Fixing Some of the issues before merge possible

Author: Niharika Karnik

examples/basis_comparison.ipynb

@@ -32,39 +32,19 @@ def __init__(self, U, n_basis_modes=10, **kwargs):
         kwargs : Not defined but added to remain consistent with prior basis functions.
         """
         if isinstance(n_basis_modes, int) and n_basis_modes > 0:
-            super(Custom, self).__init__()  # n_components=n_basis_modes, **kwargs
+            super(Custom, self).__init__()
             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):
+    def fit(self):
         """
-        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):

examples/cross_validation.ipynb

@@ -90,7 +90,6 @@ def matrix_inverse(self, n_basis_modes=None):
         B : numpy ndarray, shape (n_features, n_features)
             The inverse matrix. In this case B is the identity matrix.
         """
-        # TODO: validate this
         n_basis_modes = self._validate_input(n_basis_modes)
 
         return identity(self.basis_matrix_.shape[0])

examples/functional_constraints_class.ipynb

@@ -41,8 +41,6 @@ def fit(self, basis_matrix, **optimizer_kws):
         -------
 
         """
-
-        # TODO: implement checks on basis_matrix
         _, _, self.pivots_ = qr(basis_matrix.conj().T, pivoting=True, **optimizer_kws)
 
         return self

examples/pysensors_overview.ipynb

@@ -6,7 +6,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from ..basis import Identity
-from ..optimizers import CCQR, GQR, QR
+from ..optimizers import CCQR, QR
 from ..utils import validate_input
 
 INT_DTYPES = (int, np.int64, np.int32, np.int16, np.int8)
@@ -504,12 +504,8 @@ def _validate_n_sensors(self):
                 )
             )
 
-        # If n_sensors exceeds n_samples, the cost-constrained QR algorithm may
-        # place sensors in constrained areas.
         if (
             isinstance(self.optimizer, CCQR)
-            or isinstance(self.optimizer, QR)
-            or isinstance(self.optimizer, GQR)
         ) and self.n_sensors > self.basis_matrix_.shape[1]:
             warnings.warn(
                 "Number of sensors exceeds number of samples, which may cause CCQR to "

pysensors/basis/_custom.py

@@ -4,8 +4,6 @@
 
 import numpy as np
 
-# from sklearn.utils.validation import check_array
-
 
 def validate_input(x, sensors=None):
     """
@@ -17,12 +15,9 @@ def validate_input(x, sensors=None):
     x: numpy ndarray, shape [n_features,] or [n_examples, n_features]
         Data to be validated.
     """
-    # TODO: implement more checks
     if not isinstance(x, np.ndarray):
         raise ValueError("x must be a numpy array")
 
-    # check_array(x)
-
     if sensors is not None:
         n_features = len(x) if np.ndim(x) == 1 else x.shape[1]
         if len(sensors) != n_features:

pysensors/basis/_identity.py

@@ -46,7 +46,6 @@ def get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sens
     if not isinstance(nx, int) or not isinstance(ny, int):
         raise ValueError("nx and ny must be integers")
     n_features = len(all_sensors)
-    # image_size = int(np.sqrt(n_features))
     a = np.unravel_index(all_sensors, (nx, ny))
     constrained_sensorsx = []
     constrained_sensorsy = []
@@ -91,9 +90,6 @@ def get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max, df, **
         Name of the column in dataframe to be plotted on the X axis.
     Y-axis : string,
         Name of the column in dataframe to be plotted on the Y axis.
-    Field : string,
-        Name of the column in dataframe to be plotted as a contour map.
-        ## TODO: Field is not used here @Niha please see this.
     Returns
     -------
     idx_constrained : np.darray, shape [No. of constrained locations], array which
@@ -648,10 +644,10 @@ def annotate_sensors(
         n_samples, n_features = self.data.shape
         n_sensors = len(sensors)
         constrained = sensors[
-            np.where(np.in1d(all_sensors[:n_sensors], sensors) is False)[0]
+            np.where(np.isin(all_sensors[:n_sensors], sensors) is False)[0]
         ]
         unconstrained = sensors[
-            np.where(np.in1d(all_sensors[:n_sensors], sensors) is True)[0]
+            np.where(np.isin(all_sensors[:n_sensors], sensors) is True)[0]
         ]
         if isinstance(self.data, np.ndarray):
             xTop = np.mod(sensors, np.sqrt(n_features))
@@ -1169,7 +1165,6 @@ def draw(self, ax, **kwargs):
         )
         ax.add_patch(c)
         ax.autoscale_view()
-        # ax.axes.set_aspect('equal')
 
     def constraint_function(self, coords):
         """