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3e7aac6e8b
* simplify the initiation of chat * version update * include openai * completion * load config list from json * initiate_chat * oai config list * oai config list * config list * config_list * raise_error * retry_time * raise condition * oai config list * catch file not found * catch openml error * handle openml error * handle openml error * handle openml error * handle openml error * handle openml error * handle openml error * close #1139 * use property * termination msg * AIUserProxyAgent * smaller dev container * update notebooks * match * document code execution and AIUserProxyAgent * gpt 3.5 config list * rate limit * variable visibility * remove unnecessary import * quote * notebook comments * remove mathchat from init import * two users * import location * expose config * return str not tuple * rate limit * ipython user proxy * message * None result * rate limit * rate limit * rate limit * rate limit * make auto_reply a common method for all agents * abs path * refactor and doc * set mathchat_termination * code format * modified * emove import * code quality * sender -> messages * system message * clean agent hierarchy * dict check * invalid oai msg * return * openml error * docstr --------- Co-authored-by: kevin666aa <yrwu000627@gmail.com>
78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
from flaml.automl.data import load_openml_dataset
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from flaml import AutoML
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from flaml.tune.spark.utils import check_spark
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import os
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import pytest
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spark_available, _ = check_spark()
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skip_spark = not spark_available
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pytestmark = pytest.mark.skipif(skip_spark, reason="Spark is not installed. Skip all spark tests.")
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os.environ["FLAML_MAX_CONCURRENT"] = "2"
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def base_automl(n_concurrent_trials=1, use_ray=False, use_spark=False, verbose=0):
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from minio.error import ServerError
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try:
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X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir="./")
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except (ServerError, Exception):
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from sklearn.datasets import fetch_california_housing
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X_train, y_train = fetch_california_housing(return_X_y=True)
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automl = AutoML()
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settings = {
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"time_budget": 3, # total running time in seconds
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"metric": "r2", # primary metrics for regression can be chosen from: ['mae','mse','r2','rmse','mape']
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"estimator_list": ["lgbm", "rf", "xgboost"], # list of ML learners
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"task": "regression", # task type
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"log_file_name": "houses_experiment.log", # flaml log file
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"seed": 7654321, # random seed
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"n_concurrent_trials": n_concurrent_trials, # the maximum number of concurrent learners
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"use_ray": use_ray, # whether to use Ray for distributed training
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"use_spark": use_spark, # whether to use Spark for distributed training
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"verbose": verbose,
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}
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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print("Best ML leaner:", automl.best_estimator)
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print("Best hyperparmeter config:", automl.best_config)
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print("Best accuracy on validation data: {0:.4g}".format(1 - automl.best_loss))
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print("Training duration of best run: {0:.4g} s".format(automl.best_config_train_time))
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def test_both_ray_spark():
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with pytest.raises(ValueError):
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base_automl(n_concurrent_trials=2, use_ray=True, use_spark=True)
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def test_verboses():
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for verbose in [1, 3, 5]:
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base_automl(verbose=verbose)
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def test_import_error():
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from importlib import reload
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import flaml.tune.spark.utils as utils
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reload(utils)
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utils._have_spark = False
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spark_available, spark_error_msg = utils.check_spark()
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assert not spark_available
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assert isinstance(spark_error_msg, ImportError)
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reload(utils)
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utils._spark_major_minor_version = (1, 1)
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spark_available, spark_error_msg = utils.check_spark()
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assert not spark_available
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assert isinstance(spark_error_msg, ImportError)
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reload(utils)
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if __name__ == "__main__":
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base_automl()
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test_import_error()
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