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157b149 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | """Tests for core/model.py — POMDP state space definitions."""
import numpy as np
import pytest
from mindsphere.core.model import SphereModel, SKILL_FACTORS, SKILL_LEVEL_VALUES
@pytest.fixture
def model():
return SphereModel()
class TestSphereModel:
def test_all_factors_have_matrices(self, model):
"""Every factor should have A, B, C, D matrices."""
for factor in model.get_all_factor_names():
assert factor in model.A, f"Missing A matrix for {factor}"
assert factor in model.B, f"Missing B matrix for {factor}"
assert factor in model.C, f"Missing C matrix for {factor}"
assert factor in model.D, f"Missing D matrix for {factor}"
def test_a_matrices_are_valid(self, model):
"""A matrix columns should sum to 1 (valid likelihood)."""
for factor, A in model.A.items():
col_sums = A.sum(axis=0)
np.testing.assert_allclose(
col_sums, 1.0, atol=0.01,
err_msg=f"A[{factor}] columns don't sum to 1: {col_sums}"
)
def test_b_matrices_are_valid(self, model):
"""B matrix columns should sum to 1 (valid transition)."""
for factor, B in model.B.items():
for a in range(B.shape[0]):
col_sums = B[a].sum(axis=0)
np.testing.assert_allclose(
col_sums, 1.0, atol=0.01,
err_msg=f"B[{factor}][action={a}] columns don't sum to 1"
)
def test_d_matrices_are_valid(self, model):
"""D vectors should sum to 1 (valid prior)."""
for factor, D in model.D.items():
np.testing.assert_allclose(
D.sum(), 1.0, atol=0.01,
err_msg=f"D[{factor}] doesn't sum to 1"
)
def test_initial_beliefs_match_d(self, model):
"""get_initial_beliefs should return copies of D vectors."""
beliefs = model.get_initial_beliefs()
for factor in model.get_all_factor_names():
np.testing.assert_array_equal(beliefs[factor], model.D[factor])
def test_skill_score_range(self, model):
"""Skill scores should be in [0, 100]."""
# All probability on lowest level
low_belief = np.array([1.0, 0.0, 0.0, 0.0, 0.0])
assert model.get_skill_score(low_belief) == pytest.approx(10.0)
# All probability on highest level
high_belief = np.array([0.0, 0.0, 0.0, 0.0, 1.0])
assert model.get_skill_score(high_belief) == pytest.approx(90.0)
def test_eight_skill_factors(self, model):
"""Should have exactly 8 skill factors."""
assert len(model.spec.skill_factors) == 8
def test_get_all_skill_scores(self, model):
"""get_all_skill_scores should return dict with 8 entries."""
beliefs = model.get_initial_beliefs()
scores = model.get_all_skill_scores(beliefs)
assert len(scores) == 8
for skill in SKILL_FACTORS:
assert skill in scores
assert 0 <= scores[skill] <= 100
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