ó ù`]c@ sÊdZddlmZmZddlZddlZddlmZdd„Z d„Z d„Z d„Z d efd „ƒYZd „Zd „Zed „Zed„Ziadd„Zd„ZdS(s0 Utility functions and classes for classifiers. iÿÿÿÿ(tprint_functiontdivisionN(tLazyMapc sa|dkr.|o(t|dttfƒ}n|rP‡fd†}t||ƒStˆ|ƒSdS(sÖ Use the ``LazyMap`` class to construct a lazy list-like object that is analogous to ``map(feature_func, toks)``. In particular, if ``labeled=False``, then the returned list-like object's values are equal to:: [feature_func(tok) for tok in toks] If ``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 dict mapping feature names to feature values. :param toks: The list of tokens to which ``feature_func`` should be applied. If ``labeled=True``, then the list elements will be passed directly to ``feature_func()``. If ``labeled=False``, then the list elements should be tuples ``(tok,label)``, and ``tok`` will be passed to ``feature_func()``. :param labeled: If true, then ``toks`` contains labeled tokens -- i.e., tuples of the form ``(tok, label)``. (Default: auto-detect based on types.) ic sˆ|dƒ|dfS(Nii((t labeled_token(t feature_func(s1lib/python2.7/site-packages/nltk/classify/util.pyt lazy_funcAsN(tNonet isinstancettupletlistR(RttokstlabeledR((Rs1lib/python2.7/site-packages/nltk/classify/util.pytapply_featuress ! " cC sttd„|DƒƒƒS(s! :return: A list of all labels that are attested in the given list of tokens. :rtype: list of (immutable) :param tokens: The list of classified tokens from which to extract labels. A classified token has the form ``(token, label)``. :type tokens: list cs s|]\}}|VqdS(N((t.0ttoktlabel((s1lib/python2.7/site-packages/nltk/classify/util.pys Rs(Rtset(ttokens((s1lib/python2.7/site-packages/nltk/classify/util.pytattested_labelsIs cC s||jg|D]\}}|^q ƒ}gt||ƒD]!\\}}}|j|ƒ^q8}tjt|ƒt|ƒƒS(N(tprob_classify_manytziptprobtmathtlogtsumtlen(t classifiertgoldtfstltresultstpdisttll((s1lib/python2.7/site-packages/nltk/classify/util.pytlog_likelihoodUs(7cC s~|jg|D]\}}|^q ƒ}gt||ƒD]\\}}}||k^q8}|rvt|ƒt|ƒSdSdS(Ni(t classify_manyRRR(RRRRRtrtcorrect((s1lib/python2.7/site-packages/nltk/classify/util.pytaccuracy[s (4t CutoffCheckercB s 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. cC su|jƒ|_d|kr3t|dƒ |d,ss Senses: t sSplitting into test & train...i@âgš™™™™™é?sTraining classifier...sTesting classifier...sAccuracy: %6.4fsAvg. log likelihood: %6.4f(RPRkRQRUt _inst_cachet instancestsensesRR RtjoinRSRTtintR%RRRVRRW(RXtwordRGR\RkRQRlRoRpRZR[RRR,R^R_RFRRR ((s1lib/python2.7/site-packages/nltk/classify/util.pytwsd_demo s8   6     1 4%7" cC s8ytWn)tk r3tdƒ}t|ƒ‚nXdS(s8 Checks whether the MEGAM binary is configured. s\Please configure your megam binary first, e.g. >>> nltk.config_megam('/usr/bin/local/megam')N(t _megam_bint NameErrortstr(terr_msg((s1lib/python2.7/site-packages/nltk/classify/util.pytcheck_megam_configMs   (R?t __future__RRRtnltk.classify.utilR4t nltk.utilRRR RR!R%tobjectR&RIRKRbRjRnRtRy(((s1lib/python2.7/site-packages/nltk/classify/util.pyt s    -  < . 7 -