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* rm classification head in nlp * rm classification head in nlp * rm classification head in nlp * adding test cases for switch classification head * adding test cases for switch classification head * Update test/nlp/test_autohf_classificationhead.py Co-authored-by: Chi Wang <wang.chi@microsoft.com> * adding test cases for switch classification head * run each test separately * skip classification head test on windows * disabling wandb reporting * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * fix test nlp custom metric * Update website/docs/Examples/AutoML-NLP.md Co-authored-by: Chi Wang <wang.chi@microsoft.com> * Update website/docs/Examples/AutoML-NLP.md Co-authored-by: Chi Wang <wang.chi@microsoft.com> * fix test nlp custom metric Co-authored-by: Chi Wang <wang.chi@microsoft.com>
59 lines
1.5 KiB
Python
59 lines
1.5 KiB
Python
import sys
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import pytest
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import requests
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from utils import get_toy_data_summarization, get_automl_settings
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import os
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import shutil
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@pytest.mark.skipif(
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sys.platform == "darwin" or sys.version < "3.7",
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reason="do not run on mac os or py3.6",
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)
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def test_summarization():
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# TODO: manual test for how effective postprocess_seq2seq_prediction_label is
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from flaml import AutoML
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X_train, y_train, X_val, y_val, X_test = get_toy_data_summarization()
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automl = AutoML()
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automl_settings = get_automl_settings()
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automl_settings["task"] = "summarization"
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automl_settings["metric"] = "rouge1"
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automl_settings["time_budget"] = 2 * automl_settings["time_budget"]
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automl_settings["fit_kwargs_by_estimator"]["transformer"][
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"model_path"
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] = "patrickvonplaten/t5-tiny-random"
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try:
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automl.fit(
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X_train=X_train,
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y_train=y_train,
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X_val=X_val,
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y_val=y_val,
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**automl_settings
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)
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except requests.exceptions.HTTPError:
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return
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automl_settings.pop("max_iter", None)
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automl_settings.pop("use_ray", None)
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automl_settings.pop("estimator_list", None)
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automl.retrain_from_log(
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X_train=X_train,
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y_train=y_train,
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train_full=True,
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record_id=0,
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**automl_settings
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)
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automl.predict(X_test)
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if os.path.exists("test/data/output/"):
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shutil.rmtree("test/data/output/")
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if __name__ == "__main__":
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test_summarization()
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