DESCRIPTION

VW options:

--random_seed arg

seed random number generator

--ring_size arg

size of example ring

Update options:

-l [ --learning_rate ] arg

Set learning rate

--power_t arg

t power value

--decay_learning_rate arg

Set Decay factor for learning_rate between passes

--initial_t arg

initial t value

--feature_mask arg

Use existing regressor to determine which parameters may be updated. If no initial_regressor given, also used for initial weights.

Weight options:

-i [ --initial_regressor ] arg

Initial regressor(s)

--initial_weight arg

Set all weights to an initial value of arg.

--random_weights arg

make initial weights random

--input_feature_regularizer arg

Per feature regularization input file

Parallelization options:

--span_server arg

Location of server for setting up spanning tree

--threads

Enable multi-threading

--unique_id arg (=0)

unique id used for cluster parallel jobs

--total arg (=1)

total number of nodes used in cluster parallel job

--node arg (=0)

node number in cluster parallel job

Diagnostic options:

--version

Version information

-a [ --audit ]

print weights of features

-P [ --progress ] arg

Progress update frequency. int: additive, float: multiplicative

--quiet

Don't output disgnostics and progress updates

-h [ --help ]

Look here: http://hunch.net/~vw/ and click on Tutorial.

Feature options:

--hash arg

how to hash the features. Available options: strings, all

--ignore arg

ignore namespaces beginning with character <arg>

--keep arg

keep namespaces beginning with character <arg>

--redefine arg

redefine namespaces beginning with characters of string S as namespace N. <arg> shall be in form 'N:=S' where := is operator. Empty N or S are treated as default namespace. Use ':' as a wildcard in S.

-b [ --bit_precision ] arg

number of bits in the feature table

--noconstant

Don't add a constant feature

-C [ --constant ] arg

Set initial value of constant

--ngram arg

Generate N grams. To generate N grams for a single namespace 'foo', arg should be fN.

--skips arg

Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. To generate n-skips for a single namespace 'foo', arg should be fN.

--feature_limit arg

limit to N features. To apply to a single namespace 'foo', arg should be fN

--affix arg

generate prefixes/suffixes of features; argument '+2a,-3b,+1' means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

--spelling arg

compute spelling features for a give namespace (use '_' for default namespace)

--dictionary arg

read a dictionary for additional features (arg either 'x:file' or just 'file')

--dictionary_path arg

look in this directory for dictionaries; defaults to current directory or env{PATH}

--interactions arg

Create feature interactions of any level between namespaces.

--permutations

Use permutations instead of combinations for feature interactions of same namespace.

--leave_duplicate_interactions

Don't remove interactions with duplicate combinations of namespaces. For ex. this is a duplicate: '-q ab -q ba' and a lot more in '-q ::'.

-q [ --quadratic ] arg

Create and use quadratic features

--q: arg

: corresponds to a wildcard for all printable characters

--cubic arg

Create and use cubic features

Example options:

-t [ --testonly ]

Ignore label information and just test

--holdout_off

no holdout data in multiple passes

--holdout_period arg

holdout period for test only, default 10

--holdout_after arg

holdout after n training examples, default off (disables holdout_period)

--early_terminate arg

Specify the number of passes tolerated when holdout loss doesn't decrease before early termination, default is 3

--passes arg

Number of Training Passes

--initial_pass_length arg

initial number of examples per pass

--examples arg

number of examples to parse

--min_prediction arg

Smallest prediction to output

--max_prediction arg

Largest prediction to output

--sort_features

turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

--loss_function arg (=squared)

Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.

--quantile_tau arg (=0.5)

Parameter \tau associated with Quantile loss. Defaults to 0.5

--l1 arg

l_1 lambda

--l2 arg

l_2 lambda

--named_labels arg

use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep, eg "--named_labels Noun,Verb,Adj,Punc"

Output model:

-f [ --final_regressor ] arg

Final regressor

--readable_model arg

Output human-readable final regressor with numeric features

--invert_hash arg

Output human-readable final regressor with feature names. Computationally expensive.

--save_resume

save extra state so learning can be resumed later with new data

--save_per_pass

Save the model after every pass over data

--output_feature_regularizer_binary arg

Per feature regularization output file

--output_feature_regularizer_text arg Per feature regularization output file,

in text

Output options:

-p [ --predictions ] arg

File to output predictions to

-r [ --raw_predictions ] arg

File to output unnormalized predictions to

Reduction options, use [option] --help for more info:

--bootstrap arg

k-way bootstrap by online importance resampling

--search arg

Use learning to search, argument=maximum action id or 0 for LDF

--replay_c arg

use experience replay at a specified level [b=classification/regression, m=multiclass, c=cost sensitive] with specified buffer size

--cbify arg

Convert multiclass on <k> classes into a contextual bandit problem

--cb_adf

Do Contextual Bandit learning with multiline action dependent features.

--cb arg

Use contextual bandit learning with <k> costs

--csoaa_ldf arg

Use one-against-all multiclass learning with label dependent features. Specify singleline or multiline.

--wap_ldf arg

Use weighted all-pairs multiclass learning with label dependent features.

Specify singleline or multiline.

--interact arg

Put weights on feature products from namespaces <n1> and <n2>

--csoaa arg

One-against-all multiclass with <k> costs

--multilabel_oaa arg

One-against-all multilabel with <k> labels

--log_multi arg

Use online tree for multiclass

--ect arg

Error correcting tournament with <k> labels

--boosting arg

Online boosting with <N> weak learners

--oaa arg

One-against-all multiclass with <k> labels

--top arg

top k recommendation

--replay_m arg

use experience replay at a specified level [b=classification/regression, m=multiclass, c=cost sensitive] with specified buffer size

--binary

report loss as binary classification on -1,1

--link arg (=identity)

Specify the link function: identity, logistic or glf1

--stage_poly

use stagewise polynomial feature learning

--lrqfa arg

use low rank quadratic features with field aware weights

--lrq arg

use low rank quadratic features

--autolink arg

create link function with polynomial d

--new_mf arg

rank for reduction-based matrix factorization

--nn arg

Sigmoidal feedforward network with <k> hidden units

--confidence

Get confidence for binary predictions

--active_cover

enable active learning with cover

--active

enable active learning

--replay_b arg

use experience replay at a specified level [b=classification/regression, m=multiclass, c=cost sensitive] with specified buffer size

--bfgs

use bfgs optimization

--conjugate_gradient

use conjugate gradient based optimization

--lda arg

Run lda with <int> topics

--noop

do no learning

--print

print examples

--rank arg

rank for matrix factorization.

--sendto arg

send examples to <host>

--svrg

Streaming Stochastic Variance Reduced Gradient

--ftrl

FTRL: Follow the Proximal Regularized Leader

--pistol

FTRL: Parameter-free Stochastic Learning

--ksvm

kernel svm

Gradient Descent options:

--sgd

use regular stochastic gradient descent update.

--adaptive

use adaptive, individual learning rates.

--invariant

use safe/importance aware updates.

--normalized

use per feature normalized updates

--sparse_l2 arg (=0)

use per feature normalized updates

Input options:

-d [ --data ] arg

Example Set

--daemon

persistent daemon mode on port 26542

--port arg

port to listen on; use 0 to pick unused port

--num_children arg

number of children for persistent daemon mode

--pid_file arg

Write pid file in persistent daemon mode

--port_file arg

Write port used in persistent daemon mode

-c [ --cache ]

Use a cache. The default is <data>.cache

--cache_file arg

The location(s) of cache_file.

-k [ --kill_cache ]

do not reuse existing cache: create a new one always

--compressed

use gzip format whenever possible. If a cache file is being created, this option creates a compressed cache file. A mixture of raw-text & compressed inputs are supported with autodetection.

--no_stdin

do not default to reading from stdin