<s> """<STR_LIT>""" <EOL> from __future__ import print_function <EOL> import time <EOL> import tempfile <EOL> import os <EOL> import subprocess <EOL> import re <EOL> import zipfile <EOL> from sys import stdin <EOL> from nltk import compat <EOL> from nltk . probability import DictionaryProbDist <EOL> from nltk . internals import java , config_java <EOL> from nltk . classify . api import ClassifierI <EOL> _weka_classpath = None <EOL> _weka_search = [ '<STR_LIT:.>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> '<STR_LIT>' , ] <EOL> def config_weka ( classpath = None ) : <EOL> global _weka_classpath <EOL> config_java ( ) <EOL> if classpath is not None : <EOL> _weka_classpath = classpath <EOL> if _weka_classpath is None : <EOL> searchpath = _weka_search <EOL> if '<STR_LIT>' in os . environ : <EOL> searchpath . insert ( <NUM_LIT:0> , os . environ [ '<STR_LIT>' ] ) <EOL> for path in searchpath : <EOL> if os . path . exists ( os . path . join ( path , '<STR_LIT>' ) ) : <EOL> _weka_classpath = os . path . join ( path , '<STR_LIT>' ) <EOL> version = _check_weka_version ( _weka_classpath ) <EOL> if version : <EOL> print ( ( '<STR_LIT>' % <EOL> ( _weka_classpath , version ) ) ) <EOL> else : <EOL> print ( '<STR_LIT>' % _weka_classpath ) <EOL> _check_weka_version ( _weka_classpath ) <EOL> if _weka_classpath is None : <EOL> raise LookupError ( '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' ) <EOL> def _check_weka_version ( jar ) : <EOL> try : <EOL> zf = zipfile . ZipFile ( jar ) <EOL> except ( SystemExit , KeyboardInterrupt ) : <EOL> raise <EOL> except : <EOL> return None <EOL> try : <EOL> try : <EOL> return zf . read ( '<STR_LIT>' ) <EOL> except KeyError : <EOL> return None <EOL> finally : <EOL> zf . close ( ) <EOL> class WekaClassifier ( ClassifierI ) : <EOL> def __init__ ( self , formatter , model_filename ) : <EOL> self . _formatter = formatter <EOL> self . _model = model_filename <EOL> def prob_classify_many ( self , featuresets ) : <EOL> return self . _classify_many ( featuresets , [ '<STR_LIT>' , '<STR_LIT:0>' , '<STR_LIT>' ] ) <EOL> def classify_many ( self , featuresets ) : <EOL> return self . _classify_many ( featuresets , [ '<STR_LIT>' , '<STR_LIT:0>' ] ) <EOL> def _classify_many ( self , featuresets , options ) : <EOL> config_weka ( ) <EOL> temp_dir = tempfile . mkdtemp ( ) <EOL> try : <EOL> test_filename = os . path . join ( temp_dir , '<STR_LIT>' ) <EOL> self . _formatter . write ( test_filename , featuresets ) <EOL> cmd = [ '<STR_LIT>' , <EOL> '<STR_LIT>' , self . _model , '<STR_LIT>' , test_filename ] + options <EOL> ( stdout , stderr ) = java ( cmd , classpath = _weka_classpath , <EOL> stdout = subprocess . PIPE , <EOL> stderr = subprocess . PIPE ) <EOL> if stderr and not stdout : <EOL> if '<STR_LIT>' in stderr : <EOL> raise ValueError ( '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' ) <EOL> else : <EOL> raise ValueError ( '<STR_LIT>' <EOL> % stderr ) <EOL> return self . parse_weka_output ( stdout . decode ( stdin . encoding ) . split ( '<STR_LIT:\n>' ) ) <EOL> finally : <EOL> for f in os . listdir ( temp_dir ) : <EOL> os . remove ( os . path . join ( temp_dir , f ) ) <EOL> os . rmdir ( temp_dir ) <EOL> def parse_weka_distribution ( self , s ) : <EOL> probs = [ float ( v ) for v in re . split ( '<STR_LIT>' , s ) if v . strip ( ) ] <EOL> probs = dict ( zip ( self . _formatter . labels ( ) , probs ) ) <EOL> return DictionaryProbDist ( probs ) <EOL> def parse_weka_output ( self , lines ) : <EOL> for i , line in enumerate ( lines ) : <EOL> if line . strip ( ) . startswith ( "<STR_LIT>" ) : <EOL> lines = lines [ i : ] <EOL> break <EOL> if lines [ <NUM_LIT:0> ] . split ( ) == [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT:error>' , '<STR_LIT>' ] : <EOL> return [ line . split ( ) [ <NUM_LIT:2> ] . split ( '<STR_LIT::>' ) [ <NUM_LIT:1> ] <EOL> for line in lines [ <NUM_LIT:1> : ] if line . strip ( ) ] <EOL> elif lines [ <NUM_LIT:0> ] . split ( ) == [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' , <EOL> '<STR_LIT:error>' , '<STR_LIT>' ] : <EOL> return [ self . parse_weka_distribution ( line . split ( ) [ - <NUM_LIT:1> ] ) <EOL> for line in lines [ <NUM_LIT:1> : ] if line . strip ( ) ] <EOL> elif re . match ( r'<STR_LIT>' , lines [ <NUM_LIT:0> ] ) : <EOL> return [ line . split ( ) [ <NUM_LIT:1> ] for line in lines if line . strip ( ) ] <EOL> else : <EOL> for line in lines [ : <NUM_LIT:10> ] : <EOL> print ( line ) <EOL> raise ValueError ( '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' % lines [ <NUM_LIT:0> ] ) <EOL> _CLASSIFIER_CLASS = { <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> } <EOL> @ classmethod <EOL> def train ( cls , model_filename , featuresets , <EOL> classifier = '<STR_LIT>' , options = [ ] , quiet = True ) : <EOL> config_weka ( ) <EOL> formatter = ARFF_Formatter . from_train ( featuresets ) <EOL> temp_dir = tempfile . mkdtemp ( ) <EOL> try : <EOL> train_filename = os . path . join ( temp_dir , '<STR_LIT>' ) <EOL> formatter . write ( train_filename , featuresets ) <EOL> if classifier in cls . _CLASSIFIER_CLASS : <EOL> javaclass = cls . _CLASSIFIER_CLASS [ classifier ] <EOL> elif classifier in cls . _CLASSIFIER_CLASS . values ( ) : <EOL> javaclass = classifier <EOL> else : <EOL> raise ValueError ( '<STR_LIT>' % classifier ) <EOL> cmd = [ javaclass , '<STR_LIT>' , model_filename , '<STR_LIT>' , train_filename ] <EOL> cmd += list ( options ) <EOL> if quiet : <EOL> stdout = subprocess . PIPE <EOL> else : stdout = None <EOL> java ( cmd , classpath = _weka_classpath , stdout = stdout ) <EOL> return WekaClassifier ( formatter , model_filename ) <EOL> finally : <EOL> for f in os . listdir ( temp_dir ) : <EOL> os . remove ( os . path . join ( temp_dir , f ) ) <EOL> os . rmdir ( temp_dir ) <EOL> class ARFF_Formatter : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , labels , features ) : <EOL> """<STR_LIT>""" <EOL> self . _labels = labels <EOL> self . _features = features <EOL> def format ( self , tokens ) : <EOL> """<STR_LIT>""" <EOL> return self . header_section ( ) + self . data_section ( tokens ) <EOL> def labels ( self ) : <EOL> """<STR_LIT>""" <EOL> return list ( self . _labels ) <EOL> def write ( self , outfile , tokens ) : <EOL> """<STR_LIT>""" <EOL> if not hasattr ( outfile , '<STR_LIT>' ) : <EOL> outfile = open ( outfile , '<STR_LIT:w>' ) <EOL> outfile . write ( self . format ( tokens ) ) <EOL> outfile . close ( ) <EOL> @ staticmethod <EOL> def from_train ( tokens ) : <EOL> """<STR_LIT>""" <EOL> labels = set ( label for ( tok , label ) in tokens ) <EOL> features = { } <EOL> for tok , label in tokens : <EOL> for ( fname , fval ) in tok . items ( ) : <EOL> if issubclass ( type ( fval ) , bool ) : <EOL> ftype = '<STR_LIT>' <EOL> elif issubclass ( type ( fval ) , ( compat . integer_types , float , bool ) ) : <EOL> ftype = '<STR_LIT>' <EOL> elif issubclass ( type ( fval ) , compat . string_types ) : <EOL> ftype = '<STR_LIT>' <EOL> elif fval is None : <EOL> continue <EOL> else : <EOL> raise ValueError ( '<STR_LIT>' % ftype ) <EOL> if features . get ( fname , ftype ) != ftype : <EOL> raise ValueError ( '<STR_LIT>' % fname ) <EOL> features [ fname ] = ftype <EOL> features = sorted ( features . items ( ) ) <EOL> return ARFF_Formatter ( labels , features ) <EOL> def header_section ( self ) : <EOL> """<STR_LIT>""" <EOL> s = ( '<STR_LIT>' + <EOL> '<STR_LIT>' + <EOL> '<STR_LIT>' % time . ctime ( ) ) <EOL> s += '<STR_LIT>' <EOL> for fname , ftype in self . _features : <EOL> s += '<STR_LIT>' % ( fname , ftype ) <EOL> s += '<STR_LIT>' % ( '<STR_LIT>' , '<STR_LIT:U+002C>' . join ( self . _labels ) ) <EOL> return s <EOL> def data_section ( self , tokens , labeled = None ) : <EOL> """<STR_LIT>""" <EOL> if labeled is None : <EOL> labeled = tokens and isinstance ( tokens [ <NUM_LIT:0> ] , ( tuple , list ) ) <EOL> if not labeled : <EOL> tokens = [ ( tok , None ) for tok in tokens ] <EOL> s = '<STR_LIT>' <EOL> for ( tok , label ) in tokens : <EOL> for fname , ftype in self . _features : <EOL> s += '<STR_LIT>' % self . _fmt_arff_val ( tok . get ( fname ) ) <EOL> s += '<STR_LIT>' % self . _fmt_arff_val ( label ) <EOL> return s <EOL> def _fmt_arff_val ( self , fval ) : <EOL> if fval is None : <EOL> return '<STR_LIT:?>' <EOL> elif isinstance ( fval , ( bool , compat . integer_types ) ) : <EOL> return '<STR_LIT:%s>' % fval <EOL> elif isinstance ( fval , float ) : <EOL> return '<STR_LIT>' % fval <EOL> else : <EOL> return '<STR_LIT>' % fval <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> from nltk . classify . util import names_demo , binary_names_demo_features <EOL> def make_classifier ( featuresets ) : <EOL> return WekaClassifier . train ( '<STR_LIT>' , featuresets , <EOL> '<STR_LIT>' ) <EOL> classifier = names_demo ( make_classifier , binary_names_demo_features ) </s>
