diff options
Diffstat (limited to 'lib/model.rb')
| -rw-r--r-- | lib/model.rb | 104 |
1 files changed, 35 insertions, 69 deletions
diff --git a/lib/model.rb b/lib/model.rb index 227d4d3..8e657b8 100644 --- a/lib/model.rb +++ b/lib/model.rb @@ -34,7 +34,6 @@ module OpenTox def initialize training_dataset, params={} super params - bad_request_error "More than one prediction feature found in training_dataset #{training_dataset.id}" unless training_dataset.features.size == 1 # TODO document convention prediction_feature = training_dataset.features.first @@ -48,13 +47,34 @@ module OpenTox self end - def predict object, use_database_values=true + def predict_compound compound + prediction_feature = Feature.find prediction_feature_id + neighbors = compound.send(neighbor_algorithm, neighbor_algorithm_parameters) + # remove neighbors without prediction_feature + # check for database activities (neighbors may include query compound) + database_activities = nil + prediction = {} + if neighbors.collect{|n| n["_id"]}.include? compound.id + + database_activities = neighbors.select{|n| n["_id"] == compound.id}.first["features"][prediction_feature.id.to_s].uniq + prediction[:database_activities] = database_activities + prediction[:warning] = "#{database_activities.size} compounds have been removed from neighbors, because they have the same structure as the query compound." + neighbors.delete_if{|n| n["_id"] == compound.id} + end + neighbors.delete_if{|n| n['features'].empty? or n['features'][prediction_feature.id.to_s] == [nil] } + if neighbors.empty? + prediction.merge!({:value => nil,:confidence => nil,:warning => "Could not find similar compounds with experimental data in the training dataset.",:neighbors => []}) + else + prediction.merge!(Algorithm.run(prediction_algorithm, compound, {:neighbors => neighbors,:training_dataset_id=> training_dataset_id,:prediction_feature_id => prediction_feature.id})) + prediction[:neighbors] = neighbors + prediction[:neighbors] ||= [] + end + prediction + end - t = Time.now - at = Time.now + def predict object training_dataset = Dataset.find training_dataset_id - prediction_feature = Feature.find prediction_feature_id # parse data compounds = [] @@ -71,63 +91,33 @@ module OpenTox # make predictions predictions = [] - neighbors = [] - compounds.each_with_index do |compound,c| - t = Time.new - - neighbors = compound.send(neighbor_algorithm, neighbor_algorithm_parameters) - # add activities - # TODO: improve efficiency, takes 3 times longer than previous version - 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 - - database_activities = training_dataset.values(compound,prediction_feature) - if use_database_values and database_activities and !database_activities.empty? - database_activities = database_activities.first if database_activities.size == 1 - predictions << {:compound => compound, :value => database_activities, :confidence => "measured", :warning => "Compound #{compound.smiles} occurs in training dataset with activity '#{database_activities}'."} - next - end - predictions << Algorithm.run(prediction_algorithm, compound, {:neighbors => neighbors,:training_dataset_size => training_dataset.data_entries.size}) -=begin -# TODO scaled dataset for physchem - p neighbor_algorithm_parameters - p (neighbor_algorithm_parameters["feature_dataset_id"]) - d = Dataset.find(neighbor_algorithm_parameters["feature_dataset_id"]) - p d - p d.class - if neighbor_algorithm_parameters["feature_dataset_id"] and Dataset.find(neighbor_algorithm_parameters["feature_dataset_id"]).kind_of? ScaledDataset - p "SCALED" - end -=end - end + predictions = compounds.collect{|c| predict_compound c} # serialize result case object.class.to_s when "OpenTox::Compound" prediction = predictions.first - prediction[:neighbors] = neighbors.sort{|a,b| b[1] <=> a[1]} # sort according to similarity + prediction[:neighbors].sort!{|a,b| b[1] <=> a[1]} # sort according to similarity return prediction when "Array" return predictions when "OpenTox::Dataset" # prepare prediction dataset + measurement_feature = Feature.find prediction_feature_id + + prediction_feature = OpenTox::NumericFeature.find_or_create_by( "name" => measurement_feature.name + " (Prediction)" ) prediction_dataset = LazarPrediction.new( :name => "Lazar prediction for #{prediction_feature.name}", :creator => __FILE__, :prediction_feature_id => prediction_feature.id ) - confidence_feature = OpenTox::NumericFeature.find_or_create_by( "name" => "Prediction confidence" ) - # TODO move into warnings field + confidence_feature = OpenTox::NumericFeature.find_or_create_by( "name" => "Model RMSE" ) warning_feature = OpenTox::NominalFeature.find_or_create_by("name" => "Warnings") - prediction_dataset.features = [ prediction_feature, confidence_feature, warning_feature ] + prediction_dataset.features = [ prediction_feature, confidence_feature, measurement_feature, warning_feature ] prediction_dataset.compounds = compounds - prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:confidence], p[:warning]]} - prediction_dataset.save_all + prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:rmse] , p[:dataset_activities].to_s, p[:warning]]} + prediction_dataset.save return prediction_dataset end @@ -164,15 +154,12 @@ module OpenTox def self.create training_dataset, params={} model = self.new training_dataset, params model.neighbor_algorithm ||= "fingerprint_neighbors" - model.prediction_algorithm ||= "OpenTox::Algorithm::Regression.weighted_average" + model.prediction_algorithm ||= "OpenTox::Algorithm::Regression.local_fingerprint_regression" model.neighbor_algorithm_parameters ||= {} { :type => "MP2D", :training_dataset_id => training_dataset.id, :min_sim => 0.1 - #:type => "FP4", - #:training_dataset_id => training_dataset.id, - #:min_sim => 0.7 }.each do |key,value| model.neighbor_algorithm_parameters[key] ||= value end @@ -181,31 +168,11 @@ module OpenTox end end - class LazarFminerClassification < LazarClassification - field :feature_calculation_parameters, type: Hash - - def self.create training_dataset, fminer_params={} - model = super(training_dataset) - model.update "_type" => self.to_s # adjust class - model = self.find model.id # adjust class - model.neighbor_algorithm = "fminer_neighbors" - model.neighbor_algorithm_parameters = { - :feature_calculation_algorithm => "OpenTox::Algorithm::Descriptor.smarts_match", - :feature_dataset_id => Algorithm::Fminer.bbrc(training_dataset,fminer_params).id, - :min_sim => 0.3 - } - model.feature_calculation_parameters = fminer_params - model.save - model - end - end - class Prediction include OpenTox include Mongoid::Document include Mongoid::Timestamps - # TODO cv -> repeated cv # TODO field Validations field :endpoint, type: String field :species, type: String @@ -249,7 +216,6 @@ module OpenTox training_dataset = Dataset.from_csv_file file model = nil if training_dataset.features.first.nominal? - #model = LazarFminerClassification.create training_dataset model = LazarClassification.create training_dataset elsif training_dataset.features.first.numeric? model = LazarRegression.create training_dataset |