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isotonic.cpython-35.opt-1.pyc
��(X7 � @ s d d l Z d d l m Z d d l m Z d d l m Z m Z m Z d d l m Z m Z m Z d d l m Z d d l m Z d d l m Z m Z d d l Z d d l Z d d d g Z d d � Z d d d d d d � Z Gd d � d e e e � Z d S)� N)� interpolate)� spearmanr� )� BaseEstimator�TransformerMixin�RegressorMixin)�as_float_array� check_array�check_consistent_length)� deprecated)�astype)�'_inplace_contiguous_isotonic_regression� _make_unique�check_increasing�isotonic_regression�IsotonicRegressionc C s� t | | � \ } } | d k } | d k r� d t j d | d | � } d t j t | � d � } t j | d | � } t j | d | � } t j | � t j | � k r� t j d � | S) aE Determine 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, shape=(n_samples,) Training data. y : array-like, 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 �?r � g\��(\�?zwConfidence interval of the Spearman correlation coefficient spans zero. Determination of ``increasing`` may be suspect.� �)r r ) r �math�log�sqrt�len�tanh�npZsign�warnings�warn) �x�yZrho�_Zincreasing_bool�FZF_seZrho_0Zrho_1� r! � /isotonic.pyr s " Tc C s | r t j d d � n t j d d d � } t j | | d t j �} | d k rx t j t | � d t j �} n t j | | d t j �} t | | � | d k s� | d k r� | d k r� t j } | d k r� t j } t j | | | | � | | S)a� Solve the isotonic regression model:: min sum w[i] (y[i] - y_[i]) ** 2 subject to y_min = y_[1] <= y_[2] ... <= y_[n] = y_max where: - y[i] are inputs (real numbers) - y_[i] are fitted - w[i] are optional strictly positive weights (default to 1.0) Read more in the :ref:`User Guide <isotonic>`. Parameters ---------- y : iterable of floating-point values The data. sample_weight : iterable of floating-point values, optional, default: None Weights on each point of the regression. If None, weight is set to 1 (equal weights). y_min : optional, default: None If not None, set the lowest value of the fit to y_min. y_max : optional, default: None If not None, set the highest value of the fit to y_max. increasing : boolean, optional, default: True Whether to compute ``y_`` is increasing (if set to True) or decreasing (if set to False) Returns ------- y_ : list of floating-point values Isotonic fit of y. References ---------- "Active set algorithms for isotonic regression; A unifying framework" by Michael J. Best and Nilotpal Chakravarti, section 3. Nr �dtype���) r Zs_�array�float64�onesr r �inf�clip)r � sample_weight�y_min�y_max� increasing�orderr! r! r" r O s ,/ ! c s3 e Z d Z d Z d d d d d d � Z e e d � d d � � � Z e j d d � � Z e j d d � � Z e e d � d d � � � Z e j d d � � Z e j d d � � Z d d d � Z d d � Z d d d � Z d d d � Z d d � Z d d � Z � f d d � Z � f d d � Z � S)!r aB Isotonic regression model. The isotonic regression optimization problem is defined by:: min sum w_i (y[i] - y_[i]) ** 2 subject to y_[i] <= y_[j] whenever X[i] <= X[j] and min(y_) = y_min, max(y_) = y_max where: - ``y[i]`` are inputs (real numbers) - ``y_[i]`` are fitted - ``X`` specifies the order. If ``X`` is non-decreasing then ``y_`` is non-decreasing. - ``w[i]`` are optional strictly positive weights (default to 1.0) Read more in the :ref:`User Guide <isotonic>`. Parameters ---------- y_min : optional, default: None If not None, set the lowest value of the fit to y_min. y_max : optional, default: None If not None, set the highest value of the fit to y_max. increasing : boolean or string, optional, default: True If boolean, whether or not to fit the isotonic regression with y increasing or decreasing. The string value "auto" determines whether y should increase or decrease based on the Spearman correlation estimate's sign. out_of_bounds : string, optional, default: "nan" The ``out_of_bounds`` parameter handles how x-values outside of the training domain are handled. When set to "nan", predicted y-values will be NaN. When set to "clip", predicted y-values will be set to the value corresponding to the nearest train interval endpoint. When set to "raise", allow ``interp1d`` to throw ValueError. 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. f_ : function The stepwise interpolating function that covers the domain `X_`. Notes ----- Ties are broken using the secondary method from 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 Leeuw, Hornik, Mair Journal of Statistical Software 2009 Correctness of Kruskal's algorithms for monotone regression with ties Leeuw, Psychometrica, 1977 NT�nanc C s( | | _ | | _ | | _ | | _ d S)N)r+ r, r- � out_of_bounds)�selfr+ r, r- r0 r! r! r" �__init__� s zIsotonicRegression.__init__zSAttribute ``X_`` is deprecated in version 0.18 and will be removed in version 0.20.c C s | j S)N)�_X_)r1 r! r! r" �X_� s zIsotonicRegression.X_c C s | | _ d S)N)r3 )r1 �valuer! r! r" r4 � s c C s | ` d S)N)r3 )r1 r! r! r" r4 � s zSAttribute ``y_`` is deprecated in version 0.18 and will be removed in version 0.20.c C s | j S)N)�_y_)r1 r! r! r" �y_� s zIsotonicRegression.y_c C s | | _ d S)N)r6 )r1 r5 r! r! r" r7 � s c C s | ` d S)N)r6 )r1 r! r! r" r7 � s c C s% t | j � d k r! t d � � d S)Nr zX should be a 1d array)r �shape� ValueError)r1 �Xr r* r! r! r" �_check_fit_data� s z"IsotonicRegression._check_fit_datac s� | j d k r' t d j | j � � � | j d k } t � � d k r` � f d d � | _ n! t j | � d d d | �| _ d S) z Build the f_ interp1d function.�raiser/ r) zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0}r c s � j | j � S)N)�repeatr8 )r )r r! r" �<lambda> s z-IsotonicRegression._build_f.<locals>.<lambda>�kindZlinear� bounds_errorN)r< r/ r) )r0 r9 �formatr �f_r Zinterp1d)r1 r: r r@ r! )r r" �_build_f� s zIsotonicRegression._build_fc s8 t | | | � d d � | | g D� \ } } t | � } | j | | | � | j d k rr t | | � | _ n | j | _ | d k r� t | d d �} | d k } | | | | | | } } } n t j t | � � } t j | | f � � � f d d � | | | g D� \ } } } t | | | � \ } } } | | _ } t | | | j | j d | j �| _ } t j | � t j | � | _ | _ | r*t j t | � f d t �} t j t j | d d � | d d � � t j | d d � | d d � � � | d d � <| | | | f S| | f Sd S)z Build the y_ IsotonicRegression.c S s" g | ] } t | d d �� q S)� ensure_2dF)r )�.0r r! r! r" � <listcomp> s z/IsotonicRegression._build_y.<locals>.<listcomp>ZautoNrD Fr c s, g | ]" } t | � t j d d �� q S)�copyF)r r r& )rE r% )r. r! r" rF & s r- r# r � r$ ���r$ r$ )r r r; r- r Z increasing_r r r' r Zlexsortr r3 r r+ r, r6 �min�max�X_min_�X_max_�boolZ logical_orZ not_equal) r1 r: r r* Ztrim_duplicates�maskZunique_XZunique_yZunique_sample_weightZ keep_datar! )r. r" �_build_y s: # % #3zIsotonicRegression._build_yc C sB | j | | | � \ } } | | | _ | _ | j | | � | S)ax Fit the model using X, y as training data. Parameters ---------- X : array-like, shape=(n_samples,) Training data. y : array-like, shape=(n_samples,) Training target. sample_weight : array-like, shape=(n_samples,), optional, 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 `transform` needs X to interpolate new input data. )rP � _necessary_X_� _necessary_y_rC )r1 r: r r* r! r! r" �fitF s zIsotonicRegression.fitc C s� t | � } t | j � d k r- t d � � | j d k rT t d j | j � � � | j d k r~ t j | | j | j � } | j | � S)a Transform new data by linear interpolation Parameters ---------- T : array-like, shape=(n_samples,) Data to transform. Returns ------- T_ : array, shape=(n_samples,) The transformed data r z.Isotonic regression input should be a 1d arrayr< r/ r) zIThe argument ``out_of_bounds`` must be in 'nan', 'clip', 'raise'; got {0})r< r/ r) ) r r r8 r9 r0 rA r r) rL rM rB )r1 �Tr! r! r" � transformm s z IsotonicRegression.transformc C s | j | � S)a Predict new data by linear interpolation. Parameters ---------- T : array-like, shape=(n_samples,) Data to transform. Returns ------- T_ : array, shape=(n_samples,) Transformed data. )rU )r1 rT r! r! r" �predict� s zIsotonicRegression.predictc s) t t | � j � } | j d d � | S)z1Pickle-protocol - return state of the estimator. rB N)�superr � __getstate__�pop)r1 �state)� __class__r! r" rX � s z IsotonicRegression.__getstate__c sN t t | � j | � t | d � rJ t | d � rJ | j | j | j � d S)znPickle-protocol - set state of the estimator. We need to rebuild the interpolation function. rQ rR N)rW r � __setstate__�hasattrrC rQ rR )r1 rZ )r[ r! r" r\ � s z IsotonicRegression.__setstate__)�__name__� __module__� __qualname__�__doc__r2 �propertyr r4 �setter�deleterr7 r; rC rP rS rU rV rX r\ r! r! )r[ r" r � s& H 8' )Znumpyr Zscipyr Z scipy.statsr �baser r r Zutilsr r r r Z utils.fixesr Z _isotonicr r r r �__all__r r r r! r! r! r" �<module> s 9 =
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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calibration.cpython-35.opt-1.pyc
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calibration.cpython-35.pyc
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cross_validation.cpython-35.opt-1.pyc
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cross_validation.cpython-35.pyc
68580 bytes
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discriminant_analysis.cpython-35.opt-1.pyc
26569 bytes
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discriminant_analysis.cpython-35.pyc
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dummy.cpython-35.opt-1.pyc
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dummy.cpython-35.pyc
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exceptions.cpython-35.opt-1.pyc
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exceptions.cpython-35.pyc
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grid_search.cpython-35.opt-1.pyc
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grid_search.cpython-35.pyc
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isotonic.cpython-35.opt-1.pyc
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isotonic.cpython-35.pyc
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kernel_approximation.cpython-35.opt-1.pyc
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kernel_approximation.cpython-35.pyc
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kernel_ridge.cpython-35.opt-1.pyc
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kernel_ridge.cpython-35.pyc
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lda.cpython-35.opt-1.pyc
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lda.cpython-35.pyc
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learning_curve.cpython-35.opt-1.pyc
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learning_curve.cpython-35.pyc
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multiclass.cpython-35.opt-1.pyc
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multiclass.cpython-35.pyc
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multioutput.cpython-35.opt-1.pyc
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multioutput.cpython-35.pyc
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naive_bayes.cpython-35.opt-1.pyc
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naive_bayes.cpython-35.pyc
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pipeline.cpython-35.opt-1.pyc
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pipeline.cpython-35.pyc
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qda.cpython-35.opt-1.pyc
815 bytes
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qda.cpython-35.pyc
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random_projection.cpython-35.opt-1.pyc
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random_projection.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
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setup.cpython-35.pyc
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