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alkaline-ml / scikit-learn python

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scikit-learn / tree / _tree.pypy36-pp73-x86_64-linux-gnu.so
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Ë8éd÷ÿÿHìHÄÃmemviewslice is already initialized!Unable to initialize pickling for %s%.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 PyObjectinvalid vtable found for imported type%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Cannot convert %.200s to %.200sArgument '%.200s' has incorrect type (expected %.200s, got %.200s)sklearn.tree._tree._PathFinder.save_metrics%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'__int__ returned non-int (type %.200s).  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next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Does not understand character buffer dtype format string ('%c')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)sklearn.tree._tree.Tree.value.__get__sklearn.tree._tree.Tree.children_left.__get__sklearn.tree._tree.Tree.n_node_samples.__get__sklearn.tree._tree.Tree.children_right.__get__sklearn.tree._tree.Tree.feature.__get__sklearn.tree._tree.Tree.impurity.__get__sklearn.tree._tree.Tree.weighted_n_node_samples.__get__Buffer exposes suboffsets but no stridesC-contiguous buffer is not indirect in dimension %dBuffer and memoryview are not contiguous in the same dimension.Buffer is not indirectly contiguous in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not compatible with direct access in dimension %d.Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer not fortran contiguous.sklearn.tree._tree._cost_complexity_prunesklearn.tree._tree.TreeBuilder.__reduce_cython__sklearn.tree._tree.Tree._get_node_ndarrayc-string too long to convert to PythonAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)local variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__C-contiguous buffer is not contiguous in dimension %dBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!sklearn.tree._tree.Tree._decision_path_densesklearn.tree._tree.Tree.capacity.__set__sklearn.tree._tree.Tree.n_outputs.__set__sklearn.tree._tree.Tree.max_n_classes.__set__sklearn.tree._tree.Tree.n_features.__set__sklearn.tree._tree.Tree.node_count.__set__sklearn.tree._tree.Tree.max_depth.__set__View.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectsklearn.tree._tree.Tree.__setstate__View.MemoryView.memoryview.__cinit__object of type 'NoneType' has no len()hasattr(): attribute name must be stringsklearn.tree._tree.__pyx_unpickle__CCPPruneController__set_statesklearn.tree._tree._CCPPruneController.__setstate_cython__integer division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointersklearn.tree._tree.Tree.__cinit__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneexpected bytes, NoneType foundsklearn.tree._tree.Tree._decision_path_sparse_csrsklearn.tree._tree.Tree.predictsklearn.tree._tree._build_pruned_treesklearn.tree._tree._build_pruned_tree_ccp'NoneType' object is not subscriptableView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__sklearn.tree._tree.BestFirstTreeBuilder.__cinit__sklearn.tree._tree.__pyx_unpickle_TreeBuilder__set_statesklearn.tree._tree.TreeBuilder.__setstate_cython__sklearn.tree._tree.ccp_pruning_pathsklearn.tree._tree.Tree._apply_densesklearn.tree._tree.Tree._get_value_ndarraysklearn.tree._tree.Tree.decision_pathsklearn.tree._tree.__pyx_unpickle_TreeBuilder__pyx_unpickle__CCPPruneControllersklearn.tree._tree.__pyx_unpickle__CCPPruneControllerCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.memoryview.setitem_slice_assignmentsklearn.tree._tree.Tree._apply_sparse_csrsklearn.tree._tree.TreeBuilder._check_inputsklearn.tree._tree._add_to_frontiersklearn.tree._tree.DepthFirstTreeBuilder.__cinit__sklearn.tree._tree.Tree.compute_partial_dependencesklearn.tree._tree._PathFinder.__cinit__View.MemoryView.__pyx_unpickle_Enumcompiletime version %s of module '%.100s' does not match runtime version %ssklearn/neighbors/_quad_tree.pxd%.200s does not export expected C variable %.200sC 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)struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *(struct __pyx_t_7sklearn_4tree_6_utils_StackRecord **, size_t)struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *(struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord **, size_t)_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimesklearn.tree._tree._memoryviewsliceInternal class for passing memoryview slices to Pythonsklearn.tree._tree._PathFinderRecord metrics used to return the cost complexity path.sklearn.tree._tree._AlphaPrunerUse alpha to control when to stop pruning.sklearn.tree._tree._CCPPruneControllerBase class used by build_pruned_tree_ccp and ccp_pruning_path
    to control pruning.
    sklearn.tree._tree.BestFirstTreeBuilderBuild 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.DepthFirstTreeBuilderBuild a decision tree in depth-first fashion.sklearn.tree._tree.TreeBuilderInterface 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 int, shape [node_count]
        weighted_n_node_samples[i] holds the weighted number of training samples
        reaching node i.
    sklearn/tree/_tree.pyxstringsource__init__.pxd__pyx_capi__name '%U' is not definedMissing type objectcannot import name %SMemoryview is not initializedsklearn/tree/_tree.c%s (%s:%d)View.MemoryView._unellipsify'NoneType' is not iterableExpected %.16s, got %.200sexactlyView.MemoryView.Enum.__init__at leastat mostbuildsample_weightX_idx_sortedsklearn/tree/_tree.pxdcompute_feature_importancesan integer is required__cinit__end'bool''char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''complex float''float''complex double''double''complex long double''long double'a structPython objecta pointera stringunparseable format stringbuffer dtypeBuffer not C contiguous.View.MemoryView._err_dimIndex out of bounds (axis %d)memviewsliceobjView.MemoryView.memview_slice__del__tupleformatshape_build_pruned_tree_ccporig_tree__pyx_unpickle_TreeBuilderView.MemoryView._errView.MemoryView._err_extentsDimension %d is not 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0%ÀÐkÀ!J	þÿÿo!ÿÿÿoðÿÿo( ùÿÿoóø,%ÆxÖxæxöxyy&y6yFyVyfyvyyy¦y¶yÆyÖyæyöyzz&z6zFzVzfzvzzz¦z¶zÆzÖzæzöz{{&{6{F{V{f{v{{{¦{¶{Æ{Ö{æ{ö{||&|6|F|V|f|v|||¦|¶|Æ|Ö|æ|ö|}}&}6}F}V}f}v}}}¦}¶}Æ}Ö}æ}ö}~~&~6~F~V~f~v~~~¦~¶~Æ~Ö~æ~ö~&6FVfv¦¶ÆÖæö&6FVfv¦¶ƀրæö&6`4%Computes the cost complexity pruning path.

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    ----------
    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.

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    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).Finds the decision path (=node) for each sample in X.Finds the terminal region (=leaf node) for each sample in X.Predict target for X.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).Build a decision tree from the training set (X, y).Build a decision tree from the training set (X, y).ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ%ؑ%%p%(%0%%è%ؑ%%°%ȑ%è%ؑ%%°%ȑ%h%H%P%@%%`%X%è%ؑ%%°%ȑ%h%H%P%@%%p%`%X%H%؎%%¸%H%8%¨%%%`% %è%x%H%%0%%%0%%%0%%H%´Ý@%0Ú
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