vw - Online in the Cloud

PROGRAM:

NAME

vw - Vowpal Wabbit -- fast online learning tool

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

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