Merge branch 'bbrc-sample' into development

5 files changed, 405 insertions, 57 deletions

diff --git a/.gitmodules b/.gitmodules
index 61a4b92..af7a8f4 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -3,4 +3,7 @@
 	url = git://github.com/amaunz/fminer2.git
 [submodule "last-utils"]
 	path = last-utils
-	url = git://github.com/amaunz/last-utils.git
+  url = git://github.com/amaunz/last-utils.git
+[submodule "bbrc-sample"]
+	path = bbrc-sample
+	url = git://github.com/amaunz/bbrc-sample
diff --git a/application.rb b/application.rb
index ef123da..75d1e21 100644
--- a/application.rb
+++ b/application.rb
@@ -20,6 +20,7 @@ require File.join(File.expand_path(File.dirname(__FILE__)), 'last-utils/lu.rb')
 gem "opentox-ruby", "~> 3"
 require 'opentox-ruby'
 require 'rjb'
+require 'rinruby'
 
 
 # main
@@ -38,7 +39,7 @@ end
 #
 # @return [text/uri-list] algorithm URIs
 get '/?' do
-	list = [ url_for('/lazar', :full), url_for('/fminer/bbrc', :full), url_for('/fminer/last', :full), url_for('/feature_selection/rfe', :full), url_for('/pc', :full) ].join("\n") + "\n"
+	list = [ url_for('/lazar', :full), url_for('/fminer/bbrc', :full), url_for('/fminer/bbrc/sample', :full), url_for('/fminer/last', :full), url_for('/fminer/bbrc/match', :full), url_for('/fminer/last/match', :full), url_for('/feature_selection/rfe', :full), url_for('/pc', :full) ].join("\n") + "\n"
   case request.env['HTTP_ACCEPT']
   when /text\/html/
     content_type "text/html"
diff --git a/bbrc-sample b/bbrc-sample
new file mode 160000
+Subproject 0d1d349ac33ae2fcc1bbdf31617ed9132c7527c
diff --git a/fminer.rb b/fminer.rb
index da06fc8..052d20d 100644
--- a/fminer.rb
+++ b/fminer.rb
@@ -4,14 +4,16 @@ ENV['FMINER_PVALUES'] = 'true'
 ENV['FMINER_SILENT'] = 'true'
 ENV['FMINER_NR_HITS'] = 'true'
 
-@@bbrc = Bbrc::Bbrc.new 
-@@last = Last::Last.new 
+@@bbrc = Bbrc::Bbrc.new
+@@last = Last::Last.new
+
+
 
 # Get list of fminer algorithms
 #
 # @return [text/uri-list] URIs of fminer algorithms
 get '/fminer/?' do
-  list = [ url_for('/fminer/bbrc', :full), url_for('/fminer/last', :full) ].join("\n") + "\n"
+  list = [ url_for('/fminer/bbrc', :full), url_for('/fminer/bbrc/sample', :full), url_for('/fminer/last', :full), url_for('/fminer/bbrc/match', :full), url_for('/fminer/last/match', :full) ].join("\n") + "\n"
   case request.env['HTTP_ACCEPT']
   when /text\/html/
     content_type "text/html"
@@ -22,6 +24,8 @@ get '/fminer/?' do
   end
 end
 
+
+
 # Get RDF/XML representation of fminer bbrc algorithm
 # @return [application/rdf+xml] OWL-DL representation of fminer bbrc algorithm
 get "/fminer/bbrc/?" do
@@ -50,7 +54,40 @@ get "/fminer/bbrc/?" do
     content_type "application/x-yaml"
     algorithm.to_yaml
   else
-    response['Content-Type'] = 'application/rdf+xml'  
+    response['Content-Type'] = 'application/rdf+xml'
+    algorithm.to_rdfxml
+  end
+end
+
+# Get RDF/XML representation of fminer bbrc algorithm
+# @return [application/rdf+xml] OWL-DL representation of fminer bbrc algorithm
+get "/fminer/bbrc/sample/?" do
+  algorithm = OpenTox::Algorithm::Generic.new(url_for('/fminer/bbrc/sample',:full))
+  algorithm.metadata = {
+    DC.title => 'fminer backbone refinement class representatives, obtained from samples of a dataset',
+    DC.creator => "andreas@maunz.de",
+#    BO.instanceOf => "http://opentox.org/ontology/ist-algorithms.owl#fminer_bbrc",
+    RDF.type => [OT.Algorithm,OTA.PatternMiningSupervised],
+    OT.parameters => [
+      { DC.description => "Dataset URI", OT.paramScope => "mandatory", DC.title => "dataset_uri" },
+      { DC.description => "Feature URI for dependent variable", OT.paramScope => "mandatory", DC.title => "prediction_feature" },
+      { DC.description => "Number of bootstrap samples", OT.paramScope => "optional", DC.title => "num_boots" },
+      { DC.description => "Minimum sampling support", OT.paramScope => "optional", DC.title => "min_sampling_support" },
+      { DC.description => "Minimum frequency", OT.paramScope => "optional", DC.title => "min_frequency" },
+      { DC.description => "Whether subgraphs should be weighted with their occurrence counts in the instances (frequency)", OT.paramScope => "optional", DC.title => "nr_hits" },
+      { DC.description => "BBRC classes, pass 'false' to switch off mining for BBRC representatives.", OT.paramScope => "optional", DC.title => "backbone" },
+      { DC.description => "Chisq estimation method, pass 'mean' to use simple mean estimate for chisq test.", OT.paramScope => "optional", DC.title => "method" }
+  ]
+  }
+  case request.env['HTTP_ACCEPT']
+  when /text\/html/
+    content_type "text/html"
+    OpenTox.text_to_html algorithm.to_yaml
+  when /yaml/
+    content_type "application/x-yaml"
+    algorithm.to_yaml
+  else
+    response['Content-Type'] = 'application/rdf+xml'
     algorithm.to_rdfxml
   end
 end
@@ -81,41 +118,44 @@ get "/fminer/last/?" do
     content_type "application/x-yaml"
     algorithm.to_yaml
   else
-    response['Content-Type'] = 'application/rdf+xml'  
+    response['Content-Type'] = 'application/rdf+xml'
     algorithm.to_rdfxml
   end
 end
 
-# Creates same features for dataset <dataset_uri> that have been created
-# with fminer in dataset <feature_dataset_uri>
-# accept params[:nr_hits] as used in other fminer methods 
-post '/fminer/:method/match?' do 
-  raise OpenTox::BadRequestError.new "feature_dataset_uri not given" unless params[:feature_dataset_uri]
-  raise OpenTox::BadRequestError.new "dataset_uri not given" unless params[:dataset_uri] 
-  task = OpenTox::Task.create("Matching features", url_for('/fminer/match',:full)) do |task|
-    f_dataset = OpenTox::Dataset.find params[:feature_dataset_uri],@subjectid
-    c_dataset = OpenTox::Dataset.find params[:dataset_uri],@subjectid
-    res_dataset = OpenTox::Dataset.create CONFIG[:services]["dataset"],@subjectid
-    f_dataset.features.each do |f,m|
-      res_dataset.add_feature(f,m)
-    end
-    c_dataset.compounds.each do |c|
-      res_dataset.add_compound(c)
-      comp = OpenTox::Compound.new(c)
-      f_dataset.features.each do |f,m|
-        if params[:nr_hits] == "true"
-          hits = comp.match_hits([m[OT.smarts]])
-          res_dataset.add(c,f,hits[m[OT.smarts]]) if hits[m[OT.smarts]]          
-        else
-          res_dataset.add(c,f,1) if comp.match?(m[OT.smarts])
-        end
-      end
-    end
-    res_dataset.save @subjectid
-    res_dataset.uri
+
+# Get RDF/XML representation of fminer matching algorithm
+# @param [String] dataset_uri URI of the dataset 
+# @param [String] feature_dataset_uri URI of the feature dataset (i.e. dependent variable)
+# @param [optional] parameters Accepted parameters are
+# - prediction_feature URI of prediction feature to calculate p-values for
+get "/fminer/:method/match?" do
+  algorithm = OpenTox::Algorithm::Generic.new(url_for("/fminer/#{params[:method]}/match",:full))
+  algorithm.metadata = {
+    DC.title => 'fminer feature matching',
+    DC.creator => "mguetlein@gmail.com, andreas@maunz.de",
+    RDF.type => [OT.Algorithm,OTA.PatternMiningSupervised],
+    OT.parameters => [
+      { DC.description => "Dataset URI", OT.paramScope => "mandatory", DC.title => "dataset_uri" },
+      { DC.description => "Feature Dataset URI", OT.paramScope => "mandatory", DC.title => "feature_dataset_uri" },
+      { DC.description => "Feature URI for dependent variable", OT.paramScope => "optional", DC.title => "prediction_feature" }
+  ]
+  }
+  case request.env['HTTP_ACCEPT']
+  when /text\/html/
+    content_type "text/html"
+    OpenTox.text_to_html algorithm.to_yaml
+  when /application\/x-yaml/
+    content_type "application/x-yaml"
+    algorithm.to_yaml
+  else
+    response['Content-Type'] = 'application/rdf+xml'
+    algorithm.to_rdfxml
   end
-  return_task(task)
-end 
+end
+
+
+
 
 # Run bbrc algorithm on dataset
 #
@@ -128,7 +168,7 @@ end
 #   - min_chisq_significance Significance threshold (between 0 and 1)
 #   - nr_hits Set to "true" to get hit count instead of presence
 # @return [text/uri-list] Task URI
-post '/fminer/bbrc/?' do 
+post '/fminer/bbrc/?' do
 
   fminer=OpenTox::Algorithm::Fminer.new
   fminer.check_params(params,5,@subjectid)
@@ -144,7 +184,7 @@ post '/fminer/bbrc/?' do
     end
     @@bbrc.SetMinfreq(fminer.minfreq)
     @@bbrc.SetType(1) if params[:feature_type] == "paths"
-    @@bbrc.SetBackbone(eval params[:backbone]) if params[:backbone] and ( params[:backbone] == "true" or params[:backbone] == "false" ) # convert string to boolean
+    @@bbrc.SetBackbone(false) if params[:backbone] == "false"
     @@bbrc.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance]
     @@bbrc.SetConsoleOut(false)
 
@@ -155,7 +195,11 @@ post '/fminer/bbrc/?' do
       OT.hasSource => url_for('/fminer/bbrc', :full),
       OT.parameters => [
         { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
-        { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] }
+        { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
+        { DC.title => "min_frequency", OT.paramValue => fminer.minfreq },
+        { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") },
+        { DC.title => "backbone", OT.paramValue => (params[:backbone] == "false" ? "false" : "true") }
+
     ]
     })
     feature_dataset.save(@subjectid)
@@ -185,20 +229,20 @@ post '/fminer/bbrc/?' do
         smarts = f[0]
         p_value = f[1]
 
-        if (!@@bbrc.GetRegression) 
+        if (!@@bbrc.GetRegression)
           id_arrs = f[2..-1].flatten
-          max = OpenTox::Algorithm.effect(f[2..-1], fminer.db_class_sizes)
-          effect = f[2..-1].size-max
+          max = OpenTox::Algorithm.effect(f[2..-1].reverse, fminer.db_class_sizes) # f needs reversal for bbrc
+          effect = max+1
         else #regression part
           id_arrs = f[2]
           # DV: effect calculation
           f_arr=Array.new
           f[2].each do |id|
             id=id.keys[0] # extract id from hit count hash
-            f_arr.push(fminer.all_activities[id]) 
-          end 
+            f_arr.push(fminer.all_activities[id])
+          end
           f_median=f_arr.to_scale.median
-          if g_median >= f_median 
+          if g_median >= f_median
             effect = 'activating'
           else
             effect = 'deactivating'
@@ -232,13 +276,13 @@ post '/fminer/bbrc/?' do
           end
         }
 
-      end # end of 
+      end # end of
     end   # feature parsing
 
     # AM: add feature values for non-present features
-    # feature_dataset.complete_data_entries 
+    # feature_dataset.complete_data_entries
 
-    feature_dataset.save(@subjectid) 
+    feature_dataset.save(@subjectid)
     feature_dataset.uri
   end
   response['Content-Type'] = 'text/uri-list'
@@ -247,6 +291,191 @@ post '/fminer/bbrc/?' do
 end
 #end
 
+
+# Run bbrc/sample algorithm on a dataset
+#
+# @param [String] dataset_uri URI of the training dataset
+# @param [String] prediction_feature URI of the prediction feature (i.e. dependent variable)
+# @param [optional] BBRC sample parameters, accepted are
+#   - num_boots Number of bootstrap samples (default 150)
+#   - min_sampling_support Minimum sampling support (default 30% of num_boots)
+#   - min_frequency  Minimum frequency (default 10% of dataset size)
+#   - nr_hits Whether subgraphs should be weighted with their occurrence counts in the instances (frequency)
+#   - random_seed Random seed ensures same datasets in bootBbrc
+#   - backbone BBRC classes, pass 'false' to switch off mining for BBRC representatives. (default "true")
+#   - method Chisq estimation method, pass 'mean' to use simple mean estimate (default 'mle').
+#
+# @return [text/uri-list] Task URI
+post '/fminer/bbrc/sample/?' do
+
+  fminer=OpenTox::Algorithm::Fminer.new
+  fminer.check_params(params,100,@subjectid) # AM: 100 per-mil (10%) as default minfreq
+
+  # num_boots
+  unless params[:num_boots]
+    num_boots = 150
+    LOGGER.debug "Set num_boots to default value #{num_boots}"
+  else
+    raise OpenTox::BadRequestError.new "num_boots is not numeric" unless OpenTox::Algorithm.numeric? params[:num_boots]
+	  num_boots = params[:num_boots].to_i.ceil
+  end
+
+  # min_sampling_support
+  unless params[:min_sampling_support]
+    min_sampling_support = (num_boots * 0.3).ceil
+    LOGGER.debug "Set min_sampling_support to default value #{min_sampling_support}"
+  else
+    raise OpenTox::BadRequestError.new "min_sampling_support is not numeric" unless OpenTox::Algorithm.numeric? params[:min_sampling_support]
+	  min_sampling_support= params[:min_sampling_support].to_i.ceil
+  end
+
+  # random_seed
+  unless params[:random_seed]
+    random_seed = 1
+    LOGGER.debug "Set random seed to default value #{random_seed}"
+  else
+    raise OpenTox::BadRequestError.new "random_seed is not numeric" unless OpenTox::Algorithm.numeric? params[:random_seed]
+    random_seed= params[:random_seed].to_i.ceil
+  end
+
+  # backbone
+  unless params[:backbone]
+    backbone = "true"
+    LOGGER.debug "Set backbone to default value #{backbone}"
+  else
+    raise OpenTox::BadRequestError.new "backbone is neither 'true' nor 'false'" unless (params[:backbone] == "true" or params[:backbone] == "false")
+    backbone = params[:backbone]
+  end
+
+  # method
+  unless params[:method]
+    method="mle"
+    LOGGER.debug "Set method to default value #{method}"
+  else
+    raise OpenTox::BadRequestError.new "method is neither 'mle' nor 'mean'" unless (params[:method] == "mle" or params[:method] == "mean")
+    method = params[:method]
+  end
+
+  task = OpenTox::Task.create("Mining BBRC sample features", url_for('/fminer',:full)) do |task|
+    if fminer.prediction_feature.feature_type == "regression"
+      raise OpenTox::BadRequestError.new "BBRC sampling is only for classification"
+    else
+      raise "no accept values for dataset '"+fminer.training_dataset.uri.to_s+"' and feature '"+fminer.prediction_feature.uri.to_s+
+        "'" unless fminer.training_dataset.accept_values(fminer.prediction_feature.uri)
+      @value_map=fminer.training_dataset.value_map(fminer.prediction_feature.uri)
+    end
+
+    feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
+    feature_dataset.add_metadata({
+      DC.title => "BBRC representatives for " + fminer.training_dataset.metadata[DC.title].to_s + "(bootstrapped)",
+      DC.creator => url_for('/fminer/bbrc/sample',:full),
+      OT.hasSource => url_for('/fminer/bbrc/sample', :full)
+    })
+    feature_dataset.save(@subjectid)
+
+    # filled by add_fminer_data:
+    fminer.compounds = [] # indexed by id, starting from 1 (not 0)
+    fminer.db_class_sizes = Array.new # for effect calculation
+    fminer.all_activities = Hash.new # for effect calculation, indexed by id, starting from 1 (not 0)
+    fminer.smi = [] # needed for matching the patterns back, indexed by id, starting from 1 (not 0)
+    fminer.add_fminer_data(nil, @value_map) # To only fill in administrative data (no fminer priming) pass 'nil' as instance
+
+    raise "No compounds in dataset #{fminer.training_dataset.uri}" if fminer.compounds.size==0
+
+
+    # run bbrc-sample, obtain smarts and p-values
+    features = Set.new
+    task.progress 10
+    @r = RinRuby.new(true,false) # global R instance leads to Socket errors after a large number of requests
+    @r.assign "dataset.uri", params[:dataset_uri]
+    @r.assign "prediction.feature.uri", fminer.prediction_feature.uri
+    @r.assign "num.boots", num_boots
+    @r.assign "min.frequency.per.sample", fminer.minfreq
+    @r.assign "min.sampling.support", min_sampling_support
+    @r.assign "random.seed", random_seed
+    @r.assign "backbone", backbone
+    @r.assign "bbrc.service", File.join(CONFIG[:services]["opentox-algorithm"], "fminer/bbrc")
+    @r.assign "dataset.service", CONFIG[:services]["opentox-dataset"]
+    @r.assign "method", method
+    @r.eval "source(\"bbrc-sample/bbrc-sample.R\")"
+    begin
+      @r.eval "bootBbrc(dataset.uri, prediction.feature.uri, num.boots, min.frequency.per.sample, min.sampling.support, NULL, bbrc.service, dataset.service, T, random.seed, as.logical(backbone), method)"
+      smarts = (@r.pull "ans.patterns").collect! { |id| id.gsub(/\'/,"") } # remove extra quotes around smarts
+      r_p_values = @r.pull "ans.p.values"
+      smarts_p_values = {}; smarts.size.times { |i| smarts_p_values[ smarts[i] ] = r_p_values[i] }
+      merge_time = @r.pull "merge.time"
+      n_stripped_mss = @r.pull "n.stripped.mss"
+      n_stripped_cst = @r.pull "n.stripped.cst"
+    rescue Exception => e
+      LOGGER.debug "#{e.class}: #{e.message}"
+      LOGGER.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
+    end
+    @r.quit # free R
+
+    # matching
+    task.progress 90
+    lu = LU.new                             # AM LAST: uses last-utils here
+    params[:nr_hits] == "true" ? hit_count=true: hit_count=false
+    matches, counts = lu.match_rb(fminer.smi,smarts,hit_count)       # AM LAST: creates instantiations
+    
+    feature_dataset.add_metadata({
+          OT.parameters => [
+        { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
+        { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
+        { DC.title => "min_sampling_support", OT.paramValue => min_sampling_support },
+        { DC.title => "num_boots", OT.paramValue => num_boots },
+        { DC.title => "min_frequency_per_sample", OT.paramValue => fminer.minfreq },
+        { DC.title => "nr_hits", OT.paramValue => hit_count.to_s },
+        { DC.title => "merge_time", OT.paramValue => merge_time.to_s },
+        { DC.title => "n_stripped_mss", OT.paramValue => n_stripped_mss.to_s },
+        { DC.title => "n_stripped_cst", OT.paramValue => n_stripped_cst.to_s },
+        { DC.title => "random_seed", OT.paramValue => random_seed.to_s },
+        { DC.title => "backbone", OT.paramValue => backbone.to_s },
+        { DC.title => "method", OT.paramValue => method.to_s }
+          ]
+    })
+
+    matches.each do |smarts, ids|
+      feat_hash = Hash[*(fminer.all_activities.select { |k,v| ids.include?(k) }.flatten)] # AM LAST: get activities of feature occurrences; see http://www.softiesonrails.com/2007/9/18/ruby-201-weird-hash-syntax
+      g = Array.new
+      @value_map.each { |y,act| g[y-1]=Array.new }
+      feat_hash.each  { |x,y|   g[y-1].push(x)   }
+      max = OpenTox::Algorithm.effect(g, fminer.db_class_sizes)
+      effect = max + 1
+      feature_uri = File.join feature_dataset.uri,"feature","bbrc", features.size.to_s
+      unless features.include? smarts
+        features << smarts
+        metadata = {
+          RDF.type => [OT.Feature, OT.Substructure],
+          OT.hasSource => feature_dataset.uri,
+          OT.smarts => smarts,
+          OT.pValue => smarts_p_values[smarts],
+          OT.effect => effect,
+          OT.parameters => [
+            { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
+            { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] }
+        ]
+        }
+        feature_dataset.add_feature feature_uri, metadata
+      end
+      if !hit_count
+        ids.each { |id| feature_dataset.add(fminer.compounds[id], feature_uri, 1)}
+      else
+        ids.each_with_index { |id,i| feature_dataset.add(fminer.compounds[id], feature_uri, counts[smarts][i])}
+      end
+    end
+
+    # AM: add feature values for non-present features
+    # feature_dataset.complete_data_entries
+
+    feature_dataset.save(@subjectid)
+    feature_dataset.uri
+  end
+  response['Content-Type'] = 'text/uri-list'
+  raise OpenTox::ServiceUnavailableError.newtask.uri+"\n" if task.status == "Cancelled"
+  halt 202,task.uri.to_s+"\n"
+end
+
 # Run last algorithm on a dataset
 #
 # @param [String] dataset_uri URI of the training dataset
@@ -282,8 +511,10 @@ post '/fminer/last/?' do
       OT.hasSource => url_for('/fminer/last', :full),
       OT.parameters => [
         { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
-        { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] }
-    ]
+        { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] },
+        { DC.title => "min_frequency", OT.paramValue => fminer.minfreq },
+        { DC.title => "nr_hits", OT.paramValue => (params[:nr_hits] == "true" ? "true" : "false") }
+      ]
     })
     feature_dataset.save(@subjectid)
 
@@ -312,14 +543,14 @@ post '/fminer/last/?' do
     end
 
     lu = LU.new                             # AM LAST: uses last-utils here
-    dom=lu.read(xml)                        # AM LAST: parse GraphML 
+    dom=lu.read(xml)                        # AM LAST: parse GraphML
     smarts=lu.smarts_rb(dom,'nls')          # AM LAST: converts patterns to LAST-SMARTS using msa variant (see last-pm.maunz.de)
     params[:nr_hits] == "true" ? hit_count=true: hit_count=false
     matches, counts = lu.match_rb(fminer.smi,smarts,hit_count)       # AM LAST: creates instantiations
 
     matches.each do |smarts, ids|
       feat_hash = Hash[*(fminer.all_activities.select { |k,v| ids.include?(k) }.flatten)] # AM LAST: get activities of feature occurrences; see http://www.softiesonrails.com/2007/9/18/ruby-201-weird-hash-syntax
-      if @@last.GetRegression() 
+      if @@last.GetRegression()
         p_value = @@last.KSTest(fminer.all_activities.values, feat_hash.values).to_f # AM LAST: use internal function for test
         effect = (p_value > 0) ? "activating" : "deactivating"
       else
@@ -328,7 +559,7 @@ post '/fminer/last/?' do
         @value_map.each { |y,act| g[y-1]=Array.new }
         feat_hash.each  { |x,y|   g[y-1].push(x)   }
         max = OpenTox::Algorithm.effect(g, fminer.db_class_sizes)
-        effect = g.size-max
+        effect = max+1
       end
       feature_uri = File.join feature_dataset.uri,"feature","last", features.size.to_s
       unless features.include? smarts
@@ -343,23 +574,136 @@ post '/fminer/last/?' do
             { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
             { DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] }
         ]
-        } 
+        }
         feature_dataset.add_feature feature_uri, metadata
       end
       if !hit_count
         ids.each { |id| feature_dataset.add(fminer.compounds[id], feature_uri, 1)}
       else
-        ids.each_with_index { |id,i| feature_dataset.add(fminer.compounds[id], feature_uri, counts[smarts][i])} 
+        ids.each_with_index { |id,i| feature_dataset.add(fminer.compounds[id], feature_uri, counts[smarts][i])}
       end
     end
 
     # AM: add feature values for non-present features
-    # feature_dataset.complete_data_entries 
+    # feature_dataset.complete_data_entries
 
-    feature_dataset.save(@subjectid) 
+    feature_dataset.save(@subjectid)
     feature_dataset.uri
   end
   response['Content-Type'] = 'text/uri-list'
   raise OpenTox::ServiceUnavailableError.newtask.uri+"\n" if task.status == "Cancelled"
   halt 202,task.uri.to_s+"\n"
 end
+
+# Matches features of a a feature dataset onto instances of another dataset. 
+# The latter is referred to as 'training dataset', since p-values are computed,
+# if user passes a prediction feature, or if the training dataset has only one feature.
+# The result does not contain the prediction feature.
+# @param [String] dataset_uri URI of the dataset 
+# @param [String] feature_dataset_uri URI of the feature dataset (i.e. dependent variable)
+# @param [optional] parameters Accepted parameters are
+# - prediction_feature URI of prediction feature to calculate p-values for
+# @return [text/uri-list] Task URI
+post '/fminer/:method/match?' do
+  raise OpenTox::BadRequestError.new "feature_dataset_uri not given" unless params[:feature_dataset_uri]
+  raise OpenTox::BadRequestError.new "dataset_uri not given" unless params[:dataset_uri]
+
+  training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}"
+  unless params[:prediction_feature] # try to read prediction_feature from dataset
+    prediction_feature = OpenTox::Feature.find(training_dataset.features.keys.first) if training_dataset.features.size == 1
+  end
+  prediction_feature = OpenTox::Feature.find(params[:prediction_feature]) if params[:prediction_feature]
+
+  task = OpenTox::Task.create("Matching features", url_for('/fminer/match',:full)) do |task|
+
+    # get endpoint statistics
+    if prediction_feature
+      db_class_sizes = Array.new # for effect calculation
+      all_activities = Hash.new # for effect calculation, indexed by id, starting from 1 (not 0)
+      id = 1
+      training_dataset.compounds.each do |compound|
+        entry=training_dataset.data_entries[compound]
+        entry.each do |feature,values|
+          if feature == prediction_feature.uri
+            values.each { |val|
+              if val.nil? 
+                LOGGER.warn "No #{feature} activity for #{compound.to_s}."
+              else
+                if prediction_feature.feature_type == "classification"
+                  activity= training_dataset.value_map(prediction_feature.uri).invert[val].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= val.to_f 
+                end
+                begin
+                  all_activities[id]=activity # DV: insert global information
+                  id += 1
+                rescue Exception => e
+                  LOGGER.warn "Could not add " + smiles + "\t" + val.to_s + " to fminer"
+                  LOGGER.warn e.backtrace
+                end
+              end
+            }
+          end
+        end
+      end
+    end
+
+    # Intialize result by adding compounds
+    f_dataset = OpenTox::Dataset.find params[:feature_dataset_uri],@subjectid
+    c_dataset = OpenTox::Dataset.find params[:dataset_uri],@subjectid
+    res_dataset = OpenTox::Dataset.create CONFIG[:services]["dataset"],@subjectid
+    c_dataset.compounds.each do |c|
+      res_dataset.add_compound(c)
+    end
+
+    # Run matching, put data entries in result. Features are recreated.
+    smi = [nil]; smi += c_dataset.compounds.collect { |c| OpenTox::Compound.new(c).to_smiles }
+    smarts = f_dataset.features.collect { |f,m| m[OT.smarts] }
+    params[:nr_hits] == "true" ? hit_count=true: hit_count=false
+    matches, counts = LU.new.match_rb(smi, smarts, hit_count) if smarts.size>0
+
+    f_dataset.features.each do |f,m|
+      if (matches[m[OT.smarts]] && matches[m[OT.smarts]].size>0)
+
+        feature_uri = File.join res_dataset.uri,"feature","match", res_dataset.features.size.to_s
+        metadata = {
+          RDF.type => [OT.Feature, OT.Substructure],
+          OT.hasSource => f_dataset.uri,
+          OT.smarts => m[OT.smarts],
+          OT.parameters => [
+            { DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] }
+          ]
+        }
+
+        if (prediction_feature) 
+          feat_hash = Hash[*(all_activities.select { |k,v| matches[m[OT.smarts]].include?(k) }.flatten)]
+          if prediction_feature.feature_type == "regression"
+            p_value = @@last.KSTest(all_activities.values, feat_hash.values).to_f # AM LAST: use internal function for test
+            effect = (p_value > 0) ? "activating" : "deactivating"
+          else
+            p_value = @@last.ChisqTest(all_activities.values, feat_hash.values).to_f
+            g=Array.new # g is filled in *a*scending activity
+            training_dataset.value_map(prediction_feature.uri).each { |y,act| g[y-1]=Array.new }
+            feat_hash.each  { |x,y|   g[y-1].push(x)   }
+            max = OpenTox::Algorithm.effect(g, db_class_sizes) # db_class_sizes is filled in *a*scending activity
+            effect = max+1
+          end
+          metadata[OT.effect] = effect
+          metadata[OT.pValue] = p_value.abs
+          metadata[OT.parameters] << { DC.title => "prediction_feature", OT.paramValue => prediction_feature.uri }
+        end
+        
+        res_dataset.add_feature feature_uri, metadata
+
+        matches[m[OT.smarts]].each_with_index {|id,idx| 
+          res_dataset.add(c_dataset.compounds[id-1],feature_uri,counts[m[OT.smarts]][idx])
+        }
+      end
+    end
+    res_dataset.save @subjectid
+    res_dataset.uri
+  end
+  return_task(task)
+end
+
diff --git a/last-utils b/last-utils
-Subproject cf0238477127e54509b6ab8b5c38f50dd6ffce0
+Subproject efcc3f41dd9e2f590a1520dfee3bf709120b2e4