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module OpenTox
module Model
class LazarRegression < Lazar
# Create a lazar model from a training_dataset and a feature_dataset
# @param [OpenTox::Dataset] training_dataset
# @param [OpenTox::Dataset] feature_dataset
# @return [OpenTox::Model::Lazar] Regression or classification model
def self.create training_dataset#, feature_dataset
#bad_request_error "No features found in feature dataset #{feature_dataset.id}." if feature_dataset.features.empty?
bad_request_error "More than one prediction feature found in training_dataset #{training_dataset.id}" unless training_dataset.features.size == 1
#bad_request_error "Training dataset compounds do not match feature dataset compounds. Please ensure that they are in the same order." unless training_dataset.compounds == feature_dataset.compounds
prediction_feature = training_dataset.features.first
prediction_feature.nominal ? lazar = OpenTox::Model::LazarClassification.new : lazar = OpenTox::Model::LazarRegression.new
#lazar.feature_dataset_id = feature_dataset.id
lazar.training_dataset_id = training_dataset.id
lazar.prediction_feature_id = prediction_feature.id
lazar.title = prediction_feature.title
# log transform activities (create new dataset)
# scale, normalize features, might not be necessary
# http://stats.stackexchange.com/questions/19216/variables-are-often-adjusted-e-g-standardised-before-making-a-model-when-is
# http://stats.stackexchange.com/questions/7112/when-and-how-to-use-standardized-explanatory-variables-in-linear-regression
# zero-order correlation and the semi-partial correlation
# seems to be necessary for svm
# http://stats.stackexchange.com/questions/77876/why-would-scaling-features-decrease-svm-performance?lq=1
# http://stackoverflow.com/questions/15436367/svm-scaling-input-values
# use lasso or elastic net??
# select relevant features
# remove features with a single value
# remove correlated features
# remove features not correlated with endpoint
lazar.save
lazar
end
def predict object
t = Time.now
at = Time.now
training_dataset = OpenTox::Dataset.find(training_dataset_id)
compounds = []
case object.class.to_s
when "OpenTox::Compound"
compounds = [object]
when "Array"
compounds = object
when "OpenTox::Dataset"
compounds = object.compounds
else
bad_request_error "Please provide a OpenTox::Compound an Array of OpenTox::Compounds or an OpenTox::Dataset as parameter."
end
$logger.debug "Setup: #{Time.now-t}"
t = Time.now
$logger.debug "Query fingerprint calculation: #{Time.now-t}"
t = Time.now
predictions = []
prediction_feature = OpenTox::Feature.find prediction_feature_id
tt = 0
pt = 0
nt = 0
st = 0
nit = 0
predictions = []
compounds.each_with_index do |compound,c|
t = Time.new
neighbors = compound.neighbors
weighted_sum = 0
sim_sum = 0
neighbors.each do |row|
n,sim = row
i = training_dataset.compound_ids.index n.id
if i
act = training_dataset.data_entries[i].first
if act
weighted_sum += sim*Math.log10(act)
sim_sum += sim
end
end
end
weighted_average = 10**(weighted_sum/sim_sum)
p weighted_average
end
$logger.debug "Transform time: #{tt}"
$logger.debug "Neighbor search time: #{nt} (Similarity calculation: #{st}, Neighbor insert: #{nit})"
$logger.debug "Prediction time: #{pt}"
$logger.debug "Total prediction time: #{Time.now-at}"
# serialize result
case object.class.to_s
when "OpenTox::Compound"
return predictions.first
when "Array"
return predictions
when "OpenTox::Dataset"
# prepare prediction dataset
prediction_dataset = LazarPrediction.new(
:title => "Lazar prediction for #{prediction_feature.title}",
:creator => __FILE__,
:prediction_feature_id => prediction_feature.id
)
confidence_feature = OpenTox::NumericFeature.find_or_create_by( "title" => "Prediction confidence" )
warning_feature = OpenTox::NominalFeature.find_or_create_by("title" => "Warnings")
prediction_dataset.features = [ prediction_feature, confidence_feature, warning_feature ]
prediction_dataset.compounds = compounds
prediction_dataset.data_entries = predictions.collect{|p| [p[:value], p[:confidence],p[:warning]]}
prediction_dataset.save_all
return prediction_dataset
end
end
def training_dataset
Dataset.find training_dataset_id
end
def prediction_feature
Feature.find prediction_feature_id
end
def training_activities
i = @training_dataset.feature_ids.index prediction_feature_id
@training_dataset.data_entries.collect{|de| de[i]}
end
end
end
end
|
