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helpers do
def embedded_svg image, options={}
doc = Nokogiri::HTML::DocumentFragment.parse image
svg = doc.at_css 'svg'
title = doc.at_css 'title'
if options[:class].present?
svg['class'] = options[:class]
end
if options[:title].present?
title.children.remove
text_node = Nokogiri::XML::Text.new(options[:title], doc)
title.add_child(text_node)
end
doc.to_html.html_safe
end
def to_csv(m,predictions,compounds)
model = (m != "Cramer" ? Model::Validation.find(m.to_s) : "Cramer")
csv = ""
if model == "Cramer"
compounds = compounds.collect{|c| c.smiles}
prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
output = {}
output["model_name"] = "Oral toxicity (Cramer rules)"
output["model_type"] = false
output["model_unit"] = false
["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
output["#{key}"] = false
end
output["toxtree"] = true
output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
# header
csv = "ID,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
compounds.each_with_index do |smiles, idx|
csv << "#{idx+1},#{output["model_name"]},#{smiles},"\
"#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
"#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
end
else
output = {}
predictions.each_with_index do |prediction,idx|
compound = compounds[idx]
line = ""
output["model_name"] = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
output["model_type"] = model.model.class.to_s.match("Classification") ? type = "Classification" : type = "Regression"
output["model_unit"] = (type == "Regression") ? "(#{model.unit})" : ""
output["converted_model_unit"] = (type == "Regression") ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
output["#{key}"] = false
end
if prediction[:value]
inApp = (prediction[:warnings].join(" ") =~ /Cannot/ ? "no" : (prediction[:warnings].join(" ") =~ /may|Insufficient/ ? "maybe" : "yes"))
if prediction[:info] =~ /\b(identical)\b/i
prediction[:info] = "This compound was part of the training dataset. All information "\
"from this compound was removed from the training data before the "\
"prediction, to obtain unbiased results."
end
note = "\"#{prediction[:warnings].uniq.join(" ")}\""
output["prediction_value"] = (type == "Regression") ? "#{prediction[:value].delog10.signif(3)}" : "#{prediction[:value]}"
output["converted_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" if type == "Regression"
output["db_hit"] = prediction[:info] if prediction[:info]
if prediction[:measurements].is_a?(Array)
output["measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{model.unit})"} : prediction[:measurements].collect{|value| "#{value}"}
output["converted_measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{model.unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} : false
else
output["measurements"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{model.unit})}" : "#{prediction[:measurements]}"
output["converted_measurements"] = (type == "Regression") ? "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(model.unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : false
end #db_hit
if type == "Regression"
if !prediction[:prediction_interval].nil?
interval = prediction[:prediction_interval]
output['interval'] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)}"
output['converted_interval'] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)}"
end #prediction interval
line += "#{idx+1},#{output['model_name']},#{compound.smiles},"\
"\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
"#{output['prediction_value'] != false ? output['prediction_value'] : ""},"\
"#{output['converted_value'] != false ? output['converted_value'] : ""},"\
"#{output['interval'].split(" - ").first.strip unless output['interval'] == false},"\
"#{output['interval'].split(" - ").last.strip unless output['interval'] == false},"\
"#{output['converted_interval'].split(" - ").first.strip unless output['converted_interval'] == false},"\
"#{output['converted_interval'].split(" - ").last.strip unless output['converted_interval'] == false},"\
"#{inApp},#{note.nil? ? "" : note.chomp}\n"
else # Classification
# consensus mutagenicity
sa_prediction = KaziusAlerts.predict(compound.smiles)
lazar_mutagenicity = prediction
confidence = 0
lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
confidence = 0.85
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
confidence = 0.11
elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
end
output['sa_prediction'] = sa_prediction
output['sa_matches'] = sa_prediction[:matches].collect{|a| a.first}.join("; ") unless sa_prediction[:matches].blank?
output['confidence'] = confidence.signif(3)
output['model_name'] = "Lazar #{model.endpoint.gsub('_', ' ').downcase} (#{model.species}):"
output['probability'] = prediction[:probabilities] ? prediction[:probabilities].collect{|k,v| "#{k}: #{v.signif(3)}"} : false
line += "#{idx+1},Consensus mutagenicity,#{compound.smiles},"\
"\"#{prediction[:info] ? prediction[:info] : "no"}\",\"#{prediction[:measurements].join("; ") if prediction[:info]}\","\
"#{sa_prediction[:prediction] == false ? "non-mutagenic" : "mutagenic"},"\
"#{output['confidence']},#{output['sa_matches'] != false ? "\"#{output['sa_matches']}\"" : "none"},"\
"#{output['prediction_value']},"\
"#{output['probability'][0] != false ? output['probability'][0].split(":").last : ""},"\
"#{output['probability'][1] != false ? output['probability'][1].split(":").last : ""},"\
"#{inApp},#{note.nil? ? "" : note}\n"
end
output["warnings"] = prediction[:warnings] if prediction[:warnings]
else #no prediction value
inApp = "no"
if prediction[:info] =~ /\b(identical)\b/i
prediction[:info] = "This compound was part of the training dataset. All information "\
"from this compound was removed from the training data before the "\
"prediction, to obtain unbiased results."
end
note = "\"#{prediction[:warnings].join(" ")}\""
output["warnings"] = prediction[:warnings]
output["info"] = prediction[:info] if prediction[:info]
if type == "Regression"
line += "#{idx+1},#{output['model_name']},#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
"#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
else
line += "#{idx+1},Consensus mutagenicity,#{compound.smiles},#{prediction[:info] ? prediction[:info] : "no"},"\
"#{prediction[:measurements] if prediction[:info]},,,,,,,"+ [inApp,note].join(",")+"\n"
end
end
csv += line
end
$logger.debug csv
csv
end
end
end
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