Merge remote-tracking branch 'dask/main' into 734-pca-skip-centering

Author: hristog

.github/workflows/ci-docs.yaml renamed to .github/workflows/docs.yaml

@@ -1,4 +1,4 @@
-name: CI
+name: Documentation
 on: [push, pull_request]
 
 jobs:
@@ -12,7 +12,6 @@ jobs:
         uses: conda-incubator/setup-miniconda@v2
         with:
           miniconda-version: "latest"
-          python-version: "3.7"
           environment-file: ci/environment-docs.yaml
           activate-environment: dask-ml-docs
           auto-activate-base: false

ci/environment-docs.yaml

@@ -24,7 +24,7 @@ dependencies:
   - pytest
   - pytest-cov
   - pytest-mock
-  - python=3.6.6
+  - python=3.7
   - sortedcontainers
   - scikit-learn>=0.23.1
   - scipy

dask_ml/_compat.py

@@ -1,7 +1,7 @@
 import contextlib
 import os
 from collections.abc import Mapping  # noqa
-from typing import Any, List, Optional, Union
+from typing import Any
 
 import dask
 import dask.array as da
@@ -43,12 +43,6 @@ def dummy_context(*args: Any, **kwargs: Any):
 blockwise = da.blockwise
 
 
-def check_is_fitted(est, attributes: Optional[Union[str, List[str]]] = None):
-    args: Any = ()
-
-    return sklearn.utils.validation.check_is_fitted(est, *args)
-
-
 def _check_multimetric_scoring(estimator, scoring=None):
     # TODO: See if scikit-learn 0.24 solves the need for using
     # a private method

dask_ml/cluster/k_means.py

@@ -10,8 +10,9 @@
 from dask import compute
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.utils.extmath import squared_norm
+from sklearn.utils.validation import check_is_fitted
 
-from .._compat import SK_024, blockwise, check_is_fitted
+from .._compat import SK_024, blockwise
 from .._utils import draw_seed
 from ..metrics import (
     euclidean_distances,

dask_ml/decomposition/pca.py

@@ -7,9 +7,9 @@
 import sklearn.decomposition
 from dask import compute, delayed
 from sklearn.utils.extmath import fast_logdet
-from sklearn.utils.validation import check_random_state
+from sklearn.utils.validation import check_is_fitted, check_random_state
 
-from .._compat import DASK_2_26_0, check_is_fitted
+from .._compat import DASK_2_26_0
 from .._utils import draw_seed
 from ..utils import svd_flip
 

dask_ml/model_selection/_incremental.py

@@ -24,8 +24,9 @@
 from sklearn.model_selection import ParameterGrid, ParameterSampler
 from sklearn.utils import check_random_state
 from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.validation import check_is_fitted
 
-from .._compat import DISTRIBUTED_2021_02_0, annotate, check_is_fitted, dummy_context
+from .._compat import DISTRIBUTED_2021_02_0, annotate, dummy_context
 from .._typing import ArrayLike, Int
 from .._utils import LoggingContext
 from ..wrappers import ParallelPostFit

dask_ml/model_selection/_search.py

@@ -33,9 +33,9 @@
 from sklearn.pipeline import FeatureUnion, Pipeline
 from sklearn.utils.metaestimators import if_delegate_has_method
 from sklearn.utils.multiclass import type_of_target
-from sklearn.utils.validation import _num_samples
+from sklearn.utils.validation import _num_samples, check_is_fitted
 
-from .._compat import SK_024, SK_VERSION, check_is_fitted
+from .._compat import SK_024, SK_VERSION
 from ._normalize import normalize_estimator
 from .methods import (
     MISSING,

dask_ml/model_selection/_successive_halving.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 import toolz
+from sklearn.utils.validation import check_is_fitted
 
-from .._compat import check_is_fitted
 from ._incremental import IncrementalSearchCV
 
 

dask_ml/preprocessing/data.py

@@ -15,9 +15,9 @@
 from pandas.api.types import is_categorical_dtype
 from scipy import stats
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.utils.validation import check_random_state
+from sklearn.utils.validation import check_is_fitted, check_random_state
 
-from dask_ml._compat import blockwise, check_is_fitted
+from dask_ml._compat import blockwise
 from dask_ml._utils import copy_learned_attributes
 from dask_ml.utils import check_array, handle_zeros_in_scale
 

dask_ml/preprocessing/label.py

@@ -9,8 +9,8 @@
 import pandas as pd
 import scipy.sparse
 import sklearn.preprocessing
+from sklearn.utils.validation import check_is_fitted
 
-from .._compat import check_is_fitted
 from .._typing import ArrayLike, SeriesType
 
 

dask_ml/wrappers.py

@@ -7,10 +7,10 @@
 import numpy as np
 import sklearn.base
 import sklearn.metrics
+from sklearn.utils.validation import check_is_fitted
 
 from dask_ml.utils import _timer
 
-from ._compat import check_is_fitted
 from ._partial import fit
 from ._utils import copy_learned_attributes
 from .metrics import check_scoring, get_scorer

docs/source/xgboost.rst

@@ -22,8 +22,8 @@ classes/functions:
    XGBClassifier
    XGBRegressor
 
-The LightGBM implementation and documentation can be found at
-https://github.com/dask/dask-lightgbm.
+The LightGBM implementation can be found at https://github.com/microsoft/LightGBM and documentation can be found at
+https://lightgbm.readthedocs.io/en/latest/Parallel-Learning-Guide.html#dask.
 
 Example
 -------

github_deploy_key_dask_dask_ml.enc

@@ -13,7 +13,7 @@
 install_requires = [
     "dask[array,dataframe]>=2.4.0",
     "distributed>=2.4.0",
-    "numba",
+    "numba>=0.51.0",
     "numpy>=1.17.3",
     "pandas>=0.24.2",
     "scikit-learn>=0.23",