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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
include Mongoid::Document
include Mongoid::Timestamps
store_in collection: "model"
field :title, type: String
field :description, type: String
#field :parameters, type: Array, default: []
field :creator, type: String, default: __FILE__
# datasets
field :training_dataset_id, type: BSON::ObjectId
field :feature_dataset_id, type: BSON::ObjectId
# algorithms
field :feature_generation, type: String
field :feature_calculation_algorithm, type: String
field :prediction_algorithm, type: Symbol
field :similarity_algorithm, type: Symbol
# prediction features
field :prediction_feature_id, type: BSON::ObjectId
field :predicted_value_id, type: BSON::ObjectId
field :predicted_variables, type: Array
# parameters
field :min_sim, type: Float
field :propositionalized, type:Boolean
field :min_train_performance, type: Float
attr_accessor :prediction_dataset
attr_accessor :training_dataset
attr_accessor :feature_dataset
attr_accessor :query_fingerprint
attr_accessor :neighbors
# 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 training_dataset, feature_dataset, prediction_feature=nil, params={}
lazar = OpenTox::Model::Lazar.new
bad_request_error "No features found in feature dataset #{feature_dataset.id}." if feature_dataset.features.empty?
lazar.feature_dataset_id = feature_dataset.id
@training_dataset = training_dataset
#@training_dataset = OpenTox::Dataset.find(feature_dataset.parameters.select{|p| p["title"] == "dataset_id"}.first["paramValue"])
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
lazar.training_dataset_id = @training_dataset.id
if prediction_feature
resource_not_found_error "No feature '#{params[:prediction_feature]}' in dataset '#{@training_dataset.id}'" unless @training_dataset.features.include?( 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
prediction_feature = @training_dataset.features.first
end
lazar.prediction_feature_id = prediction_feature.id
lazar.title = prediction_feature.title
if params and params[:prediction_algorithm]
bad_request_error "Unknown prediction_algorithm #{params[:prediction_algorithm]}" unless OpenTox::Algorithm::Neighbors.respond_to?(params[:prediction_algorithm])
lazar.prediction_algorithm = params[:prediction_algorithm]
end
unless lazar.prediction_algorithm
lazar.prediction_algorithm = :weighted_majority_vote if prediction_feature.nominal
lazar.prediction_algorithm = :local_svm_regression if prediction_feature.numeric
end
lazar.prediction_algorithm =~ /majority_vote/ ? lazar.propositionalized = false : lazar.propositionalized = true
lazar.min_sim = params[:min_sim].to_f if params[:min_sim] and params[:min_sim].numeric?
lazar.nr_hits = params[:nr_hits] if params[:nr_hits]
lazar.feature_generation = feature_dataset.creator
#lazar.parameters << {"title" => "feature_generation_uri", "paramValue" => params[:feature_generation_uri]}
# TODO insert algorithm into feature dataset
# TODO store algorithms in mongodb?
if lazar.feature_generation =~ /fminer|bbrc|last/
if (lazar[:nr_hits] == "true")
lazar.feature_calculation_algorithm = "smarts_count"
else
lazar.feature_calculation_algorithm = "smarts_match"
end
lazar.similarity_algorithm = "tanimoto"
lazar.min_sim = 0.3 unless lazar.min_sim
elsif lazar.feature_generation =~/descriptor/ or lazar.feature_generation.nil?
# cosine similartiy is default (e.g. used when no fetature_generation_uri is given and a feature_dataset_uri is provided instead)
lazar.similarity_algorithm = "cosine"
lazar.min_sim = 0.7 unless lazar.min_sim
else
bad_request_error "unkown feature generation method #{lazar.feature_generation}"
end
bad_request_error "Parameter min_train_performance is not numeric." if params[:min_train_performance] and !params[:min_train_performance].numeric?
lazar.min_train_performance = params[:min_train_performance].to_f if params[:min_train_performance] and params[:min_train_performance].numeric?
lazar.min_train_performance = 0.1 unless lazar.min_train_performance
lazar.save
lazar
end
def predict params
# tailored for performance
# all consistency checks should be done during model creation
time = Time.now
# prepare prediction dataset
prediction_dataset = OpenTox::Dataset.new
prediction_feature = OpenTox::Feature.find prediction_feature_id
prediction_dataset.title = "Lazar prediction for #{prediction_feature.title}",
prediction_dataset.creator = __FILE__,
confidence_feature = OpenTox::Feature.find_or_create_by({
"title" => "Prediction confidence",
"numeric" => true
})
prediction_dataset.features = [ confidence_feature, prediction_feature ]
@training_dataset = OpenTox::Dataset.find(training_dataset_id)
@feature_dataset = OpenTox::Dataset.find(feature_dataset_id)
compounds = []
if params[:compound]
compounds = [ params[:compound]]
elsif params[:compounds]
compounds = params[:compounds]
elsif params[:dataset]
compounds = params[:dataset].compounds
else
bad_request_error "Please provide one of the parameters: :compound, :compounds, :dataset"
end
$logger.debug "Setup: #{Time.now-time}"
time = Time.now
@query_fingerprint = OpenTox::Algorithm::Descriptor.send( feature_calculation_algorithm, compounds, @feature_dataset.features.collect{|f| f["title"]} )
$logger.debug "Fingerprint calculation: #{Time.now-time}"
time = Time.now
# 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|
$logger.debug "predict compound #{c+1}/#{compounds.size} #{compound.inchi}"
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, database_activity, nil]
end
next
else
# TODO reintroduce for regression
#mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(self)
#mtf.transform
#
# find neighbors
neighbors = []
@feature_dataset.data_entries.each_with_index do |fingerprint, i|
sim = OpenTox::Algorithm::Similarity.send(similarity_algorithm,fingerprint, @query_fingerprint[c])
# TODO fix for multi feature datasets
neighbors << [@feature_dataset.compounds[i],@training_dataset.data_entries[i].first,sim] if sim > self.min_sim
end
prediction = OpenTox::Algorithm::Classification.send(prediction_algorithm, neighbors)
$logger.debug "Prediction: #{Time.now-time}"
time = Time.now
# AM: transform to original space (TODO)
confidence_value = ((confidence_value+1.0)/2.0).abs if similarity_algorithm =~ /cosine/
$logger.debug "predicted value: #{prediction[:prediction]}, confidence: #{prediction[:confidence]}"
end
prediction_dataset << [ compound, prediction[:prediction], prediction[:confidence] ]
end
prediction_dataset
end
def training_activities
# TODO select predicted variable
#@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
#}
@training_dataset.data_entries.flatten
end
end
end
end
|
