\c@sdZddlZddlZddlmZddlZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZd Zed Zd ed ZdefdYZdefdYZdefdYZdefdYZdefdYZdefdYZdefdYZdefdYZdefdYZ d efd!YZ!d"Z"d#Z#d$Z$dS(%s Base classes for all estimators.iN(t defaultdict(tsparsei(tsix(t signature(t _IS_32BIT(t __version__cCsmt|tjs!t|drWtj|r9|jn|}|jd|jdfS|d|dfSdS(s?Returns first and last element of numpy array or sparse matrix.tdataiiN(ii(ii(t isinstancetnptndarraythasattrRtissparseRtflat(tarrR((s+lib/python2.7/site-packages/sklearn/base.pyt_first_and_last_elements!c CsQt|}|ttttfkrM|g|D]}t|d|^q.St|ds|sotj|St dt |t|fn|j }|j dt }x3tj|D]"\}}t|dt ||~s(Rtget_printoptionstset_printoptionsRt enumeratetsortedRRRtfloattstrtlentappendtjointsplit( tparamstoffsettprintertoptionst params_listtthis_line_lengthtline_septitktvt this_reprtlines((s+lib/python2.7/site-packages/sklearn/base.pyt_pprintPs.  . "     "t BaseEstimatorcBsMeZdZedZedZdZdZdZ dZ RS(sBase class for all estimators in scikit-learn Notes ----- All estimators should specify all the parameters that can be set at the class level in their ``__init__`` as explicit keyword arguments (no ``*args`` or ``**kwargs``). cCst|jd|j}|tjkr+gSt|}g|jjD]-}|jdkrG|j|jkrG|^qG}x9|D]1}|j|j krt d||fqqWt g|D]}|j^qS(s%Get parameter names for the estimatortdeprecated_originaltselfsscikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). %s with constructor %s doesn't follow this convention.( tgetattrt__init__tobjectRt parameterstvaluesR%tkindt VAR_KEYWORDtVAR_POSITIONALRR6(tclstinittinit_signaturetpRP((s+lib/python2.7/site-packages/sklearn/base.pyt_get_param_namess - cst}xw|jD]it|d}|rut|dru|jj}|jfd|Dn||sN(tdictRYRMtNoneR Rtitemstupdate(RLRtouttvaluet deep_items((R\s+lib/python2.7/site-packages/sklearn/base.pyRs  c Ks|s |S|jdt}tt}x|jD]z\}}|jd\}}}||kr~td||fn|r||||__`` so that it's possible to update each component of a nested object. Returns ------- self RRZssInvalid parameter %s for estimator %s. Check the list of available parameters with `estimator.get_params().keys()`.( RtTrueRR]R_t partitiont ValueErrortsetattrt set_params( RLR=t valid_paramst nested_paramsR\Rbtdelimtsub_keyt sub_params((s+lib/python2.7/site-packages/sklearn/base.pyRhs   cCs8|jj}d|t|jdtdt|fS(Ns%s(%s)RR>(Rt__name__RIRRR9(RLt class_name((s+lib/python2.7/site-packages/sklearn/base.pyt__repr__s cCsrytt|j}Wn tk r;|jj}nXt|jjdrjt |j dt S|SdS(Nssklearn.t_sklearn_version( tsuperRJt __getstate__tAttributeErrort__dict__RRt __module__t startswithR]R_R(RLtstate((s+lib/python2.7/site-packages/sklearn/base.pyRss cCst|jjdra|jdd}|tkratjdj|jj |tt qanyt t |j |Wn!tk r|jj|nXdS(Nssklearn.Rqspre-0.18sTrying to unpickle estimator {0} from version {1} when using version {2}. This might lead to breaking code or invalid results. Use at your own risk.(RRvRwtpopRtwarningstwarntformatRRnt UserWarningRrRJt __setstate__RtRuR`(RLRxtpickle_version((s+lib/python2.7/site-packages/sklearn/base.pyR~s   ( RnRvt__doc__t classmethodRYRdRRhRpRsR~(((s+lib/python2.7/site-packages/sklearn/base.pyRJs  %  tClassifierMixincBs eZdZdZddZRS(s0Mixin class for all classifiers in scikit-learn.t classifiercCs,ddlm}|||j|d|S(sReturns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float Mean accuracy of self.predict(X) wrt. y. i(taccuracy_scoret sample_weight(tmetricsRtpredict(RLtXtyRR((s+lib/python2.7/site-packages/sklearn/base.pytscore sN(RnRvRt_estimator_typeR^R(((s+lib/python2.7/site-packages/sklearn/base.pyRstRegressorMixincBs eZdZdZddZRS(s:Mixin class for all regression estimators in scikit-learn.t regressorcCs2ddlm}|||j|d|ddS(sReturns the coefficient of determination R^2 of the prediction. The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0. Parameters ---------- X : array-like, shape = (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. Returns ------- score : float R^2 of self.predict(X) wrt. y. i(tr2_scoreRt multioutputtvariance_weighted(RRR(RLRRRR((s+lib/python2.7/site-packages/sklearn/base.pyR*sN(RnRvRRR^R(((s+lib/python2.7/site-packages/sklearn/base.pyR&st ClusterMixincBs eZdZdZddZRS(s7Mixin class for all cluster estimators in scikit-learn.t clusterercCs|j||jS(snPerforms clustering on X and returns cluster labels. Parameters ---------- X : ndarray, shape (n_samples, n_features) Input data. y : Ignored not used, present for API consistency by convention. Returns ------- labels : ndarray, shape (n_samples,) cluster labels (tfittlabels_(RLRR((s+lib/python2.7/site-packages/sklearn/base.pyt fit_predictSs N(RnRvRRR^R(((s+lib/python2.7/site-packages/sklearn/base.pyROstBiclusterMixincBs8eZdZedZdZdZdZRS(s8Mixin class for all bicluster estimators in scikit-learncCs|j|jfS(s{Convenient way to get row and column indicators together. Returns the ``rows_`` and ``columns_`` members. (trows_tcolumns_(RL((s+lib/python2.7/site-packages/sklearn/base.pyt biclusters_lscCs>|j|}|j|}tj|dtj|dfS(sRow and column indices of the i'th bicluster. Only works if ``rows_`` and ``columns_`` attributes exist. Parameters ---------- i : int The index of the cluster. Returns ------- row_ind : np.array, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : np.array, dtype=np.intp Indices of columns in the dataset that belong to the bicluster. i(RRRtnonzero(RLRDtrowstcolumns((s+lib/python2.7/site-packages/sklearn/base.pyt get_indicests  cCs#|j|}td|DS(sShape of the i'th bicluster. Parameters ---------- i : int The index of the cluster. Returns ------- shape : (int, int) Number of rows and columns (resp.) in the bicluster. css|]}t|VqdS(N(R9(R1RD((s+lib/python2.7/site-packages/sklearn/base.pys s(RR(RLRDtindices((s+lib/python2.7/site-packages/sklearn/base.pyt get_shapes cCsXddlm}||dd}|j|\}}||ddtjf|fS(sReturns the submatrix corresponding to bicluster `i`. Parameters ---------- i : int The index of the cluster. data : array The data. Returns ------- submatrix : array The submatrix corresponding to bicluster i. Notes ----- Works with sparse matrices. Only works if ``rows_`` and ``columns_`` attributes exist. i(t check_arrayt accept_sparsetcsrN(tutils.validationRRRtnewaxis(RLRDRRtrow_indtcol_ind((s+lib/python2.7/site-packages/sklearn/base.pyt get_submatrixs(RnRvRtpropertyRRRR(((s+lib/python2.7/site-packages/sklearn/base.pyRis   tTransformerMixincBseZdZddZRS(s1Mixin class for all transformers in scikit-learn.cKsE|dkr%|j||j|S|j|||j|SdS(sFit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters ---------- X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values. Returns ------- X_new : numpy array of shape [n_samples, n_features_new] Transformed array. N(R^Rt transform(RLRRt fit_params((s+lib/python2.7/site-packages/sklearn/base.pyt fit_transforms N(RnRvRR^R(((s+lib/python2.7/site-packages/sklearn/base.pyRst DensityMixincBs eZdZdZddZRS(s7Mixin class for all density estimators in scikit-learn.tDensityEstimatorcCsdS(sReturns the score of the model on the data X Parameters ---------- X : array-like, shape = (n_samples, n_features) Returns ------- score : float N((RLRR((s+lib/python2.7/site-packages/sklearn/base.pyRs N(RnRvRRR^R(((s+lib/python2.7/site-packages/sklearn/base.pyRst OutlierMixincBs eZdZdZddZRS(sAMixin class for all outlier detection estimators in scikit-learn.toutlier_detectorcCs|j|j|S(sPerforms fit on X and returns labels for X. Returns -1 for outliers and 1 for inliers. Parameters ---------- X : ndarray, shape (n_samples, n_features) Input data. y : Ignored not used, present for API consistency by convention. Returns ------- y : ndarray, shape (n_samples,) 1 for inliers, -1 for outliers. (RR(RLRR((s+lib/python2.7/site-packages/sklearn/base.pyRsN(RnRvRRR^R(((s+lib/python2.7/site-packages/sklearn/base.pyRstMetaEstimatorMixincBseZdZRS(s4Mixin class for all meta estimators in scikit-learn.(RnRvR(((s+lib/python2.7/site-packages/sklearn/base.pyRst_UnstableArchMixincBseZdZdZRS(s=Mark estimators that are non-determinstic on 32bit or PowerPCcCs itptjjdd6S(Ntppctpowerpctnon_deterministic(RR(RtplatformtmachineRw(RL((s+lib/python2.7/site-packages/sklearn/base.pyt _more_tags s(RnRvRR(((s+lib/python2.7/site-packages/sklearn/base.pyRscCst|dddkS(sReturns True if the given estimator is (probably) a classifier. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is a classifier and False otherwise. RRN(RMR^(R ((s+lib/python2.7/site-packages/sklearn/base.pyt is_classifiers cCst|dddkS(sReturns True if the given estimator is (probably) a regressor. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is a regressor and False otherwise. RRN(RMR^(R ((s+lib/python2.7/site-packages/sklearn/base.pyt is_regressors cCst|dddkS(sReturns True if the given estimator is (probably) an outlier detector. Parameters ---------- estimator : object Estimator object to test. Returns ------- out : bool True if estimator is an outlier detector and False otherwise. RRN(RMR^(R ((s+lib/python2.7/site-packages/sklearn/base.pytis_outlier_detector/s (%RRRzt collectionsRRtnumpyRtscipyRt externalsRt utils.fixesRtutilsRR/RRRdRRRIRORJRRRRRRRRRRRR(((s+lib/python2.7/site-packages/sklearn/base.pyts4     03!)L!