diff options
Diffstat (limited to 'lib/regression.rb')
| -rw-r--r-- | lib/regression.rb | 144 |
1 files changed, 7 insertions, 137 deletions
diff --git a/lib/regression.rb b/lib/regression.rb index 5021fb3..3890987 100644 --- a/lib/regression.rb +++ b/lib/regression.rb @@ -3,148 +3,18 @@ module OpenTox class Regression - def self.local_weighted_average compound, params + def self.weighted_average dependent_variables:, independent_variables:nil, weights:, query_variables:nil + # TODO: prediction_interval weighted_sum = 0.0 sim_sum = 0.0 - neighbors = params[:neighbors] - neighbors.each do |row| - sim = row["tanimoto"] - if row["features"][params[:prediction_feature_id].to_s] - row["features"][params[:prediction_feature_id].to_s].each do |act| - weighted_sum += sim*Math.log10(act) - sim_sum += sim - end - end - end - sim_sum == 0 ? prediction = nil : prediction = 10**(weighted_sum/sim_sum) + dependent_variables.each_with_index do |v,i| + weighted_sum += weights[i]*dependent_variables[i] + sim_sum += weights[i] + end if dependent_variables + sim_sum == 0 ? prediction = nil : prediction = weighted_sum/sim_sum {:value => prediction} end - # TODO explicit neighbors, also for physchem - def self.local_fingerprint_regression compound, params, method='pls'#, method_params="sigma=0.05" - neighbors = params[:neighbors] - return {:value => nil, :confidence => nil, :warning => "No similar compounds in the training data"} unless neighbors.size > 0 - activities = [] - fingerprints = {} - weights = [] - fingerprint_ids = neighbors.collect{|row| Compound.find(row["_id"]).fingerprint}.flatten.uniq.sort - - neighbors.each_with_index do |row,i| - neighbor = Compound.find row["_id"] - fingerprint = neighbor.fingerprint - if row["features"][params[:prediction_feature_id].to_s] - row["features"][params[:prediction_feature_id].to_s].each do |act| - activities << Math.log10(act) - weights << row["tanimoto"] - fingerprint_ids.each_with_index do |id,j| - fingerprints[id] ||= [] - fingerprints[id] << fingerprint.include?(id) - end - end - end - end - - variables = [] - data_frame = [activities] - fingerprints.each do |k,v| - unless v.uniq.size == 1 - data_frame << v.collect{|m| m ? "T" : "F"} - variables << k - end - end - - if variables.empty? - result = local_weighted_average(compound, params) - result[:warning] = "No variables for regression model. Using weighted average of similar compounds." - return result - - else - compound_features = variables.collect{|f| compound.fingerprint.include?(f) ? "T" : "F"} - prediction = r_model_prediction method, data_frame, variables, weights, compound_features - if prediction.nil? or prediction[:value].nil? - prediction = local_weighted_average(compound, params) - prediction[:warning] = "Could not create local PLS model. Using weighted average of similar compounds." - return prediction - else - prediction[:prediction_interval] = [10**(prediction[:value]-1.96*prediction[:rmse]), 10**(prediction[:value]+1.96*prediction[:rmse])] - prediction[:value] = 10**prediction[:value] - prediction[:rmse] = 10**prediction[:rmse] - prediction - end - end - - end - - def self.local_physchem_regression compound, params, method="plsr"#, method_params="ncomp = 4" - - neighbors = params[:neighbors] - return {:value => nil, :confidence => nil, :warning => "No similar compounds in the training data"} unless neighbors.size > 0 - return {:value => neighbors.first["features"][params[:prediction_feature_id]], :confidence => nil, :warning => "Only one similar compound in the training set"} unless neighbors.size > 1 - - activities = [] - weights = [] - physchem = {} - - neighbors.each_with_index do |row,i| - neighbor = Compound.find row["_id"] - if row["features"][params[:prediction_feature_id].to_s] - row["features"][params[:prediction_feature_id].to_s].each do |act| - activities << Math.log10(act) - weights << row["tanimoto"] # TODO cosine ? - neighbor.physchem.each do |pid,v| # insert physchem only if there is an activity - physchem[pid] ||= [] - physchem[pid] << v - end - end - end - end - - # remove properties with a single value - physchem.each do |pid,v| - physchem.delete(pid) if v.uniq.size <= 1 - end - - if physchem.empty? - result = local_weighted_average(compound, params) - result[:warning] = "No variables for regression model. Using weighted average of similar compounds." - return result - - else - data_frame = [activities] + physchem.keys.collect { |pid| physchem[pid] } - prediction = r_model_prediction method, data_frame, physchem.keys, weights, physchem.keys.collect{|pid| compound.physchem[pid]} - if prediction.nil? - prediction = local_weighted_average(compound, params) - prediction[:warning] = "Could not create local PLS model. Using weighted average of similar compounds." - return prediction - else - prediction[:value] = 10**prediction[:value] - prediction - end - end - - end - - def self.r_model_prediction method, training_data, training_features, training_weights, query_feature_values - R.assign "weights", training_weights - r_data_frame = "data.frame(#{training_data.collect{|r| "c(#{r.join(',')})"}.join(', ')})" - R.eval "data <- #{r_data_frame}" - R.assign "features", training_features - R.eval "names(data) <- append(c('activities'),features)" # - begin - R.eval "model <- train(activities ~ ., data = data, method = '#{method}')" - rescue - return nil - end - R.eval "fingerprint <- data.frame(rbind(c(#{query_feature_values.join ','})))" - R.eval "names(fingerprint) <- features" - R.eval "prediction <- predict(model,fingerprint)" - { - :value => R.eval("prediction").to_f, - :rmse => R.eval("getTrainPerf(model)$TrainRMSE").to_f, - :r_squared => R.eval("getTrainPerf(model)$TrainRsquared").to_f, - } - end - end end end |