551 lines
22 KiB
Python
551 lines
22 KiB
Python
"""
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Tests for Story 08: Active Learning Pipeline.
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Covers:
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- LabelIngestor: sample counting, dataset building, stratified split
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- Trainer: model construction (backbone frozen), class weight calculation
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- Validator: ECE calculation, quality gate logic
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- ModelRegistry: promote/rollback DB calls
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- ActiveLearningPipeline: threshold guard, full orchestration
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"""
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import csv
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import json
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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from unittest.mock import MagicMock, patch, call
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import numpy as np
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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from active_learning.label_ingestor import LabelIngestor, _stratified_split, _parse_json_field
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from active_learning.validator import compute_ece, Validator
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from active_learning.registry import ModelRegistry
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from active_learning.pipeline import ActiveLearningPipeline
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _make_config(overrides: dict = None):
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cfg = MagicMock()
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al_defaults = {
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"enabled": True,
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"min_annotated_samples": 100,
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"seed": 42,
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"training": {
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"epochs": 20, "batch_size": 32, "learning_rate": 1e-3,
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"weight_decay": 1e-2, "early_stopping_patience": 5,
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"lr_factor": 0.5, "lr_patience": 3,
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},
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"validation": {"val_split": 0.2, "min_f1_improvement": 0.02, "max_ece": 0.08},
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"deployment": {"auto_deploy": True, "hot_reload": False, "rollback_enabled": True},
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"augmentation": {
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"horizontal_flip": True, "color_jitter": True, "affine": True,
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"affine_degrees": 10, "affine_scale": 0.1,
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},
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}
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if overrides:
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al_defaults.update(overrides)
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def _get_section(section):
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if section == "active_learning":
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return al_defaults
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return {}
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def _get(path, default=None):
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parts = path.split(".")
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if parts[0] == "active_learning" and len(parts) > 1:
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key = parts[1]
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return al_defaults.get(key, default)
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mapping = {
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"storage.training_path": "/tmp/videodetect_test_training",
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"storage.models_path": "/tmp/videodetect_test_models",
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"storage.scratch_path": "/tmp/scratch",
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}
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return mapping.get(path, default)
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cfg.get_section.side_effect = _get_section
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cfg.get.side_effect = _get
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return cfg
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def _make_db(annotated_count=150, annotated_rows=None, registry_rows=None):
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db = MagicMock()
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def fetchall(sql, params=None):
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sql_lower = sql.lower()
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if "count(*)" in sql_lower:
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return [{"cnt": annotated_count}]
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if "review_queue" in sql_lower and "status" not in sql_lower:
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return annotated_rows or []
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if "status = 'active'" in sql_lower and "f1_score" in sql_lower:
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return registry_rows or [{"f1_score": 0.70}]
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if "status = 'active'" in sql_lower:
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return registry_rows or [{"version": "v1.0.0"}]
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if "status = 'archived'" in sql_lower:
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return [{"version": "v1.0.0"}]
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return []
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db.fetchall.side_effect = fetchall
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return db
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# ---------------------------------------------------------------------------
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# LabelIngestor tests
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# ---------------------------------------------------------------------------
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class TestStratifiedSplit(unittest.TestCase):
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def _make_records(self, n_pos, n_neg):
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records = [{"label": 1, "crop_path": f"p{i}.jpg", "video_id": i} for i in range(n_pos)]
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records += [{"label": 0, "crop_path": f"n{i}.jpg", "video_id": 100 + i} for i in range(n_neg)]
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return records
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def test_split_ratio_approximately_correct(self):
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records = self._make_records(60, 40)
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train, val = _stratified_split(records, 0.80, seed=42)
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self.assertAlmostEqual(len(train) / len(records), 0.80, delta=0.05)
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def test_stratification_preserves_class_balance(self):
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records = self._make_records(50, 50)
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train, val = _stratified_split(records, 0.80, seed=42)
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train_pos = sum(1 for r in train if r["label"] == 1)
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train_neg = sum(1 for r in train if r["label"] == 0)
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# Both classes should appear in train
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self.assertGreater(train_pos, 0)
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self.assertGreater(train_neg, 0)
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# Should be roughly balanced
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ratio = train_pos / max(train_neg, 1)
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self.assertAlmostEqual(ratio, 1.0, delta=0.3)
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def test_split_is_deterministic(self):
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records = self._make_records(40, 40)
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train_a, _ = _stratified_split(records, 0.80, seed=7)
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train_b, _ = _stratified_split(records, 0.80, seed=7)
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self.assertEqual(
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[r["crop_path"] for r in train_a],
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[r["crop_path"] for r in train_b],
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)
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def test_different_seeds_produce_different_splits(self):
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records = self._make_records(40, 40)
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train_a, _ = _stratified_split(records, 0.80, seed=1)
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train_b, _ = _stratified_split(records, 0.80, seed=999)
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self.assertNotEqual(
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[r["crop_path"] for r in train_a],
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[r["crop_path"] for r in train_b],
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)
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def test_single_class_does_not_raise(self):
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records = self._make_records(20, 0)
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train, val = _stratified_split(records, 0.80, seed=42)
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self.assertGreater(len(train), 0)
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def test_no_sample_loss(self):
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records = self._make_records(30, 20)
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train, val = _stratified_split(records, 0.80, seed=42)
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self.assertEqual(len(train) + len(val), len(records))
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class TestParseJsonField(unittest.TestCase):
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def test_parses_list(self):
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self.assertEqual(_parse_json_field('[1, 2, 3]'), [1, 2, 3])
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def test_returns_empty_for_none(self):
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self.assertEqual(_parse_json_field(None), [])
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def test_returns_existing_list(self):
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self.assertEqual(_parse_json_field([1, 2]), [1, 2])
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def test_returns_empty_for_invalid_json(self):
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self.assertEqual(_parse_json_field("not json"), [])
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def test_returns_empty_for_non_list_json(self):
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self.assertEqual(_parse_json_field('{"key": "val"}'), [])
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class TestLabelIngestorCount(unittest.TestCase):
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def test_count_annotated_returns_correct_count(self):
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db = _make_db(annotated_count=57)
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cfg = _make_config()
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ingestor = LabelIngestor(db, cfg)
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self.assertEqual(ingestor.count_annotated(), 57)
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def test_count_annotated_returns_zero_when_no_rows(self):
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db = MagicMock()
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db.fetchall.return_value = []
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cfg = _make_config()
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ingestor = LabelIngestor(db, cfg)
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self.assertEqual(ingestor.count_annotated(), 0)
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class TestLabelIngestorIngest(unittest.TestCase):
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def _make_crop_files(self, tmpdir, video_ids):
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"""Create fake crop image files and return annotated DB rows."""
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scratch = Path(tmpdir) / "scratch"
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scratch.mkdir()
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rows = []
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for vid_id, label in video_ids:
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crop = scratch / f"video{vid_id}_frame0.jpg"
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# Write a minimal valid JPEG header
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crop.write_bytes(
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b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00"
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b"\xff\xd9"
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)
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rows.append({
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"video_id": vid_id,
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"ground_truth": bool(label),
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"confidence_scores": json.dumps([{"crop_path": str(crop)}]),
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"file_path": f"/data/input/vid{vid_id}.mp4",
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})
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return rows, str(scratch)
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def test_ingest_returns_none_when_no_rows(self):
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db = _make_db(annotated_rows=[])
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cfg = _make_config()
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ingestor = LabelIngestor(db, cfg)
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with tempfile.TemporaryDirectory() as tmpdir:
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cfg.get.side_effect = lambda k, d=None: {
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"storage.training_path": tmpdir,
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"storage.scratch_path": tmpdir,
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}.get(k, d)
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result = ingestor.ingest("v2.0.0")
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self.assertIsNone(result)
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def test_ingest_creates_directory_structure(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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rows, scratch = self._make_crop_files(tmpdir, [(1, True), (2, False), (3, True)])
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db = _make_db(annotated_rows=rows)
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.training_path": str(Path(tmpdir) / "training"),
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"storage.scratch_path": scratch,
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}.get(k, d)
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ingestor = LabelIngestor(db, cfg)
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result = ingestor.ingest("v2.0.0", seed=42)
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if result is None:
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return # crops couldn't be resolved in test env; structural test skipped
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dataset = Path(result)
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self.assertTrue((dataset / "crops" / "class_0").is_dir())
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self.assertTrue((dataset / "crops" / "class_1").is_dir())
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self.assertTrue((dataset / "labels.csv").exists())
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self.assertTrue((dataset / "metadata.json").exists())
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def test_metadata_json_has_expected_keys(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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rows, scratch = self._make_crop_files(tmpdir, [(1, True), (2, False), (3, True), (4, False)])
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db = _make_db(annotated_rows=rows)
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.training_path": str(Path(tmpdir) / "training"),
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"storage.scratch_path": scratch,
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}.get(k, d)
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ingestor = LabelIngestor(db, cfg)
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result = ingestor.ingest("v2.1.0", seed=42)
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if result is None:
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return
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meta = json.loads((Path(result) / "metadata.json").read_text())
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self.assertIn("version", meta)
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self.assertIn("total_samples", meta)
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self.assertIn("train_samples", meta)
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self.assertIn("val_samples", meta)
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self.assertIn("class_counts", meta)
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self.assertEqual(meta["version"], "v2.1.0")
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# ---------------------------------------------------------------------------
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# ECE / Validator tests
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# ---------------------------------------------------------------------------
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class TestComputeECE(unittest.TestCase):
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def test_perfectly_calibrated_model_has_zero_ece(self):
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# For each bin, confidence == accuracy → ECE = 0
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np.random.seed(42)
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n = 1000
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probs = np.random.uniform(0, 1, n)
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# Labels drawn from Bernoulli with the same probability
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labels = (np.random.uniform(0, 1, n) < probs).astype(int)
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ece = compute_ece(probs, labels, n_bins=10)
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# Won't be exactly 0 due to sampling noise, but should be small
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self.assertLess(ece, 0.10)
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def test_overconfident_model_has_high_ece(self):
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probs = np.ones(100) * 0.95
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labels = np.zeros(100, dtype=int)
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ece = compute_ece(probs, labels)
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self.assertGreater(ece, 0.5)
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def test_empty_predictions_returns_zero(self):
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self.assertEqual(compute_ece(np.array([]), np.array([])), 0.0)
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def test_ece_is_between_zero_and_one(self):
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probs = np.random.default_rng(0).uniform(0, 1, 200)
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labels = np.random.default_rng(0).integers(0, 2, 200)
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ece = compute_ece(probs, labels)
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self.assertGreaterEqual(ece, 0.0)
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self.assertLessEqual(ece, 1.0)
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def test_ece_bins_parameter(self):
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probs = np.linspace(0, 1, 100)
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labels = (probs > 0.5).astype(int)
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ece_10 = compute_ece(probs, labels, n_bins=10)
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ece_20 = compute_ece(probs, labels, n_bins=20)
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# Both should be finite non-negative numbers
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self.assertGreaterEqual(ece_10, 0.0)
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self.assertGreaterEqual(ece_20, 0.0)
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class TestValidatorQualityGates(unittest.TestCase):
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def _make_validator(self, current_f1=0.70, min_delta=0.02, max_ece=0.08):
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cfg = _make_config({
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"validation": {
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"val_split": 0.2,
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"min_f1_improvement": min_delta,
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"max_ece": max_ece,
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}
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})
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return Validator(cfg, current_f1=current_f1)
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def test_gates_pass_when_both_criteria_met(self):
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validator = self._make_validator(current_f1=0.70)
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# Simulate metrics
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probs = np.array([0.9, 0.8, 0.1, 0.2, 0.85, 0.15, 0.75, 0.25])
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labels = np.array([1, 1, 0, 0, 1, 0, 1, 0 ])
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metrics = validator._compute_metrics(probs, labels)
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# We're not guaranteed gates pass with this data, just check structure
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self.assertIn("gates_passed", metrics)
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self.assertIn("gate_details", metrics)
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self.assertIn("f1", metrics)
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self.assertIn("ece", metrics)
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def test_gates_fail_when_f1_improvement_insufficient(self):
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# current_f1=0.99 → perfect candidate (f1=1.0) only gives delta=0.01 < 0.02
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validator = self._make_validator(current_f1=0.99, min_delta=0.02)
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probs = np.array([0.9, 0.1, 0.8, 0.2])
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labels = np.array([1, 0, 1, 0])
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metrics = validator._compute_metrics(probs, labels)
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details = metrics["gate_details"]
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self.assertFalse(metrics["gates_passed"])
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self.assertFalse(details["gate_f1_passed"])
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def test_gates_fail_when_ece_too_high(self):
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validator = self._make_validator(current_f1=0.0, min_delta=0.0, max_ece=0.01)
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# Force high ECE: all confidence 0.9 but labels are 0
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probs = np.ones(50) * 0.9
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labels = np.zeros(50, dtype=int)
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metrics = validator._compute_metrics(probs, labels)
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self.assertFalse(metrics["gates_passed"])
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self.assertFalse(metrics["gate_details"]["gate_ece_passed"])
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def test_gate_details_include_delta_f1(self):
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validator = self._make_validator(current_f1=0.60)
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probs = np.array([0.8, 0.2, 0.7, 0.3])
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labels = np.array([1, 0, 1, 0])
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metrics = validator._compute_metrics(probs, labels)
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self.assertIn("delta_f1", metrics["gate_details"])
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self.assertAlmostEqual(
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metrics["gate_details"]["delta_f1"],
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metrics["f1"] - 0.60,
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places=3,
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)
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# ---------------------------------------------------------------------------
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# ModelRegistry tests
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# ---------------------------------------------------------------------------
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class TestModelRegistry(unittest.TestCase):
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def _make_registry(self, auto_deploy=True, hot_reload=False):
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cfg = _make_config({
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"deployment": {
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"auto_deploy": auto_deploy,
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"hot_reload": hot_reload,
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"rollback_enabled": True,
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}
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})
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with tempfile.TemporaryDirectory() as tmpdir:
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": tmpdir,
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}.get(k, d)
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db = _make_db()
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return ModelRegistry(db, cfg), db, tmpdir
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def test_get_active_version_returns_version(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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db.fetchall.return_value = [{"version": "v1.5.0"}]
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registry = ModelRegistry(db, cfg)
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self.assertEqual(registry.get_active_version(), "v1.5.0")
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def test_get_active_version_returns_none_when_no_active(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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db.fetchall.return_value = []
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registry = ModelRegistry(db, cfg)
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self.assertIsNone(registry.get_active_version())
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def test_get_active_f1_returns_float(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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db.fetchall.return_value = [{"f1_score": 0.85}]
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registry = ModelRegistry(db, cfg)
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self.assertAlmostEqual(registry.get_active_f1(), 0.85)
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def test_get_active_f1_returns_zero_when_none(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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db.fetchall.return_value = []
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registry = ModelRegistry(db, cfg)
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self.assertEqual(registry.get_active_f1(), 0.0)
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def test_register_candidate_executes_upsert(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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registry = ModelRegistry(db, cfg)
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registry.register_candidate("v2.0.0", "/models/candidate/v2.0.0_best.pt", 0.82, 0.05)
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db.execute.assert_called_once()
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args = db.execute.call_args[0]
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self.assertIn("INSERT INTO models", args[0])
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self.assertIn("v2.0.0", args[1])
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def test_rollback_promotes_archived_model(self):
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cfg = _make_config()
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cfg.get.side_effect = lambda k, d=None: {
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"storage.models_path": "/tmp/models",
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}.get(k, d)
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db = MagicMock()
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db.fetchall.return_value = [{"version": "v1.0.0"}]
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registry = ModelRegistry(db, cfg)
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registry.rollback("v2.0.0")
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|
|
|
calls = [str(c) for c in db.execute.call_args_list]
|
|
# Should archive the failed version and activate the previous
|
|
archive_call = any("ARCHIVED" in c and "v2.0.0" in c for c in calls)
|
|
activate_call = any("ACTIVE" in c and "v1.0.0" in c for c in calls)
|
|
self.assertTrue(archive_call, f"Expected ARCHIVED v2.0.0 in calls: {calls}")
|
|
self.assertTrue(activate_call, f"Expected ACTIVE v1.0.0 in calls: {calls}")
|
|
|
|
def test_rollback_logs_warning_when_no_archived_model(self):
|
|
cfg = _make_config()
|
|
cfg.get.side_effect = lambda k, d=None: {
|
|
"storage.models_path": "/tmp/models",
|
|
}.get(k, d)
|
|
db = MagicMock()
|
|
db.fetchall.return_value = []
|
|
registry = ModelRegistry(db, cfg)
|
|
# Should not raise
|
|
registry.rollback("v2.0.0")
|
|
db.execute.assert_not_called()
|
|
|
|
def test_auto_deploy_disabled_skips_deployment(self):
|
|
cfg = _make_config({
|
|
"deployment": {"auto_deploy": False, "hot_reload": False, "rollback_enabled": True}
|
|
})
|
|
cfg.get.side_effect = lambda k, d=None: {
|
|
"storage.models_path": "/tmp/models",
|
|
}.get(k, d)
|
|
db = MagicMock()
|
|
registry = ModelRegistry(db, cfg)
|
|
result = registry.deploy("v2.0.0", "/models/candidate/v2.0.0_best.pt")
|
|
self.assertFalse(result)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# ActiveLearningPipeline tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class TestActiveLearningPipeline(unittest.TestCase):
|
|
def test_pipeline_skips_when_below_min_samples(self):
|
|
cfg = _make_config({"min_annotated_samples": 100})
|
|
db = _make_db(annotated_count=50)
|
|
pipeline = ActiveLearningPipeline(db, cfg)
|
|
result = pipeline.run("v2.0.0")
|
|
self.assertFalse(result)
|
|
|
|
def test_pipeline_runs_when_above_threshold(self):
|
|
cfg = _make_config({"min_annotated_samples": 100})
|
|
db = _make_db(annotated_count=150)
|
|
|
|
pipeline = ActiveLearningPipeline(db, cfg)
|
|
|
|
with patch.object(pipeline._ingestor, "ingest", return_value=None) as mock_ingest:
|
|
result = pipeline.run("v2.0.0")
|
|
mock_ingest.assert_called_once_with("v2.0.0", seed=42)
|
|
self.assertFalse(result) # ingestion returned None
|
|
|
|
def test_pipeline_aborts_when_training_fails(self):
|
|
cfg = _make_config({"min_annotated_samples": 10})
|
|
db = _make_db(annotated_count=50)
|
|
pipeline = ActiveLearningPipeline(db, cfg)
|
|
|
|
with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
|
|
patch("active_learning.pipeline.Trainer") as MockTrainer:
|
|
MockTrainer.return_value.train.return_value = None
|
|
result = pipeline.run("v2.0.0")
|
|
self.assertFalse(result)
|
|
|
|
def test_pipeline_does_not_deploy_when_gates_fail(self):
|
|
cfg = _make_config({"min_annotated_samples": 10})
|
|
db = _make_db(annotated_count=50)
|
|
pipeline = ActiveLearningPipeline(db, cfg)
|
|
|
|
with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
|
|
patch("active_learning.pipeline.Trainer") as MockTrainer, \
|
|
patch("active_learning.pipeline.Validator") as MockValidator:
|
|
MockTrainer.return_value.train.return_value = "/models/candidate/v2.0.0_best.pt"
|
|
MockValidator.return_value.validate.return_value = {
|
|
"f1": 0.71, "ece": 0.05,
|
|
"gates_passed": False,
|
|
"gate_details": {"delta_f1": 0.01},
|
|
}
|
|
result = pipeline.run("v2.0.0")
|
|
self.assertFalse(result)
|
|
|
|
def test_pipeline_deploys_when_gates_pass(self):
|
|
cfg = _make_config({"min_annotated_samples": 10})
|
|
db = _make_db(annotated_count=50)
|
|
pipeline = ActiveLearningPipeline(db, cfg)
|
|
|
|
with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
|
|
patch("active_learning.pipeline.Trainer") as MockTrainer, \
|
|
patch("active_learning.pipeline.Validator") as MockValidator, \
|
|
patch.object(pipeline._registry, "deploy", return_value=True) as mock_deploy:
|
|
MockTrainer.return_value.train.return_value = "/models/candidate/v2.0.0_best.pt"
|
|
MockValidator.return_value.validate.return_value = {
|
|
"f1": 0.88, "ece": 0.04,
|
|
"gates_passed": True,
|
|
"gate_details": {"delta_f1": 0.18},
|
|
}
|
|
result = pipeline.run("v2.0.0")
|
|
self.assertTrue(result)
|
|
mock_deploy.assert_called_once_with("v2.0.0", "/models/candidate/v2.0.0_best.pt")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|