Merge pull request #173 from decargroup/bugfix/172-prediction-with-no…

…-inputs-bug

Bugfix/172 prediction with no inputs bug

.github/workflows/test-package.yml

@@ -10,7 +10,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{matrix.python-version}}

pykoop/centers.py

@@ -11,6 +11,7 @@
 from scipy import stats
 
 log = logging.getLogger(__name__)
+log.addHandler(logging.NullHandler())
 
 
 class Centers(sklearn.base.BaseEstimator, metaclass=abc.ABCMeta):

pykoop/kernel_approximation.py

@@ -426,7 +426,7 @@ def fit(
         X_hashed = self._hash_samples(X_scaled)
         encoder_args = {
             'categories': 'auto',
-            'sparse': False,
+            'sparse_output': False,
             'handle_unknown': 'ignore',
         }
         if self.encoder_kw is not None:

pykoop/lmi_regressors.py

@@ -191,7 +191,7 @@ def __init__(
         inv_method: str = 'svd',
         tsvd: Optional[tsvd.Tsvd] = None,
         square_norm: bool = False,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiEdmd`.
@@ -494,7 +494,7 @@ def __init__(
         tsvd_shifted: Optional[tsvd.Tsvd] = None,
         reg_method: str = 'tikhonov',
         square_norm: bool = False,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiDmdc`.
@@ -672,7 +672,7 @@ def _create_base_problem(
                     - big_constant.T * U_hat.T, U_hat * Q_bar_Sigma_tld
                 ],
                 [Q_bar_Sigma_tld.T * U_hat.T, m1],
-            ]) << picos_eps)
+            ]) << -1 * picos_eps)
         problem.set_objective('min', picos.trace(W_hat))
         return problem
 
@@ -736,7 +736,7 @@ def __init__(
         alpha: float = 0,
         inv_method: str = 'svd',
         tsvd: Optional[tsvd.Tsvd] = None,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiEdmdSpectralRadiusConstr`.
@@ -988,7 +988,7 @@ def __init__(
         alpha: float = 0,
         tsvd_unshifted: Optional[tsvd.Tsvd] = None,
         tsvd_shifted: Optional[tsvd.Tsvd] = None,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiDmdcSpectralRadiusConstr`.
@@ -1264,7 +1264,7 @@ def __init__(
         inv_method: str = 'svd',
         tsvd: Optional[tsvd.Tsvd] = None,
         square_norm: bool = False,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiEdmdHinfReg`.
@@ -1603,7 +1603,7 @@ def __init__(
         tsvd_unshifted: Optional[tsvd.Tsvd] = None,
         tsvd_shifted: Optional[tsvd.Tsvd] = None,
         square_norm: bool = False,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiDmdcHinfReg`.
@@ -1924,7 +1924,7 @@ def __init__(
         iter_rtol: float = 0,
         inv_method: str = 'svd',
         tsvd: Optional[tsvd.Tsvd] = None,
-        picos_eps: float = 0,
+        picos_eps: float = 1e-6,
         solver_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiate :class:`LmiEdmdDissipativityConstr`.

requirements.txt

@@ -1,7 +1,7 @@
 # Requirements to install ``pykoop`` and run examples
 numpy>=1.21.0
-scipy>=1.7.0
-scikit-learn>=1.0.0
+scipy>=1.8.0
+scikit-learn>=1.2.0
 PICOS>=2.4.0
 optht>=0.2.0
 Deprecated>=1.2.13

tests/test_koopman_pipeline.py

@@ -1097,6 +1097,95 @@ def test_strip_initial_conditons(self):
         np.testing.assert_allclose(X1s, X2s)
 
 
+@pytest.mark.filterwarnings(
+    'ignore:Call to deprecated method predict_multistep')
+@pytest.mark.parametrize(
+    'kp',
+    [
+        pykoop.KoopmanPipeline(
+            lifting_functions=None,
+            regressor=pykoop.Edmd(),
+        ),
+        pykoop.KoopmanPipeline(
+            lifting_functions=[
+                ('pl', pykoop.PolynomialLiftingFn(order=2)),
+            ],
+            regressor=pykoop.Edmd(),
+        ),
+        pykoop.KoopmanPipeline(
+            lifting_functions=[
+                ('dl',
+                 pykoop.DelayLiftingFn(
+                     n_delays_state=2,
+                     n_delays_input=2,
+                 )),
+            ],
+            regressor=pykoop.Edmd(),
+        ),
+        pykoop.KoopmanPipeline(
+            lifting_functions=[
+                ('sp',
+                 pykoop.SplitPipeline(
+                     lifting_functions_state=[
+                         ('pl', pykoop.PolynomialLiftingFn(order=2)),
+                     ],
+                     lifting_functions_input=None,
+                 ))
+            ],
+            regressor=pykoop.Edmd(),
+        ),
+    ],
+)
+class TestPredictionNoInput:
+    """Test fit Koopman pipeline prediction without input."""
+
+    def test_predict_trajectory_no_input(
+        self,
+        kp,
+        mass_spring_damper_no_input,
+    ):
+        """Test :func:`predict_trajectory` with no input."""
+        msg = 'Test only works when there is no episode feature.'
+        assert (not mass_spring_damper_no_input['episode_feature']), msg
+        # Fit estimator
+        kp.fit(
+            mass_spring_damper_no_input['X_train'],
+            n_inputs=mass_spring_damper_no_input['n_inputs'],
+            episode_feature=False,
+        )
+        # Extract initial conditions
+        x0 = pykoop.extract_initial_conditions(
+            mass_spring_damper_no_input['X_train'],
+            kp.min_samples_,
+            n_inputs=mass_spring_damper_no_input['n_inputs'],
+            episode_feature=False,
+        )
+        # Extract input
+        u = pykoop.extract_input(
+            mass_spring_damper_no_input['X_train'],
+            n_inputs=mass_spring_damper_no_input['n_inputs'],
+            episode_feature=False,
+        )
+        # Predict new states
+        X_sim = kp.predict_trajectory(
+            x0,
+            u,
+            episode_feature=False,
+            relift_state=True,
+        )
+        # Predict manually
+        X_sim_exp = np.zeros(X_sim.shape)
+        X_sim_exp[:kp.min_samples_, :] = x0
+        for k in range(kp.min_samples_, u.shape[0]):
+            X = np.hstack((
+                X_sim_exp[(k - kp.min_samples_):k, :],
+                u[(k - kp.min_samples_):k, :],
+            ))
+            Xp = kp.predict(X)
+            X_sim_exp[[k], :] = Xp[[-1], :]
+        np.testing.assert_allclose(X_sim, X_sim_exp)
+
+
 @pytest.mark.filterwarnings(
     'ignore:Call to deprecated method predict_multistep')
 @pytest.mark.parametrize(

tests/test_lmi_regressors.py

@@ -499,18 +499,38 @@ class TestSkLearn:
     """Test ``scikit-learn`` compatibility."""
 
     @sklearn.utils.estimator_checks.parametrize_with_checks([
-        pykoop.lmi_regressors.LmiEdmd(alpha=1e-3, ),
-        pykoop.lmi_regressors.LmiEdmdSpectralRadiusConstr(max_iter=1, ),
-        pykoop.lmi_regressors.LmiEdmdHinfReg(alpha=1, ratio=1, max_iter=1),
-        pykoop.lmi_regressors.LmiEdmdDissipativityConstr(max_iter=1, ),
-        pykoop.lmi_regressors.LmiDmdc(alpha=1e-3, ),
-        pykoop.lmi_regressors.LmiDmdcSpectralRadiusConstr(max_iter=1, ),
-        pykoop.lmi_regressors.LmiDmdcHinfReg(alpha=1, ratio=1, max_iter=1),
+        pykoop.lmi_regressors.LmiEdmd(alpha=1e-3, picos_eps=0),
+        pykoop.lmi_regressors.LmiEdmdSpectralRadiusConstr(
+            max_iter=1,
+            picos_eps=0,
+        ),
+        pykoop.lmi_regressors.LmiEdmdHinfReg(
+            alpha=1,
+            ratio=1,
+            max_iter=1,
+            picos_eps=0,
+        ),
+        pykoop.lmi_regressors.LmiEdmdDissipativityConstr(
+            max_iter=1,
+            picos_eps=0,
+        ),
+        pykoop.lmi_regressors.LmiDmdc(alpha=1e-3, picos_eps=0),
+        pykoop.lmi_regressors.LmiDmdcSpectralRadiusConstr(
+            max_iter=1,
+            picos_eps=0,
+        ),
+        pykoop.lmi_regressors.LmiDmdcHinfReg(
+            alpha=1,
+            ratio=1,
+            max_iter=1,
+            picos_eps=0,
+        ),
         pykoop.lmi_regressors.LmiHinfZpkMeta(
             pykoop.lmi_regressors.LmiEdmdHinfReg(
                 alpha=1,
                 ratio=1,
                 max_iter=1,
+                picos_eps=0,
             )),
     ])
     def test_compatible_estimator(self, estimator, check, mosek_solver_params):