diff options
Diffstat (limited to 'lib/algorithm.rb')
| -rw-r--r-- | lib/algorithm.rb | 47 |
1 files changed, 35 insertions, 12 deletions
diff --git a/lib/algorithm.rb b/lib/algorithm.rb index 9dcf6a8..b921b9c 100644 --- a/lib/algorithm.rb +++ b/lib/algorithm.rb @@ -56,9 +56,15 @@ module OpenTox def check_params(params,per_mil,subjectid=nil) raise OpenTox::NotFoundError.new "Please submit a dataset_uri." unless params[:dataset_uri] and !params[:dataset_uri].nil? - raise OpenTox::NotFoundError.new "Please submit a prediction_feature." unless params[:prediction_feature] and !params[:prediction_feature].nil? - @prediction_feature = OpenTox::Feature.find params[:prediction_feature], subjectid @training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}", subjectid + + unless params[:prediction_feature] # try to read prediction_feature from dataset + raise OpenTox::NotFoundError.new "Please provide a prediction_feature parameter" unless @training_dataset.features.size == 1 + prediction_feature = OpenTox::Feature.find(@training_dataset.features.keys.first,@subjectid) + params[:prediction_feature] = prediction_feature.uri + end + @prediction_feature = OpenTox::Feature.find params[:prediction_feature], subjectid + raise OpenTox::NotFoundError.new "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless @training_dataset.features and @training_dataset.features.include?(params[:prediction_feature]) unless params[:min_frequency].nil? @@ -69,10 +75,10 @@ module OpenTox end end - def add_fminer_data(fminer_instance, params, value_map) + def add_fminer_data(fminer_instance, value_map) id = 1 # fminer start id is not 0 - @training_dataset.data_entries.each do |compound,entry| + @training_dataset.data_entries.each do |compound,entry| #order of compounds does not influence result begin smiles = OpenTox::Compound.smiles(compound.to_s) rescue @@ -84,7 +90,6 @@ module OpenTox next end - value_map=params[:value_map] unless params[:value_map].nil? entry.each do |feature,values| if feature == @prediction_feature.uri values.each do |value| @@ -92,7 +97,7 @@ module OpenTox LOGGER.warn "No #{feature} activity for #{compound.to_s}." else if @prediction_feature.feature_type == "classification" - activity= value_map.invert[value.to_s].to_i # activities are mapped to 1..n + activity= value_map.invert[value].to_i # activities are mapped to 1..n @db_class_sizes[activity-1].nil? ? @db_class_sizes[activity-1]=1 : @db_class_sizes[activity-1]+=1 # AM effect elsif @prediction_feature.feature_type == "regression" activity= value.to_f @@ -477,10 +482,22 @@ module OpenTox # assumes a data matrix 'features' and a vector 'y' of target values row.names(features)=NULL + # features with all values missing removed + na_col = names ( which ( apply ( features, 2, function(x) all ( is.na ( x ) ) ) ) ) + features = features[,!names(features) %in% na_col] + + # features with infinite values removed + inf_col = names ( which ( apply ( features, 2, function(x) any ( is.infinite ( x ) ) ) ) ) + features = features[,!names(features) %in% inf_col] + + # features with zero variance removed + zero_var = names ( which ( apply ( features, 2, function(x) var(x, na.rm=T) ) == 0 ) ) + features = features[,!names(features) %in% zero_var] + pp = NULL if (del_missing) { # needed if rows should be removed - na_ids = apply(features,1,function(x)any(is.na(x))) + na_ids = apply ( features,1,function(x) any ( is.na ( x ) ) ) features = features[!na_ids,] y = y[!na_ids] pp = preProcess(features, method=c("scale", "center")) @@ -490,15 +507,21 @@ module OpenTox } features = predict(pp, features) + # features with nan values removed (sometimes preProcess return NaN values) + nan_col = names ( which ( apply ( features, 2, function(x) any ( is.nan ( x ) ) ) ) ) + features = features[,!names(features) %in% nan_col] + # determine subsets - subsets = dim(features)[2]*c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7) - subsets = c(2,3,4,5,7,10,subsets) + subsets = dim(features)[2]*c(0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.7) + #subsets = dim(features)[2]*c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7) + #subsets = c(2,3,4,5,7,10,subsets) + #subsets = c(2,3,4,5,7,10,13,16,19,22,25,28,30) subsets = unique(sort(round(subsets))) subsets = subsets[subsets<=dim(features)[2]] subsets = subsets[subsets>1] - + # Recursive feature elimination - rfProfile = rfe( x=features, y=y, rfeControl=rfeControl(functions=rfFuncs, number=50), sizes=subsets) + rfProfile = rfe( x=features, y=y, rfeControl=rfeControl(functions=rfFuncs, number=150), sizes=subsets) # read existing dataset and select most useful features csv=feats[,c("SMILES", rfProfile$optVariables)] @@ -528,7 +551,7 @@ module OpenTox # @param [Hash] required keys: compound, features, feature_dataset_uri, pc_type # @return [Hash] Hash with matching Smarts and number of hits def self.lookup(params) - params[:compound].lookup(params[:features], params[:feature_dataset_uri],params[:pc_type],params[:subjectid]) + params[:compound].lookup(params[:features], params[:feature_dataset_uri],params[:pc_type], params[:lib],params[:subjectid]) end end |