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H=01gp} H=01Qp H=01;p H=01%p H=n01p H=X01fAWfHAVfHnHpAUfHnHATUSHH)D$ )D$0~uHD$@flHD$p)D$P~^HD$xfl)D$`Ht'LAHM~HtHMHcHHYH>HFHH|$ HHD$(HFHHD$0HFHHD$8HF HHD$@Aƃ L$ HT$(H1LDŽ$ HH9H$R<ATLL$`1A[]%fo$ fDo$0fDo$@fDo$PfDo$`fI~fDo$p)$fo$fo$D)$fo$fo$D)$fo$fo$D)$ fo$)$`D)$0D)$@)$P)$p)$)$)$)$)$D)$D)$D)$D)$D)$)$)$)$)$)$ )$0)$@MsHT$01LHD$ H9@H|$R<ATLGL$`tAYAZfo$ fDo$0fDo$@fDo$PfDo$`fH~fDo$p)$fo$fo$D)$fo$fo$D)$fo$fo$D)$ fo$)$`D)$0D)$@)$P)$p)$)$)$)$)$PD)$`D)$pD)$D)$D)$)$)$)$)$)$)$)$HHT$81LHD$ H9ZH|$ R<ATLLL$ _AXEfo$ fI~)$fo$0)$fo$@)$fo$P)$ fo$`)$0fo$p)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$)$fo$)$fo$)$MHt$@H$ L$MHD$HL$L$8H$hH$H$TH|$0HY^Ht H/:H|$(Ht H/H|$0Ht H/H|$8Ht H/H|$@Ht H/I9tAE8H9tE8I9tAD$8I9tAG8HH[]A\A]A^A_ÐHF HHD$@HFHHD$8HFHHD$0HFHHD$(HHHD$ HGHT$PHH4IHCj1PAPLD$@6H HPH| t_HCHt>H| tJHCHt,H| t8HCHtH| t&HHtH| tH|$ f.HHHHBL i@AH AH5p/H8S1ũXZE11E1AH|$ Ht H/H|$(Ht H/@H|$0Ht H/H|$8Ht H/8H|$@Ht H/uAE8HtH;-tE8MtL;%tAD$8H(DH=4 1H$ LfDۣ(fDˣfD軣fD諣fDf)$ 胣fDsfDcfDSfDCrfDH$ fH$ HE11E1AfE11AI9AE1@I9AuIm3L菢&f.7Hm,Hgff)$ H+I,$L/ f.I9A/f)$ HT$ Ht$PHHjL ?PA\A]%Im.L觡!fHm&H臡fI,$ LgfI/LH pH=L$1pH=6$1pH= $1pH= $1pH=#1pH=#1pH=#1ifAWfHAVAUATIHUSHH'fHnHHH)D$~7HD$ flHL$@HD$H)D$0HLGMH  HtH HHHT$HWHT$0H4HIH1j1PAPLD$0TH H8H| HCHt"H| HHt H| HT$&HHH: H <AHHL :H8SH`0H5)1XZA11AH|$Ht H/ H|$Ht H/ H|$ Ht H/ EuE8 H;mtHtC8 H,#DH=/E1zDHPHHT$ HPHHT$HHHT$L$1L-LHHTH$L9& H$R=AVL:L$G_AXS fo$fDo$fDo$fDo$fDo$fH~fDo$)$pfo$)D$`fo$ fo$0D)d$pfo$@fo$Pfo$`D)$fo$D)$D)$D)$D)$)$)$)$)$)$)$ )$0D)$D)$D)$D)$)$)$)$)$)$)$)$ H Ht$H$0=H$0H HT$ Hn1LDŽ$HL9 H$R1AVL4L$aY^V fo$fDo$fDo$fDo$fDo$fI~fDo$)$pfo$Mfo$ D)$fo$0fo$@D)$fo$Pfo$`D)$fo$)$D)$D)$)$)$)$)$)$ )$0)$D)$D)$ D)$0D)$@D)$P)$`)$p)$)$)$)$)$Mq foD$`)$foD$p)$fo$)$fo$)$fo$)$fo$)$ fo$)$0fo$)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$ )$fo$0)$fo$@)$fo$P)$fo$`L$)$fo$p)$fo$)$fo$)$fo$)$fo$)$ fo$)$0fo$)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$ )$fo$0)$fo$@)$fo$P)$A$4fo$`)$fo$pD$ )$fo$)$fo$)$fo$)$fo$)$ fo$)$0M9$$4$~dH$1M9fnT$ fHt$0fnH$Ld$0H=HL$8fbH$pHL$Hfl1Lt$@)D$P蝜H$HqH1Hfo$H5$fo$D$fo$D$ fo$D$0fo$D$@fo$D$Pfo$D$`fo$D$pfo$ $fo$0$fo$@$fo$P$fo$`$HIHH|$Ht H/H|$Ht H/H|$ HtH/thL9tE8L9tC8 M9tMtAG8"HHL[]A\A]A^A_DÕ두軕vfD諕RHHHL 0AH 2H8SLHHVHHT$ HPHHT$ECrfD3NfD#*fDH \2AfDA$L$foL=)$fo)$fo)$ fo)$0fo)$@fo)$Pfo)$`fo)$pfo)$fo)$fo)$fo)$fo)$M9oMfAG8UfoD$`)$foD$p)$fo$)$fo$)$fo$)$fo$)$ fo$)$0fo$)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$ )$fo$0)$fo$@)$fo$P)$fo$`L$)$fo$p)$fo$)$fo$)$fo$)$fo$)$ fo$)$0fo$)$@fo$)$Pfo$)$`fo$)$pfo$)$fo$ )$fo$0)$fo$@)$fo$P)$A$4fo$`)$fo$pD$ )$fo$)$fo$)$fo$)$fo$)$ fo$)$0Wf.L$-u7Hm_H RfDu-H+aHTpH=1pH=1{1AADL9AAfDf)$H H=!XHT$Ht$0HHjL n!AYAZgL9AAfDHmHf.H+Hȏu:I/L謏f)$uKIpH=1.pH=w1pH=a1pyH=K1HHShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__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.__int__ returned non-int (type %.200s)Interpreter change detected - this module can only be loaded into one interpreter per process.unbound method %.200S() needs an argument__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__qualname__ must be set to a string object__name__ must be set to a string object%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Cannot copy memoryview slice with indirect dimensions (axis %d)Acquisition count is %d (line %d)invalid vtable found for imported type while calling a Python objectNULL result without error in PyObject_Callcan't convert negative value to unsigned charvalue too large to convert to unsigned charcan't convert negative value to unsigned intvalue too large to convert to unsigned intvalue too large to convert to intsklearn/ensemble/_hist_gradient_boosting/histogram.pyxsklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.X_binned.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.gradients.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.hessians.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.ordered_gradients.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.ordered_hessians.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.hessians_are_constant.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_threads.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.hessians_are_constant.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_bins.__get__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_features.__get__FromPyStructUtility.__pyx_convert__from_py_struct____pyx_t_7sklearn_8ensemble_23_hist_gradient_boosting_6common_hist_structsklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_bins.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_features.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.n_threads.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder._compute_histogram_brute_single_feature%.200s() takes %.8s %zd positional argument%.1s (%zd given)sklearn.ensemble._hist_gradient_boosting.histogram._build_histogram_build_histogram_root_no_hessiansklearn.ensemble._hist_gradient_boosting.histogram._build_histogram_root_no_hessiansklearn.ensemble._hist_gradient_boosting.histogram._build_histogram_naivesklearn.ensemble._hist_gradient_boosting.histogram._subtract_histogramssklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.__reduce_cython__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.compute_histograms_brutesklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.ordered_hessians.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.ordered_gradients.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.hessians.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.gradients.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.X_binned.__set__sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.__init__sklearn.ensemble._hist_gradient_boosting.histogram.__pyx_unpickle_HistogramBuilder__set_statecannot pass None into a C function argument that is declared 'not None'sklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.__setstate_cython____pyx_unpickle_HistogramBuildersklearn.ensemble._hist_gradient_boosting.histogram.__pyx_unpickle_HistogramBuilderIncompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))sklearn.ensemble._hist_gradient_boosting.histogram._build_histogram_rootsklearn.ensemble._hist_gradient_boosting.histogram._build_histogram_no_hessiancompute_histograms_subtractionsklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilder.compute_histograms_subtractionModule 'histogram' has already been imported. Re-initialisation is not supported.sklearn.ensemble._hist_gradient_boosting.histogramcompile time Python version %d.%d of module '%.100s' %s runtime version %d.%dFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.base class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typemultiple bases have vtable conflict: '%.200s' and '%.200s'Unable to initialize pickling for %.200sinit sklearn.ensemble._hist_gradient_boosting.histogram_cython_3_2_2.cython_function_or_method_cython_3_2_2._common_types_metatypesklearn.ensemble._hist_gradient_boosting.histogram.HistogramBuilderA Histogram builder... used to build histograms. A histogram is an array with n_bins entries of type HISTOGRAM_DTYPE. Each feature has its own histogram. A histogram contains the sum of gradients and hessians of all the samples belonging to each bin. There are different ways to build a histogram: - by subtraction: hist(child) = hist(parent) - hist(sibling) - from scratch. In this case we have routines that update the hessians or not (not useful when hessians are constant for some losses e.g. least squares). Also, there's a special case for the root which contains all the samples, leading to some possible optimizations. Overall all the implementations look the same, and are optimized for cache hit. Parameters ---------- X_binned : ndarray of int, shape (n_samples, n_features) The binned input samples. Must be Fortran-aligned. n_bins : int The total number of bins, including the bin for missing values. Used to define the shape of the histograms. gradients : ndarray, shape (n_samples,) The gradients of each training sample. Those are the gradients of the loss w.r.t the predictions, evaluated at iteration i - 1. hessians : ndarray, shape (n_samples,) The hessians of each training sample. Those are the hessians of the loss w.r.t the predictions, evaluated at iteration i - 1. hessians_are_constant : bool Whether hessians are constant. needs an argument%.200s() %stakes no keyword argumentstakes exactly one argument%.200s() %s (%zd given)_cython_3_2_2takes no argumentsBad call flags for CyFunction__loader__loader__file__origin__package__parent__path__submodule_search_locationskeywords must be stringssklearn._cyutility__pyx_capi__functionan integer is required__del__Expected %s, got %.200s_build_histogramexactly_build_histogram_naive_subtract_histogramsname '%U' is not defined__reduce_cython__at leastat mostcompute_histograms_brute__init____setstate_cython__tuple__pyx_statepicklePickleError_build_histogram_root_build_histogram_no_hessianbuiltinscython_runtime__builtins__does not matchdecompresszlibarraymemoryviewcannot import name %S__reduce____module____dictoffset____vectorcalloffset____weaklistoffset__func_doc__doc__func_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutineX_binnedn_featuresn_binsordered_gradientsordered_hessianshessians_are_constantn_threadsconst G_H_DTYPE_Chist_structsum_gradientssum_hessianscountY_DTYPE_Cconst unsigned intconst X_BINNED_DTYPE_Cp`P p`'{'n'a'T'.!t, xKsƙ6P2i)&uM,Kʢi3Ӥ)eYӉc)J܎YK$]yGqG}G| C&L4#`^8ŠM frE&R2l*`2dHaմUT5$2W]FLSD֬Ս/%ĂBFUQ6I2 .L.ɦշ nZVK:ՍzsP@S m+&}_cm[V$b~o d VM@a˴5F:֙H0pB,L%?n,Z9IH5HXQq Ullj*C!fb$P[}kX>Xuk2p$*` 5D}%ϊBBH/j QVd$UlM2T38a `EЋG4~# rD]VbS&#DHkOSuV@FsѤj]V X^ juVM݀1 6VL"h5"Җe_V,~A4dkuYVp>hR6VbSBcHwB9Ra9 'T0nhBAĔ(jh~lcAP|YbRsr&#BȮKj wscrx5=48<*zba}-`$*Qdrl*و7Av[a80$8rHx4yAn4 G̘J;# ęY5+?>IA$Իb6M*E+<y-L"#ӐJF13Qg3!㝘8)ʓyztb͸FHYD#SQq%K&Z7H꼤p}k\ǿF5gXoNEd(53;E<6pN$b*c"oF8iE Ēn1yn   ĸ#3XS&!$')D =`QSpif]z.18zGq9\n 96Ξ❒ U%pJp_ RUI0 PuG 6D:E$rc `u'^tH6YԼZ &7JT!G񲓄nGO>rD6mVKCRrx$.Fbʴ?wK%m%\'"ccjh/rV_ukk~E\os7L+7Sab>.IN`2 O`П&Nj0)PU\_aVc#s]U-(D_pQ4Hw\5О9:W7$ö 2W=q&iX5l2Any2]640#ЏXz_֤l O`Ne&؞˩駩?;^:~9pavav؅W[욻]l|κ0ƽjoN'˼*bP,yK^O3ߩeww\cԹ15~/v)\#@&ܼJ&;.vnޫ^ssx3Rof-pVQNnx5ᔜrx{-^kΕ`en[q 8N:b^^AեA 7 9x4IiT ZPyJHkwf}=Kglvp֓g 30A,;ikk5V?}s iVN>ʹ.~h!|ntMZv'w#W[ݽ>y^8}Ӡȿgnu4o/ _O(i.uj^t v^{ЇxŷXZ:KG-vA(hm\ l1ur@^`~ hn9( t "-AkI#כiMvpv-Ą&O/(ՂYc~^t>\4glE-7c-4/g,5t4 g}$PVĀ/]2/a}_P"/3 B !5  OM76/ 6"$)/            /2  EkU ::a!__pyx_fatalerrorCompute histogram of the root node, not updating hessians. Used when the hessians of the loss are constant (typically LS loss). Compute histogram of the root node. Unlike other nodes, the root node has to find the split among *all* the samples from the training set. binned_feature and all_gradients / all_hessians already have a consistent ordering. Return histogram for a given feature, not updating hessians. Used when the hessians of the loss are constant (typically LS loss). Return histogram for a given feature.compute hist_a = hist_a - hist_bBuild histogram in a naive way, without optimizing for cache hit. Used in tests to compare with the optimized version.Compute the histograms of the node using the subtraction trick. hist(parent) = hist(left_child) + hist(right_child) For a given feature, the complexity is O(n_bins). This is much more efficient than compute_histograms_brute, but it's only possible for one of the siblings. Parameters ---------- parent_histograms : ndarray of HISTOGRAM_DTYPE, shape (n_features, n_bins) The histograms of the parent. sibling_histograms : ndarray of HISTOGRAM_DTYPE, shape (n_features, n_bins) The histograms of the sibling. allowed_features : None or ndarray, dtype=np.uint32 Indices of the features that are allowed by interaction constraints to be split. Returns ------- histograms : ndarray of HISTOGRAM_DTYPE, shape(n_features, n_bins) The computed histograms of the current node. We repurpose parent_histograms for this and don't need to allocate new memory. Compute the histograms of the node by scanning through all the data. For a given feature, the complexity is O(n_samples) Parameters ---------- sample_indices : array of int, shape (n_samples_at_node,) The indices of the samples at the node to split. allowed_features : None or ndarray, dtype=np.uint32 Indices of the features that are allowed by interaction constraints to be split. Returns ------- histograms : ndarray of HISTOGRAM_DTYPE, shape (n_features, n_bins) The computed histograms of the current node. 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