mirror of
https://github.com/microsoft/autogen.git
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64 lines
1.5 KiB
Python
64 lines
1.5 KiB
Python
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import os
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import pytest
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@pytest.mark.skipif(os.name == "posix", reason="do not run on mac os")
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def test_max_iter_1():
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from flaml import AutoML
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from datasets import load_dataset
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train_dataset = load_dataset("glue", "mrpc", split="train").to_pandas().iloc[0:4]
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dev_dataset = load_dataset("glue", "mrpc", split="train").to_pandas().iloc[0:4]
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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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automl = AutoML()
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def toy_metric(
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X_test,
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y_test,
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estimator,
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labels,
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X_train,
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y_train,
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weight_test=None,
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weight_train=None,
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config=None,
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groups_test=None,
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groups_train=None,
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):
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return 0, {
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"test_loss": 0,
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"train_loss": 0,
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"pred_time": 0,
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}
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automl_settings = {
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"gpu_per_trial": 0,
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"max_iter": 1,
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"time_budget": 5,
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"task": "seq-classification",
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"metric": toy_metric,
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"log_file_name": "seqclass.log",
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}
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automl_settings["custom_hpo_args"] = {
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"model_path": "google/electra-small-discriminator",
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"output_dir": "data/output/",
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"ckpt_per_epoch": 5,
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"fp16": False,
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}
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automl.fit(
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X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings
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)
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del automl
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