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=begin
* Name: lazar.rb
* Description: Lazar model representation
* Author: Andreas Maunz <andreas@maunz.de>, Christoph Helma
* Date: 10/2012
=end
module OpenTox
module Model
class Lazar
include OpenTox
attr_accessor :prediction_dataset
# Check parameters for plausibility
# Prepare lazar object (includes graph mining)
# @param[Array] lazar parameters as strings
# @param[Hash] REST parameters, as input by user
def self.create params
lazar = OpenTox::Model::Lazar.new(File.join($model[:uri],SecureRandom.uuid))
training_dataset = OpenTox::Dataset.new(params[:dataset_uri])
lazar.parameters << {RDF::DC.title => "training_dataset_uri", RDF::OT.paramValue => training_dataset.uri}
if params[:prediction_feature]
resource_not_found_error "No feature '#{params[:prediction_feature]}' in dataset '#{params[:dataset_uri]}'" unless training_dataset.find_feature_uri( params[:prediction_feature] )
else # try to read prediction_feature from dataset
resource_not_found_error "Please provide a prediction_feature parameter" unless training_dataset.features.size == 1
params[:prediction_feature] = training_dataset.features.first.uri
end
lazar[RDF::OT.trainingDataset] = training_dataset.uri
prediction_feature = OpenTox::Feature.new(params[:prediction_feature])
predicted_variable = OpenTox::Feature.find_or_create({RDF::DC.title => "#{prediction_feature.title} prediction", RDF.type => [RDF::OT.Feature, prediction_feature[RDF.type]]})
lazar[RDF::DC.title] = prediction_feature.title
lazar.parameters << {RDF::DC.title => "prediction_feature_uri", RDF::OT.paramValue => prediction_feature.uri}
lazar[RDF::OT.dependentVariables] = prediction_feature.uri
bad_request_error "Unknown prediction_algorithm #{params[:prediction_algorithm]}" if params[:prediction_algorithm] and !OpenTox::Algorithm::Neighbors.respond_to?(params[:prediction_algorithm])
lazar.parameters << {RDF::DC.title => "prediction_algorithm", RDF::OT.paramValue => params[:prediction_algorithm]} if params[:prediction_algorithm]
confidence_feature = OpenTox::Feature.find_or_create({RDF::DC.title => "predicted_confidence", RDF.type => [RDF::OT.Feature, RDF::OT.NumericFeature]})
lazar[RDF::OT.predictedVariables] = [ predicted_variable.uri, confidence_feature.uri ]
case prediction_feature.feature_type
when "classification"
lazar.parameters << {RDF::DC.title => "prediction_algorithm", RDF::OT.paramValue => "weighted_majority_vote"} unless lazar.parameter_value "prediction_algorithm"
lazar[RDF.type] = [RDF::OT.Model, RDF::OTA.ClassificationLazySingleTarget]
when "regression"
lazar.parameters << {RDF::DC.title => "prediction_algorithm", RDF::OT.paramValue => "local_svm_regression"} unless lazar.parameter_value "prediction_algorithm"
lazar[RDF.type] = [RDF::OT.Model, RDF::OTA.RegressionLazySingleTarget]
end
lazar.parameter_value("prediction_algorithm") =~ /majority_vote/ ? lazar.parameters << {RDF::DC.title => "propositionalized", RDF::OT.paramValue => false} : lazar.parameters << {RDF::DC.title => "propositionalized", RDF::OT.paramValue => true}
lazar.parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => params[:min_sim].to_f} if params[:min_sim] and params[:min_sim].numeric?
lazar.parameters << {RDF::DC.title => "feature_generation_uri", RDF::OT.paramValue => params[:feature_generation_uri]}
#lazar.parameters["nr_hits"] = params[:nr_hits]
if params["feature_generation_uri"]=~/fminer/
if (params[:nr_hits] == "true")
lazar.parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => "smarts_count"}
else
lazar.parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => "smarts_match"}
end
lazar.parameters << {RDF::DC.title => "similarity_algorithm", RDF::OT.paramValue => "tanimoto"}
lazar.parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => 0.3} unless lazar.parameter_value("min_sim")
elsif params["feature_generation_uri"]=~/descriptor/ or params["feature_generation_uri"]==nil
if params["feature_generation_uri"]
method = params["feature_generation_uri"].split(%r{/}).last.chomp
lazar.parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => method}
end
# cosine similartiy is default (e.g. used when no fetature_generation_uri is given and a feature_dataset_uri is provided instead)
lazar.parameters << {RDF::DC.title => "similarity_algorithm", RDF::OT.paramValue => "cosine"}
lazar.parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => 0.7} unless lazar.parameter_value("min_sim")
else
bad_request_error "unnkown feature generation method #{params["feature_generation_uri"]}"
end
bad_request_error "Parameter min_train_performance is not numeric." if params[:min_train_performance] and !params[:min_train_performance].numeric?
lazar.parameters << {RDF::DC.title => "min_train_performance", RDF::OT.paramValue => params[:min_train_performance].to_f} if params[:min_train_performance] and params[:min_train_performance].numeric?
lazar.parameters << {RDF::DC.title => "min_train_performance", RDF::OT.paramValue => 0.1} unless lazar.parameter_value("min_train_performance")
if params[:feature_dataset_uri]
bad_request_error "Feature dataset #{params[:feature_dataset_uri]} does not exist." unless URI.accessible? params[:feature_dataset_uri]
lazar.parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => params[:feature_dataset_uri]}
lazar[RDF::OT.featureDataset] = params["feature_dataset_uri"]
else
# run feature generation algorithm
feature_dataset_uri = OpenTox::Algorithm::Generic.new(params[:feature_generation_uri]).run(params)
lazar.parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => feature_dataset_uri}
lazar[RDF::OT.featureDataset] = feature_dataset_uri
end
lazar.put
lazar.uri
end
def predict(params)
@prediction_dataset = OpenTox::Dataset.new
# set instance variables and prediction dataset parameters from parameters
params.each {|k,v|
self.class.class_eval { attr_accessor k.to_sym }
instance_variable_set "@#{k}", v
@prediction_dataset.parameters << {RDF::DC.title => k, RDF::OT.paramValue => v}
}
#["training_compounds", "fingerprints", "training_activities", "training_fingerprints", "query_fingerprint", "neighbors"].each {|k|
["training_compounds", "training_activities", "training_fingerprints", "query_fingerprint", "neighbors"].each {|k|
self.class.class_eval { attr_accessor k.to_sym }
instance_variable_set("@#{k}", [])
}
@prediction_feature = OpenTox::Feature.new @prediction_feature_uri
@predicted_variable = OpenTox::Feature.new @predicted_variable_uri
@predicted_confidence = OpenTox::Feature.new @predicted_confidence_uri
@prediction_dataset.metadata = {
RDF::DC.title => "Lazar prediction for #{@prediction_feature.title}",
RDF::DC.creator => @model_uri,
RDF::OT.hasSource => @model_uri,
RDF::OT.dependentVariables => @prediction_feature_uri,
RDF::OT.predictedVariables => [@predicted_variable_uri,@predicted_confidence_uri]
}
@training_dataset = OpenTox::Dataset.new(@training_dataset_uri)
@feature_dataset = OpenTox::Dataset.new(@feature_dataset_uri)
bad_request_error "No features found in feature dataset #{@feature_dataset.uri}." if @feature_dataset.features.empty?
@similarity_feature = OpenTox::Feature.find_or_create({RDF::DC.title => "#{@similarity_algorithm.capitalize} similarity", RDF.type => [RDF::OT.Feature, RDF::OT.NumericFeature]})
@prediction_dataset.features = [ @predicted_variable, @predicted_confidence, @prediction_feature, @similarity_feature ]
prediction_feature_pos = @training_dataset.features.collect{|f| f.uri}.index @prediction_feature.uri
if @dataset_uri
compounds = OpenTox::Dataset.new(@dataset_uri).compounds
else
compounds = [ OpenTox::Compound.new(@compound_uri) ]
end
# @training_fingerprints = @feature_dataset.data_entries
# select training fingerprints from feature dataset (do NOT use entire feature dataset)
feature_compound_uris = @feature_dataset.compounds.collect{|c| c.uri}
@training_fingerprints = []
@training_dataset.compounds.each do |c|
idx = feature_compound_uris.index(c.uri)
bad_request_error "training dataset compound not found in feature dataset" if idx==nil
@training_fingerprints << @feature_dataset.data_entries[idx][0..-1]
end
# fill trailing missing values with nil
@training_fingerprints = @training_fingerprints.collect do |values|
values << nil while (values.size < @feature_dataset.features.size)
values
end
@training_compounds = @training_dataset.compounds
internal_server_error "sth went wrong #{@training_compounds.size} != #{@training_fingerprints.size}" if @training_compounds.size != @training_fingerprints.size
feature_names = @feature_dataset.features.collect{ |f| f[RDF::DC.title] }
query_fingerprints = {}
# first lookup in feature dataset, than apply feature_generation_uri
compounds.each do |c|
idx = feature_compound_uris.index(c.uri) # just use first index, features should be equal for duplicates
if idx!=nil
fingerprint = {}
@feature_dataset.features.each do |f|
fingerprint[f[RDF::DC.title]] = @feature_dataset.data_entry_value(idx,f.uri)
end
query_fingerprints[c] = fingerprint
end
end
# if lookup failed, try computing!
if query_fingerprints.size!=compounds.size
bad_request_error "no feature_generation_uri provided in model AND cannot lookup all test compounds in existing feature dataset" unless @feature_calculation_algorithm
query_fingerprints = OpenTox::Algorithm::Descriptor.send( @feature_calculation_algorithm, compounds, feature_names )#.collect{|row| row.collect{|val| val ? val.to_f : 0.0 } }
end
# AM: transform to cosine space
@min_sim = (@min_sim.to_f*2.0-1.0).to_s if @similarity_algorithm =~ /cosine/
compounds.each_with_index do |compound,c_count|
$logger.debug "predict compound #{c_count+1}/#{compounds.size} #{compound.uri}"
database_activities = @training_dataset.values(compound,@prediction_feature)
if database_activities and !database_activities.empty?
database_activities.each do |database_activity|
$logger.debug "do not predict compound, it occurs in dataset with activity #{database_activity}"
@prediction_dataset << [compound, nil, nil, database_activity, nil]
end
next
elsif @prediction_dataset.compound_indices(compound.uri)
$logger.debug "compound already predicted (copy old prediction)"
predicted_value = @prediction_dataset.data_entry_value(@prediction_dataset.compound_indices(compound.uri).first,@predicted_variable.uri)
confidence_value = @prediction_dataset.data_entry_value(@prediction_dataset.compound_indices(compound.uri).first,@predicted_confidence.uri)
else
@training_activities = @training_dataset.data_entries.collect{|entry|
act = entry[prediction_feature_pos] if entry
@prediction_feature.feature_type=="classification" ? @prediction_feature.value_map.invert[act] : act
}
@query_fingerprint = @feature_dataset.features.collect { |f|
val = query_fingerprints[compound][f.title]
bad_request_error "Can not parse value '#{val}' to numeric" if val and !val.numeric?
val ? val.to_f : 0.0
} # query structure
mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(self)
mtf.transform
prediction = OpenTox::Algorithm::Neighbors.send(@prediction_algorithm,
{ :props => mtf.props,
:activities => mtf.activities,
:sims => mtf.sims,
:value_map => @prediction_feature.feature_type=="classification" ? @prediction_feature.value_map : nil,
:min_train_performance => @min_train_performance
} )
predicted_value = prediction[:prediction]#.to_f
confidence_value = prediction[:confidence]#.to_f
# AM: transform to original space
confidence_value = ((confidence_value+1.0)/2.0).abs if @similarity_algorithm =~ /cosine/
predicted_value = @prediction_feature.value_map[prediction[:prediction].to_i] if @prediction_feature.feature_type == "classification"
$logger.debug "predicted value: #{predicted_value}, confidence: #{confidence_value}"
end
@prediction_dataset << [ compound, predicted_value, confidence_value, nil, nil ]
if @compound_uri # add neighbors only for compound predictions
@neighbors.each do |neighbor|
n = neighbor[:compound]
@prediction_feature.feature_type == "classification" ? a = @prediction_feature.value_map[neighbor[:activity]] : a = neighbor[:activity]
@prediction_dataset << [ n, nil, nil, a, neighbor[:similarity] ]
end
end
end # iteration over compounds
@prediction_dataset.put
@prediction_dataset
end
end
end
end
|
