o tBh;@sddlZddlmZddlmZddlZddlZddlm Z m Z m Z ddl m Z mZddlmZddlmZmZgd Zd d Zdddd d ddZGddde e e ZdS)N) interpolate) spearmanr) BaseEstimatorTransformerMixinRegressorMixin) check_arraycheck_consistent_length)_check_sample_weight)'_inplace_contiguous_isotonic_regression _make_unique)check_increasingisotonic_regressionIsotonicRegressionc Cst||\}}|dk}|dvrNt|dkrNdtd|d|}dtt|d}t|d|}t|d|}t|t|krNt 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_1r$g/var/www/html/riverr-enterprise-integrations-main/venv/lib/python3.10/site-packages/sklearn/isotonic.pyr s"r T sample_weighty_miny_max increasingcCs|r tjddntjddd}t|ddtjtjgd}tj|||jd}t|||jdd}t||}t |||dusD|dur[|durLtj }|durStj }t ||||||S) aSolve the isotonic regression model. Read more in the :ref:`User Guide `. Parameters ---------- y : array-like of shape (n_samples,) The data. sample_weight : array-like of shape (n_samples,), default=None Weights on each point of the regression. If None, weight is set to 1 (equal weights). y_min : float, default=None Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf. y_max : float, default=None Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf. increasing : bool, default=True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False). Returns ------- y_ : list of floats Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. NFr) ensure_2d input_namedtyper.T)r.copy) rs_rfloat64float32arrayr.r ascontiguousarrayr infclip)rr'r(r)r*orderr$r$r%rPs"& rcseZdZdZdddddddZdd Zd d Zdd d ZdddZddZ ddZ dddZ fddZ fddZ ddZZS)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...]) NTnanr(r)r* out_of_boundscCs||_||_||_||_dSNr:)selfr(r)r*r;r$r$r%__init__s zIsotonicRegression.__init__cCs6|jdks|jdkr|jddksd}t|dSdS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)r=Xmsgr$r$r%_check_input_data_shapes "z*IsotonicRegression._check_input_data_shapecsZ|jdvr td|j|jdk}tdkr!fdd|_d Stj|d|d|_d S) zBuild the f_ interp1d function.raiser9r7IThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}rGrcs |jSr<)repeatrA)rrr$r%s z-IsotonicRegression._build_f..linear)kind bounds_errorN)r;rBformatrf_rinterp1d)r=rCrrNr$rJr%_build_fs    zIsotonicRegression._build_fc sP|||d}|jdkrt|||_n|j|_t|||jd}|dk}||||||}}}t||ffdd|||fD\}}}t |||\}}}|}t |||j |j |jd}t |t||_|_|rtjt|ftd} tt|dd|d d t|dd|d d | dd<|| || fS||fS) z Build the y_ IsotonicRegression.r+autor/rcsg|]}|qSr$r$).0r4r8r$r% sz/IsotonicRegression._build_y..r&rNr?)rEreshaper*r increasing_r r.rlexsortr rr(r)minmaxX_min_X_max_onesrbool logical_or not_equal) r=rCrr'trim_duplicatesmaskunique_Xunique_yunique_sample_weight keep_datar$rUr%_build_ys6    4 zIsotonicRegression._build_ycCs~tddd}t|fdtjtjgd|}t|fd|jd|}t|||||||\}}|||_|_ | |||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F) accept_sparser,rC)r-r.r) dictrrr2r3r.r ri X_thresholds_ y_thresholds_rR)r=rCrr' check_paramsr$r$r%fit7s    zIsotonicRegression.fitcCst|dr |jj}ntj}t||dd}|||d}|jdvr+t d |j|jdkr9t ||j |j }||}||j}|S)aTransform new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. Returns ------- y_pred : ndarray of shape (n_samples,) The transformed data. rlF)r.r,r+rFrHr7)hasattrrlr.rr2rrErXr;rBrOr7r]r^rPastype)r=Tr.resr$r$r% transformhs        zIsotonicRegression.transformcCs ||S)a%Predict new data by linear interpolation. Parameters ---------- T : array-like of shape (n_samples,) or (n_samples, 1) Data to transform. Returns ------- y_pred : ndarray of shape (n_samples,) Transformed data. )rt)r=rrr$r$r%predicts zIsotonicRegression.predictcCs"|jj}tj|dgtdS)aKGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Ignored. Returns ------- feature_names_out : ndarray of str objects An ndarray with one string i.e. ["isotonicregression0"]. 0r/) __class____name__lowerrasarrayobject)r=input_features class_namer$r$r%get_feature_names_outs z(IsotonicRegression.get_feature_names_outcst}|dd|S)z0Pickle-protocol - return state of the estimator.rPN)super __getstate__popr=staterwr$r%rs  zIsotonicRegression.__getstate__cs<t|t|drt|dr||j|jdSdSdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rlrmN)r __setstate__rprRrlrmrrr$r%rs zIsotonicRegression.__setstate__cCs ddgiS)NX_types1darrayr$)r=r$r$r% _more_tagss zIsotonicRegression._more_tags)Tr<)rx __module__ __qualname____doc__r>rErRrirortrur~rrr __classcell__r$r$rr%rs]  11,    r)numpyrscipyr scipy.statsrrrbaserrrutilsrr utils.validationr _isotonicr r __all__r rrr$r$r$r%s   < 7