Class: OpenTox::Dataset
- Inherits:
-
Object
- Object
- OpenTox::Dataset
- Defined in:
- lib/dataset.rb
Instance Attribute Summary collapse
-
#compounds ⇒ Object
Returns the value of attribute compounds.
-
#creator ⇒ Object
Returns the value of attribute creator.
-
#data ⇒ Object
Returns the value of attribute data.
-
#features ⇒ Object
Returns the value of attribute features.
-
#title ⇒ Object
Returns the value of attribute title.
-
#uri ⇒ Object
Returns the value of attribute uri.
Class Method Summary collapse
- .find(uri, accept_header = nil) ⇒ Object
-
.owl_to_yaml(owl_data, uri) ⇒ Object
converts a dataset represented in owl to yaml (uses a temporary dataset) note: to_yaml is overwritten, loads complete owl dataset values.
Instance Method Summary collapse
-
#create_new_dataset(new_compounds, new_features, new_title, new_creator) ⇒ Object
creates a new dataset, using only those compounsd specified in new_compounds returns uri of new dataset.
-
#get_predicted_class(compound, feature) ⇒ Object
returns classification value.
-
#get_predicted_regression(compound, feature) ⇒ Object
returns regression value.
-
#get_prediction_confidence(compound, feature) ⇒ Object
returns prediction confidence if available.
-
#get_value(compound, feature) ⇒ Object
return compound-feature value.
-
#initialize(owl = nil) ⇒ Dataset
constructor
A new instance of Dataset.
-
#load_feature_values(feature = nil) ⇒ Object
loads specified feature and removes dirty-flag, loads all features if feature is nil.
-
#save ⇒ Object
saves (changes) as new dataset in dataset service returns uri uses to yaml method (which is overwritten).
-
#to_yaml ⇒ Object
overwrite to yaml: in case dataset is loaded from owl: * load all values.
-
#to_yaml_properties ⇒ Object
-
remove @owl from yaml, not necessary.
-
Constructor Details
#initialize(owl = nil) ⇒ Dataset
Returns a new instance of Dataset.
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# File 'lib/dataset.rb', line 7 def initialize( owl=nil ) @data = {} @features = [] @compounds = [] # creates dataset object from Opentox::Owl object # use Dataset.find( <uri> ) to load dataset from rdf-supporting datasetservice # note: does not load all feature values, as this is time consuming if owl raise "invalid param" unless owl.is_a?(OpenTox::Owl) @title = owl.get("title") @creator = owl.get("creator") @uri = owl.uri # when loading a dataset from owl, only compound- and feature-uris are loaded owl.load_dataset(@compounds, @features) # all features are marked as dirty # as soon as a feature-value is requested all values for this feature are loaded from the rdf @dirty_features = @features.dclone @owl = owl end end |
Instance Attribute Details
#compounds ⇒ Object
Returns the value of attribute compounds.
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# File 'lib/dataset.rb', line 5 def compounds @compounds end |
#creator ⇒ Object
Returns the value of attribute creator.
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# File 'lib/dataset.rb', line 5 def creator @creator end |
#data ⇒ Object
Returns the value of attribute data.
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# File 'lib/dataset.rb', line 5 def data @data end |
#features ⇒ Object
Returns the value of attribute features.
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# File 'lib/dataset.rb', line 5 def features @features end |
#title ⇒ Object
Returns the value of attribute title.
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# File 'lib/dataset.rb', line 5 def title @title end |
#uri ⇒ Object
Returns the value of attribute uri.
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# File 'lib/dataset.rb', line 5 def uri @uri end |
Class Method Details
.find(uri, accept_header = nil) ⇒ Object
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# File 'lib/dataset.rb', line 29 def self.find(uri, accept_header=nil) unless accept_header if (@@config[:yaml_hosts].include?(URI.parse(uri).host)) accept_header = 'application/x-yaml' else accept_header = "application/rdf+xml" end end case accept_header when "application/x-yaml" d = YAML.load RestClientWrapper.get(uri.to_s.strip, :accept => 'application/x-yaml').to_s d.uri = uri unless d.uri when "application/rdf+xml" owl = OpenTox::Owl.from_uri(uri.to_s.strip, "Dataset") d = Dataset.new(owl) else raise "cannot get datset with accept header: "+accept_header.to_s end d end |
.owl_to_yaml(owl_data, uri) ⇒ Object
converts a dataset represented in owl to yaml (uses a temporary dataset) note: to_yaml is overwritten, loads complete owl dataset values
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# File 'lib/dataset.rb', line 55 def self.owl_to_yaml( owl_data, uri) owl = OpenTox::Owl.from_data(owl_data, uri, "Dataset") d = Dataset.new(owl) d.to_yaml end |
Instance Method Details
#create_new_dataset(new_compounds, new_features, new_title, new_creator) ⇒ Object
creates a new dataset, using only those compounsd specified in new_compounds returns uri of new dataset
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# File 'lib/dataset.rb', line 63 def create_new_dataset( new_compounds, new_features, new_title, new_creator ) LOGGER.debug "create new dataset with "+new_compounds.size.to_s+"/"+compounds.size.to_s+" compounds" raise "no new compounds selected" unless new_compounds and new_compounds.size>0 # load require features if ((defined? @dirty_features) && (@dirty_features & new_features).size > 0) (@dirty_features & new_features).each{|f| load_feature_values(f)} end dataset = OpenTox::Dataset.new dataset.title = new_title dataset.creator = new_creator dataset.features = new_features dataset.compounds = new_compounds # Copy dataset data for compounds and features # PENDING: why storing feature values in an array? new_compounds.each do |c| data_c = [] raise "no data for compound '"+c.to_s+"'" if @data[c]==nil @data[c].each do |d| m = {} new_features.each do |f| m[f] = d[f] end data_c << m end dataset.data[c] = data_c end return dataset.save end |
#get_predicted_class(compound, feature) ⇒ Object
returns classification value
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# File 'lib/dataset.rb', line 97 def get_predicted_class(compound, feature) v = get_value(compound, feature) if v.is_a?(Hash) k = v.keys.grep(/classification/).first unless k.empty? #if v.has_key?(:classification) return v[k] else return "no classification key" end elsif v.is_a?(Array) raise "predicted class value is an array\n"+ "value "+v.to_s+"\n"+ "value-class "+v.class.to_s+"\n"+ "dataset "+@uri.to_s+"\n"+ "compound "+compound.to_s+"\n"+ "feature "+feature.to_s+"\n" else return v end end |
#get_predicted_regression(compound, feature) ⇒ Object
returns regression value
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# File 'lib/dataset.rb', line 120 def get_predicted_regression(compound, feature) v = get_value(compound, feature) if v.is_a?(Hash) k = v.keys.grep(/regression/).first unless k.empty? return v[k] else return "no regression key" end elsif v.is_a?(Array) raise "predicted regression value is an array\n"+ "value "+v.to_s+"\n"+ "value-class "+v.class.to_s+"\n"+ "dataset "+@uri.to_s+"\n"+ "compound "+compound.to_s+"\n"+ "feature "+feature.to_s+"\n" else return v end end |
#get_prediction_confidence(compound, feature) ⇒ Object
returns prediction confidence if available
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# File 'lib/dataset.rb', line 142 def get_prediction_confidence(compound, feature) v = get_value(compound, feature) if v.is_a?(Hash) k = v.keys.grep(/confidence/).first unless k.empty? #if v.has_key?(:confidence) return v[k].abs #return v["http://ot-dev.in-silico.ch/model/lazar#confidence"].abs else # PENDING: return nil isntead of raising an exception raise "no confidence key" end else LOGGER.warn "no confidence for compound: "+compound.to_s+", feature: "+feature.to_s return 1 end end |
#get_value(compound, feature) ⇒ Object
return compound-feature value
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# File 'lib/dataset.rb', line 161 def get_value(compound, feature) if (defined? @dirty_features) && @dirty_features.include?(feature) load_feature_values(feature) end v = @data[compound] return nil if v == nil # missing values for all features if v.is_a?(Array) # PENDING: why using an array here? v.each do |e| if e.is_a?(Hash) if e.has_key?(feature) return e[feature] end else raise "invalid internal value type" end end return nil #missing value else raise "value is not an array\n"+ "value "+v.to_s+"\n"+ "value-class "+v.class.to_s+"\n"+ "dataset "+@uri.to_s+"\n"+ "compound "+compound.to_s+"\n"+ "feature "+feature.to_s+"\n" end end |
#load_feature_values(feature = nil) ⇒ Object
loads specified feature and removes dirty-flag, loads all features if feature is nil
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# File 'lib/dataset.rb', line 191 def load_feature_values(feature=nil) if feature raise "feature already loaded" unless @dirty_features.include?(feature) @owl.load_dataset_feature_values(@compounds, @data, [feature]) @dirty_features.delete(feature) else @data = {} unless @data @owl.load_dataset_feature_values(@compounds, @data, @dirty_features) @dirty_features.clear end end |
#save ⇒ Object
saves (changes) as new dataset in dataset service returns uri uses to yaml method (which is overwritten)
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# File 'lib/dataset.rb', line 222 def save OpenTox::RestClientWrapper.post(@@config[:services]["opentox-dataset"],{:content_type => "application/x-yaml"},self.to_yaml).strip end |
#to_yaml ⇒ Object
overwrite to yaml: in case dataset is loaded from owl:
-
load all values
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# File 'lib/dataset.rb', line 206 def to_yaml # loads all features if ((defined? @dirty_features) && @dirty_features.size > 0) load_feature_values end super end |
#to_yaml_properties ⇒ Object
-
remove @owl from yaml, not necessary
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# File 'lib/dataset.rb', line 215 def to_yaml_properties super - ["@owl"] end |