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module OpenTox
module Validation
module ClassificationStatistics
def statistics
self.accept_values = model.prediction_feature.accept_values
self.confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
self.weighted_confusion_matrix = Array.new(accept_values.size){Array.new(accept_values.size,0)}
true_rate = {}
predictivity = {}
nr_instances = 0
predictions.each do |cid,pred|
# TODO
# use predictions without probabilities (single neighbor)??
# use measured majority class??
if pred[:measurements].uniq.size == 1 and pred[:probabilities]
m = pred[:measurements].first
if pred[:value] == m
if pred[:value] == accept_values[0]
confusion_matrix[0][0] += 1
weighted_confusion_matrix[0][0] += pred[:probabilities][pred[:value]]
nr_instances += 1
elsif pred[:value] == accept_values[1]
confusion_matrix[1][1] += 1
weighted_confusion_matrix[1][1] += pred[:probabilities][pred[:value]]
nr_instances += 1
end
elsif pred[:value] != m
if pred[:value] == accept_values[0]
confusion_matrix[0][1] += 1
weighted_confusion_matrix[0][1] += pred[:probabilities][pred[:value]]
nr_instances += 1
elsif pred[:value] == accept_values[1]
confusion_matrix[1][0] += 1
weighted_confusion_matrix[1][0] += pred[:probabilities][pred[:value]]
nr_instances += 1
end
end
end
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
self.accuracy = (confusion_matrix[0][0]+confusion_matrix[1][1])/nr_instances.to_f
self.weighted_accuracy = (weighted_confusion_matrix[0][0]+weighted_confusion_matrix[1][1])/confidence_sum.to_f
$logger.debug "Accuracy #{accuracy}"
save
{
:accept_values => accept_values,
:confusion_matrix => confusion_matrix,
:weighted_confusion_matrix => weighted_confusion_matrix,
:accuracy => accuracy,
:weighted_accuracy => weighted_accuracy,
:true_rate => true_rate,
:predictivity => predictivity,
}
end
def confidence_plot
unless confidence_plot_id
tmpfile = "/tmp/#{id.to_s}_confidence.svg"
accuracies = []
confidences = []
correct_predictions = 0
incorrect_predictions = 0
predictions.each do |p|
p[:measurements].each do |db_act|
if p[:value]
p[:value] == db_act ? correct_predictions += 1 : incorrect_predictions += 1
accuracies << correct_predictions/(correct_predictions+incorrect_predictions).to_f
confidences << p[:confidence]
end
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.svg")
plot_id = $gridfs.insert_one(file)
update(:confidence_plot_id => plot_id)
end
$gridfs.find_one(_id: confidence_plot_id).data
end
end
module RegressionStatistics
def statistics
# TODO: predictions within prediction_interval
rmse = 0
mae = 0
x = []
y = []
predictions.each do |cid,pred|
if pred[:value] and pred[:measurements]
x << pred[:measurements].median
y << pred[:value]
error = pred[:value]-pred[:measurements].median
rmse += error**2
mae += error.abs
else
warnings << "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
$logger.debug "No training activities for #{Compound.find(compound_id).smiles} in training dataset #{model.training_dataset_id}."
end
end
R.assign "measurement", x
R.assign "prediction", y
R.eval "r <- cor(measurement,prediction,use='pairwise')"
r = R.eval("r").to_ruby
mae = mae/predictions.size
rmse = Math.sqrt(rmse/predictions.size)
$logger.debug "R^2 #{r**2}"
$logger.debug "RMSE #{rmse}"
$logger.debug "MAE #{mae}"
{
:mae => mae,
:rmse => rmse,
:r_squared => r**2,
}
end
def correlation_plot
unless correlation_plot_id
tmpfile = "/tmp/#{id.to_s}_correlation.pdf"
x = []
y = []
feature = Feature.find(predictions.first.last["prediction_feature_id"])
predictions.each do |sid,p|
x << p["value"]
y << p["measurements"].median
end
R.assign "measurement", x
R.assign "prediction", y
R.eval "all = c(measurement,prediction)"
R.eval "range = c(min(all), max(all))"
title = feature.name
title += "[#{feature.unit}]" if feature.unit and !feature.unit.blank?
R.eval "image = qplot(prediction,measurement,main='#{title}',xlab='Prediction',ylab='Measurement',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 => "#{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
def worst_predictions n: 5, show_neigbors: true, show_common_descriptors: false
worst_predictions = predictions.sort_by{|sid,p| -(p["value"] - p["measurements"].median).abs}[0,n]
worst_predictions.collect do |p|
substance = Substance.find(p.first)
prediction = p[1]
if show_neigbors
neighbors = prediction["neighbors"].collect do |n|
common_descriptors = []
if show_common_descriptors
common_descriptors = n["common_descriptors"].collect do |d|
f=Feature.find(d)
{
:id => f.id.to_s,
:name => "#{f.name} (#{f.conditions})",
:p_value => d[:p_value],
:r_squared => d[:r_squared],
}
end
else
common_descriptors = n["common_descriptors"].size
end
{
:name => Substance.find(n["_id"]).name,
:id => n["_id"].to_s,
:common_descriptors => common_descriptors
}
end
else
neighbors = prediction["neighbors"].size
end
{
:id => substance.id.to_s,
:name => substance.name,
:feature => Feature.find(prediction["prediction_feature_id"]).name,
:error => (prediction["value"] - prediction["measurements"].median).abs,
:prediction => prediction["value"],
:measurements => prediction["measurements"],
:neighbors => neighbors
}
end
end
end
end
end
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