Add property test for optimizers complexity

pysindy/optimizers/sr3.py

@@ -64,15 +64,6 @@ class SR3(BaseOptimizer):
     copy_X : boolean, optional (default True)
         If True, X will be copied; else, it may be overwritten.
 
-    unbias : boolean, optional (default True)
-        Whether to perform an extra step of unregularized linear regression to unbias
-        the coefficients for the identified support.
-        For example, if `STLSQ(alpha=0.1)` is used then the learned coefficients will
-        be biased toward 0 due to the L2 regularization.
-        Setting `unbias=True` will trigger an additional step wherein the nonzero
-        coefficients learned by the `STLSQ` object will be updated using an
-        unregularized least-squares fit.
-
     Attributes
     ----------
     coef_ : array, shape (n_features,) or (n_targets, n_features)

pysindy/optimizers/stlsq.py

@@ -45,15 +45,6 @@ class STLSQ(BaseOptimizer):
     copy_X : boolean, optional (default True)
         If True, X will be copied; else, it may be overwritten.
 
-    unbias : boolean, optional (default True)
-        Whether to perform an extra step of unregularized linear regression to unbias
-        the coefficients for the identified support.
-        For example, if `STLSQ(alpha=0.1)` is used then the learned coefficients will
-        be biased toward 0 due to the L2 regularization.
-        Setting `unbias=True` will trigger an additional step wherein the nonzero
-        coefficients learned by the `STLSQ` object will be updated using an
-        unregularized least-squares fit.
-
     Attributes
     ----------
     coef_ : array, shape (n_features,) or (n_targets, n_features)

requirements-dev.txt

@@ -14,3 +14,4 @@ sphinx >= 2
 sphinxcontrib-apidoc
 sphinx_rtd_theme
 pre-commit
+hypothesis

test/property_tests/test_optimizers_complexity.py

@@ -0,0 +1,52 @@
+from hypothesis import assume
+from hypothesis import given
+from hypothesis import settings
+from hypothesis.strategies import integers
+from sklearn.datasets import make_regression
+from sklearn.linear_model import ElasticNet
+from sklearn.linear_model import Lasso
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import Ridge
+
+from pysindy.optimizers import SINDyOptimizer
+from pysindy.optimizers import SR3
+from pysindy.optimizers import STLSQ
+
+
+@given(
+    n_samples=integers(min_value=100, max_value=10000),
+    n_features=integers(min_value=3, max_value=30),
+    n_informative=integers(min_value=1, max_value=10),
+    random_state=integers(min_value=0, max_value=2 ** 32 - 1),
+)
+@settings(max_examples=10)
+def test_complexity(n_samples, n_features, n_informative, random_state):
+    """Behaviour test for complexity.
+
+    We assume that more regularized optimizers are less complex on the same dataset.
+    """
+    assume(n_informative <= n_features)
+
+    x, y = make_regression(
+        n_samples, n_features, n_informative, 1, 0, noise=0.1, random_state=random_state
+    )
+    y = y.reshape(-1, 1)
+
+    opt_kwargs = dict(fit_intercept=True, normalize=False)
+    optimizers = [
+        SR3(thresholder="l0", **opt_kwargs),
+        SR3(thresholder="l1", **opt_kwargs),
+        Lasso(**opt_kwargs),
+        STLSQ(**opt_kwargs),
+        ElasticNet(**opt_kwargs),
+        Ridge(**opt_kwargs),
+        LinearRegression(**opt_kwargs),
+    ]
+
+    optimizers = [SINDyOptimizer(o, unbias=True) for o in optimizers]
+
+    for opt in optimizers:
+        opt.fit(x, y)
+
+    for less_complex, more_complex in zip(optimizers, optimizers[1:]):
+        assert less_complex.complexity <= more_complex.complexity