lint

Orange/regression/linear.py

@@ -6,6 +6,13 @@
 import sklearn.linear_model as skl_linear_model
 import sklearn.preprocessing as skl_preprocessing
 
+try:
+    import dask_ml.linear_model as dask_linear_model
+    from dask_glm.regularizers import ElasticNet
+except ImportError:
+    dask_linear_model = skl_linear_model
+    ElasticNet = ...
+
 from Orange.data import Variable, ContinuousVariable
 from Orange.preprocess import Normalize
 from Orange.preprocess.score import LearnerScorer
@@ -39,25 +46,24 @@ def __init__(self, preprocessors=None, fit_intercept=True):
 
     def _initialize_wrapped(self, X=None, Y=None):
         if isinstance(X, da.Array) or isinstance(Y, da.Array):
-            try:
-                import dask_ml.linear_model
-
+            if dask_linear_model is skl_linear_model:
+                warnings.warn("dask_ml is not installed, using sklearn instead.")
+            else:
                 params = self.params.copy()
+                penalty = self.__penalty__
                 params["solver"] = "gradient_descent"
-                params["penalty"] = self.__penalty__
-                if self.__penalty__ is not None:
+
+                if penalty is not None:
+                    if penalty == "elasticnet":
+                        penalty = ElasticNet(weight=params.pop("l1_ratio"))
+                    params["penalty"] = penalty
                     params["solver"] = "admm"
                     params["C"] = 1 / params.pop("alpha")
                     params["max_iter"] = params["max_iter"] or 100
                     for key in ["copy_X", "precompute", "positive"]:
                         params.pop(key, None)
-                if self.__penalty__ == "elasticnet":
-                    from dask_glm.regularizers import ElasticNet
-                    params["penalty"] = ElasticNet(weight=params.pop("l1_ratio"))
 
-                return dask_ml.linear_model.LinearRegression(**params)
-            except ImportError:
-                warnings.warn("dask_ml is not installed, using sklearn instead.")
+                return dask_linear_model.LinearRegression(**params)
         return self.__wraps__(**self.params)
 
     def fit(self, X, Y, W=None):

Orange/tests/test_linear_regression.py

@@ -124,6 +124,7 @@ def test_linear_regression_repr(self):
         self.assertIsInstance(learner2, LinearRegressionLearner)
 
 
+# pylint: disable=invalid-name
 class TestLinearRegressionLearnerOnDask(TestLinearRegressionLearner):
     learners = [
         RidgeRegressionLearner(),