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require 'csv'
require 'tempfile'
module OpenTox
class Dataset
#attr_writer :data_entries
# associations like has_many, belongs_to deteriorate performance
field :feature_ids, type: Array, default: []
field :compound_ids, type: Array, default: []
#field :data_entries_id, type: BSON::ObjectId
field :data_entries, type: Array, default: []
field :source, type: String
# Save all data including data_entries
# Should be used instead of save
def save_all
save
#dump = Marshal.dump(@data_entries)
#file = Mongo::Grid::File.new(dump, :filename => "#{self.id.to_s}.data_entries")
#entries_id = $gridfs.insert_one(file)
#update(:data_entries_id => entries_id)
end
# Readers
# Get all compounds
def compounds
@compounds ||= self.compound_ids.collect{|id| OpenTox::Compound.find id}
@compounds
end
# Get all features
def features
@features ||= self.feature_ids.collect{|id| OpenTox::Feature.find(id)}
@features
end
=begin
# Get all data_entries
def data_entries
unless @data_entries
t = Time.now
data_entry_file = $gridfs.find_one(_id: data_entries_id)
if data_entry_file.nil?
@data_entries = []
else
@data_entries = Marshal.load(data_entry_file.data)
bad_request_error "Data entries (#{data_entries_id}) are not a 2D-Array" unless @data_entries.is_a? Array and @data_entries.first.is_a? Array
unless @data_entries.first.size == feature_ids.size
# TODO: fix (unknown) source of empty data_entries
sleep 1
data_entry_file = $gridfs.find_one(_id: data_entries_id)
@data_entries = Marshal.load(data_entry_file.data)
end
bad_request_error "Data entries (#{data_entries_id}) have #{@data_entries.size} rows, but dataset (#{id}) has #{compound_ids.size} compounds" unless @data_entries.size == compound_ids.size
# TODO: data_entries can be empty, poorly reproducible, mongo problem?
bad_request_error "Data entries (#{data_entries_id}) have #{@data_entries.first.size} columns, but dataset (#{id}) has #{feature_ids.size} features" unless @data_entries.first.size == feature_ids.size
#$logger.debug "Retrieving data: #{Time.now-t}"
end
end
@data_entries
end
=end
# Find data entry values for a given compound and feature
# @param compound [OpenTox::Compound] OpenTox Compound object
# @param feature [OpenTox::Feature] OpenTox Feature object
# @return [Array] Data entry values
def values(compound, feature)
rows = compound_ids.each_index.select{|r| compound_ids[r] == compound.id }
col = feature_ids.index feature.id
rows.collect{|row| data_entries[row][col]}
end
# Writers
# Set compounds
def compounds=(compounds)
self.compound_ids = compounds.collect{|c| c.id}
end
# Set features
def features=(features)
self.feature_ids = features.collect{|f| f.id}
end
# Dataset operations
# Split a dataset into n folds
# @param [Integer] number of folds
# @return [Array] Array with folds [training_dataset,test_dataset]
def folds n
len = self.compound_ids.size
indices = (0..len-1).to_a.shuffle
mid = (len/n)
chunks = []
start = 0
1.upto(n) do |i|
last = start+mid
last = last-1 unless len%n >= i
test_idxs = indices[start..last] || []
test_cids = test_idxs.collect{|i| self.compound_ids[i]}
test_data_entries = test_idxs.collect{|i| self.data_entries[i]}
test_dataset = self.class.new(:compound_ids => test_cids, :feature_ids => self.feature_ids, :data_entries => test_data_entries)
test_dataset.compounds.each do |compound|
compound.dataset_ids << test_dataset.id
compound.save
end
training_idxs = indices-test_idxs
training_cids = training_idxs.collect{|i| self.compound_ids[i]}
training_data_entries = training_idxs.collect{|i| self.data_entries[i]}
training_dataset = self.class.new(:compound_ids => training_cids, :feature_ids => self.feature_ids, :data_entries => training_data_entries)
training_dataset.compounds.each do |compound|
compound.dataset_ids << training_dataset.id
compound.save
end
test_dataset.save_all
training_dataset.save_all
chunks << [training_dataset,test_dataset]
start = last+1
end
chunks
end
# Diagnostics
def duplicates feature=self.features.first
col = feature_ids.index feature.id
dups = {}
compound_ids.each_with_index do |cid,i|
rows = compound_ids.each_index.select{|r| compound_ids[r] == cid }
values = rows.collect{|row| data_entries[row][col]}
dups[cid] = values if values.size > 1
end
dups
end
def correlation_plot training_dataset
# TODO: create/store svg
R.assign "features", data_entries
R.assign "activities", training_dataset.data_entries.collect{|de| de.first}
R.eval "featurePlot(features,activities)"
end
def density_plot
# TODO: create/store svg
R.assign "acts", data_entries.collect{|r| r.first }#.compact
R.eval "plot(density(-log(acts),na.rm= TRUE), main='-log(#{features.first.name})')"
end
# Serialisation
# converts dataset to csv format including compound smiles as first column, other column headers are feature names
# @return [String]
def to_csv(inchi=false)
CSV.generate() do |csv| #{:force_quotes=>true}
csv << [inchi ? "InChI" : "SMILES"] + features.collect{|f| f.name}
compounds.each_with_index do |c,i|
csv << [inchi ? c.inchi : c.smiles] + data_entries[i]
end
end
end
# Parsers
# Create a dataset from file (csv,sdf,...)
# @param filename [String]
# @return [String] dataset uri
# TODO
#def self.from_sdf_file
#end
# Create a dataset from CSV file
# TODO: document structure
def self.from_csv_file file, source=nil, bioassay=true#, layout={}
source ||= file
name = File.basename(file,".*")
dataset = self.find_by(:source => source, :name => name)
if dataset
$logger.debug "Skipping import of #{file}, it is already in the database (id: #{dataset.id})."
else
$logger.debug "Parsing #{file}."
table = CSV.read file, :skip_blanks => true, :encoding => 'windows-1251:utf-8'
dataset = self.new(:source => source, :name => name)
dataset.parse_table table, bioassay#, layout
end
dataset
end
# parse data in tabular format (e.g. from csv)
# does a lot of guesswork in order to determine feature types
def parse_table table, bioassay=true
time = Time.now
# features
feature_names = table.shift.collect{|f| f.strip}
warnings << "Duplicate features in table header." unless feature_names.size == feature_names.uniq.size
compound_format = feature_names.shift.strip
bad_request_error "#{compound_format} is not a supported compound format. Accepted formats: SMILES, InChI." unless compound_format =~ /SMILES|InChI/i
numeric = []
# guess feature types
feature_names.each_with_index do |f,i|
metadata = {:name => f}
values = table.collect{|row| val=row[i+1].to_s.strip; val.blank? ? nil : val }.uniq.compact
types = values.collect{|v| v.numeric? ? true : false}.uniq
if values.size == 0 # empty feature
elsif values.size > 5 and types.size == 1 and types.first == true # 5 max classes
metadata["numeric"] = true
numeric[i] = true
else
metadata["nominal"] = true
metadata["accept_values"] = values
numeric[i] = false
end
if bioassay
if metadata["numeric"]
feature = NumericBioAssay.find_or_create_by(metadata)
elsif metadata["nominal"]
feature = NominalBioAssay.find_or_create_by(metadata)
end
else
metadata.merge({:measured => false, :calculated => true})
if metadata["numeric"]
feature = NumericFeature.find_or_create_by(metadata)
elsif metadata["nominal"]
feature = NominalFeature.find_or_create_by(metadata)
end
end
feature_ids << feature.id if feature
end
$logger.debug "Feature values: #{Time.now-time}"
time = Time.now
r = -1
compound_time = 0
value_time = 0
# compounds and values
#@data_entries = [] #Array.new(table.size){Array.new(table.first.size-1)}
self.data_entries = []
table.each_with_index do |vals,i|
ct = Time.now
identifier = vals.shift.strip
warnings << "No feature values for compound at position #{i+2}." if vals.compact.empty?
begin
case compound_format
when /SMILES/i
compound = OpenTox::Compound.from_smiles(identifier)
when /InChI/i
compound = OpenTox::Compound.from_inchi(identifier)
end
rescue
compound = nil
end
if compound.nil?
# compound parsers may return nil
warnings << "Cannot parse #{compound_format} compound '#{identifier}' at position #{i+2}, all entries are ignored."
next
end
compound.dataset_ids << self.id unless compound.dataset_ids.include? self.id
compound_time += Time.now-ct
r += 1
unless vals.size == feature_ids.size # way cheaper than accessing features
warnings << "Number of values at position #{i+2} is different than header size (#{vals.size} vs. #{features.size}), all entries are ignored."
next
end
compound_ids << compound.id
table.first.size == 0 ? self.data_entries << Array.new(0) : self.data_entries << Array.new(table.first.size-1)
vals.each_with_index do |v,j|
if v.blank?
warnings << "Empty value for compound '#{identifier}' (row #{r+2}) and feature '#{feature_names[j]}' (column #{j+2})."
next
elsif numeric[j]
v = v.to_f
else
v = v.strip
end
self.data_entries.last[j] = v
#i = compound.feature_ids.index feature_ids[j]
compound.features[feature_ids[j].to_s] ||= []
compound.features[feature_ids[j].to_s] << v
compound.save
end
end
compounds.duplicates.each do |compound|
positions = []
compounds.each_with_index{|c,i| positions << i+1 if !c.blank? and c.inchi and c.inchi == compound.inchi}
warnings << "Duplicate compound #{compound.smiles} at rows #{positions.join(', ')}. Entries are accepted, assuming that measurements come from independent experiments."
end
$logger.debug "Value parsing: #{Time.now-time} (Compound creation: #{compound_time})"
time = Time.now
save
$logger.debug "Saving: #{Time.now-time}"
end
# Fill unset data entries
# @param any value
def fill_nil_with n
(0 .. compound_ids.size-1).each do |i|
data_entries[i] ||= []
(0 .. feature_ids.size-1).each do |j|
data_entries[i][j] ||= n
end
end
end
def scale
scaled_data_entries = Array.new(data_entries.size){Array.new(data_entries.first.size)}
centers = []
scales = []
feature_ids.each_with_index do |feature_id,col|
R.assign "x", data_entries.collect{|de| de[col]}
R.eval "scaled = scale(x,center=T,scale=T)"
centers[col] = R.eval("attr(scaled, 'scaled:center')").to_ruby
scales[col] = R.eval("attr(scaled, 'scaled:scale')").to_ruby
R.eval("scaled").to_ruby.each_with_index do |value,row|
scaled_data_entries[row][col] = value
end
end
scaled_dataset = ScaledDataset.new(attributes)
scaled_dataset["_id"] = BSON::ObjectId.new
scaled_dataset["_type"] = "OpenTox::ScaledDataset"
scaled_dataset.centers = centers
scaled_dataset.scales = scales
scaled_dataset.data_entries = scaled_data_entries
scaled_dataset.save_all
scaled_dataset
end
end
# Dataset for lazar predictions
class LazarPrediction < Dataset
field :creator, type: String
field :prediction_feature_id, type: String
def prediction_feature
Feature.find prediction_feature_id
end
end
# Dataset for descriptors (physchem)
class DescriptorDataset < Dataset
field :feature_calculation_algorithm, type: String
end
class ScaledDataset < DescriptorDataset
field :centers, type: Array, default: []
field :scales, type: Array, default: []
def original_value value, i
value * scales[i] + centers[i]
end
end
# Dataset for fminer descriptors
class FminerDataset < DescriptorDataset
field :training_algorithm, type: String
field :training_dataset_id, type: BSON::ObjectId
field :training_feature_id, type: BSON::ObjectId
field :training_parameters, type: Hash
end
end
|