UgEA dZddlZddlZddlmZddlZddlmZddl m Z ddl m Z m Z ddlmZmZmZmZdd lmZmZdd lmZmZmZdd lmZmZgd Zed gd gdddZed gd dgeeddddgeeddddgdgddddddddZGddeeeZ dS)z8Isotonic regression for obtaining monotonic fit to data.N)Real) interpolate) spearmanr)'_inplace_contiguous_isotonic_regression _make_unique) BaseEstimatorRegressorMixinTransformerMixin _fit_context) check_arraycheck_consistent_length)Interval StrOptionsvalidate_params)_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?rrg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.) rlenmathlogsqrttanhnpsignwarningswarn) rrrho_increasing_boolFF_serho_0rho_1s O/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/sklearn/isotonic.pyrrsfq!__FCQhO +#a&&1** $(C#I#)455 549SVVaZ((( !dTk/** !dTk/** 75>>RWU^^ + + M    bothclosedboolean)r sample_weighty_miny_max increasingr5r6r7r8c|rtjddntjddd}t|ddtjtjg}tj|||j}t|||jd}tj||}t||||4| tj }| tj }tj ||||||S) a0Solve the isotonic regression model. Read more in the :ref:`User Guide `. Parameters ---------- y : array-like of shape (n_samples,) The data. sample_weight : array-like of shape (n_samples,), default=None Weights on each point of the regression. If None, weight is set to 1 (equal weights). y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool, default=True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False). Returns ------- y_ : ndarray of shape (n_samples,) Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. Examples -------- >>> from sklearn.isotonic import isotonic_regression >>> isotonic_regression([5, 3, 1, 2, 8, 10, 7, 9, 6, 4]) array([2.75 , 2.75 , 2.75 , 2.75 , 7.33..., 7.33..., 7.33..., 7.33..., 7.33..., 7.33...]) NFr) ensure_2d input_namedtyper>T)r>copy) r$s_r float64float32arrayr>rascontiguousarrayrinfclip)rr5r6r7r8orders r/rresn# 3BE!!!HHddd EA3rz2:>VWWWA 5)))A(tTTTM(u)=>>M+A}=== E- =VGE =FE 5%### U8Or0c$eZdZUdZeeddddgeeddddgdedhgehdgdZee d <ddd d dd Z d Z dZ ddZ ed ddZdZdZdZddZfdZfdZdZxZS)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...]) Nr1r2r4auto>nanrGraiser6r7r8 out_of_bounds_parameter_constraintsTrKc>||_||_||_||_dSNrM)selfr6r7r8rNs r/__init__zIsotonicRegression.__init__s%  $*r0cz|jdks-|jdkr|jddksd}t|dSdS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)rRXmsgs r/_check_input_data_shapez*IsotonicRegression._check_input_data_shapesH! !  a* S// !  r0c|jdk}tdkr fd|_dStj|d||_dS)zBuild the f_ interp1d function.rLrc8|jSrQ)repeatrWrs r/z-IsotonicRegression._build_f..&s 1 1r0linear)kind bounds_errorN)rNrf_rinterp1d)rRrYrrbs ` r/_build_fzIsotonicRegression._build_f s[)W4 q66Q;;1111DGGG!*18,DGGGr0c  |||d}|jdkrt|||_n |j|_t |||j}|dk}||||||}}}tj||f fd|||fD\}}}t|||\}}}|}t|||j |j |j}tj |tj|c|_|_|rtjt%|ft&} tjtj|dd|dd tj|dd|d d| dd<|| || fS||fS) z Build the y_ IsotonicRegression.r;rJr?rc g|] }| Srh).0rDrHs r/ z/IsotonicRegression._build_y..>sOOOuU|OOOr0r9rNrU)r[reshaper8r increasing_rr>r$lexsortrrr6r7minmaxX_min_X_max_onesrbool logical_or not_equal) rRrYrr5trim_duplicatesmaskunique_Xunique_yunique_sample_weight keep_datarHs @r/_build_yzIsotonicRegression._build_y,s $$Q''' IIbMM ?f $ $/155D  #D -]AQWMMM q gqw d0Cm1 Aq6""OOOO!Q 9NOOO1m3?1m3T3T0(0   .**'    $&6!99bfQii  T[  Q 666I!m QqtWaf--r|AadGQqrrU/K/KIadOY<9- - a4Kr0rc>tdd}t|fdtjtjgd|}t|fd|jd|}t |||||||\}}||c|_|_ | |||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F) accept_sparser<rY)r=r>r) dictr r$rBrCr>rr} X_thresholds_ y_thresholds_re)rRrYrr5 check_paramss r/fitzIsotonicRegression.fit]s:%5AAA   bj"*%=  AM    Ic I IL I I1m444}}Q=111 23A.D. a r0ct|dr |jj}n tj}t ||d}|||d}|jdkr tj ||j |j }| |}| |j}|S)a`_transform` is called by both `transform` and `predict` methods. Since `transform` is wrapped to output arrays of specific types (e.g. NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform` directly. The above behaviour could be changed in the future, if we decide to output other type of arrays when calling `predict`. rF)r>r<r;rG)hasattrrr>r$rBr r[rlrNrGrqrrrcastype)rRTr>ress r/ _transformzIsotonicRegression._transforms 4 ) ) &,EEJE % 8 8 8 $$Q''' IIbMM   ' '4; 44Aggajjjj!! r0c,||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. rrRrs r/ transformzIsotonicRegression.transforms q!!!r0c,||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 r/predictzIsotonicRegression.predictsq!!!r0ct|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"]. rc0r?)r __class____name__lowerr$asarrayobject)rRinput_features class_names r/get_feature_names_outz(IsotonicRegression.get_feature_names_outsK d###^,2244 zj+++,F;;;;r0ctt}|dd|S)z0Pickle-protocol - return state of the estimator.rcN)super __getstate__poprRstaters r/rzIsotonicRegression.__getstate__s1$$&& $ r0ct|t|dr2t|dr$||j|jdSdSdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rrN)r __setstate__rrerrrs r/rzIsotonicRegression.__setstate__sz U### 4 ) ) BgdO.L.L B MM$,d.@ A A A A A B B B Br0c ddgiS)NX_types1darrayrh)rRs r/ _more_tagszIsotonicRegression._more_tagssI;''r0)TrQ)r __module__ __qualname____doc__rrrrOr__annotations__rSr[rer}r rrrrrrrr __classcell__)rs@r/rrs[[|(4tF;;;TB(4tF;;;TB **fX"6"67$*%=%=%=>>? $$D!%DTQV+++++ """   ////b\555///65/b<"""$ " " "&<<<<"BBBBB(((((((r0r)!rr r&numbersrnumpyr$scipyr scipy.statsr _isotonicrrbaser r r r utilsr rutils._param_validationrrrutils.validationrr__all__rrrrhr0r/rs!>> !!!!!!LLLLLLLLOOOOOOOOOOOO77777777JJJJJJJJJJCCCCCCCC K K K^^#' BBBJ^&-(4tF;;;TB(4tF;;;TB k #'   D;;;;  ;|G(G(G(G(G()9=G(G(G(G(G(r0