Rename sensorselector to sspor in tests and source code

Author: briandesilva

pysensors/__init__.py

@@ -7,14 +7,17 @@
     pass
 
 
-from .pysensors import SensorSelector
+from .classification import SSPOC
+from .reconstruction import SSPOR
 
 __all__ = [
     # Modules:
     "basis",
     "classification",
+    "reconstruction",
     "optimizers",
     "utils",
     # Non-modules:
-    "SensorSelector",
+    "SSPOR",
+    "SSPOC",
 ]

pysensors/pysensors.py pysensors/reconstruction/_sspor.pypysensors/pysensors.py renamed to pysensors/reconstruction/_sspor.py

@@ -1,6 +1,3 @@
-"""
-SensorSelector object definition.
-"""
 import warnings
 
 import numpy as np
@@ -9,20 +6,21 @@
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_is_fitted
 
-from .basis import Identity
-from .optimizers import QR
-from .utils import validate_input
+from ..basis import Identity
+from ..optimizers import QR
+from ..utils import validate_input
 
 
 INT_DTYPES = (int, np.int64, np.int32, np.int16, np.int8)
 
 
-class SensorSelector(BaseEstimator):
+class SSPOR(BaseEstimator):
     """
-    A model for selecting the best sensor locations for state reconstruction.
+    Sparse Sensor Placement Optimization for Reconstruction:
+    a model for selecting the best sensor locations for state reconstruction.
 
     Given a basis in which to represent the state (e.g. PCA modes) along with
-    measurement data, a :class:`SensorSelector` instance produces a list of
+    measurement data, a :class:`SSPOR` instance produces a list of
     sensor locations (a permutation of the numbers 0, 1, ...,
     :code:`n_input_features` - 1) ranked in descending order of importance.
     One can then select the top k sensors and take future measurements at
@@ -40,9 +38,9 @@ class SensorSelector(BaseEstimator):
 
     n_sensors: int, optional (default n_input_features)
         Number of sensors to select. Note that
-        ``s = SensorSelector(n_sensors=10); s.fit(x)``
+        ``s = SSPOR(n_sensors=10); s.fit(x)``
         is equivalent to
-        ``s = SensorSelector(); s.fit(x); s.set_number_of_sensors(10)``.
+        ``s = SSPOR(); s.fit(x); s.set_number_of_sensors(10)``.
 
     Attributes
     ----------
@@ -58,14 +56,14 @@ class SensorSelector(BaseEstimator):
     Examples
     --------
     >>> import numpy as np
-    >>> from pysensors import SensorSelector
+    >>> from pysensors import SSPOR
     >>>
     >>> x = np.linspace(0, 1, 501)
     >>> monomials = np.vander(x, 15).T
     >>>
-    >>> model = SensorSelector(n_sensors=5)
+    >>> model = SSPOR(n_sensors=5)
     >>> model.fit(monomials)
-    SensorSelector(basis=Identity(n_basis_modes=15), n_sensors=5, optimizer=QR())
+    SSPOR(basis=Identity(n_basis_modes=15), n_sensors=5, optimizer=QR())
     >>> print(model.selected_sensors)
     [500 377   0 460 185]
     >>> print(x[model.selected_sensors])
@@ -96,7 +94,7 @@ def __init__(self, basis=None, optimizer=None, n_sensors=None):
 
     def fit(self, x, quiet=False, prefit_basis=False, seed=None, **optimizer_kws):
         """
-        Fit the SensorSelector model, determining which sensors are relevant.
+        Fit the SSPOR model, determining which sensors are relevant.
 
         Parameters
         ----------
@@ -124,7 +122,7 @@ def fit(self, x, quiet=False, prefit_basis=False, seed=None, **optimizer_kws):
 
         Returns
         -------
-        self: a fitted :class:`SensorSelector` instance
+        self: a fitted :class:`SSPOR` instance
         """
 
         # Fit basis functions to data
@@ -311,7 +309,7 @@ def set_n_sensors(self, n_sensors):
 
     def update_n_basis_modes(self, n_basis_modes, x=None):
         """
-        Re-fit the SensorSelector object using a different value of
+        Re-fit the :class:`SSPOR` object using a different value of
         ``n_basis_modes``.
 
         This method allows one to relearn sensor locations for a

tests/test_pysensors.py tests/reconstruction/test_sspor.pytests/test_pysensors.py renamed to tests/reconstruction/test_sspor.py

@@ -1,5 +1,5 @@
 """
-Unit tests for SensorSelector class (and any other classes implemented
+Unit tests for SSPOR class (and any other classes implemented
 in pysensors.py).
 
 Note: all tests should be encapsulated in functions whose
@@ -21,16 +21,16 @@
 from sklearn.exceptions import NotFittedError
 from sklearn.utils.validation import check_is_fitted
 
-from pysensors import SensorSelector
 from pysensors.basis import Identity
 from pysensors.basis import RandomProjection
 from pysensors.basis import SVD
 from pysensors.optimizers import CCQR
+from pysensors.reconstruction import SSPOR
 
 
 def test_not_fitted(data_vandermonde):
     x = data_vandermonde
-    model = SensorSelector()
+    model = SSPOR()
 
     # Should not be able to call any of these methods before fitting
     with pytest.raises(NotFittedError):
@@ -51,7 +51,7 @@ def test_set_number_of_sensors(data_vandermonde):
     x = data_vandermonde
     max_sensors = x.shape[1]
 
-    model = SensorSelector()
+    model = SSPOR()
     model.fit(x)
 
     with pytest.raises(ValueError):
@@ -75,7 +75,7 @@ def test_get_all_sensors(data):
     x = data
     max_sensors = x.shape[1]
 
-    model = SensorSelector()
+    model = SSPOR()
     model.fit(x)
     assert len(model.get_all_sensors()) == max_sensors
 
@@ -85,7 +85,7 @@ def test_get_all_sensors(data):
 )
 def test_basis_compatibility(data_vandermonde, basis):
     x = data_vandermonde
-    model = SensorSelector(basis=basis)
+    model = SSPOR(basis=basis)
     model.fit(x)
     check_is_fitted(model)
 
@@ -94,17 +94,17 @@ def test_n_sensors(data_random):
 
     # Check for bad inputs
     with pytest.raises(ValueError):
-        model = SensorSelector(n_sensors=0)
+        model = SSPOR(n_sensors=0)
     with pytest.raises(ValueError):
-        model = SensorSelector(n_sensors=5.4)
+        model = SSPOR(n_sensors=5.4)
     with pytest.raises(ValueError):
-        model = SensorSelector(n_sensors="1")
+        model = SSPOR(n_sensors="1")
     with pytest.raises(ValueError):
-        model = SensorSelector(n_sensors=[1])
+        model = SSPOR(n_sensors=[1])
 
     n_sensors = 5
     x = data_random
-    model = SensorSelector(n_sensors=n_sensors)
+    model = SSPOR(n_sensors=n_sensors)
     model.fit(x)
 
     assert len(model.get_selected_sensors()) == n_sensors
@@ -114,7 +114,7 @@ def test_predict(data_random):
     data = data_random
 
     n_sensors = 5
-    model = SensorSelector(n_sensors=n_sensors)
+    model = SSPOR(n_sensors=n_sensors)
     model.fit(data)
 
     # Wrong size input for predict
@@ -130,7 +130,7 @@ def test_predict(data_random):
 def test_square_predict(data_random_square):
     data = data_random_square
 
-    model = SensorSelector()
+    model = SSPOR()
     model.fit(data)
     sensors = model.get_selected_sensors()
     assert data.shape == model.predict(data[:, sensors]).shape
@@ -140,7 +140,7 @@ def test_predict_accuracy(data_vandermonde_testing):
     # Polynomials up to degree 10 on [0, 1]
     data, x_test = data_vandermonde_testing
 
-    model = SensorSelector()
+    model = SSPOR()
     model.fit(data, seed=1)
     model.set_number_of_sensors(8)
     sensors = model.get_selected_sensors()
@@ -154,7 +154,7 @@ def test_predict_accuracy(data_vandermonde_testing):
 def test_reconstruction_error(data_vandermonde_testing):
     data, x_test = data_vandermonde_testing
 
-    model = SensorSelector(n_sensors=3)
+    model = SSPOR(n_sensors=3)
     model.fit(data)
 
     assert len(model.reconstruction_error(x_test)) == min(
@@ -168,14 +168,14 @@ def test_reconstruction_error(data_vandermonde_testing):
 def test_score(data_vandermonde):
     data = data_vandermonde
 
-    weak_model = SensorSelector(n_sensors=3)
+    weak_model = SSPOR(n_sensors=3)
     weak_model.fit(data)
 
     # You must pass in data with as many features as the training set
     with pytest.raises(ValueError):
         weak_model.score(data[:, :5])
 
-    strong_model = SensorSelector(n_sensors=8)
+    strong_model = SSPOR(n_sensors=8)
     strong_model.fit(data)
 
     assert weak_model.score(data) < strong_model.score(data)
@@ -189,15 +189,15 @@ def test_prefit_basis(data_random):
     # This data should be ignored during the fit
     data_to_ignore = nan * data_random
 
-    model = SensorSelector(basis=basis)
+    model = SSPOR(basis=basis)
     model.fit(data_to_ignore, prefit_basis=True)
     assert not any(isnan(model.get_selected_sensors()))
 
 
 def test_update_n_basis_modes_errors(data_random):
     data = data_random
     n_basis_modes = 5
-    model = SensorSelector(basis=Identity(n_basis_modes=n_basis_modes))
+    model = SSPOR(basis=Identity(n_basis_modes=n_basis_modes))
 
     model.fit(data)
 
@@ -215,7 +215,7 @@ def test_update_n_basis_modes_errors(data_random):
 
 def test_update_n_basis_modes(data_random):
     data = data_random
-    model = SensorSelector()
+    model = SSPOR()
     model.fit(data)
     assert model.basis.n_basis_modes == data.shape[0]
     assert model.basis_matrix_.shape[1] == data.shape[0]
@@ -229,7 +229,7 @@ def test_update_n_basis_modes(data_random):
 def test_update_n_basis_modes_refit(data_random):
     data = data_random
     n_basis_modes = 5
-    model = SensorSelector(basis=Identity(n_basis_modes=n_basis_modes))
+    model = SSPOR(basis=Identity(n_basis_modes=n_basis_modes))
     model.fit(data)
     assert model.basis_matrix_.shape[1] == n_basis_modes
 
@@ -240,7 +240,7 @@ def test_update_n_basis_modes_refit(data_random):
 def test_update_n_basis_modes_unfit_basis(data_random):
     data = data_random
     n_basis_modes = 5
-    model = SensorSelector()
+    model = SSPOR()
     model.update_n_basis_modes(n_basis_modes, data)
 
     assert model.basis_matrix_.shape[1] == n_basis_modes
@@ -252,14 +252,14 @@ def test_ccqr_integration(data_random):
     costs[[1, 3, 5]] = 100
 
     optimizer = CCQR(sensor_costs=costs)
-    model = SensorSelector(optimizer=optimizer).fit(data)
+    model = SSPOR(optimizer=optimizer).fit(data)
 
     check_is_fitted(model)
 
 
 def test_sensor_selector_properties(data_random):
     data = data_random
-    model = SensorSelector().fit(data)
+    model = SSPOR().fit(data)
 
     assert all(model.get_all_sensors() == model.all_sensors)
     assert all(model.get_selected_sensors() == model.selected_sensors)