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-rw-r--r--application.rb321
1 files changed, 194 insertions, 127 deletions
diff --git a/application.rb b/application.rb
index 70eecb5..a4f632a 100644
--- a/application.rb
+++ b/application.rb
@@ -71,10 +71,173 @@ get '/predict/dataset/:name' do
csv
end
-get '/predict/:tmppath/:filename/?' do
+get '/predict/:tmppath/:model/:filename?' do
response['Content-Type'] = "text/csv"
- path = "/tmp/#{params[:tmppath]}"
- send_file path, :filename => "lazar_batch_prediction_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
+ path = File.join("tmp", params[:tmppath])
+ `sort -gk1 #{path} -o #{path}`
+
+ send_file path, :filename => "#{Time.now.strftime("%Y-%m-%d")}_lazar_batch_prediction_#{params[:model]}_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
+end
+
+get '/batch/:model/' do
+
+ if params[:model] == "Cramer"
+ dataset = Dataset.find params[:dataset]
+ compounds = dataset.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
+
+ # td paths to insert results in GUI
+ compound_ids = dataset.compounds.collect{|c| c.id}
+ output["tds"] = compound_ids.each_with_index.map{|cid,idx| "prediction_#{cid}_Cramer_#{idx}"}
+
+ # write to file
+ # 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
+ File.open(File.join("tmp", params[:tmppath]),"a+"){|file| file.write(csv)}
+
+ # cleanup
+ dataset.delete
+
+ # return output
+ response['Content-Type'] = "application/json"
+ return JSON.pretty_generate output
+
+ else
+ idx = params[:idx].to_i
+ compound = Compound.find params[:compound]
+
+ model = Model::Validation.find params[:model]
+ prediction = model.predict(compound)
+ output = {}
+ 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[:neighbors] ? "yes" : "no"
+ inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "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].uniq.join(" ")}" + ( prediction[:info] ? "#{prediction[:info]}\"" : "\"" )
+
+ 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
+
+ csv = "#{idx+1},#{output['model_name']},#{output['model_type']},#{compound.smiles},"\
+ "#{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},#{inT},#{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].flatten.first 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
+
+ csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},"\
+ "#{output['sa_prediction']['prediction'] == false ? "non-mutagenic" : "mutagenic"},"\
+ "#{output['confidence']},#{output['sa_matches'] != false ? "\"#{output['sa_matches']}\"" : "none"}, ,"\
+ "#{output['model_type']},#{output['prediction_value']},"\
+ "#{output['probability'][0] != false ? output['probability'][0].split(":").last : ""},"\
+ "#{output['probability'][1] != false ? output['probability'][1].split(":").last : ""},"\
+ "#{inApp},#{inT},#{note.nil? ? "" : note}\n"
+
+ end
+
+ output["warnings"] = prediction[:warnings] if prediction[:warnings]
+
+ else #no prediction value
+ inApp = "no"
+ inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "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(" ")}\"" + ( prediction[:info] ? "\"#{prediction[:info]}\"" : "" )
+
+ output["warnings"] = prediction[:warnings]
+ output["info"] = prediction[:info] if prediction[:info]
+
+ if type == "Regression"
+ csv = "#{idx+1},#{output['model_name']},#{output['model_type']},#{compound.smiles},,,,,,,"+ [inApp,inT,note].join(",")+"\n"
+ else
+ csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},,,,,#{output['model_type']},,,,"+ [inApp,inT,note].join(",")+"\n"
+ end
+
+ end #prediction value
+
+ # write to file
+ File.open(File.join("tmp", params[:tmppath]),"a"){|file| file.write(csv)}
+
+ # return output
+ response['Content-Type'] = "application/json"
+ return JSON.pretty_generate output
+
+ end# if Cramer
end
post '/predict/?' do
@@ -90,152 +253,56 @@ post '/predict/?' do
@filename = params[:fileselect][:filename]
begin
input = Dataset.from_csv_file File.join("tmp", params[:fileselect][:filename]), true
+ $logger.debug "save dataset #{params[:fileselect][:filename]}"
if input.class == OpenTox::Dataset
- dataset = Dataset.find input
+ @dataset = Dataset.find input
+ @compounds = @dataset.compounds
else
bad_request_error "Could not serialize file '#{@filename}'."
end
rescue
bad_request_error "Could not serialize file '#{@filename}'."
end
- @compounds = dataset.compounds
+
if @compounds.size == 0
message = dataset[:warnings]
- dataset.delete
+ @dataset.delete
bad_request_error message
end
-
- # for csv export
- @batch = {}
- # for haml table
- @view = {}
-
- @compounds.each{|c| @view[c] = []}
- params[:selection].keys.each do |model_id|
- model = Model::Validation.find model_id
- @batch[model] = []
- @compounds.each_with_index do |compound,idx|
- prediction = model.predict(compound)
- @batch[model] << [compound, prediction]
- @view[compound] << [model,prediction]
- end
- end
-
- @csvhash = {}
- @warnings = dataset[:warnings]
- dupEntries = {}
- delEntries = ""
- # split duplicates and deleted entries
- @warnings.each do |w|
- substring = w.match(/line .* of/)
- unless substring.nil?
- delEntries += "\"#{w.sub(/\b(tmp\/)\b/,"")}\"\n"
+ @models = params[:selection].keys
+ @tmppaths = {}
+ @models.each do |model|
+ m = Model::Validation.find model
+ type = (m.regression? ? "Regression" : "Classification") unless model == "Cramer"
+ # add header for regression
+ if type == "Regression"
+ unit = (type == "Regression") ? "(#{m.unit})" : ""
+ converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
+ header = "ID,Endpoint,Type,Unique SMILES,Prediction #{unit},Prediction #{converted_unit},"\
+ "Interval Low #{unit},Interval High #{unit},Interval Low #{converted_unit},Interval High #{converted_unit},"\
+ "inApplicabilityDomain,inTrainningSet,Note\n"
end
- substring = w.match(/rows .* Entries/)
- unless substring.nil?
- lines = []
- substring[0].split(",").each{|s| lines << s[/\d+/]}
- lines.shift
- lines.each{|l| dupEntries[l.to_i] = w.split(".").first}
+ # add header for classification
+ if type == "Classification"
+ av = m.prediction_feature.accept_values
+ header = "ID,Endpoint,Unique SMILES,Structural alerts prediction,Structural alerts confidence,"\
+ "Structural alerts for mutagenicity,Lazar mutagenicity (Salmonella typhimurium),Type,Prediction,"\
+ "predProbability #{av[0]},predProbability #{av[1]},inApplicabilityDomain,inTrainningSet,Note\n"
end
+ path = File.join("tmp", "#{Time.now.strftime("%Y-%m-%d")}_#{SecureRandom.urlsafe_base64(5)}")
+ File.open(path, "w"){|f| f.write(header) if header}
+ @tmppaths[model] = path.split("/").last
end
- @batch.each_with_index do |hash, idx|
- @csvhash[idx] = ""
- model = hash[0]
- # create header
- if model.regression?
- predAunit = "(#{model.unit})"
- predBunit = "(#{model.unit =~ /mmol\/L/ ? "(mol/L)" : "(mg/kg_bw/day)"})"
- @csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction #{predAunit}\",\"Prediction #{predBunit}\",\"95% Prediction interval (low) #{predAunit}\",\"95% Prediction interval (high) #{predAunit}\",\"95% Prediction interval (low) #{predBunit}\",\"95% Prediction interval (high) #{predBunit}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
- else #classification
- av = model.prediction_feature.accept_values
- probFirst = av[0].capitalize
- probLast = av[1].capitalize
- @csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction\",\"predProbability#{probFirst}\",\"predProbability#{probLast}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
- end
- values = hash[1]
- dupEntries.keys.each{|k| values.insert(k-1, dupEntries[k])}.compact!
-
- values.each_with_index do |array, id|
- type = (model.regression? ? "Regression" : "Classification")
- endpoint = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
-
- if id == 0
- @csvhash[idx] += delEntries unless delEntries.blank?
- end
- unless array.kind_of? String
- compound = array[0]
- prediction = array[1]
- smiles = compound.smiles
-
- if prediction[:neighbors]
- if prediction[:value]
- pred = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
- predA = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
- predAunit = prediction[:value].numeric? ? "(#{model.unit})" : ""
- predB = prediction[:value].numeric? ? "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" : prediction[:value]
- predBunit = prediction[:value].numeric? ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
- int = (prediction[:prediction_interval].nil? ? nil : prediction[:prediction_interval])
- intervalLow = (int.nil? ? "" : "#{int[1].delog10.signif(3)}")
- intervalHigh = (int.nil? ? "" : "#{int[0].delog10.signif(3)}")
- intervalLowMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[1].delog10).signif(3)}")
- intervalHighMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[0].delog10).signif(3)}")
- inApp = "yes"
- inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
- note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
-
- unless prediction[:probabilities].nil?
- av = model.prediction_feature.accept_values
- propA = "#{prediction[:probabilities][av[0]].to_f.signif(3)}"
- propB = "#{prediction[:probabilities][av[1]].to_f.signif(3)}"
- end
- else
- # no prediction value only one neighbor
- inApp = "no"
- inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
- note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
- end
- else
- # no prediction value
- inApp = "no"
- inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
- note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
- end
- if @warnings
- @warnings.each do |w|
- note += (w.split(".").first + ".") if /\b(#{Regexp.escape(smiles)})\b/ === w
- end
- end
- else
- # string note for duplicates
- endpoint = type = smiles = pred = predA = predB = propA = propB = intervalLow = intervalHigh = intervalLowMg = intervalHighMg = inApp = inT = ""
- note = array
- end
- if model.regression?
- @csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{predA}\",\"#{predB}\",\"#{intervalLow}\",\"#{intervalHigh}\",\"#{intervalLowMg}\",\"#{intervalHighMg}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
- else
- @csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{pred}\",\"#{propA}\",\"#{propB}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
- end
- end
- end
- t = Tempfile.new
- @csvhash.each do |model, csv|
- t.write(csv)
- t.write("\n")
- end
- t.rewind
- @tmppath = t.path.split("/").last
-
- dataset.delete
File.delete File.join("tmp", params[:fileselect][:filename])
return haml :batch
end
+ # single compound prediction
# validate identifier input
if !params[:identifier].blank?
- @identifier = params[:identifier]
+ @identifier = params[:identifier].strip
$logger.debug "input:#{@identifier}"
# get compound from SMILES
@compound = Compound.from_smiles @identifier
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-02 08:56:32 +0000