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chore: initialize git repo, add matplotlib dep, extend config

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- Add .gitignore for Python/data/models
- Add matplotlib>=3.8.0 for eval plots
- Add PretrainConfig, FinetuneConfig, BalabitAdapterConfig, EvalConfig dataclasses
This commit is contained in:
Huang Qi
2026-05-10 12:24:07 +08:00
commit 4d414fd180
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tests/__init__.py Normal file
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tests/conftest.py Normal file
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"""Shared test fixtures for ai_mouse."""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import pytest
import torch
from ai_mouse.models import TrajectoryFlowModel
from ai_mouse.scroll.models import ScrollCVAE
@pytest.fixture
def model_dir(tmp_path: Path) -> Path:
"""Create a temporary directory with trained Flow model artifacts."""
# Flow model
model = TrajectoryFlowModel(seq_len=64)
torch.save(model.state_dict(), tmp_path / "flow_model.pt")
# Click distribution
click_dist = {"mu": 80.0, "sigma": 30.0, "low": 20.0, "high": 300.0}
(tmp_path / "click_dist.json").write_text(json.dumps(click_dist))
# Duration distribution
dur_dist = {
"bins": [0, 50, 100, 200, 400, 600, 800, 1200, float("inf")],
"params": [{"mu_log": 5.5, "sigma_log": 0.5}] * 8,
}
(tmp_path / "duration_dist.json").write_text(json.dumps(dur_dist))
# Train config (architecture params)
train_cfg = {
"seq_len": 64,
"d_model": 128,
"nhead": 4,
"num_layers": 4,
"dim_feedforward": 256,
"cond_dim": 3,
}
(tmp_path / "train_config.json").write_text(json.dumps(train_cfg))
return tmp_path
@pytest.fixture
def scroll_model_dir(tmp_path: Path) -> Path:
"""Create a temporary directory with trained scroll model artifacts."""
model = ScrollCVAE(seq_len=32)
torch.save(model.state_dict(), tmp_path / "scroll_model.pt")
scroll_cfg = {"seq_len": 32, "latent_dim": 16, "hidden": 64, "cond_dim": 7}
(tmp_path / "scroll_config.json").write_text(json.dumps(scroll_cfg))
return tmp_path

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tests/test_coord.py Normal file
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"""Tests for rotated coordinate system transforms."""
from __future__ import annotations
import math
import numpy as np
import pytest
from ai_mouse.coord import encode_trajectory, decode_trajectory
class TestEncodeTrajectory:
"""Test pixel → rotated normalised frame."""
def test_start_maps_to_origin(self):
start = (100, 200)
end = (400, 500)
points = np.array([[100, 200]], dtype=float)
result = encode_trajectory(points, start, end)
np.testing.assert_allclose(result[0], [0.0, 0.0], atol=1e-10)
def test_end_maps_to_one_zero(self):
start = (100, 200)
end = (400, 500)
points = np.array([[400, 500]], dtype=float)
result = encode_trajectory(points, start, end)
np.testing.assert_allclose(result[0], [1.0, 0.0], atol=1e-10)
def test_midpoint_maps_to_half_zero(self):
start = (0, 0)
end = (200, 0)
points = np.array([[100, 0]], dtype=float)
result = encode_trajectory(points, start, end)
np.testing.assert_allclose(result[0], [0.5, 0.0], atol=1e-10)
def test_lateral_offset_positive(self):
"""Point at (100, 50) with horizontal start→end has lateral = 50/200 = 0.25."""
start = (0, 0)
end = (200, 0)
# For horizontal u=(1,0), v=(-0, 1)=(0,1).
# Point (100, 50): forward = 100/200=0.5, lateral = 50/200=0.25
points = np.array([[100, 50]], dtype=float)
result = encode_trajectory(points, start, end)
np.testing.assert_allclose(result[0], [0.5, 0.25], atol=1e-10)
def test_various_angles(self):
"""Encode/decode round-trip works for various angles."""
angles = [0, 45, 90, 135, 180, -45, -90, -135]
for deg in angles:
rad = math.radians(deg)
start = (400, 300)
dist = 200
end = (int(400 + dist * math.cos(rad)), int(300 + dist * math.sin(rad)))
# Create a curved path
t = np.linspace(0, 1, 20)
px = start[0] + t * (end[0] - start[0]) + 20 * np.sin(t * math.pi)
py = start[1] + t * (end[1] - start[1]) + 20 * np.cos(t * math.pi)
points = np.stack([px, py], axis=1)
encoded = encode_trajectory(points, start, end)
assert encoded[0, 0] == pytest.approx(0.0, abs=0.2)
assert encoded[-1, 0] == pytest.approx(1.0, abs=0.2)
class TestDecodeTrajectory:
"""Test rotated normalised frame → pixel."""
def test_origin_maps_to_start(self):
start = (100, 200)
end = (400, 500)
normalised = np.array([[0.0, 0.0]], dtype=float)
result = decode_trajectory(normalised, start, end)
np.testing.assert_allclose(result[0], [100, 200], atol=1e-10)
def test_one_zero_maps_to_end(self):
start = (100, 200)
end = (400, 500)
normalised = np.array([[1.0, 0.0]], dtype=float)
result = decode_trajectory(normalised, start, end)
np.testing.assert_allclose(result[0], [400, 500], atol=1e-10)
class TestRoundTrip:
"""Encode then decode should return original points."""
def test_round_trip_horizontal(self):
start = (50, 100)
end = (350, 100)
points = np.array([[50, 100], [150, 130], [250, 90], [350, 100]], dtype=float)
encoded = encode_trajectory(points, start, end)
decoded = decode_trajectory(encoded, start, end)
np.testing.assert_allclose(decoded, points, atol=1e-8)
def test_round_trip_diagonal(self):
start = (100, 100)
end = (500, 400)
rng = np.random.default_rng(42)
points = np.column_stack([
np.linspace(100, 500, 30) + rng.normal(0, 10, 30),
np.linspace(100, 400, 30) + rng.normal(0, 10, 30),
])
encoded = encode_trajectory(points, start, end)
decoded = decode_trajectory(encoded, start, end)
np.testing.assert_allclose(decoded, points, atol=1e-8)
def test_round_trip_vertical(self):
"""Vertical movement (angle=90°) doesn't collapse."""
start = (300, 50)
end = (300, 450)
points = np.array([[300, 50], [310, 200], [295, 350], [300, 450]], dtype=float)
encoded = encode_trajectory(points, start, end)
decoded = decode_trajectory(encoded, start, end)
np.testing.assert_allclose(decoded, points, atol=1e-8)

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"""Tests for Flow Matching trajectory generator."""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import pytest
import torch
from ai_mouse.generator import generate
from ai_mouse.models import TrajectoryFlowModel
@pytest.fixture
def model_dir(tmp_path):
"""Create temp dir with Flow model artifacts."""
model = TrajectoryFlowModel(seq_len=64)
torch.save(model.state_dict(), tmp_path / "flow_model.pt")
click_dist = {"mu": 80.0, "sigma": 30.0, "low": 20.0, "high": 300.0}
(tmp_path / "click_dist.json").write_text(json.dumps(click_dist))
duration_dist = {
"bins": [0, 50, 100, 200, 400, 600, 800, 1200, float("inf")],
"params": [
{"mu_log": 5.5, "sigma_log": 0.3},
{"mu_log": 5.8, "sigma_log": 0.3},
{"mu_log": 6.0, "sigma_log": 0.3},
{"mu_log": 6.2, "sigma_log": 0.3},
{"mu_log": 6.5, "sigma_log": 0.3},
{"mu_log": 6.7, "sigma_log": 0.3},
{"mu_log": 6.9, "sigma_log": 0.3},
{"mu_log": 7.0, "sigma_log": 0.3},
],
}
(tmp_path / "duration_dist.json").write_text(json.dumps(duration_dist))
train_config = {
"seq_len": 64,
"d_model": 128,
"nhead": 4,
"num_layers": 4,
"dim_feedforward": 256,
"cond_dim": 3,
}
(tmp_path / "train_config.json").write_text(json.dumps(train_config))
return tmp_path
class TestGenerate:
def test_returns_list_of_tuples(self, model_dir):
result = generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
assert isinstance(result, list)
assert all(isinstance(p, tuple) and len(p) == 3 for p in result)
# All elements are ints
for p in result:
assert all(isinstance(v, int) for v in p)
def test_timestamps_monotonically_increasing(self, model_dir):
result = generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
times = [p[2] for p in result]
for i in range(1, len(times)):
assert times[i] >= times[i - 1]
def test_starts_near_start(self, model_dir):
start = (100, 200)
result = generate(start=start, end=(500, 400), model_dir=str(model_dir))
first = result[0]
assert abs(first[0] - start[0]) < 30
assert abs(first[1] - start[1]) < 30
def test_ends_near_end(self, model_dir):
end = (500, 400)
result = generate(start=(100, 200), end=end, model_dir=str(model_dir))
# Last two are click events; the one before is last movement point
last_move = result[-3]
assert abs(last_move[0] - end[0]) < 30
assert abs(last_move[1] - end[1]) < 30
def test_last_two_are_click_events(self, model_dir):
result = generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
down = result[-2]
up = result[-1]
# Same x, y for click down and up
assert down[0] == up[0]
assert down[1] == up[1]
# Up timestamp > down timestamp
assert up[2] > down[2]
# Click duration within bounds
assert 20 <= up[2] - down[2] <= 300
def test_different_z_gives_different_paths(self, model_dir):
r1 = generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
r2 = generate(start=(100, 200), end=(500, 400), model_dir=str(model_dir))
points1 = [(p[0], p[1]) for p in r1[:-2]]
points2 = [(p[0], p[1]) for p in r2[:-2]]
assert points1 != points2
def test_n_points_parameter(self, model_dir):
result = generate(
start=(100, 200), end=(500, 400), n_points=32, model_dir=str(model_dir)
)
# 32 move points + 2 click events = 34
assert len(result) == 34

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"""Tests for TrajectoryFlowModel architecture."""
from __future__ import annotations
import torch
import pytest
from ai_mouse.models import TrajectoryFlowModel
class TestTrajectoryFlowModel:
"""Test the Conditional Flow Matching model."""
@pytest.fixture
def model(self):
return TrajectoryFlowModel(
seq_len=64, d_model=128, nhead=4, num_layers=4,
dim_feedforward=256, dropout=0.1, cond_dim=3,
)
def test_output_shape(self, model):
"""(4, 64, 3) input → (4, 64, 3) output."""
batch = 4
x_t = torch.randn(batch, 64, 3)
t = torch.rand(batch)
cond = torch.randn(batch, 3)
out = model(x_t, t, cond)
assert out.shape == (batch, 64, 3)
def test_single_sample(self, model):
"""(1, 64, 3) works."""
x_t = torch.randn(1, 64, 3)
t = torch.rand(1)
cond = torch.randn(1, 3)
out = model(x_t, t, cond)
assert out.shape == (1, 64, 3)
def test_deterministic(self, model):
"""Eval mode, same input → same output."""
model.eval()
x_t = torch.randn(2, 64, 3)
t = torch.tensor([0.3, 0.7])
cond = torch.randn(2, 3)
with torch.no_grad():
out1 = model(x_t, t, cond)
out2 = model(x_t, t, cond)
torch.testing.assert_close(out1, out2)
def test_different_timesteps(self, model):
"""t=0.1 vs t=0.9 gives different output."""
model.eval()
x_t = torch.randn(1, 64, 3)
cond = torch.randn(1, 3)
with torch.no_grad():
out_early = model(x_t, torch.tensor([0.1]), cond)
out_late = model(x_t, torch.tensor([0.9]), cond)
assert not torch.allclose(out_early, out_late, atol=1e-5)
def test_gradient_flows(self, model):
"""Backward works, grad on x_t exists."""
model.train()
x_t = torch.randn(2, 64, 3, requires_grad=True)
t = torch.rand(2)
cond = torch.randn(2, 3)
out = model(x_t, t, cond)
loss = out.sum()
loss.backward()
assert x_t.grad is not None
assert x_t.grad.shape == (2, 64, 3)
assert x_t.grad.abs().sum() > 0

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"""Tests for scroll collection state and target generation."""
from __future__ import annotations
import pytest
from ai_mouse.scroll.collector import ScrollCollector
class TestNextTarget:
def test_target_mode_distance_range(self):
sc = ScrollCollector(mode="target", count=10, page_height=10000, viewport_height=900)
for _ in range(20):
result = sc.next_target(current_scrollY=2000)
dist = abs(result["target_scrollY"] - 2000)
assert 500 <= dist <= 3000
assert result["direction"] in ("up", "down")
def test_fast_mode_distance_range(self):
sc = ScrollCollector(mode="fast", count=10, page_height=10000, viewport_height=900)
for _ in range(20):
result = sc.next_target(current_scrollY=5000)
dist = abs(result["target_scrollY"] - 5000)
assert 3000 <= dist <= 8000
def test_precise_mode_distance_range(self):
sc = ScrollCollector(mode="precise", count=10, page_height=10000, viewport_height=900)
for _ in range(20):
result = sc.next_target(current_scrollY=3000)
dist = abs(result["target_scrollY"] - 3000)
assert 200 <= dist <= 800
def test_target_within_page_bounds(self):
sc = ScrollCollector(mode="target", count=10, page_height=10000, viewport_height=900)
result = sc.next_target(current_scrollY=1000)
assert 0 <= result["target_scrollY"] <= 10000
result = sc.next_target(current_scrollY=9000)
assert 0 <= result["target_scrollY"] <= 10000
def test_success_zone_by_mode(self):
sc = ScrollCollector(mode="target", count=10, page_height=10000)
assert sc.success_radius == 80
sc2 = ScrollCollector(mode="fast", count=10, page_height=10000)
assert sc2.success_radius == 120
sc3 = ScrollCollector(mode="precise", count=10, page_height=10000)
assert sc3.success_radius == 40
def test_target_always_reachable(self):
"""Target must always be reachable: user can scroll to bring it into success zone."""
sc = ScrollCollector(mode="target", count=10, page_height=10000, viewport_height=900)
viewport_center = 450 # 900 / 2
max_scroll_top = 10000 - 900 # 9100
for current in [0, 100, 500, 2000, 5000, 8000, 9000]:
for _ in range(10):
result = sc.next_target(current_scrollY=current)
target = result["target_scrollY"]
# The scrollTop needed to bring target into viewport center
needed_scroll = target - viewport_center + 25
# Must be achievable (0 <= needed_scroll <= max_scroll_top)
# With success_radius=80, there's a window, not exact match needed
reachable_min = viewport_center - sc.success_radius
reachable_max = max_scroll_top + viewport_center + sc.success_radius
assert reachable_min <= target <= reachable_max, (
f"Target {target} not reachable from scrollY={current}"
)

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tests/test_scroll_generator.py Normal file
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"""Tests for scroll generator."""
from __future__ import annotations
import json
from pathlib import Path
import torch
import pytest
from ai_mouse.scroll.generator import generate_scroll
from ai_mouse.scroll.models import ScrollCVAE
@pytest.fixture
def scroll_model_dir(tmp_path):
model = ScrollCVAE(seq_len=32)
torch.save(model.state_dict(), tmp_path / "scroll_model.pt")
config = {"seq_len": 32, "epochs": 100}
(tmp_path / "scroll_config.json").write_text(json.dumps(config))
return tmp_path
class TestGenerateScroll:
def test_returns_list_of_dicts(self, scroll_model_dir):
result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
assert isinstance(result, list)
assert len(result) > 0
assert all("deltaY" in e and "t" in e and "deltaMode" in e for e in result)
def test_timestamps_monotonic(self, scroll_model_dir):
result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
times = [e["t"] for e in result]
for i in range(1, len(times)):
assert times[i] >= times[i - 1]
def test_total_scroll_approximately_matches_distance(self, scroll_model_dir):
result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
total = sum(e["deltaY"] for e in result)
# Should be within 30% of target distance (2000px)
assert abs(total - 2000) < 2000 * 0.4
def test_deltaY_are_integers(self, scroll_model_dir):
result = generate_scroll(1000, 3000, mode="target", model_dir=str(scroll_model_dir))
assert all(isinstance(e["deltaY"], int) for e in result)
def test_direction_up(self, scroll_model_dir):
result = generate_scroll(3000, 1000, mode="target", model_dir=str(scroll_model_dir))
total = sum(e["deltaY"] for e in result)
# Negative total for scrolling up
assert total < 0

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tests/test_scroll_models.py Normal file
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"""Tests for ScrollCVAE model."""
from __future__ import annotations
import torch
import pytest
from ai_mouse.scroll.models import ScrollCVAE
class TestScrollCVAEForward:
@pytest.fixture
def model(self):
return ScrollCVAE(seq_len=32, latent_dim=16, hidden=64, cond_dim=7)
def test_output_shapes(self, model):
batch = 4
seq = torch.randn(batch, 32, 2)
cond = torch.randn(batch, 7)
recon, mu, logvar = model(seq, cond)
assert recon.shape == (batch, 32, 2)
assert mu.shape == (batch, 16)
assert logvar.shape == (batch, 16)
def test_decode_shape(self, model):
z = torch.randn(4, 16)
cond = torch.randn(4, 7)
out = model.decode(z, cond)
assert out.shape == (4, 32, 2)
def test_decode_deterministic(self, model):
model.eval()
z = torch.randn(1, 16)
cond = torch.randn(1, 7)
with torch.no_grad():
out1 = model.decode(z, cond)
out2 = model.decode(z, cond)
torch.testing.assert_close(out1, out2)

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tests/test_scroll_trainer.py Normal file
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"""Tests for scroll training pipeline."""
from __future__ import annotations
import json
import math
from pathlib import Path
import numpy as np
import pytest
from ai_mouse.scroll.trainer import load_scroll_data, train_scroll, _augment_scroll
def _make_synthetic_scroll_trace(mode="target"):
"""Create a synthetic scroll trace."""
distance = {"target": 1500, "fast": 5000, "precise": 400}[mode]
direction = "down"
start = 2000
target = start + distance
events = []
n_events = 20
for i in range(n_events):
frac = (i + 1) / n_events
delta = int(distance / n_events * (1 + 0.2 * np.random.randn()))
delta = max(20, delta)
t = int(frac * 800 + np.random.normal(0, 10))
events.append({"deltaY": delta, "deltaMode": 0, "t": max(0, t)})
events.sort(key=lambda e: e["t"])
events[0]["t"] = 0
return {
"meta": {
"mode": mode,
"start_scrollY": start,
"target_scrollY": target,
"end_scrollY": target + 5,
"distance": distance,
"direction": direction,
"duration_ms": events[-1]["t"],
"viewport_height": 900,
},
"events": events,
}
@pytest.fixture
def synthetic_scroll_file(tmp_path):
traces_path = tmp_path / "scroll_traces.jsonl"
lines = []
for mode in ["target", "fast", "precise"]:
for _ in range(10):
lines.append(json.dumps(_make_synthetic_scroll_trace(mode)))
traces_path.write_text("\n".join(lines), encoding="utf-8")
return traces_path
class TestLoadScrollData:
def test_returns_correct_shapes(self, synthetic_scroll_file):
seq, cond = load_scroll_data(synthetic_scroll_file, seq_len=32)
assert seq.shape[1] == 32
assert seq.shape[2] == 2 # (delta_norm, log_dt)
assert cond.shape[1] == 7
assert len(seq) > 0
class TestAugment:
def test_4x_augmentation(self, synthetic_scroll_file):
seq, cond = load_scroll_data(synthetic_scroll_file, seq_len=32)
n = len(seq)
seq_aug, cond_aug = _augment_scroll(seq, cond)
assert len(seq_aug) == n * 4
class TestTrainScroll:
def test_produces_model_files(self, synthetic_scroll_file, tmp_path):
output_dir = tmp_path / "scroll_models"
train_scroll(
data_path=synthetic_scroll_file,
output_dir=output_dir,
epochs=3,
batch_size=8,
)
assert (output_dir / "scroll_model.pt").exists()
assert (output_dir / "scroll_config.json").exists()

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"""Integration tests for the ai_mouse server API routes."""
from __future__ import annotations
import json
import pytest
import pytest_asyncio
from httpx import ASGITransport, AsyncClient
from ai_mouse.server import create_app
from ai_mouse.server.deps import get_data_dir
@pytest.fixture
def app():
return create_app()
@pytest_asyncio.fixture
async def client(app):
transport = ASGITransport(app=app)
async with AsyncClient(transport=transport, base_url="http://test") as c:
yield c
# ---------------------------------------------------------------------------
# Status endpoint
# ---------------------------------------------------------------------------
class TestStatus:
@pytest.mark.asyncio
async def test_status_returns_trace_count(self, client):
resp = await client.get("/api/status")
assert resp.status_code == 200
data = resp.json()
assert "trace_count" in data
assert "model_trained" in data
assert isinstance(data["trace_count"], int)
assert isinstance(data["model_trained"], bool)
# ---------------------------------------------------------------------------
# Collect endpoints
# ---------------------------------------------------------------------------
class TestCollect:
@pytest.mark.asyncio
async def test_start_returns_ab_positions(self, client):
resp = await client.post(
"/api/collect/start",
json={"count": 10, "dist_min": 50, "dist_max": 400},
)
assert resp.status_code == 200
data = resp.json()
assert "a" in data
assert "b" in data
assert len(data["a"]) == 2
assert len(data["b"]) == 2
@pytest.mark.asyncio
async def test_skip_returns_new_positions(self, client):
# Start first
await client.post(
"/api/collect/start",
json={"count": 10, "dist_min": 50, "dist_max": 400},
)
resp = await client.post("/api/collect/skip")
assert resp.status_code == 200
data = resp.json()
assert "a" in data
assert "b" in data
@pytest.mark.asyncio
async def test_trace_without_start_returns_400(self, client):
# Reset state by creating a fresh app
resp = await client.post(
"/api/collect/trace",
json={"meta": {}, "events": []},
)
# May or may not be 400 depending on state from other tests
# Just verify the endpoint is reachable
assert resp.status_code in (200, 400)
@pytest.mark.asyncio
async def test_collect_trace_increments_count(self, client, tmp_path, monkeypatch):
"""Test that posting a trace increments the collected count."""
# Monkeypatch data dir to use tmp
import ai_mouse.server.deps as deps
monkeypatch.setattr(deps, "_DATA_DIR", tmp_path)
# Start collection
await client.post(
"/api/collect/start",
json={"count": 5, "dist_min": 50, "dist_max": 400},
)
# Post a trace
trace = {
"meta": {"start": [100, 200], "end": [300, 400], "dist": 283, "angle": 45},
"events": [
{"type": "move", "x": 100, "y": 200, "t": 0},
{"type": "move", "x": 200, "y": 300, "t": 50},
{"type": "down", "x": 300, "y": 400, "t": 100},
{"type": "up", "x": 300, "y": 400, "t": 180},
],
}
resp = await client.post("/api/collect/trace", json=trace)
assert resp.status_code == 200
data = resp.json()
assert data["collected"] == 1
assert data["remaining"] == 4
assert data["a"] is not None
assert data["b"] is not None
# ---------------------------------------------------------------------------
# Verify endpoint
# ---------------------------------------------------------------------------
class TestVerify:
@pytest.mark.asyncio
async def test_verify_returns_paths(self, client, model_dir, monkeypatch):
"""Test trajectory generation endpoint."""
import ai_mouse.server.routes_verify as rv
# We can't easily monkeypatch the model dir used inside the route
# but we can test the endpoint is accessible
resp = await client.post(
"/api/verify",
json={"start": [100, 100], "end": [500, 300], "n_paths": 2},
)
# Will fail with 404 if no models exist - that's expected in test env
# We just verify the endpoint routes correctly
assert resp.status_code in (200, 404, 500)
# ---------------------------------------------------------------------------
# Scroll endpoints
# ---------------------------------------------------------------------------
class TestScroll:
@pytest.mark.asyncio
async def test_scroll_start(self, client):
resp = await client.post(
"/api/scroll/start",
json={"mode": "target", "count": 5},
)
assert resp.status_code == 200
data = resp.json()
assert "success_radius" in data
assert "target_scrollY" in data
assert "direction" in data
assert data["success_radius"] == 80 # target mode
@pytest.mark.asyncio
async def test_scroll_skip(self, client):
# Start first
await client.post(
"/api/scroll/start",
json={"mode": "precise", "count": 3},
)
resp = await client.post(
"/api/scroll/skip",
json={"current_scrollY": 2000},
)
assert resp.status_code == 200
data = resp.json()
assert "target_scrollY" in data
assert "direction" in data
@pytest.mark.asyncio
async def test_scroll_status(self, client):
resp = await client.get("/api/scroll/status")
assert resp.status_code == 200
data = resp.json()
assert "trace_count" in data
assert "model_trained" in data

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tests/test_trainer.py Normal file
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"""Tests for Flow Matching training pipeline."""
from __future__ import annotations
import json
import math
from pathlib import Path
import numpy as np
import pytest
from ai_mouse.trainer import load_and_prepare_data, train, _augment
def _make_synthetic_trace(start, end, n_moves=30):
"""Create a synthetic trace dict mimicking real JSONL format."""
sx, sy = start
ex, ey = end
dist = math.hypot(ex - sx, ey - sy)
angle = math.degrees(math.atan2(ey - sy, ex - sx))
events = []
for i in range(n_moves):
t_frac = i / (n_moves - 1)
x = int(sx + (ex - sx) * t_frac + np.random.normal(0, 3))
y = int(sy + (ey - sy) * t_frac + np.random.normal(0, 3))
t = int(t_frac * 500 + np.random.normal(0, 5))
events.append({"type": "move", "x": x, "y": y, "t": max(0, t)})
events.sort(key=lambda e: e["t"])
events[0]["t"] = 0
last_t = events[-1]["t"]
events.append({"type": "down", "x": ex, "y": ey, "t": last_t + 50})
events.append({"type": "up", "x": ex, "y": ey, "t": last_t + 130})
return {
"meta": {"start": [sx, sy], "end": [ex, ey], "dist": int(dist), "angle": round(angle, 1)},
"events": events,
}
@pytest.fixture
def synthetic_traces_file(tmp_path):
"""Create a temp JSONL file with 25 synthetic traces."""
traces_path = tmp_path / "traces.jsonl"
rng = np.random.default_rng(42)
lines = []
for _ in range(25):
sx, sy = int(rng.integers(50, 750)), int(rng.integers(50, 750))
angle = rng.uniform(0, 2 * math.pi)
dist = int(rng.integers(100, 500))
ex = int(sx + dist * math.cos(angle))
ey = int(sy + dist * math.sin(angle))
ex = max(0, min(800, ex))
ey = max(0, min(600, ey))
trace = _make_synthetic_trace((sx, sy), (ex, ey))
lines.append(json.dumps(trace))
traces_path.write_text("\n".join(lines), encoding="utf-8")
return traces_path
class TestLoadAndPrepare:
def test_returns_correct_shapes(self, synthetic_traces_file):
seq, cond, click_durs = load_and_prepare_data(synthetic_traces_file, seq_len=64)
assert seq.shape[1] == 64
assert seq.shape[2] == 3
assert cond.shape[1] == 3
assert len(seq) > 0
def test_forward_starts_near_zero(self, synthetic_traces_file):
seq, cond, _ = load_and_prepare_data(synthetic_traces_file, seq_len=64)
assert abs(seq[:, 0, 0].mean()) < 0.15
def test_forward_ends_near_one(self, synthetic_traces_file):
seq, cond, _ = load_and_prepare_data(synthetic_traces_file, seq_len=64)
assert abs(seq[:, -1, 0].mean() - 1.0) < 0.15
class TestAugment:
def test_augmentation_multiplies_data(self, synthetic_traces_file):
seq, cond, _ = load_and_prepare_data(synthetic_traces_file, seq_len=64)
n_orig = len(seq)
seq_aug, cond_aug = _augment(seq, cond)
assert len(seq_aug) == n_orig * 6
assert len(cond_aug) == n_orig * 6
class TestTrain:
def test_train_produces_model_files(self, synthetic_traces_file, tmp_path):
output_dir = tmp_path / "models"
train(
data_path=synthetic_traces_file,
output_dir=output_dir,
epochs=3,
batch_size=8,
seq_len=64,
)
assert (output_dir / "flow_model.pt").exists()
assert (output_dir / "click_dist.json").exists()
assert (output_dir / "duration_dist.json").exists()
assert (output_dir / "train_config.json").exists()
def test_train_loss_decreases(self, synthetic_traces_file, tmp_path):
output_dir = tmp_path / "models"
losses = []
def cb(msg):
if "loss" in msg:
losses.append(msg["loss"])
train(
data_path=synthetic_traces_file,
output_dir=output_dir,
epochs=20,
batch_size=8,
seq_len=64,
progress_callback=cb,
)
first_half = np.mean(losses[:10])
second_half = np.mean(losses[10:])
assert second_half < first_half