U |e<@sddlZddlmZddlmZddlmZddlZddl Z ddl m Z m Z m Z ddlmZmZddlmZdd lmZmZdd lmZmZd d d gZdd Zddddddd ZGdd d e e e ZdS)N) interpolate) spearmanr)Real) BaseEstimatorTransformerMixinRegressorMixin) check_arraycheck_consistent_length)_check_sample_weight)Interval StrOptions)'_inplace_contiguous_isotonic_regression _make_uniquecheck_increasingisotonic_regressionIsotonicRegressionc Cst||\}}|dk}|dkrt|dkrdtd|d|}dtt|d}t|d|}t|d|}t|t|krt 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_Zincreasing_boolFZF_seZrho_0Zrho_1r#M/var/www/website-v5/atlas_env/lib/python3.8/site-packages/sklearn/isotonic.pyrs"T sample_weighty_miny_max increasingcCs|rtjddntjddd}t|ddtjtjgd}tj|||jd}t|||jdd}t||}t |||dk s|dk r|dkrtj }|dkrtj }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_r float64float32arrayr-r ascontiguousarrayrinfclip)rr&r'r(r)orderr#r#r$rRs&" cseZdZUdZeedddddgeedddddgdedhgeddd hgd Zee d <ddd dd d dZ ddZ ddZ d%ddZ d&ddZddZddZddZd'ddZfdd Zfd!d"Zd#d$ZZS)(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)closedbooleanautonanr6raiser'r(r) out_of_bounds_parameter_constraintsTcCs||_||_||_||_dSNr>)selfr'r(r)r?r#r#r$__init__szIsotonicRegression.__init__cCs2|jdks.|jdkr"|jddks.d}t|dS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)rBXmsgr#r#r$_check_input_data_shapes"z*IsotonicRegression._check_input_data_shapecs>|jdk}tdkr&fdd|_ntj|d|d|_dS)zBuild the f_ interp1d function.r=rcs |jSrA)repeatrF)rrr#r$z-IsotonicRegression._build_f..linear)kind bounds_errorN)r?rf_rinterp1d)rBrHrrQr#rLr$_build_fs  zIsotonicRegression._build_fc sV|||d}|jdkr,t|||_n|j|_t|||jd}|dk}||||||}}}t||ffdd|||fD\}}}t |||\}}}|}t |||j |j |jd}t |t||_|_|rJtjt|ftd} tt|dd|d d t|dd|d d | dd<|| || fS||fSd S) z Build the y_ IsotonicRegression.r*r;r.rcsg|] }|qSr#r#).0r3r7r#r$ sz/IsotonicRegression._build_y..r%rNrD)rJreshaper)rZ increasing_r r-rlexsortrrr'r(minmaxX_min_X_max_onesrbool logical_or not_equal) rBrHrr&Ztrim_duplicatesmaskZunique_XZunique_yZunique_sample_weightZ keep_datar#rVr$_build_ys8     zIsotonicRegression._build_ycCs|tddd}t|fdtjtjgd|}t|fd|jd|}t|||||||\}}|||_ |_ | |||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F) accept_sparser+rH)r,r-r) _validate_paramsdictr rr1r2r-r rd X_thresholds_ y_thresholds_rT)rBrHrr& check_paramsr#r#r$fit9s     zIsotonicRegression.fitcCsrt|dr|jj}ntj}t||dd}|||d}|jdkrXt ||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`. rhF)r-r+r*r6)hasattrrhr-rr1r rJrYr?r6r]r^rRastype)rBTr-resr#r#r$ _transformks       zIsotonicRegression._transformcCs ||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. rprBrnr#r#r$ transformszIsotonicRegression.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. rqrrr#r#r$predicts zIsotonicRegression.predictcCs"|jj}tj|dgtdS)aKGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Ignored. Returns ------- feature_names_out : ndarray of str objects An ndarray with one string i.e. ["isotonicregression0"]. 0r.) __class____name__lowerrasarrayobject)rBinput_features class_namer#r#r$get_feature_names_outs z(IsotonicRegression.get_feature_names_outcst}|dd|S)z0Pickle-protocol - return state of the estimator.rRN)super __getstate__poprBstatervr#r$rs  zIsotonicRegression.__getstate__cs4t|t|dr0t|dr0||j|jdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rhriN)r~ __setstate__rlrTrhrirrr#r$rs zIsotonicRegression.__setstate__cCs ddgiS)NX_typesZ1darrayr#)rBr#r#r$ _more_tagsszIsotonicRegression._more_tags)T)N)N)rw __module__ __qualname____doc__r rr r@rg__annotations__rCrJrTrdrkrprsrtr}rrr __classcell__r#r#rr$rs$ ^  1 2   )numpyrscipyr scipy.statsrnumbersrrrbaserrrutilsr r utils.validationr utils._param_validationr r Z _isotonicrr__all__rrrr#r#r#r$s$     < 7