Merge branch 'master' into simpler-inputs

docs/conf.py

@@ -61,7 +61,8 @@
 html_sourcelink_suffix = ""
 
 intersphinx_mapping = {
-    "derivative": ("https://derivative.readthedocs.io/en/latest/", None)
+    "derivative": ("https://derivative.readthedocs.io/en/latest/", None),
+    "sklearn": ("https://scikit-learn.org/stable/", None),
 }
 
 # -- Extensions to the  Napoleon GoogleDocstring class ---------------------

pysindy/feature_library/base.py

@@ -25,29 +25,8 @@ class BaseFeatureLibrary(TransformerMixin):
 
     Forces subclasses to implement ``fit``, ``transform``,
     and ``get_feature_names`` methods.
-
-    Parameters
-    ----------
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library)
-
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library.
     """
 
-    def __init__(self, library_ensemble=None, ensemble_indices=[0]):
-        if library_ensemble is not None:
-            warnings.warn(
-                "Library ensembling is no longer performed by feature libraries.  Use "
-                "EnsemblingOptimizer to fit an ensemble model.",
-                DeprecationWarning,
-            )
-        self.library_ensemble = library_ensemble
-        if np.any(np.asarray(ensemble_indices) < 0):
-            raise ValueError("Library ensemble indices must be 0 or positive integers.")
-        self.ensemble_indices = ensemble_indices
-
     def validate_input(self, x, *args, **kwargs):
         return validate_no_reshape(x, *args, **kwargs)
 
@@ -145,29 +124,6 @@ def get_feature_names(self, input_features=None):
         """
         raise NotImplementedError
 
-    def _ensemble(self, xp):
-        """
-        If library bagging, return xp without
-        the terms at ensemble_indices
-        """
-        warnings.warn(
-            "Library ensembling is no longer performed by feature libraries.  Use "
-            "EnsemblingOptimizer to fit an ensemble model.",
-            UserWarning,
-        )
-
-        if self.library_ensemble:
-            if self.n_output_features_ <= len(self.ensemble_indices):
-                raise ValueError(
-                    "Error: you are trying to chop more library terms "
-                    "than are available to remove!"
-                )
-            inds = range(self.n_output_features_)
-            inds = np.delete(inds, self.ensemble_indices)
-            return [x[..., inds] for x in xp]
-        else:
-            return xp
-
     def __add__(self, other):
         return ConcatLibrary([self, other])
 
@@ -222,14 +178,6 @@ class ConcatLibrary(BaseFeatureLibrary):
     libraries : list of libraries
         Library instances to be applied to the input matrix.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library).
-
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library. For instance, if
-        ensemble_indices = [0], it chops off the first column of the library.
-
     Attributes
     ----------
     n_features_in_ : int
@@ -257,12 +205,8 @@ class ConcatLibrary(BaseFeatureLibrary):
     def __init__(
         self,
         libraries: list,
-        library_ensemble=False,
-        ensemble_indices=[0],
     ):
-        super(ConcatLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         self.libraries = libraries
 
     @x_sequence_or_item
@@ -319,8 +263,6 @@ def transform(self, x_full):
 
             xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full
 
     def get_feature_names(self, input_features=None):
@@ -355,20 +297,12 @@ class TensoredLibrary(BaseFeatureLibrary):
     libraries : list of libraries
         Library instances to be applied to the input matrix.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library).
-
     inputs_per_library_ : Sequence of Sequences of ints (default None)
         list that specifies which input indexes should be passed as
         inputs for each of the individual feature libraries.
         length must equal the number of feature libraries.  Default is
         that all inputs are used for every library.
 
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library. For instance, if
-        ensemble_indices = [0], it chops off the first column of the library.
-
     Attributes
     ----------
     libraries_ : list of libraries
@@ -399,14 +333,10 @@ class TensoredLibrary(BaseFeatureLibrary):
     def __init__(
         self,
         libraries: list,
-        library_ensemble=False,
         inputs_per_library: Optional[Sequence[Sequence[int]]] = None,
-        ensemble_indices=[0],
     ):
-        super(TensoredLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
-        self.libraries_ = libraries
+        super().__init__()
+        self.libraries = libraries
         self.inputs_per_library = inputs_per_library
 
     def _combinations(self, lib_i, lib_j):
@@ -470,13 +400,13 @@ def fit(self, x_full, y=None):
         # If parameter is not set, use all the inputs
         if self.inputs_per_library is None:
             self.inputs_per_library = list(
-                repeat(list(range(n_features)), len(self.libraries_))
+                repeat(list(range(n_features)), len(self.libraries))
             )
 
         # First fit all libs provided below
         fitted_libs = [
             lib.fit([x[..., _unique(self.inputs_per_library[i])] for x in x_full], y)
-            for i, lib in enumerate(self.libraries_)
+            for i, lib in enumerate(self.libraries)
         ]
 
         # Calculate the sum of output features
@@ -486,7 +416,7 @@ def fit(self, x_full, y=None):
             self.n_output_features_ *= osize
 
         # Save fitted libs
-        self.libraries_ = fitted_libs
+        self.libraries = fitted_libs
 
         return self
 
@@ -511,17 +441,17 @@ def transform(self, x_full):
         xp_full = []
         for x in x_full:
             xp = []
-            for i in range(len(self.libraries_)):
-                lib_i = self.libraries_[i]
+            for i in range(len(self.libraries)):
+                lib_i = self.libraries[i]
                 if self.inputs_per_library is None:
                     xp_i = lib_i.transform([x])[0]
                 else:
                     xp_i = lib_i.transform(
                         [x[..., _unique(self.inputs_per_library[i])]]
                     )[0]
 
-                for j in range(i + 1, len(self.libraries_)):
-                    lib_j = self.libraries_[j]
+                for j in range(i + 1, len(self.libraries)):
+                    lib_j = self.libraries[j]
                     xp_j = lib_j.transform(
                         [x[..., _unique(self.inputs_per_library[j])]]
                     )[0]
@@ -531,8 +461,6 @@ def transform(self, x_full):
             xp = np.concatenate(xp, axis=xp[0].ax_coord)
             xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full
 
     def get_feature_names(self, input_features=None):
@@ -549,8 +477,8 @@ def get_feature_names(self, input_features=None):
         output_feature_names : list of string, length n_output_features
         """
         feature_names = list()
-        for i in range(len(self.libraries_)):
-            lib_i = self.libraries_[i]
+        for i in range(len(self.libraries)):
+            lib_i = self.libraries[i]
             if input_features is None:
                 input_features_i = [
                     "x%d" % k for k in _unique(self.inputs_per_library[i])
@@ -560,8 +488,8 @@ def get_feature_names(self, input_features=None):
                     _unique(self.inputs_per_library[i])
                 ].tolist()
             lib_i_feat_names = lib_i.get_feature_names(input_features_i)
-            for j in range(i + 1, len(self.libraries_)):
-                lib_j = self.libraries_[j]
+            for j in range(i + 1, len(self.libraries)):
+                lib_j = self.libraries[j]
                 if input_features is None:
                     input_features_j = [
                         "x%d" % k for k in _unique(self.inputs_per_library[j])
@@ -577,7 +505,7 @@ def get_feature_names(self, input_features=None):
         return feature_names
 
     def calc_trajectory(self, diff_method, x, t):
-        return self.libraries_[0].calc_trajectory(diff_method, x, t)
+        return self.libraries[0].calc_trajectory(diff_method, x, t)
 
 
 def _unique(s: Sequence) -> Sequence:

pysindy/feature_library/custom_library.py

@@ -41,13 +41,6 @@ class CustomLibrary(BaseFeatureLibrary):
         will be included.
         If False, all combinations will be included.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library)
-
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library.
-
     include_bias : boolean, optional (default False)
         If True (default), then include a bias column, the feature in which
         all polynomial powers are zero (i.e. a column of ones - acts as an
@@ -92,13 +85,9 @@ def __init__(
         library_functions,
         function_names=None,
         interaction_only=True,
-        library_ensemble=False,
-        ensemble_indices=[0],
         include_bias=False,
     ):
-        super(CustomLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         self.functions = library_functions
         self.function_names = function_names
         if function_names and (
@@ -219,6 +208,4 @@ def transform(self, x_full):
 
             xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full

pysindy/feature_library/fourier_library.py

@@ -25,13 +25,6 @@ class FourierLibrary(BaseFeatureLibrary):
     include_cos : boolean, optional (default True)
         If True, include cosine terms in the library.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library)
-
-    ensemble_indices : integer array, optional (default 0)
-        The indices to use for ensembling the library.
-
     Attributes
     ----------
     n_features_in_ : int
@@ -61,12 +54,8 @@ def __init__(
         n_frequencies=1,
         include_sin=True,
         include_cos=True,
-        library_ensemble=False,
-        ensemble_indices=[0],
     ):
-        super(FourierLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         if not (include_sin or include_cos):
             raise ValueError("include_sin and include_cos cannot both be False")
         if n_frequencies < 1 or not isinstance(n_frequencies, int):
@@ -164,6 +153,4 @@ def transform(self, x_full):
                         idx += 1
             xp = AxesArray(xp, comprehend_axes(xp))
             xp_full.append(xp)
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full

pysindy/feature_library/generalized_library.py

@@ -42,14 +42,6 @@ class GeneralizedLibrary(BaseFeatureLibrary):
         length must equal the number of feature libraries.  Default is
         that all inputs are used for every library.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library).
-
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library. For instance, if
-        ensemble_indices = [0], it chops off the first column of the library.
-
     Attributes
     ----------
     self.libraries_full_: list[BaseFeatureLibrary]
@@ -82,13 +74,9 @@ def __init__(
         libraries: list,
         tensor_array=None,
         inputs_per_library: Optional[Sequence[Sequence[int]]] = None,
-        library_ensemble=False,
-        ensemble_indices=[0],
         exclude_libraries=[],
     ):
-        super(GeneralizedLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         if len(libraries) > 0:
             self.libraries = libraries
 
@@ -252,8 +240,6 @@ def transform(self, x_full):
 
             xp = AxesArray(np.concatenate(xps, axis=xps[0].ax_coord), xps[0].__dict__)
             xp_full = xp_full + [xp]
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full
 
     def get_feature_names(self, input_features=None):

pysindy/feature_library/identity_library.py

@@ -18,13 +18,6 @@ class IdentityLibrary(BaseFeatureLibrary):
         The total number of output features. The number of output features
         is equal to the number of input features.
 
-    library_ensemble : boolean, optional (default False)
-        Whether or not to use library bagging (regress on subset of the
-        candidate terms in the library)
-
-    ensemble_indices : integer array, optional (default [0])
-        The indices to use for ensembling the library.
-
     Examples
     --------
     >>> import numpy as np
@@ -39,15 +32,6 @@ class IdentityLibrary(BaseFeatureLibrary):
     ['x0', 'x1']
     """
 
-    def __init__(
-        self,
-        library_ensemble=False,
-        ensemble_indices=[0],
-    ):
-        super(IdentityLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
-
     def get_feature_names(self, input_features=None):
         """
         Return feature names for output features
@@ -114,6 +98,4 @@ def transform(self, x_full):
                 raise ValueError("x shape does not match training shape")
 
             xp_full = xp_full + [x.copy()]
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full

pysindy/feature_library/parameterized_library.py

@@ -52,8 +52,6 @@ def __init__(
         feature_library: BaseFeatureLibrary = PolynomialLibrary(),
         num_parameters: int = 3,
         num_features: int = 3,
-        library_ensemble=False,
-        ensemble_indices=[0],
     ):
         if num_parameters <= 0 or num_features <= 0:
             raise ValueError("Both num_parameter and num_feature must be positive.")
@@ -67,8 +65,6 @@ def __init__(
             tensor_array=[[1, 1]],
             exclude_libraries=[0, 1],
             inputs_per_library=inputs_per_library,
-            library_ensemble=library_ensemble,
-            ensemble_indices=ensemble_indices,
         )
 
     def calc_trajectory(self, diff_method, x, t):