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module OpenTox
class CrossValidation
field :validation_ids, type: Array, default: []
field :model_id, type: BSON::ObjectId
field :folds, type: Integer
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
field :predictions, type: Hash, default: {}
field :finished_at, type: Time
def time
finished_at - created_at
end
def validations
validation_ids.collect{|vid| Validation.find vid}
end
def model
Model::Lazar.find model_id
end
def self.create model, n=10
klass = ClassificationCrossValidation if model.is_a? Model::LazarClassification
klass = RegressionCrossValidation if model.is_a? Model::LazarRegression
bad_request_error "Unknown model class #{model.class}." unless klass
cv = klass.new(
name: model.name,
model_id: model.id,
folds: n
)
cv.save # set created_at
nr_instances = 0
nr_unpredicted = 0
predictions = {}
training_dataset = Dataset.find model.training_dataset_id
training_dataset.folds(n).each_with_index do |fold,fold_nr|
#fork do # parallel execution of validations can lead to Rserve and memory problems
$logger.debug "Dataset #{training_dataset.name}: Fold #{fold_nr} started"
t = Time.now
validation = Validation.create(model, fold[0], fold[1],cv)
$logger.debug "Dataset #{training_dataset.name}, Fold #{fold_nr}: #{Time.now-t} seconds"
#end
end
#Process.waitall
cv.validation_ids = Validation.where(:crossvalidation_id => cv.id).distinct(:_id)
cv.validations.each do |validation|
nr_instances += validation.nr_instances
nr_unpredicted += validation.nr_unpredicted
predictions.merge! validation.predictions
end
cv.update_attributes(
nr_instances: nr_instances,
nr_unpredicted: nr_unpredicted,
predictions: predictions
)
$logger.debug "Nr unpredicted: #{nr_unpredicted}"
cv.statistics
cv
end
end
class ClassificationCrossValidation < CrossValidation
field :accept_values, type: Array
field :confusion_matrix, type: Array
field :weighted_confusion_matrix, type: Array
field :accuracy, type: Float
field :weighted_accuracy, type: Float
field :true_rate, type: Hash
field :predictivity, type: Hash
field :confidence_plot_id, type: BSON::ObjectId
# TODO auc, f-measure (usability??)
def statistics
stat = ValidationStatistics.classification(predictions, Feature.find(model.prediction_feature_id).accept_values)
update_attributes(stat)
end
def confidence_plot
unless confidence_plot_id
tmpfile = "/tmp/#{id.to_s}_confidence.png"
accuracies = []
confidences = []
correct_predictions = 0
incorrect_predictions = 0
predictions.each do |p|
if p[1] and p[2]
p[1] == p[2] ? correct_predictions += 1 : incorrect_predictions += 1
accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
confidences << p[3]
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.png")
plot_id = $gridfs.insert_one(file)
update(:confidence_plot_id => plot_id)
end
$gridfs.find_one(_id: confidence_plot_id).data
end
#Average area under roc 0.646
#Area under roc 0.646
#F measure carcinogen: 0.769, noncarcinogen: 0.348
end
class RegressionCrossValidation < CrossValidation
field :rmse, type: Float
field :mae, type: Float
field :r_squared, type: Float
field :correlation_plot_id, type: BSON::ObjectId
def statistics
stat = ValidationStatistics.regression predictions
update_attributes(stat)
end
def misclassifications n=nil
n ||= 10
model = Model::Lazar.find(self.model_id)
training_dataset = Dataset.find(model.training_dataset_id)
prediction_feature = training_dataset.features.first
predictions.collect do |p|
unless p.include? nil
compound = Compound.find(p[0])
neighbors = compound.send(model.neighbor_algorithm,model.neighbor_algorithm_parameters)
neighbors.collect! do |n|
neighbor = Compound.find(n[0])
{ :smiles => neighbor.smiles, :similarity => n[1], :measurements => neighbor.toxicities[prediction_feature.id.to_s]}
end
{
:smiles => compound.smiles,
#:fingerprint => compound.fp4.collect{|id| Smarts.find(id).name},
:measured => p[1],
:predicted => p[2],
#:relative_error => (Math.log10(p[1])-Math.log10(p[2])).abs/Math.log10(p[1]).to_f.abs,
:error => (p[1]-p[2]).abs,
:relative_error => (p[1]-p[2]).abs/p[1],
:confidence => p[3],
:neighbors => neighbors
}
end
end.compact.sort{|a,b| b[:relative_error] <=> a[:relative_error]}[0..n-1]
end
def confidence_plot
tmpfile = "/tmp/#{id.to_s}_confidence.png"
sorted_predictions = predictions.collect{|p| [(p[1]-p[2]).abs,p[3]] if p[1] and p[2]}.compact
R.assign "error", sorted_predictions.collect{|p| p[0]}
R.assign "confidence", sorted_predictions.collect{|p| p[1]}
# TODO fix axis names
R.eval "image = qplot(confidence,error)"
R.eval "image = image + stat_smooth(method='lm', se=FALSE)"
R.eval "ggsave(file='#{tmpfile}', plot=image)"
file = Mongo::Grid::File.new(File.read(tmpfile), :filename => "#{self.id.to_s}_confidence_plot.png")
plot_id = $gridfs.insert_one(file)
update(:confidence_plot_id => plot_id)
$gridfs.find_one(_id: confidence_plot_id).data
end
def correlation_plot
unless correlation_plot_id
tmpfile = "/tmp/#{id.to_s}_correlation.png"
x = predictions.collect{|p| p[1]}
y = predictions.collect{|p| p[2]}
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", x
R.assign "prediction", y
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.png")
plot_id = $gridfs.insert_one(file)
update(:correlation_plot_id => plot_id)
end
$gridfs.find_one(_id: correlation_plot_id).data
end
end
class RepeatedCrossValidation
field :crossvalidation_ids, type: Array, default: []
def self.create model, folds=10, repeats=3
repeated_cross_validation = self.new
repeats.times do |n|
$logger.debug "Crossvalidation #{n+1} for #{model.name}"
repeated_cross_validation.crossvalidation_ids << CrossValidation.create(model, folds).id
end
repeated_cross_validation.save
repeated_cross_validation
end
def crossvalidations
crossvalidation_ids.collect{|id| CrossValidation.find(id)}
end
end
end
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