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produces correct results on small toy dataset: ------------------ | X | y | weight | ------------------ | 3 | 3 | 0.1 | | 5 | 3 | 0.3 | | 8 | 4 | 1.0 | | 3 | 6 | 0.6 | | 5 | 7 | 0.3 | ------------------ |sum wt:| 2.3 | ------------------ Because we are dealing with sample weights, we cannot find the median by simply choosing/averaging the centre value(s), instead we consider the median where 50% of the cumulative weight is found (in a y sorted data set) . Therefore with regards to this test data, the cumulative weight is >= 50% when y = 4. Therefore: Median = 4 For all the samples, we can get the total error by summing: Absolute(Median - y) * weight I.e., total error = (Absolute(4 - 3) * 0.1) + (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 6) * 0.6) + (Absolute(4 - 7) * 0.3) = 2.5 Impurity = Total error / total weight = 2.5 / 2.3 = 1.08695652173913 ------------------ From this root node, the next best split is between X values of 3 and 5. Thus, we have left and right child nodes: LEFT RIGHT ------------------ ------------------ | X | y | weight | | X | y | weight | ------------------ ------------------ | 3 | 3 | 0.1 | | 5 | 3 | 0.3 | | 3 | 6 | 0.6 | | 8 | 4 | 1.0 | ------------------ | 5 | 7 | 0.3 | |sum wt:| 0.7 | ------------------ ------------------ |sum wt:| 1.6 | ------------------ Impurity is found in the same way: Left node Median = 6 Total error = (Absolute(6 - 3) * 0.1) + (Absolute(6 - 6) * 0.6) = 0.3 Left Impurity = Total error / total weight = 0.3 / 0.7 = 0.428571428571429 ------------------- Likewise for Right node: Right node Median = 4 Total error = (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 7) * 0.3) = 1.2 Right Impurity = Total error / total weight = 1.2 / 1.6 = 0.75 ------ rr3rL)rWrrr;r:rK)rIrr;r7r;)333333?333333?rrNr)r_r`r)g,d?gܶm۶m?g?)@g@r)ffffff?rMgUUUUUU?)r7rTrr^N) rrrrrxr'ryrrrr)dt_maerrrtest_maes J.&rc Csd}tjdtjd}d}dd}tjtj|fD][}tD]'\}}|||}||}|\} \} } }|| ks9J|| ks?Jt|| qt 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dS)NrZr)rr rWr8)rrzipr!r) estimator_clsr_r`r estimatorsr rprev_estnext_estrrrrAs rc Cs|j|jksJ|j|jksJ|j}|j}|j}|j}dg}|r|\}}t|j||j|t|j||j|t|j ||j |t|j ||j |||||krdtt |j |n!t|j ||j || ||||f| ||||f|s!dSdS)N)rr)rnrrqrppopr&ryr%rxrvr2rrur) rsubtree tree_c_left tree_c_rightsubtree_c_leftsubtree_c_rightstack tree_node_idxsubtree_node_idxrrrr!Ps@  r!rrrwcCstd}|djtjjdd}|durt|}n!||d}tj|jtjjd|_t|j|j |j f\|_|_ |_ ttjt tjjd}t ||d}| ||t||||t||||dS)Nrer_Frr)r)rrrrrr(rrrrrrrr6rr'rrtodense)rrrrr X_readonly y_readonlyrrrr"test_apply_path_readonly_all_treesxs(   r4)r2r4r5cCsLtjtj}}||d}|||t||tt|ks$JdS)Nr) rbrrrrrrwrrr)rr#r_r`rrrrtest_balance_propertys  (r5seedc Csddgddgddgddgddgddgddgddgg}gd}td|d}|||t||dks6Jtd|d}|||t||dksNJd }tj|ddd d ||dd|d \}}d|d |k|dk@<t|}td|d}|||t||dksJdS)Nrr8rLr;)rrrrr8rLr;r7r2rr5rCrr)effective_rank tail_strengthrXrYrrWr>) rrrraminrrrmake_regressionr)r6r_r`rrYrrrtest_poisson_zero_nodess,4          r;cCsBtjd}d\}}}tj||||d}|jdd|dtj|dd}|jt||d }t ||||d \}}} } t d d |d } t dd |d } | || | || t dd || } || df|| dffD]6\}}}t || |}t |t| |dd}t || |}|dkr|d|ksJ|d|ksJqhdS)Nr)r<rCrXrYrWrArL)lowhighr\rr)lam) test_sizerWr5rC)rrrWr2mean)strategytraintestgV瞯 with criterion 'entropy' and 'log_loss' gave different trees.N)rrrrrrr)r#rJrXrYr_r` tree_log_loss tree_entropyrrr'test_criterion_entropy_same_as_log_losss" rYcsvtjdd\}}tddd||||}ddfdd}t|}|||}t||s9JdS) Nrrr;rcSs||jSr)byteswapviewr newbyteorderr)arrrrrreduce_ndarraysz8test_different_endianness_pickle..reduce_ndarraycsBt}t|}tj|_|jtj<| | d|SNr) ioBytesIOrQPicklercopyregdispatch_tablerrrndarraydumpseekfprr^rr get_pickle_non_native_endiannesss     zJtest_different_endianness_pickle..get_pickle_non_native_endianness) rrrrrrQloadrrisclose)r_r`rrlnew_clf new_scorerrkr test_different_endianness_pickles    rqcs~tjdd\}}tddd||||}Gdddtfdd}t|}|||}t ||s=JdS) Nrrr;rcseZdZfddZZS)zPtest_different_endianness_joblib_pickle..NonNativeEndiannessNumpyPicklercs0t|tjr||j}t|dSr) isinstancerrrerZr[rr\supersave)selfrrUrrrt(s zUtest_different_endianness_joblib_pickle..NonNativeEndiannessNumpyPickler.save)__name__ __module__ __qualname__rt __classcell__rrrvrNonNativeEndiannessNumpyPickler'sr{cs(t}|}||d|Sr_)r`rarfrgrhr{rrr'get_joblib_pickle_non_native_endianness-s   zXtest_different_endianness_joblib_pickle..get_joblib_pickle_non_native_endianness) rrrrrrjoblibrmrrrn)r_r`rr}rorprr|r'test_different_endianness_joblib_pickle s     rcCsntrtjntj}gd}dd|jjD}|D]}|||<qtt|t| d}|j |ddS)N) left_child right_childrtrvcSi|] \}\}}||qSrrrLrrrrrrrO@rPz6get_different_bitness_node_ndarray..namesformats same_kindcasting) r+rrint64rrfieldsrlistrvaluesr) node_ndarraynew_dtype_for_indexing_fieldsindexing_field_namesnew_dtype_dictr new_dtyperrr"get_different_bitness_node_ndarray:s  rcCsjdd|jjD}dd|jjD}dd|D}tt|t||d}|j|ddS) NcSrrrrrrrrOMrPz8get_different_alignment_node_ndarray..cSsg|]\}}|qSrr)rLrrrrrrPz8get_different_alignment_node_ndarray..cSsg|]}d|qS)rKr)rLrrrrrQr)rroffsetsrr)rrrrrrrrr)rrrshifted_offsetsrrrr$get_different_alignment_node_ndarrayLs   rc CsZtrtjntj}|\}\}}}}|j|dd}|}t|d|d<||||f|fS)Nrrnodes)r+rrrrrrrr) rrrrYrJrstate new_n_classes new_staterrr"reduce_tree_with_different_bitness]s rcntjdd\}}tddd||||}fdd}t|}|||}|t|ks5JdS)Nrrr;rcs@t}t|}tj|_t|jt<| | d|Sr_) r`rarQrbrcrdrr CythonTreerfrgrhrrr"pickle_dump_with_different_bitnessos     zItest_different_bitness_pickle..pickle_dump_with_different_bitness) rrrrrrQrmrr)r_r`rrrorprrrtest_different_bitness_picklehs     rcr)Nrrr;rcs>t}t|}tj|_t|jt<|| d|Sr_) r`rarrcrdrrrrfrgrhrrr"joblib_dump_with_different_bitnesss    zPtest_different_bitness_joblib_pickle..joblib_dump_with_different_bitness) rrrrrr~rmrr)r_r`rrrorprrr$test_different_bitness_joblib_pickle~s     rcCstrttjnttj}ttjttjg}|dd|D7}tjddg|d}|D] }t|||q.tj t ddtjddgg|d}t||Wdn1sZwYtj t dd|tj }t||WddS1s}wYdS) NcSsg|]}|qSr)r\)rLdtrrrrrz(test_check_n_classes..rr8rzWrong dimensions.+n_classesrzn_classes.+incompatible dtype) r+rrrrrrr rrrrrm)expected_dtypeallowed_dtypesrJrwrong_dim_n_classeswrong_dtype_n_classesrrrtest_check_n_classess   "rc Cs0ttj}d}tj||d}||g}|D] }t|||dqtjtddt||ddWdn1s:wY|ddddddft |fD]!}tjtddt|||j dWdn1smwYqQtjtd dt| tj ||dWddS1swYdS) N)r:r8rLr)rexpected_shapezWrong shape.+value arrayr)r8rLr8zvalue array.+C-contiguouszvalue array.+incompatible dtype) rrrrmrkr\r"rrrrrrr)rr value_ndarrayrrproblematic_arrrrrtest_check_value_ndarrays:  ( "rcCst}tjd|d}|t|t|g}|dd|D7}|D]}t||dqtjtddtjd|d}t||dWdn1sEwYtjtd d|ddd }t||dWdn1siwYd d |j j D}| }tj |d <t t|t|d}||}tjtddt||dWdn1swY| }tj|d<t t|t|d}||}tjtddt||dWddS1swYdS)N)r:rcSsg|] }||jqSr)rrr\)rLr]rrrrsz+test_check_node_ndarray..)rzWrong dimensions.+node arrayr)r:rLznode array.+C-contiguousrLcSrrrrrrrrOsz+test_check_node_ndarray..rurznode array.+incompatible dtyper)rrrrkrrr!rrrrrrrrrrrrrm)rrvalid_node_ndarraysr]problematic_node_ndarray dtype_dictrrrrrtest_check_node_ndarraysL    "rSplitterc Cstjd}d}dtjddgtjd}}td||}|||dd|d d }t|}t|}|j |ks7Jt ||s>Jd S) z&Check that splitters are serializable.rrCrLr;rr0r:rJN) monotonic_cst) rrrwrrrrrQrRrSrrr) rrrrrJrrsplitter_serialize splitter_backrrrtest_splitter_serializable s   rcCsRt|d}tdd}|ttt||tj|dd}t |j |j ddS)zhCheck that Trees can be deserialized with read only buffers. Non-regression test for gh-25584. z clf.joblibrrr) mmap_modez?The trees of the original and loaded classifiers are not equal.N) strjoinrrrrr~rfrmrr)tmpdir pickle_pathr loaded_clfrrr/test_tree_deserialization_from_read_only_buffer s   rcCstddgddgg}tddg}|dd|||dd}d}tjt|d|||WddS1s;wYdS)zhCheck that an error is raised when min_sample_split=1. non-regression test for issue gh-25481. rr8rN)rzb'min_samples_split' .* must be an int in the range \[2, inf\) or a float in the range \(0.0, 1.0\]rN)rrrrrrr)r#r_r`rmsgrrrtest_min_sample_split_1_error% s "rcCstgdgj}tgd}tdd|d}||||tjgg}t|t|ddg|dd}|dd}tdd|d}||||tjgg}t|t|d dgdS) z=Check missing values goes to correct node during predictions. rr8rLr;rKrrH r rr=rr=rrrMg?g@rr8rr<Nr>r9) rrrrrrrnanrrB)rr_r`dtcrX_equaly_equalrrr;test_missing_values_best_splitter_on_equal_nodes_no_missing; s    rc Cstgdgj}tgd}t|d|d}||||jjd}|jjd}|jj|}|jj|}||k} |jj |d} |jj |d} | tj gg} | rZt | | dSt | | dS)zCheck missing values go to the correct node during predictions for ExtraTree. Since ETC use random splits, we use different seeds to verify that the left/right node is chosen correctly when the splits occur. rrr8rrN) rrrrrrrrqrpr2ryrrr) rr6r_r`etrrr left_samples right_samples went_left y_pred_left y_pred_rightrrrr=test_missing_values_random_splitter_on_equal_nodes_no_missingU s     rrVr0cCsd}ttjgdgdgj}t|gddgddgd}tdd|d}|||ttjdd ggj}||}t||ddgd S) zITest when missing values are uniquely present in a class among 3 classes.rr7)rr8rLr;rKrrHrr8rLrrr;rNrrrrrrrrr')rmissing_values_classr_r`rr y_nan_predrrr/test_missing_values_best_splitter_three_classesx s$  rcCsttjgdgdgj}tdgddgd}tdd|d}|||ttjd tjggj}||}t|gd d S) zMissing values spanning only one class at fit-time must make missing values at predict-time be classified has belonging to this class.r7rr8rLr;r7r:rr8rIrrLrr:)rr8rNrrr_r`rrrrrr)test_missing_values_best_splitter_to_left s  rcCsttjgdgdgj}tdgddgddgd}tdd|d}|||ttjdd ggj}||}t|gd d S) zMissing values and non-missing values sharing one class at fit-time must make missing values at predict-time be classified has belonging to this class.r7rr8rrLrrrOg333333@rNrrrrr*test_missing_values_best_splitter_to_right s$  rc Cstddddtjddddtjg gj}td gddgd}td d|d }|||ttjd d ggj}||}t|gddS)zNCheck behavior of missing value when there is one missing value in each class.r8rLr;r:rCrZrVrrrrgffffff@gA@rNrrrrr>test_missing_values_best_splitter_missing_both_classes_has_nan s&  rrrc Cstddddtjddddtjg gj}td gddgd}|d ur(||}tjtd d |||Wd d S1sAwYd S) z4Check unsupported configurations for missing values.r8rLr;r:rCrZrVrrNzInput X contains NaNr)rrrrrrrrr)rrr_r`rrrtest_missing_value_errors s& "rcCsptjtj}}tj|ddddf<tj|ddddf<|ddd}|||||}|d ks6JdS) z5Smoke test for poisson regression and missing values.Nr:rrIr>r5rrrS) rbrrrrrrrrr)r#r_r`rrrrrtest_missing_values_poisson s   rcOs$tj|i|\}}|dk}||fS)N)rmake_friedman1)argskwargsr_r`rrrmake_friedman1_classification srzmake_data, Tree, tolerancegQ?gQ?gQ?sample_weight_trainrcCsd\}}|||d|d\}}|} tj|} tj| | jddg|jddgd<t| ||d \} } } }|d kr@t| jd }nd }d }|||d}|j | | |d| | |}t t |||d}| | | | | |}|||ksJd|d|d|d S)zFCheck that trees can deal with missing values have decent performance.)rrCrN)rXrYnoiserWFTrUrr\rjrrrNrCrrzscore_native_tree=z + z! should be strictly greater than ) rrrrwrrchoicerrrrrrr ) make_datar#rr3 tolerancerXrYr_r` X_missingrX_missing_trainX_missing_testrrrr native_treescore_native_treetree_with_imputerscore_tree_with_imputerrrr!test_missing_values_is_resilience s<       rzTree, expected_scoreg333333?g(\?c Cstjd}d}|j|dfd}tt|dt|dg}|jddg|dd gd }| t }||||<|j|d} tj | |<| |d d d f<||d } t | ||d d } | |kskJd|d| d S)z@Check the tree learns when only the missing value is predictive.rr<rZr[rLFTgffffff?rPrNr:r)cvzExpected CV score: z but got )rrrwrstandard_normalrrkrrrrboolrrrB) r#expected_scorer3rrXr_r` X_random_masky_mask X_predictiver tree_cv_scorerrr test_missing_value_is_predictive s "    rzmake_data, Treec Cstjd}d\}}||||d\}}tj||jddg|jddgd<t|jd}d |d d d <|dd }|j|||d |dd } | |dd d d d f|dd d t| || |d S)z=Check sample weight is correctly handled with missing values.r)rrCr=FTrUrrrSNrLrrr8) rrrwrrrrrrrr) rr#rrXrYr_r`r tree_with_swtree_samples_removedrrrtest_sample_weight_non_uniform> s    (rcCsPtddtjtj}tddtjtj}t|}t|}||ks&JdSr)rrrarrrQrR)tree1tree2pickle1pickle2rrrtest_deterministic_pickle[ s   rr_r:rIcCs|dd}td}||dd||}t||dd|}|jj}t|dks1J|t |jjdd|jjddt |jj dk|jj dk@}t |jj|ddS) a'Check that we properly handle missing values in regression trees using a toy dataset. The regression targeted by this test was that we were not reinitializing the criterion when it comes to the number of missing values. Therefore, the value of the critetion (i.e. MSE) was completely wrong. This test check that the MSE is null when there is a single sample in the leaf. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/28254 https://github.com/scikit-learn/scikit-learn/issues/28316 r>r8rIrrNrLrS) rrrrrrrrxrrr flatnonzerorqrv)r#r_rr`rtree_refrx leaves_idxrrr'test_regression_tree_missing_values_toyh s  "rcCstj|}d}tj|tjddd}tj|ddddf<||t|}t|dd ||}|j j }t |dksAJ|dS) Nr-rr>r8ir:rr) rrrwrrrmrrrrrrrxr)r3rrXr_r`rrxrrr-test_regression_extra_tree_missing_values_toy s   rc Cstjdd\}}tjd}|}|jtjdtjd|dddgfdd  t }tj ||<t ||d d \}}}}tj gd tjd }tdddd} | ||||t| jjdks`Jt| jjdk| jjdk@} t| jj| ddS)aCheck that we properly handle missing values in clasification trees using a toy dataset. The test is more involved because we use a case where we detected a regression in a random forest. We therefore define the seed and bootstrap indices to detect one of the non-frequent regression. Here, we check that the impurity is null or positive in the leaves. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/28254 T) return_X_yr)r8r7)rrNrLrK)nrj r)prLQ'a[&.erYRr-rErIJ3/kKnrZrh9rrO#MZrr r^r rK]r$rr rrrmrrCr#rr\4rZr%rKrKrrrrrrUrrVr Nrr&irqrrrfr+rr r8rrH rr!jr"r8rr>UrrPr?rIr2Tr;r;Lr*rr;riHnrrr>r8rS)r load_irisrrrwrrrrrrrrrrrrrrrxrrqrvr) r_r`rrmaskrLrrrrrrrr+test_classification_tree_missing_values_toy s, "   r8cCsHtddd}|tjtjt|j}t|j ||j }tj |j j tjd}d|d<t||j ||j j dks9J|j dks@Jttt|j j|jWdn1sXwYt|j jd|jdt|j ||j }tj |j j tjd}d|dd<t||j ||j j dksJ|j dksJ|j t|j j|jdS)zHTest pruning a tree with the Python caller of the Cythonized prune tree.rr8rrr;N)rrrarrrr atleast_1drarn_features_in_rrkrrnuint8rrrAssertionErrorr'ryrrJ pruned_treeleave_in_subtreerrrtest_build_pruned_tree_py s(    r@cCstddd}|tjtjt|j}t|j ||j }tj |j j tjd}d|d<tjtddt||j |WddS1sDwYdS)z8Test pruning a tree does not result in an infinite loop.rr8rrz,Node has reached a leaf in the original treerN)rrrarrrrr9rarr:rrkrrnr;rrrrr=rrr$test_build_pruned_tree_infinite_loop s  "rAcCs`tjd}|jddddtj}t|gd}td}t||dgd}t ||d S) zNon-regression test for gh-30554. 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