mirror of
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af423463c3
* fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
91 lines
2.4 KiB
Python
91 lines
2.4 KiB
Python
import sys
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import pytest
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import requests
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@pytest.mark.skipif(sys.platform == "darwin", reason="do not run on mac os")
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def test_summarization():
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from flaml import AutoML
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from pandas import DataFrame
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train_dataset = DataFrame(
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[
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("The cat is alive", "The cat is dead"),
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("The cat is alive", "The cat is dead"),
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("The cat is alive", "The cat is dead"),
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("The cat is alive", "The cat is dead"),
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]
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)
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dev_dataset = DataFrame(
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[
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("The old woman is beautiful", "The old woman is ugly"),
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("The old woman is beautiful", "The old woman is ugly"),
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("The old woman is beautiful", "The old woman is ugly"),
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("The old woman is beautiful", "The old woman is ugly"),
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]
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)
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test_dataset = DataFrame(
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[
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("The purse is cheap", "The purse is expensive"),
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("The purse is cheap", "The purse is expensive"),
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("The purse is cheap", "The purse is expensive"),
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("The purse is cheap", "The purse is expensive"),
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]
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)
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for each_dataset in [train_dataset, dev_dataset, test_dataset]:
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each_dataset.columns = ["document", "summary"]
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custom_sent_keys = ["document"]
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label_key = "summary"
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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": 0,
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"max_iter": 3,
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"time_budget": 20,
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"task": "summarization",
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"metric": "rouge1",
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"log_file_name": "seqclass.log",
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}
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automl_settings["hf_args"] = {
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"model_path": "patrickvonplaten/t5-tiny-random",
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"output_dir": "test/data/output/",
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"ckpt_per_epoch": 1,
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"fp16": False,
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}
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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 = AutoML()
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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 __name__ == "__main__":
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test_summarization()
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