0PhC dZddlZddlZddlmZddlZddlmZm Z ddl m Z ddl m Z ddlmZmZdd lmZmZmZmZdd lmZmZdd lmZmZmZdd lmZmZdd l m!Z!m"Z"gdZ#edgdgdddZ$edgddgeeddddgeeddddgdgddddddddZ%GddeeeZ&dS)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)check_increasingisotonic_regressionIsotonicRegressionz array-likexyTprefer_skip_nested_validationct||\}}|dk}|dvrt|dkrdtjd|zd|z z z}dtjt|dz z }tj|d|zz }tj|d|zz}t j|t j|krtj d|S) a?Determine 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 P/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/isotonic.pyrrsfq!__FCQhO +#a&&1** $(C#I#)455 549SVVaZ((( !dTk/** !dTk/** 75>>RWU^^ + + M    bothclosedboolean)r sample_weighty_miny_max increasingr9r:r;r<ct|ddtjtjg}tt dkr8t j|||}tj|j |j }n|rtj ddntj ddd}tj |||j }t|||j d }tj||}t||||}||4| tj }| tj}tj|||||S) a0Solve 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 r3rrfs8n A3rz2:>VWWWA-1111*:    JsuAG , , ,'7aaaBE$$B$K HQuXQW - - -,]AQWSWXXX ,]5-ABB /=AAA eH E- =VGE =FE 5%### Hr4cLeZdZUdZdejiZdejiZee ddddgee ddddgde dhge hdgd Z e e d <ddd d d d ZdZdZddZed ddZdZdZdZddZfdZfdZfdZxZS)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_constraintsTrSc>||_||_||_||_dSNrU)selfr:r;r<rVs r3__init__zIsotonicRegression.__init__ s%  $*r4cz|jdks-|jdkr|jddksd}t|dSdS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)rZXmsgs r3_check_input_data_shapez*IsotonicRegression._check_input_data_shape&sH! !  a* S// !  r4c|jdk}tdkr fd|_dStj|d||_dS)zBuild the f_ interp1d function.rTrc8|jSrY)repeatr_rs r3z-IsotonicRegression._build_f..4s 1 1r4linear)kind bounds_errorN)rVr#f_rinterp1d)rZrarrjs ` r3_build_fzIsotonicRegression._build_f.s[)W4 q66Q;;1111DGGG!*18,DGGGr4c  |||d}|jdkrt|||_n |j|_t |||j}|dk}||||||}}}tj||f fd|||fD\}}}t|||\}}}|}t|||j |j |j}tj |tj|c|_|_|rtjt%|ft&} tjtj|dd|dd tj|dd|d d| dd<|| || fS||fS) z Build the y_ IsotonicRegression.rDrRrCrc g|] }| Srp).0rJrOs r3 z/IsotonicRegression._build_y..LsOOOuU|OOOr4r=rNr])rcreshaper<r increasing_rrAr(lexsortr rr:r;minmaxX_min_X_max_onesr#bool logical_or not_equal) rZrarr9trim_duplicatesmaskunique_Xunique_yunique_sample_weight keep_datarOs @r3_build_yzIsotonicRegression._build_y:s $$Q''' IIbMM ?f $ $/155D  #D -]AQWMMM q gqw d0Cm1 Aq6""OOOO!Q 9NOOO1m3?1m3T3T0(0   .**'    $&6!99bfQii  T[  Q 666I!m QqtWaf--r|AadGQqrrU/K/KIadOY<9- - a4Kr4rc>tdd}t|fdtjtjgd|}t|fd|jd|}t |||||||\}}||c|_|_ | |||S)aFit 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?ra)r@rAr) dictrr(rFrGrArr X_thresholds_ y_thresholds_rm)rZrarr9 check_paramss r3fitzIsotonicRegression.fitks:%5AAA   bj"*%=  AM    Ic I IL I I1m444}}Q=111 23A.D. a r4ct|dr |jj}n tj}t ||d}|||d}|jdkr tj ||j |j }| |}| |j}|S)a`_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(rFrrcrtrVrMryrzrkastype)rZrQrArNs r3 _transformzIsotonicRegression._transforms 4 ) ) &,EEJE % 8 8 8 $$Q''' IIbMM   ' '4; 44Aggajjjj!! r4c,||S)aTransform new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. Returns ------- y_pred : ndarray of shape (n_samples,) The transformed data. rrZrQs r3 transformzIsotonicRegression.transforms q!!!r4c,||S)a%Predict new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. Returns ------- y_pred : ndarray of shape (n_samples,) Transformed data. rrs r3predictzIsotonicRegression.predictsq!!!r4ct|d|jj}t j|dgt S)aKGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Ignored. Returns ------- feature_names_out : ndarray of str objects An ndarray with one string i.e. ["isotonicregression0"]. rk0rC)r __class____name__lowerr(rHobject)rZinput_features class_names r3get_feature_names_outz(IsotonicRegression.get_feature_names_outsK d###^,2244 zj+++,F;;;;r4ctt}|dd|S)z0Pickle-protocol - return state of the estimator.rkN)super __getstate__poprZstaters r3rzIsotonicRegression.__getstate__s1$$&& $ r4ct|t|dr2t|dr$||j|jdSdSdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rrN)r __setstate__rrmrrrs r3rzIsotonicRegression.__setstate__sz U### 4 ) ) BgdO.L.L B MM$,d.@ A A A A A B B B Br4cxt}d|j_d|j_|S)NTF)r__sklearn_tags__ input_tags one_d_array two_d_array)rZtagsrs r3rz#IsotonicRegression.__sklearn_tags__s1ww''))&*#&+# r4)TrY)r __module__ __qualname____doc__rUNUSED._IsotonicRegression__metadata_request__predict0_IsotonicRegression__metadata_request__transformrrrrWr__annotations__r[rcrmrrrrrrrrrr __classcell__)rs@r3rrs[[|$'(8(?"@%(*:*A$B!(4tF;;;TB(4tF;;;TB **fX"6"67$*%=%=%=>>? $$D!%DTQV+++++ """   ////b\555///65/b<"""$ " " "&<<<<"BBBBBr4r)'rr$r*numbersrnumpyr(scipyrr scipy.statsr sklearn.utilsr _isotonicr r baser r r rutilsrrutils._param_validationrrr utils.fixesrrutils.validationrr__all__rrrrpr4r3rsV>>  ''''''''!!!!!!******LLLLLLLLOOOOOOOOOOOO77777777JJJJJJJJJJ77777777CCCCCCCC K K K^^#' BBBJ^&-(4tF;;;TB(4tF;;;TB k #'   DD D D D   D NNNNNN)9=NNNNNr4