logistic-regression-sgd: A C++ repository from MGwave

L1-regularized logistic regression using stochastic gradient descent
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(c) Tim Nugent

Compile by running 'make'. Uses -std=c++11 - on older compilers you may need to change this to -std=c++0x in the Makefile.

Run all tests with 'make test'

Run the train/classify tool as follows:

	./lr_sgd -o weights.out -p predict.out -t test.dat train.dat

This trains using train.dat, writes the weights to weights.out, then classifies test.dat and writes predictions to predict.out.

Just train and write weights:
	
	./lr_sgd -o weights.out train.dat

Just classify using weights and test files:

	./lr_sgd -m weights.out -p predict.out -t test.dat

Training and test data should be in svm-light/libsvm format, e.g.

+1 1:4.12069 2:11.3896 3:18.5742 4:2.85764 5:53.4406 
-1 1:3.14565 2:17.4338 3:19.3353 4:2.63431 5:56.4233

Sparse data is OK. Stochastic gradient descent is sensitive to feature scaling, so it is highly recommended that you scale your data e.g. by standardising to Z-scores, or scaling in the range [0,1]. Only 2 data classes are permitted (e.g. 0 and 1, or -1 and 1). Full options are as follows:

	Usage:
	./lr_sgd [options] [training_data]

	Options:
	-s <int>   Shuffle dataset after each iteration. default 1
	-i <int>   Maximum iterations. default 50000
	-e <float> Convergence rate. default 0.005
	-a <float> Learning rate. default 0.001
	-l <float> L1 regularization weight. default 0.0001
	-m <file>  Read weights from file
	-o <file>  Write weights to file
	-t <file>  Test file to classify
	-p <file>  Write predictions to file
	-r         Randomise weights between -1 and 1, otherwise 0
	-v         Verbose.

L1-regularization is via the cumulative approach described in:

Tsuruoka, Y., Tsujii, J., and Ananiadou, S., 2009
http://aclweb.org/anthology/P/P09/P09-1054.pdf

Test example
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This uses training/test data from Reuters articles about corporate acquisitions, borrowed from here: http://svmlight.joachims.org/. Use the -v flag to see the prediction results.

With L1-regularization:

./lr_sgd -o weights.out -p predict.out -t test.dat train.dat
# called with:       ./lr_sgd -o weights.out -p predict.out -t test.dat train.dat 
# learning rate:     0.001
# convergence rate:  0.005
# l1 penalty weight: 0.0001
# max. iterations:   50000
# training data:     train.dat
# model output:      weights.out
# test data:         test.dat
# predictions:       predict.out
# training examples: 2000
# features:          7034
# stochastic gradient descent
# convergence: 0.0488 l1-norm: 1.4560e+02 iterations: 100
# convergence: 0.0356 l1-norm: 2.4081e+02 iterations: 200
# convergence: 0.0281 l1-norm: 3.1019e+02 iterations: 300
# convergence: 0.0233 l1-norm: 3.6375e+02 iterations: 400
# convergence: 0.0198 l1-norm: 4.0679e+02 iterations: 500
# convergence: 0.0173 l1-norm: 4.4234e+02 iterations: 600
# convergence: 0.0154 l1-norm: 4.7222e+02 iterations: 700
# convergence: 0.0139 l1-norm: 4.9784e+02 iterations: 800
# convergence: 0.0127 l1-norm: 5.1999e+02 iterations: 900
# convergence: 0.0117 l1-norm: 5.3936e+02 iterations: 1000
# convergence: 0.0108 l1-norm: 5.5645e+02 iterations: 1100
# convergence: 0.0101 l1-norm: 5.7178e+02 iterations: 1200
# convergence: 0.0094 l1-norm: 5.8565e+02 iterations: 1300
# convergence: 0.0089 l1-norm: 5.9820e+02 iterations: 1400
# convergence: 0.0084 l1-norm: 6.0956e+02 iterations: 1500
# convergence: 0.0079 l1-norm: 6.2004e+02 iterations: 1600
# convergence: 0.0076 l1-norm: 6.2980e+02 iterations: 1700
# convergence: 0.0072 l1-norm: 6.3883e+02 iterations: 1800
# convergence: 0.0069 l1-norm: 6.4721e+02 iterations: 1900
# convergence: 0.0066 l1-norm: 6.5499e+02 iterations: 2000
# convergence: 0.0064 l1-norm: 6.6228e+02 iterations: 2100
# convergence: 0.0061 l1-norm: 6.6908e+02 iterations: 2200
# convergence: 0.0059 l1-norm: 6.7543e+02 iterations: 2300
# convergence: 0.0057 l1-norm: 6.8134e+02 iterations: 2400
# convergence: 0.0055 l1-norm: 6.8684e+02 iterations: 2500
# convergence: 0.0053 l1-norm: 6.9202e+02 iterations: 2600
# convergence: 0.0052 l1-norm: 6.9694e+02 iterations: 2700
# convergence: 0.0050 l1-norm: 7.0160e+02 iterations: 2800
# sparsity:    0.1483 (1043/7034)
# written weights to file weights.out
# classifying
# accuracy:    0.9733 (584/600)
# precision:   0.9581
# recall:      0.9900
# mcc:         0.9472
# tp:          297
# tn:          287
# fp:          13
# fn:          3
# written predictions to file predict.out

Without L1-regularization:

./lr_sgd -l 0.0 -o weights.out -p predict.out -t test.dat train.dat
# called with:       ./lr_sgd -l 0.0 -o weights.out -p predict.out -t test.dat train.dat 
# learning rate:     0.001
# convergence rate:  0.005
# l1 penalty weight: 0
# max. iterations:   50000
# training data:     train.dat
# model output:      weights.out
# test data:         test.dat
# predictions:       predict.out
# training examples: 2000
# features:          7034
# stochastic gradient descent
# convergence: 0.0529 l1-norm: 2.3400e+02 iterations: 100
# convergence: 0.0390 l1-norm: 4.0495e+02 iterations: 200
# convergence: 0.0311 l1-norm: 5.4213e+02 iterations: 300
# convergence: 0.0260 l1-norm: 6.5733e+02 iterations: 400
# convergence: 0.0224 l1-norm: 7.5714e+02 iterations: 500
# convergence: 0.0197 l1-norm: 8.4550e+02 iterations: 600
# convergence: 0.0177 l1-norm: 9.2504e+02 iterations: 700
# convergence: 0.0161 l1-norm: 9.9751e+02 iterations: 800
# convergence: 0.0147 l1-norm: 1.0642e+03 iterations: 900
# convergence: 0.0136 l1-norm: 1.1260e+03 iterations: 1000
# convergence: 0.0127 l1-norm: 1.1838e+03 iterations: 1100
# convergence: 0.0119 l1-norm: 1.2380e+03 iterations: 1200
# convergence: 0.0112 l1-norm: 1.2892e+03 iterations: 1300
# convergence: 0.0106 l1-norm: 1.3376e+03 iterations: 1400
# convergence: 0.0100 l1-norm: 1.3836e+03 iterations: 1500
# convergence: 0.0096 l1-norm: 1.4276e+03 iterations: 1600
# convergence: 0.0091 l1-norm: 1.4695e+03 iterations: 1700
# convergence: 0.0087 l1-norm: 1.5097e+03 iterations: 1800
# convergence: 0.0084 l1-norm: 1.5483e+03 iterations: 1900
# convergence: 0.0080 l1-norm: 1.5854e+03 iterations: 2000
# convergence: 0.0077 l1-norm: 1.6211e+03 iterations: 2100
# convergence: 0.0075 l1-norm: 1.6556e+03 iterations: 2200
# convergence: 0.0072 l1-norm: 1.6890e+03 iterations: 2300
# convergence: 0.0070 l1-norm: 1.7213e+03 iterations: 2400
# convergence: 0.0068 l1-norm: 1.7525e+03 iterations: 2500
# convergence: 0.0066 l1-norm: 1.7829e+03 iterations: 2600
# convergence: 0.0064 l1-norm: 1.8124e+03 iterations: 2700
# convergence: 0.0062 l1-norm: 1.8410e+03 iterations: 2800
# convergence: 0.0060 l1-norm: 1.8689e+03 iterations: 2900
# convergence: 0.0059 l1-norm: 1.8960e+03 iterations: 3000
# convergence: 0.0057 l1-norm: 1.9225e+03 iterations: 3100
# convergence: 0.0056 l1-norm: 1.9482e+03 iterations: 3200
# convergence: 0.0054 l1-norm: 1.9734e+03 iterations: 3300
# convergence: 0.0053 l1-norm: 1.9979e+03 iterations: 3400
# convergence: 0.0052 l1-norm: 2.0219e+03 iterations: 3500
# convergence: 0.0051 l1-norm: 2.0453e+03 iterations: 3600
# sparsity:    1.0000 (7034/7034)
# written weights to file weights.out
# classifying
# accuracy:    0.9783 (587/600)
# precision:   0.9674
# recall:      0.9900
# mcc:         0.9569
# tp:          297
# tn:          290
# fp:          10
# fn:          3
# written predictions to file predict.out
MGwave/logistic-regression-sgd
