Improved fit checking

Author: sichinaga

pydmd/bopdmd.py

@@ -214,8 +214,6 @@ def A(self):
                 "Set parameter compute_A = True to compute A."
             )
             raise ValueError(msg)
-        if self._A is None:
-            raise ValueError("You need to call fit before")
         return self._A
 
     @property
@@ -1126,7 +1124,7 @@ def init_alpha(self):
                 "fit() hasn't been called "
                 "and no initial value for alpha has been given."
             )
-            raise RuntimeError(msg)
+            raise ValueError(msg)
         return self._init_alpha
 
     @init_alpha.setter
@@ -1148,7 +1146,7 @@ def proj_basis(self):
                 "fit() hasn't been called "
                 "and no projection basis has been given."
             )
-            raise RuntimeError(msg)
+            raise ValueError(msg)
         return self._proj_basis
 
     @proj_basis.setter
@@ -1183,8 +1181,9 @@ def time(self):
         :return: the vector that contains the original time points.
         :rtype: numpy.ndarray
         """
-        if self._time is None:
-            raise RuntimeError("fit() hasn't been called.")
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self._time
 
     @property
@@ -1195,6 +1194,9 @@ def atilde(self):
         :return: the reduced Koopman operator A.
         :rtype: numpy.ndarray
         """
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self.operator.as_numpy_array
 
     @property
@@ -1205,6 +1207,9 @@ def A(self):
         :return: the full Koopman operator A.
         :rtype: numpy.ndarray
         """
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self.operator.A
 
     @property
@@ -1215,7 +1220,7 @@ def dynamics(self):
         :return: matrix that contains all the time evolution, stored by row.
         :rtype: numpy.ndarray
         """
-        t_omega = np.exp(np.outer(self.eigs, self._time))
+        t_omega = np.exp(np.outer(self.eigs, self.time))
         return np.diag(self.amplitudes).dot(t_omega)
 
     @property
@@ -1226,6 +1231,9 @@ def amplitudes_std(self):
         :return: amplitudes standard deviation.
         :rtype: numpy.ndarray
         """
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self.operator.amplitudes_std
 
     @property
@@ -1236,6 +1244,9 @@ def eigenvalues_std(self):
         :return: eigenvalues standard deviation.
         :rtype: numpy.ndarray
         """
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self.operator.eigenvalues_std
 
     @property
@@ -1246,6 +1257,9 @@ def modes_std(self):
         :return: modes standard deviation.
         :rtype: numpy.ndarray
         """
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
+
         return self.operator.modes_std
 
     @property
@@ -1263,8 +1277,8 @@ def print_varpro_opts(self):
         Prints a formatted information string that displays all chosen
         variable projection parameter values.
         """
-        if self._Atilde is None:
-            raise ValueError("You need to call fit before")
+        if not self.fitted:
+            raise ValueError("You need to call fit() before.")
 
         opt_names = [
             "init_lambda",

tests/test_bopdmd.py

@@ -125,7 +125,7 @@ def test_A():
     bopdmd.fit(Z_uneven, t_uneven)
     assert compute_error(bopdmd.A, expected_A) < 1e-3
 
-    bopdmd = BOPDMD(svd_rank=2, compute_A=True, varpro_opts_dict={"tol": 0.05})
+    bopdmd = BOPDMD(svd_rank=2, compute_A=True)
     bopdmd.fit(Z_noisy, t)
     assert compute_error(bopdmd.A, expected_A) < 1e-3
 
@@ -444,6 +444,19 @@ def test_bag_int():
     bopdmd.fit(Z, t)
 
 
+def test_bag_getters():
+    """
+    Test calls to the num_trials and trial_size parameters.
+    """
+    bopdmd = BOPDMD(svd_rank=2, num_trials=0, trial_size=0.8)
+    assert bopdmd.num_trials == 0
+    assert bopdmd.trial_size == 0.8
+
+    bopdmd = BOPDMD(svd_rank=2, num_trials=100, trial_size=3200)
+    assert bopdmd.num_trials == 100
+    assert bopdmd.trial_size == 3200
+
+
 def test_bag_error():
     """
     Test that errors are thrown if invalid bagging parameters are given.
@@ -498,7 +511,7 @@ def test_init_alpha_initializer():
     bopdmd = BOPDMD(svd_rank=2)
 
     # Initial eigs shouldn't be defined yet.
-    with raises(RuntimeError):
+    with raises(ValueError):
         _ = bopdmd.init_alpha
 
     # After fitting, the initial eigs used should be fairly accurate.
@@ -517,7 +530,7 @@ def test_proj_basis_initializer():
     bopdmd = BOPDMD(svd_rank=2)
 
     # Projection basis shouldn't be defined yet.
-    with raises(RuntimeError):
+    with raises(ValueError):
         _ = bopdmd.proj_basis
 
     # After fitting, the projection basis used should be accurate.
@@ -587,3 +600,41 @@ def test_std_shape():
     assert bopdmd.eigenvalues_std.shape == bopdmd.eigs.shape
     assert bopdmd.modes_std.shape == bopdmd.modes.shape
     assert bopdmd.amplitudes_std.shape == bopdmd.amplitudes.shape
+
+
+def test_std_nobags():
+    """
+    Test that std attributes are simply None if no bags were used.
+    """
+    bopdmd = BOPDMD(svd_rank=2, num_trials=0)
+    bopdmd.fit(Z, t)
+
+    assert bopdmd.eigenvalues_std is None
+    assert bopdmd.modes_std is None
+    assert bopdmd.amplitudes_std is None
+
+
+def test_getter_errors():
+    """
+    Test that error occurs if the following properties are called before fit:
+    time, atilde, A, eigenvalues_std, modes_std, amplitudes_std.
+    """
+    bopdmd = BOPDMD(compute_A=True)
+
+    with raises(ValueError):
+        _ = bopdmd.time
+
+    with raises(ValueError):
+        _ = bopdmd.atilde
+
+    with raises(ValueError):
+        _ = bopdmd.A
+
+    with raises(ValueError):
+        _ = bopdmd.eigenvalues_std
+
+    with raises(ValueError):
+        _ = bopdmd.modes_std
+
+    with raises(ValueError):
+        _ = bopdmd.amplitudes_std