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module OpenTox
class LeaveOneOutValidation
field :model_id, type: BSON::ObjectId
field :dataset_id, type: BSON::ObjectId
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
field :predictions, type: Array
field :finished_at, type: Time
def self.create model
model.training_dataset.features.first.nominal? ? klass = ClassificationLeaveOneOutValidation : klass = RegressionLeaveOneOutValidation
loo = klass.new :model_id => model.id, :dataset_id => model.training_dataset_id
compound_ids = model.training_dataset.compound_ids
predictions = model.predict model.training_dataset.compounds
predictions = predictions.each_with_index {|p,i| p[:compound_id] = compound_ids[i]}
predictions.select!{|p| p[:database_activities] and !p[:database_activities].empty?}
loo.nr_instances = predictions.size
predictions.select!{|p| p[:value]} # remove unpredicted
loo.predictions = predictions#.sort{|a,b| b[:confidence] <=> a[:confidence]}
loo.nr_unpredicted = loo.nr_instances - loo.predictions.size
loo.statistics
loo.save
loo
end
def model
Model::Lazar.find model_id
end
end
class ClassificationLeaveOneOutValidation < LeaveOneOutValidation
field :accept_values, type: Array
field :confusion_matrix, type: Array, default: []
field :weighted_confusion_matrix, type: Array, default: []
field :accuracy, type: Float
field :weighted_accuracy, type: Float
field :true_rate, type: Hash, default: {}
field :predictivity, type: Hash, default: {}
field :confidence_plot_id, type: BSON::ObjectId
def statistics
accept_values = Feature.find(model.prediction_feature_id).accept_values
confusion_matrix = Array.new(accept_values.size,0){Array.new(accept_values.size,0)}
weighted_confusion_matrix = Array.new(accept_values.size,0){Array.new(accept_values.size,0)}
predictions.each do |pred|
pred[:database_activities].each do |db_act|
if pred[:value]
if pred[:value] == db_act
if pred[:value] == accept_values[0]
confusion_matrix[0][0] += 1
weighted_confusion_matrix[0][0] += pred[:confidence]
elsif pred[:value] == accept_values[1]
confusion_matrix[1][1] += 1
weighted_confusion_matrix[1][1] += pred[:confidence]
end
else
if pred[:value] == accept_values[0]
confusion_matrix[0][1] += 1
weighted_confusion_matrix[0][1] += pred[:confidence]
elsif pred[:value] == accept_values[1]
confusion_matrix[1][0] += 1
weighted_confusion_matrix[1][0] += pred[:confidence]
end
end
end
end
end
accept_values.each_with_index do |v,i|
true_rate[v] = confusion_matrix[i][i]/confusion_matrix[i].reduce(:+).to_f
predictivity[v] = confusion_matrix[i][i]/confusion_matrix.collect{|n| n[i]}.reduce(:+).to_f
end
confidence_sum = 0
weighted_confusion_matrix.each do |r|
r.each do |c|
confidence_sum += c
end
end
update_attributes(
accept_values: accept_values,
confusion_matrix: confusion_matrix,
weighted_confusion_matrix: weighted_confusion_matrix,
accuracy: (confusion_matrix[0][0]+confusion_matrix[1][1])/(nr_instances-nr_unpredicted).to_f,
weighted_accuracy: (weighted_confusion_matrix[0][0]+weighted_confusion_matrix[1][1])/confidence_sum.to_f,
true_rate: true_rate,
predictivity: predictivity,
finished_at: Time.now
)
$logger.debug "Accuracy #{accuracy}"
end
def confidence_plot
unless confidence_plot_id
tmpfile = "/tmp/#{id.to_s}_confidence.svg"
accuracies = []
confidences = []
correct_predictions = 0
incorrect_predictions = 0
predictions.each do |p|
p[:database_activities].each do |db_act|
if p[:value]
p[:value] == db_act ? correct_predictions += 1 : incorrect_predictions += 1
accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
confidences << p[:confidence]
end
end
end
R.assign "accuracy", accuracies
R.assign "confidence", confidences
R.eval "image = qplot(confidence,accuracy)+ylab('accumulated accuracy')+scale_x_reverse()"
R.eval "ggsave(file='#{tmpfile}', plot=image)"
file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_confidence_plot.svg")
plot_id = $gridfs.insert_one(file)
update(:confidence_plot_id => plot_id)
end
$gridfs.find_one(_id: confidence_plot_id).data
end
end
class RegressionLeaveOneOutValidation < LeaveOneOutValidation
field :rmse, type: Float, default: 0.0
field :mae, type: Float, default: 0
#field :weighted_rmse, type: Float, default: 0
#field :weighted_mae, type: Float, default: 0
field :r_squared, type: Float
field :correlation_plot_id, type: BSON::ObjectId
field :confidence_plot_id, type: BSON::ObjectId
def statistics
confidence_sum = 0
predicted_values = []
measured_values = []
predictions.each do |pred|
pred[:database_activities].each do |activity|
if pred[:value]
predicted_values << pred[:value]
measured_values << activity
error = Math.log10(pred[:value])-Math.log10(activity)
self.rmse += error**2
#self.weighted_rmse += pred[:confidence]*error**2
self.mae += error.abs
#self.weighted_mae += pred[:confidence]*error.abs
#confidence_sum += pred[:confidence]
end
end
if pred[:database_activities].empty?
warnings << "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
$logger.debug "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
end
end
R.assign "measurement", measured_values
R.assign "prediction", predicted_values
R.eval "r <- cor(-log(measurement),-log(prediction),use='complete')"
r = R.eval("r").to_ruby
self.mae = self.mae/predictions.size
#self.weighted_mae = self.weighted_mae/confidence_sum
self.rmse = Math.sqrt(self.rmse/predictions.size)
#self.weighted_rmse = Math.sqrt(self.weighted_rmse/confidence_sum)
self.r_squared = r**2
self.finished_at = Time.now
save
$logger.debug "R^2 #{r**2}"
$logger.debug "RMSE #{rmse}"
$logger.debug "MAE #{mae}"
end
def correlation_plot
unless correlation_plot_id
tmpfile = "/tmp/#{id.to_s}_correlation.svg"
predicted_values = []
measured_values = []
predictions.each do |pred|
pred[:database_activities].each do |activity|
if pred[:value]
predicted_values << pred[:value]
measured_values << activity
end
end
end
attributes = Model::Lazar.find(self.model_id).attributes
attributes.delete_if{|key,_| key.match(/_id|_at/) or ["_id","creator","name"].include? key}
attributes = attributes.values.collect{|v| v.is_a?(String) ? v.sub(/OpenTox::/,'') : v}.join("\n")
R.assign "measurement", measured_values
R.assign "prediction", predicted_values
R.eval "all = c(-log(measurement),-log(prediction))"
R.eval "range = c(min(all), max(all))"
R.eval "image = qplot(-log(prediction),-log(measurement),main='#{self.name}',asp=1,xlim=range, ylim=range)"
R.eval "image = image + geom_abline(intercept=0, slope=1)"
R.eval "ggsave(file='#{tmpfile}', plot=image)"
file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_correlation_plot.svg")
plot_id = $gridfs.insert_one(file)
update(:correlation_plot_id => plot_id)
end
$gridfs.find_one(_id: correlation_plot_id).data
end
end
end
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