³ò 4ÒÇIc @sèddkZddkZddkZyddklZWnej o dZnXddklZl Z ddk l Z ddk l Z d„Zd„Zd„Zd d „Zd „Zd „Zd „Zedjo eƒndS(iÿÿÿÿN(tbetai(tLazyConcatenationtLazyMap(tizip(tFreqDistcCs~t|ƒt|ƒjotdƒ‚nd}x8t||ƒD]'\}}||jo|d7}q?q?Wt|ƒt|ƒS(s2 Given a list of reference values and a corresponding list of test values, return the percentage of corresponding values that are equal. In particular, return the percentage of indices C{0‡s (RRtsumtzip(RR ttotal_likelihood((s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pytlog_likelihoodws  c s‡|iddƒ}t|td„tdt|ƒt|ƒdƒƒƒ}|idd„ƒ}|idtƒ}|o d|GHnti||ƒ||ƒƒ}|od |GHd d GHnd }t||gƒ‰t t|ƒt|ƒƒ}x t |ƒD]ÿ} |o| d djo d| GHnt i |ƒ|t ‡fd†|t|ƒ ƒƒ} |t ‡fd†|t|ƒƒƒ} ti| | ƒ} | |jo|d7}n|oB| d djo1d| GHdt |dƒ| dGHd d GHqqWt |dƒ|d} |oVd| GHtoBx?ddddddgD]!}d|t|||ƒfGHqMWqzn| ||fS(s Returns an approximate significance level between two lists of independently generated test values. Approximate randomization calculates significance by randomly drawing from a sample of the possible permutations. At the limit of the number of possible permutations, the significance level is exact. The approximate significance level is the sample mean number of times the statistic of the permutated lists varies from the actual statistic of the unpermuted argument lists. @return: a tuple containing an approximate significance level, the count of the number of times the pseudo-statistic varied from the actual statistic, and the number of shuffles @rtype: C{tuple} @param a: a list of test values @type a: C{list} @param b: another list of independently generated test values @type b: C{list} tshufflesiçcSs||S(((R R ((s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pyt£sit statisticcSstt|ƒƒt|ƒS((RRR(tlst((s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pyR!¤stverboses shuffles: %dsactual statistic: %ft-i<g0Žä.ÿ++i is shuffle: %dcsˆ|S(((ti(R#(s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pyR!ºscsˆ|S(((R&(R#(s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pyR!»sspseudo-statistic: %fssignificance: %fg{®Gáz„?gš™™™™™©?gš™™™™™¹?g333333Ã?gÐ?gà?sprob(phi<=%f): %f(tgettmintreducetxrangeRtFalsetmathtfabsRtrangetrandomtshuffleRRR(tatbtkwargsR tstatR$t actual_stattctindicesR&t pseudo_stat_at pseudo_stat_bt pseudo_statt significancetphi((R#s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pyt approxrand‹sF8      ((   'cCs»ddGHdiƒ}diƒ}dG|GHdG|GHdGt||ƒGHddGHt|ƒ}t|ƒ}dG|GHdG|GHd Gt||ƒGHd Gt||ƒGHd Gt||ƒGHddGHdS( NR%iKs DET NN VB DET JJ NN NN IN DET NNs DET VB VB DET NN NN NN IN DET NNs Reference =s Test =s Accuracy:s Test = s Precision:s Recall:s F-Measure:(tsplitR tsetRRR(RR t reference_setttest_set((s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pytdemoÑs          t__main__(tsysR,R/tscipy.stats.statsRt ImportErrorRt nltk.utilRRt itertoolsRtnltk.probabilityRR RRRRR=RBt__name__(((s)/p/zhu/06/nlp/nltk/nltk/metrics/scores.pys s$          F