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require "lib/prediction_data.rb"
require "lib/predictions.rb"
module Lib
class OTPredictions < Predictions
def initialize(data, compounds=nil)
raise unless data.is_a?(Hash)
super(data)
@compounds = compounds
end
def identifier(instance_index)
compound(instance_index)
end
def compound(instance_index)
@compounds[instance_index]
end
def compounds()
@compounds
end
def compute_stats()
res = {}
case feature_type
when "classification"
(Validation::VAL_CLASS_PROPS).each{ |s| res[s] = send(s)}
when "regression"
(Validation::VAL_REGR_PROPS).each{ |s| res[s] = send(s) }
end
return res
end
def to_array()
OTPredictions.to_array( [self] )
end
def self.to_array( predictions, add_pic=false, format=false, validation_uris=nil )
confidence_available = false
predictions.each do |p|
confidence_available |= p.confidence_values_available?
end
res = []
conf_column = nil
count = 0
predictions.each do |p|
v_uris = validation_uris[count] if validation_uris
count += 1
cmpds_mw = {}
ds = OpenTox::Dataset.new()
ds.save
# Get MW of compounds
p.compounds.each do |c_uri|
ds.add_compound(c_uri)
ds.save
end
mw_algorithm_uri = File.join(CONFIG[:services]["opentox-algorithm"],"pc/MW")
mw_uri = OpenTox::RestClientWrapper.post(mw_algorithm_uri, {:dataset_uri=>ds.uri})
mw_ds = OpenTox::Dataset.find(mw_uri)
p.compounds.each do |c_uri|
cmpds_mw[c_uri] = mw_ds.data_entries[c_uri][mw_uri.to_s + "/feature/MW"].first
end
mw_ds.delete
ds.delete
# Get prediction feature
val = OpenTox::Validation.find(v_uris.first)
p_feature = val.metadata[OT::predictionFeature].to_s.split("/").last
(0..p.num_instances-1).each do |i|
a = []
#PENDING!
begin
#a.push( "http://ambit.uni-plovdiv.bg:8080/ambit2/depict/cdk?search="+
# URI.encode(OpenTox::Compound.new(:uri=>p.identifier(i)).smiles) ) if add_pic
a << p.identifier(i)+"?media=image/png"
rescue => ex
raise ex
#a.push("Could not add pic: "+ex.message)
#a.push(p.identifier(i))
end
if cmpds_mw[p.identifier(i)].nil? || (p.feature_type=="classification")
a << (format ? p.actual_value(i).to_nice_s : p.actual_value(i))
a << (format ? p.predicted_value(i).to_nice_s : p.predicted_value(i))
else # Converting values to mg values if found in prediction feature name
if p.actual_value(i).nil?
a << (format ? p.actual_value(i).to_nice_s : p.actual_value(i))
else
if p_feature.downcase.include? "ptd50"
td50 = (((10**(-1.0*p.actual_value(i)))*(cmpds_mw[p.identifier(i)].to_f*1000))*1000).round / 1000.0
a << (format ? "pTD50: " + p.actual_value(i).to_nice_s + "\n[TD50: " + td50.to_s.chomp + "]" : p.actual_value(i))
elsif p_feature.downcase.include? "mol"
mg = (((10**(-1.0*p.actual_value(i)))*(cmpds_mw[p.identifier(i)].to_f*1000))*1000).round / 1000.0
a << (format ? "log mol/kg bw/day: " + p.actual_value(i).to_nice_s + "\n[mg/kg bw/day: " + mg.to_s.chomp + "]": p.actual_value(i))
elsif p_feature.downcase.include? "mg"
mg = ((10**p.actual_value(i))*1000).round / 1000.0
a << (format ? "log mg/kg bw/day: " + p.actual_value(i).to_nice_s + "\n[mg/kg bw/day: " + mg.to_s.chomp + "]": p.actual_value(i))
else
a << (format ? p.actual_value(i).to_nice_s : p.actual_value(i))
end
end
if p.predicted_value(i).nil?
a << (format ? p.predicted_value(i).to_nice_s : p.predicted_value(i))
else
if p_feature.downcase.include? "ptd50"
td50 = (((10**(-1.0*p.predicted_value(i)))*(cmpds_mw[p.identifier(i)].to_f*1000))*1000).round / 1000.0
a << (format ? "pTD50: " + p.predicted_value(i).to_nice_s + "\n[TD50: " + td50.to_s.chomp + "]": p.predicted_value(i))
elsif p_feature.downcase.include? "mol"
mg = (((10**(-1.0*p.predicted_value(i)))*(cmpds_mw[p.identifier(i)].to_f*1000))*1000).round / 1000.0
a << (format ? "log mol/kg bw/day: " + p.predicted_value(i).to_nice_s + "\n[mg/kg bw/day: " + mg.to_s.chomp + "]": p.predicted_value(i))
elsif p_feature.downcase.include? "mg"
mg = ((10**p.predicted_value(i))*1000).round / 1000.0
a << (format ? "log mg/kg bw/day: " + p.predicted_value(i).to_nice_s + "\n[mg/kg bw/day: " + mg.to_s.chomp + "]": p.predicted_value(i))
else
a << (format ? p.predicted_value(i).to_nice_s : p.predicted_value(i))
end
end
end
if p.feature_type=="classification"
if (p.predicted_value(i)!=nil and p.actual_value(i)!=nil)
if p.classification_miss?(i)
a << (format ? ICON_ERROR : 1)
else
a << (format ? ICON_OK : 0)
end
else
a << nil
end
end
if confidence_available
conf_column = a.size if conf_column==nil
a << p.confidence_value(i)
end
if validation_uris
a << v_uris[i]
end
a << p.identifier(i)
# Get chemical names and add pubchem_iupac_name
c = OpenTox::Compound.new(p.identifier(i))
c_names = c.to_names_hash
a << c_names["pubchem_iupac_name"]
a << cmpds_mw[p.identifier(i)]
res << a
end
end
if conf_column!=nil
LOGGER.debug "sort via confidence: "+res.collect{|n| n[conf_column]}.inspect
res = res.sort_by{ |n| n[conf_column] || 0 }.reverse
if format
res.each do |a|
a[conf_column] = a[conf_column].to_nice_s
end
end
end
header = []
header << "Compound" if add_pic
header << "Actual value"
header << "Predicted value"
header << "Classification" if predictions[0].feature_type=="classification"
header << "Confidence value" if predictions[0].confidence_values_available?
header << "Validation URI" if validation_uris
header << "Compound URI"
header << "Chemical name (pubchem_iupac_name)"
header << "Molecular weight (openbabel)"
res.insert(0, header)
return res
end
end
end
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