test: add test for init error check of active learning

tests/test_active_learning.py

+import pytest
+import numpy as np
+from psimpy.inference.active_learning import ActiveLearning
+from psimpy.simulator.run_simulator import RunSimulator
+from psimpy.simulator.mass_point_model import MassPointModel
+from psimpy.sampler.latin import LHS
+from psimpy.sampler.saltelli import Saltelli
+from psimpy.emulator.robustgasp import ScalarGaSP, PPGaSP
+from scipy.stats import uniform, norm
+from scipy import optimize
+from beartype.roar import BeartypeCallHintParamViolation
+
+@pytest.mark.parametrize(
+    "run_sim_obj, prior, likelihood, lhs_sampler, scalar_gasp, optimizer",
+    [
+        (MassPointModel(), uniform.pdf, norm.pdf, LHS(1), ScalarGaSP(1),
+        optimize.brute), 
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        None, norm.pdf, LHS(1), ScalarGaSP(1), optimize.brute),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        uniform.pdf, None, LHS(1), ScalarGaSP(1), optimize.brute),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        uniform.pdf, norm.pdf, Saltelli(1), ScalarGaSP(1), optimize.brute),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        uniform.pdf, norm.pdf, LHS(1), PPGaSP(1), optimize.brute),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        uniform.pdf, norm.pdf, LHS(1), ScalarGaSP(1), None)       
+    ]
+)  
+def test_ActiveLearning_init_TypeError(run_sim_obj, prior, likelihood,
+    lhs_sampler, scalar_gasp, optimizer):
+    ndim = 1
+    bounds = np.array([[0,1]])
+    data  = np.array([1,2,3])
+    n0 = 20
+    nt = 100
+    with pytest.raises(BeartypeCallHintParamViolation):
+        _ = ActiveLearning(ndim, bounds, data, run_sim_obj, prior, likelihood,
+            n0, nt, lhs_sampler, scalar_gasp, optimizer=optimizer)
+
+
+@pytest.mark.parametrize(
+    "run_sim_obj, lhs_sampler, scalar_gasp",
+    [
+        (RunSimulator(MassPointModel.run,
+            ['coulomb_friction', 'turbulent_friction']),
+        LHS(1), ScalarGaSP(1)
+        ),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        LHS(2), ScalarGaSP(1)),
+        (RunSimulator(MassPointModel.run, ['coulomb_friction']),
+        LHS(1), ScalarGaSP(3))      
+    ]
+)  
+def test_ActiveLearning_init_RuntimeError(run_sim_obj, lhs_sampler,
+    scalar_gasp):
+    ndim = 1
+    bounds = np.array([[0,1]])
+    data  = np.array([1,2,3])
+    prior = uniform.pdf
+    likelihood = norm.pdf
+    n0 = 20
+    nt = 100
+    with pytest.raises(RuntimeError):
+        _ = ActiveLearning(ndim, bounds, data, run_sim_obj, prior, likelihood,
+            n0, nt, lhs_sampler, scalar_gasp)
+
+
+@pytest.mark.parametrize(
+    "scalar_gasp_mean, indicator",
+    [
+        ('cubic', 'entropy'),
+        ('linear', 'divergence')
+    ]
+)  
+def test_ActiveLearning_init_NotImplementedError(scalar_gasp_mean, indicator):
+    ndim = 1
+    bounds = np.array([[0,1]])
+    data  = np.array([1,2,3])
+    run_sim_obj = RunSimulator(MassPointModel.run, ['coulomb_friction'])
+    lhs_sampler = LHS(1)
+    scalar_gasp = ScalarGaSP(1)
+    prior = uniform.pdf
+    likelihood = norm.pdf
+    n0 = 20
+    nt = 100
+    with pytest.raises(NotImplementedError):
+        _ = ActiveLearning(ndim, bounds, data, run_sim_obj, prior, likelihood,
+            n0, nt, lhs_sampler, scalar_gasp, scalar_gasp_mean=scalar_gasp_mean,
+            indicator=indicator)
+
+def test_ActiveLearning_init_ValueError():
+    ndim = 1
+    bounds = np.array([[0,1]])
+    data  = np.array([1,2,3])
+    run_sim_obj = RunSimulator(MassPointModel.run, ['coulomb_friction'])
+    lhs_sampler = LHS(1)
+    scalar_gasp = ScalarGaSP(1)
+    prior = uniform.pdf
+    likelihood = norm.pdf
+    n0 = 20
+    nt = 100
+    kwgs_optimizer = {"NS":50}
+    with pytest.raises(ValueError):
+        _ = ActiveLearning(ndim, bounds, data, run_sim_obj, prior, likelihood,
+            n0, nt, lhs_sampler, scalar_gasp, kwgs_optimizer=kwgs_optimizer)