diff options
Diffstat (limited to 'lib/algorithm/transform.rb')
| -rw-r--r-- | lib/algorithm/transform.rb | 282 |
1 files changed, 116 insertions, 166 deletions
diff --git a/lib/algorithm/transform.rb b/lib/algorithm/transform.rb index afc76f9..ec25526 100644 --- a/lib/algorithm/transform.rb +++ b/lib/algorithm/transform.rb @@ -18,42 +18,27 @@ module OpenTox # @param [GSL::Vector] values Values to transform using AutoScaling. def initialize values - begin - raise "Cannot transform, values empty." if values.size==0 - vs = values.clone - @mean = vs.to_scale.mean - @stdev = vs.to_scale.standard_deviation_population - @stdev = 0.0 if @stdev.nan? - @vs = transform vs - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + bad_request_error "Cannot transform, values empty." if values.size==0 + vs = values.clone + @mean = vs.to_scale.mean + @stdev = vs.to_scale.standard_deviation_population + @stdev = 0.0 if @stdev.nan? + @vs = transform vs end # @param [GSL::Vector] values Values to transform. # @return [GSL::Vector] transformed values. def transform values - begin - raise "Cannot transform, values empty." if values.size==0 - autoscale values.clone - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + bad_request_error "Cannot transform, values empty." if values.size==0 + autoscale values.clone end # @param [GSL::Vector] values Values to restore. # @return [GSL::Vector] transformed values. def restore values - begin - raise "Cannot transform, values empty." if values.size==0 - rv_ss = values.clone.to_scale * @stdev unless @stdev == 0.0 - (rv_ss + @mean).to_gsl - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + bad_request_error "Cannot transform, values empty." if values.size==0 + rv_ss = values.clone.to_scale * @stdev unless @stdev == 0.0 + (rv_ss + @mean).to_gsl end # @param [GSL::Vector] values to transform. @@ -77,68 +62,63 @@ module OpenTox # @return [GSL::Matrix] Data transformed matrix. def initialize data_matrix, compression=0.05, maxcols=(1.0/0.0) - begin - @data_matrix = data_matrix.clone - @compression = compression.to_f - @mean = Array.new - @autoscaler = Array.new - @cols = Array.new - @maxcols = maxcols - - # Objective Feature Selection - raise "Error! PCA needs at least two dimensions." if data_matrix.size2 < 2 - @data_matrix_selected = nil - (0..@data_matrix.size2-1).each { |i| - if !@data_matrix.col(i).to_a.zero_variance? - if @data_matrix_selected.nil? - @data_matrix_selected = GSL::Matrix.alloc(@data_matrix.size1, 1) - @data_matrix_selected.col(0)[0..@data_matrix.size1-1] = @data_matrix.col(i) - else - @data_matrix_selected = @data_matrix_selected.horzcat(GSL::Matrix.alloc(@data_matrix.col(i).to_a,@data_matrix.size1, 1)) - end - @cols << i - end - } - raise "Error! PCA needs at least two dimensions." if (@data_matrix_selected.nil? || @data_matrix_selected.size2 < 2) - - # PCA uses internal centering on 0 - @data_matrix_scaled = GSL::Matrix.alloc(@data_matrix_selected.size1, @cols.size) - (0..@cols.size-1).each { |i| - as = OpenTox::Algorithm::Transform::AutoScale.new(@data_matrix_selected.col(i)) - @data_matrix_scaled.col(i)[0..@data_matrix.size1-1] = as.vs * as.stdev # re-adjust by stdev - @mean << as.mean - @autoscaler << as - } - - # PCA - data_matrix_hash = Hash.new - (0..@cols.size-1).each { |i| - column_view = @data_matrix_scaled.col(i) - data_matrix_hash[i] = column_view.to_scale - } - dataset_hash = data_matrix_hash.to_dataset # see http://goo.gl/7XcW9 - cor_matrix=Statsample::Bivariate.correlation_matrix(dataset_hash) - pca=Statsample::Factor::PCA.new(cor_matrix) - - # Select best eigenvectors - pca.eigenvalues.each { |ev| raise "PCA failed!" unless !ev.nan? } - @eigenvalue_sums = Array.new - (0..@cols.size-1).each { |i| - @eigenvalue_sums << pca.eigenvalues[0..i].inject{ |sum, ev| sum + ev } - } - eigenvectors_selected = Array.new - pca.eigenvectors.each_with_index { |ev, i| - if (@eigenvalue_sums[i] <= ((1.0-@compression)*@cols.size)) || (eigenvectors_selected.size == 0) - eigenvectors_selected << ev.to_a unless @maxcols <= eigenvectors_selected.size + @data_matrix = data_matrix.clone + @compression = compression.to_f + @mean = Array.new + @autoscaler = Array.new + @cols = Array.new + @maxcols = maxcols + + # Objective Feature Selection + bad_request_error "Error! PCA needs at least two dimensions." if data_matrix.size2 < 2 + @data_matrix_selected = nil + (0..@data_matrix.size2-1).each { |i| + if !@data_matrix.col(i).to_a.zero_variance? + if @data_matrix_selected.nil? + @data_matrix_selected = GSL::Matrix.alloc(@data_matrix.size1, 1) + @data_matrix_selected.col(0)[0..@data_matrix.size1-1] = @data_matrix.col(i) + else + @data_matrix_selected = @data_matrix_selected.horzcat(GSL::Matrix.alloc(@data_matrix.col(i).to_a,@data_matrix.size1, 1)) end - } - @eigenvector_matrix = GSL::Matrix.alloc(eigenvectors_selected.flatten, eigenvectors_selected.size, @cols.size).transpose - @data_transformed_matrix = (@eigenvector_matrix.transpose * @data_matrix_scaled.transpose).transpose + @cols << i + end + } + bad_request_error "Error! PCA needs at least two dimensions." if (@data_matrix_selected.nil? || @data_matrix_selected.size2 < 2) + + # PCA uses internal centering on 0 + @data_matrix_scaled = GSL::Matrix.alloc(@data_matrix_selected.size1, @cols.size) + (0..@cols.size-1).each { |i| + as = OpenTox::Algorithm::Transform::AutoScale.new(@data_matrix_selected.col(i)) + @data_matrix_scaled.col(i)[0..@data_matrix.size1-1] = as.vs * as.stdev # re-adjust by stdev + @mean << as.mean + @autoscaler << as + } + + # PCA + data_matrix_hash = Hash.new + (0..@cols.size-1).each { |i| + column_view = @data_matrix_scaled.col(i) + data_matrix_hash[i] = column_view.to_scale + } + dataset_hash = data_matrix_hash.to_dataset # see http://goo.gl/7XcW9 + cor_matrix=Statsample::Bivariate.correlation_matrix(dataset_hash) + pca=Statsample::Factor::PCA.new(cor_matrix) + + # Select best eigenvectors + pca.eigenvalues.each { |ev| bad_request_error "PCA failed!" unless !ev.nan? } + @eigenvalue_sums = Array.new + (0..@cols.size-1).each { |i| + @eigenvalue_sums << pca.eigenvalues[0..i].inject{ |sum, ev| sum + ev } + } + eigenvectors_selected = Array.new + pca.eigenvectors.each_with_index { |ev, i| + if (@eigenvalue_sums[i] <= ((1.0-@compression)*@cols.size)) || (eigenvectors_selected.size == 0) + eigenvectors_selected << ev.to_a unless @maxcols <= eigenvectors_selected.size + end + } + @eigenvector_matrix = GSL::Matrix.alloc(eigenvectors_selected.flatten, eigenvectors_selected.size, @cols.size).transpose + @data_transformed_matrix = (@eigenvector_matrix.transpose * @data_matrix_scaled.transpose).transpose - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end end # Transforms data to feature space found by PCA. @@ -146,35 +126,25 @@ module OpenTox # @param [GSL::Matrix] values Data matrix. # @return [GSL::Matrix] Transformed data matrix. def transform values - begin - vs = values.clone - raise "Error! Too few columns for transformation." if vs.size2 < @cols.max - data_matrix_scaled = GSL::Matrix.alloc(vs.size1, @cols.size) - @cols.each_with_index { |i,j| - data_matrix_scaled.col(j)[0..data_matrix_scaled.size1-1] = @autoscaler[j].transform(vs.col(i).to_a) * @autoscaler[j].stdev - } - (@eigenvector_matrix.transpose * data_matrix_scaled.transpose).transpose - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + vs = values.clone + bad_request_error "Error! Too few columns for transformation." if vs.size2 < @cols.max + data_matrix_scaled = GSL::Matrix.alloc(vs.size1, @cols.size) + @cols.each_with_index { |i,j| + data_matrix_scaled.col(j)[0..data_matrix_scaled.size1-1] = @autoscaler[j].transform(vs.col(i).to_a) * @autoscaler[j].stdev + } + (@eigenvector_matrix.transpose * data_matrix_scaled.transpose).transpose end # Restores data in the original feature space (possibly with compression loss). # # @return [GSL::Matrix] Data matrix. def restore - begin - data_matrix_restored = (@eigenvector_matrix * @data_transformed_matrix.transpose).transpose # reverse pca - # reverse scaling - (0..@cols.size-1).each { |i| - data_matrix_restored.col(i)[0..data_matrix_restored.size1-1] += @mean[i] - } - data_matrix_restored - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + data_matrix_restored = (@eigenvector_matrix * @data_transformed_matrix.transpose).transpose # reverse pca + # reverse scaling + (0..@cols.size-1).each { |i| + data_matrix_restored.col(i)[0..data_matrix_restored.size1-1] += @mean[i] + } + data_matrix_restored end end @@ -191,45 +161,40 @@ module OpenTox # @return [GSL::Matrix] Data transformed matrix def initialize data_matrix, compression=0.05 - begin - @data_matrix = data_matrix.clone - @compression = compression - - # Compute the SV Decomposition X=USV - # vt is *not* the transpose of V here, but V itself (see http://goo.gl/mm2xz)! - u, vt, s = data_matrix.SV_decomp - - # Determine cutoff index - s2 = s.mul(s) ; s2_sum = s2.sum - s2_run = 0 - k = s2.size - 1 - s2.to_a.reverse.each { |v| - s2_run += v - frac = s2_run / s2_sum - break if frac > compression - k -= 1 - } - k += 1 if k == 0 # avoid uni-dimensional (always cos sim of 1) - - # Take the k-rank approximation of the Matrix - # - Take first k columns of u - # - Take first k columns of vt - # - Take the first k eigenvalues - @uk = u.submatrix(nil, (0..k)) # used to transform column format data - @vk = vt.submatrix(nil, (0..k)) # used to transform row format data - s = GSL::Matrix.diagonal(s) - @eigk = s.submatrix((0..k), (0..k)) - @eigk_inv = @eigk.inv - - # Transform data - @data_transformed_matrix = @uk # = u for all SVs - # NOTE: @data_transformed_matrix is also equal to - # @data_matrix * @vk * @eigk_inv - - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + @data_matrix = data_matrix.clone + @compression = compression + + # Compute the SV Decomposition X=USV + # vt is *not* the transpose of V here, but V itself (see http://goo.gl/mm2xz)! + u, vt, s = data_matrix.SV_decomp + + # Determine cutoff index + s2 = s.mul(s) ; s2_sum = s2.sum + s2_run = 0 + k = s2.size - 1 + s2.to_a.reverse.each { |v| + s2_run += v + frac = s2_run / s2_sum + break if frac > compression + k -= 1 + } + k += 1 if k == 0 # avoid uni-dimensional (always cos sim of 1) + + # Take the k-rank approximation of the Matrix + # - Take first k columns of u + # - Take first k columns of vt + # - Take the first k eigenvalues + @uk = u.submatrix(nil, (0..k)) # used to transform column format data + @vk = vt.submatrix(nil, (0..k)) # used to transform row format data + s = GSL::Matrix.diagonal(s) + @eigk = s.submatrix((0..k), (0..k)) + @eigk_inv = @eigk.inv + + # Transform data + @data_transformed_matrix = @uk # = u for all SVs + # NOTE: @data_transformed_matrix is also equal to + # @data_matrix * @vk * @eigk_inv + end @@ -238,12 +203,7 @@ module OpenTox # @param [GSL::Matrix] values Data matrix (1 x m). # @return [GSL::Matrix] Transformed data matrix. def transform_instance values - begin - values * @vk * @eigk_inv - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + values * @vk * @eigk_inv end alias :transform :transform_instance # make this the default (see PCA interface) @@ -252,12 +212,7 @@ module OpenTox # @param [GSL::Matrix] values Data matrix (1 x n). # @return [GSL::Matrix] Transformed data matrix. def transform_feature values - begin - values * @uk * @eigk_inv - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + values * @uk * @eigk_inv end @@ -265,12 +220,7 @@ module OpenTox # # @return [GSL::Matrix] Data matrix. def restore - begin - @data_transformed_matrix * @eigk * @vk.transpose # reverse svd - rescue Exception => e - $logger.debug "#{e.class}: #{e.message}" - $logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}" - end + @data_transformed_matrix * @eigk * @vk.transpose # reverse svd end |