CLN: Remove deprecated ensembling code

Removing/fixing tests will happen in a separate commit
dynamicslab · Jun 30, 2023 · 819893f · 819893f
1 parent ac61b4a
commit 819893f
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Showing 12 changed files with 10 additions and 290 deletions.
diff --git a/pysindy/feature_library/base.py b/pysindy/feature_library/base.py
@@ -23,29 +23,8 @@ 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)
 
@@ -143,29 +122,6 @@ 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])
 
@@ -220,14 +176,6 @@ 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
     ----------
     libraries_ : list of libraries
@@ -258,12 +206,8 @@ 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
@@ -320,8 +264,6 @@ 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):
@@ -356,14 +298,6 @@ 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).
-
-    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
@@ -399,13 +333,9 @@ class TensoredLibrary(BaseFeatureLibrary):
     def __init__(
         self,
         libraries: list,
-        library_ensemble=False,
         inputs_per_library=None,
-        ensemble_indices=[0],
     ):
-        super(TensoredLibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         self.libraries_ = libraries
         self.inputs_per_library_ = inputs_per_library
 
@@ -534,8 +464,6 @@ 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):

diff --git a/pysindy/feature_library/custom_library.py b/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
diff --git a/pysindy/feature_library/fourier_library.py b/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
diff --git a/pysindy/feature_library/generalized_library.py b/pysindy/feature_library/generalized_library.py
@@ -38,14 +38,6 @@ class GeneralizedLibrary(BaseFeatureLibrary):
         subsets of the input variables. Note that this must be specified for
         all the individual feature libraries.
 
-    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
     ----------
     libraries_ : list of libraries
@@ -96,13 +88,9 @@ def __init__(
         libraries: list,
         tensor_array=None,
         inputs_per_library=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
 
@@ -262,8 +250,6 @@ 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):

diff --git a/pysindy/feature_library/identity_library.py b/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
diff --git a/pysindy/feature_library/parameterized_library.py b/pysindy/feature_library/parameterized_library.py
@@ -70,8 +70,6 @@ def __init__(
         feature_library=PolynomialLibrary(),
         num_parameters=3,
         num_features=3,
-        library_ensemble=False,
-        ensemble_indices=[0],
     ):
         if not isinstance(feature_library, BaseFeatureLibrary) or not isinstance(
             parameter_library, BaseFeatureLibrary
@@ -101,8 +99,6 @@ def __init__(
             tensor_array=tensor_array,
             exclude_libraries=[0, 1],
             inputs_per_library=inputs_per_libraries,
-            library_ensemble=library_ensemble,
-            ensemble_indices=ensemble_indices,
         )
 
     def calc_trajectory(self, diff_method, x, t):

diff --git a/pysindy/feature_library/pde_library.py b/pysindy/feature_library/pde_library.py
@@ -63,13 +63,6 @@ class PDELibrary(BaseFeatureLibrary):
         will consist of only pure no-derivative terms and pure derivative
         terms, with no mixed terms.
 
-    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.
-
     implicit_terms : boolean
         Flag to indicate if SINDy-PI (temporal derivatives) is being used
         for the right-hand side of the SINDy fit.
@@ -118,18 +111,14 @@ def __init__(
         function_names=None,
         include_bias=False,
         include_interaction=True,
-        library_ensemble=False,
-        ensemble_indices=[0],
         implicit_terms=False,
         multiindices=None,
         differentiation_method=FiniteDifference,
         diff_kwargs={},
         is_uniform=None,
         periodic=None,
     ):
-        super(PDELibrary, self).__init__(
-            library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
+        super().__init__()
         self.functions = library_functions
         self.derivative_order = derivative_order
         self.function_names = function_names
@@ -472,8 +461,6 @@ def transform(self, x_full):
                 library_idx += n_library_terms * self.num_derivatives * n_features
             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_spatial_grid(self):

diff --git a/pysindy/feature_library/polynomial_library.py b/pysindy/feature_library/polynomial_library.py
@@ -44,13 +44,6 @@ class PolynomialLibrary(PolynomialFeatures, BaseFeatureLibrary):
         Order of output array in the dense case. 'F' order is faster to
         compute, but may slow down subsequent estimators.
 
-    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
     ----------
     powers_ : array, shape (n_output_features, n_input_features)
@@ -71,18 +64,13 @@ def __init__(
         interaction_only=False,
         include_bias=True,
         order="C",
-        library_ensemble=False,
-        ensemble_indices=[0],
     ):
         super(PolynomialLibrary, self).__init__(
             degree=degree,
             interaction_only=interaction_only,
             include_bias=include_bias,
             order=order,
         )
-        BaseFeatureLibrary.__init__(
-            self, library_ensemble=library_ensemble, ensemble_indices=ensemble_indices
-        )
         if degree < 0 or not isinstance(degree, int):
             raise ValueError("degree must be a nonnegative integer")
         if (not include_interaction) and interaction_only:
@@ -284,6 +272,4 @@ def transform(self, x_full):
                     for i, comb in enumerate(combinations):
                         xp[..., i] = x[..., comb].prod(-1)
             xp_full = xp_full + [xp]
-        if self.library_ensemble:
-            xp_full = self._ensemble(xp_full)
         return xp_full