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"(tune-lightgbm-example)=\n",
"\n",
"# Using LightGBM with Tune\n",
"\n",
"```{image} /images/lightgbm_logo.png\n",
":align: center\n",
":alt: LightGBM Logo\n",
":height: 120px\n",
":target: https://lightgbm.readthedocs.io\n",
"```\n",
"\n",
"```{contents}\n",
":backlinks: none\n",
":local: true\n",
"```\n",
"\n",
"## Example"
]
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"2022-07-22 15:30:02,623\tINFO services.py:1483 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n",
"2022-07-22 15:30:05,042\tWARNING function_trainable.py:619 -- Function checkpointing is disabled. This may result in unexpected behavior when using checkpointing features or certain schedulers. To enable, set the train function arguments to be `func(config, checkpoint_dir=None)`.\n"
]
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"== Status ==<br>Current time: 2022-07-22 15:30:18 (running for 00:00:12.88)<br>Memory usage on this node: 10.1/16.0 GiB<br>Using AsyncHyperBand: num_stopped=4\n",
"Bracket: Iter 64.000: -0.32867132867132864 | Iter 16.000: -0.32867132867132864 | Iter 4.000: -0.32867132867132864 | Iter 1.000: -0.35664335664335667<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/5.3 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: c7534_00003 with binary_error=0.3146853146853147 and parameters={'objective': 'binary', 'metric': ['binary_error', 'binary_logloss'], 'verbose': -1, 'boosting_type': 'dart', 'num_leaves': 702, 'learning_rate': 4.858514533326432e-08}<br>Result logdir: /Users/kai/ray_results/train_breast_cancer_2022-07-22_15-29-59<br>Number of trials: 4/4 (4 TERMINATED)<br><table>\n",
"<thead>\n",
"<tr><th>Trial name </th><th>status </th><th>loc </th><th>boosting_type </th><th style=\"text-align: right;\"> learning_rate</th><th style=\"text-align: right;\"> num_leaves</th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> binary_error</th><th style=\"text-align: right;\"> binary_logloss</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>train_breast_cancer_c7534_00000</td><td>TERMINATED</td><td>127.0.0.1:46947</td><td>gbdt </td><td style=\"text-align: right;\"> 1.09528e-08</td><td style=\"text-align: right;\"> 926</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 4.04621 </td><td style=\"text-align: right;\"> 0.370629</td><td style=\"text-align: right;\"> 0.659303</td></tr>\n",
"<tr><td>train_breast_cancer_c7534_00001</td><td>TERMINATED</td><td>127.0.0.1:46965</td><td>dart </td><td style=\"text-align: right;\"> 9.07058e-05</td><td style=\"text-align: right;\"> 512</td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 0.0379331</td><td style=\"text-align: right;\"> 0.391608</td><td style=\"text-align: right;\"> 0.670769</td></tr>\n",
"<tr><td>train_breast_cancer_c7534_00002</td><td>TERMINATED</td><td>127.0.0.1:46987</td><td>gbdt </td><td style=\"text-align: right;\"> 0.00110605 </td><td style=\"text-align: right;\"> 186</td><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\"> 0.0196211</td><td style=\"text-align: right;\"> 0.405594</td><td style=\"text-align: right;\"> 0.678443</td></tr>\n",
"<tr><td>train_breast_cancer_c7534_00003</td><td>TERMINATED</td><td>127.0.0.1:46988</td><td>dart </td><td style=\"text-align: right;\"> 4.85851e-08</td><td style=\"text-align: right;\"> 702</td><td style=\"text-align: right;\"> 100</td><td style=\"text-align: right;\"> 0.417179 </td><td style=\"text-align: right;\"> 0.314685</td><td style=\"text-align: right;\"> 0.655626</td></tr>\n",
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"2022-07-22 15:30:06,224\tINFO plugin_schema_manager.py:52 -- Loading the default runtime env schemas: ['/Users/kai/coding/ray/python/ray/_private/runtime_env/../../runtime_env/schemas/working_dir_schema.json', '/Users/kai/coding/ray/python/ray/_private/runtime_env/../../runtime_env/schemas/pip_schema.json'].\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46947)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46947)\u001b[0m _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for train_breast_cancer_c7534_00000:\n",
" binary_error: 0.3706293706293706\n",
" binary_logloss: 0.6593043583564255\n",
" date: 2022-07-22_15-30-11\n",
" done: false\n",
" experiment_id: 9fbbf2cd94b24a14aa5ef2d552e78b70\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 1\n",
" node_ip: 127.0.0.1\n",
" pid: 46947\n",
" time_since_restore: 0.10576009750366211\n",
" time_this_iter_s: 0.10576009750366211\n",
" time_total_s: 0.10576009750366211\n",
" timestamp: 1658500211\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: c7534_00000\n",
" warmup_time: 0.0033888816833496094\n",
" \n",
"Result for train_breast_cancer_c7534_00001:\n",
" binary_error: 0.3916083916083916\n",
" binary_logloss: 0.670769405026208\n",
" date: 2022-07-22_15-30-14\n",
" done: true\n",
" experiment_id: 10df796f3d2e4627ba7526014b21f426\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 1\n",
" node_ip: 127.0.0.1\n",
" pid: 46965\n",
" time_since_restore: 0.0379331111907959\n",
" time_this_iter_s: 0.0379331111907959\n",
" time_total_s: 0.0379331111907959\n",
" timestamp: 1658500214\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: c7534_00001\n",
" warmup_time: 0.0033578872680664062\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(train_breast_cancer pid=46965)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46965)\u001b[0m _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for train_breast_cancer_c7534_00000:\n",
" binary_error: 0.3706293706293706\n",
" binary_logloss: 0.6593034612409915\n",
" date: 2022-07-22_15-30-15\n",
" done: true\n",
" experiment_id: 9fbbf2cd94b24a14aa5ef2d552e78b70\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 100\n",
" node_ip: 127.0.0.1\n",
" pid: 46947\n",
" time_since_restore: 4.046205043792725\n",
" time_this_iter_s: 0.002338886260986328\n",
" time_total_s: 4.046205043792725\n",
" timestamp: 1658500215\n",
" timesteps_since_restore: 0\n",
" training_iteration: 100\n",
" trial_id: c7534_00000\n",
" warmup_time: 0.0033888816833496094\n",
" \n",
"Result for train_breast_cancer_c7534_00003:\n",
" binary_error: 0.3146853146853147\n",
" binary_logloss: 0.635705942279978\n",
" date: 2022-07-22_15-30-18\n",
" done: false\n",
" experiment_id: d370b87343ea4a8e994bcf99a4f6f28d\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 1\n",
" node_ip: 127.0.0.1\n",
" pid: 46988\n",
" time_since_restore: 0.04007911682128906\n",
" time_this_iter_s: 0.04007911682128906\n",
" time_total_s: 0.04007911682128906\n",
" timestamp: 1658500218\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: c7534_00003\n",
" warmup_time: 0.0032351016998291016\n",
" \n",
"Result for train_breast_cancer_c7534_00002:\n",
" binary_error: 0.40559440559440557\n",
" binary_logloss: 0.6784426899984863\n",
" date: 2022-07-22_15-30-18\n",
" done: true\n",
" experiment_id: 96e95ab236aa40aea3e9a1218293b562\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 1\n",
" node_ip: 127.0.0.1\n",
" pid: 46987\n",
" time_since_restore: 0.01962113380432129\n",
" time_this_iter_s: 0.01962113380432129\n",
" time_total_s: 0.01962113380432129\n",
" timestamp: 1658500218\n",
" timesteps_since_restore: 0\n",
" training_iteration: 1\n",
" trial_id: c7534_00002\n",
" warmup_time: 0.0026988983154296875\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(train_breast_cancer pid=46987)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46987)\u001b[0m _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46988)\u001b[0m /Users/kai/.pyenv/versions/3.7.7/lib/python3.7/site-packages/lightgbm/engine.py:239: UserWarning: 'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
"\u001b[2m\u001b[36m(train_breast_cancer pid=46988)\u001b[0m _log_warning(\"'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result for train_breast_cancer_c7534_00003:\n",
" binary_error: 0.3146853146853147\n",
" binary_logloss: 0.6556262981958247\n",
" date: 2022-07-22_15-30-18\n",
" done: true\n",
" experiment_id: d370b87343ea4a8e994bcf99a4f6f28d\n",
" hostname: Kais-MacBook-Pro.local\n",
" iterations_since_restore: 100\n",
" node_ip: 127.0.0.1\n",
" pid: 46988\n",
" time_since_restore: 0.4171791076660156\n",
" time_this_iter_s: 0.0024061203002929688\n",
" time_total_s: 0.4171791076660156\n",
" timestamp: 1658500218\n",
" timesteps_since_restore: 0\n",
" training_iteration: 100\n",
" trial_id: c7534_00003\n",
" warmup_time: 0.0032351016998291016\n",
" \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-07-22 15:30:18,873\tINFO tune.py:738 -- Total run time: 13.83 seconds (12.87 seconds for the tuning loop).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best hyperparameters found were: {'objective': 'binary', 'metric': ['binary_error', 'binary_logloss'], 'verbose': -1, 'boosting_type': 'dart', 'num_leaves': 702, 'learning_rate': 4.858514533326432e-08}\n"
]
}
],
"source": [
"import lightgbm as lgb\n",
"import numpy as np\n",
"import sklearn.datasets\n",
"import sklearn.metrics\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from ray import tune\n",
"from ray.air import session\n",
"from ray.tune.schedulers import ASHAScheduler\n",
"from ray.tune.integration.lightgbm import TuneReportCheckpointCallback\n",
"\n",
"\n",
"def train_breast_cancer(config):\n",
"\n",
" data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
" train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)\n",
" train_set = lgb.Dataset(train_x, label=train_y)\n",
" test_set = lgb.Dataset(test_x, label=test_y)\n",
" gbm = lgb.train(\n",
" config,\n",
" train_set,\n",
" valid_sets=[test_set],\n",
" valid_names=[\"eval\"],\n",
" verbose_eval=False,\n",
" callbacks=[\n",
" TuneReportCheckpointCallback(\n",
" {\n",
" \"binary_error\": \"eval-binary_error\",\n",
" \"binary_logloss\": \"eval-binary_logloss\",\n",
" }\n",
" )\n",
" ],\n",
" )\n",
" preds = gbm.predict(test_x)\n",
" pred_labels = np.rint(preds)\n",
" session.report({\n",
" \"mean_accuracy\": sklearn.metrics.accuracy_score(test_y, pred_labels), \"done\": True\n",
" })\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" import argparse\n",
"\n",
" parser = argparse.ArgumentParser()\n",
" parser.add_argument(\n",
" \"--server-address\",\n",
" type=str,\n",
" default=None,\n",
" required=False,\n",
" help=\"The address of server to connect to if using \" \"Ray Client.\",\n",
" )\n",
" args, _ = parser.parse_known_args()\n",
"\n",
" if args.server_address:\n",
" import ray\n",
"\n",
" ray.init(f\"ray://{args.server_address}\")\n",
"\n",
" config = {\n",
" \"objective\": \"binary\",\n",
" \"metric\": [\"binary_error\", \"binary_logloss\"],\n",
" \"verbose\": -1,\n",
" \"boosting_type\": tune.grid_search([\"gbdt\", \"dart\"]),\n",
" \"num_leaves\": tune.randint(10, 1000),\n",
" \"learning_rate\": tune.loguniform(1e-8, 1e-1),\n",
" }\n",
" \n",
" tuner = tune.Tuner(\n",
" train_breast_cancer,\n",
" tune_config=tune.TuneConfig(\n",
" metric=\"binary_error\",\n",
" mode=\"min\",\n",
" scheduler=ASHAScheduler(),\n",
" num_samples=2,\n",
" ),\n",
" param_space=config,\n",
" )\n",
" results = tuner.fit()\n",
"\n",
" print(\"Best hyperparameters found were: \", results.get_best_result().config)\n"
]
}
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