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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>
121 lines
2.9 KiB
Python
121 lines
2.9 KiB
Python
from utils import (
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get_toy_data_regression,
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get_toy_data_binclassification,
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get_toy_data_multiclassclassification,
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get_automl_settings,
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)
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import sys
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import pytest
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import os
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import shutil
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data_list = [
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"get_toy_data_regression",
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"get_toy_data_binclassification",
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"get_toy_data_multiclassclassification",
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]
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model_path_list = [
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"textattack/bert-base-uncased-STS-B",
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"textattack/bert-base-uncased-SST-2",
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"textattack/bert-base-uncased-MNLI",
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]
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def test_switch_1_1():
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data_idx, model_path_idx = 0, 0
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_1_2():
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data_idx, model_path_idx = 0, 1
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_1_3():
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data_idx, model_path_idx = 0, 2
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_2_1():
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data_idx, model_path_idx = 1, 0
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_2_2():
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data_idx, model_path_idx = 1, 1
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_2_3():
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data_idx, model_path_idx = 1, 2
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_3_1():
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data_idx, model_path_idx = 2, 0
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_3_2():
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data_idx, model_path_idx = 2, 1
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def test_switch_3_3():
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data_idx, model_path_idx = 2, 2
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_test_switch_classificationhead(
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data_list[data_idx], model_path_list[model_path_idx]
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)
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def _test_switch_classificationhead(each_data, each_model_path):
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from flaml import AutoML
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import requests
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automl = AutoML()
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X_train, y_train, X_val, y_val = globals()[each_data]()
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automl_settings = get_automl_settings()
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automl_settings["model_path"] = each_model_path
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if each_data == "get_toy_data_regression":
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automl_settings["task"] = "seq-regression"
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automl_settings["metric"] = "pearsonr"
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else:
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automl_settings["task"] = "seq-classification"
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automl_settings["metric"] = "accuracy"
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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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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_switch_classificationhead(data_list[0], model_path_list[0])
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