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bicluster.cpython-35.opt-1.pyc
��(X�M � @ sm d Z d d l m Z m Z d d l Z d d l m Z d d l m Z d d l m Z m Z d d l m Z m Z d d l m Z d d l m Z d d l m Z m Z d d l m Z m Z m Z m Z d d l m Z m Z d d g Z d d � Z d d d d � Z d d � Z! Gd d � d e j" e e e � � Z# Gd d � d e# � Z$ Gd d � d e# � Z% d S) zOSpectral biclustering algorithms. Authors : Kemal Eren License: BSD 3 clause � )�ABCMeta�abstractmethodN)� dia_matrix)�issparse� )�KMeans�MiniBatchKMeans� )� BaseEstimator�BiclusterMixin)�six)�check_random_state)�eigsh�svds)�make_nonnegative�norm�randomized_svd�safe_sparse_dot)�assert_all_finite� check_array�SpectralCoclustering�SpectralBiclusteringc C s@ t | � } t j d t j | j d d � � � j � } t j d t j | j d d � � � j � } t j t j | � d | � } t j t j | � d | � } t | � r| j \ } } t | d g f d | | f �} t | d g f d | | f �} | | | } n! | d d � t j f | | } | | | f S)z�Normalize ``X`` by scaling rows and columns independently. Returns the normalized matrix and the row and column scaling factors. g �?�axisr r �shapeN) r �npZasarray�sqrt�sumZsqueezeZwhere�isnanr r r �newaxis)�X�row_diag�col_diag�n_rowsZn_cols�r�cZan� r% � /bicluster.py�_scale_normalize s .. !!!r' i� g�h㈵��>c C s� t | � } | } d } x} t | � D]o } t | � \ } } } t | � re t | j | j � } n t | | � } | } | d k r% | | k r% Pq% W| S)z�Normalize rows and columns of ``X`` simultaneously so that all rows sum to one constant and all columns sum to a different constant. N)r �ranger' r r �data)r Zmax_iterZtolZX_scaledZdist�_ZX_newr% r% r& �_bistochastic_normalize4 s r+ c C s� t | d d �} t | � r* t d � � t j | � } | j d d � d d � t j f } | j d d � } | j � } | | | | S)z>Normalize ``X`` according to Kluger's log-interactions scheme.Z min_valuer z[Cannot compute log of a sparse matrix, because log(x) diverges to -infinity as x goes to 0.r Nr )r r � ValueErrorr �logZmeanr )r �LZrow_avgZcol_avg�avgr% r% r& �_log_normalizeK s % r0 c @ sp e Z d Z d Z e d d d d d d d d d d � � Z d d � Z d d � Z d d � Z d d � Z d S)� BaseSpectralz%Base class for spectral biclustering.� � randomizedNFz k-means++� r c C sL | | _ | | _ | | _ | | _ | | _ | | _ | | _ | | _ d S)N)� n_clusters� svd_method� n_svd_vecs� mini_batch�init�n_init�n_jobs� random_state) �selfr5 r6 r7 r8 r9 r: r; r< r% r% r& �__init__] s zBaseSpectral.__init__c C s4 d } | j | k r0 t d j | j | � � � d S)Nr3 �arpackz9Unknown SVD method: '{0}'. svd_method must be one of {1}.)r3 r? )r6 r, �format)r= Zlegal_svd_methodsr% r% r& �_check_parametersj s z BaseSpectral._check_parametersc C s6 t | d d d t j �} | j � | j | � | S)z�Creates a biclustering for X. Parameters ---------- X : array-like, shape (n_samples, n_features) Z accept_sparseZcsrZdtype)r r Zfloat64rA �_fit)r= r r% r% r& �fitq s zBaseSpectral.fitc C s� | j d k rX i } | j d k r1 | j | d <t | | d | j | �\ } } } n(| j d k r�t | d | d | j �\ } } } t j t j | � � r t | j | � } t | j � } | j d d | j d � } t | d | j d | �\ } } | j } t j t j | � � r�t | | j � } t | j � } | j d d | j d � } t | d | j d | �\ } } t | � t | � | d d � | d � f } | | d � } | | j f S) zyReturns first `n_components` left and right singular vectors u and v, discarding the first `n_discard`. r3 NZ n_oversamplesr< r? �kZncvr r �v0���rF )r6 r7 r r< r r �anyr r �Tr �uniformr r r ) r= �array� n_components� n_discard�kwargs�ur* �vt�Ar< rE �vr% r% r& �_svd~ s2 $ ! ! zBaseSpectral._svdc C s� | j r3 t | d | j d | j d | j �} n0 t | d | j d | j d | j d | j �} | j | � | j } | j } | | f S)Nr9 r: r< r; ) r8 r r9 r: r< r r; rC Zcluster_centers_Zlabels_)r= r) r5 Zmodel�centroid�labelsr% r% r& �_k_means� s zBaseSpectral._k_means) �__name__� __module__� __qualname__�__doc__r r> rA rC rR rU r% r% r% r& r1 Y s &r1 c sL e Z d Z d Z d d d d d d d d � f d d � Z d d � Z � S) r a� Spectral Co-Clustering algorithm (Dhillon, 2001). Clusters rows and columns of an array `X` to solve the relaxed normalized cut of the bipartite graph created from `X` as follows: the edge between row vertex `i` and column vertex `j` has weight `X[i, j]`. The resulting bicluster structure is block-diagonal, since each row and each column belongs to exactly one bicluster. Supports sparse matrices, as long as they are nonnegative. Read more in the :ref:`User Guide <spectral_coclustering>`. Parameters ---------- n_clusters : integer, optional, default: 3 The number of biclusters to find. svd_method : string, optional, default: 'randomized' Selects the algorithm for finding singular vectors. May be 'randomized' or 'arpack'. If 'randomized', use :func:`sklearn.utils.extmath.randomized_svd`, which may be faster for large matrices. If 'arpack', use :func:`sklearn.utils.arpack.svds`, which is more accurate, but possibly slower in some cases. n_svd_vecs : int, optional, default: None Number of vectors to use in calculating the SVD. Corresponds to `ncv` when `svd_method=arpack` and `n_oversamples` when `svd_method` is 'randomized`. mini_batch : bool, optional, default: False Whether to use mini-batch k-means, which is faster but may get different results. init : {'k-means++', 'random' or an ndarray} Method for initialization of k-means algorithm; defaults to 'k-means++'. n_init : int, optional, default: 10 Number of random initializations that are tried with the k-means algorithm. If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen. n_jobs : int, optional, default: 1 The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. random_state : int seed, RandomState instance, or None (default) A pseudo random number generator used by the K-Means initialization. Attributes ---------- rows_ : array-like, shape (n_row_clusters, n_rows) Results of the clustering. `rows[i, r]` is True if cluster `i` contains row `r`. Available only after calling ``fit``. columns_ : array-like, shape (n_column_clusters, n_columns) Results of the clustering, like `rows`. row_labels_ : array-like, shape (n_rows,) The bicluster label of each row. column_labels_ : array-like, shape (n_cols,) The bicluster label of each column. References ---------- * Dhillon, Inderjit S, 2001. `Co-clustering documents and words using bipartite spectral graph partitioning <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__. r2 r3 NFz k-means++r4 r c s/ t t | � j | | | | | | | | � d S)N)�superr r> ) r= r5 r6 r7 r8 r9 r: r; r< )� __class__r% r& r> s z SpectralCoclustering.__init__c sI t | � \ } } } d t t j t j � j � � � } � j | | d d �\ } } t j | d d � t j f | | d d � t j f | f � } � j | � j � \ } } | j d } | d | � � _ | | d � � _ t j � f d d � t � j � D� � � _ t j � f d d � t � j � D� � � _ d S)Nr rL r c 3 s | ] } � j | k Vq d S)N)� row_labels_)�.0r$ )r= r% r& � <genexpr># s z,SpectralCoclustering._fit.<locals>.<genexpr>c 3 s | ] } � j | k Vq d S)N)�column_labels_)r] r$ )r= r% r& r^ % s )r' �intr �ceil�log2r5 rR �vstackr rU r r\ r_ r( �rows_�columns_) r= r �normalized_datar r! �n_svrN rQ �zr* rT r" r% )r= r& rB s % # zSpectralCoclustering._fit)rV rW rX rY r> rB r% r% )r[ r& r � s U c s e Z d Z d Z d d d d d d d d d d d � f d d � Z � f d d � Z d d � Z d d � Z d d � Z � S)r a_ Spectral biclustering (Kluger, 2003). Partitions rows and columns under the assumption that the data has an underlying checkerboard structure. For instance, if there are two row partitions and three column partitions, each row will belong to three biclusters, and each column will belong to two biclusters. The outer product of the corresponding row and column label vectors gives this checkerboard structure. Read more in the :ref:`User Guide <spectral_biclustering>`. Parameters ---------- n_clusters : integer or tuple (n_row_clusters, n_column_clusters) The number of row and column clusters in the checkerboard structure. method : string, optional, default: 'bistochastic' Method of normalizing and converting singular vectors into biclusters. May be one of 'scale', 'bistochastic', or 'log'. The authors recommend using 'log'. If the data is sparse, however, log normalization will not work, which is why the default is 'bistochastic'. CAUTION: if `method='log'`, the data must not be sparse. n_components : integer, optional, default: 6 Number of singular vectors to check. n_best : integer, optional, default: 3 Number of best singular vectors to which to project the data for clustering. svd_method : string, optional, default: 'randomized' Selects the algorithm for finding singular vectors. May be 'randomized' or 'arpack'. If 'randomized', uses `sklearn.utils.extmath.randomized_svd`, which may be faster for large matrices. If 'arpack', uses `sklearn.utils.arpack.svds`, which is more accurate, but possibly slower in some cases. n_svd_vecs : int, optional, default: None Number of vectors to use in calculating the SVD. Corresponds to `ncv` when `svd_method=arpack` and `n_oversamples` when `svd_method` is 'randomized`. mini_batch : bool, optional, default: False Whether to use mini-batch k-means, which is faster but may get different results. init : {'k-means++', 'random' or an ndarray} Method for initialization of k-means algorithm; defaults to 'k-means++'. n_init : int, optional, default: 10 Number of random initializations that are tried with the k-means algorithm. If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen. n_jobs : int, optional, default: 1 The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. random_state : int seed, RandomState instance, or None (default) A pseudo random number generator used by the K-Means initialization. Attributes ---------- rows_ : array-like, shape (n_row_clusters, n_rows) Results of the clustering. `rows[i, r]` is True if cluster `i` contains row `r`. Available only after calling ``fit``. columns_ : array-like, shape (n_column_clusters, n_columns) Results of the clustering, like `rows`. row_labels_ : array-like, shape (n_rows,) Row partition labels. column_labels_ : array-like, shape (n_cols,) Column partition labels. References ---------- * Kluger, Yuval, et. al., 2003. `Spectral biclustering of microarray data: coclustering genes and conditions <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.1608>`__. r2 � bistochastic� r3 NFz k-means++r4 r c sJ t t | � j | | | | | | | | � | | _ | | _ | | _ d S)N)rZ r r> �methodrK �n_best) r= r5 rk rK rl r6 r7 r8 r9 r: r; r< )r[ r% r& r> � s z SpectralBiclustering.__init__c s9 t t | � j � d } | j | k rC t d j | j | � � � y t | j � Wn` t k r� y' | j \ } } t | � t | � Wn$ t t f k r� t d � � Yn XYn X| j d k r� t d j | j � � � | j d k rt d j | j � � � | j | j k r5t d j | j | j � � � d S) Nri �scaler- z1Unknown method: '{0}'. method must be one of {1}.z�Incorrect parameter n_clusters has value: {}. It should either be a single integer or an iterable with two integers: (n_row_clusters, n_column_clusters)r zBParameter n_components must be greater than 0, but its value is {}z<Parameter n_best must be greater than 0, but its value is {}z7n_best cannot be larger than n_components, but {} > {})ri rm r- ) rZ r rA rk r, r@ r` r5 � TypeErrorrK rl )r= Z legal_methodsr# r$ )r[ r% r& rA � s. z&SpectralBiclustering._check_parametersc s� � j } � j d k r1 t | � } | d 7} nL � j d k rb t | � \ } } } | d 7} n � j d k r} t | � } � j d k r� d n d } � j | | | � \ } } | j } | j } y � j \ } � Wn t k r� � j } � Yn X� j | � j | � } � j | � j � � } � j | | j | � � _ � j | j | j � � � _ t j � � f d d � t | � D� � � _ t j � � f d d � t | � D� � � _ d S) Nri r rm r- r c 3 s1 | ]' } t � � D] } � j | k Vq q d S)N)r( r\ )r] �labelr* )�n_col_clustersr= r% r& r^ � s z,SpectralBiclustering._fit.<locals>.<genexpr>c 3 s1 | ]' } t � � D] } � j | k Vq q d S)N)r( r_ )r] r* ro )rp r= r% r& r^ � s )rK rk r+ r' r0 rR rH r5 rn �_fit_best_piecewiserl �_project_and_clusterr\ r_ r rc r( rd re ) r= r rg rf r* rL rN rQ �utrO Zn_row_clustersZbest_utZbest_vtr% )rp r= r& rB � s: zSpectralBiclustering._fitc sp � � f d d � } t j | d d d | �} t j t d d d | | �} | t j | � d | � } | S)z�Find the ``n_best`` vectors that are best approximated by piecewise constant vectors. The piecewise vectors are found by k-means; the best is chosen according to Euclidean distance. c s2 � j | j d d � � � \ } } | | j � S)Nr rF )rU ZreshapeZravel)rQ rS rT )r5 r= r% r& �make_piecewise� s $z@SpectralBiclustering._fit_best_piecewise.<locals>.make_piecewiser r ZarrN)r Zapply_along_axisr Zargsort)r= �vectorsrl r5 rt Zpiecewise_vectorsZdists�resultr% )r5 r= r& rq � s z(SpectralBiclustering._fit_best_piecewisec C s+ t | | � } | j | | � \ } } | S)z7Project ``data`` to ``vectors`` and cluster the result.)r rU )r= r) ru r5 Z projectedr* rT r% r% r& rr � s z)SpectralBiclustering._project_and_cluster) rV rW rX rY r> rA rB rq rr r% r% )r[ r& r ) s b ' )&rY �abcr r Znumpyr Z scipy.sparser r � r r �baser r Z externalsr Zutilsr Z utils.arpackr r Z utils.extmathr r r r Zutils.validationr r �__all__r' r+ r0 Zwith_metaclassr1 r r r% r% r% r& �<module> s( " Zu
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__init__.cpython-35.opt-1.pyc
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__init__.cpython-35.pyc
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_feature_agglomeration.cpython-35.opt-1.pyc
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_feature_agglomeration.cpython-35.pyc
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affinity_propagation_.cpython-35.opt-1.pyc
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affinity_propagation_.cpython-35.pyc
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bicluster.cpython-35.opt-1.pyc
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bicluster.cpython-35.pyc
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birch.cpython-35.opt-1.pyc
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birch.cpython-35.pyc
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dbscan_.cpython-35.opt-1.pyc
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dbscan_.cpython-35.pyc
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hierarchical.cpython-35.opt-1.pyc
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hierarchical.cpython-35.pyc
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k_means_.cpython-35.opt-1.pyc
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k_means_.cpython-35.pyc
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mean_shift_.cpython-35.opt-1.pyc
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mean_shift_.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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spectral.cpython-35.opt-1.pyc
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spectral.cpython-35.pyc
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