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-rw-r--r--lib/model.rb48
1 files changed, 30 insertions, 18 deletions
diff --git a/lib/model.rb b/lib/model.rb
index b18610d..475a346 100644
--- a/lib/model.rb
+++ b/lib/model.rb
@@ -57,7 +57,7 @@ module OpenTox
model.version = {:warning => "git is not installed"}
end
- # set defaults
+ # 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
@@ -68,10 +68,6 @@ module OpenTox
:method => "fingerprint",
:type => "MP2D",
},
- :similarity => {
- :method => "Algorithm::Similarity.tanimoto",
- :min => 0.1
- },
:feature_selection => nil
}
@@ -79,9 +75,17 @@ module OpenTox
model.algorithms[:prediction] = {
:method => "Algorithm::Classification.weighted_majority_vote",
}
+ model.algorithms[:similarity] = {
+ :method => "Algorithm::Similarity.tanimoto",
+ :min => 0.1,
+ }
elsif model.class == LazarRegression
model.algorithms[:prediction] = {
- :method => "Algorithm::Caret.pls",
+ :method => "Algorithm::Caret.rf",
+ }
+ model.algorithms[:similarity] = {
+ :method => "Algorithm::Similarity.tanimoto",
+ :min => 0.5,
}
end
@@ -93,7 +97,7 @@ module OpenTox
},
:similarity => {
:method => "Algorithm::Similarity.weighted_cosine",
- :min => 0.5
+ :min => 0.5,
},
:prediction => {
:method => "Algorithm::Caret.rf",
@@ -141,7 +145,6 @@ module OpenTox
end
model.descriptor_ids = model.fingerprints.flatten.uniq
model.descriptor_ids.each do |d|
- # resulting model may break BSON size limit (e.g. f Kazius dataset)
model.independent_variables << model.substance_ids.collect_with_index{|s,i| model.fingerprints[i].include? d} if model.algorithms[:prediction][:method].match /Caret/
end
# calculate physchem properties
@@ -191,7 +194,7 @@ module OpenTox
# Predict a substance (compound or nanoparticle)
# @param [OpenTox::Substance]
# @return [Hash]
- def predict_substance substance
+ def predict_substance substance, threshold = self.algorithms[:similarity][:min]
@independent_variables = Marshal.load $gridfs.find_one(_id: self.independent_variables_id).data
case algorithms[:similarity][:method]
@@ -221,20 +224,19 @@ module OpenTox
bad_request_error "Unknown descriptor type '#{descriptors}' for similarity method '#{similarity[:method]}'."
end
- prediction = {}
+ prediction = {:warnings => [], :measurements => []}
+ prediction[:warnings] << "Similarity threshold #{threshold} < #{algorithms[:similarity][:min]}, prediction may be out of applicability domain." if threshold < algorithms[:similarity][:min]
neighbor_ids = []
neighbor_similarities = []
neighbor_dependent_variables = []
neighbor_independent_variables = []
- prediction = {}
# find neighbors
substance_ids.each_with_index do |s,i|
# handle query substance
if substance.id.to_s == s
- prediction[:measurements] ||= []
prediction[:measurements] << dependent_variables[i]
- prediction[:warning] = "Substance '#{substance.name}, id:#{substance.id}' has been excluded from neighbors, because it is identical with the query substance."
+ prediction[:info] = "Substance '#{substance.name}, id:#{substance.id}' has been excluded from neighbors, because it is identical with the query substance."
else
if fingerprints?
neighbor_descriptors = fingerprints[i]
@@ -243,7 +245,7 @@ module OpenTox
neighbor_descriptors = scaled_variables.collect{|v| v[i]}
end
sim = Algorithm.run algorithms[:similarity][:method], [similarity_descriptors, neighbor_descriptors, descriptor_weights]
- if sim >= algorithms[:similarity][:min]
+ if sim >= threshold
neighbor_ids << s
neighbor_similarities << sim
neighbor_dependent_variables << dependent_variables[i]
@@ -258,17 +260,27 @@ module OpenTox
measurements = nil
if neighbor_similarities.empty?
- prediction.merge!({:value => nil,:warning => "Could not find similar substances with experimental data in the training dataset.",:neighbors => []})
+ prediction[:value] = nil
+ prediction[:warnings] << "Could not find similar substances with experimental data in the training dataset."
elsif neighbor_similarities.size == 1
- prediction.merge!({:value => dependent_variables.first, :probabilities => nil, :warning => "Only one similar compound in the training set. Predicting its experimental value.", :neighbors => [{:id => neighbor_ids.first, :similarity => neighbor_similarities.first}]})
+ prediction[:value] = nil
+ prediction[:warnings] << "Cannot create prediction: Only one similar compound in the training set."
+ prediction[:neighbors] = [{:id => neighbor_ids.first, :similarity => neighbor_similarities.first}]
else
query_descriptors.collect!{|d| d ? 1 : 0} if algorithms[:feature_selection] and algorithms[:descriptors][:method] == "fingerprint"
# call prediction algorithm
result = Algorithm.run algorithms[:prediction][:method], dependent_variables:neighbor_dependent_variables,independent_variables:neighbor_independent_variables ,weights:neighbor_similarities, query_variables:query_descriptors
prediction.merge! result
prediction[:neighbors] = neighbor_ids.collect_with_index{|id,i| {:id => id, :measurement => neighbor_dependent_variables[i], :similarity => neighbor_similarities[i]}}
+ #if neighbor_similarities.max < algorithms[:similarity][:warn_min]
+ #prediction[:warnings] << "Closest neighbor has similarity < #{algorithms[:similarity][:warn_min]}. Prediction may be out of applicability domain."
+ #end
+ end
+ if prediction[:warnings].empty? or threshold < algorithms[:similarity][:min] or threshold <= 0.2
+ prediction
+ else # try again with a lower threshold
+ predict_substance substance, 0.2
end
- prediction
end
# Predict a substance (compound or nanoparticle), an array of substances or a dataset
@@ -300,7 +312,7 @@ module OpenTox
# 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
+ prediction[:neighbors].sort!{|a,b| b[1] <=> a[1]} if prediction[:neighbors]# sort according to similarity
return prediction
elsif object.is_a? Array
return predictions