mirror of
https://github.com/vale981/ray
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128 lines
No EOL
3.6 KiB
Text
128 lines
No EOL
3.6 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"(tune-lightgbm-example)=\n",
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"\n",
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"# Using LightGBM with Tune\n",
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"\n",
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"```{image} /images/lightgbm_logo.png\n",
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":align: center\n",
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":alt: LightGBM Logo\n",
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":height: 120px\n",
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":target: https://lightgbm.readthedocs.io\n",
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"```\n",
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"\n",
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"```{contents}\n",
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":backlinks: none\n",
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":local: true\n",
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"```\n",
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"\n",
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"## Example"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import lightgbm as lgb\n",
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"import numpy as np\n",
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"import sklearn.datasets\n",
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"import sklearn.metrics\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"from ray import tune\n",
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"from ray.tune.schedulers import ASHAScheduler\n",
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"from ray.tune.integration.lightgbm import TuneReportCheckpointCallback\n",
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"\n",
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"\n",
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"def train_breast_cancer(config):\n",
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"\n",
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" data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
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" train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)\n",
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" train_set = lgb.Dataset(train_x, label=train_y)\n",
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" test_set = lgb.Dataset(test_x, label=test_y)\n",
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" gbm = lgb.train(\n",
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" config,\n",
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" train_set,\n",
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" valid_sets=[test_set],\n",
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" valid_names=[\"eval\"],\n",
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" verbose_eval=False,\n",
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" callbacks=[\n",
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" TuneReportCheckpointCallback(\n",
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" {\n",
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" \"binary_error\": \"eval-binary_error\",\n",
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" \"binary_logloss\": \"eval-binary_logloss\",\n",
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" }\n",
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" )\n",
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" ],\n",
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" )\n",
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" preds = gbm.predict(test_x)\n",
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" pred_labels = np.rint(preds)\n",
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" tune.report(\n",
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" mean_accuracy=sklearn.metrics.accuracy_score(test_y, pred_labels), done=True\n",
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" )\n",
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"\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" import argparse\n",
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"\n",
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" parser = argparse.ArgumentParser()\n",
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" parser.add_argument(\n",
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" \"--server-address\",\n",
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" type=str,\n",
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" default=None,\n",
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" required=False,\n",
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" help=\"The address of server to connect to if using \" \"Ray Client.\",\n",
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" )\n",
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" args, _ = parser.parse_known_args()\n",
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"\n",
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" if args.server_address:\n",
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" import ray\n",
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"\n",
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" ray.init(f\"ray://{args.server_address}\")\n",
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"\n",
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" config = {\n",
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" \"objective\": \"binary\",\n",
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" \"metric\": [\"binary_error\", \"binary_logloss\"],\n",
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" \"verbose\": -1,\n",
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" \"boosting_type\": tune.grid_search([\"gbdt\", \"dart\"]),\n",
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" \"num_leaves\": tune.randint(10, 1000),\n",
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" \"learning_rate\": tune.loguniform(1e-8, 1e-1),\n",
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" }\n",
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"\n",
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" analysis = tune.run(\n",
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" train_breast_cancer,\n",
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" metric=\"binary_error\",\n",
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" mode=\"min\",\n",
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" config=config,\n",
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" num_samples=2,\n",
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" scheduler=ASHAScheduler(),\n",
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" )\n",
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"\n",
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" print(\"Best hyperparameters found were: \", analysis.best_config)\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"orphan": true
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},
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"nbformat": 4,
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"nbformat_minor": 5
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} |