Specify HLG layer name during delayed object creation in fit

dask_ml/_compat.py

@@ -22,6 +22,7 @@
 DASK_2_26_0 = DASK_VERSION >= packaging.version.parse("2.26.0")
 DASK_2_28_0 = DASK_VERSION > packaging.version.parse("2.27.0")
 DASK_2021_02_0 = DASK_VERSION >= packaging.version.parse("2021.02.0")
+DASK_2022_01_0 = DASK_VERSION > packaging.version.parse("2021.12.0")
 DISTRIBUTED_2_5_0 = DISTRIBUTED_VERSION > packaging.version.parse("2.5.0")
 DISTRIBUTED_2_11_0 = DISTRIBUTED_VERSION > packaging.version.parse("2.10.0")  # dev
 DISTRIBUTED_2021_02_0 = DISTRIBUTED_VERSION >= packaging.version.parse("2021.02.0")

dask_ml/_partial.py

@@ -10,6 +10,8 @@
 from dask.highlevelgraph import HighLevelGraph
 from toolz import partial
 
+from ._compat import DASK_2022_01_0
+
 logger = logging.getLogger(__name__)
 
 
@@ -125,7 +127,11 @@ def fit(
     if y is not None:
         dependencies.append(y)
     new_dsk = HighLevelGraph.from_collections(name, dsk, dependencies=dependencies)
-    value = Delayed((name, nblocks - 1), new_dsk)
+
+    if DASK_2022_01_0:
+        value = Delayed((name, nblocks - 1), new_dsk, layer=name)
+    else:
+        value = Delayed((name, nblocks - 1), new_dsk)
 
     if compute:
         return value.compute()

dask_ml/model_selection/methods.py

@@ -2,11 +2,11 @@
 
 import warnings
 from collections import defaultdict
-from distutils.version import LooseVersion
 from threading import Lock
 from timeit import default_timer
 
 import numpy as np
+import packaging.version
 from dask.base import normalize_token
 from scipy import sparse
 from scipy.stats import rankdata
@@ -20,7 +20,7 @@
 
 # Copied from scikit-learn/sklearn/utils/fixes.py, can be removed once we drop
 # support for scikit-learn < 0.18.1 or numpy < 1.12.0.
-if LooseVersion(np.__version__) < "1.12.0":
+if packaging.version.parse(np.__version__) < packaging.version.parse("1.12.0"):
 
     class MaskedArray(np.ma.MaskedArray):
         # Before numpy 1.12, np.ma.MaskedArray object is not picklable

dask_ml/model_selection/utils.py

@@ -1,20 +1,20 @@
 import copy
 import warnings
-from distutils.version import LooseVersion
 from itertools import compress
 
 import dask
 import dask.array as da
 import dask.dataframe as dd
 import numpy as np
+import packaging.version
 import scipy.sparse as sp
 from dask.base import tokenize
 from dask.delayed import Delayed, delayed
 from sklearn.utils.validation import _is_arraylike, indexable
 
 from ..utils import _num_samples
 
-if LooseVersion(dask.__version__) > "0.15.4":
+if packaging.version.parse(dask.__version__) > packaging.version.parse("0.15.4"):
     from dask.base import is_dask_collection
 else:
     from dask.base import Base

dask_ml/preprocessing/data.py

@@ -4,12 +4,12 @@
 import multiprocessing
 import numbers
 from collections import OrderedDict
-from distutils.version import LooseVersion
 from typing import Any, List, Optional, Sequence, Union
 
 import dask.array as da
 import dask.dataframe as dd
 import numpy as np
+import packaging.version
 import pandas as pd
 import sklearn.preprocessing
 from dask import compute
@@ -26,8 +26,8 @@
 from .._typing import ArrayLike, DataFrameType, NDArrayOrScalar, SeriesType
 from ..base import DaskMLBaseMixin
 
-_PANDAS_VERSION = LooseVersion(pd.__version__)
-_HAS_CTD = _PANDAS_VERSION >= "0.21.0"
+_PANDAS_VERSION = packaging.version.parse(pd.__version__)
+_HAS_CTD = _PANDAS_VERSION >= packaging.version.parse("0.21.0")
 BOUNDS_THRESHOLD = 1e-7
 
 

tests/linear_model/test_glm.py

@@ -64,9 +64,11 @@ def test_fit(fit_intercept, solver):
 )
 def test_fit_solver(solver):
     import dask_glm
-    from distutils.version import LooseVersion
+    import packaging.version
 
-    if LooseVersion(dask_glm.__version__) <= "0.2.0":
+    if packaging.version.parse(dask_glm.__version__) <= packaging.version.parse(
+        "0.2.0"
+    ):
         pytest.skip("FutureWarning for dask config.")
 
     X, y = make_classification(n_samples=100, n_features=5, chunks=50)