TST: update tests for DiscreteSINDy

Author: yb6599

test/test_pysindy.py

@@ -23,7 +23,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from pysindy import pysindy
-from pysindy import SINDy
+from pysindy import SINDy, DiscreteSINDy
 from pysindy.differentiation import SINDyDerivative
 from pysindy.differentiation import SmoothedFiniteDifference
 from pysindy.feature_library import FourierLibrary
@@ -331,18 +331,18 @@ def test_score_multiple_trajectories(data_multiple_trajectories):
 def test_fit_discrete_time(data_discrete_time):
     x = data_discrete_time
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     check_is_fitted(model)
 
-    model = SINDy(discrete_time=True)
-    model.fit(x[:-1], x_dot=x[1:], t=1)
+    model = DiscreteSINDy()
+    model.fit(x[:-1], x_next=x[1:], t=1)
     check_is_fitted(model)
 
 
 def test_simulate_discrete_time(data_discrete_time):
     x = data_discrete_time
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     n_steps = x.shape[0]
     x1 = model.simulate(x[0], n_steps)
@@ -359,17 +359,17 @@ def stop_func(xi):
 
 def test_predict_discrete_time(data_discrete_time):
     x = data_discrete_time
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     assert len(model.predict(x)) == len(x)
 
 
 def test_score_discrete_time(data_discrete_time):
     x = data_discrete_time
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     assert model.score(x, t=1) > 0.75
-    assert model.score(x, x_dot=x, t=1) < 1
+    assert model.score(x, x_next=x, t=1) < 1
 
 
 def test_bad_multiple_trajectories(data_multiple_trajectories):
@@ -384,20 +384,20 @@ def test_fit_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories,
 ):
     x = data_discrete_time_multiple_trajectories
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     check_is_fitted(model)
 
-    model = SINDy(discrete_time=True)
-    model.fit(x, x_dot=x, t=1)
+    model = DiscreteSINDy()
+    model.fit(x, x_next=x, t=1)
     check_is_fitted(model)
 
 
 def test_predict_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories,
 ):
     x = data_discrete_time_multiple_trajectories
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
 
     y = model.predict(x)
@@ -408,14 +408,14 @@ def test_score_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories,
 ):
     x = data_discrete_time_multiple_trajectories
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
 
     s = model.score(x, t=1)
     assert s > 0.75
 
     # x is not its own derivative, so we expect bad performance here
-    s = model.score(x, x_dot=x, t=1)
+    s = model.score(x, x_next=x, t=1)
     assert s < 1
 
 
@@ -445,7 +445,7 @@ def test_equations(data, capsys):
 
 def test_print_discrete_time(data_discrete_time, capsys):
     x = data_discrete_time
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
     model.print()
 
@@ -455,20 +455,6 @@ def test_print_discrete_time(data_discrete_time, capsys):
     assert "(x0)[k+1] = " in out
 
 
-def test_differentiate(data_lorenz, data_multiple_trajectories):
-    x, t = data_lorenz
-
-    model = SINDy()
-    model.differentiate(x, t)
-
-    x, t = data_multiple_trajectories
-    model.differentiate(x, t)
-
-    model = SINDy(discrete_time=True)
-    with pytest.raises(RuntimeError):
-        model.differentiate(x, t)
-
-
 def test_coefficients_equals_complexity(data_lorenz):
     x, t = data_lorenz
     model = SINDy()
@@ -486,15 +472,15 @@ def test_simulate_errors(data_lorenz):
     with pytest.raises(ValueError):
         model.simulate(x[0], t=1)
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     with pytest.raises(ValueError):
         model.simulate(x[0], t=[1, 2])
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     with pytest.raises(ValueError):
         model.simulate(x[0], t=-1)
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     with pytest.raises(ValueError):
         model.simulate(x[0], t=0.5)
 

test/test_sindyc.py

@@ -8,7 +8,7 @@
 from sklearn.linear_model import Lasso
 from sklearn.utils.validation import check_is_fitted
 
-from pysindy import SINDy
+from pysindy import SINDy, DiscreteSINDy
 from pysindy.optimizers import SR3
 from pysindy.optimizers import STLSQ
 
@@ -257,12 +257,12 @@ def test_score_multiple_trajectories(data_multiple_trajectories):
 def test_fit_discrete_time(data):
     x, u = data
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
     check_is_fitted(model)
 
-    model = SINDy(discrete_time=True)
-    model.fit(x[:-1], u=u[:-1], x_dot=x[1:], t=1)
+    model = DiscreteSINDy()
+    model.fit(x[:-1], u=u[:-1], x_next=x[1:], t=1)
     check_is_fitted(model)
 
 
@@ -275,7 +275,7 @@ def test_fit_discrete_time(data):
 )
 def test_simulate_discrete_time(data):
     x, u = data
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
     n_steps = x.shape[0]
     x1 = model.simulate(x[0], t=n_steps, u=u)
@@ -294,7 +294,7 @@ def test_simulate_discrete_time(data):
 )
 def test_predict_discrete_time(data):
     x, u = data
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     print(x, u)
     model.fit(x, u=u, t=1)
     assert len(model.predict(x, u=u)) == len(x)
@@ -309,30 +309,30 @@ def test_predict_discrete_time(data):
 )
 def test_score_discrete_time(data):
     x, u = data
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
     assert model.score(x, u=u, t=1) > 0.75
-    assert model.score(x, u=u, x_dot=x, t=1) < 1
+    assert model.score(x, u=u, x_next=x, t=1) < 1
 
 
 def test_fit_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories_c,
 ):
     x, u = data_discrete_time_multiple_trajectories_c
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
     check_is_fitted(model)
 
-    model = SINDy(discrete_time=True)
-    model.fit(x, u=u, x_dot=x, t=1)
+    model = DiscreteSINDy()
+    model.fit(x, u=u, x_next=x, t=1)
     check_is_fitted(model)
 
 
 def test_predict_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories_c,
 ):
     x, u = data_discrete_time_multiple_trajectories_c
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
 
     y = model.predict(x, u=u)
@@ -343,14 +343,14 @@ def test_score_discrete_time_multiple_trajectories(
     data_discrete_time_multiple_trajectories_c,
 ):
     x, u = data_discrete_time_multiple_trajectories_c
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, u=u, t=1)
 
     s = model.score(x, u=u, t=1)
     assert s > 0.75
 
     # x is not its own derivative, so we expect bad performance here
-    s = model.score(x, u=u, x_dot=x, t=1)
+    s = model.score(x, u=u, x_next=x, t=1)
     assert s < 1
 
 
@@ -362,7 +362,7 @@ def test_simulate_errors(data_lorenz_c_1d):
     with pytest.raises(ValueError):
         model.simulate(x[0], t=1, u=u)
 
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     with pytest.raises(ValueError):
         model.simulate(x[0], t=[1, 2], u=u)
 
@@ -412,7 +412,7 @@ def test_extra_u_warn(data_lorenz_c_1d):
 
 def test_extra_u_warn_discrete(data_discrete_time_c):
     x, u = data_discrete_time_c
-    model = SINDy(discrete_time=True)
+    model = DiscreteSINDy()
     model.fit(x, t=1)
 
     with pytest.warns(UserWarning):
@@ -422,4 +422,4 @@ def test_extra_u_warn_discrete(data_discrete_time_c):
         model.score(x, u=u, t=1)
 
     with pytest.warns(UserWarning):
-        model.simulate(x[0], u=u, t=10, integrator_kws={"rtol": 0.1})
+        model.simulate(x[0], u=u, t=10)

test/utils/test_utils.py

@@ -50,20 +50,6 @@ def test_reorder_constraints_2D():
     np.testing.assert_array_equal(result, target_order)
 
 
-def test_validate_controls():
-    with pytest.raises(ValueError):
-        validate_control_variables(1, [])
-    with pytest.raises(ValueError):
-        validate_control_variables([], 1)
-    with pytest.raises(ValueError):
-        validate_control_variables([], [1])
-    arr = AxesArray(np.ones(4).reshape((2, 2)), axes={"ax_time": 0, "ax_coord": 1})
-    with pytest.raises(ValueError):
-        validate_control_variables([arr], [arr[:1]])
-    u_mod = validate_control_variables([arr], [arr], trim_last_point=True)
-    assert u_mod[0].n_time == 1
-
-
 @pytest.mark.parametrize(
     ["regularization", "lam", "expected"],
     [