fix issues on the pytest version

src/pykoopman/koopman.py

@@ -21,8 +21,8 @@
 from .regression import DMDc
 from .regression import EDMDc
 from .regression import EnsembleBaseRegressor
-from .regression import PyDMDRegressor
 from .regression import NNDMD
+from .regression import PyDMDRegressor
 
 
 class Koopman(BaseEstimator):

src/pykoopman/regression/_base_ensemble.py

@@ -4,7 +4,6 @@
 """
 from __future__ import annotations
 
-import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import clone
 from sklearn.base import TransformerMixin
@@ -78,16 +77,10 @@ def fit(self, X, y, **fit_params):
             functions.
         """
 
-        # if (
-        #         isinstance(X, np.ndarray)
-        #         and isinstance(y, np.ndarray)
-        #         and X.ndim == 2
-        #         and y.ndim == 2
-        # ):
-        # case 2: x, y are 2D np.ndarray, must be 1-step, no validation
-        self._training_dim = y.ndim
         # transformers are designed to modify X which is 2d dimensional, we
         # need to modify y accordingly.
+
+        self._training_dim = y.ndim
         if y.ndim == 1:
             y_2d = y.reshape(-1, 1)
         else:
@@ -104,17 +97,12 @@ def fit(self, X, y, **fit_params):
 
         if self.regressor is None:
             from sklearn.linear_model import LinearRegression
+
             self.regressor_ = LinearRegression()
         else:
             self.regressor_ = clone(self.regressor)
 
         self.regressor_.fit(X, y_trans, **fit_params)
-        # elif isinstance(X, list) and isinstance(y, list):
-        #     # case 4: x, y are two lists of trajectories, we have validation data
-        #     for
-        #
-        # else:
-        #     raise ValueError("check `x` and `y` for `self.fit`")
 
         if hasattr(self.regressor_, "feature_names_in_"):
             self.feature_names_in_ = self.regressor_.feature_names_in_