a hDf@ @s*ddlZddlZddlmZddlZddlmZddlm Z ddl m Z m Z ddl mZmZmZmZddlmZmZdd lmZmZmZdd lmZmZgd Zed gd gd ddddZed gd dgeedddddgeedddddgdgddddddddddZGdddeeeZdS)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-like)xyTZprefer_skip_nested_validationc Cst||\}}|dk}|dvrt|dkrdtd|d|}dtt|d}t|d|}t|d|}t|t|krt d|S) a7Determine 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) True >>> y = [10, 8, 6, 4, 2] >>> check_increasing(x, y) False r)g?g?rrg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.) rlenmathlogsqrttanhnpsignwarningswarn) rrrho_Zincreasing_boolFZF_seZrho_0Zrho_1r']/nfs/NAS7/SABIOD/METHODE/ermites/ermites_venv/lib/python3.9/site-packages/sklearn/isotonic.pyrs3rbothclosedboolean)r sample_weighty_miny_max increasingr-r.r/r0cCs|rtjddntjddd}t|ddtjtjgd}tj|||jd}t|||jdd}t||}t |||dus|dur|durtj }|durtj }t ||||||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_namedtyper5T)r5copy) r Zs_r float64float32arrayr5rZascontiguousarrayrinfclip)rr-r.r/r0orderr'r'r(rcs7" rcseZdZUdZeedddddgeedddddgdedhgehdgdZee d <ddd d dd d Z ddZ ddZ d%ddZ ed dd&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...]) Nr)r*r,auto>r<nanraiser.r/r0 out_of_bounds_parameter_constraintsTr?cCs||_||_||_||_dSNrA)selfr.r/r0rBr'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)rEXmsgr'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 |jSrD)repeatrI)rrr'r($z-IsotonicRegression._build_f..Zlinear)kind bounds_errorN)rBrf_rZinterp1d)rErKrrSr'rOr(_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.r2r>r6rcsg|] }|qSr'r').0r:r=r'r( <rQz/IsotonicRegression._build_y..r1rNrG)rMreshaper0rZ increasing_rr5r Zlexsortrrr.r/minmaxX_min_X_max_Zonesrbool logical_or not_equal) rErKrr-Ztrim_duplicatesmaskZunique_XZunique_yZunique_sample_weightZ keep_datar'rWr(_build_y*s6    4 zIsotonicRegression._build_yrcCs~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)Z accept_sparser3rK)r4r5r) dictr r r8r9r5r rc X_thresholds_ y_thresholds_rU)rErKrr-Z check_paramsr'r'r(fit[s    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`. reF)r5r3r2r<)hasattrrer5r r8r rMrZrBr<r]r^rTZastype)rETr5resr'r'r( _transforms       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. rkrErir'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. rlrmr'r'r(predicts zIsotonicRegression.predictcCs,t|d|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"]. rT0r6)r __class____name__lowerr Zasarrayobject)rEZinput_features class_namer'r'r(get_feature_names_outs  z(IsotonicRegression.get_feature_names_outcst}|dd|S)z0Pickle-protocol - return state of the estimator.rTN)super __getstate__poprEstaterqr'r(rxs  zIsotonicRegression.__getstate__cs4t|t|dr0t|dr0||j|jdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rerfN)rw __setstate__rhrUrerfrzr|r'r(r}s zIsotonicRegression.__setstate__cCs ddgiS)NZX_typesZ1darrayr')rEr'r'r( _more_tagsszIsotonicRegression._more_tags)T)N)N)rr __module__ __qualname____doc__rrrrCrd__annotations__rFrMrUrcr rgrkrnrorvrxr}r~ __classcell__r'r'r|r(rs& ^   1 1   r) rr"Znumbersrnumpyr ZscipyrZ scipy.statsrZ _isotonicrrbaserr r r utilsr r Zutils._param_validationrrrZutils.validationrr__all__rrrr'r'r'r(s<    E >