chore: import upstream snapshot with attribution

2026-07-13 13:24:47 +08:00
commit dc6079821b
1384 changed files with 261110 additions and 0 deletions
@@ -0,0 +1,102 @@
+from typing import Dict, Text
+from pathlib import Path
+
+import pytest
+from _pytest.tmpdir import TempPathFactory
+
+from rasa.core import training
+
+from rasa.core.policies.ted_policy import TEDPolicy
+from rasa.engine.graph import ExecutionContext, GraphSchema
+from rasa.engine.storage.local_model_storage import LocalModelStorage
+from rasa.engine.storage.resource import Resource
+from rasa.shared.core.domain import Domain
+from rasa.utils.tensorflow.callback import RasaModelCheckpoint
+from rasa.utils.tensorflow.constants import EPOCHS
+
+
+@pytest.mark.parametrize(
+    "previous_best, current_values, improved",
+    [
+        (
+            {"val_i_acc": 0.5, "val_f1": 0.5},
+            {"val_i_acc": 0.65, "val_f1": 0.7},
+            True,
+        ),  # both improved
+        (
+            {"val_i_acc": 0.54, "val_f1": 0.5},
+            {"val_i_acc": 0.54, "val_f1": 0.7},
+            True,
+        ),  # one equal, one improved
+        (
+            {"val_i_acc": 0.8, "val_f1": 0.55},
+            {"val_i_acc": 0.8, "val_f1": 0.55},
+            False,
+        ),  # both equal
+        (
+            {"val_i_acc": 0.64, "val_f1": 0.5},
+            {"val_i_acc": 0.41, "val_f1": 0.7},
+            False,
+        ),  # one improved, one worse
+        (
+            {"val_i_acc": 0.71, "val_f1": 0.35},
+            {"val_i_acc": 0.52, "val_f1": 0.35},
+            False,
+        ),  # one worse, one equal
+    ],
+)
+def test_does_model_improve(
+    previous_best: Dict[Text, float],
+    current_values: Dict[Text, float],
+    improved: bool,
+    tmpdir: Path,
+):
+    checkpoint = RasaModelCheckpoint(tmpdir)
+    checkpoint.best_metrics_so_far = previous_best
+    # true iff all values are equal or better and at least one is better
+    assert checkpoint._does_model_improve(current_values) == improved
+
+
+@pytest.fixture(scope="module")
+def trained_ted(
+    tmp_path_factory: TempPathFactory, moodbot_domain_path: Path
+) -> TEDPolicy:
+    training_files = "data/test_moodbot/data/stories.yml"
+    domain = Domain.load(moodbot_domain_path)
+    trackers = training.load_data(str(training_files), domain)
+    policy = TEDPolicy.create(
+        {**TEDPolicy.get_default_config(), EPOCHS: 1},
+        LocalModelStorage.create(tmp_path_factory.mktemp("storage")),
+        Resource("ted"),
+        ExecutionContext(GraphSchema({})),
+    )
+    policy.train(trackers, domain)
+
+    return policy
+
+
+@pytest.mark.parametrize(
+    "previous_best, current_values, improved",
+    [
+        ({"val_i_acc": 0.5, "val_f1": 0.5}, {"val_i_acc": 0.5, "val_f1": 0.7}, True),
+        ({"val_i_acc": 0.5, "val_f1": 0.5}, {"val_i_acc": 0.4, "val_f1": 0.5}, False),
+    ],
+)
+def test_on_epoch_end_saves_checkpoints_file(
+    previous_best: Dict[Text, float],
+    current_values: Dict[Text, float],
+    improved: bool,
+    tmp_path: Path,
+    trained_ted: TEDPolicy,
+):
+    model_name = "checkpoint"
+    best_model_file = tmp_path / model_name
+    assert not best_model_file.exists()
+    checkpoint = RasaModelCheckpoint(tmp_path)
+    checkpoint.best_metrics_so_far = previous_best
+    checkpoint.model = trained_ted.model
+    checkpoint.on_epoch_end(1, current_values)
+    if improved:
+        assert best_model_file.exists()
+    else:
+        assert not best_model_file.exists()