Add slow marker to multiple test cases

tests/test_data_sanity.py

@@ -11,6 +11,7 @@ def cpn():
     return Path(__file__).parent / "fixtures" / "ddm_cpn.onnx"
 
 
+@pytest.mark.slow
 def test_data_sanity_check(data_ddm, cpn, caplog):
     # Case 1: raise error if there are missing fields in data
     with pytest.raises(ValueError, match="Field rt not found in data."):

tests/test_distribution_utils.py

@@ -81,6 +81,7 @@ def test_lapse_distribution():
     np.testing.assert_array_equal(random_sample_a, random_sample_b)
 
 
+@pytest.mark.slow
 def test_apply_param_bounds_to_loglik():
     """Tests the function in separation."""
     logp = np.random.normal(size=1000)
@@ -121,6 +122,7 @@ def test_apply_param_bounds_to_loglik():
     )
 
 
+@pytest.mark.slow
 def test_make_distribution():
     def fake_logp_function(data, param1, param2):
         """Make up a fake log likelihood function for this test only."""
@@ -165,6 +167,7 @@ def fake_logp_function(data, param1, param2):
     )
 
 
+@pytest.mark.slow
 def test_extra_fields(data_ddm):
     ones = np.ones(data_ddm.shape[0])
     x = ones * 0.5
@@ -231,6 +234,7 @@ def logp_ddm_extra_fields(data, v, a, z, t, x, y):
     )
 
 
+@pytest.mark.slow
 def test_ensure_positive_ndt():
     data = np.zeros((1000, 2))
     data[:, 0] = np.random.uniform(size=1000)

tests/test_graphing.py

@@ -1,6 +1,9 @@
+import pytest
+
 import hssm
 
 
+@pytest.mark.slow
 def test_simple_graphing(data_ddm):
     model = hssm.HSSM(data=data_ddm, model="ddm")
     graph = model.graph()

tests/test_hssm.py

@@ -22,6 +22,7 @@
 param_a = param_v | dict(name="a", formula="a ~ 1 + x + y")
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(
     "include, should_raise_exception",
     [
@@ -80,6 +81,7 @@ def test_transform_params_general(data_ddm_reg, include, should_raise_exception)
         assert len(model.params) == 5
 
 
+@pytest.mark.slow
 def test_custom_model(data_ddm):
     with pytest.raises(
         ValueError, match="When using a custom model, please provide a `loglik_kind.`"
@@ -132,6 +134,7 @@ def test_custom_model(data_ddm):
     assert model.list_params == ["v", "a", "z", "t", "p_outlier"]
 
 
+@pytest.mark.slow
 def test_model_definition_outside_include(data_ddm):
     model_with_one_param_fixed = HSSM(data_ddm, a=0.5)
 
@@ -151,6 +154,7 @@ def test_model_definition_outside_include(data_ddm):
         HSSM(data_ddm, include=[{"name": "a", "prior": 0.5}], a=0.5)
 
 
+@pytest.mark.slow
 def test_sample_prior_predictive(data_ddm_reg):
     data_ddm_reg = data_ddm_reg.iloc[:10, :]
 
@@ -195,6 +199,7 @@ def test_sample_prior_predictive(data_ddm_reg):
     )
 
 
+@pytest.mark.slow
 def test_override_default_link(caplog, data_ddm_reg):
     param_v = {
         "name": "v",
@@ -225,6 +230,7 @@ def test_override_default_link(caplog, data_ddm_reg):
     assert "strange" in caplog.records[0].message
 
 
+@pytest.mark.slow
 def test_resampling(data_ddm):
     model = HSSM(data=data_ddm)
     sample_1 = model.sample(draws=10, chains=1, tune=0)
@@ -236,6 +242,7 @@ def test_resampling(data_ddm):
     assert sample_1 is not sample_2
 
 
+@pytest.mark.slow
 def test_add_likelihood_parameters_to_data(data_ddm):
     """Test if the likelihood parameters are added to the InferenceData object."""
     model = HSSM(data=data_ddm)
@@ -271,6 +278,7 @@ def test_add_likelihood_parameters_to_data(data_ddm):
 
 
 # Setting any parameter to a fixed value should work:
+@pytest.mark.slow
 def test_model_creation_constant_parameter(data_ddm):
     for param_name in ["v", "a", "z", "t"]:
         model = HSSM(data=data_ddm, **{param_name: 1.0})
@@ -279,6 +287,7 @@ def test_model_creation_constant_parameter(data_ddm):
 
 
 # Setting any single parameter to a regression should respect the default bounds:
+@pytest.mark.slow
 @pytest.mark.parametrize(
     "param_name, dist_name",
     [("v", "Normal"), ("a", "Gamma"), ("z", "Beta"), ("t", "Gamma")],
@@ -299,6 +308,7 @@ def test_model_creation_all_parameters_constant(data_ddm):
 
 
 # Prior settings
+@pytest.mark.slow
 def test_prior_settings_basic(cavanagh_test):
     model_1 = HSSM(
         data=cavanagh_test,
@@ -321,6 +331,7 @@ def test_prior_settings_basic(cavanagh_test):
     )
 
 
+@pytest.mark.slow
 def test_compile_logp(cavanagh_test):
     model_1 = HSSM(
         data=cavanagh_test,

tests/test_initvals.py

@@ -36,6 +36,7 @@
 ]
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(parameter_names, parameter_grid)
 def test_sample_map(caplog, loglik_kind, model, sampler, initvals):
     """Test sampling from MAP starting point."""
@@ -136,6 +137,7 @@ def _check_initval_defaults_correctness(model) -> None:
             pass
 
 
+@pytest.mark.slow
 def test_basic_model(caplog):
     """Test basic model with p_outlier distribution defined."""
     caplog.set_level(logging.INFO)
@@ -149,6 +151,7 @@ def test_basic_model(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_basic_model_p_outlier(caplog):
     """Test basic model with p_outlier distribution defined."""
     caplog.set_level(logging.INFO)
@@ -163,6 +166,7 @@ def test_basic_model_p_outlier(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_basic_model_p_outlier_initval(caplog):
     """Test basic model with p_outlier distribution defined."""
     caplog.set_level(logging.INFO)
@@ -180,6 +184,7 @@ def test_basic_model_p_outlier_initval(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_reg_model(caplog):
     """Test regression model, with regression on all parameters."""
     caplog.set_level(logging.INFO)
@@ -199,6 +204,7 @@ def test_reg_model(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_reg_model_subset(caplog):
     """Test regression model, with subset of parameters being regressions."""
     caplog.set_level(logging.INFO)
@@ -217,6 +223,7 @@ def test_reg_model_subset(caplog):
     )
 
 
+@pytest.mark.slow
 def test_angle_model_reg(caplog):
     """Test with angle model regression."""
     caplog.set_level(logging.INFO)
@@ -237,6 +244,7 @@ def test_angle_model_reg(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_angle_model(caplog):
     """Test with angle model basic."""
     caplog.set_level(logging.INFO)
@@ -250,6 +258,7 @@ def test_angle_model(caplog):
     _check_initval_defaults_correctness(model)
 
 
+@pytest.mark.slow
 def test_process_no_process(caplog):
     """Test mismatch with and without preprocessing."""
     caplog.set_level(logging.INFO)

tests/test_likelihoods.py

@@ -64,6 +64,7 @@ def shared_params():
 parameters = [(name, np.arange(value, 5.1, 0.5)) for name, value in zip(names, values)]
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("param_name, param_values", parameters)
 def test_no_inf_values(data_ddm, shared_params, param_name, param_values):
     for value in param_values:
@@ -82,6 +83,7 @@ def test_no_inf_values(data_ddm, shared_params, param_name, param_values):
 parameters = [standard, svd]  # type: ignore
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("logp_func, logp_bbox_func, true_values", parameters)
 def test_bbox(data_ddm, logp_func, logp_bbox_func, true_values):
     data = data_ddm.values
@@ -101,6 +103,7 @@ def test_bbox(data_ddm, logp_func, logp_bbox_func, true_values):
 nan_guard_mode = NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=False)
 
 
+@pytest.mark.slow
 def test_analytical_gradient():
     v = pt.dvector()
     a = pt.dvector()
@@ -145,6 +148,7 @@ def test_analytical_gradient():
     )
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("p_outlier, loglik_kind", param_matrix)
 def test_lapse_distribution_cav(p_outlier, loglik_kind):
     true_values = (1.5, 0.5, 0.5)

tests/test_likelihoods_lba.py

@@ -59,6 +59,7 @@ def vectorize_param(theta, param, size):
 theta_lba3 = theta_lba2 | {"v2": 1.0}
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(
     "logp_func, model, theta",
     [(logp_lba2, "lba2", theta_lba2), (logp_lba3, "lba3", theta_lba3)],

tests/test_missing_and_deadline.py

@@ -36,6 +36,7 @@ def data():
 cases = product(["cpn", "opn"], [True, False])
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize("cpn, is_vector", cases)
 def test_make_missing_data_callable(data, fixture_path, cpn, is_vector):
     is_cpn = cpn == "cpn"

tests/test_onnx.py

@@ -28,6 +28,7 @@ def onnx_session(fixture_path):
     )
 
 
+@pytest.mark.slow
 def test_interpret_onnx(onnx_session, fixture_path):
     """Tests whether both versions of interpret_onnx return similar values as does the
     ONNX runtime.
@@ -100,6 +101,7 @@ def test_make_jax_logp_funcs_from_onnx(fixture_path):
     )
 
 
+@pytest.mark.slow
 def test_make_jax_logp_ops(fixture_path):
     """Tests whether the logp Op returned from make_jax_logp_ops with different backends
     work the same way.

tests/test_plotting.py

@@ -151,6 +151,7 @@ def test__plot_posterior_predictive_2D(cav_idata, cavanagh_test):
     assert len(g2.figure.axes[0].get_lines()) == 1
 
 
+@pytest.mark.slow
 def test_plot_posterior_predictive(cav_idata, cavanagh_test):
     # Mock model object
     model = hssm.HSSM(
@@ -289,6 +290,7 @@ def test__plot_quantile_probability_2D(cav_idata, cavanagh_test):
     assert len(g.figure.axes) == 5 * 4
 
 
+@pytest.mark.slow
 def test_plot_quantile_probability(cav_idata, cavanagh_test):
     # Mock model object
     model = hssm.HSSM(

tests/test_plotting_cartoon.py

@@ -10,6 +10,7 @@
 
 
 # I want to parameter
+@pytest.mark.slow
 @pytest.mark.parametrize(
     ["n_trajectories", "groups", "plot_pp_mean", "plot_pp_samples", "row", "col"],
     [
@@ -66,6 +67,7 @@ def test_plot_model_cartoon_2_choice(
             assert len(ax) == len(cav_model_cartoon.data[groups[0]].unique())
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(
     ["n_trajectories", "groups", "plot_pp_mean", "plot_pp_samples", "row", "col"],
     [

tests/test_register.py

@@ -10,6 +10,7 @@
 )
 
 
+@pytest.mark.slow
 def test_register_model():
     """Test registering a custom model"""
 

tests/test_sample_posterior_predictive.py

@@ -29,6 +29,7 @@
 ]
 
 
+@pytest.mark.slow
 @pytest.mark.parametrize(PARAMETER_NAMES, PARAMETER_GRID)
 def test_sample_posterior_predictive(
     cav_idata, cavanagh_test, draws, safe_mode, inplace

tests/test_save_load.py

@@ -1,6 +1,9 @@
 import numpy as np
 import shutil
 from pathlib import Path
+
+import pytest
+
 import hssm
 
 
@@ -19,6 +22,7 @@ def compare_hssm_class_attributes(model_a, model_b):
     ], "Basic RVs not the same"
 
 
+@pytest.mark.slow
 def test_save_load_model_only(basic_hssm_model):
     tmp_folder_name = "models_pytest"
     tmp_model_name = "hssm_model_pytest"
@@ -32,6 +36,7 @@ def test_save_load_model_only(basic_hssm_model):
     shutil.rmtree(tmp_folder_name)
 
 
+@pytest.mark.slow
 def test_save_load_vi_mcmc(basic_hssm_model):
     tmp_folder_name = "models_pytest"
     tmp_model_name = "hssm_model_pytest"

tests/test_utils.py

@@ -15,6 +15,7 @@
 hssm.set_floatX("float32")
 
 
+@pytest.mark.slow
 def test_get_alias_dict():
     # Simulate some data:
     v_true, a_true, z_true, t_true = [0.5, 1.5, 0.5, 0.5]