Merge pull request #82 from dynamicslab/fix-check-fitted

Fix check fitted

pysindy/feature_library/custom_library.py

@@ -89,7 +89,7 @@ def get_feature_names(self, input_features=None):
         output_feature_names : list of string, length n_output_features
 
         """
-        check_is_fitted(self, "n_output_features_")
+        check_is_fitted(self)
         if input_features is None:
             input_features = ["x%d" % i for i in range(self.n_input_features_)]
         feature_names = []
@@ -147,7 +147,7 @@ def transform(self, X):
             generated from applying the custom functions to the inputs.
 
         """
-        check_is_fitted(self, "n_output_features_")
+        check_is_fitted(self)
 
         X = check_array(X)
 

pysindy/optimizers/base.py

@@ -8,6 +8,7 @@
 from sklearn.linear_model import LinearRegression
 from sklearn.multioutput import MultiOutputRegressor
 from sklearn.utils.extmath import safe_sparse_dot
+from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.validation import check_X_y
 
 
@@ -27,6 +28,7 @@ def _rescale_data(X, y, sample_weight):
 class ComplexityMixin:
     @property
     def complexity(self):
+        check_is_fitted(self)
         return np.count_nonzero(self.coef_) + np.count_nonzero(self.intercept_)
 
 
@@ -73,9 +75,6 @@ def __init__(self, max_iter=20, normalize=False, fit_intercept=False, copy_X=Tru
 
         self.max_iter = max_iter
         self.iters = 0
-        self.coef_ = None
-        self.ind_ = None
-        self.history_ = []
 
     # Force subclasses to implement this
     @abc.abstractmethod
@@ -127,7 +126,7 @@ def fit(self, x_, y, sample_weight=None, **reduce_kws):
         self.iters = 0
         self.ind_ = np.ones((y.shape[1], x.shape[1]), dtype=bool)
         self.coef_ = np.linalg.lstsq(x, y, rcond=None)[0].T  # initial guess
-        self.history_.append(self.coef_)
+        self.history_ = [self.coef_]
 
         self._reduce(x, y, **reduce_kws)
         self.ind_ = np.abs(self.coef_) > 1e-14

requirements.txt

@@ -1,3 +1,3 @@
-scikit-learn[alldeps]>=0.21
+scikit-learn[alldeps]>=0.23
 numpy
 scipy

test/optimizers/test_optimizers.py

@@ -5,6 +5,7 @@
 import pytest
 from numpy.linalg import norm
 from sklearn.base import BaseEstimator
+from sklearn.exceptions import NotFittedError
 from sklearn.linear_model import ElasticNet
 from sklearn.linear_model import Lasso
 from sklearn.utils.validation import check_is_fitted
@@ -81,6 +82,22 @@ def test_fit(data, optimizer):
         assert opt.coef_.shape == (1, x.shape[1])
 
 
+@pytest.mark.parametrize(
+    "optimizer", [STLSQ(), SR3()],
+)
+def test_not_fitted(optimizer):
+    with pytest.raises(NotFittedError):
+        optimizer.predict(np.ones((1, 3)))
+
+
+@pytest.mark.parametrize(
+    "optimizer", [STLSQ(), SR3()],
+)
+def test_complexity_not_fitted(optimizer):
+    with pytest.raises(NotFittedError):
+        optimizer.complexity
+
+
 @pytest.mark.parametrize(
     "kwargs", [{"normalize": True}, {"fit_intercept": True}, {"copy_X": False}]
 )