Module: Discretizer
- Includes:
- Consistency, Entropy
- Included in:
- FSelector::BaseDiscrete, FSelector::CFS_d, FSelector::KS_CCBF, FSelector::ReliefF_d, FSelector::Relief_d
- Defined in:
- lib/fselector/discretizer.rb
Overview
discretize continuous feature
Instance Method Summary collapse
-
#discretize_by_Chi2!(delta = 0.02)
discretize by Chi2 algorithm.
-
#discretize_by_ChiMerge!(alpha = 0.10)
discretize by ChiMerge algorithm.
-
#discretize_by_equal_frequency!(n_interval)
discretize by equal-frequency intervals.
-
#discretize_by_equal_width!(n_interval)
discretize by equal-width intervals.
-
#discretize_by_MID!
discretize by Multi-Interval Discretization (MID) algorithm.
-
#discretize_by_TID!
discretize by Three-Interval Discretization (TID) algorithm.
Methods included from Entropy
#get_conditional_entropy, #get_information_gain, #get_joint_entropy, #get_marginal_entropy, #get_symmetrical_uncertainty
Methods included from Consistency
#get_IR, #get_IR_by_count, #get_IR_by_feature, #get_instance_count
Instance Method Details
#discretize_by_Chi2!(delta = 0.02)
Chi2 does some feature reduction if a discretized feature has only one interval. Using delta==0.02 reproduces exactly the same results as that of the original Chi2 algorithm
discretize by Chi2 algorithm
ref: Chi2: Feature Selection and Discretization of Numeric Attributes
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# File 'lib/fselector/discretizer.rb', line 159 def discretize_by_Chi2!(delta=0.02) # degree of freedom equals one less than number of classes df = get_classes.size-1 # # Phase 1 # sig_level = 0.5 sig_level0 = sig_level inst_cnt = get_instance_count inconsis_rate = get_IR_by_count(inst_cnt) # f2bs = { # :'sepal-length' => [4.4], # :'sepal-width' => [2.0], # :'petal-length' => [1.0, 3.0, 5.0], # :'petal-width' => [0.1, 1.0, 1.7], # } while true chisq = pval2chisq(sig_level, df) f2bs = {} # cut ponts each_feature do |f| bs, cs, qs = chi2_init(f) chi2_merge(bs, cs, qs, chisq) f2bs[f] = bs end inconsis_rate = chi2_get_inconsistency_rate(inst_cnt, f2bs) if inconsis_rate <= delta sig_level -= 0.1 sig_level0 = sig_level break if sig_level0 <= 0.2 # phase 1 stop at level == 0.2 else # data inconsistency break end end # # Phase 2 # try_levels = [0.1, 0.01, 0.001, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10, 1e-11, 1e-12] mergeble_fs = [] f2sig_level = {} each_feature do |f| mergeble_fs << f f2sig_level[f] = sig_level0 end f2bs = {} # cut ponts while not mergeble_fs.empty? mergeble_fs.each do |f| #pp f bs, cs, qs = chi2_init(f) chisq_now = pval2chisq(f2sig_level[f], df) chi2_merge(bs, cs, qs, chisq_now) # backup bs_bak = nil if f2bs.has_key? f bs_bak = f2bs[f] end f2bs[f] = bs inconsis_rate = chi2_get_inconsistency_rate(inst_cnt, f2bs) if (inconsis_rate <= delta) # try next level next_level = chi2_decrease_sig_level(f2sig_level[f], try_levels) f2sig_level[f] = next_level if not next_level # we've tried all levels mergeble_fs.delete(f) else f2bs[f] = bs # record cut points for this level end else # cause more inconsistency f2bs[f] = bs_bak if bs_bak # restore last cut points mergeble_fs.delete(f) # not mergeble end end end #pp f2bs #pp f2sig_level # if there is only one interval, remove this feature each_sample do |k, s| s.delete_if { |f, v| f2bs[f].size <= 1 } end # discretize according to each feature's cut points discretize_at_cutpoints!(f2bs) end |
#discretize_by_ChiMerge!(alpha = 0.10)
data structure will be altered
discretize by ChiMerge algorithm
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# File 'lib/fselector/discretizer.rb', line 72 def discretize_by_ChiMerge!(alpha=0.10) # degree of freedom equals one less than number of classes df = get_classes.size-1 chisq = pval2chisq(alpha, df) # for intialization hzero = {} each_class do |k| hzero[k] = 0.0 end # determine the final boundaries for each feature f2bs = {} each_feature do |f| #f = :"sepal-length" # 1a. initialize boundaries bs, cs, qs = [], [], [] fvs = get_feature_values(f).uniq.sort fvs.each do |v| bs << v cs << hzero.dup end # 1b. initialize counts for each interval each_sample do |k, s| next if not s.has_key? f i = bs.rindex { |x| s[f] >= x } cs[i][k] += 1.0 end # 1c. initialize chi-squared values between two adjacent intervals cs.each_with_index do |c, i| if i+1 < cs.size qs << chisq_calc(c, cs[i+1]) end end # 2. iteratively merge intervals until qs.empty? or qs.min > chisq qs.each_with_index do |q, i| next if q != qs.min # update cs for merged two intervals cm = {} each_class do |k| cm[k] = cs[i][k]+cs[i+1][k] end # update qs if necessary # before merged intervals if i-1 >= 0 qs[i-1] = chisq_calc(cs[i-1], cm) end # after merged intervals if i+1 < qs.size qs[i+1] = chisq_calc(cm, cs[i+2]) end # merge up bs.delete_at(i+1) cs.delete_at(i);cs.delete_at(i);cs.insert(i, cm) qs.delete_at(i) # break out break end end # 3. record the final boundaries f2bs[f] = bs end # discretize according to each feature's boundaries discretize_at_cutpoints!(f2bs) end |
#discretize_by_equal_frequency!(n_interval)
data structure will be altered
discretize by equal-frequency intervals
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# File 'lib/fselector/discretizer.rb', line 43 def discretize_by_equal_frequency!(n_interval) n_interval = 1 if n_interval < 1 # at least one interval # first determine the boundaries f2bs = Hash.new { |h,k| h[k] = [] } each_feature do |f| fvs = get_feature_values(f).sort # number of samples in each interval ns = (fvs.size.to_f/n_interval).round fvs.each_with_index do |v, i| if i%ns == 0 f2bs[f] << v end end end # then discretize based on cut points discretize_at_cutpoints!(f2bs) end |
#discretize_by_equal_width!(n_interval)
data structure will be altered
discretize by equal-width intervals
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# File 'lib/fselector/discretizer.rb', line 16 def discretize_by_equal_width!(n_interval) n_interval = 1 if n_interval < 1 # at least one interval # first determine the boundary of each feature f2bs = Hash.new { |h,k| h[k] = [] } each_feature do |f| fvs = get_feature_values(f) fmin, fmax = fvs.min, fvs.max delta = (fmax-fmin)/n_interval n_interval.times do |i| f2bs[f] << fmin + i*delta end end # then discretize based on cut points discretize_at_cutpoints!(f2bs) end |
#discretize_by_MID!
no missing feature value is allowed, and data structure will be altered
discretize by Multi-Interval Discretization (MID) algorithm
ref: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
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# File 'lib/fselector/discretizer.rb', line 272 def discretize_by_MID! # determine the final boundaries f2cp = {} # cut points for each feature each_feature do |f| cv = get_class_labels fv = get_feature_values(f) n = cv.size abort "[#{__FILE__}@#{__LINE__}]: \n"+ " missing feature value is not allowed!" if n != fv.size # sort cv and fv according to ascending order of fv sis = (0...n).to_a.sort { |i,j| fv[i] <=> fv[j] } cv = cv.values_at(*sis) fv = fv.values_at(*sis) # get initial boundaries bs = [] fv.each_with_index do |v, i| # cut point (Ta) for feature A must always be a value between # two examples of different classes in the sequence of sorted examples # see orginal reference if i < n-1 and cv[i] != cv[i+1] bs << v end end bs.uniq! # remove duplicates # main algorithm, iteratively determine cut point cp = [] partition(cv, fv, bs, cp) # record cut points for feature (f) f2cp[f] = cp.sort # sorted cut points end # discretize based on cut points discretize_at_cutpoints!(f2cp) end |
#discretize_by_TID!
no missing feature value is allowed, and data structure will be altered
discretize by Three-Interval Discretization (TID) algorithm
ref: Filter versus wrapper gene selection approaches in DNA microarray domains
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# File 'lib/fselector/discretizer.rb', line 320 def discretize_by_TID! # cut points for each feature f2cp = {} each_feature do |f| cv = get_class_labels fv = get_feature_values(f) n = cv.size abort "[#{__FILE__}@#{__LINE__}]: \n"+ " missing feature value is not allowed!" if n != fv.size # sort cv and fv according to ascending order of fv sis = (0...n).to_a.sort { |i,j| fv[i] <=> fv[j] } cv = cv.values_at(*sis) fv = fv.values_at(*sis) # get initial boundaries bs = [] fv_u = fv.uniq fv_u.each_with_index do |v, i| # cut points are the mean of two adjacent data points if i < fv_u.size-1 bs << (v+fv_u[i+1])/2.0 end end # test each pair cut point s_best, h1_best, h2_best = nil, nil, nil bs.each_with_index do |h1, i| bs.each_with_index do |h2, j| next if j <= i n_h1 = (0...n).to_a.select { |x| fv[x] < h1 }.size.to_f n_h1_h2 = (0...n).to_a.select { |x| fv[x] > h1 and fv[x] < h2 }.size.to_f n_h2 = (0...n).to_a.select { |x| fv[x] > h2 }.size.to_f s = 0.0 each_class do |k| n_h1_k = (0...n).to_a.select { |x| fv[x] < h1 and cv[x] == k }.size.to_f n_h1_h2_k = (0...n).to_a.select { |x| fv[x] > h1 and fv[x] < h2 and cv[x] == k }.size.to_f n_h2_k = (0...n).to_a.select { |x| fv[x] > h2 and cv[x] == k }.size.to_f s += n_h1_k * Math.log2(n_h1_k/n_h1) if not n_h1_k.zero? s += n_h1_h2_k * Math.log2(n_h1_h2_k/n_h1_h2) if not n_h1_h2_k.zero? s += n_h2_k * Math.log2(n_h2_k/n_h2) if not n_h2_k.zero? #pp [s_best, s, h1, h2] + [n_h1, n_h1_k] + [n_h1_h2, n_h1_h2_k] + [n_h2, n_h2_k] end if not s_best or s > s_best s_best, h1_best, h2_best = s, h1, h2 #pp [s_best, h1_best, h2_best] end break if s_best.zero? # allow early temination at maximum value 0.0 end break if s_best.zero? # allow early temination at maximum value 0.0 end #pp [s_best, h1_best, h2_best] f2cp[f] = [h1_best, h2_best] end # discretize based on cut points discretize_at_cutpoints!(f2cp, true) end |