Merge branch 'Jimmy-adding-customBasis' into niharika-krithika-mohammad-regionConstraints

Author: Jimmy-INL

examples/basis_comparison-Copy1.ipynb

@@ -1,6 +1,6 @@
 from ._identity import Identity
 from ._random_projection import RandomProjection
 from ._svd import SVD
+from ._custom import Custom
 
-
-__all__ = ["Identity", "SVD", "RandomProjection"]
+__all__ = ["Identity", "SVD", "RandomProjection","Custom"]

pysensors/basis/__init__.py

@@ -43,9 +43,9 @@ def matrix_representation(self, n_basis_modes=None, copy=False):
         n_basis_modes = self._validate_input(n_basis_modes)
 
         if copy:
-            return self.basis_matrix_[:, :n_basis_modes].copy()
+            return self.basis_matrix_[:, :n_basis_modes].copy()#self.original_data @
         else:
-            return self.basis_matrix_[:, :n_basis_modes]
+            return self.basis_matrix_[:, :n_basis_modes]#self.original_data @
 
     def _validate_input(self, n_basis_modes):
         """

pysensors/basis/_base.py

@@ -0,0 +1,92 @@
+"""
+custom mode basis class.
+"""
+from ._base import InvertibleBasis
+from ._base import MatrixMixin
+
+class Custom(InvertibleBasis, MatrixMixin):
+    """
+<<<<<<< HEAD
+    Use a custom transformation to maps input features to
+=======
+    Generate a custom transformation which maps input features to
+>>>>>>> 1ccd23fafed0ca39dac6cde204efa228d133df1c
+    custom modes.
+
+    Assumes the data has already been centered (to have mean 0).
+
+    Parameters
+    ----------
+    n_basis_modes : int, optional (default 10)
+        Number of basis modes to retain. Cannot be larger than
+        the number of features ``n_features``, or the number of examples
+        ``n_examples``.
+    U: The custom basis matrix
+
+    Attributes
+    ----------
+    basis_matrix_ : numpy ndarray, shape (n_features, n_basis_modes)
+        The top n_basis_modes left singular vectors of the training data.
+
+    """
+
+    def __init__(self, U, n_basis_modes=10, **kwargs):
+        if isinstance(n_basis_modes, int) and n_basis_modes > 0:
+            super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs
+            self._n_basis_modes = n_basis_modes
+            self.custom_basis_ = U
+        else:
+            raise ValueError("n_basis_modes must be a positive integer.")
+
+    def fit(self, X):
+        """
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            The training data.
+
+        Returns
+        -------
+        self : instance
+        """
+        # self.basis_matrix_ = self.custom_basis_[:,: self.n_basis_modes] @ self.custom_basis_[:,: self.n_basis_modes].T @ X[: self.n_basis_modes, :].T.copy()
+        # self.basis_matrix_ = self.custom_basis_ @ self.custom_basis_.T @ X[: self.n_basis_modes, :].T.copy()
+        self.basis_matrix_ = self.custom_basis_[:,:self.n_basis_modes]
+        # self.basis_matrix_ = (X @ self.custom_basis_[:,:self.n_basis_modes] @ self.custom_basis_[:,:self.n_basis_modes].T)[:self.n_basis_modes,:].T
+
+        # self.basis_matrix_ = ((X @ self.custom_basis_).T)[:,:self.n_basis_modes]
+        # self.basis_matrix_ = ((X @ self.custom_basis_ @ self.custom_basis_.T).T)[:,:self.n_basis_modes]
+        return self
+
+    def matrix_inverse(self, n_basis_modes=None):
+        """
+        Get the inverse matrix mapping from measurement space to
+        coordinates with respect to the basis.
+
+        Note that this is not the inverse of the matrix returned by
+        ``self.matrix_representation``. It is the (pseudo) inverse of
+        the matrix whose columns are the basis modes.
+
+        Parameters
+        ----------
+        n_basis_modes : positive int, optional (default None)
+            Number of basis modes to be used to compute inverse.
+
+        Returns
+        -------
+        B : numpy ndarray, shape (n_basis_modes, n_features)
+            The inverse matrix.
+        """
+        n_basis_modes = self._validate_input(n_basis_modes)
+
+        return self.basis_matrix_[:, :n_basis_modes].T
+
+    @property
+    def n_basis_modes(self):
+        """Number of basis modes."""
+        return self._n_basis_modes
+
+    @n_basis_modes.setter
+    def n_basis_modes(self, n_basis_modes):
+        self._n_basis_modes = n_basis_modes
+        self.n_components = n_basis_modes

pysensors/basis/_custom.py

@@ -6,6 +6,7 @@
 from warnings import warn
 
 from numpy import identity
+import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_array
 
@@ -52,7 +53,8 @@ def fit(self, X):
         -------
         self : instance
         """
-
+        # Store original data
+        self.original_data = X
         # Note that we take a transpose here, so columns correspond to examples
         if self.n_basis_modes is None:
             self.basis_matrix_ = check_array(X).T.copy()
@@ -65,10 +67,10 @@ def fit(self, X):
                     )
                 )
 
-            self.basis_matrix_ = check_array(X)[: self.n_basis_modes, :].T.copy()
+            self.basis_matrix_ = np.eye(X.shape[1])[:,:self.n_basis_modes] #check_array(X)[: self.n_basis_modes, :].T.copy()
 
-            if self.n_basis_modes < X.shape[0]:
-                warn(f"Only the first {self.n_basis_modes} examples were retained.")
+            # if self.n_basis_modes < X.shape[0]:
+            #     warn(f"Only the first {self.n_basis_modes} examples were retained.")
         return self
 
     def matrix_inverse(self, n_basis_modes=None):