Command line arguments

Dmitry Spikhalskiy edited this page Dec 12, 2017 · 135 revisions

Running VW with the -h or --help option produces a message which briefly explains each argument. If you want to know all parameters of a given reduction (e.g. --ksvm or --nn), you must add the reduction parameter to the help command (e.g. vw -h --ksvm or vw -h --nn 1). Below arguments are grouped according to their function and each argument is explained in more detail.

VW options

-h [ --help ]                Look here: and
                             click on Tutorial.
--version                    Version information
--random_seed arg            seed random number generator
--noop                       do no learning

Input options

-d [ --data ] arg            Example Set
--ring_size arg              size of example ring
--examples arg               number of examples to parse
--daemon                     read data from port 26542
--port arg                   port to listen on
--num_children arg (=10)     number of children for 
                             persistent daemon mode
--pid_file arg               Write pid file in 
                             persistent daemon mode
--passes arg (=1)            Number of Training Passes
-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
--save_resume                save extra state so learning can be resumed
                             later with new data

Raw training/testing data (in the proper plain text input format) can be passed to VW in a number of ways:

  • Using the -d or --data options which expect a file name as an argument (specifying a file name that is not associated with any option also works);
  • Via stdin;
  • Via a TCP/IP port if the --daemon option is specified. The port itself is specified by --port otherwise the default port 26542 is used. The daemon by default creates 10 child processes which share the model state, allowing answering multiple simultaneous queries. The number of child processes can be controlled with --num_children, and you can create a file with the jobid using --pid_file which is later useful for killing the job.

Parsing raw data is slow so there are options to create or load data in VW's native format. Files containing data in VW's native format are called caches. The exact contents of a cache file depend on the input as well as a few options (-b, --affix, --spelling) that are passed to VW during the creation of the cache. This implies that using the cache file with different options might cause VW to rebuild the cache. The easiest way to use a cache is to always specify the -c option. This way, VW will first look for a cache file and create it if it doesn't exist. To override the default cache file name use --cache_file followed by the file name.

--compressed can be used for reading gzipped raw training data, writing gzipped caches, and reading gzipped caches.

--passes takes as an argument the number of times the algorithm will cycle over the data (epochs).

Output options

-a [ --audit ]                 print weights of features  
-p [ --predictions ] arg       File to output predictions to
-r [ --raw_predictions ] arg   File to output unnormalized predictions
--sendto arg                   to send compressed examples to <host>
--quiet                        Don't output diagnostics
-P [ --progress ] arg          Progress update frequency.
                               integer: additive; float: multiplicative
--min_prediction arg           Smallest prediction to output
--max_prediction arg           Largest prediction to output
--progressive_validation       File to record progressive validation
                               for FTRL-Proximal (option in ftrl)

-p /dev/stdout is often a handy trick for seeing outputs.

-r is rarely used.

--quiet shuts off the normal diagnostic printout of progress updates.

--progress arg changes the frequency of the diagnostic progress-update printouts. If arg is an integer, the printouts happen every arg (fixed) interval, e.g: arg is 10, we get printouts at 10, 20, 30, ... Alternatively, if arg has a dot in it, it is interpreted as a floating point number, and the printouts happen on a multiplicative schedule: e.g. when arg is 2.0 (the default) progress updates will be printed on examples numbered: 1, 2, 4, 8, ..., 2^n

--sendto is used with another VW using --daemon to send examples and get back predictions from the daemon VW.

--min_prediction and --max_prediction control the range of the output prediction by clipping. By default, it automatically adjusts to the range of labels observed. If you set this, there is no auto-adjusting.

The -a or --audit option is useful for debugging and for accessing the features and values for each example as well as the values in VW's weight vector. The format depends on the mode VW is running on. The format used for the non-LDA case is:

`prediction tag (namespace^feature:hashindex:value:weight[@ssgrad] )*`

prediction is VW's prediction on the example with tag tag. Then there's a list of feature information. namespace is the namespace where the feature belongs, feature is the name of the feature, hashindex is the position where it hashes, value is the value of the feature, weight is the current learned weight associated with that feature and finally ssgrad is the sum of squared gradients (plus 1) if adaptive updates are used.

Example Manipulation options

-t [ --testonly ]        Ignore label information and just test
-q [ --quadratic ] arg   Create and use quadratic features
--cubic arg              Create and use cubic features
--interactions arg       Create feature interactions of any level 
                         between namespaces.
--ignore arg             ignore namespaces beginning
                         with character <arg>
--keep arg               keep namespaces beginning with 
                         character <arg>
--redefine arg           redefine namespaces beginning with chars
                         of string S as namespace N.
                         <arg> format is 'N:=S' where := is the
                         redefine operator. Empty N or S are the
                         default namespace.
                         Use ':' as a wildcard in S.
--holdout_off            no holdout data in multiple passes
--holdout_period         holdout period for test only, default 10
--sort_features          turn this on to disregard order in which 
                         features have been defined. This will lead 
                         to smaller cache sizes
--permutations           Use permutations instead of combinations for 
                         feature interactions of same namespace.
--noconstant             Don't add a constant feature
-C [ --constant ] arg    Set initial value of the constant feature to arg
                         (Useful for faster convergence on data-sets
                          where the label isn't centered around zero)
--ngram arg              Generate N grams. 
                         To target a specific namespace write its name as a prefix to arg 
                         (e.g. --ngram a2 --ngram c3).
--skips arg              Generate skips in N grams. This in conjunction 
                         with the ngram tag can be used to generate 
                         generalized n-skip-k-gram.
--hash arg               how to hash the features. Available options:
                             strings, all
                         Don't remove order-dependent duplicate
                         interactions when crossing namespaces.
                         e.g: '-q ab -q ba' is a duplicate, and
                         '-q ::' may create many more duplicates
--classweight arg        Importance-weight multiplier for a target class,
                         e.g. for binary classification you could use:
                             --classweight -1:0.25
                         which will under-weight negative (-1) examples
                         by a factor 0.25

-t and --testonly makes VW run in testing mode. The labels are ignored so this is useful for assessing the generalization performance of the learned model on a test set. This has the same effect as passing a 0 importance weight on every example.

-q is a very powerful option. It takes as an argument a pair of two letters. Its effect is to create interactions between the features of two namespaces. Suppose each example has a namespace user and a namespace document, then specifying -q ud will create an interaction feature for every pair of features (x,y) where x is a feature from the user namespace and y is a feature from the document namespace. If a letter matches more than one namespace then all the matching namespaces are used. In our example if there is another namespace url then interactions between url and document will also be modeled. The letter : is a wildcard to interact with all namespaces. -q a: (or -q :a) will create an interaction feature for every pair of features (x,y) where x is a feature from the namespaces starting with a and y is a feature from the all namespaces. -q :: would interact any combination of pairs of features.

note: \xFF notation could be used to define namespace by its character's hex code (FF in this example). \x is case sensitive and \ shall be escaped in some shells like bash (-q \\xC0\\xC1). This format is supported in any command line argument which accepts namespaces.

--cubic is similar to -q, but it takes three letters as the argument, thus enabling interaction among the features of three namespaces.

--interactions same as -q and --cubic but can create feature interactions of any level, like --interactions abcde. For example --interactions abc is equal to --cubic abc.

--ignore ignores a namespace, effectively making the features not there. You can use it multiple times.

--keep keeps namespace(s) ignoring those not listed, it is a counterpart to --ignore. You can use it multiple times. Useful for example to train a baseline using just a single namespace.

--redefine allows namespace(s) renaming without any changes in input data. Its argument takes the form N:=S where := is the redefine operator, S is the list of old namespaces and N is the new namespace character. Empty S or N refer to the default namespace (features without namespace explicitly specified). The wildcard character : may be used to represent all namespaces, including default. For example, --redefine :=: will rename all namespaces to the default one (all features will be stored in default namespace). The order of --redefine, --ignore, and other name-space options (like -q or --cubic) matters. For example:

   --redefine A:=: --redefine B:= --redefine B:=q --ignore B -q AA

will ignore features of namespaces starting with q and the default namespace, put all other features into one namespace A and finally generate quadratic interactions between the newly defined A namespace.

--holdout_off disables holdout validation for multiple pass learning. By default, VW holds out a (controllable default = 1/10th) subset of examples whenever --passes > 1 and reports the test loss on the print out. This is used to prevent overfitting in multiple pass learning. An extra h is printed at the end of the line to specify the reported losses are holdout validation loss, instead of progressive validation loss.

--holdout_period specifies the period of holdout example used for holdout validation in multiple pass learning. For example, if user specifies --holdout_period 5, every one in 5 examples is used for holdout validation. In other words, 80% of the data is used for training.

--noconstant eliminates the constant feature that exists by default in VW.

--ngram and --skip can be used to generate ngram features possibly with skips (a.k.a. don't cares). For example --ngram 2 will generate (unigram and) bigram features by creating new features from features that appear next to each other, and --ngram 2 --skip 1 will generate (unigram, bigram, and) trigram features plus trigram features where we don't care about the identity of the middle token.

Unlike --ngram where the order of the features matters, --sort_features destroys the order in which features are presented and writes them in cache in a way that minimizes the cache size. --sort_features and --ngram are mutually exclusive

--permutations defines how VW interacts features of the same namespace. For example, in case -q aa. If namespace a contains 3 features than by default VW generates only simple combinations of them: aa:{(1,2),(1,3),(2,3)}. With --permutations specified it will generate permutations of interacting features aa:{(1,1),(1,2),(1,3),(2,1),(2,2),(2,3),(3,1),(3,2),(3,3)}. It's recommended to not use --permutations without a good reason as it may cause generation of a lot more features than usual.

By default VW hashes string features and does not hash integer features. --hash all hashes all feature identifiers. This is useful if your features are integers and you want to use parallelization as it will spread the features almost equally among the threads or cluster nodes, having a load balancing effect.

VW removes duplicate interactions of same set of namespaces. For example in -q ab -q ba -q ab only first -q ab will be used. That is helpful to remove unnecessary interactions generated by wildcards, like -q ::. You can switch off this behavior with --leave_duplicate_interactions.

Update Rule options

--sgd                        use regular/classic/simple stochastic
                             gradient descent update, i.e., non adaptive,
                             non normalized, non invariant
                             (this is no longer the default since it is
                              often sub-optimal)
--adaptive                   use adaptive, individual learning rates
                             (on by default)
--normalized                 use per feature normalized updates.
                             (on by default)
--invariant                  use safe/importance aware updates
                             (on by default)
--conjugate_gradient         use conjugate gradient based optimization
                             (option in bfgs)
--bfgs                       use bfgs optimization
--ftrl                       use FTRL-Proximal optimization
--ftrl_alpha (=0.005)        ftrl alpha parameter (option in ftrl)
--ftrl_beta (=0.1)           ftrl beta patameter (option in ftrl)
--mem arg (=15)              memory in bfgs
--termination arg (=0.001)   Termination threshold
--hessian_on                 use second derivative in line search
--initial_pass_length arg    initial number of examples per pass
--l1 arg (=0)                l_1 lambda (L1 regularization)
--l2 arg (=0)                l_2 lambda (L2 regularization)
--decay_learning_rate arg (=1)
                             Set Decay factor for learning_rate
                             between passes
--initial_t arg (=0)         initial t value
--power_t arg (=0.5)         t power value
-l [ --learning_rate ] arg (=0.5)
                             Set (initial) learning Rate
--loss_function arg (=squared)
                             Specify the loss function to be used,
                             uses squared by default. Currently available
                             ones are:
--quantile_tau arg (=0.5)    Parameter \tau associated with Quantile loss
                             Defaults to 0.5
--minibatch arg (=1)         Minibatch size
--feature_mask arg           Use existing regressor to determine which 
                             parameters may be updated

Currently, --adaptive, --normalized and --invariant are on by default, but if you specify any of those flags explicitly, the effect is that the rest of these flags is turned off.

--adaptive turns on an individual learning rate for each feature. These learning rates are adjusted automatically according to a data-dependent schedule. For details the relevant papers are Adaptive Bound Optimization for Online Convex Optimization and Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. These learning rates give an improvement when the data have many features, but they can be slightly slower especially when used in conjunction with options that cause examples to have many non-zero features such as -q and --ngram.

--bfgs and --conjugate_gradient uses a batch optimizer based on LBFGS or nonlinear conjugate gradient method. Of the two, --bfgs is recommended. To avoid overfitting, you should specify --l2. You may also want to adjust --mem which controls the rank of an inverse hessian approximation used by LBFGS. --termination causes bfgs to terminate early when only a very small gradient remains.

--ftrl and --ftrl_alpha and --ftrl_beta uses a per-Coordinate FTRL-Proximal with L1 and L2 Regularization for Logistic Regression. Detailed information about the algorithm can be found in this paper.

--initial_pass_length is a trick to make LBFGS quasi-online. You must first create a cache file, and then it will treat initial_pass_length as the number of examples in a pass, resetting to the beginning of the file after each pass. After running --passes many times, it starts over warmstarting from the final solution with twice as many examples.

--hessian_on is a rarely used option for LBFGS which changes the way a step size is computed. Instead of using the inverse hessian approximation directly, you compute a second derivative in the update direction and use that to compute the step size via a parabolic approximation.

--l1 and --l2 specify the level (lambda values) of L1 and L2 regularization, and can be nonzero at the same time. These values are applied on a per-example basis in online learning (sgd),

\sum_i \left(L(x_i,y_i,w) + \lambda_1 |w|_1 + 1/2 \cdot \lambda_2 |w|_2^2\right)

but on an aggregate level in batch learning (conjugate gradient and bfgs).

\left(\sum_i L(x_i,y_i,w)\right) + \lambda_1 |w|_1 + 1/2 \cdot\lambda_2 |w|_2^2 .

-l <lambda>,

--initial_t <t_0>,

--power_t <p>,


--decay_learning_rate <d>

specify the learning rate schedule whose generic form in the (k+1)^{th} epoch is

\eta_t = \lambda d^k \left(\frac{t_0}{t_0 + w_t}\right)^p

where (w_t) is the sum of the importance weights of all examples seen so far ((w_t = t) if all examples have importance weight 1).

There is no single rule for the best learning rate form. For standard learning from an i.i.d. sample, typically  p \in {0, 0.5, 1}, \   d \in (0.5,1]

and \lambda,t_0 are searched in a logarithmic scale. Very often, the defaults are reasonable and only the -l option (\lambda) needs to be explored. For other problems the defaults may be inadequate, e.g. for tracking (p=0) is more sensible.

To specify a loss function use --loss_function followed by either squared, logistic, hinge, or quantile. The latter is parametrized by \tau \in (0,1)) whose value can be specified by --quantile_tau. By default this is 0.5. For more information see Loss functions

To average the gradient from k examples and update the weights once every k examples use --minibatch <k>. Minibatch updates make a big difference for Latent Dirichlet Allocation and it's only enabled there.

--feature_mask allows to specify directly a set of parameters which can update, from a model file. This is useful in combination with --l1. One can use --l1 to discover which features should have a nonzero weight and do -f model, then use --feature_mask model without --l1 to learn a better regressor.

Weight options

-b [ --bit_precision ] arg         number of bits in the feature table
-i [ --initial_regressor ] arg     Initial regressor(s) to load into
                                   memory (arg is filename)
-f [ --final_regressor ] arg       Final regressor to save
                                   (arg is filename)
--random_weights arg               make initial weights random
--sparse_weights                   Use a sparse datastructure for weights
--initial_weight arg (=0)          Set all weights to initial value of arg
--readable_model arg               Output human-readable final regressor
--invert_hash arg                  Output human-readable final regressor
                                   with feature names
--audit_regressor arg              stores feature names and their regressor
                                   values. Same dataset must be used for 
                                   both regressor training and this mode.
--save_per_pass                    Save model after every pass over data
--input_feature_regularizer arg    Per feature regularization input file
--output_feature_regularizer_binary arg
                                   Per feature regularization output file
--output_feature_regularizer_text arg
                                   Per feature regularization output file
                                   in text format

VW hashes all features to a predetermined range  [0,2^b-1] and uses a fixed weight vector with 2^b components. The argument of -b option determines the value of (b) which is 18 by default. Hashing the features allows the algorithm to work with very raw data (since there's no need to assign a unique id to each feature) and has only a negligible effect on generalization performance (see for example Feature Hashing for Large Scale Multitask Learning.

Use the -f option to write the weight vector to a file named after its argument. For testing purposes or to resume training, one can load a weight vector using the -i option.

--readable_model is identical to -f, except that the model is output in a human readable format.

--invert_hash is similar to --readable_model, but the model is output in a more human readable format with feature names followed by weights, instead of hash indexes and weights. Note that running vw with --invert_hash is much slower and needs much more memory. Feature names are not stored in the cache files (so if -c is on and the cache file exists and you want to use --invert_hash, either delete the cache or use -k to do it automatically). For multi-pass learning (where -c is necessary), it is recommended to first train the model without --invert_hash and then do another run with no learning (-t) which will just read the previously created binary model (-i my.model) and store it in human-readable format (--invert_hash my.invert_hash).

--audit_regressor mode works like --invert_hash but is designed to have much smaller RAM usage overhead. To use it you shall perform two steps. Firstly, train your model as usual and save your regressor with -f. Secondly, test your model against the same dataset that was used for training with the additional --audit_regressor result_file option in the command line. Technically, this loads the regressor and prints out feature details when it's encountered in the dataset for the first time. Thus, the second step may be used on any dataset that contains the same features. It cannot process features that have hash collisions - the first one encountered will be printed out and the others ignored. If your model isn't too big you may prefer to use --invert_hash or the vw-varinfo script for the same purpose.

--save_per_pass saves the model after every pass over the data. This is useful for early stopping.

--input_feature_regularizer, --output_feature_regularizer_binary, --output_feature_regularizer_text are analogs of -i, -f, and --readable_model for batch optimization where want to do per feature regularization. This is advanced, but allows efficient simulation of online learning with a batch optimizer.

By default VW starts with the zero vector as its hypothesis. The --random_weights option initializes with random weights. This is often useful for symmetry breaking in advanced models. It's also possible to initialize with a fixed value such as the all-ones vector using --initial_weight.

Holdout options

--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

Feature namespace options

--hash arg              how to hash the features.
                        Available options:
--ignore arg            ignore namespaces starting with character <arg>
--keep arg              keep namespaces starting with character <arg>
--noconstant            Don't add a constant feature
-C [ --constant ] arg   Set initial value of constant
--sort_features         turn this on to disregard order in which features
                        have been defined. Leads 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.
--affix arg             generate prefixes/suffixes of features;
                        argument '+2a,-3b,+1' means 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 given namespace
                        (use '_' for default namespace)

Latent Dirichlet Allocation options

--lda arg                        Run lda with <int> topics
--lda_alpha arg (=0.100000001)   Prior on sparsity of per-document
                                 topic weights
--lda_rho arg (=0.100000001)     Prior on sparsity of topic distributions
--lda_D arg (=10000)             Number of documents
--lda_epsilon arg (=0.00100000005) Loop convergence threshold
--minibatch arg (=1)               Minibatch size, for LDA
--math-mode arg (=0)               Math mode: simd, accuracy, fast-approx

The --lda option switches VW to LDA mode. The argument is the number of topics. --lda_alpha and --lda_rho specify prior hyperparameters. --lda_D specifies the number of documents. VW will still work the same if this number is incorrect, just the diagnostic information will be wrong. For details see Online Learning for Latent Dirichlet Allocation [pdf].

Matrix Factorization options

--rank arg (=0)       rank for matrix factorization.

--rank sticks VW in matrix factorization mode. You'll need a relatively small learning rate like -l 0.01.

Low Rank Quadratic options

--lrq arg             use low rank quadratic features
--lrqdropout          use dropout training for low rank quadratic features


--binary          Reports loss as binary classification with -1,1 labels

Multiclass options

--oaa arg         Use one-against-all multiclass learning with  labels
--ect arg         Use error correcting tournament with  labels
--csoaa arg       Use one-against-all multiclass learning with  costs
--wap arg         Use weighted all-pairs multiclass learning w/  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.
--log_multi arg   Use online (decision) trees for  classes
(in log(arg) time). See theoretic paper

Stagewise Polynomial options

--stage_poly                     stagewise polynomial features
--sched_exponent arg1 ( = 1.0)   exponent controlling quantity
                                 of included features
--batch_sz arg2 ( = 1000)        multiplier on batch size before
                                 including more features
--batch_sz_no_doubling           batch_sz does not double

--stage_poly tells VW to maintain polynomial features: training examples are augmented with features obtained by producting together subsets (and even sub-multisets) of features. VW starts with the original feature set, and uses --batch_sz and (and --batch_sz_no_doubling if present) to determine when to include new features (otherwise, the feature set is held fixed), with --sched_exponent controlling the quantity of new features.

--batch_sz arg2 (together with --batch_sz_no_doubling), on a single machine, causes three types of behaviors: arg2 = 0 means features are constructed at the end of every non-final pass, arg2 > 0 with --batch_sz_no_doubling means features are constructed every arg2 examples, and arg2 > 0 without --batch_sz_no_doubling means features are constructed when the number of examples seen so far is equal to arg2, then 2*arg2, 4*arg2, and so on. When VW is run on multiple machines, then the options are similar, except that no feature set updates occur after the first pass (so that features have more time to stabilize across multiple machines). The default setting is arg2 = 1000 (and doubling is enabled).

--sched_exponent arg1 tells VW to include s^arg1 features every time it updates the feature set (as according to --batch_sz above), where s is the (running) average number of nonzero features (in the input representation). The default is arg1 = 1.0.

While care was taken to choose sensible defaults, the choices do matter. For instance, good performance was obtained by using arg2 = #examples / 6 and --batch_sz_no_doubling, however arg2 = 1000 (without --batch_sz_no_doubling) was made default since #examples is not available to VW a priori. As usual, including too many features (by updating the support to frequently, or by including too many features each time) can lead to overfitting.

Active Learning options

--active                  active learning mode
--simulation              active learning simulation mode
--mellowness arg (=8)     active learning mellowness parameter c_0. 
                          Default 8

Given a fully labeled dataset, experimenting with active learning can be done with --simulation. All active learning algorithms need a parameter that defines the trade off between label complexity and generalization performance. This is specified here with --mellowness. A value of 0 means that the algorithm will not ask for any label. A large value means that the algorithm will ask for all the labels. If instead of --simulation, --active is specified (together with --daemon) real active learning is implemented (examples are passed to VW via a TCP/IP port and VW responds with its prediction as well as how much it wants this example to be labeled if at all). If this is confusing, watch Daniel's explanation at the VW tutorial. The active learning algorithm is described in detail in Agnostic Active Learning without Constraints [pdf].

Parallelization options

--span_server arg       Location of server for setting up spanning tree
--unique_id arg (=0)    unique id used for cluster parallel job
--total arg (=1)        total number of nodes used in cluster parallel job
--node arg (=0)         node number in cluster parallel job

VW supports cluster parallel learning, potentially on thousands of nodes (it's known to work well on 1000 nodes) using the algorithms discussed here.

--span_server specifies the network address of a little server that sets up spanning trees over the nodes.

--unique_id should be a number that is the same for all nodes executing a particular job and different for all others.

--total is the total number of nodes.

--node should be unique for each node and range from {0,total-1}.

More details are in the cluster directory.

Warning: Make sure to disable the holdout feature in parallel learning using --holdout_off. Otherwise, some nodes might attempt to terminate earlier while others continue running. If nodes become out of sync in this fashion, usually a deadlock will take place. You can detect this situation if you see all your vw instances hanging with a CPU usage of 0% for a long time.

Label dependent features

--csoaa_ldf multiline|singleline
--wap_ldf multiline|singleline

See and

Learning algorithm / reduction options

--bootstrap K  bootstrap with K rounds by online importance resampling
--bs_type arg  the bootstrap mode - arg can be 'mean' or 'vote'
--top K        top K recommendation
--autolink N   create link function with polynomial N
--cb K         use contextual bandit learning with K costs
--cbify K      convert multiclass on K classes into a
contextual bandit problem
--lda N        run LDA with N topics
--nn N         use sigmoidal feedforward network w/ N hidden units
--search N     use search-based structured prediction (SEARN or DAgger),
N=maximum action id or 0 for LDF
--ksvm         online kernel Support Vector Machine. See documentation
--boosting N   online boosting with  weak learners. See theoretic paper

Neural Network options

--inpass                              Train or test sigmoidal feedforward
network with input passthrough.
--multitask                           Share hidden layer across all reduced
--dropout                             Train or test sigmoidal feedforward
network using dropout.
--meanfield                           Train or test sigmoidal feedforward
network using mean field.