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Diffstat (limited to 'lib/prediction_data.rb')
| -rw-r--r-- | lib/prediction_data.rb | 297 |
1 files changed, 297 insertions, 0 deletions
diff --git a/lib/prediction_data.rb b/lib/prediction_data.rb new file mode 100644 index 0000000..42da5fc --- /dev/null +++ b/lib/prediction_data.rb @@ -0,0 +1,297 @@ + +module Lib + + class PredictionData + + CHECK_VALUES = ENV['RACK_ENV'] =~ /debug|test/ + + def self.filter_data( data, compounds, min_confidence, min_num_predictions, max_num_predictions, prediction_index=nil ) + + raise "cannot filter anything, no confidence values available" if data[:confidence_values][0]==nil + raise OpenTox::BadRequestError.new "please specify either min_confidence or max_num_predictions" if + (min_confidence!=nil and max_num_predictions!=nil) || (min_confidence==nil and max_num_predictions==nil) + raise OpenTox::BadRequestError.new "min_num_predictions only valid for min_confidence" if + (min_confidence==nil and min_num_predictions!=nil) + min_num_predictions = 0 if min_num_predictions==nil + + LOGGER.debug("filtering predictions, conf:'"+min_confidence.to_s+"' min_num_predictions: '"+ + min_num_predictions.to_s+"' max_num_predictions: '"+max_num_predictions.to_s+"' ") + #LOGGER.debug("to filter:\nconf: "+data[:confidence_values].inspect) + + orig_size = data[:predicted_values].size + valid_indices = [] + data[:confidence_values].size.times do |i| + next if prediction_index!=nil and prediction_index!=data[:predicted_values][i] + valid = false + if min_confidence!=nil + valid = (valid_indices.size<=min_num_predictions or + (data[:confidence_values][i]!=nil and data[:confidence_values][i]>=min_confidence)) + else + valid = valid_indices.size<max_num_predictions + end + valid_indices << i if valid + end + [ :predicted_values, :actual_values, :confidence_values ].each do |key| + arr = [] + valid_indices.each{|i| arr << data[key][i]} + data[key] = arr + end + if compounds!=nil + new_compounds = [] + valid_indices.each{|i| new_compounds << compounds[i]} + end + LOGGER.debug("filtered predictions remaining: "+data[:predicted_values].size.to_s+"/"+orig_size.to_s) + + PredictionData.new(data, new_compounds) + end + + def data + @data + end + + def compounds + @compounds + end + + def self.create( feature_type, test_dataset_uris, test_target_dataset_uris, + prediction_feature, prediction_dataset_uris, predicted_variables, predicted_confidences, + subjectid=nil, task=nil ) + + test_dataset_uris = [test_dataset_uris] unless test_dataset_uris.is_a?(Array) + test_target_dataset_uris = [test_target_dataset_uris] unless test_target_dataset_uris.is_a?(Array) + prediction_dataset_uris = [prediction_dataset_uris] unless prediction_dataset_uris.is_a?(Array) + predicted_variables = [predicted_variables] unless predicted_variables.is_a?(Array) + predicted_confidences = [predicted_confidences] unless predicted_confidences.is_a?(Array) + LOGGER.debug "loading prediction -- test-dataset: "+test_dataset_uris.inspect + LOGGER.debug "loading prediction -- test-target-datset: "+test_target_dataset_uris.inspect + LOGGER.debug "loading prediction -- prediction-dataset: "+prediction_dataset_uris.inspect + LOGGER.debug "loading prediction -- predicted_variable: "+predicted_variables.inspect + LOGGER.debug "loading prediction -- predicted_confidence: "+predicted_confidences.inspect + LOGGER.debug "loading prediction -- prediction_feature: "+prediction_feature.to_s + raise "prediction_feature missing" unless prediction_feature + + all_compounds = [] + all_predicted_values = [] + all_actual_values = [] + all_confidence_values = [] + accept_values = nil + + if task + task_step = 100 / (test_dataset_uris.size*2 + 1) + task_status = 0 + end + + test_dataset_uris.size.times do |i| + + test_dataset_uri = test_dataset_uris[i] + test_target_dataset_uri = test_target_dataset_uris[i] + prediction_dataset_uri = prediction_dataset_uris[i] + predicted_variable = predicted_variables[i] + predicted_confidence = predicted_confidences[i] + + predicted_variable=prediction_feature if predicted_variable==nil + + test_dataset = Lib::DatasetCache.find test_dataset_uri,subjectid + raise "test dataset not found: '"+test_dataset_uri.to_s+"'" unless test_dataset + + if test_target_dataset_uri == nil || test_target_dataset_uri.strip.size==0 || test_target_dataset_uri==test_dataset_uri + test_target_dataset_uri = test_dataset_uri + test_target_dataset = test_dataset + raise "prediction_feature not found in test_dataset, specify a test_target_dataset\n"+ + "prediction_feature: '"+prediction_feature.to_s+"'\n"+ + "test_dataset: '"+test_target_dataset_uri.to_s+"'\n"+ + "available features are: "+test_target_dataset.features.inspect if test_target_dataset.features.keys.index(prediction_feature)==nil + else + test_target_dataset = Lib::DatasetCache.find test_target_dataset_uri,subjectid + raise "test target datset not found: '"+test_target_dataset_uri.to_s+"'" unless test_target_dataset + if CHECK_VALUES + test_dataset.compounds.each do |c| + raise "test compound not found on test class dataset "+c.to_s unless test_target_dataset.compounds.include?(c) + end + end + raise "prediction_feature not found in test_target_dataset\n"+ + "prediction_feature: '"+prediction_feature.to_s+"'\n"+ + "test_target_dataset: '"+test_target_dataset_uri.to_s+"'\n"+ + "available features are: "+test_target_dataset.features.inspect if test_target_dataset.features.keys.index(prediction_feature)==nil + end + + compounds = test_dataset.compounds + LOGGER.debug "test dataset size: "+compounds.size.to_s + raise "test dataset is empty "+test_dataset_uri.to_s unless compounds.size>0 + + if feature_type=="classification" + av = test_target_dataset.accept_values(prediction_feature) + raise "'"+OT.acceptValue.to_s+"' missing/invalid for feature '"+prediction_feature.to_s+"' in dataset '"+ + test_target_dataset_uri.to_s+"', acceptValues are: '"+av.inspect+"'" if av==nil or av.length<2 + if accept_values==nil + accept_values=av + else + raise "accept values (in folds) differ "+av.inspect+" != "+accept_values.inspect if av!=accept_values + end + end + + actual_values = [] + tmp_compounds = [] + compounds.each do |c| + case feature_type + when "classification" + vals = classification_vals(test_target_dataset, c, prediction_feature, accept_values) + when "regression" + vals = regression_vals(test_target_dataset, c, prediction_feature) + end + vals.each do |v| + actual_values << v + tmp_compounds << c + end + end + compounds = tmp_compounds + task.progress( task_status += task_step ) if task # loaded actual values + + prediction_dataset = Lib::DatasetCache.find prediction_dataset_uri,subjectid + raise "prediction dataset not found: '"+prediction_dataset_uri.to_s+"'" unless prediction_dataset + + # allow missing prediction feature if there are no compounds in the prediction dataset + raise "predicted_variable not found in prediction_dataset\n"+ + "predicted_variable '"+predicted_variable.to_s+"'\n"+ + "prediction_dataset: '"+prediction_dataset_uri.to_s+"'\n"+ + "available features are: "+prediction_dataset.features.inspect if prediction_dataset.features.keys.index(predicted_variable)==nil and prediction_dataset.compounds.size>0 + raise "predicted_confidence not found in prediction_dataset\n"+ + "predicted_confidence '"+predicted_confidence.to_s+"'\n"+ + "prediction_dataset: '"+prediction_dataset_uri.to_s+"'\n"+ + "available features are: "+prediction_dataset.features.inspect if predicted_confidence and prediction_dataset.features.keys.index(predicted_confidence)==nil and prediction_dataset.compounds.size>0 + + raise "more predicted than test compounds, #test: "+compounds.size.to_s+" < #prediction: "+ + prediction_dataset.compounds.size.to_s+", test-dataset: "+test_dataset_uri.to_s+", prediction-dataset: "+ + prediction_dataset_uri if compounds.size < prediction_dataset.compounds.size + if CHECK_VALUES + prediction_dataset.compounds.each do |c| + raise "predicted compound not found in test dataset:\n"+c+"\ntest-compounds:\n"+ + compounds.collect{|c| c.to_s}.join("\n") if compounds.index(c)==nil + end + end + + predicted_values = [] + confidence_values = [] + count = 0 + compounds.each do |c| + if prediction_dataset.compounds.index(c)==nil + predicted_values << nil + confidence_values << nil + else + case feature_type + when "classification" + vals = classification_vals(prediction_dataset, c, predicted_variable, accept_values) + when "regression" + vals = regression_vals(prediction_dataset, c, predicted_variable) + end + raise "not yet implemented: more than one prediction for one compound" if vals.size>1 + predicted_values << vals[0] + if predicted_confidence + confidence_values << confidence_val(prediction_dataset, c, predicted_confidence) + else + confidence_values << nil + end + end + count += 1 + end + all_compounds += compounds + all_predicted_values += predicted_values + all_actual_values += actual_values + all_confidence_values += confidence_values + + task.progress( task_status += task_step ) if task # loaded predicted values and confidence + end + + #sort according to confidence if available + if all_confidence_values.compact.size>0 + values = [] + all_predicted_values.size.times do |i| + values << [all_predicted_values[i], all_actual_values[i], all_confidence_values[i], all_compounds[i]] + end + values = values.sort_by{ |v| v[2] || 0 }.reverse # sorting by confidence + all_predicted_values = [] + all_actual_values = [] + all_confidence_values = [] + all_compounds = [] + values.each do |v| + all_predicted_values << v[0] + all_actual_values << v[1] + all_confidence_values << v[2] + all_compounds << v[3] + end + end + + raise "illegal num compounds "+all_compounds.size.to_s+" != "+all_predicted_values.size.to_s if + all_compounds.size != all_predicted_values.size + task.progress(100) if task # done with the mathmatics + data = { :predicted_values => all_predicted_values, :actual_values => all_actual_values, :confidence_values => all_confidence_values, + :feature_type => feature_type, :accept_values => accept_values } + + PredictionData.new(data, all_compounds) + end + + private + def initialize( data, compounds ) + @data = data + @compounds = compounds + end + + private + def self.regression_vals(dataset, compound, feature) + v_num = [] + values(dataset, compound, feature).each do |v| + if v==nil or v.is_a?(Numeric) + v_num << v + else + begin + v_num << v.to_f + rescue + LOGGER.warn "no numeric value for regression: '"+v.to_s+"'" + v_num << nil + end + end + end + v_num + end + + def self.confidence_val(dataset, compound, confidence) + v = values(dataset, compound, confidence) + raise "not yet implemented: duplicate conf value" if v.size>1 + begin + v = v[0] + v = v.to_f unless v==nil or v.is_a?(Numeric) + v + rescue + LOGGER.warn "no numeric value for confidence '"+v.to_s+"'" + nil + end + end + + def self.classification_vals(dataset, compound, feature, accept_values) + v_indices = [] + values(dataset, compound, feature).each do |v| + i = accept_values.index(v.to_s) + raise "illegal class_value of prediction (value is '"+v.to_s+"'), accept values are "+ + accept_values.inspect unless v==nil or i!=nil + v_indices << i + end + v_indices + end + + def self.values(dataset, compound, feature) + return [nil] if dataset.data_entries[compound]==nil + if feature==nil + v = dataset.data_entries[compound].values[0] + else + v = dataset.data_entries[compound][feature] + end + return [nil] if v==nil + # sanitiy checks + raise "no array "+v.class.to_s+" : '"+v.to_s+"'" unless v.is_a?(Array) + v.each{|vv| raise "array-elem is array" if vv.is_a?(Array)} + # replace empty strings with nil + v_mod = v.collect{|vv| (vv.to_s().size==0 ? nil : vv)} + v_mod + end + end +end
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