h<ddlZddlmZddlmZddlmZddlZddl Z ddl m Z m Z m Z ddlmZmZddlmZdd lmZmZdd lmZmZgd Zd Zdddd ddZGdde e e ZdS)N) interpolate) spearmanr)Real) BaseEstimatorTransformerMixinRegressorMixin) check_arraycheck_consistent_length)_check_sample_weight)Interval StrOptions)'_inplace_contiguous_isotonic_regression _make_unique)check_increasingisotonic_regressionIsotonicRegressionct||\}}|dk}|dvrt|dkrdtjd|zd|z z z}dtjt|dz z }tj|d|zz }tj|d|zz}t j|t j|krtj d|S) aGDetermine 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 r)g?g?rrg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.) rlenmathlogsqrttanhnpsignwarningswarn) xyrho_increasing_boolFF_serho_0rho_1s L/var/www/html/Sam_Eipo/venv/lib/python3.11/site-packages/sklearn/isotonic.pyrrsDq!__FCQhO +#a&&1** $(C#I#)455 549SVVaZ((( !dTk/** !dTk/** 75>>RWU^^ + + M    T sample_weighty_miny_max increasingc|rtjddntjddd}t|ddtjtjg}tj|||j}t|||jd}tj||}t||||4| tj }| tj }tj ||||||S) 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_ : list of floats Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. NFr!) ensure_2d input_namedtyper4T)r4copy) rs_r float64float32arrayr4r ascontiguousarrayrinfclip)r!r,r-r.r/orders r)rrRsL# 3BE!!!HHddd EA3rz2:>VWWWA 5)))A(tTTTM(u)=>>M+A}=== E- =VGE =FE 5%### U8Or*ceZdZUdZeeddddgeeddddgdedhgehdgdZee d <ddd d dd Z d Z dZ ddZ 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...]) Nboth)closedbooleanauto>nanr=raiser-r.r/ out_of_bounds_parameter_constraintsTrDc>||_||_||_||_dSNrF)selfr-r.r/rGs r)__init__zIsotonicRegression.__init__s%  $*r*cz|jdks-|jdkr|jddksd}t|dSdS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)rKXmsgs r)_check_input_data_shapez*IsotonicRegression._check_input_data_shapesH! !  a* S// !  r*c|jdk}tdkr fd|_dStj|d||_dS)zBuild the f_ interp1d function.rErc8|jSrJ)repeatrP)r r!s r)z-IsotonicRegression._build_f..s 1 1r*linear)kind bounds_errorN)rGrf_rinterp1d)rKrRr!r[s ` r)_build_fzIsotonicRegression._build_fs[)W4 q66Q;;1111DGGG!*18,DGGGr*c  |||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.r1rCr5rc g|] }| Sra).0r:r>s r) z/IsotonicRegression._build_y..sOOOuU|OOOr*r+rNrN)rTreshaper/r increasing_r r4rlexsortrrr-r.minmaxX_min_X_max_onesrbool logical_or not_equal) rKrRr!r,trim_duplicatesmaskunique_Xunique_yunique_sample_weight keep_datar>s @r)_build_yzIsotonicRegression._build_ys $$Q''' IIbMM ?f $ $/155D  #D -]AQWMMM q gqw d0Cm1 Aq6""OOOO!Q 9NOOO1m3?1m3T3T0(0   .**'    $&6!99bfQii  T[  Q 666I!m QqtWaf--r|AadGQqrrU/K/KIadOY<9- - a4Kr*cf|tdd}t|fdtjtjgd|}t|fd|jd|}t|||||||\}}||c|_ |_ | |||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F) accept_sparser2rR)r3r4r!) _validate_paramsdictr rr8r9r4r rv X_thresholds_ y_thresholds_r^)rKrRr!r, check_paramss r)fitzIsotonicRegression.fit9s8 %5AAA   bj"*%=  AM    Ic I IL I I1m444}}Q=111 23A.D. a r*ct|dr |jj}n tj}t ||d}|||d}|jdkr tj ||j |j }| |}| |j}|S)a`_transform` is called by both `transform` and `predict` methods. Since `transform` is wrapped to output arrays of specific types (e.g. NumPy arrays, pandas DataFrame), we cannot make `predict` call `transform` directly. The above behaviour could be changed in the future, if we decide to output other type of arrays when calling `predict`. r{F)r4r2r1r=)hasattrr{r4rr8r rTrerGr=rjrkr\astype)rKTr4ress r) _transformzIsotonicRegression._transformks 4 ) ) &,EEJE % 8 8 8 $$Q''' IIbMM   ' '4; 44Aggajjjj!! r*c,||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. rrKrs r) transformzIsotonicRegression.transforms q!!!r*c,||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!!!r*c||jj}tj|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"]. 0r5) __class____name__lowerrasarrayobject)rKinput_features class_names r)get_feature_names_outz(IsotonicRegression.get_feature_names_outs;^,2244 zj+++,F;;;;r*ctt}|dd|S)z0Pickle-protocol - return state of the estimator.r\N)super __getstate__poprKstaters r)rzIsotonicRegression.__getstate__s1$$&& $ r*ct|t|dr2t|dr$||j|jdSdSdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. r{r|N)r __setstate__rr^r{r|rs r)rzIsotonicRegression.__setstate__sz U### 4 ) ) BgdO.L.L B MM$,d.@ A A A A A B B B Br*c ddgiS)NX_types1darrayra)rKs r) _more_tagszIsotonicRegression._more_tagssI;''r*)TrJ)r __module__ __qualname____doc__r rrrHrz__annotations__rLrTr^rvr~rrrrrrr __classcell__)rs@r)rrs[[|(4tF;;;TB(4tF;;;TB **fX"6"67$*%=%=%=>>? $$D!%DTQV+++++ """   ////b0000d<"""$ " " "&<<<< BBBBB(((((((r*r)numpyrscipyr scipy.statsrnumbersrrrbaserrr utilsr r utils.validationr utils._param_validationr r _isotonicrr__all__rrrrar*r)rsY !!!!!! AAAAAAAAAA7777777722222299999999LLLLLLLL L K K888xD44444nF(F(F(F(F()9=F(F(F(F(F(r*