3 [h7@sddlZddlmZddlmZddlZddlZddlm Z m Z m Z ddl m Z mZddlmZmZddlmZmZd d d gZd d Zedddd ddd ZGdd d e e e ZdS)N) interpolate) spearmanr) BaseEstimatorTransformerMixinRegressorMixin) check_arraycheck_consistent_length)_check_sample_weight_deprecate_positional_args)'_inplace_contiguous_isotonic_regression _make_uniquecheck_increasingisotonic_regressionIsotonicRegressionc Cst||\}}|dk}|d krt|dkrdtjd|d|}dtjt|d}tj|d|}tj|d|}tj|tj|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?rg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.)rr) rlenmathlogsqrttanhnpsignwarningswarn) xyrho_Zincreasing_boolFZF_seZrho_0Zrho_1r"8/tmp/pip-build-zwgx3nbq/scikit-learn/sklearn/isotonic.pyrs" T) sample_weighty_miny_max increasingcCs|rtjddntjddd}t|dtjtjgd}tj|||jd}t|||jd}tj||}t |||dk s|dk r|dkrtj }|dkrtj }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. NrF) ensure_2ddtype)r)) rZs_rfloat64float32arrayr)r Zascontiguousarrayr infclip)rr$r%r&r'orderr"r"r#rOs&" cseZdZdZedddddddZdd Zd d Zdd d ZdddZ ddZ ddZ fddZ fddZ ddZZS)rau 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``. 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...]) NTnan)r%r&r' out_of_boundscCs||_||_||_||_dS)N)r%r&r'r2)selfr%r&r'r2r"r"r#__init__szIsotonicRegression.__init__cCs2|jdkp |jdko |jddks.d}t|dS)NrzKIsotonic regression input X should be a 1d array or 2d array with 1 feature)ndimshape ValueError)r3Xmsgr"r"r#_check_input_data_shapes"z*IsotonicRegression._check_input_data_shapecsX|jd krtdj|j|jdk}tdkr@fdd|_ntj|d|d |_d S) zBuild the f_ interp1d function.raiser1r/zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}rcs j|jS)N)repeatr7)r)rr"r#sz-IsotonicRegression._build_f..Zlinear)kind bounds_errorN)r<r1r/)r2r8formatrf_rZinterp1d)r3r9rr@r")rr#_build_fs     zIsotonicRegression._build_fc sV|j||jd }|jdkr,t|||_n|j|_t|||jd}|dk}||||||}}}tj||ffdd|||gD\}}}t |||\}}}|}t |||j |j |jd}tj |tj||_|_|rJtjt|ftd} tjtj|dd |dd tj|dd |d d| dd<|| || fS||fSdS)z Build the y_ IsotonicRegression.rauto)r)rcsg|] }|qSr"r").0r-)r0r"r# sz/IsotonicRegression._build_y..)r$r%r&r'Nr5r*r*r*r*)r;reshaper'rZ increasing_r r)rZlexsortr rr%r&minmaxX_min_X_max_Zonesrbool logical_or not_equal) r3r9rr$Ztrim_duplicatesmaskZunique_XZunique_yZunique_sample_weightZ keep_datar")r0r#_build_ys0    &zIsotonicRegression._build_ycCsztddd}t|fdtjtjgi|}t|fd|ji|}t||||j|||\}}|||_|_ |j |||S)aFit the model using X, y as training data. Parameters ---------- X : array-like of shape (n_samples,) or (n_samples, 1) Training data. .. versionchanged:: 0.24 Also accepts 2d array with 1 feature. y : array-like of shape (n_samples,) Training target. sample_weight : array-like of shape (n_samples,), default=None Weights. If set to None, all weights will be set to 1 (equal weights). Returns ------- self : object Returns an instance of self. Notes ----- X is stored for future use, as :meth:`transform` needs X to interpolate new input data. F)Z accept_sparser(r)) dictrrr+r,r)r rQ X_thresholds_ y_thresholds_rC)r3r9rr$Z check_paramsr"r"r#fit)s   zIsotonicRegression.fitcCst|dr|jj}ntj}t||dd}|j||jd }|jd krVt dj |j|jdkrrtj ||j |j }|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 rSF)r)r(rr<r1r/zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}r*)r<r1r/)hasattrrSr)rr+rr;rHr2r8rAr/rKrLrBZastype)r3Tr)resr"r"r# transformXs         zIsotonicRegression.transformcCs |j|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. )rY)r3rWr"r"r#predicts zIsotonicRegression.predictcstj}|jdd|S)z1Pickle-protocol - return state of the estimator. rBN)super __getstate__pop)r3state) __class__r"r#r\s  zIsotonicRegression.__getstate__cs4tj|t|dr0t|dr0|j|j|jdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rSrTN)r[ __setstate__rVrCrSrT)r3r^)r_r"r#r`s zIsotonicRegression.__setstate__cCs ddgiS)NZX_typesZ1darrayr")r3r"r"r# _more_tagsszIsotonicRegression._more_tags)T)N)__name__ __module__ __qualname____doc__r r4r;rCrQrUrYrZr\r`ra __classcell__r"r")r_r#rsT / /+  )ZnumpyrZscipyrZ scipy.statsrrrbaserrrutilsrr Zutils.validationr r Z _isotonicr r __all__rrrr"r"r"r#s  95