mirror of
https://github.com/microsoft/FLAML.git
synced 2026-02-09 02:09:16 +08:00
840f76e5e5
* Changed tune.report import for ray>=2 * env: Changed pydantic restriction in env * Reverted Pydantic install conditions * Reverted Pydantic install conditions * test: Check if GPU is available * tests: uncommented a line * tests: Better fix for Ray GPU checking * tests: Added timeout to dataset loading * tests: Deleted _test_hf_data() * test: Reduce lrl2 dataset size * bug: timeout error * bug: timeout error * fix: Added threading check for timout issue * Undo old commits * Timeout fix from #1406 --------- Co-authored-by: Daniel Grindrod <dannycg1996@gmail.com>
129 lines
3.4 KiB
Python
129 lines
3.4 KiB
Python
import pickle
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import shutil
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import sys
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import pytest
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def test_xgboost():
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import numpy as np
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import scipy.sparse
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from sklearn.datasets import make_moons
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from xgboost.core import XGBoostError
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from flaml import AutoML
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try:
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X_train = scipy.sparse.eye(900000)
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y_train = np.random.randint(2, size=900000)
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automl = AutoML()
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automl.fit(
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X_train,
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y_train,
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estimator_list=["xgb_limitdepth", "xgboost"],
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time_budget=5,
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gpu_per_trial=1,
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)
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train, label = make_moons(n_samples=300000, shuffle=True, noise=0.3, random_state=None)
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automl = AutoML()
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automl.fit(
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train,
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label,
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estimator_list=["xgb_limitdepth", "xgboost"],
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time_budget=5,
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gpu_per_trial=1,
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)
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automl.fit(
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train,
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label,
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estimator_list=["xgb_limitdepth", "xgboost"],
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time_budget=5,
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)
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except XGBoostError:
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# No visible GPU is found for XGBoost.
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return
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@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
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def _test_hf_data():
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import requests
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from datasets import load_dataset
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from flaml import AutoML
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try:
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train_dataset = load_dataset("glue", "mrpc", split="train[:1%]").to_pandas()
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dev_dataset = load_dataset("glue", "mrpc", split="validation[:1%]").to_pandas()
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test_dataset = load_dataset("glue", "mrpc", split="test[:1%]").to_pandas()
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except requests.exceptions.ConnectionError:
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return
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# Tests will only run if there is a GPU available
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try:
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import ray
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pg = ray.util.placement_group([{"CPU": 1, "GPU": 1}])
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if not pg.wait(timeout_seconds=10): # Wait 10 seconds for resources
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raise RuntimeError("No available node types can fulfill resource request!")
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except RuntimeError:
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return
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custom_sent_keys = ["sentence1", "sentence2"]
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label_key = "label"
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X_train = train_dataset[custom_sent_keys]
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y_train = train_dataset[label_key]
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X_val = dev_dataset[custom_sent_keys]
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y_val = dev_dataset[label_key]
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X_test = test_dataset[custom_sent_keys]
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automl = AutoML()
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automl_settings = {
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"gpu_per_trial": 1,
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"max_iter": 2,
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"time_budget": 5000,
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"task": "seq-classification",
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"metric": "accuracy",
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"log_file_name": "seqclass.log",
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"use_ray": True,
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}
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automl_settings["fit_kwargs_by_estimator"] = {
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"transformer": {
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"model_path": "facebook/muppet-roberta-base",
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"output_dir": "test/data/output/",
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"fp16": True,
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}
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}
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automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)
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automl = AutoML()
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automl.retrain_from_log(X_train=X_train, y_train=y_train, train_full=True, record_id=0, **automl_settings)
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with open("automl.pkl", "wb") as f:
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pickle.dump(automl, f, pickle.HIGHEST_PROTOCOL)
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with open("automl.pkl", "rb") as f:
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automl = pickle.load(f)
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shutil.rmtree("test/data/output/")
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automl.predict(X_test)
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automl.predict(["test test", "test test"])
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automl.predict(
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[
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["test test", "test test"],
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["test test", "test test"],
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["test test", "test test"],
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]
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)
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automl.predict_proba(X_test)
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print(automl.classes_)
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if __name__ == "__main__":
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_test_hf_data()
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