diff options
Diffstat (limited to 'lib/lazar.rb')
| -rw-r--r-- | lib/lazar.rb | 166 |
1 files changed, 112 insertions, 54 deletions
diff --git a/lib/lazar.rb b/lib/lazar.rb index 2bb89cd..b56a747 100644 --- a/lib/lazar.rb +++ b/lib/lazar.rb @@ -13,34 +13,26 @@ module OpenTox field :creator, type: String, default: __FILE__ # datasets field :training_dataset_id, type: BSON::ObjectId - field :feature_dataset_id, type: BSON::ObjectId # algorithms - field :feature_calculation_algorithm, type: String field :prediction_algorithm, type: String - field :similarity_algorithm, type: String - field :min_sim, type: Float + field :neighbor_algorithm, type: String + field :neighbor_algorithm_parameters, type: Hash # prediction feature field :prediction_feature_id, type: BSON::ObjectId attr_accessor :prediction_dataset attr_accessor :training_dataset - attr_accessor :feature_dataset - attr_accessor :query_fingerprint - attr_accessor :neighbors # Create a lazar model from a training_dataset and a feature_dataset # @param [OpenTox::Dataset] training_dataset - # @param [OpenTox::Dataset] feature_dataset # @return [OpenTox::Model::Lazar] Regression or classification model - def self.create training_dataset, feature_dataset + def self.create training_dataset - bad_request_error "No features found in feature dataset #{feature_dataset.id}." if feature_dataset.features.empty? bad_request_error "More than one prediction feature found in training_dataset #{training_dataset.id}" unless training_dataset.features.size == 1 - bad_request_error "Training dataset compounds do not match feature dataset compounds. Please ensure that they are in the same order." unless training_dataset.compounds == feature_dataset.compounds + # TODO document convention prediction_feature = training_dataset.features.first prediction_feature.nominal ? lazar = OpenTox::Model::LazarClassification.new : lazar = OpenTox::Model::LazarRegression.new - lazar.feature_dataset_id = feature_dataset.id lazar.training_dataset_id = training_dataset.id lazar.prediction_feature_id = prediction_feature.id lazar.title = prediction_feature.title @@ -54,6 +46,105 @@ module OpenTox t = Time.now at = Time.now + training_dataset = Dataset.find training_dataset_id + prediction_feature = Feature.find prediction_feature_id + + # parse data + compounds = [] + case object.class.to_s + when "OpenTox::Compound" + compounds = [object] + when "Array" + compounds = object + when "OpenTox::Dataset" + compounds = object.compounds + else + bad_request_error "Please provide a OpenTox::Compound an Array of OpenTox::Compounds or an OpenTox::Dataset as parameter." + end + + # make predictions + predictions = [] + compounds.each_with_index do |compound,c| + t = Time.new + neighbors = Algorithm.run(neighbor_algorithm, compound, neighbor_algorithm_parameters) + # add activities + # TODO: improve efficiency, takes 3 times longer than previous version + # TODO database activity?? + neighbors.collect! do |n| + rows = training_dataset.compound_ids.each_index.select{|i| training_dataset.compound_ids[i] == n.first} + acts = rows.collect{|row| training_dataset.data_entries[row][0]}.compact + acts.empty? ? nil : n << acts + end + neighbors.compact! # remove neighbors without training activities + predictions << Algorithm.run(prediction_algorithm, neighbors) + end + + # serialize result + case object.class.to_s + when "OpenTox::Compound" + return predictions.first + when "Array" + return predictions + when "OpenTox::Dataset" + # prepare prediction dataset + prediction_dataset = LazarPrediction.new( + :title => "Lazar prediction for #{prediction_feature.title}", + :creator => __FILE__, + :prediction_feature_id => prediction_feature.id + + ) + confidence_feature = OpenTox::NumericFeature.find_or_create_by( "title" => "Prediction confidence" ) + # TODO move into warnings field + warning_feature = OpenTox::NominalFeature.find_or_create_by("title" => "Warnings") + prediction_dataset.features = [ prediction_feature, confidence_feature, warning_feature ] + prediction_dataset.compounds = compounds + prediction_dataset.data_entries = predictions + prediction_dataset.save_all + return prediction_dataset + end + + end + + def training_activities + i = training_dataset.feature_ids.index prediction_feature_id + training_dataset.data_entries.collect{|de| de[i]} + end + + end + + class LazarClassification < Lazar + def initialize + super + self.prediction_algorithm = "OpenTox::Algorithm::Classification.weighted_majority_vote" + self.neighbor_algorithm = "OpenTox::Algorithm::Neighbor.fingerprint_similarity" + self.neighbor_algorithm_parameters = {:min_sim => 0.7} + end + end + + class LazarFminerClassification < LazarClassification + field :feature_dataset_id, type: BSON::ObjectId + field :feature_calculation_algorithm, type: String + + def self.create training_dataset + model = super(training_dataset) + model.update "_type" => self.to_s # adjust class + model = self.find model.id # adjust class + model.neighbor_algorithm = "OpenTox::Algorithm::Neighbor.fminer_similarity" + model.neighbor_algorithm_parameters = { + :feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.smarts_match", + :feature_dataset_id => Algorithm::Fminer.bbrc(training_dataset).id, + :min_sim => 0.3 + } + model.save + model + end + +=begin + def predict object + + t = Time.now + at = Time.now + @training_dataset = OpenTox::Dataset.find(training_dataset_id) @feature_dataset = OpenTox::Dataset.find(feature_dataset_id) @@ -98,17 +189,9 @@ module OpenTox next else - if prediction_algorithm =~ /Regression/ - mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(self) - mtf.transform - @training_fingerprints = mtf.n_prop - query_fingerprint = mtf.q_prop - neighbors = [[nil,nil,nil,query_fingerprint]] - else - #training_fingerprints = @feature_dataset.data_entries - query_fingerprint = @query_fingerprint[c] - neighbors = [] - end + #training_fingerprints = @feature_dataset.data_entries + query_fingerprint = @query_fingerprint[c] + neighbors = [] tt += Time.now-t t = Time.new @@ -146,7 +229,7 @@ module OpenTox # AM: transform to original space (TODO) - confidence_value = ((confidence_value+1.0)/2.0).abs if prediction.first and similarity_algorithm =~ /cosine/ + #confidence_value = ((confidence_value+1.0)/2.0).abs if prediction.first and similarity_algorithm =~ /cosine/ $logger.debug "predicted value: #{prediction[:value]}, confidence: #{prediction[:confidence]}" @@ -184,43 +267,18 @@ module OpenTox end end - - def training_dataset - Dataset.find training_dataset_id - end - - def prediction_feature - Feature.find prediction_feature_id - end - - def training_activities - i = @training_dataset.feature_ids.index prediction_feature_id - @training_dataset.data_entries.collect{|de| de[i]} - end - +=end end class LazarRegression < Lazar - field :min_train_performance, type: Float, default: 0.1 - def initialize - super - self.prediction_algorithm = "OpenTox::Algorithm::Regression.local_svm_regression" - self.similarity_algorithm = "OpenTox::Algorithm::Similarity.cosine" - self.min_sim = 0.7 - - # AM: transform to cosine space - min_sim = (min_sim.to_f*2.0-1.0).to_s if similarity_algorithm =~ /cosine/ - end - end - class LazarClassification < Lazar def initialize super - self.prediction_algorithm = "OpenTox::Algorithm::Classification.weighted_majority_vote" - self.similarity_algorithm = "OpenTox::Algorithm::Similarity.tanimoto" - self.feature_calculation_algorithm = "OpenTox::Algorithm::Descriptor.smarts_match" - self.min_sim = 0.3 + self.neighbor_algorithm = "OpenTox::Algorithm::Neighbor.fingerprint_similarity" + self.prediction_algorithm = "OpenTox::Algorithm::Regression.weighted_average" + self.neighbor_algorithm_parameters = {:min_sim => 0.7} end + end end |