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module OpenTox
class Validation
include OpenTox
include Mongoid::Document
include Mongoid::Timestamps
store_in collection: "validations"
field :prediction_dataset_id, type: BSON::ObjectId
field :test_dataset_id, type: BSON::ObjectId
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
field :accept_values, type: String
field :confusion_matrix, type: Array
field :weighted_confusion_matrix, type: Array
field :predictions, type: Array
# TODO classification und regression in subclasses
def self.create model, training_set, test_set
validation = self.class.new
feature_dataset = Dataset.find model.feature_dataset_id
if feature_dataset.is_a? FminerDataset
features = Algorithm.run feature_dataset.training_algorithm, training_set, feature_dataset.training_parameters
else
# TODO search for descriptors
end
validation_model = Model::Lazar.create training_set, features
test_set_without_activities = Dataset.new(:compound_ids => test_set.compound_ids) # just to be sure that activities cannot be used
prediction_dataset = validation_model.predict test_set_without_activities
accept_values = prediction_dataset.prediction_feature.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)}
predictions = []
prediction_dataset.data_entries.each_with_index do |pe,i|
if pe[0] and pe[1] and pe[1].numeric?
prediction = pe[0]
# TODO prediction_feature, convention??
# TODO generalize for multiple classes
activity = test_set.data_entries[i].first
confidence = prediction_dataset.data_entries[i][1]
predictions << [prediction_dataset.compound_ids[i], activity, prediction, confidence]
if prediction == activity
if prediction == accept_values[0]
confusion_matrix[0][0] += 1
weighted_confusion_matrix[0][0] += confidence
elsif prediction == accept_values[1]
confusion_matrix[1][1] += 1
weighted_confusion_matrix[1][1] += confidence
end
elsif prediction != activity
if prediction == accept_values[0]
confusion_matrix[0][1] += 1
weighted_confusion_matrix[0][1] += confidence
elsif prediction == accept_values[1]
confusion_matrix[1][0] += 1
weighted_confusion_matrix[1][0] += confidence
end
end
end
end
validation = self.new(
:prediction_dataset_id => prediction_dataset.id,
:test_dataset_id => test_set.id,
:nr_instances => test_set.compound_ids.size,
:nr_unpredicted => prediction_dataset.data_entries.count{|de| de.first.nil?},
:accept_values => accept_values,
:confusion_matrix => confusion_matrix,
:weighted_confusion_matrix => weighted_confusion_matrix,
:predictions => predictions.sort{|a,b| b[3] <=> a[3]} # sort according to confidence
)
validation.save
validation
end
def prediction_dataset
Dataset.find prediction_dataset_id
end
def test_dataset
Dataset.find test_dataset_id
end
end
class CrossValidation
include OpenTox
include Mongoid::Document
include Mongoid::Timestamps
store_in collection: "crossvalidations"
field :validation_ids, type: Array, default: []
field :folds, type: Integer
field :nr_instances, type: Integer
field :nr_unpredicted, type: Integer
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 :predictions, type: Array
# TODO auc, f-measure (usability??)
def self.create model, n=10
validation_ids = []
nr_instances = 0
nr_unpredicted = 0
accept_values = model.prediction_feature.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 = {}
predictions = []
model.training_dataset.folds(n).each do |fold|
validation = Validation.create(model, fold[0], fold[1])
validation_ids << validation.id
nr_instances += validation.nr_instances
nr_unpredicted += validation.nr_unpredicted
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
predictions << validation.predictions
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 = CrossValidation.new(
:folds => n,
:validation_ids => validation_ids,
: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
)
cv.save
cv
end
#Average area under roc 0.646
#Area under roc 0.646
#F measure carcinogen: 0.769, noncarcinogen: 0.348
end
end
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