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{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"slideshow":{"slide_type":"slide"}},"source":["Copyright (c) Microsoft Corporation. All rights reserved. \n","\n","Licensed under the MIT License.\n","\n","# Run FLAML Parallel tuning with Spark\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 economical. 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 run FLAML parallel tuning using Spark as the backend.\n","\n","FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the following options:\n","```bash\n","pip install flaml[spark,notebook,blendsearch]>=1.1.0\n","```\n","*Spark support is added in v1.1.0*"]},{"cell_type":"code","execution_count":null,"metadata":{"cellStatus":"{\"Li Jiang\":{\"queued_time\":\"2022-12-07T08:16:51.6335768Z\",\"session_start_time\":null,\"execution_start_time\":\"2022-12-07T08:17:21.9028602Z\",\"execution_finish_time\":\"2022-12-07T08:18:52.3646576Z\",\"state\":\"finished\",\"livy_statement_state\":\"available\"}}"},"outputs":[],"source":["# %pip install flaml[spark,notebook,blendsearch]>=1.1.0"]},{"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":null,"metadata":{"cellStatus":"{\"Li Jiang\":{\"queued_time\":\"2022-12-07T08:20:53.4783943Z\",\"session_start_time\":null,\"execution_start_time\":\"2022-12-07T08:20:55.7666047Z\",\"execution_finish_time\":\"2022-12-07T08:21:10.9050139Z\",\"state\":\"finished\",\"livy_statement_state\":\"available\"}}","slideshow":{"slide_type":"subslide"},"tags":[]},"outputs":[],"source":["from flaml.data import load_openml_dataset\n","X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir='./')"]},{"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. \n","\n","Notice that here `use_spark` is set to `True` in order to use Spark as the parallel training backend."]},{"cell_type":"code","execution_count":null,"metadata":{"cellStatus":"{\"Li Jiang\":{\"queued_time\":\"2022-12-07T08:20:53.7001471Z\",\"session_start_time\":null,\"execution_start_time\":\"2022-12-07T08:21:10.9846131Z\",\"execution_finish_time\":\"2022-12-07T08:21:11.3604062Z\",\"state\":\"finished\",\"livy_statement_state\":\"available\"}}","slideshow":{"slide_type":"slide"},"tags":[]},"outputs":[],"source":["''' import AutoML class from flaml package '''\n","from flaml import AutoML\n","automl = AutoML()"]},{"cell_type":"code","execution_count":null,"metadata":{"cellStatus":"{\"Li Jiang\":{\"queued_time\":\"2022-12-07T08:20:53.8983341Z\",\"session_start_time\":null,\"execution_start_time\":\"2022-12-07T08:21:11.4417491Z\",\"execution_finish_time\":\"2022-12-07T08:21:11.8242955Z\",\"state\":\"finished\",\"livy_statement_state\":\"available\"}}","slideshow":{"slide_type":"slide"}},"outputs":[],"source":["settings = {\n"," \"time_budget\": 30, # total running time in seconds\n"," \"metric\": 'r2', # primary metrics for regression can be chosen from: ['mae','mse','r2','rmse','mape']\n"," \"esti
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