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|
- require 'zipf'
!!!
%html
%head
%title debug view (for session ##{session_key})
%link(rel="stylesheet" type="text/css" href="debug.css")
%body
%h1 debug view
%p (for session ##{session_key})
%p
%a{:href => "/reset", :target => "_blank"} reset progress,
%a{:href => "/reset_weights", :target => "_blank"} reset weights,
%a{:href => "/reset_extractor", :target => "_blank"} reset extractor,
%a{:href => "/shutdown", :target => "_blank"} shutdown,
%span learning rate
%select
- [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001].each do |i|
%option{:value => i, :onclick => "window.open(\"http://\"+window.location.host+\"/set_learning_rate/#{i}\");"} #{i}
%span , learning rate (sparse)
%select
- [1000,100,10,1,0.1,0.01,0.001,0.0001,0.00001,0.000001].each do |i|
%option{:value => i, :onclick => "window.open(\"http://\"+window.location.host+\"/set_sparse_learning_rate/#{i}\");"} #{i}
%table
%tr
%td.noborder
%strong source:
%td.left #{data["source"]}
%tr
%td.noborder
%strong post-edit:
%td.left #{data["target"]}
%tr
%td.noborder
%strong original mt:
%td.left #{data["1best"]}
%tr
%td.noborder
%strong best match (bleu=#{data["best_match_score"]}):
%td.left #{data["best_match"]}
%h2 meta
%p <strong>k:</strong> #{data["samples_size"]}
%p <strong>number of updates:</strong> #{data["num_up"]}
%p <strong>updated features:</strong> #{data["updated_features"]}
%p <strong>learning rate:</strong> #{data["learning_rate"]}
%p <strong>learning rate (sparse):</strong> #{data["learning_rate_sparse"]}
%h2 k-best
%p bleu | model score | original rank | \|e\| | translation \n features
%p.red update needed
%ol
- kbest = []
- 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
%h2 weight updates
%table
%tr
%th feature
%th before
%th after
%th diff
%th raw diff
- data["weights_before"].default = 0
- data["weights_after"].keys.each.sort { |a,b| a[0] <=> b[0] }.each do |k|
%tr
%td.noborder <strong> #{k} </strong>
%td #{"%+.3f"%data["weights_before"][k].round(4)}
%td #{"%+.3f"%data["weights_after"][k].round(4)}
- diff = data["weights_after"][k]-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"%((data["weights_after"][k]-data["weights_before"][k])/data["learning_rate"]).round(2)}
- else
%td #{"%+.1f"%((data["weights_after"][k]-data["weights_before"][k])/data["learning_rate_sparse"]).round(2)}
%h3 dense features explained
%table
%tr
%td.noborder EgivenFCoherent (TM)
%td.left -log10[ c(e, f) / sample c(f) ]
%tr
%td.noborder Glue
%td.left absolute number of rules used from glue grammar
%tr
%td.noborder IsSingletonF/E (TM)
%td.left true|false (1|0) (sum)
%tr
%td.noborder IsSingletonFE (TM)
%td.left true|false (1|0) (sum)
%tr
%td.noborder LanguageModel
%td.left -log?[ score ]
%tr
%td.noborder LanguageModel_OOV
%td.left abs. count of OOV unigrams
%tr
%td.noborder MaxLexFgivenE (TM)
%td.left Sum_f -log10(maxScore) (maxScore = max_e(ttable(f))
%tr
%td.noborder MaxLexEgivenF (TM)
%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
%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
|
