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.predictions
%table
%tr
%th= @title.gsub(/_lazar_.*$/,' ').capitalize
%th Prediction
%th
%a{:href => "#", :id => "linkConfidence#{p.object_id}"} Confidence
:javascript
$("a#linkConfidence#{p.object_id}").click(function () {
$("dl#confidence").toggle();
});
%th Relevant features
%tr
%th
%img{:src => @compound.image_uri, :alt => @compound.smiles}
%td
- if @measured_activities
%br
- @measured_activities.each do |a|
- if activity(a) == 'active'
.active
= activity(a)
- elsif activity(a) == 'inactive'
.inactive
= activity(a)
- else
= a
%br
(
%a{:href => "#", :id => "linkTrainingData#{p.object_id}"} Training data
:javascript
$("a#linkTrainingData#{p.object_id}").click(function () {
$("dl#training_data").toggle();
});
)
- else
- if activity(@activity) == 'active'
.active
= activity(@activity)
- elsif activity(@activity) == 'inactive'
.inactive
= activity(@activity)
- elsif @activity.is_a?(Float)
.other
= sprintf('%.03g', @activity)
- else
.other
%em= @activity.to_s
%td
= sprintf('%.03g', @confidence.to_f.abs) if @confidence
%td
%table
%tr
%th{:colspan => 2} activating
%th p value
- if @features[:activating]
- @features[:activating].sort{|a,b| b.last <=> a.last }.each do |f|
%tr
%th= f[0]
%td= f[1]
%tr
%th{:colspan => 2} deactivating
%th p value
- if @features[:deactivating]
- @features[:deactivating].sort{|a,b| b.last <=> a.last }.each do |f|
%tr
%th= f[0]
%td= f[1]
%tr
%th Neighbors
%th Activity
%th Similarity (activity specific)
%th Relevant features
- @neighbors.sort{|a,b| b.last[:similarity] <=> a.last[:similarity]}.each do |uri,data|
- c = OpenTox::Compound.new(:uri => uri)
%tr
%th
%br= c.smiles
%br
%a{:href => c.image_uri, :target => "_blank"} Image
%br
%img{:src => c.image_uri, :alt => c.smiles}
%td
- data[:activities].each do |act|
- if activity(act) == 'active'
.active
= activity(act)
- elsif activity(act) == 'inactive'
.inactive
= activity(act)
- elsif act.is_a?(Float)
.other
= sprintf('%.03g', act)
- else
.other
%em= act.to_s
%td
= sprintf('%.03g', data[:similarity])
%td
%table
%tr
%th{:colspan => 2} activating
%th p value
-#%td= data[:features].inspect
-# data[:features][:activating].each do |f|
- data[:features][:activating].sort{|a,b| b.last[:p_value] <=> a.last[:p_value] }.each do |f|
-# f.inspect
%tr
%th= f[:smarts]
%td= f[:p_value]
%td=# f[:p_value]
%tr
%th{:colspan => 2} deactivating
%th p value
-# data[:features][:deactivating].sort{|a,b| b.last[:p_value] <=> a.last[:p_value] }.each do |f|
- data[:features][:deactivating].each do |f|
%tr
%th= f[:smarts]
%td= f[:p_value]
%dl#confidence{ :style => "display: none;" }
%dt Confidence:
%dd Indicates the applicability domain of a model. Predictions with a high confidence can be expected to be more reliable than predictions with low confidence. Confidence values may take any value between 0 and 1. For most models confidence > 0.025 is a sensible (hard) cutoff to distiguish between reliable and unreliable predictions.
%dl#training_data{ :style => "display: none;" }
%dt Training data:
%dd Experimental result(s) from the training dataset are displayed here.
|
