89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
import sys
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import unittest
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from pathlib import Path
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import torch
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MODULE_DIR = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(MODULE_DIR))
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import train
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class MetricsTests(unittest.TestCase):
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def test_classification_metrics_include_every_label(self):
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actual = list(range(8))
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predicted = [0, 1, 2, 3, 4, 5, 6, 0]
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metrics = train.classification_metrics(actual, predicted)
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self.assertEqual(7 / 8, metrics["accuracy"])
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self.assertEqual(0.0, metrics["perPurposeRecall"]["writing"])
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self.assertEqual(1.0, metrics["perPurposeRecall"]["planning"])
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def test_thresholds_preserve_confidence_nesting(self):
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probabilities = [0.99, 0.95, 0.85, 0.75, 0.65]
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margins = [0.95, 0.85, 0.60, 0.40, 0.20]
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correct = [True, True, True, False, False]
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thresholds = train.choose_confidence_thresholds(
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probabilities,
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margins,
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correct,
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high_precision=1.0,
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accepted_precision=0.75,
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)
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self.assertGreaterEqual(
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thresholds["high"]["minimumScore"],
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thresholds["medium"]["minimumScore"],
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)
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self.assertGreater(thresholds["medium"]["validationAcceptedCoverage"], 0)
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def test_expected_calibration_error_is_zero_for_perfect_extremes(self):
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self.assertEqual(
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0.0,
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train.expected_calibration_error([1.0, 0.0], [True, False]),
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)
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class FixedShapeTokenizerTests(unittest.TestCase):
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class Tokenizer:
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pad_token_id = 0
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cls_token_id = 1
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sep_token_id = 2
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padding_side = "right"
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model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
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def __call__(self, texts, **_):
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return {
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"input_ids": [
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list(range(10, 10 + int(text.split()[-1]))) for text in texts
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]
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}
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@staticmethod
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def num_special_tokens_to_add(pair=False):
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return 3 if pair else 2
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def test_long_input_keeps_head_and_tail_in_fixed_pair_shape(self):
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encoded = train.encode_fixed_shape(
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self.Tokenizer(), ["tokens 200"], torch
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)
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self.assertEqual((1, train.MAX_LENGTH), tuple(encoded["input_ids"].shape))
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row = encoded["input_ids"][0].tolist()
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self.assertEqual(list(range(10, 10 + train.HEAD_TOKENS)), row[1:64])
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self.assertEqual(2, row[64])
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self.assertEqual(
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list(range(10 + 200 - train.TAIL_TOKENS, 10 + 200)),
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row[65:127],
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)
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self.assertEqual(2, row[127])
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self.assertEqual(1, encoded["token_type_ids"][0, 65].item())
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def test_short_input_is_padded_as_one_sequence(self):
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encoded = train.encode_fixed_shape(self.Tokenizer(), ["tokens 3"], torch)
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self.assertEqual([1, 10, 11, 12, 2], encoded["input_ids"][0, :5].tolist())
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self.assertEqual(0, encoded["attention_mask"][0, 5].item())
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if __name__ == "__main__":
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unittest.main()
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