³ò 4ÒÇIc@sŸdZddkZddkZddklZdd„Zd„Zd„Z d„Z de fd „ƒYZ d „Z d „Ze d „Zhad d„ZdS(s0 Utility functions and classes for classifiers. iÿÿÿÿN(tLazyMappedListcsi|djo$|ot|dttfƒ}n|o ‡fd†}t||ƒSnt|ˆƒSdS(sÓ Use the L{LazyMappedList} class to construct a lazy list-like object that is analogous to C{map(feature_func, toks)}. In particular, if C{labeled=False}, then the returned list-like object's values are equal to:: [feature_func(tok) for tok in toks] If C{labeled=True}, then the returned list-like object's values are equal to:: [(feature_func(tok), label) for (tok, label) in toks] The primary purpose of this function is to avoid the memory overhead involved in storing all the featuresets for every token in a corpus. Instead, these featuresets are constructed lazily, as-needed. The reduction in memory overhead can be especially significant when the underlying list of tokens is itself lazy (as is the case with many corpus readers). @param feature_func: The function that will be applied to each token. It should return a featureset -- i.e., a C{dict} mapping feature names to feature values. @param toks: The list of tokens to which C{feature_func} should be applied. If C{labeled=True}, then the list elements will be passed directly to C{feature_func()}. If C{labeled=False}, then the list elements should be tuples C{(tok,label)}, and C{tok} will be passed to C{feature_func()}. @param labeled: If true, then C{toks} contains labeled tokens -- i.e., tuples of the form C{(tok, label)}. (Default: auto-detect based on types.) icsˆ|dƒ|dfS(Nii((t labeled_token(t feature_func(s(/p/zhu/06/nlp/nltk/nltk/classify/util.pyt lazy_func>sN(tNonet isinstancettupletlistR(RttokstlabeledR((Rs(/p/zhu/06/nlp/nltk/nltk/classify/util.pytapply_featuress ! $cCs1ttg}|D]\}}||q~ƒƒS(s& @return: A list of all labels that are attested in the given list of tokens. @rtype: C{list} of (immutable) @param tokens: The list of classified tokens from which to extract labels. A classified token has the form C{(token, label)}. @type tokens: C{list} (Rtset(ttokenst_[1]ttoktlabel((s(/p/zhu/06/nlp/nltk/nltk/classify/util.pytattested_labelsDs c Cs’|ig}|D]\}}||q~ƒ}g}t||ƒD]"\\}}}||i|ƒqD~}titt|ƒƒt|ƒƒS(N(tbatch_prob_classifytziptprobtmathtlogtfloattsumtlen( t classifiertgoldR tfstltresultst_[2]tpdisttll((s(/p/zhu/06/nlp/nltk/nltk/classify/util.pytlog_likelihoodOs0?c Cs†|ig}|D]\}}||q~ƒ}g}t||ƒD]\\}}}|||jqD~}tt|ƒƒt|ƒS(N(tbatch_classifyRRRR( RRR RRRRtrtcorrect((s(/p/zhu/06/nlp/nltk/nltk/classify/util.pytaccuracyTs0<t CutoffCheckercBs eZdZd„Zd„ZRS(sÉ A helper class that implements cutoff checks based on number of iterations and log likelihood. Accuracy cutoffs are also implemented, but they're almost never a good idea to use. cCsy|iƒ|_d|jot|dƒ |dÙss Senses: t sSplitting into test & train...i@âgš™™™™™é?sTraining classifier...sTesting classifier...sAccuracy: %6.4fsAvg. log likelihood: %6.4f(ROReRPt _inst_cachet instancestsensesRRR tjoinRRRStintR%RRRTRRU(RVtwordRFR[ReRPR RgRjRkRXRYRRRRZR,R]R_R`R^RERRR ((s(/p/zhu/06/nlp/nltk/nltk/classify/util.pytwsd_demoÎs8  ? #  9< -8(R>Rtnltk.classify.utilR4t nltk.utilRRR RR!R%tobjectR&RHRJRdRiRo(((s(/p/zhu/06/nlp/nltk/nltk/classify/util.pys s   *  1 -