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module OpenTox
module Algorithm
class Fminer
#
# 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
# - get_target Set to "true" to obtain target variable as feature
# @return [text/uri-list] Task URI
def self.bbrc dataset, params={}
table_of_elements = [
"H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", "Rg", "Cn", "Uut", "Fl", "Uup", "Lv", "Uus", "Uuo"]
@fminer=OpenTox::Algorithm::Fminer.new
@fminer.check_params(dataset,params,5)
time = Time.now
@bbrc = Bbrc::Bbrc.new
@bbrc.Reset
if @fminer.prediction_feature.numeric
@bbrc.SetRegression(true) # AM: DO NOT MOVE DOWN! Must happen before the other Set... operations!
else
bad_request_error "No accept values for "\
"dataset '#{@fminer.training_dataset.id}' and "\
"feature '#{@fminer.prediction_feature.id}'" unless @fminer.prediction_feature.accept_values
value_map = @fminer.prediction_feature.accept_values.each_index.inject({}) { |h,idx| h[idx+1]=@fminer.prediction_feature.accept_values[idx]; h }
end
@bbrc.SetMinfreq(@fminer.minfreq)
@bbrc.SetType(1) if params[:feature_type] == "paths"
@bbrc.SetBackbone(false) if params[:backbone] == "false"
@bbrc.SetChisqSig(params[:min_chisq_significance].to_f) if params[:min_chisq_significance]
@bbrc.SetConsoleOut(false)
params[:nr_hits] ? nr_hits = params[:nr_hits] : nr_hits = false
feature_dataset = FminerDataset.new(
:training_dataset_id => dataset.id,
:training_algorithm => "#{self.to_s}.bbrc",
:training_feature_id => params[:prediction_feature].id ,
:training_parameters => {
:min_frequency => @fminer.minfreq,
:nr_hits => nr_hits,
:backbone => (params[:backbone] == false ? false : true)
}
)
feature_dataset.compounds = dataset.compounds
# Add data to fminer
@fminer.add_fminer_data(@bbrc, value_map)
g_median=@fminer.all_activities.values.to_scale.median
#task.progress 10
#step_width = 80 / @bbrc.GetNoRootNodes().to_f
$logger.debug "Setup: #{Time.now-time}"
time = Time.now
ftime = 0
itime = 0
rtime = 0
# run @bbrc
(0 .. @bbrc.GetNoRootNodes()-1).each do |j|
results = @bbrc.MineRoot(j)
results.each do |result|
rt = Time.now
f = YAML.load(result)[0]
smarts = f.shift
# convert fminer SMARTS representation into a more human readable format
smarts.gsub!(%r{\[#(\d+)&(\w)\]}) do
element = table_of_elements[$1.to_i-1]
$2 == "a" ? element.downcase : element
end
p_value = f.shift
f.flatten!
=begin
if (!@bbrc.GetRegression)
id_arrs = f[2..-1].flatten
max = OpenTox::Algorithm::Fminer.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_median=f_arr.to_scale.median
if g_median >= f_median
effect = 'activating'
else
effect = 'deactivating'
end
end
=end
rtime += Time.now - rt
ft = Time.now
feature = OpenTox::FminerSmarts.find_or_create_by({
"smarts" => smarts,
"pValue" => p_value.to_f.abs.round(5),
#"effect" => effect,
"dataset_id" => feature_dataset.id
})
feature_dataset.feature_ids << feature.id
ftime += Time.now - ft
it = Time.now
f.each do |id_count_hash|
id_count_hash.each do |id,count|
nr_hits ? count = count.to_i : count = 1
feature_dataset[id-1, feature_dataset.feature_ids.size-1] = count
end
end
itime += Time.now - it
end
end
$logger.debug "Fminer: #{Time.now-time} (read: #{rtime}, iterate: #{itime}, find/create Features: #{ftime})"
time = Time.now
feature_dataset.fill_nil_with 0
$logger.debug "Prepare save: #{Time.now-time}"
time = Time.now
feature_dataset.save_all
$logger.debug "Save: #{Time.now-time}"
feature_dataset
end
end
end
end
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