Implement Ruby binding

[abicky]

I have implemented Ruby binding and have confirmed that it works as expected on Mac OS X 10.10.2 with Swig 3.0.5, Debian 6.0.5 with Swig 3.0.5, and Debian 6.0.5 with Swig 2.0.4.

Installation

$ cd swig/ruby
$ ./prepare.sh --swig
$ ruby extconf.rb
$ make install 

Execute sample codes

$ cd swig/ruby
$ curl -LO http://www.cnts.ua.ac.be/conll2000/chunking/train.txt.gz
$ zcat train.txt.gz | ../../example/chunking.py > train.crfsuite.txt
$ ruby sample_train.rb CoNLL2000.model < train.crfsuite.txt
0.12.2
feature.minfreq 0.000000 The minimum frequency of features.
feature.possible_states 0 Force to generate possible state features.
feature.possible_transitions 0 Force to generate possible transition features.
c2 1.000000 Coefficient for L2 regularization.
max_iterations 1000 The maximum number of iterations (epochs) for SGD optimization.
period 10 The duration of iterations to test the stopping criterion.
delta 0.000001 The threshold for the stopping criterion; an optimization process stops when
the improvement of the log likelihood over the last ${period} iterations is no
greater than this threshold.
calibration.eta 0.100000 The initial value of learning rate (eta) used for calibration.
calibration.rate 2.000000 The rate of increase/decrease of learning rate for calibration.
calibration.samples 1000 The number of instances used for calibration.
calibration.candidates 10 The number of candidates of learning rate.
calibration.max_trials 20 The maximum number of trials of learning rates for calibration.
Feature generation
type: CRF1d
feature.minfreq: 0.000000
feature.possible_states: 0
feature.possible_transitions: 0
0....1....2....3....4....5....6....7....8....9....10
Number of features: 452755
Seconds required: 2.648

Stochastic Gradient Descent (SGD)
c2: 0.100000
max_iterations: 1000
period: 10
delta: 0.000001

Calibrating the learning rate (eta)
calibration.eta: 0.100000
calibration.rate: 2.000000
calibration.samples: 1000
calibration.candidates: 10
calibration.max_trials: 20
Initial loss: 73427.713480
Trial #1 (eta = 0.100000): 6582.519447
Trial #2 (eta = 0.200000): 6730.455920
Trial #3 (eta = 0.400000): 9512.830812
Trial #4 (eta = 0.800000): 18893.911403
Trial #5 (eta = 1.600000): 37728.232850
Trial #6 (eta = 3.200000): 80183.389088 (worse)
Trial #7 (eta = 0.050000): 7630.312307
Trial #8 (eta = 0.025000): 9621.753885
Trial #9 (eta = 0.012500): 12638.018534
Trial #10 (eta = 0.006250): 16982.966482
Trial #11 (eta = 0.003125): 23074.675027
Trial #12 (eta = 0.001563): 31343.061039
Trial #13 (eta = 0.000781): 41540.646706
Trial #14 (eta = 0.000391): 52696.836433
Trial #15 (eta = 0.000195): 61945.207748
Trial #16 (eta = 0.000098): 67498.204541
Best learning rate (eta): 0.100000
Seconds required: 2.078

***** Epoch #1 *****
Loss: 33583.103783
Feature L2-norm: 63.611729
Learning rate (eta): 0.098039
Total number of feature updates: 8936
Seconds required for this iteration: 1.046

(snip)

SGD terminated with the stopping criteria
Loss: 3523.303519
Total seconds required for training: 164.998

Storing the model
Number of active features: 452755 (452755)
Number of active attributes: 335674 (335674)
Number of active labels: 22 (22)
Writing labels
Writing attributes
Writing feature references for transitions
Writing feature references for attributes
Seconds required: 1.120

$ ruby sample_tag.rb CoNLL2000.model < train.crfsuite.txt > result