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module OpenTox
class ValidationStatistics
include OpenTox
def self.classification predictions, accept_values
confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
weighted_confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
true_rate = {}
predictivity = {}
nr_instances = 0
predictions.each do |cid,pred|
# TODO use measured majority class
if pred[:measured].uniq.size == 1
m = pred[:measured].first
#pred[:measured].each do |m|
if pred[:value] == m
if pred[:value] == accept_values[0]
confusion_matrix[0][0] += 1
weighted_confusion_matrix[0][0] += pred[:probabilities][pred[:value]]
nr_instances += 1
elsif pred[:value] == accept_values[1]
confusion_matrix[1][1] += 1
weighted_confusion_matrix[1][1] += pred[:probabilities][pred[:value]]
nr_instances += 1
end
elsif pred[:value] != m
if pred[:value] == accept_values[0]
confusion_matrix[0][1] += 1
weighted_confusion_matrix[0][1] += pred[:probabilities][pred[:value]]
nr_instances += 1
elsif pred[:value] == accept_values[1]
confusion_matrix[1][0] += 1
weighted_confusion_matrix[1][0] += pred[:probabilities][pred[:value]]
nr_instances += 1
end
end
end
end
true_rate = {}
predictivity = {}
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
accuracy = (confusion_matrix[0][0]+confusion_matrix[1][1])/nr_instances.to_f
weighted_accuracy = (weighted_confusion_matrix[0][0]+weighted_confusion_matrix[1][1])/confidence_sum.to_f
$logger.debug "Accuracy #{accuracy}"
{
:accept_values => accept_values,
:confusion_matrix => confusion_matrix,
:weighted_confusion_matrix => weighted_confusion_matrix,
:accuracy => accuracy,
:weighted_accuracy => weighted_accuracy,
:true_rate => true_rate,
:predictivity => predictivity,
:finished_at => Time.now
}
end
def self.regression predictions
rmse = 0
mae = 0
x = []
y = []
predictions.each do |cid,pred|
if pred[:value] and pred[:measured] #and pred[:measured] != [nil]
x << pred[:measured].median
y << pred[:value]
error = pred[:value]-pred[:measured].median
rmse += error**2
mae += error.abs
else
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", x
R.assign "prediction", y
R.eval "r <- cor(measurement,prediction,use='complete')"
r = R.eval("r").to_ruby
mae = mae/predictions.size
rmse = Math.sqrt(rmse/predictions.size)
$logger.debug "R^2 #{r**2}"
$logger.debug "RMSE #{rmse}"
$logger.debug "MAE #{mae}"
{
:mae => mae,
:rmse => rmse,
:r_squared => r**2,
:finished_at => Time.now
}
end
end
end
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