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duality-group
duality-group / scikit-learn python
Repository URL to install this package:
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Version:
1.2.0 ▾
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ÿÿ¨ ÿÿÁÿÿéÿÿÿÿ9ÿÿaÿÿÿÿÀÿÿ83ÿÿX5ÿÿû2ÿÿò2ÿÿé2ÿÿà2ÿÿ+3ÿÿP3ÿÿu3ÿÿ3ÿÿ¿3ÿÿØPÿÿëRÿÿRÿÿRÿÿwRÿÿnRÿÿeRÿÿ\RÿÿSRÿÿGRÿÿ;Rÿÿ/Rÿÿ#RÿÿRÿÿRÿÿùQÿÿ«Pÿÿ¿RÿÿäRÿÿ Sÿÿ.SÿÿSSÿÿxSÿÿSÿÿÂSÿÿêSÿÿTÿÿ:TÿÿbTÿÿTÿÿ²TÿÿÿÿÿÿåÿÿܗÿÿӗÿÿʗÿÿùÿÿÿÿµÿÿڛÿÿIÿÿ\ÊÿÿÊÿÿÄÆÿÿ¼ÆÿÿDÊÿÿ__pyx_fatalerror Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name)) sklearn/ensemble/_hist_gradient_boosting/splitting.pyx unable to allocate shape and strides. numpy.core.umath failed to import no default __reduce__ due to non-trivial __cinit__ got differing extents in dimension %d (got %d and %d) Unable to convert item to object This module contains routines and data structures to:
- Find the best possible split of a node. For a given node, a split is
characterized by a feature and a bin.
- Apply a split to a node, i.e. split the indices of the samples at the node
into the newly created left and right children.
Pure data class to store information about a potential split.
Parameters
----------
gain : float
The gain of the split.
feature_idx : int
The index of the feature to be split.
bin_idx : int
The index of the bin on which the split is made. Should be ignored if
`is_categorical` is True: `left_cat_bitset` will be used to determine
the split.
missing_go_to_left : bool
Whether missing values should go to the left child. This is used
whether the split is categorical or not.
sum_gradient_left : float
The sum of the gradients of all the samples in the left child.
sum_hessian_left : float
The sum of the hessians of all the samples in the left child.
sum_gradient_right : float
The sum of the gradients of all the samples in the right child.
sum_hessian_right : float
The sum of the hessians of all the samples in the right child.
n_samples_left : int, default=0
The number of samples in the left child.
n_samples_right : int
The number of samples in the right child.
is_categorical : bool
Whether the split is done on a categorical feature.
left_cat_bitset : ndarray of shape=(8,), dtype=uint32 or None
Bitset representing the categories that go to the left. This is used
only when `is_categorical` is True.
Note that missing values are part of that bitset if there are missing
values in the training data. For missing values, we rely on that
bitset for splitting, but at prediction time, we rely on
missing_go_to_left.
Out of bounds on buffer access (axis %d) Invalid mode, expected 'c' or 'fortran', got %s Indirect dimensions not supported Incompatible checksums (0x%x vs (0x2be2789, 0x2e8a02e, 0xd1743ed) = (X_binned, has_missing_values, hessians_are_constant, is_categorical, l2_regularization, left_indices_buffer, min_gain_to_split, min_hessian_to_split, min_samples_leaf, missing_values_bin_idx, monotonic_cst, n_bins_non_missing, n_features, n_threads, partition, right_indices_buffer)) Empty shape tuple for cython.array Cannot create writable memory view from read-only memoryview Cannot assign to read-only memoryview Can only create a buffer that is contiguous in memory. Buffer view does not expose strides sklearn.ensemble._hist_gradient_boosting.splitting numpy.core.multiarray failed to import <strided and direct or indirect> unable to allocate array data. itemsize <= 0 for cython.array Invalid shape in axis %d: %d. Cannot index with type '%s' <contiguous and indirect> <MemoryView of %r at 0x%x> missing_values_bin_idx <MemoryView of %r object> __pyx_unpickle_Splitter hessians_are_constant <contiguous and direct> <strided and indirect> min_hessian_to_split sum_gradient_right <strided and direct> n_bins_non_missing missing_go_to_left has_missing_values cline_in_traceback sum_hessian_right sum_gradient_left __pyx_unpickle_Enum min_gain_to_split l2_regularization sum_hessian_left min_samples_leaf allowed_features SplitInfo.__init__ __setstate_cython__ __pyx_PickleError n_samples_right left_cat_bitset dtype_is_object allocate_buffer View.MemoryView sample_indices n_samples_left is_categorical sum_gradients __reduce_cython__ __pyx_getbuffer monotonic_cst sum_hessians sum_gradient stringsource __pyx_checksum value_right upper_bound sum_hessian lower_bound feature_idx PickleError MemoryError ImportError value_left split_info __pyx_vtable__ __pyx_result histograms empty_like ValueError IndexError __reduce_ex__ __pyx_state n_threads n_samples __metaclass__ enumerate TypeError SplitInfo __setstate__ __qualname__ __pyx_type itemsize __getstate__ X_binned Splitter Ellipsis __prepare__ memview fortran bin_idx asarray update unpack uint32 struct __reduce__ pickle __name__ __module__ __import__ format encode arange zeros value start shape range numpy int32 flags error empty dtype __class__ ASCII __test__ stop step size self pack ndim name mode __main__ __init__ gain full __dict__ base obj __new__ __doc__ np id c O %.200s() takes %.8s %zd positional argument%.1s (%zd given) Buffer dtype mismatch, expected %s%s%s but got %s Buffer dtype mismatch, expected '%s' but got %s in '%s.%s' %.200s() takes no arguments (%zd given) %.200s() takes exactly one argument (%zd given) Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported! %.200s() takes no keyword arguments while calling a Python object NULL result without error in PyObject_Call memviewslice is already initialized! %.200s.%.200s is not a type object %.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject %.200s does not export expected C function %.200s C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s) Interpreter change detected - this module can only be loaded into one interpreter per process. Unexpected format string character: '%c' Expected a dimension of size %zu, got %zu Expected %d dimensions, got %d Python does not define a standard format string size for long double ('g').. Buffer dtype mismatch; next field is at offset %zd but %zd expected Acquisition count is %d (line %d) %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' Big-endian buffer not supported on little-endian compiler Buffer acquisition: Expected '{' after 'T' Cannot handle repeated arrays in format string Does not understand character buffer dtype format string ('%c') Expected a dimension of size %zu, got %d Expected a comma in format string, got '%c' Expected %d dimension(s), got %d Unexpected end of format string, expected ')' Cannot convert %.200s to %.200s __defaults__ must be set to a tuple object __kwdefaults__ must be set to a dict object __annotations__ must be set to a dict object __qualname__ must be set to a string object __name__ must be set to a string object function's dictionary may not be deleted setting function's dictionary to a non-dict __int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python. __%.4s__ returned non-%.4s (type %.200s) can't convert negative value to npy_uint8 value too large to convert to npy_uint8 can't convert negative value to unsigned char value too large to convert to unsigned char can't convert negative value to unsigned int value too large to convert to unsigned int value too large to convert to int unbound method %.200S() needs an argument calling %R should have returned an instance of BaseException, not %R raise: exception class must be a subclass of BaseException '%.200s' object is not subscriptable cannot fit '%.200s' into an index-sized integer Unable to initialize pickling for %s sklearn/ensemble/_hist_gradient_boosting/splitting.c View.MemoryView.memoryview.itemsize.__get__ View.MemoryView.memoryview.ndim.__get__ View.MemoryView.get_slice_from_memview View.MemoryView.memoryview.is_f_contig View.MemoryView.memoryview.is_c_contig View.MemoryView.array.memview.__get__ sklearn/ensemble/_hist_gradient_boosting/splitting.pyx sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_threads.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_gain_to_split.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_samples_leaf.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_hessian_to_split.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.l2_regularization.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.hessians_are_constant.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.missing_values_bin_idx.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_features.__set__ 'NoneType' object is not subscriptable hasattr(): attribute name must be string View.MemoryView.__pyx_unpickle_Enum__set_state View.MemoryView.memoryview_cwrapper Buffer has wrong number of dimensions (expected %d, got %d) Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s) Buffer is not indirectly contiguous in dimension %d. Buffer and memoryview are not contiguous in the same dimension. C-contiguous buffer is not contiguous in dimension %d C-contiguous buffer is not indirect in dimension %d Buffer exposes suboffsets but no strides Buffer not compatible with direct access in dimension %d. Buffer is not indirectly accessible in dimension %d. Buffer not fortran contiguous. Module 'splitting' has already been imported. Re-initialisation is not supported. sklearn.ensemble._hist_gradient_boosting.splitting compiletime version %s of module '%.100s' does not match runtime version %s Shared Cython type %.200s is not a type object Shared Cython type %.200s has the wrong size, try recompiling sklearn.ensemble._hist_gradient_boosting._bitset void (__pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_BITSET_INNER_DTYPE_C *) void (__pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_BITSET_INNER_DTYPE_C *, __pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_X_BINNED_DTYPE_C) unsigned char (__pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_BITSET_INNER_DTYPE_C *, __pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_X_BINNED_DTYPE_C) init sklearn.ensemble._hist_gradient_boosting.splitting View.MemoryView.memoryview.size.__get__ View.MemoryView.memoryview.nbytes.__get__ View.MemoryView.memoryview.suboffsets.__get__ View.MemoryView.memoryview.shape.__get__ View.MemoryView.memoryview_fromslice View.MemoryView.memoryview_copy_from_slice sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.right_indices_buffer.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.left_indices_buffer.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.partition.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.monotonic_cst.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.is_categorical.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.has_missing_values.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_bins_non_missing.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.X_binned.__get__ Cannot transpose memoryview with indirect dimensions View.MemoryView.transpose_memslice View.MemoryView.memoryview_copy View.MemoryView.memoryview.T.__get__ Step may not be zero (axis %d) All dimensions preceding dimension %d must be indexed and not sliced View.MemoryView.slice_memviewslice View.MemoryView.memoryview.__str__ View.MemoryView.memoryview.__repr__ View.MemoryView.memoryview.setitem_indexed View.MemoryView.assert_direct_dimensions View.MemoryView.memoryview.setitem_slice_assign_scalar View.MemoryView.Enum.__setstate_cython__ View.MemoryView.array.__getattr__ View.MemoryView.array.__setitem__ Subscript deletion not supported by %.200s View.MemoryView.array.__getitem__ View.MemoryView.array.get_memview View.MemoryView._memoryviewslice.__setstate_cython__ View.MemoryView._memoryviewslice.__reduce_cython__ View.MemoryView.memoryview.__setstate_cython__ View.MemoryView.memoryview.__reduce_cython__ View.MemoryView.array.__setstate_cython__ View.MemoryView.array.__reduce_cython__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_threads.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_gain_to_split.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_samples_leaf.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.min_hessian_to_split.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.l2_regularization.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.hessians_are_constant.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.missing_values_bin_idx.__get__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_features.__get__ View.MemoryView.__pyx_unpickle_Enum integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer View.MemoryView.memoryview.__cinit__ too many values to unpack (expected %zd) need more than %zd value%.1s to unpack 'NoneType' object is not iterable View.MemoryView.memoryview.__setitem__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.n_bins_non_missing.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.left_indices_buffer.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.right_indices_buffer.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.monotonic_cst.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.has_missing_values.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.is_categorical.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.partition.__set__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.X_binned.__set__ View.MemoryView.memoryview.strides.__get__ View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object View.MemoryView.copy_data_to_temp View.MemoryView.memoryview_copy_contents View.MemoryView.memoryview.setitem_slice_assignment sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.__init__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.__reduce_cython__ View.MemoryView.memoryview.is_slice PyObject_GetBuffer: view==NULL argument is obsolete View.MemoryView.memoryview.__getbuffer__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.find_node_split carray.to_py.__Pyx_carray_to_py___pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_BITSET_INNER_DTYPE_C View.MemoryView.memoryview.convert_item_to_object View.MemoryView._memoryviewslice.convert_item_to_object sklearn.ensemble._hist_gradient_boosting.splitting.compute_node_value local variable '%s' referenced before assignment View.MemoryView.memoryview.__getitem__ sklearn.ensemble._hist_gradient_boosting.splitting.SplitInfo.__init__ sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.split_indices Argument '%.200s' has incorrect type (expected %.200s, got %.200s) View.MemoryView.array.__cinit__ Argument '%.200s' must not be None object of type 'NoneType' has no len() expected bytes, NoneType found View.MemoryView.array.__getbuffer__ sklearn.ensemble._hist_gradient_boosting.splitting.__pyx_unpickle_Splitter__set_state sklearn.ensemble._hist_gradient_boosting.splitting.Splitter.__setstate_cython__ sklearn.ensemble._hist_gradient_boosting.splitting.__pyx_unpickle_Splitter Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.array_cwrapper View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy View.MemoryView.Enum.__reduce_cython__ sklearn.ensemble._hist_gradient_boosting.splitting._memoryviewslice Internal class for passing memoryview slices to Python sklearn.ensemble._hist_gradient_boosting.splitting.memoryview sklearn.ensemble._hist_gradient_boosting.splitting.Enum sklearn.ensemble._hist_gradient_boosting.splitting.array sklearn.ensemble._hist_gradient_boosting.splitting.Splitter Splitter used to find the best possible split at each node.
A split (see SplitInfo) is characterized by a feature and a bin.
The Splitter is also responsible for partitioning the samples among the
leaves of the tree (see split_indices() and the partition attribute).
Parameters
----------
X_binned : ndarray of int, shape (n_samples, n_features)
The binned input samples. Must be Fortran-aligned.
n_bins_non_missing : ndarray, shape (n_features,)
For each feature, gives the number of bins actually used for
non-missing values.
missing_values_bin_idx : uint8
Index of the bin that is used for missing values. This is the index of
the last bin and is always equal to max_bins (as passed to the GBDT
classes), or equivalently to n_bins - 1.
has_missing_values : ndarray, shape (n_features,)
Whether missing values were observed in the training data, for each
feature.
is_categorical : ndarray of bool of shape (n_features,)
Indicates categorical features.
monotonic_cst : ndarray of int of shape (n_features,), dtype=int
Indicates the monotonic constraint to enforce on each feature.
- 1: monotonic increase
- 0: no constraint
- -1: monotonic decrease
Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.
l2_regularization : float
The L2 regularization parameter.
min_hessian_to_split : float, default=1e-3
The minimum sum of hessians needed in each node. Splits that result in
at least one child having a sum of hessians less than
min_hessian_to_split are discarded.
min_samples_leaf : int, default=20
The minimum number of samples per leaf.
min_gain_to_split : float, default=0.0
The minimum gain needed to split a node. Splits with lower gain will
be ignored.
hessians_are_constant: bool, default is False
Whether hessians are constant.
n_threads : int, default=1
Number of OpenMP threads to use.
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1ýÿ BBE A(D0W
(D BBBA j
(D EBBI L
(D BBBA 4 |
È1ýÿÇ AAG
CAD I
CAC ( ´
`2ýÿF AG0
AI A , à
3ýÿz BAA ±
CBF Ô5ýÿ) Ac D , è5ýÿÒ BIA [
ABF L
ABA \
AEF \ t 6ýÿÎ BBD D(D0X
(D ABBG L
(G ABBH Q
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D p
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EA I
EB \ $ 8ýÿ( BEI A(G0u
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(D ABBD \
(D DBBH , `9ýÿ¿ yD `ABÆp XÆQ ( ´ ð9ýÿÑ AG0
AF §A P à ¤;ýÿ< KDD D0
AABF
FABK HÃÆÌH0d 4 =ýÿ¬ BGB E(D0A8Jà_
8A0A(B BBBH º
8C0A(B BBBE ØDýÿ¿ BBB B(A0D8G¦BBE A¨A°A¸AÀAÈAÐBØBàGèGðGøGGGGG G¨G°G¸GÀGÈGÐbÀEDA A¨A°A¸CÀBÈBÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐX_
BDA A¨A°C¸AÀBÈBÐBØBàGèGðGøGGGGG G¨G°G¸GÀGÈGÐM p
8A0A(B BBBF ( 4 Lýÿ^ BAD |
DBG ( ` 4Mýÿ^ BAD |
DBG L hNýÿÇ BED D(GPè
(C ABBJ |
(F ABBI 4 Ü èOýÿ| BAD B
DBA @
AEJ < 0Qýÿ AD A
AI G
AH V
AI G
AC 8 T Rýÿ BAD S
ABC M
ABH L ôSýÿâ BBE D(G0V
(G BBBI I
(D BBBE P à Týÿí BBA q
BBF D
BBH H
IBM A
EBH ( 4 0UýÿÌ AAG m
CAF 8 ` ÔUýÿÌ RD kAFÆH m
DÆO j
CÆE ` hVýÿÂ BAA D0
DABK i
AABC f
AABF `
CABA 0 ÔYýÿ BAA D0q
AABC 8 4 ¼Ñüÿa bBA A(A00(D ABB H p áÒüÿ½ BBE B(A0A8DP8D0A(B BBB , ¼ 8Zýÿ AG0Õ
DO O
AH ` ì ¨[ýÿI BBB E(D0D8F`?
8A0A(B BBBF N
8A0A(B BBBE P býÿL D R
J k p ÄbýÿL D R
J k ( ôbýÿl B]
A J
F M
C 0 ¼ 8dýÿ AJÀ$
AH R
AE nA0 ð eýÿx AJÀ
AG R
AE nA $ ÐfýÿD D O
E k , D gýÿé BAA U
ABD t Àhýÿ¨ AD y
AA , Liýÿï BAA _
ABJ È kýÿ¨ AD y
AA ì kýÿ¨ AD y
AA , $lýÿ¿ BAA J
ABG , @ ´mýÿ¿ BAA J
ABG , p Doýÿï BAA _
ABJ d qýÿU BBB B(A0A8D@
8D0A(B BBBF N
8C0A(B BBBA l üsýÿø BBH A(C0G@²
0D(A BBBD d
0D(A BBBI I
0D(A BBBD \ x uýÿr BHE E(D0A8G0
8A0A(B BBBF _H NA < Ø ¬zýÿN BQfHWAÒ
BC Î
BH < ¼|ýÿV BQfMUAÒ
BH Î
BH ü X ¶Ðüÿ BIB E(A0A8GÐç Ø[àMèMðMøFFFFF IÐ[ØQàBèFðFøAAAFA IÐZØQàBèFðFøAAAFA IÐØQàBèFðFøAAAFA IÐ8A0A(B BBB H X Ü}ýÿ BBB B(A0A8DPV
8D0A(B BBBG D ¤ 0ýÿ; BID h
BBE L
BBH @
BBD L ì (ýÿ BBA A(G@¢
(A ABBK
(C ABBD 8 < øýÿf BBA A(I@ß
(A ABBD H x ,ýÿ BBB B(A0A8D`3
8D0A(B BBBB | Ä pýÿ\ D d(Q0F8F@FHFPFXF`FhFpCxCCCCC C¨C°C¸CÀCÈCÐCØCàCèBðO I
H c | D Pýÿü D F(O0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
F k | Ä Ћýÿü D F(O0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
F k | D Pýÿü D F(O0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
F k | Ä Ќýÿü D F(J0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
K k | D Pýÿü D F(J0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
K k | Ä Ѝýÿü D F(J0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
K k | D Pýÿü D F(J0M8F@FHFPFXF`FhFpFxFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðQ I
K k | Ä ЎýÿÌ D F(J0M8F@FHFPFXF`FhFpFxFFCCC C¨C°C¸CÀCÈCÐCØCàCèCðQ I
B k H D! ýÿê BBB B(D0D8DPJ
8F0A(B BBBC ` ! Đýÿü BBB B(A0A8D@
8F0A(B BBBH G8C0A(B BBB P ô! `ýÿÿ BDGðP
DBE @
DBB D
DBF ¢
DBH H H" ýÿg BBB E(D0D8D@Z
8F0A(B BBBH ` " 0ýÿ× BEB E(D0D8GPt
8A0A(B BBBE ^
8A0A(B BBBH P ø" ¬ýÿè BBA D0å
ABBE v
ABBE e
CBBD | L# HýÿA BBB E(A0A8DPb
8A0A(B BBBC x
8C0A(B BBBD F
8A0A(B BBBH 4 Ì# ýÿ BDG x
ABH q
CBJ L $ ýÿ¨ BBB E(D0G8GÐü
8A0A(B BBBE T$ à£ýÿ¢ At
K p
H @ x$ l¤ýÿ BDD0i
ABJ V
ABG v
CBE ¼$ H¥ýÿ 4 Ð$ D¥ýÿP BEA T
BBH _BB P % \¥ýÿ) BBA D0b
DBBE L
DBBD q
DBBG @ \% 8¦ýÿ BDD0a
ABB V
ABG v
CBE \ % §ýÿÈ BBA A(L@¤
(D ABBA d
(D ABBC z
(D ABBE , & t¨ýÿÒ BHH h
CBA , 0& $©ýÿÒ BHH h
CBA , `& ԩýÿÒ BHH h
CBA , & ªýÿÒ BHH h
CBA , À& 4«ýÿÒ BHH h
CBA , ð& ä«ýÿÒ BHH h
CBA ' ¬ýÿL D U
G k @' ĬýÿL D V
F k `' ô¬ýÿL D T
H k ' $ýÿL D V
F k ' TýÿL D V
F k À' ýÿL D U
G k à' ´ýÿL D U
G k ( äýÿL D T
H k p ( ®ýÿ BBB B(A0D8DpÛ
8A0A(B BBBJ |xW_xFp^
8A0A(B BBBH H ( 0·ýÿ´ BEB B(D0A8D`Ò
8D0A(B BBBE X à( ¤½ýÿÊ BBE B(A0D8D`±hWpRhF`
8D0A(B BBBG <) Âýÿ; Dv H T) @Âýÿ1 BBB B(D0A8Dp!
8D0A(B BBBA H ) 4Îýÿß BBB E(A0A8DP
8D0A(B BBBE < ì) ÈÔýÿS BHJðiøMUøAð
ABC ,* èØýÿ. N_ < D* ÙýÿS BHJðiøMUøAð
ABC * Ýýÿ. N_ < * 8ÝýÿS BHJðiøMUøAð
ABC Ü* Xáýÿ. N_ < ô* páýÿS BHJðiøMUøAð
ABC 4+ åýÿ. N_ < L+ ¨åýÿS BHJðiøMUøAð
ABC + Èéýÿ. N_ < ¤+ àéýÿS BHJðiøMUøAð
ABC ä+ îýÿ. N_ < ü+ îýÿS BHJðiøMUøAð
ABC <, 8òýÿ. N_ < T, Pòýÿ; BHJðpøMUøAðï
ABG , Pöýÿ. N_ @ ¬, höýÿ4 BBB A(A0D@
0A(A BBBI H ð, døýÿR BBE E(A0A8I`P
8D0A(B BBBJ <- x þÿ Dj
B C
E `- ä þÿZ BBB G(D0A8G Ø
8D0A(B BBBB ¨G°Y¨A ϨJ°S¨A ¨F°s¨B ä- Àþÿß BEB E(A0A8GðüøFFFFF F¨F°F¸FÀCÈCÐCØCàCèCðCøCCCCC C¨C°C¸CÀFÈFÐFØFàFèFðFøFFFCC C¨C°C¸CÀCÈCÐCØCàCèCðCøCCBLðX
8A0A(B BBBF / þÿr4 BLB B(A0A8J"HOBJHOBRHOBAHNA=FZAúLGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐLöLGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐL
8A0A(B BBBG ¬ 0 |Iþÿ´ BMB B(A0A8JÀFÈFÐFØFàFèFðFøFFFFF F¨C°C¸CÀCÈCÐCØCàCèCðCøCCCQÀOÈOÐPØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀjÈOÐMØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFWÀjÈVÐKØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀ}ÈOÐMØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀTÈOÐMØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀGÈVÐKØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀTÈVÐKØFàFèFðFøFFFFF F¨F°F¸FÀFÈFÐFØFàFèFðFøFFFLÀ
8D0A(B BBBC d 84 `þÿY BBB E(D0A8D
8A0A(B BBBB 6
8A0A(B BBBH 8 4 gþÿ> BBA A(D0²
(A ABBF l Ü4 iþÿø0 BFB B(A0D8JàëèKð]èAàcèDðNèAày
8D0A(B BBBE d L5 þÿa
BBB E(F0A8DÉ
8A0A(B BBBG |
8A0A(B BBBJ ´5 ¤þÿd DZ
B C \ Ô5 p¤þÿ BBA A(G£
(A ABBJ TW_F\
(C ABBD p 46 0ªþÿò BIB E(A0A8GÀÜÈBÐHØAàBèDðDøDVÀtÈBÐBØBàBèBðAøDSÀÜÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGSÀ8
8D0A(B BBBA /ÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGSÀ ` ¨7 ¼·þÿo IBB A(D0º
(D BBBJ H
(D BBBF M
(D BBBA L 8 ȹþÿW BBB A(D0G@x
0A(A BBBF tHWP_HF@ ( \8 غþÿ¯ ADD H
AAE \ 8 \¿þÿº BLB B(A0A8GIW_Fa
8A0A(B BBBE ¸ è8 ¼Êþÿü1 BLB E(A0A8GÐÿØKàZØAÐØBàBèBðBøBBGaÐO lÐaØBàBèBðBøBBBaÐS lÐ]
8D0A(B BBBC ä ¤9 üþÿî BBB D(A0JkMUAúGGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐLG
0A(A BBBI W_F_
0C(A BBBG X : ÿÿ BBB E(A0D8D ·¨E°l¨F i
8D0A(B BBBH L è: Ìÿÿ© BBA A(D@º
(A ABBF
(F ABBH ´ 8; ,ÿÿÛ BBB B(A0A8G Y¨ G° Z¨ A ¨ I° O¨ B Û¨ I° O¨ B ð¨ I° O¨ B ¨ N° X¨ A ¨ L° W¨ A M
8A0A(B BBBH ð; T4ÿÿ¢ At
K p
H p < à4ÿÿ BBB B(A0D8DpÛ
8A0A(B BBBJ |xW_xFp^
8A0A(B BBBH < ü=ÿÿ" BGB J(D0A8GÐ\
8A0A(B BBBI -ØMàGèGðGøGGGGG G¨G°G¸GÀGÈGÐGØGàGèGðGøGGGGG G¨G°G¸GÀGÈGÐGØGàGèGðDøDDDDD D¨D°D¸DÀDÈDÐDØDàDèCðSÐ 8 ¬= Cÿÿ AJlOKA
AA dC è= \Dÿÿ @ ü= XDÿÿz BAJÀrÈLÐMÀî
ABG dCB @> Eÿÿ d T> Eÿÿ BBB B(A0A8LPe
8A0A(B BBBK æ
8A0A(B BBBH ¼> qÉüÿ ` Øì XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX àì 6p Fp Vp fp vp p p ¦p ¶p Æp Öp æp öp q q &q 6q Fq Vq fq vq q q ¦q ¶q Æq Öq æq öq r r &r 6r Fr Vr fr vr r r ¦r ¶r Ær Ör ær ör s s &s 6s Fs Vs fs vs s s ¦s ¶s Æs Ös æs ös t t &t 6t Ft Vt ft vt t t ¦t ¶t Æt Öt æt öt u u &u 6u Fu Vu fu vu u u ¦u ¶u Æu Öu æu öu v v &v 6v Fv Vv fv vv v v ¦v ¶v Æv Öv æv öv w w &w 6w Fw Vw fw vw w w ¦w ¶w Æw Öw æw öw x x &x 6x Fx Vx fx vx x x ¦x ¶x Æx Öx æx öx y y ÿÿÿÿÿÿÿÿ Compute a node's value.
The value is capped in the [lower_bound, upper_bound] interval to respect
monotonic constraints. Shrinkage is ignored.
See Equation 5 of:
:arxiv:`T. Chen, C. Guestrin, (2016) XGBoost: A Scalable Tree Boosting System,
<1603.02754>.`
For each feature, find the best bin to split on at a given node.
Return the best split info among all features.
Parameters
----------
n_samples : int
The number of samples at the node.
histograms : ndarray of HISTOGRAM_DTYPE of shape (n_features, max_bins)
The histograms of the current node.
sum_gradients : float
The sum of the gradients for each sample at the node.
sum_hessians : float
The sum of the hessians for each sample at the node.
value : float
The bounded value of the current node. We directly pass the value
instead of re-computing it from sum_gradients and sum_hessians,
because we need to compute the loss and the gain based on the
*bounded* value: computing the value from
sum_gradients / sum_hessians would give the unbounded value, and
the interaction with min_gain_to_split would not be correct
anymore. Side note: we can't use the lower_bound / upper_bound
parameters either because these refer to the bounds of the
children, not the bounds of the current node.
lower_bound : float
Lower bound for the children values for respecting the monotonic
constraints.
upper_bound : float
Upper bound for the children values for respecting the monotonic
constraints.
allowed_features : None or ndarray, dtype=np.uint32
Indices of the features that are allowed by interaction constraints to be
split.
Returns
-------
best_split_info : SplitInfo
The info about the best possible split among all features.
Split samples into left and right arrays.
The split is performed according to the best possible split
(split_info).
Ultimately, this is nothing but a partition of the sample_indices
array with a given pivot, exactly like a quicksort subroutine.
Parameters
----------
split_info : SplitInfo
The SplitInfo of the node to split.
sample_indices : ndarray of unsigned int, shape (n_samples_at_node,)
The indices of the samples at the node to split. This is a view
on self.partition, and it is modified inplace by placing the
indices of the left child at the beginning, and the indices of
the right child at the end.
Returns
-------
left_indices : ndarray of int, shape (n_left_samples,)
The indices of the samples in the left child. This is a view on
self.partition.
right_indices : ndarray of int, shape (n_right_samples,)
The indices of the samples in the right child. This is a view on
self.partition.
right_child_position : int
The position of the right child in ``sample_indices``.
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