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|
ENV['FMINER_SMARTS'] = 'true'
ENV['FMINER_NO_AROMATIC'] = 'true'
ENV['FMINER_PVALUES'] = 'true'
ENV['FMINER_SILENT'] = 'true'
ENV['FMINER_NR_HITS'] = 'true'
@@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/bbrc/sample', :full), url_for('/fminer/last', :full) ].join("\n") + "\n"
case request.env['HTTP_ACCEPT']
when /text\/html/
content_type "text/html"
OpenTox.text_to_html list
else
content_type 'text/uri-list'
list
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
algorithm = OpenTox::Algorithm::Generic.new(url_for('/fminer/bbrc',:full))
algorithm.metadata = {
DC.title => 'fminer backbone refinement class representatives',
DC.creator => "andreas@maunz.de, helma@in-silico.ch",
DC.contributor => "vorgrimmlerdavid@gmx.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 => "Minimum frequency", OT.paramScope => "optional", DC.title => "min_frequency" },
{ DC.description => "Feature type, can be 'paths' or 'trees'", OT.paramScope => "optional", DC.title => "feature_type" },
{ DC.description => "BBRC classes, pass 'false' to switch off mining for BBRC representatives.", OT.paramScope => "optional", DC.title => "backbone" },
{ DC.description => "Significance threshold (between 0 and 1)", OT.paramScope => "optional", DC.title => "min_chisq_significance" },
{ DC.description => "Whether subgraphs should be weighted with their occurrence counts in the instances (frequency)", OT.paramScope => "optional", DC.title => "nr_hits" },
]
}
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
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" },
]
}
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
# Get RDF/XML representation of fminer last algorithm
# @return [application/rdf+xml] OWL-DL representation of fminer last algorithm
get "/fminer/last/?" do
algorithm = OpenTox::Algorithm::Generic.new(url_for('/fminer/last',:full))
algorithm.metadata = {
DC.title => 'fminer latent structure class representatives',
DC.creator => "andreas@maunz.de, helma@in-silico.ch",
DC.contributor => "vorgrimmlerdavid@gmx.de",
# BO.instanceOf => "http://opentox.org/ontology/ist-algorithms.owl#fminer_last",
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 => "Minimum frequency", OT.paramScope => "optional", DC.title => "min_frequency" },
{ DC.description => "Feature type, can be 'paths' or 'trees'", OT.paramScope => "optional", DC.title => "feature_type" },
{ DC.description => "Whether subgraphs should be weighted with their occurrence counts in the instances (frequency)", OT.paramScope => "optional", DC.title => "nr_hits" },
]
}
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
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
end
return_task(task)
end
# Run bbrc algorithm on 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] parameters BBRC parameters, accepted parameters are
# - min_frequency Minimum frequency (default 5)
# - feature_type Feature type, can be 'paths' or 'trees' (default "trees")
# - backbone BBRC classes, pass 'false' to switch off mining for BBRC representatives. (default "true")
# - 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
fminer=OpenTox::Algorithm::Fminer.new
fminer.check_params(params,5,@subjectid)
task = OpenTox::Task.create("Mining BBRC features", url_for('/fminer',:full)) do |task|
@@bbrc.Reset
if fminer.prediction_feature.feature_type == "regression"
@@bbrc.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
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
@@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.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance]
@@bbrc.SetConsoleOut(false)
feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
feature_dataset.add_metadata({
DC.title => "BBRC representatives for " + fminer.training_dataset.metadata[DC.title].to_s,
DC.creator => url_for('/fminer/bbrc',:full),
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] }
]
})
feature_dataset.save(@subjectid)
fminer.compounds = []
fminer.db_class_sizes = Array.new # AM: effect
fminer.all_activities = Hash.new # DV: for effect calculation in regression part
fminer.smi = [] # AM LAST: needed for matching the patterns back
# Add data to fminer
fminer.add_fminer_data(@@bbrc, @value_map)
g_array=fminer.all_activities.values # DV: calculation of global median for effect calculation
g_median=g_array.to_scale.median
raise "No compounds in dataset #{fminer.training_dataset.uri}" if fminer.compounds.size==0
task.progress 10
step_width = 80 / @@bbrc.GetNoRootNodes().to_f
features = Set.new
# run @@bbrc
(0 .. @@bbrc.GetNoRootNodes()-1).each do |j|
results = @@bbrc.MineRoot(j)
task.progress 10+step_width*(j+1)
results.each do |result|
f = YAML.load(result)[0]
smarts = f[0]
p_value = f[1]
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
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_median=f_arr.to_scale.median
if g_median >= f_median
effect = 'activating'
else
effect = 'deactivating'
end
end
feature_uri = File.join feature_dataset.uri,"feature","bbrc", features.size.to_s
unless features.include? smarts
features << smarts
metadata = {
OT.hasSource => url_for('/fminer/bbrc', :full),
RDF.type => [OT.Feature, OT.Substructure],
OT.smarts => smarts,
OT.pValue => p_value.to_f,
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
#feature_dataset.add_feature_parameters feature_uri, feature_dataset.parameters
end
id_arrs.each { |id_count_hash|
id=id_count_hash.keys[0].to_i
count=id_count_hash.values[0].to_i
if params[:nr_hits] == "true"
feature_dataset.add(fminer.compounds[id], feature_uri, count)
else
feature_dataset.add(fminer.compounds[id], feature_uri, 1)
end
}
end # end of
end # feature parsing
# 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
#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)
#
# @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 num_boots 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
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 sampled representatives for " + fminer.training_dataset.metadata[DC.title].to_s,
DC.creator => url_for('/fminer/bbrc/sample',:full),
OT.hasSource => url_for('/fminer/bbrc/sample', :full),
OT.parameters => [
{ DC.title => "dataset_uri", OT.paramValue => params[:dataset_uri] },
{ DC.title => "prediction_feature", OT.paramValue => params[:prediction_feature] }
# TODO: add more params
]
})
feature_dataset.save(@subjectid)
fminer.compounds = []
fminer.db_class_sizes = Array.new # AM: effect
fminer.all_activities = Hash.new # DV: for effect calculation (class and regr)
fminer.smi = [] # AM LAST: needed for matching the patterns back
# Add data to fminer
fminer.add_fminer_data(nil, @value_map) # AM: 'nil' as instance to only fill in administrative data
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
# 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
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
p_value = @@last.ChisqTest(fminer.all_activities.values, feat_hash.values).to_f
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 = g.size-max
feature_uri = File.join feature_dataset.uri,"feature","last", 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 => p_value.abs,
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
# @param [String] prediction_feature URI of the prediction feature (i.e. dependent variable)
# @param [optional] parameters LAST parameters, accepted parameters are
# - min_frequency freq Minimum frequency (default 5)
# - feature_type Feature type, can be 'paths' or 'trees' (default "trees")
# - nr_hits Set to "true" to get hit count instead of presence
# @return [text/uri-list] Task URI
post '/fminer/last/?' do
fminer=OpenTox::Algorithm::Fminer.new
fminer.check_params(params,80,@subjectid)
task = OpenTox::Task.create("Mining LAST features", url_for('/fminer',:full)) do |task|
@@last.Reset
if fminer.prediction_feature.feature_type == "regression"
@@last.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
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
@@last.SetMinfreq(fminer.minfreq)
@@last.SetType(1) if params[:feature_type] == "paths"
@@last.SetConsoleOut(false)
feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
feature_dataset.add_metadata({
DC.title => "LAST representatives for " + fminer.training_dataset.metadata[DC.title].to_s,
DC.creator => url_for('/fminer/last',:full),
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] }
]
})
feature_dataset.save(@subjectid)
fminer.compounds = []
fminer.db_class_sizes = Array.new # AM: effect
fminer.all_activities = Hash.new # DV: for effect calculation (class and regr)
fminer.smi = [] # AM LAST: needed for matching the patterns back
# Add data to fminer
fminer.add_fminer_data(@@last, @value_map)
raise "No compounds in dataset #{fminer.training_dataset.uri}" if fminer.compounds.size==0
# run @@last
features = Set.new
xml = ""
task.progress 10
step_width = 80 / @@last.GetNoRootNodes().to_f
(0 .. @@last.GetNoRootNodes()-1).each do |j|
results = @@last.MineRoot(j)
task.progress 10+step_width*(j+1)
results.each do |result|
xml << result
end
end
lu = LU.new # AM LAST: uses last-utils here
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()
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
p_value = @@last.ChisqTest(fminer.all_activities.values, feat_hash.values).to_f
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 = g.size-max
end
feature_uri = File.join feature_dataset.uri,"feature","last", 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 => p_value.abs,
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
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