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duality-group
duality-group / scikit-learn python
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Version:
1.2.0 ▾
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View.MemoryView._err_dim tuple Expected %.16s, got %.200s %d.%d builtins cython_runtime __builtins__ numpy flatiter broadcast ndarray generic number unsignedinteger inexact complexfloating flexible character ufunc sklearn.tree._splitter Splitter sklearn.tree._tree TreeBuilder sklearn.neighbors._quad_tree _QuadTree sklearn.tree._utils WeightedPQueue WeightedMedianCalculator EPSILON float scipy.special.cython_special __pyx_fuse_0xlogy __pyx_fuse_1xlogy double (double) log numpy.core._multiarray_umath _ARRAY_API _ARRAY_API not found _ARRAY_API is NULL pointer __init__.pxd numpy.import_array getbuffer(obj, view, flags) init sklearn.tree._criterion __cinit__ View.MemoryView._unellipsify 'NoneType' is not iterable View.MemoryView.memview_slice Index out of bounds (axis %d) memviewsliceobj __pyx_unpickle_Enum cannot import name %S shape format n_classes View.MemoryView._err View.MemoryView._err_extents Dimension %d is not direct base __reduce_cython__ __setstate_cython__ T strides suboffsets ndim itemsize nbytes is_c_contig is_f_contig copy copy_fortran sklearn.tree._criterion.Enum sklearn.tree._criterion.array memview __getattr__ sklearn.tree._criterion.MAE sklearn.tree._criterion.MSE sklearn.tree._criterion.Gini __reduce__ __getstate__ __setstate__ DOUBLE_t double SIZE_t 2*þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿ*þÿÀ)þÿÀ)þÿ**þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿb*þÿZ*þÿÀ)þÿÀ)þÿR*þÿÀ)þÿÀ)þÿJ*þÿB*þÿ:*þÿÀ)þÿÀ)þÿø)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿÀ)þÿð)þÿ*þÿ*þÿÀ)þÿ *þÿr*þÿj*þÿè)þÿÀ)þÿÀ)þÿà)þÿÀ)þÿÀ)þÿÀ)þÿÐ)þÿØ)þÿÀ)þÿÐ)þÿ`6þÿ06þÿ06þÿ`6þÿ06þÿ06þÿ06þÿ06þÿ06þÿ6þÿ 6þÿ06þÿ06þÿ6þÿ06þÿ06þÿ6þÿ6þÿ6þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ06þÿ`6þÿ`6þÿ6þÿ06þÿ 6þÿp6þÿ6þÿ 6þÿ06þÿ06þÿ6þÿ06þÿ06þÿ06þÿ`6þÿ6þÿ06þÿ`6þÿ9þÿ6þÿ6þÿ9þÿ6þÿ6þÿ6þÿ6þÿ6þÿ9þÿ9þÿ6þÿ6þÿ9þÿ6þÿ6þÿ¶9þÿ¶9þÿ9þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ6þÿ9þÿÏ:þÿ£9þÿ6þÿ£9þÿ£9þÿ9þÿ9þÿ6þÿ6þÿ9þÿ6þÿ6þÿ6þÿ9þÿ9þÿ6þÿ9þÿ7þÿx6þÿx6þÿ7þÿx6þÿx6þÿx6þÿx6þÿx6þÿh8þÿX8þÿx6þÿx6þÿX8þÿx6þÿx6þÿH8þÿH8þÿH8þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿx6þÿ7þÿ7þÿ¦8þÿx6þÿ8þÿr8þÿh8þÿX8þÿx6þÿx6þÿX8þÿx6þÿx6þÿx6þÿ7þÿH8þÿx6þÿ7þÿ¬6þÿ¤5þÿ¤5þÿ¬6þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿT8þÿD8þÿ¤5þÿ¤5þÿ48þÿ¤5þÿ¤5þÿ48þÿ48þÿ48þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¤5þÿ¬6þÿ¬6þÿq8þÿ¤5þÿ8þÿ^8þÿT8þÿD8þÿ¤5þÿ¤5þÿ48þÿ¤5þÿ¤5þÿ¤5þÿ¬6þÿ48þÿ¤5þÿ¬6þÿ<þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿØ;þÿ;þÿ;þÿØ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿØ;þÿo<þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿá;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ;þÿñ<þÿ;þÿÔ;þÿc<þÿo<þÿ0;þÿc<þÿ;þÿ0;þÿ;þÿ;þÿ;þÿ;þÿ;þÿ0;þÿ0;þÿ;þÿ;þÿ0;þÿ;þÿ;þÿ0;þÿ;þÿ0;þÿ;þÿ;þÿÀ=þÿ;þÿ;þÿ;þÿ;þÿ;þÿ\=þÿ;þÿ;þÿ;þÿc<þÿ;þÿ;þÿ;þÿ0;þÿ0;þÿ0;þÿ;þÿ0;þÿ0;þÿ0;þÿ0;þÿ;þÿ;þÿ0;þÿ;þÿ;þÿ;þÿ0;þÿ0;þÿ;þÿ;?þÿ;þÿ;þÿ;þÿ;þÿ¸<þÿ;þÿ;þÿ;þÿ;þÿ=þÿ(Xþÿ@XþÿXþÿXþÿXþÿÌaþÿôaþÿtaþÿaþÿaþÿ eþÿÐdþÿàdþÿ eþÿeþÿ,fþÿDfþÿ fþÿfþÿ fþÿ@EÿÿxEÿÿ(Eÿÿ8EÿÿEÿÿLFÿÿ4FÿÿFÿÿôEÿÿdFÿÿKÿÿ@KÿÿGÿÿxGÿÿðJÿÿ¬}ÿÿä}ÿÿ,}ÿÿ$}ÿÿ}ÿÿø}ÿÿx~ÿÿ`}ÿÿX}ÿÿà}ÿÿdÿÿÿÿìÿÿäÿÿTÿÿ°ÿÿ0ÿÿÿÿÿÿÿÿÿÿÿÿ!ÿÿÿÿÿÿÿÿøÿÿÿÿ´ÿÿّÿÿMÿÿ½ÿÿ ¾ÿÿ8½ÿÿ0½ÿÿ½ÿÿ __pyx_fatalerror unable to allocate shape and strides. self.samples cannot be converted to a Python object for pickling 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 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 (0xb068931, 0x82a3537, 0x6ae9995) = (name)) 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 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> ClassificationCriterion <MemoryView of %r object> <contiguous and direct> <strided and indirect> RegressionCriterion <strided and direct> cline_in_traceback __pyx_unpickle_Enum __setstate_cython__ __pyx_PickleError dtype_is_object allocate_buffer View.MemoryView __reduce_cython__ __pyx_getbuffer stringsource __pyx_checksum PickleError MemoryError ImportError FriedmanMSE __pyx_vtable__ __pyx_result ValueError IndexError __reduce_ex__ __pyx_state n_samples n_outputs n_classes enumerate TypeError Criterion __setstate__ __pyx_type itemsize __getstate__ Ellipsis memview fortran float64 asarray Poisson Entropy update unpack struct __reduce__ pickle object __name__ __import__ format encode double zeros start shape range numpy flags finfo error empty dtype __class__ ASCII __test__ stop step size pack ndim name mode __main__ intp __dict__ base Gini obj __new__ eps MSE MAE 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' 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 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. %s() got multiple values for keyword argument '%U' %.200s() keywords must be strings %s() got an unexpected keyword argument '%U' 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) 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 Unable to initialize pickling for %s __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) value too large to convert to int 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 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.tree._criterion.MAE.update sklearn.tree._criterion.MAE.reverse_reset sklearn.tree._criterion.MAE.reset sklearn.tree._criterion.MAE.init sklearn.tree._criterion.RegressionCriterion.update sklearn.tree._criterion.RegressionCriterion.init sklearn.tree._criterion.ClassificationCriterion.update sklearn.tree._criterion.ClassificationCriterion.init 'NoneType' object is not subscriptable hasattr(): attribute name must be string View.MemoryView.__pyx_unpickle_Enum__set_state 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 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. 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 Module '_criterion' 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) __pyx_t_double_complex (__pyx_t_double_complex, __pyx_t_double_complex, int __pyx_skip_dispatch) double (double, double, int __pyx_skip_dispatch) _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 View.MemoryView._memoryviewslice.__setstate_cython__ View.MemoryView._memoryviewslice.__reduce_cython__ View.MemoryView.memoryview.__setstate_cython__ View.MemoryView.memoryview.__reduce_cython__ View.MemoryView.memoryview.__cinit__ View.MemoryView.array.__setstate_cython__ View.MemoryView.array.__reduce_cython__ sklearn.tree._criterion.Criterion.__setstate_cython__ sklearn.tree._criterion.Criterion.__reduce_cython__ sklearn.tree._criterion.Criterion.__getstate__ integer division or modulo by zero value too large to perform division View.MemoryView.pybuffer_index View.MemoryView.memoryview.get_item_pointer sklearn.tree._criterion.ClassificationCriterion.__reduce__ 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.tree._criterion.RegressionCriterion.__reduce__ View.MemoryView.memoryview.strides.__get__ View.MemoryView.memoryview.assign_item_from_object View.MemoryView._memoryviewslice.assign_item_from_object PyObject_GetBuffer: view==NULL argument is obsolete View.MemoryView.memoryview.__getbuffer__ View.MemoryView.memoryview.is_slice View.MemoryView.memoryview.convert_item_to_object View.MemoryView._memoryviewslice.convert_item_to_object View.MemoryView.array.__getbuffer__ View.MemoryView.Enum.__reduce_cython__ 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.memoryview.__getitem__ View.MemoryView.__pyx_unpickle_Enum sklearn.tree._criterion.MAE.__cinit__ sklearn.tree._criterion.RegressionCriterion.__cinit__ 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 Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s) sklearn.tree._criterion.ClassificationCriterion.__cinit__ 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 Cannot copy memoryview slice with indirect dimensions (axis %d) View.MemoryView.array_cwrapper View.MemoryView.memoryview.copy_fortran View.MemoryView.memoryview.copy View.MemoryView.memoryview.setitem_slice_assignment sklearn.tree._criterion._memoryviewslice Internal class for passing memoryview slices to Python sklearn.tree._criterion.memoryview sklearn.tree._criterion.Poisson Half Poisson deviance as impurity criterion.
Poisson deviance = 2/n * sum(y_true * log(y_true/y_pred) + y_pred - y_true)
Note that the deviance is >= 0, and since we have `y_pred = mean(y_true)`
at the leaves, one always has `sum(y_pred - y_true) = 0`. It remains the
implemented impurity (factor 2 is skipped):
1/n * sum(y_true * log(y_true/y_pred)
sklearn.tree._criterion.FriedmanMSE Mean squared error impurity criterion with improvement score by Friedman.
Uses the formula (35) in Friedman's original Gradient Boosting paper:
diff = mean_left - mean_right
improvement = n_left * n_right * diff^2 / (n_left + n_right)
Mean absolute error impurity criterion.
MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
value and f_i is the predicted value. Mean squared error impurity criterion.
MSE = var_left + var_right
Gini Index impurity criterion.
This handles cases where the target is a classification taking values
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
then let
count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k)
be the proportion of class k observations in node m.
The Gini Index is then defined as:
index = \sum_{k=0}^{K-1} count_k (1 - count_k)
= 1 - \sum_{k=0}^{K-1} count_k ** 2
sklearn.tree._criterion.Entropy Cross Entropy impurity criterion.
This handles cases where the target is a classification taking values
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
then let
count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k)
be the proportion of class k observations in node m.
The cross-entropy is then defined as
cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k)
sklearn.tree._criterion.RegressionCriterion Abstract regression criterion.
This handles cases where the target is a continuous value, and is
evaluated by computing the variance of the target values left and right
of the split point. The computation takes linear time with `n_samples`
by using ::
var = \sum_i^n (y_i - y_bar) ** 2
= (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2
sklearn.tree._criterion.ClassificationCriterion Abstract criterion for classification. sklearn.tree._criterion.Criterion Interface for impurity criteria.
This object stores methods on how to calculate how good a split is using
different metrics.
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