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duality-group / scikit-learn python
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Version:
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Ҹ HEÂLlH¸ÿÿÿÿÿÿÿI9ÅÞ J<í ènÍûÿHs(IÇLèL)ðHÑèM4ÇHCHHPH9òt H)òL÷è"ÊûÿHCH;H4Å èNÍûÿL;LkIO,&Ls(LkHHCH HC IE HC8H HC@éæþÿÿ L)òIÅHÑêL4ÑLêH)òL9övL9ît«L÷èªÉûÿë¡ L9îtI|$H)×L÷èÉûÿëH=Úk è-ÃûÿèËûÿ é[Äûÿ HìHÄà 'bool' 'complex long double' 'signed char' 'unsigned char' 'short' 'unsigned short' 'int' 'unsigned int' 'long' 'unsigned long' 'long long' 'unsigned long long' 'char' 'float' 'double' a struct Python object a pointer a string end unparseable format string 'complex double' 'complex float' 'long double' at least at most exactly Unknown exception numpy.core._multiarray_umath _ARRAY_API _ARRAY_API not found _ARRAY_API is NULL pointer __pyx_capi__ name __loader__ loader __file__ origin __package__ parent __path__ submodule_search_locations __pyx_fatalerror Missing type object name '%U' is not defined cannot import name %S buffer dtype int an integer is required sklearn/tree/_tree.cpp %s (%s:%d) stringsource sklearn/tree/_tree.pyx sklearn/tree/_tree.pxd __del__ compute_feature_importances Buffer not C contiguous. View.MemoryView._err_dim __cinit__ 'NoneType' is not iterable Expected %.16s, got %.200s build sample_weight View.MemoryView.Enum.__init__ compute_partial_dependence tuple View.MemoryView.memview_slice Index out of bounds (axis %d) memviewsliceobj View.MemoryView._err View.MemoryView._err_extents Dimension %d is not direct _check_node_ndarray _check_n_classes items shape format __pyx_unpickle_TreeBuilder __pyx_unpickle_Enum _check_value_ndarray sklearn.tree._tree.Tree.apply View.MemoryView._unellipsify %d.%d builtins cython_runtime __builtins__ bool complex numpy flatiter broadcast generic number unsignedinteger inexact complexfloating flexible character ufunc sklearn.tree._criterion ClassificationCriterion RegressionCriterion sklearn.tree._splitter Splitter sklearn.neighbors._quad_tree _QuadTree sklearn.tree._utils WeightedPQueue WeightedMedianCalculator EPSILON float sizet_ptr_to_ndarray __pyx_fuse_0safe_realloc __pyx_fuse_1safe_realloc __pyx_fuse_2safe_realloc __pyx_fuse_3safe_realloc __pyx_fuse_4safe_realloc __pyx_fuse_5safe_realloc __pyx_fuse_6safe_realloc __pyx_fuse_7safe_realloc __pyx_fuse_8safe_realloc __init__.pxd numpy.import_array (n) getbuffer(obj, view, flags) init sklearn.tree._tree orig_tree _build_pruned_tree_ccp base __reduce_cython__ __setstate_cython__ sklearn.tree._tree.memoryview T strides suboffsets ndim itemsize nbytes is_c_contig is_f_contig copy copy_fortran sklearn.tree._tree.Enum sklearn.tree._tree.array memview __getattr__ sklearn.tree._tree.Tree children_left children_right n_leaves feature threshold impurity weighted_n_node_samples value n_features n_outputs max_n_classes max_depth node_count capacity __reduce__ __getstate__ __setstate__ predict decision_path ccp_pruning_path _all_compatible_dtype_dicts _dtype_to_dict unsigned char const DTYPE_t const DOUBLE_t double Node left_child right_child float64_t INT32_t SIZE_t Òüÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ'üÿ`üÿ`üÿÊüÿ`üÿ`üÿ`üÿ`üÿ`üÿüÿúüÿ`üÿ`üÿòüÿ`üÿ`üÿêüÿâüÿÚüÿ`üÿ`üÿüÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿ`üÿüÿ/üÿµüÿ`üÿ üÿüÿ
üÿüÿ`üÿ`üÿüÿ`üÿ`üÿ`üÿpüÿxüÿ`üÿpüÿt¾ûÿ¾ûÿ¼¾ûÿûÿ ¿ûÿ%¿ûÿJ¿ûÿo¿ûÿ¿ûÿ¹¿ûÿûÿôüÿÄüÿÄüÿôüÿÄüÿÄüÿÄüÿÄüÿÄüÿüÿ´üÿÄüÿÄüÿ¤üÿÄüÿÄüÿ¤üÿ¤üÿ¤üÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿÄüÿôüÿôüÿ¤üÿÄüÿ´üÿüÿüÿ´üÿÄüÿÄüÿ¤üÿÄüÿÄüÿÄüÿôüÿ¤üÿÄüÿôüÿ üÿCüÿCüÿ üÿCüÿCüÿCüÿCüÿCüÿ üÿ üÿCüÿCüÿ üÿCüÿCüÿ3 üÿ3 üÿ üÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿCüÿà üÿc üÿ üÿCüÿ üÿ üÿà üÿà üÿCüÿCüÿà üÿCüÿCüÿCüÿà üÿà üÿCüÿà üÿ üÿüÿüÿ üÿüÿüÿüÿüÿüÿäüÿÔüÿüÿüÿÔüÿüÿüÿÄüÿÄüÿÄüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿüÿ üÿ üÿ. üÿüÿ üÿùüÿäüÿÔüÿüÿüÿÔüÿüÿüÿüÿ üÿÄüÿüÿ üÿ8üÿ0üÿ0üÿ8üÿ0üÿ0üÿ0üÿ0üÿ0üÿÐüÿÀüÿ0üÿ0üÿ°üÿ0üÿ0üÿ°üÿ°üÿ°üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ0üÿ8üÿ8üÿøüÿ0üÿ üÿàüÿÐüÿÀüÿ0üÿ0üÿ°üÿ0üÿ0üÿ0üÿ8üÿ°üÿ0üÿ8üÿ"üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿl!üÿ!üÿ!üÿl!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿl!üÿÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿu!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ!üÿ"üÿ!üÿh!üÿó!üÿÿ!üÿÄ üÿó!üÿ!üÿÄ üÿ!üÿ!üÿ!üÿ!üÿ!üÿÄ üÿÄ üÿ!üÿ!üÿÄ üÿ!üÿ!üÿÄ üÿ!üÿÄ üÿ!üÿ!üÿT#üÿ!üÿ!üÿ!üÿ!üÿ!üÿð"üÿ!üÿ!üÿ!üÿó!üÿ!üÿ!üÿ!üÿÄ üÿÄ üÿÄ üÿ!üÿÄ üÿÄ üÿÄ üÿÄ üÿ!üÿ!üÿÄ üÿ!üÿ!üÿ!üÿÄ üÿÄ üÿ!üÿþ$üÿ!üÿ!üÿ!üÿ!üÿH"üÿ!üÿ!üÿ!üÿ!üÿ¦"üÿ9üÿ¬9üÿ,9üÿ<9üÿL9üÿXHüÿHüÿHüÿ8HüÿHHüÿdIüÿ|IüÿDIüÿLIüÿTIüÿUüÿ`UüÿðTüÿèTüÿ UüÿVüÿìVüÿ|VüÿtVüÿVüÿ(XüÿxXüÿXüÿ XüÿXüÿ´YüÿZüÿYüÿYüÿ¤Yüÿ@[üÿ[üÿ [üÿ[üÿ0[üÿÌ\üÿ ]üÿ¬\üÿ¤\üÿ¼\üÿ¸Óüÿ`ÖüÿªÔüÿ¡ÔüÿÔüÿÔ×üÿ|ÚüÿÆØüÿ½Øüÿ´ØüÿðÛüÿÞüÿâÜüÿÙÜüÿÐÜüÿ,äüÿäüÿ¤âüÿâüÿäüÿ äüÿØäüÿÐâüÿÈâüÿäüÿ åüÿÔäüÿ<ãüÿ4ãüÿ$åüÿøåüÿ çüÿ¼åüÿ³åüÿªåüÿ¡åüÿåüÿçåüÿ æüÿ1æüÿVæüÿ{æüÿ æüÿøüÿLøüÿ$öüÿ öüÿü÷üÿ`øüÿøüÿPöüÿHöüÿHøüÿ¬øüÿäøüÿöüÿöüÿøüÿàøüÿ ùüÿ¸öüÿ°öüÿ0ùüÿ<ùüÿúüÿIúüÿ@úüÿ7úüÿ.úüÿ%úüÿ úüÿ'ùüÿxúüÿúüÿÂúüÿçúüÿ ûüÿ1ûüÿð¬ýÿ ¬ýÿ@¬ýÿ8¬ýÿýÿ|þÿ¼þÿÔþÿÌþÿdþÿðþÿ¸þÿ@þÿ8þÿþÿTþÿ$þÿtþÿlþÿ þÿ+þÿ +þÿ0$þÿ($þÿè*þÿ´+þÿô+þÿ$þÿ$þÿ+þÿ7WþÿÅVþÿRþÿRþÿRþÿzRþÿ*WþÿÁVþÿæVþÿ WþÿWþÿ¤hþÿÜhþÿ<eþÿ4eþÿhþÿsþÿHsþÿ¨oþÿ oþÿørþÿ|}þÿ´}þÿzþÿ zþÿd}þÿ0ÿÿ°ÿÿ°ÿÿ°ÿÿ°ÿÿÿÿ ÿÿ°ÿÿ°ÿÿ°ÿÿ°ÿÿ°ÿÿ°ÿÿÿÿ°ÿÿXÿÿ°ÿÿ°ÿÿ ÿÿ dtype_dict_with_modified_bitness_and_endianness No value specified for struct attribute 'weighted_n_node_samples' No value specified for struct attribute 'n_node_samples' No value specified for struct attribute 'impurity' No value specified for struct attribute 'threshold' No value specified for struct attribute 'feature' No value specified for struct attribute 'right_child' Incompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name)) Incompatible checksums (0x%x vs (0xd41d8cd, 0xe3b0c44, 0xda39a3e) = ()) value array from the pickle should be a C-contiguous array unable to allocate shape and strides. numpy.core.umath failed to import node array from the pickle should be a C-contiguous array node array from the pickle has an incompatible dtype:
- expected: no default __reduce__ due to non-trivial __cinit__ n_classes from the pickle has an incompatible dtype:
- expected: got differing extents in dimension %d (got %d and %d) dtype_dict_with_modified_endianness dtype_dict_with_modified_bitness You have loaded Tree version which cannot be imported X should be in np.ndarray format, got %s X should be in csr_matrix format, got %s X.dtype should be np.float32, got %s Wrong shape for value array from the pickle: expected Wrong dimensions for n_classes from the pickle: expected 1, got Unable to convert item to object Total weight should be 1.0 but was %.9f Out of bounds on buffer access (axis %d) No value specified for struct attribute 'left_child' No support for np.int64 index based sparse matrices Invalid mode, expected 'c' or 'fortran', got %s Indirect dimensions not supported Incompatible checksums (0x%x vs (0x1c9830e, 0x662fb7f, 0xfb57f19) = (max_depth, min_impurity_decrease, min_samples_leaf, min_samples_split, min_weight_leaf, splitter)) 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 value array from the pickle has an incompatible dtype:
- expected: numpy.core.multiarray failed to import _dtype_dict_with_modified_bitness Wrong dimensions for node array from the pickle: expected 1, got <strided and direct or indirect> __pyx_unpickle__CCPPruneController unable to allocate array data. all_compatible_dtype_dicts itemsize <= 0 for cython.array compute_feature_importances _all_compatible_dtype_dicts Invalid shape in axis %d: %d. __pyx_unpickle_TreeBuilder Cannot index with type '%s' weighted_n_node_samples node_ndarray_dtype_dict <contiguous and indirect> <MemoryView of %r at 0x%x> sklearn/tree/_tree.pyx <MemoryView of %r object> Can't initialize array. min_impurity_decrease <contiguous and direct> _build_pruned_tree_ccp DepthFirstTreeBuilder <strided and indirect> indexing_field_names BestFirstTreeBuilder n_constant_features expected_dtype_size _check_value_ndarray <strided and direct> sklearn.tree._tree resizing tree to %d pruning_controller node_ndarray_dtype cline_in_traceback _check_node_ndarray allowed_dtype_size allowed_dtype_dict _CCPPruneController __pyx_unpickle_Enum min_samples_split leaves_in_subtree ascontiguousarray min_samples_leaf ccp_pruning_path target_features __setstate_cython__ __pyx_PickleError min_weight_leaf dtype_is_object _check_n_classes allocate_buffer View.MemoryView n_node_samples max_leaf_nodes expected_shape expected_dtype children_right asfortranarray TREE_UNDEFINED value_ndarray sample_weight __reduce_cython__ __pyx_getbuffer _dtype_to_dict decision_path children_left stringsource sort_indices scipy.sparse __pyx_checksum pruning tree node_ndarray newbyteorder c_contiguous total_items right_child path_finder logical_and TreeBuilder PickleError MemoryError ImportError _AlphaPruner __pyx_vtable__ __pyx_result node_count n_features left_child impurities dtype_dict csr_matrix contiguous ccp_alphas ValueError _PathFinder NODE_DTYPE IndexError threshold same_kind __reduce_ex__ __pyx_state orig_tree normalize n_outputs n_classes max_depth enumerate ccp_alpha TypeError TREE_LEAF splitter __setstate__ __pyx_type node_idx itemsize issparse impurity __getstate__ endswith calcsize KeyError Ellipsis reshape replace predict ndarray n_nodes memview is_left indices fortran float64 float32 feature casting asarray values update unpack struct __reduce__ pickle parent __name__ indptr __import__ format fields encode double astype DOUBLE zeros uint8 tocsc start shape range order numpy nodes items int32
- got : , got flags finfo error equiv empty dtype depth count __class__ build array apply DTYPE ASCII tree __test__ take stop step size rest pack ones ndim name mode __main__ kind join intp __dict__ data copy clip base axis Tree sum str out obj __new__ max inf
- got: eps end , } : ^ np id f8 dt y (%s) i c X T{ P O C 8 4 %.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' memviewslice is already initialized! while calling a Python object NULL result without error in PyObject_Call _ARRAY_API is not PyCapsule object module compiled against ABI version 0x%x but this version of numpy is 0x%x module compiled against API version 0x%x but this version of numpy is 0x%x FATAL: module compiled as unknown endian FATAL: module compiled as little endian, but detected different endianness at runtime %.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 invalid vtable found for imported type %.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 Argument '%.200s' has incorrect type (expected %.200s, got %.200s) sklearn.tree._tree._add_to_frontier join() result is too long for a Python string __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) Unable to initialize pickling for %s Buffer has wrong number of dimensions (expected %d, got %d) Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) value too large to convert to int '%.200s' object is not subscriptable cannot fit '%.200s' into an index-sized integer raise: exception class must be a subclass of BaseException calling %R should have returned an instance of BaseException, not %R View.MemoryView.memoryview.itemsize.__get__ View.MemoryView.memoryview.ndim.__get__ View.MemoryView.array.memview.__get__ sklearn.tree._tree.BestFirstTreeBuilder._add_split_node sklearn.tree._tree.Tree.capacity.__set__ sklearn.tree._tree.Tree.node_count.__set__ sklearn.tree._tree.Tree.max_depth.__set__ sklearn.tree._tree.Tree.max_n_classes.__set__ sklearn.tree._tree.Tree.n_outputs.__set__ sklearn.tree._tree.Tree.n_features.__set__ sklearn.tree._tree.Tree.compute_feature_importances sklearn.tree._tree.Tree._resize_c sklearn.tree._tree.Tree._resize sklearn.tree._tree.Tree._add_node View.MemoryView.memoryview_cwrapper 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. View.MemoryView.memoryview.size.__get__ View.MemoryView.memoryview.nbytes.__get__ View.MemoryView.memoryview.suboffsets.__get__ View.MemoryView.memoryview.shape.__get__ View.MemoryView.memoryview.__str__ View.MemoryView.memoryview.__repr__ View.MemoryView.memoryview.setitem_indexed View.MemoryView.array.__getattr__ View.MemoryView.array.__setitem__ Subscript deletion not supported by %.200s View.MemoryView.array.get_memview sklearn.tree._tree.TreeBuilder.__reduce_cython__ '%.200s' object is unsliceable sklearn.tree._tree.Tree.value.__get__ sklearn.tree._tree.Tree.__getstate__ View.MemoryView.array.__getitem__ sklearn.tree._tree.Tree.weighted_n_node_samples.__get__ sklearn.tree._tree.Tree.n_node_samples.__get__ sklearn.tree._tree.Tree.impurity.__get__ sklearn.tree._tree.Tree.threshold.__get__ sklearn.tree._tree.Tree.feature.__get__ sklearn.tree._tree.Tree.children_right.__get__ sklearn.tree._tree.Tree.children_left.__get__ View.MemoryView.memoryview_fromslice View.MemoryView.memoryview_copy_from_slice 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.tree._tree._PathFinder.__setstate_cython__ sklearn.tree._tree._PathFinder.__reduce_cython__ sklearn.tree._tree._AlphaPruner.__setstate_cython__ sklearn.tree._tree._AlphaPruner.__reduce_cython__ sklearn.tree._tree.BestFirstTreeBuilder.__setstate_cython__ sklearn.tree._tree.BestFirstTreeBuilder.__reduce_cython__ sklearn.tree._tree.DepthFirstTreeBuilder.__setstate_cython__ sklearn.tree._tree.DepthFirstTreeBuilder.__reduce_cython__ sklearn.tree._tree.Tree.capacity.__get__ sklearn.tree._tree.Tree.node_count.__get__ sklearn.tree._tree.Tree.max_depth.__get__ sklearn.tree._tree.Tree.max_n_classes.__get__ sklearn.tree._tree.Tree.n_outputs.__get__ sklearn.tree._tree.Tree.n_features.__get__ sklearn.tree._tree.Tree.n_classes.__get__ View.MemoryView.memoryview.__cinit__ View.MemoryView.get_slice_from_memview View.MemoryView.memoryview.is_f_contig View.MemoryView.memoryview.is_c_contig View.MemoryView.assert_direct_dimensions View.MemoryView.memoryview.setitem_slice_assign_scalar View.MemoryView.memoryview.strides.__get__ View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object View.MemoryView.array_cwrapper PyObject_GetBuffer: view==NULL argument is obsolete View.MemoryView.memoryview.__getbuffer__ sklearn.tree._tree.DepthFirstTreeBuilder.build sklearn.tree._tree.BestFirstTreeBuilder.build sklearn.tree._tree.TreeBuilder.build View.MemoryView.array.__getbuffer__ sklearn.tree._tree.DepthFirstTreeBuilder.__cinit__ sklearn.tree._tree.Tree._get_value_ndarray View.MemoryView.memoryview.is_slice sklearn.tree._tree.BestFirstTreeBuilder.__cinit__ sklearn.tree._tree.Tree.compute_partial_dependence object of type 'NoneType' has no len() hasattr(): attribute name must be string sklearn.tree._tree.__pyx_unpickle__CCPPruneController__set_state sklearn.tree._tree._CCPPruneController.__setstate_cython__ sklearn.tree._tree._CCPPruneController.__reduce_cython__ Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too! 'NoneType' object is not subscriptable View.MemoryView.__pyx_unpickle_Enum__set_state View.MemoryView.Enum.__setstate_cython__ View.MemoryView.memoryview.convert_item_to_object View.MemoryView._memoryviewslice.convert_item_to_object All dimensions preceding dimension %d must be indexed and not sliced View.MemoryView.slice_memviewslice Step may not be zero (axis %d) local variable '%s' referenced before assignment View.MemoryView.Enum.__reduce_cython__ sklearn.tree._tree.Tree.n_leaves.__get__ sklearn.tree._tree._AlphaPruner.__cinit__ sklearn.tree._tree.Tree._get_node_ndarray Cannot transpose memoryview with indirect dimensions View.MemoryView.transpose_memslice View.MemoryView.memoryview_copy View.MemoryView.memoryview.T.__get__ View.MemoryView.copy_data_to_temp View.MemoryView.memoryview_copy_contents View.MemoryView.memoryview.setitem_slice_assignment Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy sklearn.tree._tree._check_node_ndarray sklearn.tree._tree.Tree.__cinit__ sklearn.tree._tree.Tree._decision_path_dense sklearn.tree._tree._check_n_classes 'NoneType' object has no attribute '%.30s' '%.50s' object has no attribute '%U' dictionary changed size during iteration too many values to unpack (expected %zd) 'NoneType' object is not iterable need more than %zd value%.1s to unpack sklearn.tree._tree._dtype_to_dict sklearn.tree._tree.Tree.__reduce__ sklearn.tree._tree.Tree._apply_dense sklearn.tree._tree.__pyx_unpickle_TreeBuilder__set_state sklearn.tree._tree.TreeBuilder.__setstate_cython__ sklearn.tree._tree._all_compatible_dtype_dicts sklearn.tree._tree.Tree.__setstate__ sklearn.tree._tree._dtype_dict_with_modified_bitness View.MemoryView.array.__cinit__ Argument '%.200s' must not be None expected bytes, NoneType found integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer sklearn.tree._tree.__pyx_unpickle_TreeBuilder View.MemoryView.__pyx_unpickle_Enum __pyx_unpickle__CCPPruneController sklearn.tree._tree.__pyx_unpickle__CCPPruneController sklearn.tree._tree._PathFinder.__cinit__ sklearn.tree._tree._check_value_ndarray sklearn.tree._tree.Tree._apply_sparse_csr sklearn.tree._tree.Tree._decision_path_sparse_csr sklearn.tree._tree.Tree.decision_path View.MemoryView.memoryview.__setitem__ View.MemoryView.memoryview.__getitem__ BufferFormatFromTypeInfo.format_from_typeinfo Module '_tree' has already been imported. Re-initialisation is not supported. compiletime version %s of module '%.100s' does not match runtime version %s %.200s does not export expected C variable %.200s C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s) PyArrayObject *(__pyx_t_7sklearn_4tree_6_utils_SIZE_t *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t) __pyx_t_7sklearn_4tree_6_utils_DTYPE_t *(__pyx_t_7sklearn_4tree_6_utils_DTYPE_t **, size_t) __pyx_t_7sklearn_4tree_6_utils_SIZE_t *(__pyx_t_7sklearn_4tree_6_utils_SIZE_t **, size_t) unsigned char *(unsigned char **, size_t) struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord *(struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord **, size_t) __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *(__pyx_t_7sklearn_4tree_6_utils_DOUBLE_t **, size_t) __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t **(__pyx_t_7sklearn_4tree_6_utils_DOUBLE_t ***, size_t) struct __pyx_t_7sklearn_4tree_5_tree_Node *(struct __pyx_t_7sklearn_4tree_5_tree_Node **, size_t) struct __pyx_t_7sklearn_9neighbors_10_quad_tree_Cell *(struct __pyx_t_7sklearn_9neighbors_10_quad_tree_Cell **, size_t) struct __pyx_t_7sklearn_4tree_5_tree_Node **(struct __pyx_t_7sklearn_4tree_5_tree_Node ***, size_t) sklearn.tree._tree.TreeBuilder._check_input sklearn.tree._tree.Tree.predict sklearn.tree._tree._cost_complexity_prune sklearn.tree._tree.ccp_pruning_path sklearn.tree._tree._build_pruned_tree_ccp sklearn.tree._tree._build_pruned_tree sklearn.tree._tree._memoryviewslice Internal class for passing memoryview slices to Python sklearn.tree._tree._PathFinder Record metrics used to return the cost complexity path. sklearn.tree._tree._AlphaPruner Use alpha to control when to stop pruning. sklearn.tree._tree._CCPPruneController Base class used by build_pruned_tree_ccp and ccp_pruning_path
to control pruning.
sklearn.tree._tree.BestFirstTreeBuilder Build a decision tree in best-first fashion.
The best node to expand is given by the node at the frontier that has the
highest impurity improvement.
sklearn.tree._tree.DepthFirstTreeBuilder Build a decision tree in depth-first fashion. sklearn.tree._tree.TreeBuilder Interface for different tree building strategies. Array-based representation of a binary decision tree.
The binary tree is represented as a number of parallel arrays. The i-th
element of each array holds information about the node `i`. Node 0 is the
tree's root. You can find a detailed description of all arrays in
`_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split
nodes, resp. In this case the values of nodes of the other type are
arbitrary!
Attributes
----------
node_count : int
The number of nodes (internal nodes + leaves) in the tree.
capacity : int
The current capacity (i.e., size) of the arrays, which is at least as
great as `node_count`.
max_depth : int
The depth of the tree, i.e. the maximum depth of its leaves.
children_left : array of int, shape [node_count]
children_left[i] holds the node id of the left child of node i.
For leaves, children_left[i] == TREE_LEAF. Otherwise,
children_left[i] > i. This child handles the case where
X[:, feature[i]] <= threshold[i].
children_right : array of int, shape [node_count]
children_right[i] holds the node id of the right child of node i.
For leaves, children_right[i] == TREE_LEAF. Otherwise,
children_right[i] > i. This child handles the case where
X[:, feature[i]] > threshold[i].
feature : array of int, shape [node_count]
feature[i] holds the feature to split on, for the internal node i.
threshold : array of double, shape [node_count]
threshold[i] holds the threshold for the internal node i.
value : array of double, shape [node_count, n_outputs, max_n_classes]
Contains the constant prediction value of each node.
impurity : array of double, shape [node_count]
impurity[i] holds the impurity (i.e., the value of the splitting
criterion) at node i.
n_node_samples : array of int, shape [node_count]
n_node_samples[i] holds the number of training samples reaching node i.
weighted_n_node_samples : array of double, shape [node_count]
weighted_n_node_samples[i] holds the weighted number of training samples
reaching node i.
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Parameters
----------
tree : Tree
Original tree.
Returns
-------
path_info : dict
Information about pruning path with attributes:
ccp_alphas : ndarray
Effective alphas of subtree during pruning.
impurities : ndarray
Sum of the impurities of the subtree leaves for the
corresponding alpha value in ``ccp_alphas``.
Build a pruned tree from the original tree using cost complexity
pruning.
The values and nodes from the original tree are copied into the pruned
tree.
Parameters
----------
tree : Tree
Location to place the pruned tree
orig_tree : Tree
Original tree
ccp_alpha : positive double
Complexity parameter. The subtree with the largest cost complexity
that is smaller than ``ccp_alpha`` will be chosen. By default,
no pruning is performed.
Partial dependence of the response on the ``target_feature`` set.
For each sample in ``X`` a tree traversal is performed.
Each traversal starts from the root with weight 1.0.
At each non-leaf node that splits on a target feature, either
the left child or the right child is visited based on the feature
value of the current sample, and the weight is not modified.
At each non-leaf node that splits on a complementary feature,
both children are visited and the weight is multiplied by the fraction
of training samples which went to each child.
At each leaf, the value of the node is multiplied by the current
weight (weights sum to 1 for all visited terminal nodes).
Parameters
----------
X : view on 2d ndarray, shape (n_samples, n_target_features)
The grid points on which the partial dependence should be
evaluated.
target_features : view on 1d ndarray, shape (n_target_features)
The set of target features for which the partial dependence
should be evaluated.
out : view on 1d ndarray, shape (n_samples)
The value of the partial dependence function on each grid
point.
Computes the importance of each feature (aka variable). ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Finds the decision path (=node) for each sample in X. ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Finds the terminal region (=leaf node) for each sample in X. ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿPredict target for X. ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Setstate re-implementation, for unpickling. Getstate re-implementation, for pickling. Reduce re-implementation, for pickling. Build a decision tree from the training set (X, y). ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Build a decision tree from the training set (X, y). ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ Build a decision tree from the training set (X, y). ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿX, C P, A H, R
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