583478411346, 'reg_alpha': 0.007099803569534422, 'reg_lambda': 3.0138982869145257}\nBest r2 on validation data: 0.8292\nTraining duration of best run: 2.752 s\n"
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}
],
"source": [
"''' retrieve best config'''\n",
"print('Best hyperparmeter config:', automl.best_config)\n",
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"print('Best r2 on validation data: {0:.4g}'.format(1 - automl.best_loss))\n",
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"print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/plain": "<xgboost.core.Booster at 0x7ffb8617d910>"
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},
"metadata": {},
"execution_count": 6
}
],
"source": [
"automl.model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
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"''' pickle and save the automl object '''\n",
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"import pickle\n",
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"with open('automl.pkl', 'wb') as f:\n",
" pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)"
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]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "Predicted labels[139985.8 258609.5 147321.94 ... 245122.38 243654.11 271184.34]\nTrue labels[136900. 241300. 200700. ... 160900. 227300. 265600.]\n"
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}
],
"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)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "r2=0.8324355914726785\nmse=2214951230.2261615\nmae=31314.021581274224\n"
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}
],
"source": [
"''' compute different metric values on testing dataset'''\n",
"from flaml.ml import sklearn_metric_loss_score\n",
"print('r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))\n",
"print('mse', '=', sklearn_metric_loss_score('mse', y_pred, y_test))\n",
"print('mae', '=', sklearn_metric_loss_score('mae', y_pred, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 4, 'max_leaves': 4, 'min_child_weight': 1, 'learning_rate': 0.1, 'subsample': 1.0, 'colsample_bylevel': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 4, 'max_leaves': 4, 'min_child_weight': 1, 'learning_rate': 0.1, 'subsample': 1.0, 'colsample_bylevel': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.0009765625, 'reg_lambda': 1.0}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 4.0, 'max_leaves': 4.0, 'min_child_weight': 1.8630223791107017, 'learning_rate': 1.0, 'subsample': 0.8513627344387318, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9684145930669938, 'reg_alpha': 0.001831177697321707, 'reg_lambda': 0.2790165919053839}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 4.0, 'max_leaves': 4.0, 'min_child_weight': 1.8630223791107017, 'learning_rate': 1.0, 'subsample': 0.8513627344387318, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9684145930669938, 'reg_alpha': 0.001831177697321707, 'reg_lambda': 0.2790165919053839}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 20.0, 'max_leaves': 4.0, 'min_child_weight': 12.035965728320889, 'learning_rate': 1.0, 'subsample': 0.9814787163243813, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9534346594834143, 'reg_alpha': 0.002208534076096185, 'reg_lambda': 0.5460627024738886}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 20.0, 'max_leaves': 4.0, 'min_child_weight': 12.035965728320889, 'learning_rate': 1.0, 'subsample': 0.9814787163243813, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9534346594834143, 'reg_alpha': 0.002208534076096185, 'reg_lambda': 0.5460627024738886}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 11.0, 'max_leaves': 15.0, 'min_child_weight': 8.517629386811183, 'learning_rate': 0.4743416464891248, 'subsample': 0.9233328006239466, 'colsample_bylevel': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.034996420228767956, 'reg_lambda': 0.6169079461473814}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 11.0, 'max_leaves': 15.0, 'min_child_weight': 8.517629386811183, 'learning_rate': 0.4743416464891248, 'subsample': 0.9233328006239466, 'colsample_bylevel': 1.0, 'colsample_bytree': 1.0, 'reg_alpha': 0.034996420228767956, 'reg_lambda': 0.6169079461473814}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 7.0, 'max_leaves': 41.0, 'min_child_weight': 36.70427494512747, 'learning_rate': 0.829848065653668, 'subsample': 1.0, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9462273188918029, 'reg_alpha': 0.03855312356344618, 'reg_lambda': 0.11780549418358223}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 7.0, 'max_leaves': 41.0, 'min_child_weight': 36.70427494512747, 'learning_rate': 0.829848065653668, 'subsample': 1.0, 'colsample_bylevel': 1.0, 'colsample_bytree': 0.9462273188918029, 'reg_alpha': 0.03855312356344618, 'reg_lambda': 0.11780549418358223}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 19.0, 'max_leaves': 67.0, 'min_child_weight': 50.99082180813152, 'learning_rate': 0.2715137524235054, 'subsample': 1.0, 'colsample_bylevel': 0.9209151683339658, 'colsample_bytree': 0.9707158427317836, 'reg_alpha': 0.07779983160764462, 'reg_lambda': 0.11002505912010285}, 'Best Learner': 'xgboost', 'Best Hyper-parameters': {'n_estimators': 19.0, 'max_leaves': 67.0, 'min_child_weight': 50.99082180813152, 'learning_rate': 0.2715137524235054, 'subsample': 1.0, 'colsample_bylevel': 0.9209151683339658, 'colsample_bytree': 0.9707158427317836, 'reg_alpha': 0.07779983160764462, 'reg_lambda': 0.11002505912010285}}\n{'Current Learner': 'xgboost', 'Current Sample': 15480, 'Current Hyper-parameters': {'n_estimators': 30.0, 'm
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}
],
"source": [
"from flaml.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, train_loss_history = \\\n",
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" get_output_from_log(filename=settings['log_file_name'], time_budget=60)\n",
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"\n",
"for config in config_history:\n",
" print(config)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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},
"metadata": {
"needs_background": "light"
}
}
],
"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 r2')\n",
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"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",
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"plt.show()"
]
},
{
"source": [
"## 3. Comparison with untuned XGBoost\n",
"\n",
"### FLAML's accuracy"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
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"metadata": {
"tags": []
},
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "flaml (60s) r2=0.8324355914726785\n"
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}
],
"source": [
"print('flaml (60s) r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))"
]
},
{
"source": [
"### Default XGBoost"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from xgboost import XGBRegressor\n",
"xgb = XGBRegressor()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
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"text/plain": "XGBRegressor(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)"
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},
"metadata": {},
"execution_count": 14
}
],
"source": [
"xgb.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
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"metadata": {
"tags": []
},
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
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"text": "default xgboost r2=0.8265451174596482\n"
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}
],
"source": [
"y_pred = xgb.predict(X_test)\n",
"from flaml.ml import sklearn_metric_loss_score\n",
"print('default xgboost r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))"
]
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Add customized XGBoost learners in FLAML\n",
"You can easily enable a custom objective function by adding a customized XGBoost learner (XGBoostEstimator for regression tasks, and XGBoostSklearnEstimator for classification tasks) in FLAML. In the following example, we show how to add such a customized XGBoostEstimator with a custom objective function. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"tags": [
"outputPrepend"
]
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "NFO - Using RepeatedKFold\n[flaml.automl: 04-10 12:00:32] {911} INFO - Minimizing error metric: 1-r2\n[flaml.automl: 04-10 12:00:32] {930} INFO - List of ML learners in AutoML Run: ['my_xgb1', 'my_xgb2']\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 0, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.1s,\tbest my_xgb1's error=53750617.1059,\tbest my_xgb1's error=53750617.1059\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 1, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.1s,\tbest my_xgb1's error=260718.5183,\tbest my_xgb1's error=260718.5183\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 2, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.1s,\tbest my_xgb1's error=260718.5183,\tbest my_xgb1's error=260718.5183\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 3, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.2s,\tbest my_xgb1's error=2075.8695,\tbest my_xgb1's error=2075.8695\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 4, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.2s,\tbest my_xgb2's error=4.1611,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 5, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.2s,\tbest my_xgb1's error=2075.8695,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 6, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.3s,\tbest my_xgb1's error=289.1186,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 7, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.3s,\tbest my_xgb1's error=289.1186,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 8, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.3s,\tbest my_xgb1's error=289.1186,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 9, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.4s,\tbest my_xgb2's error=4.1611,\tbest my_xgb2's error=4.1611\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 10, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.4s,\tbest my_xgb2's error=4.1603,\tbest my_xgb2's error=4.1603\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 11, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.4s,\tbest my_xgb2's error=4.1603,\tbest my_xgb2's error=4.1603\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 12, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.5s,\tbest my_xgb1's error=7.0764,\tbest my_xgb2's error=4.1603\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 13, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.6s,\tbest my_xgb2's error=0.3612,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 14, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.6s,\tbest my_xgb1's error=7.0764,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 15, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.7s,\tbest my_xgb2's error=0.3612,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 16, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.7s,\tbest my_xgb1's error=7.0764,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 17, current learner my_xgb2\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.8s,\tbest my_xgb2's error=0.3612,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 18, current learner my_xgb1\n[flaml.automl: 04-10 12:00:32] {1147} INFO - at 0.8s,\tbest my_xgb1's error=7.0764,\tbest my_xgb2's error=0.3612\n[flaml.automl: 04-10 12:00:32] {994} INFO - iteration 19, current learner m
}
],
"source": [
"import numpy as np \n",
"\n",
"''' define your customized objective function '''\n",
"def logregobj(preds, dtrain):\n",
" labels = dtrain.get_label()\n",
" preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight\n",
" grad = preds - labels\n",
" hess = preds * (1.0 - preds)\n",
" return grad, hess\n",
"\n",
"''' create customized XGBoost learners class with your objective function '''\n",
"from flaml.model import XGBoostEstimator\n",
"\n",
"\n",
"class MyXGB1(XGBoostEstimator):\n",
" '''XGBoostEstimator with the logregobj function as the objective function\n",
" '''\n",
"\n",
" def __init__(self, **params):\n",
" super().__init__(objective=logregobj, **params) \n",
"\n",
"\n",
"class MyXGB2(XGBoostEstimator):\n",
" '''XGBoostEstimator with 'reg:squarederror' as the objective function\n",
" '''\n",
"\n",
" def __init__(self, **params):\n",
" super().__init__(objective='reg:gamma', **params)\n",
"\n",
"\n",
"from flaml import AutoML\n",
"automl = AutoML()\n",
"automl.add_learner(learner_name='my_xgb1', learner_class=MyXGB1)\n",
"automl.add_learner(learner_name='my_xgb2', learner_class=MyXGB2)\n",
"settings = {\n",
" \"time_budget\": 30, # total running time in seconds\n",
" \"metric\": 'r2', # primary metrics for regression can be chosen from: ['mae','mse','r2']\n",
" \"estimator_list\": ['my_xgb1', 'my_xgb2'], # list of ML learners; we tune lightgbm in this example\n",
" \"task\": 'regression', # task type \n",
" \"log_file_name\": 'houses_experiment_my_xgb.log', # flaml log file\n",
"}\n",
"automl.fit(X_train=X_train, y_train=y_train, **settings)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Best hyperparmeter config:{'n_estimators': 308.0, 'max_leaves': 106.0, 'min_child_weight': 23.491005464979025, 'learning_rate': 0.12163628930660118, 'subsample': 1.0, 'colsample_bylevel': 0.8088347023339904, 'colsample_bytree': 1.0, 'reg_alpha': 0.0017299741585977452, 'reg_lambda': 0.2417100801313181}\nBest r2 on validation data: 0.8289\nTraining duration of best run: 1.589 s\nPredicted labels[149762.39 238168. 150789.95 ... 180301.3 205223.98 262682.8 ]\nTrue labels[136900. 241300. 200700. ... 160900. 227300. 265600.]\nr2=0.8390991924308188\nmse=2126868377.371629\nmae=29728.29839662064\n"
}
],
"source": [
"print('Best hyperparmeter config:', automl.best_config)\n",
"print('Best r2 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))\n",
"\n",
"y_pred = automl.predict(X_test)\n",
"print('Predicted labels', y_pred)\n",
"print('True labels', y_test)\n",
"\n",
"from flaml.ml import sklearn_metric_loss_score\n",
"print('r2', '=', 1 - sklearn_metric_loss_score('r2', y_pred, y_test))\n",
"print('mse', '=', sklearn_metric_loss_score('mse', y_pred, y_test))\n",
"print('mae', '=', sklearn_metric_loss_score('mae', y_pred, y_test))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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}
],
"metadata": {
"kernelspec": {
2021-04-10 21:14:28 -04:00
"name": "python37764bitbsconda5b158f6acec0414d8c5c2401992dd9e1",
"display_name": "Python 3.7.7 64-bit ('bs': conda)",
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"metadata": {
"interpreter": {
2021-04-08 09:29:55 -07:00
"hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544"
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}
}
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.7.7-final"
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}
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"nbformat": 4,
"nbformat_minor": 2
}
