'[fldZddlZddlmZ ddlZn #e$rdZYnwxYwdad dZd dZd dZ dZ d Z dS) aP A set of functions used to interface with the external megam_ maxent optimization package. Before megam can be used, you should tell NLTK where it can find the megam binary, using the ``config_megam()`` function. Typical usage: >>> from nltk.classify import megam >>> megam.config_megam() # pass path to megam if not found in PATH # doctest: +SKIP [Found megam: ...] Use with MaxentClassifier. Example below, see MaxentClassifier documentation for details. nltk.classify.MaxentClassifier.train(corpus, 'megam') .. _megam: https://www.umiacs.umd.edu/~hal/megam/index.html N) find_binaryc4td|dggddadS)aA Configure NLTK's interface to the ``megam`` maxent optimization package. :param bin: The full path to the ``megam`` binary. If not specified, then nltk will search the system for a ``megam`` binary; and if one is not found, it will raise a ``LookupError`` exception. :type bin: str megamMEGAM)z megam.optr megam_686zmegam_i686.optz0https://www.umiacs.umd.edu/~hal/megam/index.html)env_vars binary_namesurlN)r _megam_bin)bins G/var/www/piapp/venv/lib/python3.11/site-packages/nltk/classify/megam.py config_megamr)s3 JJJ > JJJTc8 }dt|D}|D]\ tdr7|d fd|Dn|d| z|s&t  ||n?|D]<}|dt |||=|ddS) a Generate an input file for ``megam`` based on the given corpus of classified tokens. :type train_toks: list(tuple(dict, str)) :param train_toks: Training data, represented as a list of pairs, the first member of which is a feature dictionary, and the second of which is a classification label. :type encoding: MaxentFeatureEncodingI :param encoding: A feature encoding, used to convert featuresets into feature vectors. May optionally implement a cost() method in order to assign different costs to different class predictions. :type stream: stream :param stream: The stream to which the megam input file should be written. :param bernoulli: If true, then use the 'bernoulli' format. I.e., all joint features have binary values, and are listed iff they are true. Otherwise, list feature values explicitly. If ``bernoulli=False``, then you must call ``megam`` with the ``-fvals`` option. :param explicit: If true, then use the 'explicit' format. I.e., list the features that would fire for any of the possible labels, for each token. If ``explicit=True``, then you must call ``megam`` with the ``-explicit`` option. ci|]\}}|| Sr).0ilabels r z$write_megam_file..bs===Zaq===rcost:c3`K|](}t|V)dSN)strr)rlencoding featuresetrs r z#write_megam_file..is;RRaX]]:ua@@AARRRRRRrz%dz # N)labels enumeratehasattrwritejoin_write_megam_featuresencode) train_toksrstream bernoulliexplicitr!labelnumrrrs ` @@r write_megam_filer-BsR>__  F==9V+<+<===H( E 8V $ $ 1 LLRRRRRR6RRRRR     LL/ 0 0 0 Y !(//*e"D"Dfi X X X X  Y Y T"""%hooj!&D&DfiXXXX  T-rc`ttd|s Jd|d}tj|d}|D]L}|r6|\}}t ||t |<M|S)z Given the stdout output generated by ``megam`` when training a model, return a ``numpy`` array containing the corresponding weight vector. This function does not currently handle bias features. Nz.This function requires that numpy be installedznon-explicit not supported yetr d)numpy ValueErrorstripsplitzerosfloatint)sfeatures_countr+linesweightslinefidweights r parse_megam_weightsr>~s  }IJJJ 555555 GGIIOOD ! !Ek.#..G.. ::<< .**,,KC %f GCHH  Nrc|std|D]W\}}|r5|dkr|d|z&|dkrtd<|d|d|XdS)Nz:MEGAM classifier requires the use of an always-on feature.z %srz3If bernoulli=True, then allfeatures must be binary. )r1r$)vectorr)r*r<fvals r r&r&s   K    + + d  +qyy US[)))) L LL)S))4)) * * * * + +rct|trtdtt tg|z}t j|t j}|\}}|j dkr,tt|tdt|tr|S| dS)z= Call the ``megam`` binary with the given arguments. z args should be a list of stringsN)stdoutrzmegam command failed!zutf-8) isinstancer TypeErrorr r subprocessPopenPIPE communicate returncodeprintOSErrordecode)argscmdprEstderrs r call_megamrTs$<:;;; , CZ_555A}}VV |q  f -...&#& }}W%%%rr)TT)T) __doc__rHnltk.internalsrr0 ImportErrorr rr-r>r&rTrrr rXs"&&&&&&LLLL EEE 29999x$+++"&&&&&s