NAME¶
vw - Vowpal Wabbit -- fast online learning tool
DESCRIPTION¶
VW options:¶
- -h [ --help ]
- Look here: http://hunch.net/~vw/ and click on Tutorial.
- --active_learning
- active learning mode
- --active_simulation
- active learning simulation mode
- --active_mellowness arg
- active learning mellowness parameter c_0. Default 8
- --binary
- report loss as binary classification on -1,1
- --autolink arg
- create link function with polynomial d
- --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
- --exact_adaptive_norm
- use current default invariant normalized adaptive update rule
- -a [ --audit ]
- print weights of features
- -b [ --bit_precision ] arg
- number of bits in the feature table
- --bfgs
- use bfgs optimization
- -c [ --cache ]
- Use a cache. The default is <data>.cache
- --cache_file arg
- The location(s) of cache_file.
- --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
- --conjugate_gradient
- use conjugate gradient based optimization
- --csoaa arg
- Use one-against-all multiclass learning with <k> costs
- --wap arg
- Use weighted all-pairs multiclass 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.
- --cb arg
- Use contextual bandit learning with <k> costs
- --l1 arg
- l_1 lambda
- --l2 arg
- l_2 lambda
- -d [ --data ] arg
- Example Set
- --daemon
- persistent daemon mode on port 26542
- --num_children arg
- number of children for persistent daemon mode
- --pid_file arg
- Write pid file in persistent daemon mode
- --decay_learning_rate arg
- Set Decay factor for learning_rate between passes
- --input_feature_regularizer arg
- Per feature regularization input file
- -f [ --final_regressor ] arg
- Final regressor
- --readable_model arg
- Output human-readable final regressor
- --hash arg
- how to hash the features. Available options: strings, all
- --hessian_on
- use second derivative in line search
- --version
- Version information
- --ignore arg
- ignore namespaces beginning with character <arg>
- --keep arg
- keep namespaces beginning with character <arg>
- -k [ --kill_cache ]
- do not reuse existing cache: create a new one always
- --initial_weight arg
- Set all weights to an initial value of 1.
- -i [ --initial_regressor ] arg
- Initial regressor(s)
- --initial_pass_length arg
- initial number of examples per pass
- --initial_t arg
- initial t value
- --lda arg
- Run lda with <int> topics
- --span_server arg
- Location of server for setting up spanning tree
- --min_prediction arg
- Smallest prediction to output
- --max_prediction arg
- Largest prediction to output
- --mem arg
- memory in bfgs
- --nn arg
- Use sigmoidal feedforward network with <k> hidden units
- --noconstant
- Don't add a constant feature
- --noop
- do no learning
- --oaa arg
- Use one-against-all multiclass learning with <k> labels
- --ect arg
- Use error correcting tournament with <k> labels
- --output_feature_regularizer_binary arg
- Per feature regularization output file
- --output_feature_regularizer_text arg Per feature regularization
output file,
- in text
- --port arg
- port to listen on
- --power_t arg
- t power value
- -l [ --learning_rate ] arg
- Set Learning Rate
- --passes arg
- Number of Training Passes
- --termination arg
- Termination threshold
- -p [ --predictions ] arg
- File to output predictions to
- -q [ --quadratic ] arg
- Create and use quadratic features
- --cubic arg
- Create and use cubic features
- --quiet
- Don't output diagnostics
- --rank arg
- rank for matrix factorization.
- --random_weights arg
- make initial weights random
- --random_seed arg
- seed random number generator
- -r [ --raw_predictions ] arg
- File to output unnormalized predictions to
- --ring_size arg
- size of example ring
- --examples arg
- number of examples to parse
- --save_per_pass
- Save the model after every pass over data
- --save_resume
- save extra state so learning can be resumed later with new data
- --sendto arg
- send examples to <host>
- --searn arg
- use searn, argument=maximum action id
- --searnimp arg
- use searn, argument=maximum action id or 0 for LDF
- -t [ --testonly ]
- Ignore label information and just test
- --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
- --unique_id arg
- unique id used for cluster parallel jobs
- --total arg
- total number of nodes used in cluster parallel job
- --node arg
- node number in cluster parallel job
- --sort_features
- turn this on to disregard order in which features have been defined. This
will lead to smaller cache sizes
- --ngram arg
- Generate N grams
- --skips arg
- Generate skips in N grams. This in conjunction with the ngram tag can be
used to generate generalized n-skip-k-gram.