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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
class LazarPrediction < Model
attr_accessor :prediction_dataset
def initialize(params)
@prediction_dataset = OpenTox::Dataset.new(nil, @subjectid)
# 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}
}
["cmpds", "fps", "acts", "n_prop", "q_prop", "neighbors"].each {|k|
self.class.class_eval { attr_accessor k.to_sym }
instance_variable_set("@#{k}", [])
}
@prediction_feature = OpenTox::Feature.new @prediction_feature_uri, @subjectid
@predicted_variable = OpenTox::Feature.new @predicted_variable_uri, @subjectid
@predicted_confidence = OpenTox::Feature.new @predicted_confidence_uri, @subjectid
@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,@subjectid)
@feature_dataset = OpenTox::Dataset.new(@feature_dataset_uri, @subjectid)
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]}, @subjectid)
@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, @subjectid).compounds
else
compounds = [ OpenTox::Compound.new(@compound_uri, @subjectid) ]
end
compounds.each do |compound|
#database_activity = @training_dataset.database_activity(params)
database_activities = @training_dataset.values(compound,@prediction_feature)
if database_activities and !database_activities.empty?
database_activities.each do |database_activity|
@prediction_dataset.add_data_entry compound, @prediction_feature, database_activity
end
next
else
# AM: transform to cosine space
@min_sim = (@min_sim.to_f*2.0-1.0).to_s if @similarity_algorithm =~ /cosine/
compound_params = {
:compound => compound,
:feature_dataset => @feature_dataset,
}
#compound_fingerprints = OpenTox::Algorithm::FeatureValues.send( @feature_calculation_algorithm, compound_params, @subjectid )
# TODO: fix for pc descriptors
#compound_fingerprints = OpenTox::Algorithm::Descriptor.send( @feature_calculation_algorithm, compound, @feature_dataset.features.collect{ |f| f[RDF::DC.title] } )
compound_fingerprints = eval("#{@feature_calculation_algorithm}(compound, @feature_dataset.features.collect{ |f| f[RDF::DC.title] } )")
@training_dataset.compounds.each_with_index { |cmpd, idx|
act = @training_dataset.data_entries[idx][prediction_feature_pos]
@acts << (@prediction_feature.feature_type=="classification" ? @prediction_feature.value_map.invert[act] : nil)
@n_prop << @feature_dataset.data_entries[idx]#.collect.to_a
@cmpds << cmpd.uri
}
=begin
@q_prop = @feature_dataset.features.collect { |f|
val = compound_fingerprints[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
=end
@q_prop = compound_fingerprints.first.collect{|v| v.to_f}
mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(self)
mtf.transform
prediction = OpenTox::Algorithm::Neighbors.send(@prediction_algorithm,
{ :props => mtf.props,
:acts => mtf.acts,
: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"
end
@prediction_dataset.add_data_entry compound, predicted_variable, predicted_value
@prediction_dataset.add_data_entry compound, predicted_confidence, confidence_value
if @compound_uri # add neighbors only for compound predictions
@neighbors.each do |neighbor|
n = OpenTox::Compound.new(neighbor[:compound], @subjectid)
@prediction_dataset.add_data_entry n, @prediction_feature, @prediction_feature.value_map[neighbor[:activity]]
@prediction_dataset.add_data_entry n, @similarity_feature, neighbor[:similarity]
#@prediction_dataset << [ n, @prediction_feature.value_map[neighbor[:activity]], nil, nil, neighbor[:similarity] ]
end
end
end # iteration over compounds
@prediction_dataset.put
end
end
class Model
class Lazar < Model
# 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 create(params)
training_dataset = OpenTox::Dataset.new(params[:dataset_uri], @subjectid)
@parameters << {RDF::DC.title => "training_dataset_uri", RDF::OT.paramValue => training_dataset.uri}
# TODO: This is inconsistent, it would be better to have prediction_feature_uri in the API
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
self[RDF::OT.trainingDataset] = training_dataset.uri
prediction_feature = OpenTox::Feature.new(params[:prediction_feature], @subjectid)
predicted_variable = OpenTox::Feature.find_or_create({RDF::DC.title => "#{prediction_feature.title} prediction", RDF.type => [RDF::OT.Feature, prediction_feature[RDF.type]]}, @subjectid)
self[RDF::DC.title] = prediction_feature.title
@parameters << {RDF::DC.title => "prediction_feature_uri", RDF::OT.paramValue => prediction_feature.uri}
self[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])
@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]}, @subjectid)
self[RDF::OT.predictedVariables] = [ predicted_variable.uri, confidence_feature.uri ]
case prediction_feature.feature_type
when "classification"
@parameters << {RDF::DC.title => "prediction_algorithm", RDF::OT.paramValue => "weighted_majority_vote"} unless parameter_value "prediction_algorithm"
self[RDF.type] = [RDF::OT.Model, RDF::OTA.ClassificationLazySingleTarget]
when "regression"
@parameters << {RDF::DC.title => "prediction_algorithm", RDF::OT.paramValue => "local_svm_regression"} unless parameter_value "prediction_algorithm"
self[RDF.type] = [RDF::OT.Model, RDF::OTA.RegressionLazySingleTarget]
end
parameter_value("prediction_algorithm") =~ /majority_vote/ ? @parameters << {RDF::DC.title => "propositionalized", RDF::OT.paramValue => false} : @parameters << {RDF::DC.title => "propositionalized", RDF::OT.paramValue => true}
@parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => params[:min_sim].to_f} if params[:min_sim] and params[:min_sim].numeric?
@parameters << {RDF::DC.title => "feature_generation_uri", RDF::OT.paramValue => params[:feature_generation_uri]}
#@parameters["nr_hits"] = params[:nr_hits]
case params["feature_generation_uri"]
when /fminer/
if (params[:nr_hits] == "true")
@parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => "OpenTox::Descriptor::Smarts.count"}
else
@parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => "OpenTox::Descriptor::Smarts.fingerprint"}
end
@parameters << {RDF::DC.title => "similarity_algorithm", RDF::OT.paramValue => "tanimoto"}
@parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => 0.3} unless parameter_value("min_sim")
when /descriptor/
@parameters << {RDF::DC.title => "feature_calculation_algorithm", RDF::OT.paramValue => "lookup"}
@parameters << {RDF::DC.title => "similarity_algorithm", RDF::OT.paramValue => "cosine"}
@parameters << {RDF::DC.title => "min_sim", RDF::OT.paramValue => 0.7} unless parameter_value("min_sim")
end
bad_request_error "Parameter min_train_performance is not numeric." if params[:min_train_performance] and !params[:min_train_performance].numeric?
@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?
@parameters << {RDF::DC.title => "min_train_performance", RDF::OT.paramValue => 0.1} unless 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]
@parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => params[:feature_dataset_uri]}
self[RDF::OT.featureDataset] = params["feature_dataset_uri"]
else
# run feature generation algorithm
feature_dataset_uri = OpenTox::Algorithm.new(params[:feature_generation_uri]).run(params)
@parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => feature_dataset_uri}
self[RDF::OT.featureDataset] = feature_dataset_uri
end
put
@uri
<<<<<<< HEAD
=======
if params[:feature_dataset_uri]
bad_request_error "Feature dataset #{params[:feature_dataset_uri]} does not exist." unless URI.accessible? params[:feature_dataset_uri], @subjectid
@parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => params[:feature_dataset_uri]}
self[RDF::OT.featureDataset] = params["feature_dataset_uri"]
else
# run feature generation algorithm
feature_dataset_uri = OpenTox::Algorithm.new(params[:feature_generation_uri], @subjectid).run(params)
@parameters << {RDF::DC.title => "feature_dataset_uri", RDF::OT.paramValue => feature_dataset_uri}
self[RDF::OT.featureDataset] = feature_dataset_uri
>>>>>>> ad386110267ecc3e0c5301769b4880a7e555a44e
end
end
end
end
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