Revert trying to make distributed a soft dependency

dask_ml/model_selection/__init__.py

@@ -3,18 +3,8 @@
 These estimators will operate in parallel. Their scalability depends
 on the underlying estimators being used.
 """
-from warnings import warn
-
 from ._search import GridSearchCV, RandomizedSearchCV, compute_n_splits, check_cv
 from ._split import ShuffleSplit, KFold, train_test_split
-try:
-    import distributed
-except ImportError:
-    warn(
-        "The Dask Distributed library has not been found. The IncrementalSearchCV"
-        "class and it's children depend on it. For installation instructions, "
-        "see http://docs.dask.org/en/latest/install.html", ImportWarning
-    )
 
 
 __all__ = [
@@ -32,5 +22,5 @@
     from ._incremental import IncrementalSearchCV  # noqa: F401
 
     __all__.extend(["IncrementalSearchCV"])
-except ImportError as e:
+except ImportError:
     pass

tests/conftest.py

@@ -4,10 +4,7 @@
 import dask.dataframe as dd
 import numpy as np
 import pytest
-try:
-    from distributed.utils_test import cluster
-except ImportError:
-    pass
+from distributed.utils_test import cluster
 
 from dask_ml.datasets import (
     make_blobs,

tests/model_selection/test_incremental.py

@@ -3,22 +3,17 @@
 import dask.array as da
 import numpy as np
 import toolz
-
 from dask.distributed import Future
 from distributed.utils_test import cluster, gen_cluster, loop  # noqa: F401
-
-import pytest
 from sklearn.base import BaseEstimator
 from sklearn.cluster import MiniBatchKMeans
 from sklearn.linear_model import SGDClassifier
 from sklearn.model_selection import ParameterGrid, ParameterSampler
 from tornado import gen
-import pytest
 
 from dask_ml.datasets import make_classification
 from dask_ml.model_selection import IncrementalSearchCV
 from dask_ml.model_selection._incremental import _partial_fit, _score, fit
-import dask_ml.model_selection
 
 
 @gen_cluster(client=True, timeout=500)