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	Merge branch 'microsoft:main' into main
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				| @ -4,7 +4,10 @@ import requests | ||||
| from utils import get_toy_data_summarization, get_automl_settings | ||||
| 
 | ||||
| 
 | ||||
| @pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os") | ||||
| @pytest.mark.skipif( | ||||
|     sys.platform == "darwin" or sys.version < "3.7", | ||||
|     reason="do not run on mac os or py < 3.7", | ||||
| ) | ||||
| def test_summarization(): | ||||
|     # TODO: manual test for how effective postprocess_seq2seq_prediction_label is | ||||
|     from flaml import AutoML | ||||
|  | ||||
| @ -63,7 +63,6 @@ def test_starting_point_not_in_search_space(): | ||||
|         } | ||||
|     } | ||||
|     automl_settings["starting_points"] = "data:test/nlp/default/" | ||||
|     del automl_settings["fit_kwargs_by_estimator"][this_estimator_name]["model_path"] | ||||
| 
 | ||||
|     automl.fit(X_train, y_train, **automl_settings) | ||||
|     assert len(automl._search_states[this_estimator_name].init_config) == len( | ||||
| @ -90,10 +89,13 @@ def test_points_to_evaluate(): | ||||
|     automl = AutoML() | ||||
|     automl_settings = get_automl_settings(estimator_name="transformer_ms") | ||||
| 
 | ||||
|     automl_settings["estimator_list"] = ["transformer_ms"] | ||||
|     automl_settings["starting_points"] = "data" | ||||
|     automl_settings["starting_points"] = "data:test/nlp/default/" | ||||
| 
 | ||||
|     del automl_settings["fit_kwargs_by_estimator"]["transformer_ms"]["model_path"] | ||||
|     automl_settings["custom_hp"] = { | ||||
|         "transformer_ms": { | ||||
|             "model_path": {"domain": "google/electra-small-discriminator"} | ||||
|         } | ||||
|     } | ||||
| 
 | ||||
|     automl.fit(X_train, y_train, **automl_settings) | ||||
| 
 | ||||
| @ -109,8 +111,6 @@ def test_zero_shot_nomodel(): | ||||
| 
 | ||||
|     automl_settings = get_automl_settings(estimator_name) | ||||
| 
 | ||||
|     del automl_settings["fit_kwargs_by_estimator"][estimator_name]["model_path"] | ||||
| 
 | ||||
|     ( | ||||
|         hyperparams, | ||||
|         estimator_class, | ||||
| @ -146,10 +146,6 @@ def test_build_error_portfolio(path="./test/nlp/default", strategy="greedy"): | ||||
|     location = "test/nlp/default" | ||||
|     X_train, y_train, X_val, y_val, X_test = get_toy_data_seqclassification() | ||||
| 
 | ||||
|     automl_settings = get_automl_settings(estimator_name) | ||||
| 
 | ||||
|     del automl_settings["fit_kwargs_by_estimator"][estimator_name]["model_path"] | ||||
| 
 | ||||
|     try: | ||||
|         ( | ||||
|             hyperparams, | ||||
|  | ||||
| @ -1514,14 +1514,23 @@ def get_automl_settings(estimator_name="transformer"): | ||||
|         "use_ray": False, | ||||
|     } | ||||
| 
 | ||||
|     automl_settings["fit_kwargs_by_estimator"] = { | ||||
|         estimator_name: { | ||||
|             "model_path": "google/electra-small-discriminator", | ||||
|             "output_dir": "test/data/output/", | ||||
|             "ckpt_per_epoch": 1, | ||||
|             "fp16": False, | ||||
|     if estimator_name.endswith("ms"): | ||||
|         automl_settings["fit_kwargs_by_estimator"] = { | ||||
|             estimator_name: { | ||||
|                 "output_dir": "test/data/output/", | ||||
|                 "ckpt_per_epoch": 1, | ||||
|                 "fp16": False, | ||||
|             } | ||||
|         } | ||||
|     else: | ||||
|         automl_settings["fit_kwargs_by_estimator"] = { | ||||
|             estimator_name: { | ||||
|                 "model_path": "google/electra-small-discriminator", | ||||
|                 "output_dir": "test/data/output/", | ||||
|                 "ckpt_per_epoch": 1, | ||||
|                 "fp16": False, | ||||
|             } | ||||
|         } | ||||
|     } | ||||
| 
 | ||||
|     automl_settings["estimator_list"] = [estimator_name] | ||||
|     return automl_settings | ||||
|  | ||||
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