Create properties for getting sensors

Author: briandesilva

pysensors/classification/_sspoc.py

@@ -401,6 +401,24 @@ def update_n_basis_modes(self, n_basis_modes, xy, **fit_kws):
 
     @property
     def selected_sensors(self):
-        """Get the selected sensors."""
+        """
+        Get the indices of the selected sensors.
+
+        Returns
+        -------
+        sensors: numpy array, shape (n_sensors,)
+            Indices of the selected sensors.
+        """
         check_is_fitted(self, "sparse_sensors_")
         return self.sparse_sensors_
+
+    def get_selected_sensors(self):
+        """
+        Convenience function for getting indices of the selected sensors.
+
+        Returns
+        -------
+        sensors: numpy array, shape (n_sensors,)
+            Indices of the selected sensors.
+        """
+        return self.selected_sensors

pysensors/pysensors.py

@@ -212,11 +212,38 @@ def get_selected_sensors(self):
         check_is_fitted(self, "ranked_sensors_")
         return self.ranked_sensors_[: self.n_sensors]
 
+    @property
+    def selected_sensors(self):
+        """
+        Get the indices of the sensors chosen by the model.
+
+        Returns
+        -------
+        sensors: numpy array, shape (n_sensors,)
+            Indices of the sensors chosen by the model
+            (i.e. the sensor locations) ranked in descending order
+            of importance.
+        """
+        return self.get_selected_sensors()
+
     def get_all_sensors(self):
         """
         Get a ranked list consisting of all the sensors.
         The sensors are given in descending order of importance.
 
+        Returns
+        -------
+        sensors: numpy array, shape (n_features,)
+            Indices of sensors in descending order of importance.
+        """
+        return self.all_sensors
+
+    @property
+    def all_sensors(self):
+        """
+        Get a ranked list consisting of all the sensors.
+        The sensors are given in descending order of importance.
+
         Returns
         -------
         sensors: numpy array, shape (n_features,)
@@ -249,6 +276,20 @@ def set_number_of_sensors(self, n_sensors):
         else:
             self.n_sensors = n_sensors
 
+    def set_n_sensors(self, n_sensors):
+        """
+        A convenience function accomplishing the same thing as
+        :meth:`set_number_of_sensors`.
+        Set ``n_sensors``, the number of sensors to be used for prediction.
+
+        Parameters
+        ----------
+        n_sensors: int
+            The number of sensors. Must be a positive integer.
+            Cannot exceed the number of available sensors (n_features).
+        """
+        self.set_number_of_sensors(n_sensors)
+
     def update_n_basis_modes(self, n_basis_modes, x=None):
         """
         Re-fit the SensorSelector object using a different value of
@@ -418,6 +459,10 @@ def score(x, y):
         return error
 
     def _validate_n_sensors(self):
+        """
+        Check that number of sensors does not exceed the maximimum number
+        allowed by the chosen basis.
+        """
         check_is_fitted(self, "basis_matrix_")
 
         # Maximum number of sensors (= dimension of basis vectors)

test/classification/test_sspoc.py

@@ -263,3 +263,11 @@ def test_update_n_basis_modes_unfit_basis(basis, data_binary_classification):
     model.update_n_basis_modes(n_basis_modes, (x, y), quiet=True)
 
     assert model.basis_matrix_inverse_.shape[0] == n_basis_modes
+
+
+def test_sspoc_selector_equivalence(data_multiclass_classification):
+    x, y, _ = data_multiclass_classification
+
+    model = SSPOC().fit(x, y)
+
+    np.testing.assert_array_equal(model.get_selected_sensors(), model.selected_sensors)

test/test_pysensors.py

@@ -255,3 +255,11 @@ def test_ccqr_integration(data_random):
     model = SensorSelector(optimizer=optimizer).fit(data)
 
     check_is_fitted(model)
+
+
+def test_sensor_selector_properties(data_random):
+    data = data_random
+    model = SensorSelector().fit(data)
+
+    assert all(model.get_all_sensors() == model.all_sensors)
+    assert all(model.get_selected_sensors() == model.selected_sensors)