Change to SINDy init defaults for optimizer, feature_library, and differentiation_method

pysindy/optimizers/stlsq.py

@@ -9,8 +9,7 @@
 
 
 class STLSQ(BaseOptimizer):
-    """
-    Sequentially thresholded least squares algorithm.
+    """Sequentially thresholded least squares algorithm.
 
     Attempts to minimize the objective function
     :math:`\\|y - Xw\\|^2_2 + alpha \\times \\|w\\|^2_2`
@@ -128,8 +127,7 @@ def _no_change(self):
         return all(bool(i) == bool(j) for i, j in zip(this_coef, last_coef))
 
     def _reduce(self, x, y):
-        """
-        Iterates the thresholding. Assumes an initial guess is saved in
+        """Iterates the thresholding. Assumes an initial guess is saved in
         self.coef_ and self.ind_
         """
         ind = self.ind_
@@ -140,8 +138,8 @@ def _reduce(self, x, y):
         for _ in range(self.max_iter):
             if np.count_nonzero(ind) == 0:
                 warnings.warn(
-                    """Sparsity parameter is too big ({}) and eliminated all
-                    coefficients""".format(
+                    "Sparsity parameter is too big ({}) and eliminated all "
+                    "coefficients".format(
                         self.threshold
                     )
                 )
@@ -152,8 +150,8 @@ def _reduce(self, x, y):
             for i in range(n_targets):
                 if np.count_nonzero(ind[i]) == 0:
                     warnings.warn(
-                        """Sparsity parameter is too big ({}) and eliminated all
-                        coefficients""".format(
+                        "Sparsity parameter is too big ({}) and eliminated all "
+                        "coefficients".format(
                             self.threshold
                         )
                     )

pysindy/pysindy.py

@@ -91,18 +91,21 @@ class SINDy(BaseEstimator):
     0.999999985520653
     """
 
-    def __init__(
-        self,
-        optimizer=STLSQ(),
-        feature_library=PolynomialFeatures(),
-        differentiation_method=FiniteDifference(),
-        feature_names=None,
-        discrete_time=False,
-        n_jobs=1,
-    ):
-        self.optimizer = optimizer
-        self.feature_library = feature_library
-        self.differentiation_method = differentiation_method
+    def __init__(self, optimizer=None, feature_library=None,
+                 differentiation_method=None, feature_names=None,
+                 discrete_time=False, n_jobs=1):
+        if optimizer is None:
+            self.optimizer = STLSQ()
+        else:
+            self.optimizer = optimizer
+        if feature_library is None:
+            self.feature_library = PolynomialFeatures()
+        else:
+            self.feature_library = feature_library
+        if differentiation_method is None:
+            self.differentiation_method = FiniteDifference()
+        else:
+            self.differentiation_method = differentiation_method
         self.feature_names = feature_names
         self.discrete_time = discrete_time
         self.n_jobs = n_jobs