ó <¿CVc@s»dZddlmZddlZddlZddlmZdd„Z d„Z d„Z d„Z d e fd „ƒYZd „Zd „Zed „Zed„Ziadd„ZdS(s0 Utility functions and classes for classifiers. iÿÿÿÿ(tprint_functionN(tLazyMapcsa|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.) icsˆ|dƒ|dfS(Nii((t labeled_token(t feature_func(sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pyt lazy_func@sN(tNonet isinstancettupletlistR(RttokstlabeledR((Rsd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pytapply_featuress ! " cCsttd„|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 css|]\}}|VqdS(N((t.0ttoktlabel((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pys Os(Rtset(ttokens((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pytattested_labelsFs cCs‚|jg|D]\}}|^q ƒ}gt||ƒD]!\\}}}|j|ƒ^q8}tjtt|ƒƒt|ƒƒS(N(tprob_classify_manytziptprobtmathtlogtfloattsumtlen(t classifiertgoldtfstltresultstpdisttll((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pytlog_likelihoodQs(7cCs„|jg|D]\}}|^q ƒ}gt||ƒD]\\}}}||k^q8}|r|tt|ƒƒt|ƒSdSdS(Ni(t classify_manyRRRR(RRRRRtrtcorrect((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pytaccuracyVs (4t 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. cCsu|jƒ|_d|kr3t|dƒ |dss Senses: t sSplitting into test & train...i@âgš™™™™™é?sTraining classifier...sTesting classifier...sAccuracy: %6.4fsAvg. log likelihood: %6.4f(RPRkRQRUt _inst_cachet instancestsensesRRRtjoinRSRTtintR%RRRVRRW(RXtwordRGR\RkRQRlRoRpRZR[RRR,R^R_RFRRR ((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pytwsd_demo s:   6     1 4%4" (R?t __future__RRtnltk.classify.utilR4t nltk.utilRRR RR!R%tobjectR&RIRKRbRjRnRt(((sd/private/var/folders/cc/xm4nqn811x9b50x1q_zpkmvdjlphkp/T/pip-build-FUwmDn/nltk/nltk/classify/util.pyt s   *  4 - 6