diff options
author | mr <mr@mrautenberg.de> | 2010-12-03 09:53:25 +0100 |
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committer | mr <mr@mrautenberg.de> | 2010-12-03 09:53:25 +0100 |
commit | 13272ba7507d2b856d329637b48a64255434fea0 (patch) | |
tree | 64ee5eadacd672c5d24eff7a701bf0bd7b25088b | |
parent | cb3fc6a27be73c9f8c08c31f555f181c43b50bb2 (diff) | |
parent | 12220a1cc4b37fda3a6776c4d0fd787d90a16882 (diff) |
merge with helma/development
-rw-r--r-- | application.rb | 28 | ||||
-rw-r--r-- | config.ru | 3 | ||||
-rw-r--r-- | lazar.rb | 364 |
3 files changed, 61 insertions, 334 deletions
diff --git a/application.rb b/application.rb index 0f762eb..b522baf 100644 --- a/application.rb +++ b/application.rb @@ -1,14 +1,14 @@ require 'rubygems' -gem "opentox-ruby-api-wrapper", "= 1.6.3" -require 'opentox-ruby-api-wrapper' +gem "opentox-ruby", "~> 0" +require 'opentox-ruby' -class Model +class ModelStore include DataMapper::Resource + attr_accessor :prediction_dataset property :id, Serial property :uri, String, :length => 255 - property :owl, Text, :length => 2**32-1 - property :yaml, Text, :length => 2**32-1 - property :token_id, String, :length => 255 + property :yaml, Text, :length => 2**32-1 + property :token_id, String, :length => 255 property :created_at, DateTime after :save, :check_policy @@ -20,18 +20,19 @@ class Model end -class Prediction +class PredictionCache # cache predictions include DataMapper::Resource property :id, Serial property :compound_uri, String, :length => 255 property :model_uri, String, :length => 255 - property :yaml, Text, :length => 2**32-1 + property :dataset_uri, String, :length => 255 end DataMapper.auto_upgrade! require 'lazar.rb' +#require 'property_lazar.rb' helpers do @@ -50,14 +51,13 @@ end get '/?' do # get index of models response['Content-Type'] = 'text/uri-list' - Model.all(params).collect{|m| m.uri}.join("\n") + "\n" + ModelStore.all(params).collect{|m| m.uri}.join("\n") + "\n" end delete '/:id/?' do begin - model = Model.get(params[:id]) - uri = model.uri - model.destroy! + uri = ModelStore.get(params[:id]).uri + ModelStore.get(params[:id]).destroy! "Model #{params[:id]} deleted." if params[:token_id] and !Model.get(params[:id]) and uri begin @@ -75,8 +75,8 @@ end delete '/?' do # TODO delete datasets - Model.auto_migrate! - Prediction.auto_migrate! + ModelStore.auto_migrate! + #Prediction.auto_migrate! response['Content-Type'] = 'text/plain' "All models and cached predictions deleted." end @@ -1,6 +1,5 @@ require 'rubygems' -require 'opentox-ruby-api-wrapper' +require 'opentox-ruby' require 'config/config_ru' set :app_file, __FILE__ # to get the view path right run Sinatra::Application - @@ -1,246 +1,16 @@ -# R integration -# workaround to initialize R non-interactively (former rinruby versions did this by default) -# avoids compiling R with X -R = nil -require "rinruby" require "haml" -class Lazar < Model - - attr_accessor :prediction_dataset - - # AM begin - # regression function, created 06/10 - # ch: please properly integrate this into the workflow. You will need some criterium for distinguishing regression/classification (hardcoded regression for testing) - def regression(compound_uri,prediction,verbose=false) - - lazar = YAML.load self.yaml - compound = OpenTox::Compound.new(:uri => compound_uri) - - # obtain X values for query compound - compound_matches = compound.match lazar.features - - conf = 0.0 - features = { :activating => [], :deactivating => [] } - neighbors = {} - regression = nil - - regr_occurrences = [] # occurrence vector with {0,1} entries - sims = [] # similarity values between query and neighbors - acts = [] # activities of neighbors for supervised learning - neighbor_matches = [] # as in classification: URIs of matches - gram_matrix = [] # square matrix of similarities between neighbors; implements weighted tanimoto kernel - i = 0 - - # aquire data related to query structure - lazar.fingerprints.each do |uri,matches| - sim = OpenTox::Algorithm::Similarity.weighted_tanimoto(compound_matches,matches,lazar.p_values) - lazar.activities[uri].each do |act| - if sim > 0.3 - neighbors[uri] = {:similarity => sim} - neighbors[uri][:features] = { :activating => [], :deactivating => [] } unless neighbors[uri][:features] - matches.each do |m| - if lazar.effects[m] == 'activating' - neighbors[uri][:features][:activating] << {:smarts => m, :p_value => lazar.p_values[m]} - elsif lazar.effects[m] == 'deactivating' - neighbors[uri][:features][:deactivating] << {:smarts => m, :p_value => lazar.p_values[m]} - end - end - lazar.activities[uri].each do |act| - neighbors[uri][:activities] = [] unless neighbors[uri][:activities] - neighbors[uri][:activities] << act - end - conf += OpenTox::Utils.gauss(sim) - sims << OpenTox::Utils.gauss(sim) - #TODO check for 0 s - acts << Math.log10(act.to_f) - #acts << act.to_f - neighbor_matches[i] = matches - i+=1 - end - end - end - conf = conf/neighbors.size - LOGGER.debug "Regression: found " + neighbor_matches.size.to_s + " neighbors." - - - unless neighbor_matches.length == 0 - # gram matrix - (0..(neighbor_matches.length-1)).each do |i| - gram_matrix[i] = [] - # lower triangle - (0..(i-1)).each do |j| - sim = OpenTox::Algorithm::Similarity.weighted_tanimoto(neighbor_matches[i], neighbor_matches[j], lazar.p_values) - gram_matrix[i] << OpenTox::Utils.gauss(sim) - end - # diagonal element - gram_matrix[i][i] = 1.0 - # upper triangle - ((i+1)..(neighbor_matches.length-1)).each do |j| - sim = OpenTox::Algorithm::Similarity.weighted_tanimoto(neighbor_matches[i], neighbor_matches[j], lazar.p_values) - gram_matrix[i] << OpenTox::Utils.gauss(sim) - end - end - - @r = RinRuby.new(false,false) # global R instance leads to Socket errors after a large number of requests - @r.eval "library('kernlab')" # this requires R package "kernlab" to be installed - LOGGER.debug "Setting R data ..." - # set data - @r.gram_matrix = gram_matrix.flatten - @r.n = neighbor_matches.length - @r.y = acts - @r.sims = sims - - LOGGER.debug "Preparing R data ..." - # prepare data - @r.eval "y<-as.vector(y)" - @r.eval "gram_matrix<-as.kernelMatrix(matrix(gram_matrix,n,n))" - @r.eval "sims<-as.vector(sims)" - - # model + support vectors - LOGGER.debug "Creating SVM model ..." - @r.eval "model<-ksvm(gram_matrix, y, kernel=matrix, type=\"nu-svr\", nu=0.8)" - @r.eval "sv<-as.vector(SVindex(model))" - @r.eval "sims<-sims[sv]" - @r.eval "sims<-as.kernelMatrix(matrix(sims,1))" - LOGGER.debug "Predicting ..." - @r.eval "p<-predict(model,sims)[1,1]" - regression = 10**(@r.p.to_f) - LOGGER.debug "Prediction is: '" + regression.to_s + "'." - @r.quit # free R - - end - - if (regression != nil) - feature_uri = lazar.dependentVariables - prediction.compounds << compound_uri - prediction.features << feature_uri - prediction.data[compound_uri] = [] unless prediction.data[compound_uri] - compound_matches.each { |m| features[lazar.effects[m].to_sym] << {:smarts => m, :p_value => lazar.p_values[m] } } - tuple = { - File.join(@@config[:services]["opentox-model"],"lazar#regression") => regression, - File.join(@@config[:services]["opentox-model"],"lazar#confidence") => conf - } - if verbose - tuple[File.join(@@config[:services]["opentox-model"],"lazar#neighbors")] = neighbors - tuple[File.join(@@config[:services]["opentox-model"],"lazar#features")] = features - end - prediction.data[compound_uri] << {feature_uri => tuple} - end - - end - # AM end - - - def classification(compound_uri,prediction,verbose=false) - - lazar = YAML.load self.yaml - compound = OpenTox::Compound.new(:uri => compound_uri) - compound_matches = compound.match lazar.features - - conf = 0.0 - features = { :activating => [], :deactivating => [] } - neighbors = {} - classification = nil - - lazar.fingerprints.each do |uri,matches| - - sim = OpenTox::Algorithm::Similarity.weighted_tanimoto(compound_matches,matches,lazar.p_values) - if sim > 0.3 - neighbors[uri] = {:similarity => sim} - neighbors[uri][:features] = { :activating => [], :deactivating => [] } unless neighbors[uri][:features] - matches.each do |m| - if lazar.effects[m] == 'activating' - neighbors[uri][:features][:activating] << {:smarts => m, :p_value => lazar.p_values[m]} - elsif lazar.effects[m] == 'deactivating' - neighbors[uri][:features][:deactivating] << {:smarts => m, :p_value => lazar.p_values[m]} - end - end - lazar.activities[uri].each do |act| - neighbors[uri][:activities] = [] unless neighbors[uri][:activities] - neighbors[uri][:activities] << act - case act.to_s - when 'true' - conf += OpenTox::Utils.gauss(sim) - when 'false' - conf -= OpenTox::Utils.gauss(sim) - end - end - end - end - - conf = conf/neighbors.size - if conf > 0.0 - classification = true - elsif conf < 0.0 - classification = false - end - if (classification != nil) - feature_uri = lazar.dependentVariables - prediction.compounds << compound_uri - prediction.features << feature_uri - prediction.data[compound_uri] = [] unless prediction.data[compound_uri] - compound_matches.each { |m| features[lazar.effects[m].to_sym] << {:smarts => m, :p_value => lazar.p_values[m] } } - tuple = { - File.join(@@config[:services]["opentox-model"],"lazar#classification") => classification, - File.join(@@config[:services]["opentox-model"],"lazar#confidence") => conf - } - if verbose - tuple[File.join(@@config[:services]["opentox-model"],"lazar#neighbors")] = neighbors - tuple[File.join(@@config[:services]["opentox-model"],"lazar#features")] = features - end - prediction.data[compound_uri] << {feature_uri => tuple} - end - end - - def database_activity?(compound_uri,prediction) - # find database activities - lazar = YAML.load self.yaml - db_activities = lazar.activities[compound_uri] - if db_activities - prediction.creator = lazar.trainingDataset - feature_uri = lazar.dependentVariables - prediction.compounds << compound_uri - prediction.features << feature_uri - prediction.data[compound_uri] = [] unless prediction.data[compound_uri] - db_activities.each do |act| - prediction.data[compound_uri] << {feature_uri => act} - end - true - else - false - end - end - - def to_owl - data = YAML.load(yaml) - activity_dataset = YAML.load(RestClient.get(data.trainingDataset, :accept => 'application/x-yaml').to_s) - feature_dataset = YAML.load(RestClient.get(data.feature_dataset_uri, :accept => 'application/x-yaml').to_s) - owl = OpenTox::Owl.create 'Model', uri - owl.set("creator","http://github.com/helma/opentox-model") - owl.set("title", URI.decode(data.dependentVariables.split(/#/).last) ) - #owl.set("title","#{URI.decode(activity_dataset.title)} lazar classification") - owl.set("date",created_at.to_s) - owl.set("algorithm",data.algorithm) - owl.set("dependentVariables",activity_dataset.features.join(', ')) - owl.set("independentVariables",feature_dataset.features.join(', ')) - owl.set("predictedVariables", data.dependentVariables ) - #owl.set("predictedVariables",activity_dataset.features.join(', ') + "_lazar_classification") - owl.set("trainingDataset",data.trainingDataset) - owl.parameters = { - "Dataset URI" => - { :scope => "mandatory", :value => data.trainingDataset }, - "Feature URI for dependent variable" => - { :scope => "mandatory", :value => activity_dataset.features.join(', ')}, - "Feature generation URI" => - { :scope => "mandatory", :value => feature_dataset.creator } - } - - owl.rdf - end - +helpers do + def uri_available?(urlStr) + url = URI.parse(urlStr) + Net::HTTP.start(urlStr.host, urlStr.port) do |http| + return http.head(urlStr.request_uri).code == "200" + end + end end +# Get model representation +# @return [application/rdf+xml,application/x-yaml] Model representation get '/:id/?' do accept = request.env['HTTP_ACCEPT'] accept = "application/rdf+xml" if accept == '*/*' or accept == '' or accept.nil? @@ -253,16 +23,14 @@ get '/:id/?' do params[:id].sub!(/.rdf$/,'') accept = 'application/rdf+xml' end - model = Lazar.get(params[:id]) - halt 404, "Model #{params[:id]} not found." unless model + halt 404, "Model #{params[:id]} not found." unless model = ModelStore.get(params[:id]) + lazar = YAML.load model.yaml case accept - when "application/rdf+xml" - response['Content-Type'] = 'application/rdf+xml' - unless model.owl # lazy owl creation - model.owl = model.to_owl - model.save - end - model.owl + when /application\/rdf\+xml/ + s = OpenTox::Serializer::Owl.new + s.add_model(url_for('/lazar',:full),lazar.metadata) + response['Content-Type'] = 'application/rdf+xml' + s.to_rdfxml when /yaml/ response['Content-Type'] = 'application/x-yaml' model.yaml @@ -271,93 +39,53 @@ get '/:id/?' do end end -get '/:id/algorithm/?' do - response['Content-Type'] = 'text/plain' - YAML.load(Lazar.get(params[:id]).yaml).algorithm -end - -get '/:id/trainingDataset/?' do - response['Content-Type'] = 'text/plain' - YAML.load(Lazar.get(params[:id]).yaml).trainingDataset -end - -get '/:id/feature_dataset/?' do - response['Content-Type'] = 'text/plain' - YAML.load(Lazar.get(params[:id]).yaml).feature_dataset_uri -end - +# Store a lazar model. This method should not be called directly, use OpenTox::Algorithm::Lazr to create a lazar model +# @param [Body] lazar Model representation in YAML format +# @return [String] Model URI post '/?' do # create model halt 400, "MIME type \"#{request.content_type}\" not supported." unless request.content_type.match(/yaml/) - model = Lazar.new - model.save + model = ModelStore.create model.token_id = params[:token_id] if params[:token_id] model.token_id = request.env["HTTP_TOKEN_ID"] if !model.token_id and request.env["HTTP_TOKEN_ID"] model.uri = url_for("/#{model.id}", :full) - model.yaml = request.env["rack.input"].read + lazar = YAML.load request.env["rack.input"].read + lazar.uri = model.uri + model.yaml = lazar.to_yaml model.save model.uri end -post '/:id/?' do # create prediction +# Make a lazar prediction. Predicts either a single compound or all compounds from a dataset +# @param [optional,String] dataset_uri URI of the dataset to be predicted +# @param [optional,String] compound_uri URI of the compound to be predicted +# @param [optional,Header] Accept Content-type of prediction, can be either `application/rdf+xml or application/x-yaml` +# @return [text/uri-list] URI of prediction task (dataset prediction) or prediction dataset (compound prediction) +post '/:id/?' do - lazar = Lazar.get(params[:id]) - halt 404, "Model #{params[:id]} does not exist." unless lazar + @lazar = YAML.load ModelStore.get(params[:id]).yaml + + halt 404, "Model #{params[:id]} does not exist." unless @lazar halt 404, "No compound_uri or dataset_uri parameter." unless compound_uri = params[:compound_uri] or dataset_uri = params[:dataset_uri] - @prediction = OpenTox::Dataset.new - @prediction.creator = lazar.uri - @prediction.token_id = params[:token_id] - @prediction.token_id = request.env["HTTP_TOKEN_ID"] if !@prediction.token_id and request.env["HTTP_TOKEN_ID"] - dependent_variable = YAML.load(lazar.yaml).dependentVariables - @prediction.title = URI.decode(dependent_variable.split(/#/).last) - case dependent_variable - when /classification/ - prediction_type = "classification" - when /regression/ - prediction_type = "regression" - end + response['Content-Type'] = 'text/uri-list' if compound_uri - # look for cached prediction first - if cached_prediction = Prediction.first(:model_uri => lazar.uri, :compound_uri => compound_uri) - @prediction = YAML.load(cached_prediction.yaml) - else - begin - # AM: switch here between regression and classification - eval "lazar.#{prediction_type}(compound_uri,@prediction,true) unless lazar.database_activity?(compound_uri,@prediction)" - Prediction.create(:model_uri => lazar.uri, :compound_uri => compound_uri, :yaml => @prediction.to_yaml) - rescue - LOGGER.error "#{prediction_type} failed for #{compound_uri} with #{$!} " - halt 500, "Prediction of #{compound_uri} failed." - end + cache = PredictionCache.first(:model_uri => @lazar.uri, :compound_uri => compound_uri) + return cache.dataset_uri if cache and uri_available?(cache.dataset_uri) + begin + prediction_uri = @lazar.predict(compound_uri,true).uri + PredictionCache.create(:model_uri => @lazar.uri, :compound_uri => compound_uri, :dataset_uri => prediction_uri) + prediction_uri + rescue + LOGGER.error "Lazar prediction failed for #{compound_uri} with #{$!} " + halt 500, "Prediction of #{compound_uri} with #{@lazar.uri} failed." end - case request.env['HTTP_ACCEPT'] - when /yaml/ - @prediction.to_yaml - when 'application/rdf+xml' - @prediction.to_owl - else - halt 400, "MIME type \"#{request.env['HTTP_ACCEPT']}\" not supported." - end elsif dataset_uri - response['Content-Type'] = 'text/uri-list' - task_uri = OpenTox::Task.as_task do - input_dataset = OpenTox::Dataset.find(dataset_uri) - input_dataset.compounds.each do |compound_uri| - # AM: switch here between regression and classification - begin - eval "lazar.#{prediction_type}(compound_uri,@prediction) unless lazar.database_activity?(compound_uri,@prediction)" - rescue - LOGGER.error "#{prediction_type} failed for #{compound_uri} with #{$!} " - end - end - begin - uri = @prediction.save.chomp - rescue - halt 500, "Could not save prediction dataset" - end + task = OpenTox::Task.create("Predict dataset",url_for("/#{@lazar.id}", :full)) do + @lazar.predict_dataset(dataset_uri).uri end - halt 202,task_uri + halt 503,task.uri+"\n" if task.status == "Cancelled" + halt 202,task.uri end end |