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|
ENV['FMINER_SMARTS'] = 'true'
ENV['FMINER_NO_AROMATIC'] = 'true'
ENV['FMINER_PVALUES'] = 'true'
ENV['FMINER_SILENT'] = '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
response['Content-Type'] = 'text/uri-list'
[ url_for('/fminer/bbrc', :full), url_for('/fminer/last', :full) ].join("\n") + "\n"
end
# Get RDF/XML representation of fminer bbrc algorithm
# @return [application/rdf+xml] OWL-DL representation of fminer bbrc algorithm
get "/fminer/bbrc/?" do
response['Content-Type'] = 'application/rdf+xml'
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",
OT.isA => 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 => "minfreq" },
{ 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" },
]
}
algorithm.to_rdfxml
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",
OT.isA => 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 => "minfreq" },
{ DC.description => "Feature type, can be 'paths' or 'trees'", OT.paramScope => "optional", DC.title => "feature_type" },
{ DC.description => "Maximum number of hops", OT.paramScope => "optional", DC.title => "hops" },
]
}
algorithm.to_rdfxml
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
# - minfreq 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)
# @return [text/uri-list] Task URI
post '/fminer/bbrc/?' do
halt 404, "Please submit a dataset_uri." unless params[:dataset_uri] and !params[:dataset_uri].nil?
halt 404, "Please submit a prediction_feature." unless params[:prediction_feature] and !params[:prediction_feature].nil?
prediction_feature = params[:prediction_feature]
training_dataset = OpenTox::Dataset.find "#{params[:dataset_uri]}", @subjectid
halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
unless params[:min_frequency].nil?
minfreq=params[:min_frequency].to_i
raise "Minimum frequency must be a number >0!" unless minfreq>0
else
minfreq = 5*training_dataset.compounds.size/1000 # AM sugg. 8-10 per mil
minfreq = 2 unless minfreq > 2
end
task = OpenTox::Task.create("Mining BBRC features", url_for('/fminer',:full)) do
@@bbrc.Reset
@@bbrc.SetMinfreq(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]) if params[:min_chisq_significance]
@@bbrc.SetConsoleOut(false)
feature_dataset = OpenTox::Dataset.new(nil, @subjectid)
feature_dataset.add_metadata({
DC.title => "BBRC representatives for " + 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)
id = 1 # fminer start id is not 0
compounds = []
nr_active=0
nr_inactive=0
all_activities = Hash.new# DV: for effect calculation in regression part
training_dataset.data_entries.each do |compound,entry|
begin
smiles = OpenTox::Compound.new(compound.to_s).to_smiles
rescue
LOGGER.warn "No resource for #{compound.to_s}"
next
end
if smiles == '' or smiles.nil?
LOGGER.warn "Cannot find smiles for #{compound.to_s}."
next
end
entry.each do |feature,values|
if feature == prediction_feature
values.each do |value|
if value.nil?
LOGGER.warn "No #{feature} activiity for #{compound.to_s}."
else
case value.to_s
when "true"
nr_active += 1
activity = 1
when "false"
nr_inactive += 1
activity = 0
else
activity = value.to_f
@@bbrc.SetRegression(true)
end
begin
@@bbrc.AddCompound(smiles,id)
@@bbrc.AddActivity(activity, id)
all_activities[id]=activity # DV: insert global information
compounds[id] = compound
id += 1
rescue
LOGGER.warn "Could not add " + smiles + "\t" + value.to_s + " to fminer"
end
end
end
end
end
end
g_array=all_activities.values # DV: calculation of global median for effect calculation
g_median=OpenTox::Algorithm.median(g_array)
raise "No compounds in dataset #{training_dataset.uri}" if compounds.size==0
features = Set.new
# run @@bbrc
(0 .. @@bbrc.GetNoRootNodes()-1).each do |j|
results = @@bbrc.MineRoot(j)
results.each do |result|
f = YAML.load(result)[0]
smarts = f[0]
p_value = f[1]
if (!@@bbrc.GetRegression)
ids = f[2] + f[3]
if f[2].size.to_f/ids.size > nr_active.to_f/(nr_active+nr_inactive)
effect = 'activating'
else
effect = 'deactivating'
end
else #regression part
ids = f[2]
# DV: effect calculation
f_arr=Array.new
f[2].each do |id|
f_arr.push(all_activities[id])
end
f_median=OpenTox::Algorithm.median(f_arr)
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),
OT.isA => 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
ids.each { |id| feature_dataset.add(compounds[id], feature_uri, true)}
end
end
feature_dataset.save(@subjectid)
feature_dataset.uri
end
response['Content-Type'] = 'text/uri-list'
halt 503,task.uri+"\n" if task.status == "Cancelled"
halt 202,task.uri.to_s+"\n"
end
#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
# - minfreq Minimum frequency (default 5)
# - feature_type Feature type, can be 'paths' or 'trees' (default "trees")
# - hops Maximum number of hops
# @return [text/uri-list] Task URI
post '/fminer/last/?' do
halt 404, "Please submit a dataset_uri." unless params[:dataset_uri] and !params[:dataset_uri].nil?
halt 404, "Please submit a prediction_feature." unless params[:prediction_feature] and !params[:prediction_feature].nil?
prediction_feature = params[:prediction_feature]
training_dataset = OpenTox::Dataset.new "#{params[:dataset_uri]}", @subjectid
training_dataset.load_all(@subjectid)
halt 404, "No feature #{params[:prediction_feature]} in dataset #{params[:dataset_uri]}" unless training_dataset.features and training_dataset.features.include?(params[:prediction_feature])
unless params[:min_frequency].nil?
minfreq=params[:min_frequency].to_i
raise "Minimum frequency must be a number >0!" unless minfreq>0
else
minfreq = 8*training_dataset.compounds.size/100 # AM sugg. 5-10%
minfreq = 2 unless minfreq > 2
end
task = OpenTox::Task.create("Mining LAST features", url_for('/fminer',:full)) do
@@last.Reset
@@last.SetMinfreq(minfreq)
@@last.SetType(1) if params[:feature_type] == "paths"
@@last.SetMaxHops(params[:hops]) if params[:hops]
@@last.SetConsoleOut(false)
feature_dataset = OpenTox::Dataset.new
feature_dataset.add_metadata({
DC.title => "LAST representatives for " + 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)
id = 1 # fminer start id is not 0
compounds = []
smi = [] # AM LAST: needed for matching the patterns back
nr_active=0
nr_inactive=0
all_activities = Hash.new #DV: for effect calculation (class and regr)
training_dataset.data_entries.each do |compound,entry|
begin
smiles = OpenTox::Compound.new(compound.to_s).to_smiles
rescue
LOGGER.warn "No resource for #{compound.to_s}"
next
end
if smiles == '' or smiles.nil?
LOGGER.warn "Cannot find smiles for #{compound.to_s}."
next
end
entry.each do |feature,values|
if feature == prediction_feature
values.each do |value|
if value.nil?
LOGGER.warn "No #{feature} activiity for #{compound.to_s}."
else
case value.to_s
when "true"
nr_active += 1
activity = 1
when "false"
nr_inactive += 1
activity = 0
else
activity = value.to_f
@@last.SetRegression(true)
end
begin
@@last.AddCompound(smiles,id)
@@last.AddActivity(activity, id)
all_activities[id]=activity # DV: insert global information
compounds[id] = compound
smi[id] = smiles # AM LAST: changed this to store SMILES.
id += 1
rescue
LOGGER.warn "Could not add " + smiles + "\t" + value.to_s + " to fminer"
end
end
end
end
end
end
raise "No compounds in dataset #{training_dataset.uri}" if compounds.size==0
# run @@last
features = Set.new
xml = ""
(0 .. @@last.GetNoRootNodes()-1).each do |j|
results = @@last.MineRoot(j)
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 (needs hpricot, @ch: to be included in wrapper!)
smarts=lu.smarts_rb(dom,'nls') # AM LAST: converts patterns to LAST-SMARTS using msa variant (see last-pm.maunz.de)
instances=lu.match_rb(smi,smarts) # AM LAST: creates instantiations
instances.each do |smarts, ids|
feat_hash = Hash[*(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
@@last.GetRegression() ? p_value = @@last.KSTest(all_activities.values, feat_hash.values).to_f : p_value = @@last.ChisqTest(all_activities.values, feat_hash.values).to_f # AM LAST: use internal function for test
effect = (p_value > 0) ? "activating" : "deactivating"
feature_uri = File.join feature_dataset.uri,"feature","last", features.size.to_s
unless features.include? smarts
features << smarts
metadata = {
OT.isA => OT.Substructure,
OT.hasSource => feature_dataset.uri,
OT.smarts => smarts,
OT.pValue => p_value.to_f.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
ids.each { |id| feature_dataset.add(compounds[id], feature_uri, true)}
end
feature_dataset.save(@subjectid)
feature_dataset.uri
end
response['Content-Type'] = 'text/uri-list'
halt 503,task.uri+"\n" if task.status == "Cancelled"
halt 202,task.uri.to_s+"\n"
end
|