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%div.card.bg-light
%div.card-body
%h6.card-title Model:
Source:
%a{:href=>model.source, :rel=>"external"}
= model.source
%br
- model.classification? ? type = "Classification" : type = "Regression"
= "Type:\t"
= type
%br
= "Training compounds:\t"
= data_entries.count/3
%br
= "Training dataset:\t"
%a{:href=>"#{to("/predict/dataset/#{training_dataset.name}")}"}
= training_dataset.name
%div.card.bg-light
%div.card-body
%h6.card-title Algorithms:
%p.card-text
Similarity:
%a.card-link{:href=> "http://www.rubydoc.info/gems/lazar/OpenTox%2F#{model.model.algorithms["similarity"]["method"].sub("::", "%2F")}", :rel=>"external"}
= model.model.algorithms["similarity"]["method"]
= ", min: #{model.model.algorithms["similarity"]["min"]}"
%br
Prediction:
- if model.model.algorithms["prediction"]["method"] !~ /Caret/
%a.card-link{:href=>"http://www.rubydoc.info/gems/lazar/OpenTox%2F#{model.model.algorithms["prediction"]["method"].sub("::","%2f")}", :rel=>"external"}
= model.model.algorithms["prediction"]["method"]
- else
%a.card-link{:href=>"http://www.rubydoc.info/gems/lazar/OpenTox/Algorithm/Caret", :rel=>"external"}
= model.model.algorithms["prediction"]["method"]
%br
Descriptors:
= model.model.algorithms["descriptors"]["method"]+","
= model.model.algorithms["descriptors"]["type"]
%div.card.bg-light
%div.card-body
- if type == "Classification"
%h6.card-title Independent crossvalidations:
- else
%h6.card-title Independent crossvalidations (-log10 transformed):
- crossvalidations.each_with_index do |cv,idx|
%p.card-text
- if model.classification?
/ accuracy, confusion matrixes
- keys = cv.accuracy.collect{|key, value| key}
- acc = cv.accuracy.collect{|key, value| value.signif(3)}
- tpr = cv.true_rate.collect{|key, hash| hash[cv.accept_values[0]].signif(3)}
- fpr = cv.true_rate.collect{|key, hash| hash[cv.accept_values[1]].signif(3)}
- pp = cv.predictivity.collect{|key, hash| hash[cv.accept_values[0]].signif(3)}
- np = cv.predictivity.collect{|key, hash| hash[cv.accept_values[1]].signif(3)}
%table.table.table-borderless.table-responsive
%tr
%td.text-right
= "Nr.#{idx+1}"
%td.text-center.fit Accuracy:
%td.text-right.fit True positive rate:
%td.text-right.fit True negative rate:
%td.text-right.fit Positive predictiv value:
%td.text-right.fit Negative predictiv value:
/ mimic vertical line
%td.border-right
%td.text-center.fit Confusion matrix all:
%td.text-center.fit Confusion matrix confidence high:
%td.text-center.fit Confusion matrix confidence low:
%tr
%td.fit
/ keys:
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.fit.text-right.pr-0= key.gsub("_", " ")+":"
%td.fit
/ acc,accuray value:
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.text-left= acc[idx]
%td.fit
/ tpr,true positive rate
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.text-left= tpr[idx]
%td.fit
/ fpr,false positive rate
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.text-left= fpr[idx]
%td.fit
/ pp,positive predictions
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.text-left= pp[idx]
%td.fit
/ np,negative predictions
%table.table-sm
- keys.each_with_index do |key,idx|
%tr
%td.text-left= np[idx]
/ mimic vertical line
%td.border-right
/ confusion matrixes:
- keys.each do |key|
- matrix = cv.confusion_matrix[key]
%td
%table.table-sm
%tr
%td
%td
%td
%h6 actual
%td
%tr
%td
%td
%td active
%td inactive
%tr
%td
%h6 predicted
%td active
%td
= matrix[0][0]
%td
= matrix[0][1]
%tr
%td
%td inactive
%td
= matrix[1][0]
%td
= matrix[1][1]
/ regression
- if model.regression?
/ rmse, mae, r_squared, pred nr, within intv, out intv
- keys = cv.rmse.collect{|key, value| key}
- rmse = cv.rmse.collect{|key, value| value.signif(3)}
- mae = cv.mae.collect{|key, value| value.signif(3)}
- rsq = cv.r_squared.collect{|key, value| value.signif(3)}
- nrp = cv.nr_predictions.collect{|key,value| value}
- wpi = cv.within_prediction_interval.collect{|key,value| value}
- opi = cv.out_of_prediction_interval.collect{|key,value| value}
%table.table.table-borderless.table-responsive
%tr
%td.text-center
= "Nr.#{idx+1} | Num folds:#{cv.folds}"
%td.text-center
%a.card-link{:href=>"https://en.wikipedia.org/wiki/Root-mean-square_deviation", :rel=>"external"}
RMSE:
%td.text-center
%a.card-link{:href=>"https://en.wikipedia.org/wiki/Mean_absolute_error", :rel=>"external"}
MAE:
%td.text-center
%a.card-link{:href=>"https://en.wikipedia.org/wiki/Coefficient_of_determination", :rel=>"external"}= "R"+"<sup>2</sup>"+":"
%td.text-center Number of predictions:
%td.text-center Within prediction interval:
%td.text-center Out of prediction interval:
- keys.each_with_index do |key,idx|
%tr
%td.text-right= key.gsub("_", " ")+":"
/ rsme:
%td.text-center= rmse[idx]
/ mae:
%td.text-center= mae[idx]
/ r_squared:
%td.text-center= rsq[idx]
/ Predictions number:
%td.text-center= nrp[idx]
/ Within prediction interval:
%td.text-center= wpi[idx]
/ Out of prediction interval:
%td.text-center= opi[idx]
- unless idx == 4
%hr
%div.card.bg-light
%div.card-body
%h6.card-title QMRF:
%a.btn.btn-outline-info{:href=>"#{to("/predict/report/#{model.id}")}", :id=>"report#{model.id}", :style=>"font-size:small;"}
%span.fa.fa-download
XML
%br
|
