³ò ’B_Kc@sndZddkTddklZddkTdefd„ƒYZd„Zd„Ze djo eƒnd S( sË A classifier model that decides which label to assign to a token on the basis of a tree structure, where branches correspond to conditions on feature values, and leaves correspond to label assignments. iÿÿÿÿ(t*(t defaultdicttDecisionTreeClassifiercBsãeZeeed„Zd„Zd„Zd„Zdddd„Zddd„Zd „Z e d d d e ee d „ƒZ e d„ƒZ e d„ƒZe ee d„Ze e d„ƒZe d„ƒZe e d„ƒZRS(cCs(||_||_||_||_dS(sõ @param label: The most likely label for tokens that reach this node in the decision tree. If this decision tree has no children, then this label will be assigned to any token that reaches this decision tree. @param feature_name: The name of the feature that this decision tree selects for. @param decisions: A dictionary mapping from feature values for the feature identified by C{feature_name} to child decision trees. @param default: The child that will be used if the value of feature C{feature_name} does not match any of the keys in C{decisions}. This is used when constructing binary decision trees. N(t_labelt_fnamet _decisionst_default(tselftlabelt feature_namet decisionstdefault((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyt__init__s   cCs‡|ig}|idj o1x.|iiƒD]}|i|iƒƒq,Wn|idj o|i|iiƒƒntt|ƒƒS(N( RRtNonetvaluestextendtlabelsRtlisttset(RRtdt((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR+s cCs|idjo |iSn||i}||ijo|i|i|ƒSn,|idj o|ii|ƒSn|iSdS(N(RR RRtclassifyR(Rt featuresettfval((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR4s  cCsUd}x8|D]0\}}|i|ƒ|jo|d7}q q Wt|ƒt|ƒS(Nii(Rtfloattlen(Rtlabeled_featuresetsterrorsRR((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyterrorBs  iFtic Cs—|itjo0|t|ƒd}d|d||ifSnd}x¹tt|iiƒƒƒD]œ\}\}}d||i|f} |dt| ƒ}|d| d||if7}|itj o2|djo%||i||d|dƒ7}qbqbW|i tj o|t|ƒd}|d |d||i if7}|i itj o5|djo(||i i||d|dƒ7}q“n|S( s  Return a string containing a pretty-printed version of this decision tree. Each line in this string corresponds to a single decision tree node or leaf, and indentation is used to display the structure of the decision tree. is%s%s %s t.Rs %s%s=%s? is is%selse: %s %s ( RR RRt enumeratetsortedRtitemstppR( RtwidthtprefixtdepthtntstiRtresultthdr((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR!Is")! ,cCs†|itjod||ifSnd}x‘t|iiƒƒD]z\}}|d||i|f7}|itj o3|djo&|d|i|d|dƒ7}qA|d|i7}qAW|itj o³t|iƒdjo+|d||i|ii ƒd f7}n|d |f7}|iitj o6|djo)|d|ii|d|dƒ7}q‚|d|ii7}n|S( s© Return a string representation of this decision tree that expresses the decisions it makes as a nested set of pseudocode if statements. s %sreturn %r Rs%sif %s == %r: is s s return %r s%sif %s != %r: is%selse: ( RR RRRR t pseudocodeRRtkeys(RR#R$R&RR(((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR*bs$ & )cCs |iƒS(N(R!(R((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyt__str__}sgš™™™™™©?idi c Cstƒ}x2|D]*\}} x|D]} |i| ƒq#WqW|tjo\|oUttƒ}xF|D]:\}} x+|iƒD]\} } || i| ƒq~WqeWn|pti|||ƒ} nti||||ƒ} | i|||d||||ƒ| S(s´ @param binary: If true, then treat all feature/value pairs a individual binary features, rather than using a single n-way branch for each feature. i( RtaddR RR Rt best_stumptbest_binary_stumptrefine( Rtentropy_cutofft depth_cutofftsupport_cutofftbinarytfeature_valuestverboset feature_namesRRtfnameRttree((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyttrain€s*     cCs=tg}|D]\}}||q~ƒiƒ}t|ƒS(N(tFreqDisttmaxR(Rt_[1]RR((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pytleaf¥s )c Cs¾tg}|D]\}}||q~ƒiƒ}ttƒ}x/|D]'\}}||}||i|ƒqFWtg}|D]#}||t||iƒƒfq~ƒ} t||| ƒS(N(R;R<RtinctdictR( R RR=RRtfreqst feature_valuet_[2]tvalR ((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pytstump«s )    3cCsçt|ƒ|jodSn|idjodSn|djodSnxÈ|iD]½}g} |D]1\} } | |i|jo| | | fqcqc~ } tg} | D]\} } | | q«~ ƒ}tt|ƒƒ|jo,ti| ||||||ƒ|i|RRERR(R7RR6R.t best_errorR8REt stump_error((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR.×s c CsËtg}|D]\}}||q~ƒiƒ}tƒ}tƒ}xC|D];\}}|||jo|i|ƒqL|i|ƒqLWht|iƒƒ|<}t|iƒƒ} t|||| ƒS(N(R;R<R?R( R RBRR=RRt pos_fdistt neg_fdistR R ((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyt binary_stumpæs )   c Csàti|ƒ}|i|ƒ}xd|D]\}xS||D]G}ti|||ƒ}|i|ƒ} | |jo| }|}q6q6Wq%W|io$d|i|iiƒdf} nd} |odt|ƒ| |fGHn|S(Ns%s=%sis (default)s,best stump for %6d toks uses %-20s err=%6.4f(RR>RRQRRR+R( R7RR5R6R.RMR8RRERNtdescr((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyR/øs(    (t__name__t __module__R R RRRR!R*R,t staticmethodtFalseR:R>RER0R.RQR/(((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyRs(    " cCsti|dtdtƒS(NR4R6(RR:tTrue(tx((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pytfscCsKddkl}l}|t|ƒ}|iddƒGH|iddƒGHdS(Niÿÿÿÿ(t names_demotbinary_names_demo_featuresR$i(tnltk.classify.utilRZR[RYR!R*(RZR[t classifier((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pytdemos  t__main__N( t__doc__tnltk.probabilityt nltk.compatRtapit ClassifierIRRYR^RS(((s0/p/zhu/06/nlp/nltk/nltk/classify/decisiontree.pyss  þ