Test improvements today

examples/17_parameterized_pattern_formation/.gitignore

@@ -0,0 +1,2 @@
+*.pdf
+data

test/conftest.py

@@ -61,7 +61,7 @@ def data_1d_bad_shape():
 @pytest.fixture
 def data_lorenz():
 
-    t = np.linspace(0, 5, 500)
+    t = np.linspace(0, 1, 50)
     x0 = [8, 27, -7]
     x = solve_ivp(lorenz, (t[0], t[-1]), x0, t_eval=t).y.T
 
@@ -351,7 +351,7 @@ def data_generalized_library():
 
 
 @pytest.fixture
-def data_sindypi_library():
+def data_sindypi_library(data_lorenz):
     library_functions = [
         lambda x: x,
         lambda x: x**2,
@@ -362,7 +362,7 @@ def data_sindypi_library():
         lambda s: str(s) + "^2",
         lambda s, t: str(s) + " " + str(t),
     ]
-    t = np.linspace(0, 5, 500)
+    _, t = data_lorenz
 
     return PDELibrary(
         library_functions=library_functions,
@@ -393,8 +393,8 @@ def data_ode_library():
 
 
 @pytest.fixture
-def data_pde_library():
-    spatial_grid = np.linspace(0, 10, 500)
+def data_pde_library(data_lorenz):
+    _, spatial_grid = data_lorenz
     library_functions = [
         lambda x: x,
         lambda x: x**2,

test/differentiation/test_differentiation_methods.py → test/test_differentiation.py

@@ -45,9 +45,12 @@ def test_centered_difference_variable_timestep_length():
     assert len(centered_difference(x, t) == len(x))
 
 
-# Fixtures: data sets to be re-used in multiple tests
-# data_derivative_1d and data_derivative_2d are defined
-# in ../conftest.py
+def test_nan_derivatives(data_lorenz):
+    x, t = data_lorenz
+    x_dot = FiniteDifference(drop_endpoints=False)(x, t)
+    x_dot_nans = FiniteDifference(drop_endpoints=True)(x, t)
+    np.testing.assert_allclose(x_dot_nans[1:-1], x_dot[1:-1])
+    assert np.isnan(x_dot_nans[:1]).all() and np.isnan(x_dot_nans[-1:]).all()
 
 
 def test_forward_difference_1d(data_derivative_1d):

test/test_notebooks.py

@@ -67,6 +67,8 @@ def purge_notebook_modules():
 @pytest.mark.parametrize("directory", notebook_scripts)
 def test_notebook_script(directory: Path, purge_notebook_modules):
     # Run in native directory with modified sys.path for imports to work
+    if "17_" in directory or "5_" in directory:
+        pytest.skip("Notebook runs too slowly.  Test manually")
     with _cwd(notebook_dir / directory):
         runpy.run_path(str(notebook_dir / directory / "example.py"), run_name="testing")
 

test/optimizers/test_optimizers.py → test/test_optimizers.py

@@ -1028,25 +1028,12 @@ def test_normalize_columns(data_derivative_1d, optimizer):
         assert opt.coef_.shape == (1, x.shape[1])
 
 
-@pytest.mark.parametrize(
-    "optimizer",
-    [
-        STLSQ,
-        SSR,
-        FROLS,
-        SR3,
-        ConstrainedSR3,
-        StableLinearSR3,
-        TrappingSR3,
-        MIOSR,
-    ],
-)
-def test_legacy_ensemble_odes(data_lorenz, optimizer):
+def test_legacy_ensemble_odes(data_lorenz):
     x, t = data_lorenz
-    opt = optimizer(normalize_columns=True)
+    opt = STLSQ(normalize_columns=True)
     model = SINDy(optimizer=opt)
-    model.fit(x, ensemble=True, n_models=10, n_subset=20)
-    assert np.shape(model.coef_list) == (10, 3, 10)
+    model.fit(x, ensemble=True, n_models=2, n_subset=2)
+    assert np.shape(model.coef_list) == (2, 3, 10)
 
 
 @pytest.mark.parametrize(
@@ -1066,20 +1053,7 @@ def test_ensemble_optimizer(data_lorenz, optimizer_params):
     assert model.coefficients().shape == (3, 10)
 
 
-@pytest.mark.parametrize(
-    "optimizer",
-    [
-        STLSQ,
-        SSR,
-        FROLS,
-        SR3,
-        ConstrainedSR3,
-        StableLinearSR3,
-        TrappingSR3,
-        MIOSR,
-    ],
-)
-def test_legacy_ensemble_pdes(optimizer):
+def test_legacy_ensemble_pdes():
     u = np.random.randn(10, 10, 2)
     t = np.linspace(1, 10, 10)
     x = np.linspace(1, 10, 10)
@@ -1097,11 +1071,11 @@ def test_legacy_ensemble_pdes(optimizer):
         spatial_grid=x,
         include_bias=True,
     )
-    opt = optimizer(normalize_columns=True)
+    opt = STLSQ(normalize_columns=True)
     model = SINDy(optimizer=opt, feature_library=pde_lib)
-    model.fit(u, x_dot=u_dot, ensemble=True, n_models=10, n_subset=20)
+    model.fit(u, x_dot=u_dot, ensemble=True, n_models=2, n_subset=2)
     n_features = len(model.get_feature_names())
-    assert np.shape(model.coef_list) == (10, 2, n_features)
+    assert np.shape(model.coef_list) == (2, 2, n_features)
 
 
 def test_ssr_criteria(data_lorenz):

test/test_pysindy.py

@@ -23,7 +23,6 @@
 from sklearn.utils.validation import check_is_fitted
 
 from pysindy import SINDy
-from pysindy.differentiation import FiniteDifference
 from pysindy.differentiation import SINDyDerivative
 from pysindy.differentiation import SmoothedFiniteDifference
 from pysindy.feature_library import FourierLibrary
@@ -81,20 +80,6 @@ def test_improper_shape_input(data_1d):
     check_is_fitted(model)
 
 
-def test_nan_derivatives(data_lorenz):
-    x, t = data_lorenz
-    model0 = SINDy(differentiation_method=FiniteDifference(drop_endpoints=False))
-    model0.fit(x, t)
-
-    model = SINDy(differentiation_method=FiniteDifference(drop_endpoints=True))
-    model.fit(x, t)
-    check_is_fitted(model)
-
-    result = model.score(x, t=t)
-    expected = model0.score(x, t=t)
-    np.testing.assert_allclose(result, expected, rtol=1e-4)
-
-
 @pytest.mark.parametrize(
     "data",
     [
@@ -561,19 +546,13 @@ def test_differentiate(data_lorenz, data_multiple_trajctories):
         model.differentiate(x)
 
 
-def test_coefficients(data_lorenz):
+def test_coefficients_equals_complexity(data_lorenz):
     x, t = data_lorenz
     model = SINDy()
     model.fit(x, t)
     c = model.coefficients()
-    assert np.count_nonzero(c) < 10
-
-
-def test_complexity(data_lorenz):
-    x, t = data_lorenz
-    model = SINDy()
-    model.fit(x, t)
-    assert model.complexity < 10
+    assert model.complexity == np.count_nonzero(c)
+    assert model.complexity < 30
 
 
 def test_simulate_errors(data_lorenz):