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|
- require 'zipf'
!!!
%html
%head
%title Debug view (Session ##{session_key})
%link(rel="stylesheet" type="text/css" href="debug.css")
%script{:src =>"http://ajax.googleapis.com/ajax/libs/jquery/1.11.2/jquery.min.js", :charset=>"utf-8"}
%script{:src => "http://postedit.cl.uni-heidelberg.de/js/debug.js"}
%body
%h1 Debug view
%h2 Session [##{session_key}]
- if pairwise_ranking_data["kbest"].empty?
%p.red
%strong No data to show!
%ul
%li
%a{ :href => "#controls" } Controls
%li
%a{ :href => "#post_edit" } Post-edit & machine translation
%li
%a{ :href => "#meta" } Meta
%li
%a{ :href => "#rules" } Learned rules
%li
%a{ :href => "#pairwise_ranking" } Pairwise ranking
%hr
/=#########################################################################
%h2#controls Controls
%h3 Reset
%p
%strong [Server reply]
%span#control_reply
%ul
%li
%a.ajax{:tgt => "/reset_progress", :href => "#controls"} Reset progress
%li
%a.ajax{:tgt => "/reset_weights", :href => "#controls"} Reset weights
%li
%a.ajax{:tgt => "/reset_extractor", :href => "#controls"} Reset extractor
%li
%a.ajax{:tgt => "/reset_new_rules", :href => "#controls"} Reset new rules
%li
%a.ajax{:tgt => "/shutdown", :href => "#controls"} Initiate shutdown
%h3 Learning rate
%p Default for dense features: <strong>1.0e-05</strong>, for sparse features: <strong>1.0e-05</strong>
%select
- [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001,0.0000001,0.00000001,0.000000001,0.0000000001].each do |i|
%option.ajax{:value => i, :tgt => "/set_learning_rate/#{i}"} #{i}
%em dense features <br />
%select
- [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001,0.0000001,0.00000001,0.000000001,0.0000000001].each do |i|
%option.ajax{:value => i, :tgt => "/set_learning_rate/sparse/#{i}"} #{i}
%em sparse features
%p
%a{ :href => "#" } ^ up
%hr
/=#########################################################################
%h2#post_edit Post-edit & machine translation
%p#original_svg_b64 #{data["original_svg"][progress]}
%p#svg_b64 #{data["svg"][progress]}
%div#original_svg
%div#svg
%table
%tr
%td.noborder
%strong progress:
%td.left #{[0,progress].max}
%tr
%td.noborder
%strong MT Input
%td.left #{data["source_segments"][[0,progress].max]}
%tr
%td.noborder
%strong Raw source
%td.left #{data["raw_source_segments"][[0,progress].max]}
%tr
%td.noborder
%strong Post-edit
%td.left #{data["post_edits_raw"][progress]}
%tr
%td.noborder
%strong Post-edit (processed)
%td.left #{data["post_edits"][progress]}
%tr
%td.noborder
%strong Original MT
%td.left #{data["mt_raw"][progress]}
%tr
%td.noborder
%strong Displayed MT
%td.left #{data["mt"][progress]}
%tr
%td.noborder
%strong Best match (BLEU=#{(pairwise_ranking_data["best_match_score"]*100).round(2)}%)
%td.left #{pairwise_ranking_data["best_match"]}
%h2 Derivation
%pre #{data["derivations"][progress]}
/= %h3 Processed
/= - if data["derivations_proc"][progress]
/= %pre #{JSON.pretty_generate(JSON.parse(data["derivations_proc"][progress]))}
%h2 Client reply
- if data["feedback"][progress]
%pre #{JSON.pretty_generate(JSON.parse(data["feedback"][progress]))}
%p
%a{ :href => "#" } ^ up
%hr
/=#########################################################################
%h2#meta Meta
%p <strong>k:</strong> #{pairwise_ranking_data["samples_size"]}
%p <strong>number of updates:</strong> #{pairwise_ranking_data["num_up"]}
%p <strong>updated features:</strong> #{pairwise_ranking_data["updated_features"]}
%p <strong>learning rate:</strong> #{pairwise_ranking_data["learning_rate"]}
%p <strong>learning rate (sparse):</strong> #{pairwise_ranking_data["learning_rate_sparse"]}
%p <strong>duration:</strong> #{data["durations"][progress]}ms
%p <strong>updated:</strong> #{data["updated"][progress]}
%p
%a{ :href => "#" } ^ up
%hr
/=#########################################################################
%h2#rules New & known rules
%pre #{additional_rules.join("\n")}
%h3
Known rules
%pre #{rejected_rules.join("\n")}
%p
%a{ :href => "#" } ^ up
%hr
/=#########################################################################
%h2#pairwise_ranking Pairwise ranking updates
%h3 K-best list
%p (Empty or list from previous updates displayed if there was no change in the current post-edit.)
%pre [BLEU score | Model score | Original rank | \|e\| | Translation \n Features]
%p.red In red: Update needed, i.e. "any of the above hypotheses has a lower model score"
%ol
- kbest = []
- pairwise_ranking_data["kbest"].each { |i| x=splitpipe(i); kbest << [ x[0].to_f, x[1].to_f, x[2].to_i, x[3], x[4] ] }
- kbest.sort! { |i,j| j[0] <=> i[0] }
- kbest.each_with_index do |k,j|
- b = kbest[0,j].map { |l| l[0]>k[0] && l[1]<k[1] }.include? true
-if b
%li.red
%strong #{"%.2f"%(k[0].to_f*100)} | #{k[1]} | #{k[2]} | #{k[4].split.size} |#{k[4]} <br/>
%p{:style=>"font-size:80%"} #{k[3]}
- else
%li
%strong #{"%.2f"%(k[0].to_f*100)} | #{k[1]} | #{k[2]} | #{k[4].split.size} | #{k[4]} <br/>
%p{:style=>"font-size:80%"} #{k[3]}
- if [9,89].include? j
%hr
%h3 Weight updates
%p Raw update: #{pairwise_ranking_data["update_raw"]}
/=%p Update (learning rates applied): <pre>#{pairwise_ranking_data["update"]}</pre>
/=- "#{pairwise_ranking_data["update"]}"
%table
%tr
%th Feature
%th Before
%th After
%th Diff.
%th Raw diff.
- pairwise_ranking_data["weights_before"].default = 0
- pairwise_ranking_data["weights_after"].keys.each.sort { |a,b| a[0] <=> b[0] }.each do |k|
%tr
%td.noborder <strong> #{k} </strong>
%td #{"%+.3f"%pairwise_ranking_data["weights_before"][k].round(4)}
%td #{"%+.3f"%pairwise_ranking_data["weights_after"][k].round(4)}
- diff = pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k]
- if diff < 0
%td.red #{"%+.3f"%(diff).round(4)}
- elsif diff > 0
%td.green #{"%+.3f"%(diff).round(4)}
- else
%td #{"%+.3f"%(diff).round(4)}
- if !k.start_with? "R:"
%td #{"%+.1f"%((pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k])/pairwise_ranking_data["learning_rate"]).round(2)}
- else
%td #{"%+.1f"%((pairwise_ranking_data["weights_after"][k]-pairwise_ranking_data["weights_before"][k])/pairwise_ranking_data["learning_rate_sparse"]).round(2)}
%h3 Features explained
%table
%tr
%td.noborder EgivenFCoherent (rule)
%td.left -log10[ c(e, f) / sample c(f) ]
%tr
%td.noborder NewRule (rule)
%td.left Only feature of additional rules, weight fixed at 1
%tr
%td.noborder KnownRule (rule)
%td.left Added to existing rules if they could be extracted from previous post-edits
%tr
%td.noborder OOVFix (rule)
%td.left Manually added rules to fix OOV words
%tr
%td.noborder Glue
%td.left Absolute number of rules used from glue grammar
%tr
%td.noborder IsSingletonF/E (rule)
%td.left true|false (1|0) (sum)
%tr
%td.noborder IsSingletonFE (rule)
%td.left true|false (1|0) (sum)
%tr
%td.noborder LanguageModel
%td.left -log10[ score ]
%tr
%td.noborder LanguageModel_OOV
%td.left Abs. count of OOV unigrams
%tr
%td.noborder MaxLexFgivenE (rule)
%td.left Sum_f -log10(maxScore) (maxScore = max_e(ttable(f))
%tr
%td.noborder MaxLexEgivenF (rule)
%td.left Sum_e -log10(maxScore) (maxScore = max_f(ttable(e))
%tr
%td.noborder PassThrough
%td.left Absolute count of used PassThrough rules (1 per word)
%tr
%td.noborder SampleCountF (rule)
%td.left log10 [ sample c(f) ]
%tr
%td.noborder WordPenalty
%td.left log_10(e)*|e| = 1/log(10) * |e| (*-1) = -0.43429448190325176*|e|
%tr
%td.noborder SourceWordPenalty (per edge/rule)
%td.left ^^^ (|e| <=> |f|)
%tr
%td.noborder R:*
%td.left Rule indicator features, sum over full derivation per rule
%tr
%td.noborder Shape_*
%td.left Indicator features for rule shapes
%tr
%td.noborder IsSupportedOnline
%td.left Counts how many rules have support from local context (Denkowski)
%p
%a{ :href => "#" } ^ up
|
