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* Initial plan * Add objective parameter to LGBMEstimator search_space Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com> * Add test for LGBMEstimator objective parameter Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com> * Fix format error * Remove changes, just add a test to verify the current supported usage --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com> Co-authored-by: Li Jiang <bnujli@gmail.com> Co-authored-by: Li Jiang <lijiang1@microsoft.com>
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
import sys
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import pytest
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from flaml import AutoML, tune
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try:
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import transformers
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_transformers_installed = True
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except ImportError:
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_transformers_installed = False
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@pytest.mark.skipif(
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sys.platform == "darwin" or not _transformers_installed, reason="do not run on mac os or transformers not installed"
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)
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def test_custom_hp_nlp():
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from test.nlp.utils import get_automl_settings, get_toy_data_seqclassification
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X_train, y_train, X_val, y_val, X_test = get_toy_data_seqclassification()
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automl = AutoML()
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automl_settings = get_automl_settings()
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automl_settings["custom_hp"] = None
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automl_settings["custom_hp"] = {
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"transformer": {
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"model_path": {
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"domain": tune.choice(["google/electra-small-discriminator"]),
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},
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"num_train_epochs": {"domain": 3},
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}
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}
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automl_settings["fit_kwargs_by_estimator"] = {
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"transformer": {
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"output_dir": "test/data/output/",
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"fp16": False,
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}
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}
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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def test_custom_hp():
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from sklearn.datasets import load_iris
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X_train, y_train = load_iris(return_X_y=True)
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automl = AutoML()
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custom_hp = {
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"xgboost": {
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"n_estimators": {
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"domain": tune.lograndint(lower=1, upper=100),
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"low_cost_init_value": 1,
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},
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},
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"rf": {
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"max_leaves": {
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"domain": None, # disable search
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},
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},
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"lgbm": {
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"subsample": {
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"domain": tune.uniform(lower=0.1, upper=1.0),
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"init_value": 1.0,
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},
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"subsample_freq": {
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"domain": 1, # subsample_freq must > 0 to enable subsample
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},
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},
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}
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automl.fit(X_train, y_train, custom_hp=custom_hp, time_budget=2)
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print(automl.best_config_per_estimator)
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def test_lgbm_objective():
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"""Test that objective parameter can be set via custom_hp for LGBMEstimator"""
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import numpy as np
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# Create a simple regression dataset
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np.random.seed(42)
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X_train = np.random.rand(100, 5)
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y_train = np.random.rand(100) * 100 # Scale to avoid division issues with MAPE
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automl = AutoML()
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settings = {
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"time_budget": 3,
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"metric": "mape",
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"task": "regression",
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"estimator_list": ["lgbm"],
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"verbose": 0,
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"custom_hp": {"lgbm": {"objective": {"domain": "mape"}}}, # Fixed value, not tuned
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}
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automl.fit(X_train, y_train, **settings)
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# Verify that objective was set correctly
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assert "objective" in automl.best_config, "objective should be in best_config"
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assert automl.best_config["objective"] == "mape", "objective should be 'mape'"
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# Verify the model has the correct objective
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if hasattr(automl.model, "estimator") and hasattr(automl.model.estimator, "get_params"):
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model_params = automl.model.estimator.get_params()
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assert model_params.get("objective") == "mape", "Model should use 'mape' objective"
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print("Test passed: objective parameter works correctly with LGBMEstimator")
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if __name__ == "__main__":
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test_custom_hp()
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test_lgbm_objective()
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