API reference for structured_prediction_experiment

Command line arguments

The table below describes command line arguments for the structured prediction experiments, namely named entity recognition and visual object localization. The main files are respectively main_ner.py and main_loc.py. The common arguments apply to both these files, which specialized arguments apply only to individual files. This is followed by arguments unique to each individual main file.

Common command line arguments

Argument Name	Type	Default	Description
l2reg	float	0.1	\(\ell_2\) regularizer which taken to be l2reg\(/n\) where \(n\) is the number of examples
prefix	str	'./data'	Prefix applied to all data paths such as train_file or bbox-dir below
output-dir	str	'./outputs'	Directory where logged outputs are saved
algorithm	str	'sgd'	Which optimization algorithm to use. See below for a detailed description of options and what they imply.
num_passes	int	100	Maximum number of passes to make through data
seed	float	1234	Random seed to use for reproducibility
lr	float	1.0	Learning rate for SGD or SVRG or Casimir-SVRG ('csvrg_lr'). Ignored by 'csvrg'
lr-t	float	1.0	Learning rate decay for SGD and ignored by other algorithms. Use learning rate \(\eta_t = \eta_0 / (1 + t/t_0)\) where \(\eta_0\) is specified using --lr and \(t_0\) is specified using --lr-t
L	float	1.0	Gradient Lipschtiz coefficient for use by Casimir-SVRG ('csvrg'). Ignored by other algorithms
smoother	float	1.0	Initial smoothing coefficient for algorithms that require smoothing. Ignored by algorithms that do not require smoothing
decay_smoother	str	‘none’	Type to smoothing decay to be applied. Options are 'none' for no decay or 'expo' for \(\mu_0 c_t^{-t}\) or 'linear' for \(\mu_0 / t\) where \(\mu_0\) is specified via --smoother. Refer to documentation of casimir.optim.CasimirSVRGOptimizer for details.
K	int	5	the value of \(K\) for top-\(K\) inference for \(\ell_2\) smoothing
warm_start	int	3	Warm start strategy for Casimir-SVRG. 1 refers to prox-center while 2 to extrapolation and 3 to prev-iterate. Refer to documentation of casimir.optim.CasimirSVRGOptimizer for details.
kappa	float	1.0	scaling factor on initial Moreau coefficient for Casimir-SVRG variant 'csvrg_lr'

Command line arguments specific to main_ner.py

Argument Name	Type	Default	Description
train_file	str	'conll03_ner/eng.train'	Name of text file with training data
dev_file	str	'conll03_ner/eng.testa'	Name of text file with development data
test_file	str	'conll03_ner/eng.testb'	Name of text file with †esting data

Command line arguments specific to main_loc.py

Argument Name	Type	Default	Description
object-class	str	'dog'	Name of object class to run experiment on
bbox-dir	str	'voc2007/bboxes'	Name of directory with bounding box information. See this page on how to obtain this data
features-dir	str	'voc2007/features'	Name of directory with bounding box features. See this page on how to obtain this data

Description of algorithms accepted by --algorithm

Algorithm	Description
'csvrg'	Casimir-SVRG with learning rate specified implicitly via grad_lipshitz_parameter. Specify command line argument --L for grad_lipschitz_parameter. Requires smoothing.
'csvrg_lr'	Casimir-SVRG with learning rate specified direcly as --lr. Requires smoothing.
'svrg'	SVRG with learning rate specified via --lr. Requires smoothing.
'sgd'	SGD with learning rates \(\eta_t = \eta_0 / (1 + t/t_0)\) where \(\eta_0\) is specified using --lr and \(t_0\) is specified using --lr-t and weighted averaging \(\bar w_T = \frac{2}{T(T+1)} \sum_{t=0}^{T} t \cdot w_t\)
'sgd_const'	SGD with constant learning rate specified as \(\eta_0\) and weighted averaging \(\bar w_T = \frac{2}{T(T+1)} \sum_{t=0}^{T} t \cdot w_t\)
'pegasos'	SGD with learning rates \(\eta_t = (\lambda t)^{-1}\) where \(\lambda\) is the \(L_2\) regularization and weighted averaging \(\bar w_T = \frac{2}{T(T+1)} \sum_{t=0}^{T} t \cdot w_t\)
'bcfw'	Block Coordinate Frank Wolfe optimization for Structural SVMs.