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Tests for Part 2 — training pipeline, endpoints, and utilities.
Uses mocked DB and model so no real database or TensorFlow training is needed.
"""
import pytest
import numpy as np
from unittest.mock import AsyncMock, MagicMock, patch
from httpx import AsyncClient, ASGITransport
from app.main import app
from app.ml.serving.registry import DummyModel
from app.ml.training.data.encoding import (
encode_labels,
stratified_split_with_test_ratio_override,
)
from app.ml.training.evaluate import decode_predictions
# ───────────────────────── Fixtures ─────────────────────────
@pytest.fixture
def auth_headers():
"""Valid internal service auth headers."""
return {"X-Internal-Token": "test-secret"}
# ───────────────────── Encoding helpers ─────────────────────
class TestEncodeLabels:
"""Test one-hot label encoding."""
def test_basic_encoding(self):
labels = ["safe", "suspicious", "injection"]
encoded = encode_labels(labels)
assert encoded.shape == (3, 3)
# safe=[1,0,0], suspicious=[0,1,0], injection=[0,0,1]
np.testing.assert_array_equal(encoded[0], [1, 0, 0])
np.testing.assert_array_equal(encoded[1], [0, 1, 0])
np.testing.assert_array_equal(encoded[2], [0, 0, 1])
def test_all_same_label(self):
labels = ["safe", "safe", "safe"]
encoded = encode_labels(labels)
assert encoded.shape == (3, 3)
for row in encoded:
np.testing.assert_array_equal(row, [1, 0, 0])
def test_unknown_label_defaults_safe(self):
labels = ["unknown"]
encoded = encode_labels(labels)
np.testing.assert_array_equal(encoded[0], [1, 0, 0])
# ───────────────── Stratified split ─────────────────
class TestStratifiedSplit:
"""Test stratified_split_with_test_ratio_override."""
def test_produces_disjoint_sets(self):
labels = ["safe"] * 80 + ["injection"] * 20
train_idx, test_idx = stratified_split_with_test_ratio_override(labels)
assert set(train_idx).isdisjoint(set(test_idx))
assert len(train_idx) + len(test_idx) == len(labels)
def test_test_set_has_lower_positive_ratio(self):
"""Test set should have ~6% positives, not the training set's ~20%."""
labels = ["safe"] * 800 + ["injection"] * 200
train_idx, test_idx = stratified_split_with_test_ratio_override(
labels, test_split=0.15, test_positive_ratio=0.06
)
test_labels = [labels[i] for i in test_idx]
test_positive_count = sum(1 for l in test_labels if l == "injection")
test_ratio = test_positive_count / len(test_labels) if test_labels else 0
# Test ratio should be much lower than 20%
assert test_ratio < 0.15, (
f"Test positive ratio {test_ratio:.2%} is too high — "
f"should be closer to 6%, not the training set's ~20%"
)
def test_handles_small_dataset(self):
labels = ["safe"] * 5 + ["injection"] * 2
train_idx, test_idx = stratified_split_with_test_ratio_override(labels)
assert len(train_idx) + len(test_idx) == len(labels)
def test_deterministic_with_seed(self):
labels = ["safe"] * 80 + ["injection"] * 20
split1 = stratified_split_with_test_ratio_override(labels, seed=42)
split2 = stratified_split_with_test_ratio_override(labels, seed=42)
assert split1[0] == split2[0]
assert split1[1] == split2[1]
# ───────────────── Decode predictions ─────────────────
class TestDecodePredictions:
"""Test softmax → label string decoding."""
def test_argmax_decoding(self):
probs = np.array([
[0.9, 0.05, 0.05], # safe
[0.1, 0.8, 0.1], # suspicious
[0.05, 0.1, 0.85], # injection
])
labels = decode_predictions(probs)
assert labels == ["safe", "suspicious", "injection"]
def test_tie_breaks_to_first(self):
probs = np.array([[0.5, 0.5, 0.0]])
labels = decode_predictions(probs)
assert labels == ["safe"] # argmax returns first occurrence
# ───────────────── Training endpoints ─────────────────
@pytest.mark.asyncio
async def test_start_training_returns_job_id(auth_headers):
"""POST /train should return a job ID and queued status."""
mock_db = MagicMock()
async def noop_training_job(job_id):
pass # don't actually run training in tests
with patch("app.api.routes.train.get_firestore_db", return_value=mock_db), \
patch("app.api.routes.train.run_training_job", side_effect=noop_training_job):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
response = await client.post("/train", headers=auth_headers)
assert response.status_code == 200
data = response.json()
assert "jobId" in data
assert data["status"] == "queued"
# ───────────────── Model promotion endpoint ─────────────────
@pytest.mark.asyncio
async def test_change_active_version_success(auth_headers):
"""POST /model/change-version/{version_id} should promote a version to active."""
mock_result = {"version": "v123", "metrics": {"f1": 0.9}, "status": "active"}
with patch(
"app.api.routes.model_status.promote_model_version",
new_callable=AsyncMock,
return_value=mock_result,
):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
response = await client.post(
"/model/change-version/v123", headers=auth_headers
)
assert response.status_code == 200
data = response.json()
assert data["version"] == "v123"
assert data["status"] == "active"
@pytest.mark.asyncio
async def test_change_active_version_not_found(auth_headers):
"""POST /model/change-version/{version_id} with unknown version should return 400."""
with patch(
"app.api.routes.model_status.promote_model_version",
new_callable=AsyncMock,
side_effect=ValueError("Model version 'vXXX' not found"),
):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
response = await client.post(
"/model/change-version/vXXX", headers=auth_headers
)
assert response.status_code == 400
@pytest.mark.asyncio
async def test_get_all_models_success(auth_headers):
"""GET /model/all-models should return all models with isCurrentVersion flag."""
mock_models = [
{
"version": "run-11",
"status": "active",
"isCurrentVersion": True,
"metrics": {"test_acc": 0.50},
"createdAt": "2026-09-11T16:00:00Z",
},
{
"version": "run-10",
"status": "archived",
"isCurrentVersion": False,
"metrics": {"test_acc": 0.70},
"createdAt": "2026-09-09T14:00:00Z",
},
]
with patch(
"app.api.routes.model_status.get_all_models_metadata",
new_callable=AsyncMock,
return_value=mock_models,
):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
response = await client.get("/model/all-models", headers=auth_headers)
assert response.status_code == 200
data = response.json()
assert data["total"] == 2
assert len(data["models"]) == 2
assert data["models"][0]["version"] == "run-11"
assert data["models"][0]["isCurrentVersion"] is True
assert data["models"][1]["version"] == "run-10"
assert data["models"][1]["isCurrentVersion"] is False
# ───────────────── Classify still works with DummyModel ─────────────────
@pytest.mark.asyncio
async def test_classify_still_works_with_dummy(auth_headers):
"""POST /analyze-injection should still work with DummyModel via run_prediction."""
dummy = DummyModel()
with patch(
"app.api.routes.classify.load_active_model",
new_callable=AsyncMock,
return_value=dummy,
):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as client:
response = await client.post(
"/analyze-injection",
json={"documentId": "doc-123", "fullText": "normal document containing enough words for test"},
headers=auth_headers,
)
assert response.status_code == 200
data = response.json()
assert data["label"] == "safe"
assert data["confidence"] == 0.95
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