<s> import sys <EOL> import numpy as np <EOL> from lxmls . parsing . dependency_reader import * <EOL> from lxmls . parsing . dependency_writer import * <EOL> from lxmls . parsing . dependency_features import * <EOL> from lxmls . parsing . dependency_decoder import * <EOL> from lxmls . util . my_math_utils import * <EOL> class DependencyParser ( ) : <EOL> '''<STR_LIT>''' <EOL> def __init__ ( self ) : <EOL> self . trained = False <EOL> self . projective = False <EOL> self . language = "<STR_LIT>" <EOL> self . weights = [ ] <EOL> self . decoder = DependencyDecoder ( ) <EOL> self . reader = DependencyReader ( ) <EOL> self . writer = DependencyWriter ( ) <EOL> self . features = DependencyFeatures ( ) <EOL> def read_data ( self , language ) : <EOL> self . language = language <EOL> self . reader . load ( language ) <EOL> self . features . create_dictionary ( self . reader . train_instances ) <EOL> def train_perceptron ( self , n_epochs ) : <EOL> '''<STR_LIT>''' <EOL> self . weights = np . zeros ( self . features . n_feats ) <EOL> total = np . zeros ( self . features . n_feats ) <EOL> for epoch in range ( n_epochs ) : <EOL> print "<STR_LIT>" . format ( epoch + <NUM_LIT:1> ) <EOL> n_mistakes = <NUM_LIT:0> <EOL> n_tokens = <NUM_LIT:0> <EOL> n_instances = <NUM_LIT:0> <EOL> for instance in self . reader . train_instances : <EOL> feats = self . features . create_features ( instance ) <EOL> scores = self . features . compute_scores ( feats , self . weights ) <EOL> if self . projective : <EOL> heads_pred = self . decoder . parse_proj ( scores ) <EOL> else : <EOL> heads_pred = self . decoder . parse_nonproj ( scores ) <EOL> for m in range ( np . size ( heads_pred ) ) : <EOL> if heads_pred [ m ] != instance . heads [ m ] : <EOL> for f in feats [ instance . heads [ m ] ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> self . weights [ f ] += <NUM_LIT:1.0> <EOL> for f in feats [ heads_pred [ m ] ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> self . weights [ f ] -= <NUM_LIT:1.0> <EOL> n_mistakes += <NUM_LIT:1> <EOL> n_tokens += <NUM_LIT:1> <EOL> n_instances += <NUM_LIT:1> <EOL> print "<STR_LIT>" . format ( np . double ( n_tokens - n_mistakes ) / np . double ( n_tokens ) ) <EOL> total += self . weights <EOL> self . weights = total / np . double ( n_epochs ) <EOL> def train_crf_sgd ( self , n_epochs , sigma , eta0 = <NUM_LIT> ) : <EOL> '''<STR_LIT>''' <EOL> self . weights = np . zeros ( self . features . n_feats ) <EOL> t = <NUM_LIT:0> <EOL> t0 = <NUM_LIT:1.0> / ( sigma * eta0 ) <EOL> for epoch in range ( n_epochs ) : <EOL> print "<STR_LIT>" . format ( epoch + <NUM_LIT:1> ) <EOL> n_mistakes = <NUM_LIT:0> <EOL> n_tokens = <NUM_LIT:0> <EOL> n_instances = <NUM_LIT:0> <EOL> objective = <NUM_LIT:0.0> <EOL> for instance in self . reader . train_instances : <EOL> eta = <NUM_LIT:1.0> / ( sigma * ( t + t0 ) ) <EOL> feats = self . features . create_features ( instance ) <EOL> scores = self . features . compute_scores ( feats , self . weights ) <EOL> marginals , logZ = self . decoder . parse_marginals_nonproj ( scores ) <EOL> self . weights -= eta * sigma * self . weights <EOL> for h in range ( np . size ( marginals , <NUM_LIT:0> ) ) : <EOL> for m in range ( <NUM_LIT:1> , np . size ( marginals , <NUM_LIT:1> ) ) : <EOL> if feats [ h ] [ m ] == None : <EOL> continue <EOL> for f in feats [ h ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> self . weights [ f ] -= eta * marginals [ h , m ] <EOL> score_corr = <NUM_LIT:0.0> <EOL> for m in range ( <NUM_LIT:1> , np . size ( instance . heads ) ) : <EOL> h = instance . heads [ m ] <EOL> score_corr += scores [ h , m ] <EOL> for f in feats [ h ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> self . weights [ f ] += eta <EOL> objective += <NUM_LIT:0.5> * sigma * np . dot ( self . weights , self . weights ) - score_corr + logZ <EOL> n_instances += <NUM_LIT:1> <EOL> t += <NUM_LIT:1> <EOL> print "<STR_LIT>" . format ( objective / n_instances ) <EOL> def test ( self ) : <EOL> n_mistakes = <NUM_LIT:0> <EOL> n_tokens = <NUM_LIT:0> <EOL> n_instances = <NUM_LIT:0> <EOL> arr_heads_pred = [ ] ; <EOL> for instance in self . reader . test_instances : <EOL> feats = self . features . create_features ( instance ) <EOL> scores = self . features . compute_scores ( feats , self . weights ) <EOL> if self . projective : <EOL> heads_pred = self . decoder . parse_proj ( scores ) <EOL> else : <EOL> heads_pred = self . decoder . parse_nonproj ( scores ) <EOL> for m in range ( np . size ( heads_pred ) ) : <EOL> if heads_pred [ m ] != instance . heads [ m ] : <EOL> for f in feats [ instance . heads [ m ] ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> for f in feats [ heads_pred [ m ] ] [ m ] : <EOL> if f < <NUM_LIT:0> : <EOL> continue <EOL> n_mistakes += <NUM_LIT:1> <EOL> n_tokens += <NUM_LIT:1> <EOL> n_instances += <NUM_LIT:1> <EOL> arr_heads_pred . append ( heads_pred ) <EOL> print "<STR_LIT>" . format ( n_instances , np . double ( n_tokens - n_mistakes ) / np . double ( n_tokens ) ) <EOL> self . writer . save ( self . language , arr_heads_pred ) </s>
