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05c5f8f426
* more tolerant time limit for test_overtime * Cancel assertion becasue github VM sometimes is super slow --------- Co-authored-by: Li Jiang <lijiang1@microsoft.com>
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
import os
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import time
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import numpy as np
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import pytest
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from sklearn.datasets import load_iris
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from flaml import AutoML
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try:
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from test.spark.custom_mylearner import *
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except ImportError:
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from custom_mylearner import *
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try:
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import pyspark
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from flaml.tune.spark.utils import check_spark
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from flaml.tune.spark.mylearner import lazy_metric
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os.environ["FLAML_MAX_CONCURRENT"] = "10"
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spark = pyspark.sql.SparkSession.builder.appName("App4OvertimeTest").getOrCreate()
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spark_available, _ = check_spark()
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skip_spark = not spark_available
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except ImportError:
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skip_spark = True
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pytestmark = pytest.mark.skipif(
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skip_spark, reason="Spark is not installed. Skip all spark tests."
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)
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def test_overtime():
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time_budget = 15
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df, y = load_iris(return_X_y=True, as_frame=True)
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df["label"] = y
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automl_experiment = AutoML()
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automl_settings = {
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"dataframe": df,
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"label": "label",
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"time_budget": time_budget,
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"eval_method": "cv",
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"metric": lazy_metric,
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"task": "classification",
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"log_file_name": "test/iris_custom.log",
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"log_training_metric": True,
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"log_type": "all",
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"n_jobs": 1,
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"model_history": True,
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"sample_weight": np.ones(len(y)),
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"pred_time_limit": 1e-5,
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"estimator_list": ["lgbm"],
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"n_concurrent_trials": 2,
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"use_spark": True,
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"force_cancel": True,
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}
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start_time = time.time()
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automl_experiment.fit(**automl_settings)
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elapsed_time = time.time() - start_time
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print(
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"time budget: {:.2f}s, actual elapsed time: {:.2f}s".format(
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time_budget, elapsed_time
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)
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)
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# assert abs(elapsed_time - time_budget) < 5 # cancel assertion because github VM sometimes is super slow, causing the test to fail
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print(automl_experiment.predict(df))
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print(automl_experiment.model)
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print(automl_experiment.best_iteration)
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print(automl_experiment.best_estimator)
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if __name__ == "__main__":
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test_overtime()
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