B udQ8@sddlZddlmZddlmZddlZddlZddlm Z m Z m Z ddl m Z mZddlmZddlmZmZd d d gZd d Zdddd ddd ZGdd d e e e ZdS)N) interpolate) spearmanr) BaseEstimatorTransformerMixinRegressorMixin) check_arraycheck_consistent_length)_check_sample_weight)'_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)gg?g?g?rg\(\?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.) rlenmathlogsqrttanhnpsignwarningswarn) xyrho_Zincreasing_boolFZF_seZrho_0Zrho_1r]/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/sklearn/isotonic.pyr s"T) sample_weighty_miny_max increasingcCs|rtjddntjddd}t|dtjtjgd}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. NF) ensure_2ddtype)r'T)r'copy) rZs_rfloat64float32arrayr'r Zascontiguousarrayr infclip)rr!r"r#r$orderrrr rPs&" cs|eZdZdZdddddddZdd Zd d Zdd d ZdddZddZ ddZ fddZ fddZ ddZ ZS)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...]) NTnan)r"r#r$ out_of_boundscCs||_||_||_||_dS)N)r"r#r$r0)selfr"r#r$r0rrr __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)r1Xmsgrrr _check_input_data_shapes"z*IsotonicRegression._check_input_data_shapecsX|jdkrtd|j|jdk}tdkr@fdd|_ntj|d|d|_d S) zBuild the f_ interp1d function.)raiser/r-zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}r:rcs |jS)N)repeatr5)r)rrr z-IsotonicRegression._build_f..Zlinear)kind bounds_errorN)r0r6formatrf_rZinterp1d)r1r7rr?r)rr _build_fs    zIsotonicRegression._build_fc sV|||d}|jdkr,t|||_n|j|_t|||jd}|dk}||||||}}}t||ffdd|||gD\}}}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%auto)r'rcsg|] }|qSrr).0r+)r.rr sz/IsotonicRegression._build_y..)r!r"r#r$rNr3)r9reshaper$r Z increasing_r r'rZlexsortr rr"r#minmaxX_min_X_max_Zonesrbool logical_or not_equal) r1r7rr!Ztrim_duplicatesmaskZunique_XZunique_yZunique_sample_weightZ keep_datar)r.r _build_ys2    @zIsotonicRegression._build_ycCsztddd}t|fdtjtjgi|}t|fd|ji|}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_sparser&r') dictrrr)r*r'r rP X_thresholds_ y_thresholds_rB)r1r7rr!Z check_paramsrrr fit7s   zIsotonicRegression.fitcCst|dr|jj}ntj}t||dd}|||d}|jdkrVt d |j|jdkrrt ||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. rRF)r'r&r%)r:r/r-zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}r-)hasattrrRr'rr)rr9rGr0r6r@r-rJrKrAZastype)r1Tr'resrrr transformfs         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. )rX)r1rVrrr predicts zIsotonicRegression.predictcst}|dd|S)z0Pickle-protocol - return state of the estimator.rAN)super __getstate__pop)r1state) __class__rr r[s  zIsotonicRegression.__getstate__cs4t|t|dr0t|dr0||j|jdS)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rRrSN)rZ __setstate__rUrBrRrS)r1r])r^rr r_s zIsotonicRegression.__setstate__cCs ddgiS)NZX_typesZ1darrayr)r1rrr _more_tagsszIsotonicRegression._more_tags)T)N)__name__ __module__ __qualname____doc__r2r9rBrPrTrXrYr[r_r` __classcell__rr)r^r rs\ 1 /,  )numpyrZscipyrZ scipy.statsrrrbaserrrutilsrr Zutils.validationr Z _isotonicr r __all__r rrrrrr s    <6