³ò 4ÒÇIc@s¡dZddkZd„ZeiZd„ZdZdZdZ dZ de fd „ƒYZ d e fd „ƒYZ d e fd „ƒYZde fd„ƒYZdS(sÉ Provides scoring functions for a number of association measures through a generic, abstract implementation in L{NgramAssocMeasures}, and n-specific L{BigramAssocMeasures} and L{TrigramAssocMeasures}. iÿÿÿÿNcCsti|dƒS(g@(t_mathtlog(tx((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytscCstd„|ƒS(cSs||S(((Rty((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyRs(treduce(ts((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyRsg#B’ ¡œÇ;iiþÿÿÿtNgramAssocMeasurescBs³eZdZed„ƒZed„ƒZed„ƒZed„ƒZed„ƒZ ed„ƒZ ed„ƒZ ed„ƒZ ed „ƒZ ed „ƒZed „ƒZRS( sº An abstract class defining a collection of generic association measures. Each public method returns a score, taking the following arguments: score_fn(count_of_ngram, (count_of_n-1gram_1, ..., count_of_n-1gram_j), (count_of_n-2gram_1, ..., count_of_n-2gram_k), ..., (count_of_1gram_1, ..., count_of_1gram_n), count_of_total_words) See L{BigramAssocMeasures} and L{TrigramAssocMeasures} Inheriting classes should define a property _n, and a method _contingency which calculates contingency values from marginals in order for all association measures defined here to be usable. cGs td‚dS(s>Calculates values of a contingency table from marginal values.s?The contingency table is not availablein the general ngram caseN(tNotImplementedError(t marginals((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyt _contingency4scGs td‚dS(sACalculates values of contingency table marginals from its values.s?The contingency table is not availablein the general ngram caseN(R(t contingency((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyt _marginals:sc#s‡tˆƒ}g}t|iƒD]‰|dˆ>q ~}xFttˆƒƒD]2‰t‡‡fd†|Dƒƒt|dƒVqMWdS(s3Calculates expected values for a contingency table.ic3s)x"|]}ˆˆˆˆ|AVqWdS(N((t.0tj(titcont(s./p/zhu/06/nlp/nltk/nltk/metrics/association.pys IsiN(tsumtranget_ntlent_producttfloat(tclsRtn_allt_[1]tbits((RRs./p/zhu/06/nlp/nltk/nltk/metrics/association.pyt_expected_values@s  .cGst|tƒ|tS(s Scores ngrams by their frequency(RtNGRAMtTOTAL(R ((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytraw_freqLscGsA|t|tt|tƒ|t|id|ttdS(sScores ngrams using Student's t test with independence hypothesis for unigrams, as in Manning and Schutze 5.3.2. igà?(RRRtUNIGRAMSRt_SMALL(RR ((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyt student_tQscGs;|i|Œ}|i|ƒ}td„t||ƒDƒƒS(sZScores ngrams using Pearson's chi-square as in Manning and Schutze 5.3.3. css/x(|]!\}}||d|tVqWdS(iN(R (R tobstexp((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pys bs (R RRtzip(RR Rtexps((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytchi_sq[s cOs,|t|iddƒtt|tƒƒS(sÂScores ngrams using a variant of mutual information. The keyword argument power sets an exponent (default 3) for the numerator. No logarithm of the result is calculated. tpoweri(RtgetRRR(R tkwargs((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytmi_likeescGs5t|t|t|idƒtt|tƒƒS(s^Scores ngrams by pointwise mutual information, as in Manning and Schutze 5.4. i(t_log2RRRRR(RR ((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytpmins cGsD|i|Œ}d|idtd„t||i|ƒƒDƒƒS(sOScores ngrams using likelihood ratios as in Manning and Schutze 5.3.4. iÿÿÿÿicss;x4|]-\}}|tt|ƒ|ttƒVqWdS(N(t_lnRR (R R"R#((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pys }s (R RRR$R(RR R((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytlikelihood_ratiovs cGsGt|tƒt|t|idƒ}|tt|t|ƒdS(s1Scores ngrams using the Poisson-Stirling measure.i(RRRRRRR+(RR R#((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytpoisson_stirling€s cGs+|i|Œ}t|dƒt|d ƒS(s&Scores ngrams using the Jaccard index.iiÿÿÿÿ(R RR(RR R((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytjaccard‡s(t__name__t __module__t__doc__t staticmethodR R t classmethodRRR!R&R*R,R.R/R0(((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR#s     tBigramAssocMeasurescBsneZdZdZed„ƒZed„ƒZed„ƒZed„ƒZ ed„ƒZ ed„ƒZ RS(sk A collection of trigram association measures. Each association measure is provided as a function with three arguments: bigram_score_fn(n_ii, (n_ix, n_xi), n_xx) The arguments constitute the marginals of a contingency table, counting the occurrences of particular events in a corpus. The letter i in the suffix refers to the appearance of the word in question, while x indicates the appearance of any word. Thus, for example: n_ii counts (w1, w2), i.e. the bigram being scored n_ix counts (w1, *) n_xi counts (*, w2) n_xx counts (*, *), i.e. any bigram This may be shown with respect to a contingency table: w1 ~w1 ------ ------ w2 | n_ii | n_oi | = n_xi ------ ------ ~w2 | n_io | n_oo | ------ ------ = n_ix TOTAL = n_xx icCs<|\}}||}||}|||||||fS(sECalculates values of a bigram contingency table from marginal values.((tn_iit.1tn_xxtn_ixtn_xitn_oitn_io((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR ¨s   cCs'|||||f||||fS(sACalculates values of contingency table marginals from its values.((R7R<R=tn_oo((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR ¯sccsZt|ƒ}xGtdƒD]9}||||dA||||dAt|ƒVqWdS(s3Calculates expected values for a contingency table.iiiN(RRR(RR9R((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR´s  cGsU|i|Œ\}}}}t||||dƒ||||||||S(sdScores bigrams using phi-square, the square of the Pearson correlation coefficient. i(R R(RR R7R=R<R>((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytphi_sq¼scCs)|\}}||i|||f|ƒS(sƒScores bigrams using chi-square, i.e. phi-sq multiplied by the number of bigrams, as in Manning and Schutze 5.3.3. (R?(RR7t.2R9R:R;((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR&Æs cCs"|\}}dt|ƒ||S(s(Scores bigrams using Dice's coefficient.i(R(R7R8R9R:R;((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytdiceÍs ( R1R2R3RR4R R RR5R?R&RA(((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR6Žs tTrigramAssocMeasurescBs2eZdZdZed„ƒZed„ƒZRS(sÁ A collection of trigram association measures. Each association measure is provided as a function with four arguments: trigram_score_fn(n_iii, (n_iix, n_ixi, n_xii), (n_ixx, n_xix, n_xxi), n_xxx) The arguments constitute the marginals of a contingency table, counting the occurrences of particular events in a corpus. The letter i in the suffix refers to the appearance of the word in question, while x indicates the appearance of any word. Thus, for example: n_iii counts (w1, w2, w3), i.e. the trigram being scored n_ixx counts (w1, *, *) n_xxx counts (*, *, *), i.e. any trigram icCs°|\}}}|\}}} ||} ||} ||} | || | } ||| | }||| | }||| | | | ||}|| | | | |||fS(saCalculates values of a trigram contingency table (or cube) from marginal values. ((tn_iiiR8R@tn_xxxtn_iixtn_ixitn_xiitn_ixxtn_xixtn_xxitn_oiitn_ioitn_iiotn_ooitn_oiotn_iootn_ooo((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR æs    " c Gsv|\}}}}}}}}|||||||f||||||||||||ft|ƒfS(sACalculates values of contingency table marginals from its values.(R( R RCRKRLRNRMRORPRQ((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyR ùs(R1R2R3RR4R R (((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyRBÓstContingencyMeasurescBs&eZdZd„Zed„ƒZRS(sWraps NgramAssocMeasures classes such that the arguments of association measures are contingency table values rather than marginals. cCsd|ii|i_xpt|ƒD]b}|idƒoq#nt||ƒ}|idƒp|i||ƒ}nt|||ƒq#WdS(sAConstructs a ContingencyMeasures given a NgramAssocMeasures classt Contingencyt__t_N(t __class__R1tdirt startswithtgetattrt_make_contingency_fntsetattr(tselftmeasurestktv((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyt__init__ s cs.‡‡fd†}ˆi|_ˆi|_|S(s‡From an association measure function, produces a new function which accepts contingency table values as its arguments. csˆˆi|ŒŒS(N(R (R (told_fnR](s./p/zhu/06/nlp/nltk/nltk/metrics/association.pytress(R3R1(R]RaRb((RaR]s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyRZs  (R1R2R3R`R4RZ(((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pyRRs (R3tmathRR+RR-RR RRRtobjectRR6RBRR(((s./p/zhu/06/nlp/nltk/nltk/metrics/association.pys s    kE2