"""
Unit tests for adaptive course learner components.
Config is mocked in conftest.py so tests run without .env.
"""
from pathlib import Path
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
@pytest.fixture
def mock_config():
"""Provide mock Canvas config."""
with patch("adaptive_learner.canvas_fetcher.load_env_config") as m:
cfg = MagicMock()
cfg.base_url = "https://test.instructure.com"
cfg.api_token = "test-token"
m.return_value = cfg
yield cfg
@pytest.fixture
def sample_course_content():
"""Sample course content for testing."""
return {
"course_id": 58606,
"modules": [
{
"id": 1,
"name": "Introduction",
"items": [
{"id": 1, "title": "Welcome", "type": "Page"},
{"id": 2, "title": "Syllabus", "type": "File"},
],
},
{
"id": 2,
"name": "Week 1",
"items": [
{"id": 3, "title": "Lecture 1", "type": "ExternalUrl"},
],
},
],
}
class TestPerceptronFeatureExtractor:
"""Tests for PerceptronFeatureExtractor."""
def test_extract_features_empty(self):
from adaptive_learner.perceptron_model import PerceptronFeatureExtractor
p = PerceptronFeatureExtractor()
features = p.extract_features({})
assert features.shape == (1000,)
assert np.all(features >= 0)
def test_extract_features_with_content(self, sample_course_content):
from adaptive_learner.perceptron_model import PerceptronFeatureExtractor
p = PerceptronFeatureExtractor()
features = p.extract_features(sample_course_content)
assert len(features.shape) == 1
assert features.shape[0] >= 1
assert p.is_fitted
def test_learn_patterns(self, sample_course_content):
from adaptive_learner.perceptron_model import PerceptronFeatureExtractor
p = PerceptronFeatureExtractor()
p.learn_patterns([sample_course_content, sample_course_content])
score = p.predict_importance(sample_course_content)
assert isinstance(score, (int, float))
def test_save_load(self, sample_course_content, tmp_path):
from adaptive_learner.perceptron_model import PerceptronFeatureExtractor
p1 = PerceptronFeatureExtractor()
p1.learn_patterns([sample_course_content])
p1.save(tmp_path / "perceptron.pkl")
p2 = PerceptronFeatureExtractor()
p2.load(tmp_path / "perceptron.pkl")
assert p2.is_fitted
class TestRLContextBuilder:
"""Tests for RLContextBuilder."""
def test_get_state(self, sample_course_content):
from adaptive_learner.rl_agent import RLContextBuilder
rl = RLContextBuilder(epsilon=0.0) # No exploration
state = rl.get_state(sample_course_content, [])
assert "2_" in state # 2 modules
assert "3_" in state # 3 items total
def test_choose_action(self):
from adaptive_learner.rl_agent import RLContextBuilder
rl = RLContextBuilder(epsilon=0.0)
actions = ["add_module_1", "add_module_2"]
# With epsilon=0 and empty Q-table, should pick first by max
action = rl.choose_action("2_3_0", actions)
assert action in actions
def test_update_q_value(self):
from adaptive_learner.rl_agent import RLContextBuilder
rl = RLContextBuilder(learning_rate=0.1)
rl.update_q_value("s1", "a1", 1.0, "s2", ["a2"])
assert rl.q_table["s1"]["a1"] != 0
def test_build_context_iteratively(self, sample_course_content):
from adaptive_learner.perceptron_model import PerceptronFeatureExtractor
from adaptive_learner.rl_agent import RLContextBuilder
perceptron = PerceptronFeatureExtractor()
perceptron.learn_patterns([sample_course_content])
rl = RLContextBuilder(epsilon=0.0)
context = rl.build_context_iteratively(
sample_course_content, perceptron, max_iterations=3
)
assert len(context) <= 3
assert all(isinstance(c, str) for c in context)
class TestCanvasContentFetcher:
"""Tests for CanvasContentFetcher (with mocked HTTP)."""
def test_fetch_course_content(self, mock_config, sample_course_content):
from adaptive_learner.canvas_fetcher import CanvasContentFetcher
with patch("adaptive_learner.canvas_fetcher.httpx.Client") as mock_client:
client_instance = MagicMock()
mock_client.return_value.__enter__.return_value = client_instance
modules_resp = MagicMock()
modules_resp.status_code = 200
modules_resp.json.return_value = [
{"id": 1, "name": "Intro"},
{"id": 2, "name": "Week 1"},
]
modules_resp.raise_for_status = MagicMock()
items_resp = MagicMock()
items_resp.status_code = 200
items_resp.json.return_value = [{"id": 1, "title": "Item 1", "type": "Page"}]
client_instance.get.side_effect = [modules_resp, items_resp, items_resp]
fetcher = CanvasContentFetcher()
result = fetcher.fetch_course_content(58606)
assert result["course_id"] == 58606
assert len(result["modules"]) == 2
def test_save_to_folder(self, mock_config, sample_course_content, tmp_path):
from adaptive_learner.canvas_fetcher import CanvasContentFetcher
fetcher = CanvasContentFetcher()
fetcher.save_to_folder(sample_course_content, tmp_path)
course_dir = tmp_path / "course_58606"
assert course_dir.exists()
assert (course_dir / "module_1" / "items.json").exists()
assert (course_dir / "module_2" / "meta.json").exists()
class TestAdaptiveCourseLearner:
"""Tests for AdaptiveCourseLearner."""
def test_learn_from_course(
self, mock_config, sample_course_content, tmp_path
):
from adaptive_learner.learner import AdaptiveCourseLearner
with patch.object(
AdaptiveCourseLearner,
"get_course_content",
return_value=sample_course_content,
):
learner = AdaptiveCourseLearner(knowledge_base_path=tmp_path)
# Override fetcher to avoid real fetch
learner.fetcher.fetch_course_content = lambda cid: sample_course_content
context = learner.learn_from_course(58606, iterations=2)
assert isinstance(context, list)
assert (tmp_path / "course_58606_context.json").exists()
