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module OpenTox
module Model
class Lazar
include OpenTox
include Mongoid::Document
include Mongoid::Timestamps
store_in collection: "models"
field :name, type: String
field :creator, type: String, default: __FILE__
field :training_dataset_id, type: BSON::ObjectId
field :prediction_feature_id, type: BSON::ObjectId
field :algorithms, type: Hash
field :relevant_features, type: Hash
def self.create prediction_feature:nil, training_dataset:nil, algorithms:{}
bad_request_error "Please provide a prediction_feature and/or a training_dataset." unless prediction_feature or training_dataset
prediction_feature = training_dataset.features.first unless prediction_feature
# TODO: prediction_feature without training_dataset: use all available data
# explicit prediction algorithm
if algorithms[:prediction] and algorithms[:prediction][:method]
case algorithms[:prediction][:method]
when /Classifiction/
model = LazarClassification.new
when /Regression/
model = LazarRegression.new
end
# guess model type
elsif prediction_feature.numeric?
model = LazarRegression.new
else
model = LazarClassification.new
end
# set defaults
substance_classes = training_dataset.substances.collect{|s| s.class.to_s}.uniq
bad_request_error "Cannot create models for mixed substance classes '#{substance_classes.join ', '}'." unless substance_classes.size == 1
if substance_classes.first == "OpenTox::Compound"
model.algorithms = {
:descriptors => {
:method => "fingerprint",
:type => 'MP2D',
},
:similarity => {
:method => "Algorithm::Similarity.tanimoto",
:min => 0.1
},
:feature_selection => nil
}
if model.class == LazarClassification
model.algorithms[:prediction] = {
:method => "Algorithm::Classification.weighted_majority_vote",
}
elsif model.class == LazarRegression
model.algorithms[:prediction] = {
:method => "Algorithm::Regression.caret",
:parameters => "pls",
}
end
elsif substance_classes.first == "OpenTox::Nanoparticle"
model.algorithms = {
:descriptors => {
:method => "properties",
#:types => ["P-CHEM","Proteomics"],
:types => ["P-CHEM"],
},
:similarity => {
:method => "Algorithm::Similarity.weighted_cosine",
:min => 0.5
},
:prediction => {
:method => "Algorithm::Regression.caret",
:parameters => "rf",
},
:feature_selection => {
:method => "Algorithm::FeatureSelection.correlation_filter",
},
}
else
bad_request_error "Cannot create models for #{substance_classes.first}."
end
# overwrite defaults with explicit parameters
algorithms.each do |type,parameters|
if parameters and parameters.is_a? Hash
parameters.each do |p,v|
model.algorithms[type] ||= {}
model.algorithms[type][p] = v
end
else
model.algorithms[type] = parameters
end
end
model.prediction_feature_id = prediction_feature.id
model.training_dataset_id = training_dataset.id
model.name = "#{training_dataset.name} #{prediction_feature.name}"
if model.algorithms[:feature_selection] and model.algorithms[:feature_selection][:method]
model.relevant_features = Algorithm.run model.algorithms[:feature_selection][:method], dataset: training_dataset, prediction_feature: prediction_feature, types: model.algorithms[:descriptors][:types]
end
model.save
model
end
def predict_substance substance
neighbors = substance.neighbors dataset_id: training_dataset_id, prediction_feature_id: prediction_feature_id, descriptors: algorithms[:descriptors], similarity: algorithms[:similarity], relevant_features: relevant_features
measurements = nil
prediction = {}
# handle query substance
if neighbors.collect{|n| n["_id"]}.include? substance.id
query = neighbors.select{|n| n["_id"] == substance.id}.first
measurements = training_dataset.values(query["_id"],prediction_feature_id)
prediction[:measurements] = measurements
prediction[:warning] = "#{measurements.size} substances have been removed from neighbors, because they are identical with the query substance."
neighbors.delete_if{|n| n["_id"] == substance.id} # remove query substance for an unbiased prediction (also useful for loo validation)
end
if neighbors.empty?
prediction.merge!({:value => nil,:probabilities => nil,:warning => "Could not find similar substances with experimental data in the training dataset.",:neighbors => []})
elsif neighbors.size == 1
value = nil
m = neighbors.first["measurements"]
if m.size == 1 # single measurement
value = m.first
else # multiple measurement
if m.collect{|t| t.numeric?}.uniq == [true] # numeric
value = m.median
elsif m.uniq.size == 1 # single value
value = m.first
else # contradictory results
# TODO add majority vote??
end
end
prediction.merge!({:value => value, :probabilities => nil, :warning => "Only one similar compound in the training set. Predicting median of its experimental values.", :neighbors => neighbors}) if value
else
# call prediction algorithm
case algorithms[:descriptors][:method]
when "fingerprint"
descriptors = substance.fingerprints[algorithms[:descriptors][:type]]
when "properties"
descriptors = substance.properties
else
bad_request_error "Descriptor method '#{algorithms[:descriptors][:method]}' not available."
end
params = algorithms[:prediction].merge({:descriptors => descriptors, :neighbors => neighbors})
params.delete :method
result = Algorithm.run algorithms[:prediction][:method], params
prediction.merge! result
prediction[:neighbors] = neighbors
prediction[:neighbors] ||= []
end
prediction
end
def predict object
training_dataset = Dataset.find training_dataset_id
# parse data
substances = []
if object.is_a? Substance
substances = [object]
elsif object.is_a? Array
substances = object
elsif object.is_a? Dataset
substances = object.substances
else
bad_request_error "Please provide a OpenTox::Compound an Array of OpenTox::Substances or an OpenTox::Dataset as parameter."
end
# make predictions
predictions = {}
substances.each do |c|
predictions[c.id.to_s] = predict_substance c
predictions[c.id.to_s][:prediction_feature_id] = prediction_feature_id
end
# serialize result
if object.is_a? Substance
prediction = predictions[substances.first.id.to_s]
prediction[:neighbors].sort!{|a,b| b[1] <=> a[1]} # sort according to similarity
return prediction
elsif object.is_a? Array
return predictions
elsif object.is_a? Dataset
# prepare prediction dataset
measurement_feature = Feature.find prediction_feature_id
prediction_feature = NumericFeature.find_or_create_by( "name" => measurement_feature.name + " (Prediction)" )
prediction_dataset = LazarPrediction.create(
:name => "Lazar prediction for #{prediction_feature.name}",
:creator => __FILE__,
:prediction_feature_id => prediction_feature.id,
:predictions => predictions
)
return prediction_dataset
end
end
def training_dataset
Dataset.find(training_dataset_id)
end
def prediction_feature
Feature.find(prediction_feature_id)
end
end
class LazarClassification < Lazar
end
class LazarRegression < Lazar
end
class Prediction
include OpenTox
include Mongoid::Document
include Mongoid::Timestamps
field :endpoint, type: String
field :species, type: String
field :source, type: String
field :unit, type: String
field :model_id, type: BSON::ObjectId
field :repeated_crossvalidation_id, type: BSON::ObjectId
field :leave_one_out_validation_id, type: BSON::ObjectId
def predict object
Lazar.find(model_id).predict object
end
def training_dataset
model.training_dataset
end
def model
Lazar.find model_id
end
def repeated_crossvalidation
Validation::RepeatedCrossValidation.find repeated_crossvalidation_id
end
def crossvalidations
repeated_crossvalidation.crossvalidations
end
def leave_one_out_validation
Validation::LeaveOneOut.find leave_one_out_validation_id
end
def regression?
model.is_a? LazarRegression
end
def classification?
model.is_a? LazarClassification
end
def self.from_csv_file file
metadata_file = file.sub(/csv$/,"json")
bad_request_error "No metadata file #{metadata_file}" unless File.exist? metadata_file
prediction_model = self.new JSON.parse(File.read(metadata_file))
training_dataset = Dataset.from_csv_file file
prediction_feature = training_dataset.features.first
model = nil
if prediction_feature.nominal?
model = LazarClassification.create prediction_feature, training_dataset
elsif prediction_feature.numeric?
model = LazarRegression.create prediction_feature, training_dataset
end
prediction_model[:model_id] = model.id
prediction_model[:prediction_feature_id] = prediction_feature.id
prediction_model[:repeated_crossvalidation_id] = Validation::RepeatedCrossValidation.create(model).id
#prediction_model[:leave_one_out_validation_id] = Validation::LeaveOneOut.create(model).id
prediction_model.save
prediction_model
end
end
class NanoPrediction < Prediction
def self.from_json_dump dir, category
Import::Enanomapper.import dir
prediction_model = self.new(
:endpoint => "log2(Net cell association)",
:source => "https://data.enanomapper.net/",
:species => "A549 human lung epithelial carcinoma cells",
:unit => "log2(ug/Mg)"
)
params = {
:feature_selection_algorithm => :correlation_filter,
:feature_selection_algorithm_parameters => {:category => category},
:neighbor_algorithm => "physchem_neighbors",
:neighbor_algorithm_parameters => {:min_sim => 0.5},
:prediction_algorithm => "OpenTox::Algorithm::Regression.physchem_regression",
:prediction_algorithm_parameters => {:method => 'rf'}, # random forests
}
training_dataset = Dataset.find_or_create_by(:name => "Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold and Silver Nanoparticles")
prediction_feature = Feature.find_or_create_by(name: "log2(Net cell association)", category: "TOX")
#prediction_feature = Feature.find("579621b84de73e267b414e55")
prediction_model[:prediction_feature_id] = prediction_feature.id
model = Model::LazarRegression.create(prediction_feature, training_dataset, params)
prediction_model[:model_id] = model.id
repeated_cv = Validation::RepeatedCrossValidation.create model
prediction_model[:repeated_crossvalidation_id] = Validation::RepeatedCrossValidation.create(model).id
#prediction_model[:leave_one_out_validation_id] = Validation::LeaveOneOut.create(model).id
prediction_model.save
prediction_model
end
end
end
end
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