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|
module OpenTox
module Validation
# Crossvalidation
class CrossValidation < Validation
field :validation_ids, type: Array, default: []
field :folds, type: Integer, default: 10
# Create a crossvalidation
# @param [OpenTox::Model::Lazar]
# @param [Fixnum] number of folds
# @return [OpenTox::Validation::CrossValidation]
def self.create model, n=10
$logger.debug model.algorithms
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
training_dataset = model.training_dataset
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 = TrainTest.create(model, fold[0], fold[1])
cv.validation_ids << validation.id
cv.nr_instances += validation.nr_instances
cv.nr_unpredicted += validation.nr_unpredicted
#cv.predictions.merge! validation.predictions
$logger.debug "Dataset #{training_dataset.name}, Fold #{fold_nr}: #{Time.now-t} seconds"
#end
end
#Process.waitall
cv.save
$logger.debug "Nr unpredicted: #{nr_unpredicted}"
cv.statistics
cv.update_attributes(finished_at: Time.now)
cv
end
# Get execution time
# @return [Fixnum]
def time
finished_at - created_at
end
# Get individual validations
# @return [Array<OpenTox::Validation>]
def validations
validation_ids.collect{|vid| TrainTest.find vid}
end
# Get predictions for all compounds
# @return [Array<Hash>]
def predictions
predictions = {}
validations.each{|v| predictions.merge!(v.predictions)}
predictions
end
end
# Crossvalidation of classification models
class ClassificationCrossValidation < CrossValidation
include ClassificationStatistics
field :accept_values, type: Array
field :confusion_matrix, type: Hash
field :weighted_confusion_matrix, type: Hash
field :accuracy, type: Hash
field :weighted_accuracy, type: Hash
field :true_rate, type: Hash
field :predictivity, type: Hash
field :nr_predictions, type: Hash
field :probability_plot_id, type: BSON::ObjectId
end
# Crossvalidation of regression models
class RegressionCrossValidation < CrossValidation
include RegressionStatistics
field :rmse, type: Hash
field :mae, type: Hash
field :r_squared, type: Hash
field :within_prediction_interval, type: Hash
field :out_of_prediction_interval, type: Hash
field :nr_predictions, type: Hash
field :warnings, type: Array
field :correlation_plot_id, type: BSON::ObjectId
end
# Independent repeated crossvalidations
class RepeatedCrossValidation < Validation
field :crossvalidation_ids, type: Array, default: []
field :correlation_plot_id, type: BSON::ObjectId
# Create repeated crossvalidations
# @param [OpenTox::Model::Lazar]
# @param [Fixnum] number of folds
# @param [Fixnum] number of repeats
# @return [OpenTox::Validation::RepeatedCrossValidation]
def self.create model, folds=10, repeats=5
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
# Get crossvalidations
# @return [OpenTox::Validation::CrossValidation]
def crossvalidations
crossvalidation_ids.collect{|id| CrossValidation.find(id)}
end
end
end
end
|
