-iC fSrSSKrSSKrSSKJr SSKrSSKJrJ r SSK J r SSK J r SSKJrJr SS KJrJrJrJr SS KJrJr SS KJrJrJr SS KJrJr SS K J!r!J"r" /SQr#\"S/S/S.SS9S5r$\"S/SS/\"\SSSS9S/\"\SSSS9S/S/S.SS9SSSSS.Sj5r%"SS\\\5r&g)z8Isotonic regression for obtaining monotonic fit to data.N)Real) interpolateoptimize) spearmanr)metadata_routing)'_inplace_contiguous_isotonic_regression _make_unique) BaseEstimatorRegressorMixinTransformerMixin _fit_context) check_arraycheck_consistent_length)Interval StrOptionsvalidate_params) parse_versionsp_base_version)_check_sample_weightcheck_is_fitted)IsotonicRegressioncheck_increasingisotonic_regressionz array-likexyTprefer_skip_nested_validationc[X5up#US:nUS;a[U5S:aS[R"SU-SU- - 5-nS[R"[U5S- 5- n[R "USU-- 5n[R "USU--5n[ R"U5[ R"U5:wa[R"S5 U$) aDetermine whether y is monotonically correlated with x. y is found increasing or decreasing with respect to x based on a Spearman correlation test. Parameters ---------- x : array-like of shape (n_samples,) Training data. y : array-like of shape (n_samples,) Training target. Returns ------- increasing_bool : boolean Whether the relationship is increasing or decreasing. Notes ----- The Spearman correlation coefficient is estimated from the data, and the sign of the resulting estimate is used as the result. In the event that the 95% confidence interval based on Fisher transform spans zero, a warning is raised. References ---------- Fisher transformation. Wikipedia. https://en.wikipedia.org/wiki/Fisher_transformation Examples -------- >>> from sklearn.isotonic import check_increasing >>> x, y = [1, 2, 3, 4, 5], [2, 4, 6, 8, 10] >>> check_increasing(x, y) np.True_ >>> y = [10, 8, 6, 4, 2] >>> check_increasing(x, y) np.False_ r)g?g?r!rg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.) rlenmathlogsqrttanhnpsignwarningswarn) rrrho_increasing_boolFF_serho_0rho_1s C/var/www/html/venv/lib/python3.13/site-packages/sklearn/isotonic.pyrrsfq_FCQhO +#a&1* $((C#I#)45 5499SVaZ(( !dTk/* !dTk/* 775>RWWU^ + MM  bothclosedboolean)r sample_weighty_miny_max increasingr9r:r;r<c[USS[R[R/S9n[[ S5:a?[ R"XUS9n[R"URURS9nOU(a[RSSO[RSSS2n[R"XURS9n[XURS S 9n[R"X5n[X5 XnUcUb>Uc[R *nUc[R n[R""XX05 U$) aSolve the isotonic regression model. Read more in the :ref:`User Guide `. Parameters ---------- y : array-like of shape (n_samples,) The data. sample_weight : array-like of shape (n_samples,), default=None Weights on each point of the regression. If None, weight is set to 1 (equal weights). y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool, default=True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False). Returns ------- y_ : ndarray of shape (n_samples,) Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. Examples -------- >>> from sklearn.isotonic import isotonic_regression >>> isotonic_regression([5, 3, 1, 2, 8, 10, 7, 9, 6, 4]) array([2.75 , 2.75 , 2.75 , 2.75 , 7.33, 7.33, 7.33, 7.33, 7.33, 7.33]) Fr) ensure_2d input_namedtypez1.12.0)rweightsr<rANT)rAcopy)rr(float64float32rrrrasarrayrrAs_arrayrascontiguousarrayr infclip)rr9r:r;r<resorders r3rrfsn A3rzz2::>VWA-11**:  JJsuuAGG ,'aBEE$B$K HHQXQWW -,]QWWSWX ,,]-AB /A H E- =VVGE =FFE %# Hr4c@^\rSrSr%SrS\R 0rS\R 0r\ "\ SSSS9S/\ "\ SSSS9S/S\ "S15/\ "1S k5/S .r \ \S 'SSS S S .SjrSrSrSSjr\"S S9SSj5rSrSrSrSSjrU4SjrU4SjrU4SjrSrU=r$)ra Isotonic regression model. Read more in the :ref:`User Guide `. .. versionadded:: 0.13 Parameters ---------- y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool or 'auto', default=True Determines whether the predictions should be constrained to increase or decrease with `X`. 'auto' will decide based on the Spearman correlation estimate's sign. out_of_bounds : {'nan', 'clip', 'raise'}, default='nan' Handles how `X` values outside of the training domain are handled during prediction. - 'nan', predictions will be NaN. - 'clip', predictions will be set to the value corresponding to the nearest train interval endpoint. - 'raise', a `ValueError` is raised. Attributes ---------- X_min_ : float Minimum value of input array `X_` for left bound. X_max_ : float Maximum value of input array `X_` for right bound. X_thresholds_ : ndarray of shape (n_thresholds,) Unique ascending `X` values used to interpolate the y = f(X) monotonic function. .. versionadded:: 0.24 y_thresholds_ : ndarray of shape (n_thresholds,) De-duplicated `y` values suitable to interpolate the y = f(X) monotonic function. .. versionadded:: 0.24 f_ : function The stepwise interpolating function that covers the input domain ``X``. increasing_ : bool Inferred value for ``increasing``. See Also -------- sklearn.linear_model.LinearRegression : Ordinary least squares Linear Regression. sklearn.ensemble.HistGradientBoostingRegressor : Gradient boosting that is a non-parametric model accepting monotonicity constraints. isotonic_regression : Function to solve the isotonic regression model. Notes ----- Ties are broken using the secondary method from de Leeuw, 1977. References ---------- Isotonic Median Regression: A Linear Programming Approach Nilotpal Chakravarti Mathematics of Operations Research Vol. 14, No. 2 (May, 1989), pp. 303-308 Isotone Optimization in R : Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods de Leeuw, Hornik, Mair Journal of Statistical Software 2009 Correctness of Kruskal's algorithms for monotone regression with ties de Leeuw, Psychometrica, 1977 Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.isotonic import IsotonicRegression >>> X, y = make_regression(n_samples=10, n_features=1, random_state=41) >>> iso_reg = IsotonicRegression().fit(X, y) >>> iso_reg.predict([.1, .2]) array([1.8628, 3.7256]) TNr5r6r8auto>nanrMraiser:r;r< out_of_bounds_parameter_constraintsTrTc4XlX lX0lX@lgNrV)selfr:r;r<rWs r3__init__IsotonicRegression.__init__ s  $*r4cURS:Xd1URS:XaURSS:Xd Sn[U5egg)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)r[Xmsgs r3_check_input_data_shape*IsotonicRegression._check_input_data_shape&sC! !  a* S/ ! 1@ r4c^URS:Hn[T5S:Xa U4SjUlg[R"UTSUS9Ulg)zBuild the f_ interp1d function.rUrc:>TRUR5$rZ)repeatrars r3-IsotonicRegression._build_f..4s 1r4linear)kind bounds_errorN)rWr#f_rinterp1d)r[rcrrns ` r3_build_fIsotonicRegression._build_f.sB))W4 q6Q;1DG!**18,DGr4c NURU5 URS5nURS:Xa[X5UlOURUl[ X1UR S9nUS:nXX%X5p2n[R"X!45nXU4Vs/sHowUPM snupn[XU5upn Un[U U URURURS9n[R"U5[R"U5sUlUlU(a{[R""[%U54[&S9n [R("[R*"USSUSS5[R*"USSUS S55U SS&XX+4$X4$s snf) z Build the y_ IsotonicRegression.rDrSrCrr=rNr_)rereshaper<r increasing_rrAr(lexsortr rr:r;minmaxX_min_X_max_onesr#bool logical_or not_equal) r[rcrr9trim_duplicatesmaskrOrJunique_Xunique_yunique_sample_weight keep_datas r3_build_yIsotonicRegression._build_y:s $$Q' IIbM ??f $/5D #D -]QWWM q gqw 0Cm A6":; 9NO9NU|9NOm3?m3T00   .****''  $&66!9bffQi  T[ Q 6I!mm QqWaf-r||AaGQqrU/KIaO<- - 4K;PsF"rc$[SSS9n[U4S[R[R/S.UD6n[U4SUR S.UD6n[ XU5 URXU5upXsUlUl URX5 U$)aFFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F) accept_sparser?rc)r@rAr) dictrr(rFrGrArr X_thresholds_ y_thresholds_rq)r[rcrr9 check_paramss r3fitIsotonicRegression.fitks:%5A   bjj"**%= AM   Ic IL Im4}}Q=1 23.D. a r4c[US5(aURRnO[Rn[ XSS9nUR U5 URS5nURS:Xa+[R"XRUR5nURU5nURUR5nU$)aW`_transform` is called by both `transform` and `predict` methods. Since `transform` is wrapped to output arrays of specific types (e.g. NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform` directly. The above behaviour could be changed in the future, if we decide to output other type of arrays when calling `predict`. rF)rAr?rDrM)hasattrrrAr(rFrrerurWrMrzr{roastype)r[rRrArNs r3 _transformIsotonicRegression._transforms 4 ) )&&,,EJJE % 8 $$Q' IIbM    ';; 4Aggajjj! r4c$URU5$)a.Transform new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. Returns ------- y_pred : ndarray of shape (n_samples,) The transformed data. rr[rRs r3 transformIsotonicRegression.transforms q!!r4c$URU5$)zPredict new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. Returns ------- y_pred : ndarray of shape (n_samples,) Transformed data. rrs r3predictIsotonicRegression.predictsq!!r4c[US5 URRR5n[R "US3/[ S9$)aGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Ignored. Returns ------- feature_names_out : ndarray of str objects An ndarray with one string i.e. ["isotonicregression0"]. ro0rC)r __class____name__lowerr(rHobject)r[input_features class_names r3get_feature_names_out(IsotonicRegression.get_feature_names_outsA d#^^,,224 zzj\+,F;;r4cH>[TU]5nURSS5 U$)z0Pickle-protocol - return state of the estimator.roN)super __getstate__popr[staters r3rIsotonicRegression.__getstate__s#$& $ r4c>[TU]U5 [US5(a9[US5(a'URURUR 5 ggg)z^Pickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rrN)r __setstate__rrqrrrs r3rIsotonicRegression.__setstate__sN U# 4 ) )gdO.L.L MM$,,d.@.@ A/M )r4ch>[TU]5nSURlSURlU$)NTF)r__sklearn_tags__ input_tags one_d_array two_d_array)r[tagsrs r3r#IsotonicRegression.__sklearn_tags__s-w')&*#&+# r4) r{rzrror<rvrWr;r:r)TrZ)r __module__ __qualname____firstlineno____doc__rUNUSED._IsotonicRegression__metadata_request__predict0_IsotonicRegression__metadata_request__transformrrrrXr__annotations__r\rerqrrrrrrrrrr__static_attributes__ __classcell__)rs@r3rrs[|$'(8(?(?"@%(*:*A*A$B!4tF;TB4tF;TB *fX"67$%=>? $D!%DTQV+ " /b5/6/b<"$ "&<"Br4r)'rr$r*numbersrnumpyr(scipyrr scipy.statsr sklearn.utilsr _isotonicr r baser r r rutilsrrutils._param_validationrrr utils.fixesrrutils.validationrr__all__rrrr4r3rs>  '!*LOO7JJ7C K^^#' BBJ^&-4tF;TB4tF;TB k #' DD  D NN)9=Nr4