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module OpenTox
module Model
class Lazar
include OpenTox
include Mongoid::Document
include Mongoid::Timestamps
store_in collection: "models"
field :title, type: String
field :endpoint, type: String
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
# 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
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
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
lazar.save
lazar
end
def predict object
time = Time.now
@training_dataset = OpenTox::Dataset.find(training_dataset_id)
@feature_dataset = OpenTox::Dataset.find(feature_dataset_id)
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
$logger.debug "Setup: #{Time.now-time}"
time = Time.now
@query_fingerprint = Algorithm.run(feature_calculation_algorithm, compounds, @feature_dataset.features.collect{|f| f.name} )
$logger.debug "Query fingerprint calculation: #{Time.now-time}"
predictions = []
prediction_feature = OpenTox::Feature.find prediction_feature_id
tt = 0
pt = 0
compounds.each_with_index do |compound,c|
t = Time.new
$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 = database_activities.first if database_activities.size == 1
$logger.debug "Compound #{compound.inchi} occurs in training dataset with activity #{database_activities}"
predictions << {:compound => compound, :value => database_activities, :confidence => "measured"}
next
else
if prediction_algorithm =~ /Regression/
mtf = OpenTox::Algorithm::Transform::ModelTransformer.new(self)
mtf.transform
training_fingerprints = mtf.n_prop
query_fingerprint = mtf.q_prop
neighbors = [[nil,nil,nil,query_fingerprint]]
else
training_fingerprints = @feature_dataset.data_entries
query_fingerprint = @query_fingerprint[c]
neighbors = []
end
tt += Time.now-t
t = Time.new
# find neighbors
training_fingerprints.each_with_index do |fingerprint, i|
sim = Algorithm.run(similarity_algorithm,fingerprint, query_fingerprint)
if sim > self.min_sim
if prediction_algorithm =~ /Regression/
neighbors << [@feature_dataset.compounds[i],sim,training_activities[i], fingerprint]
else
neighbors << [@feature_dataset.compounds[i],sim,training_activities[i]]
end
end
end
if neighbors.empty?
predictions << {:compound => compound, :value => nil, :confidence => nil, :warning => "No neighbors with similarity > #{min_sim} in dataset #{training_dataset.id}"}
#$logger.warn "No neighbors found for compound #{compound}."
next
end
if prediction_algorithm =~ /Regression/
prediction = Algorithm.run(prediction_algorithm, neighbors, :min_train_performance => self.min_train_performance)
else
prediction = Algorithm.run(prediction_algorithm, neighbors)
end
prediction[:compound] = compound
prediction[:neighbors] = neighbors.sort{|a,b| b[1] <=> a[1]} # sort with ascending similarities
# AM: transform to original space (TODO)
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]}"
predictions << prediction
pt += Time.now-t
end
end
$logger.debug "Transform time: #{tt}"
$logger.debug "Prediction time: #{pt}"
# 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__
)
confidence_feature = OpenTox::NumericFeature.find_or_create_by( "title" => "Prediction confidence" )
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.collect{|p| [p[:value], p[:confidence],p[:warning]]}
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 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
end
end
end
end
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