From b515a0cfedb887a2af753db6e4a08ae1af430cad Mon Sep 17 00:00:00 2001 From: Christoph Helma Date: Tue, 31 May 2016 18:08:08 +0200 Subject: cleanup of validation modules/classes --- lib/crossvalidation.rb | 251 +++++++++++++++---------------------------------- 1 file changed, 77 insertions(+), 174 deletions(-) (limited to 'lib/crossvalidation.rb') diff --git a/lib/crossvalidation.rb b/lib/crossvalidation.rb index 420dd8c..22071d8 100644 --- a/lib/crossvalidation.rb +++ b/lib/crossvalidation.rb @@ -1,193 +1,96 @@ 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) - #p validation - $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 + module Validation + class CrossValidation < Validation + field :validation_ids, type: Array, default: [] + field :model_id, type: BSON::ObjectId + field :folds, type: Integer, default: 10 + field :nr_instances, type: Integer, default: 0 + field :nr_unpredicted, type: Integer, default: 0 + field :predictions, type: Hash, default: {} + + def time + finished_at - created_at 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) - stat - end + def validations + validation_ids.collect{|vid| TrainTest.find vid} + 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] + def model + Model::Lazar.find model_id + end - 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 = 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 - 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) + #Process.waitall + cv.save + $logger.debug "Nr unpredicted: #{nr_unpredicted}" + cv.statistics + cv.update_attributes(finished_at: Time.now) + cv 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) - 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][training_dataset.id.to_s]} - end - { - :smiles => compound.smiles, - :measured => p[1], - :predicted => p[2], - :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] + class ClassificationCrossValidation < CrossValidation + include ClassificationStatistics + 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 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 + class RegressionCrossValidation < CrossValidation + include RegressionStatistics + field :rmse, type: Float + field :mae, type: Float + field :r_squared, type: Float + field :correlation_plot_id, type: BSON::ObjectId end - def correlation_plot - unless correlation_plot_id - plot_id = ValidationStatistics.correlation_plot id, predictions - update(:correlation_plot_id => plot_id) + class RepeatedCrossValidation < Validation + 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 - $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 + def crossvalidations + crossvalidation_ids.collect{|id| CrossValidation.find(id)} end - repeated_cross_validation.save - repeated_cross_validation - end - def crossvalidations - crossvalidation_ids.collect{|id| CrossValidation.find(id)} end end -- cgit v1.2.3