MAINT Replace absolute imports with relative imports (scikit-learn#13653)

Author: aditya1702

sklearn/base.py

@@ -12,7 +12,7 @@
 import numpy as np
 
 from . import __version__
-from sklearn.utils import _IS_32BIT
+from .utils import _IS_32BIT
 
 _DEFAULT_TAGS = {
     'non_deterministic': False,

sklearn/calibration.py

@@ -16,7 +16,7 @@
 from scipy.special import expit
 from scipy.special import xlogy
 from scipy.optimize import fmin_bfgs
-from sklearn.preprocessing import LabelEncoder
+from .preprocessing import LabelEncoder
 
 from .base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
 from .preprocessing import label_binarize, LabelBinarizer

sklearn/cluster/_hierarchical.pyx

@@ -13,7 +13,7 @@ ctypedef np.int8_t INT8
 
 np.import_array()
 
-from sklearn.utils.fast_dict cimport IntFloatDict
+from ..utils.fast_dict cimport IntFloatDict
 
 # C++
 from cython.operator cimport dereference as deref, preincrement as inc

sklearn/cluster/_k_means.pyx

@@ -17,7 +17,7 @@ cimport numpy as np
 cimport cython
 from cython cimport floating
 
-from sklearn.utils.sparsefuncs_fast import assign_rows_csr
+from ..utils.sparsefuncs_fast import assign_rows_csr
 from ..utils._cython_blas cimport _dot
 
 ctypedef np.float64_t DOUBLE

sklearn/cluster/affinity_propagation_.py

@@ -8,7 +8,7 @@
 import numpy as np
 import warnings
 
-from sklearn.exceptions import ConvergenceWarning
+from ..exceptions import ConvergenceWarning
 from ..base import BaseEstimator, ClusterMixin
 from ..utils import as_float_array, check_array
 from ..utils.validation import check_is_fitted

sklearn/datasets/openml.py

@@ -14,7 +14,7 @@
 import numpy as np
 import scipy.sparse
 
-from sklearn.externals import _arff
+from ..externals import _arff
 from .base import get_data_home
 from urllib.error import HTTPError
 from ..utils import Bunch

sklearn/datasets/species_distributions.py

@@ -49,8 +49,8 @@
 from .base import _fetch_remote
 from .base import RemoteFileMetadata
 from ..utils import Bunch
-from sklearn.datasets.base import _pkl_filepath
-from sklearn.utils import _joblib
+from .base import _pkl_filepath
+from ..utils import _joblib
 
 # The original data can be found at:
 # https://biodiversityinformatics.amnh.org/open_source/maxent/samples.zip

sklearn/datasets/svmlight_format.py

@@ -140,7 +140,7 @@ def load_svmlight_file(f, n_features=None, dtype=np.float64,
     To use joblib.Memory to cache the svmlight file::
 
         from joblib import Memory
-        from sklearn.datasets import load_svmlight_file
+        from .datasets import load_svmlight_file
         mem = Memory("./mycache")
 
         @mem.cache

sklearn/ensemble/_gradient_boosting.pyx

@@ -18,12 +18,12 @@ np.import_array()
 from scipy.sparse import issparse
 from scipy.sparse import csr_matrix
 
-from sklearn.tree._tree cimport Node
-from sklearn.tree._tree cimport Tree
-from sklearn.tree._tree cimport DTYPE_t
-from sklearn.tree._tree cimport SIZE_t
-from sklearn.tree._tree cimport INT32_t
-from sklearn.tree._utils cimport safe_realloc
+from ..tree._tree cimport Node
+from ..tree._tree cimport Tree
+from ..tree._tree cimport DTYPE_t
+from ..tree._tree cimport SIZE_t
+from ..tree._tree cimport INT32_t
+from ..tree._utils cimport safe_realloc
 
 ctypedef np.int32_t int32
 ctypedef np.float64_t float64

sklearn/feature_extraction/_hashing.pyx

@@ -11,8 +11,8 @@ from libc.stdlib cimport abs
 cimport numpy as np
 import numpy as np
 
-from sklearn.utils.murmurhash cimport murmurhash3_bytes_s32
-from sklearn.utils.fixes import sp_version
+from ..utils.murmurhash cimport murmurhash3_bytes_s32
+from ..utils.fixes import sp_version
 
 np.import_array()
 

sklearn/gaussian_process/gpc.py

@@ -12,14 +12,14 @@
 from scipy.optimize import fmin_l_bfgs_b
 from scipy.special import erf, expit
 
-from sklearn.base import BaseEstimator, ClassifierMixin, clone
-from sklearn.gaussian_process.kernels \
+from ..base import BaseEstimator, ClassifierMixin, clone
+from .kernels \
     import RBF, CompoundKernel, ConstantKernel as C
-from sklearn.utils.validation import check_X_y, check_is_fitted, check_array
-from sklearn.utils import check_random_state
-from sklearn.preprocessing import LabelEncoder
-from sklearn.multiclass import OneVsRestClassifier, OneVsOneClassifier
-from sklearn.exceptions import ConvergenceWarning
+from ..utils.validation import check_X_y, check_is_fitted, check_array
+from ..utils import check_random_state
+from ..preprocessing import LabelEncoder
+from ..multiclass import OneVsRestClassifier, OneVsOneClassifier
+from ..exceptions import ConvergenceWarning
 
 
 # Values required for approximating the logistic sigmoid by

sklearn/gaussian_process/gpr.py

@@ -11,12 +11,12 @@
 from scipy.linalg import cholesky, cho_solve, solve_triangular
 from scipy.optimize import fmin_l_bfgs_b
 
-from sklearn.base import BaseEstimator, RegressorMixin, clone
-from sklearn.base import MultiOutputMixin
-from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
-from sklearn.utils import check_random_state
-from sklearn.utils.validation import check_X_y, check_array
-from sklearn.exceptions import ConvergenceWarning
+from ..base import BaseEstimator, RegressorMixin, clone
+from ..base import MultiOutputMixin
+from .kernels import RBF, ConstantKernel as C
+from ..utils import check_random_state
+from ..utils.validation import check_X_y, check_array
+from ..exceptions import ConvergenceWarning
 
 
 class GaussianProcessRegressor(BaseEstimator, RegressorMixin,

sklearn/linear_model/cd_fast.pyx

@@ -24,7 +24,7 @@ from ..utils._cython_blas cimport (_axpy, _dot, _asum, _ger, _gemv, _nrm2,
 from ..utils._cython_blas cimport RowMajor, ColMajor, Trans, NoTrans
 
 
-from sklearn.utils cimport _random 
+from ..utils cimport _random
 
 ctypedef np.float64_t DOUBLE
 ctypedef np.uint32_t UINT32_t

sklearn/linear_model/sgd_fast.pyx

@@ -21,8 +21,8 @@ from numpy.math cimport INFINITY
 cdef extern from "sgd_fast_helpers.h":
     bint skl_isfinite(double) nogil
 
-from sklearn.utils.weight_vector cimport WeightVector
-from sklearn.utils.seq_dataset cimport SequentialDataset64 as SequentialDataset
+from ..utils.weight_vector cimport WeightVector
+from ..utils.seq_dataset cimport SequentialDataset64 as SequentialDataset
 
 np.import_array()
 

sklearn/manifold/_barnes_hut_tsne.pyx

@@ -15,7 +15,7 @@ from libc.math cimport sqrt, log
 import numpy as np
 cimport numpy as np
 
-from sklearn.neighbors.quad_tree cimport _QuadTree
+from ..neighbors.quad_tree cimport _QuadTree
 
 cdef char* EMPTY_STRING = ""
 

sklearn/metrics/cluster/bicluster.py

@@ -1,4 +1,3 @@
-
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 

sklearn/metrics/cluster/expected_mutual_info_fast.pyx

@@ -11,7 +11,7 @@ from scipy.special import gammaln
 import numpy as np
 cimport numpy as np
 cimport cython
-from sklearn.utils.lgamma cimport lgamma
+from ...utils.lgamma cimport lgamma
 
 np.import_array()
 ctypedef np.float64_t DOUBLE

sklearn/metrics/cluster/unsupervised.py

@@ -16,7 +16,7 @@
 from ..pairwise import pairwise_distances_chunked
 from ..pairwise import pairwise_distances
 from ...preprocessing import LabelEncoder
-from sklearn.utils import deprecated
+from ...utils import deprecated
 
 
 def check_number_of_labels(n_labels, n_samples):

sklearn/neighbors/binary_tree.pxi

@@ -147,7 +147,7 @@ cimport numpy as np
 from libc.math cimport fabs, sqrt, exp, cos, pow, log
 from libc.stdlib cimport calloc, malloc, free
 from libc.string cimport memcpy
-from sklearn.utils.lgamma cimport lgamma
+from ..utils.lgamma cimport lgamma
 
 import numpy as np
 import warnings

sklearn/neighbors/quad_tree.pyx

@@ -12,7 +12,7 @@ from libc.stdlib cimport malloc, free
 from libc.string cimport memcpy
 from libc.stdio cimport printf
 
-from sklearn.tree._utils cimport safe_realloc, sizet_ptr_to_ndarray
+from ..tree._utils cimport safe_realloc, sizet_ptr_to_ndarray
 from ..utils import check_array
 
 import numpy as np

sklearn/tree/_utils.pxd

@@ -11,7 +11,7 @@
 import numpy as np
 cimport numpy as np
 from ._tree cimport Node
-from sklearn.neighbors.quad_tree cimport Cell
+from ..neighbors.quad_tree cimport Cell
 
 ctypedef np.npy_float32 DTYPE_t          # Type of X
 ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight

sklearn/tree/_utils.pyx

@@ -20,7 +20,7 @@ import numpy as np
 cimport numpy as np
 np.import_array()
 
-from sklearn.utils cimport _random
+from ..utils cimport _random
 
 # =============================================================================
 # Helper functions

sklearn/utils/_random.pyx

@@ -19,7 +19,7 @@ import numpy as np
 cimport numpy as np
 np.import_array()
 
-from sklearn.utils import check_random_state
+from . import check_random_state
 
 cdef UINT32_t DEFAULT_SEED = 1
 

sklearn/utils/estimator_checks.py

@@ -11,51 +11,51 @@
 from scipy import sparse
 from scipy.stats import rankdata
 
-from sklearn.utils import IS_PYPY
-from sklearn.utils import _joblib
-from sklearn.utils.testing import assert_raises, _get_args
-from sklearn.utils.testing import assert_raises_regex
-from sklearn.utils.testing import assert_raise_message
-from sklearn.utils.testing import assert_equal
-from sklearn.utils.testing import assert_not_equal
-from sklearn.utils.testing import assert_in
-from sklearn.utils.testing import assert_array_equal
-from sklearn.utils.testing import assert_array_almost_equal
-from sklearn.utils.testing import assert_allclose
-from sklearn.utils.testing import assert_allclose_dense_sparse
-from sklearn.utils.testing import assert_warns_message
-from sklearn.utils.testing import set_random_state
-from sklearn.utils.testing import assert_greater
-from sklearn.utils.testing import assert_greater_equal
-from sklearn.utils.testing import SkipTest
-from sklearn.utils.testing import ignore_warnings
-from sklearn.utils.testing import assert_dict_equal
-from sklearn.utils.testing import create_memmap_backed_data
-from sklearn.utils import is_scalar_nan
-from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-from sklearn.linear_model import Ridge
-
-
-from sklearn.base import (clone, ClusterMixin, is_classifier, is_regressor,
+from . import IS_PYPY
+from . import _joblib
+from .testing import assert_raises, _get_args
+from .testing import assert_raises_regex
+from .testing import assert_raise_message
+from .testing import assert_equal
+from .testing import assert_not_equal
+from .testing import assert_in
+from .testing import assert_array_equal
+from .testing import assert_array_almost_equal
+from .testing import assert_allclose
+from .testing import assert_allclose_dense_sparse
+from .testing import assert_warns_message
+from .testing import set_random_state
+from .testing import assert_greater
+from .testing import assert_greater_equal
+from .testing import SkipTest
+from .testing import ignore_warnings
+from .testing import assert_dict_equal
+from .testing import create_memmap_backed_data
+from . import is_scalar_nan
+from ..discriminant_analysis import LinearDiscriminantAnalysis
+from ..linear_model import Ridge
+
+
+from ..base import (clone, ClusterMixin, is_classifier, is_regressor,
                           _DEFAULT_TAGS, RegressorMixin, is_outlier_detector)
 
-from sklearn.metrics import accuracy_score, adjusted_rand_score, f1_score
-
-from sklearn.random_projection import BaseRandomProjection
-from sklearn.feature_selection import SelectKBest
-from sklearn.pipeline import make_pipeline
-from sklearn.exceptions import DataConversionWarning
-from sklearn.exceptions import SkipTestWarning
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import ShuffleSplit
-from sklearn.model_selection._validation import _safe_split
-from sklearn.metrics.pairwise import (rbf_kernel, linear_kernel,
+from ..metrics import accuracy_score, adjusted_rand_score, f1_score
+
+from ..random_projection import BaseRandomProjection
+from ..feature_selection import SelectKBest
+from ..pipeline import make_pipeline
+from ..exceptions import DataConversionWarning
+from ..exceptions import SkipTestWarning
+from ..model_selection import train_test_split
+from ..model_selection import ShuffleSplit
+from ..model_selection._validation import _safe_split
+from ..metrics.pairwise import (rbf_kernel, linear_kernel,
                                       pairwise_distances)
 
-from sklearn.utils import shuffle
-from sklearn.utils.validation import has_fit_parameter, _num_samples
-from sklearn.preprocessing import StandardScaler
-from sklearn.datasets import load_iris, load_boston, make_blobs
+from .import shuffle
+from .validation import has_fit_parameter, _num_samples
+from ..preprocessing import StandardScaler
+from ..datasets import load_iris, load_boston, make_blobs
 
 
 BOSTON = None

sklearn/utils/random.py

@@ -5,7 +5,7 @@
 import scipy.sparse as sp
 import array
 
-from sklearn.utils import check_random_state
+from . import check_random_state
 from ._random import sample_without_replacement
 
 __all__ = ['sample_without_replacement']

sklearn/utils/seq_dataset.pyx.tp

@@ -45,7 +45,7 @@ import numpy as np
 
 np.import_array()
 
-from sklearn.utils cimport _random
+from . cimport _random
 
 cdef class SequentialDataset{{name}}:
     """Base class for datasets with sequential data access.

sklearn/utils/stats.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from sklearn.utils.extmath import stable_cumsum
+from .extmath import stable_cumsum
 
 
 def _weighted_percentile(array, sample_weight, percentile=50):