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module OpenTox
class CrossValidation
field :validation_ids, type: Array, default: []
field :folds, type: Integer
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
field :predictions, type: Array
field :finished_at, type: Time
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
# TODO auc, f-measure (usability??)
def self.create model, n=10
cv = self.new
validation_ids = []
nr_instances = 0
nr_unpredicted = 0
predictions = []
validation_class = Object.const_get(self.to_s.sub(/Cross/,''))
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)}
true_rate = {}
predictivity = {}
fold_nr = 1
training_dataset = Dataset.find model.training_dataset_id
training_dataset.folds(n).each do |fold|
t = Time.now
$logger.debug "Fold #{fold_nr}"
validation = validation_class.create(model, fold[0], fold[1])
validation_ids << validation.id
nr_instances += validation.nr_instances
nr_unpredicted += validation.nr_unpredicted
predictions += validation.predictions
validation.confusion_matrix.each_with_index do |r,i|
r.each_with_index do |c,j|
confusion_matrix[i][j] += c
weighted_confusion_matrix[i][j] += validation.weighted_confusion_matrix[i][j]
end
end
$logger.debug "Fold #{fold_nr}: #{Time.now-t} seconds"
fold_nr +=1
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
cv.update_attributes(
nr_instances: nr_instances,
nr_unpredicted: nr_unpredicted,
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,
predictions: predictions.sort{|a,b| b[3] <=> a[3]}, # sort according to confidence
finished_at: Time.now
)
cv.save
cv
end
#Average area under roc 0.646
#Area under roc 0.646
#F measure carcinogen: 0.769, noncarcinogen: 0.348
end
class RegressionCrossValidation < Validation
field :validation_ids, type: Array, default: []
field :folds, type: Integer
field :rmse, type: Float
field :mae, type: Float
field :weighted_rmse, type: Float
field :weighted_mae, type: Float
def self.create model, n=10
cv = self.new
validation_ids = []
nr_instances = 0
nr_unpredicted = 0
predictions = []
validation_class = Object.const_get(self.to_s.sub(/Cross/,''))
fold_nr = 1
training_dataset = Dataset.find model.training_dataset_id
training_dataset.folds(n).each do |fold|
t = Time.now
$logger.debug "Predicting fold #{fold_nr}"
validation = validation_class.create(model, fold[0], fold[1])
validation_ids << validation.id
nr_instances += validation.nr_instances
nr_unpredicted += validation.nr_unpredicted
predictions += validation.predictions
$logger.debug "Fold #{fold_nr}: #{Time.now-t} seconds"
fold_nr +=1
end
rmse = 0
weighted_rmse = 0
rse = 0
weighted_rse = 0
mae = 0
weighted_mae = 0
rae = 0
weighted_rae = 0
n = 0
confidence_sum = 0
predictions.each do |pred|
compound_id,activity,prediction,confidence = pred
if activity and prediction
error = prediction-activity
rmse += error**2
weighted_rmse += confidence*error**2
mae += error.abs
weighted_mae += confidence*error.abs
n += 1
confidence_sum += confidence
else
# TODO: create warnings
p pred
end
end
mae = mae/n
weighted_mae = weighted_mae/confidence_sum
rmse = Math.sqrt(rmse/n)
weighted_rmse = Math.sqrt(weighted_rmse/confidence_sum)
cv.update_attributes(
folds: n,
validation_ids: validation_ids,
nr_instances: nr_instances,
nr_unpredicted: nr_unpredicted,
predictions: predictions.sort{|a,b| b[3] <=> a[3]},
mae: mae,
rmse: rmse,
weighted_mae: weighted_mae,
weighted_rmse: weighted_rmse
)
cv.save
cv
end
def plot
# RMSE
x = predictions.collect{|p| p[1]}
y = predictions.collect{|p| p[2]}
R.assign "Measurement", x
R.assign "Prediction", y
R.eval "par(pty='s')" # sets the plot type to be square
#R.eval "fitline <- lm(log(Prediction) ~ log(Measurement))"
#R.eval "error <- log(Measurement)-log(Prediction)"
R.eval "error <- Measurement-Prediction"
R.eval "rmse <- sqrt(mean(error^2,na.rm=T))"
R.eval "mae <- mean( abs(error), na.rm = TRUE)"
R.eval "r <- cor(log(Prediction),log(Measurement))"
R.eval "svg(filename='/tmp/#{id.to_s}.svg')"
R.eval "plot(log(Prediction),log(Measurement),main='#{self.name}', sub=paste('RMSE: ',rmse, 'MAE :',mae, 'r^2: ',r^2),asp=1)"
#R.eval "plot(log(Prediction),log(Measurement),main='#{self.name}', sub=paste('RMSE: ',rmse, 'MAE :',mae, 'r^2: '),asp=1)"
#R.eval "plot(log(Prediction),log(Measurement),main='#{self.name}', ,asp=1)"
R.eval "abline(0,1,col='blue')"
#R.eval "abline(fitline,col='red')"
R.eval "dev.off()"
"/tmp/#{id.to_s}.svg"
end
end
end
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