vwtest: Compute predictions using Vowpal Wabbit model
In ivan-pavlov/rvwgsoc: R Interface for Vowpal Wabbit

Description Usage Arguments Value Examples

vwtest computes predictions using VW model from vwsetup predict.vw compute predictions using parser settings from vwtrain

vwtest(vwmodel, data, probs_path = "", full_probs = FALSE,
  readable_model = NULL, readable_model_path = "", quiet = FALSE,
  passes = 1L, cache = FALSE, raw = FALSE, progress = NULL,
  namespaces = NULL, keep_space = NULL, fixed = NULL,
  targets = NULL, probabilities = NULL, weight = NULL, base = NULL,
  tag = NULL, multiline = NULL)

## S3 method for class 'vw'
predict(object, data, probs_path = "", full_probs = FALSE,
  readable_model = NULL, quiet = FALSE, ...)

`vwmodel`	[vw] Model of vw class to train.
`data`	[string or data.frame] Path to training data in .vw plain text format or data.frame. If `[data.frame]` then will be parsed using `df2vw` function.
`probs_path`	[string] Path to file where to save predictions.
`full_probs`	[bool] Output full predictions in data.frame format. If not, force predictions into a single vector (default).
`readable_model`	[string] Print trained model in human readable format ("hashed") and also with human readable features ("inverted").
`readable_model_path`	[string] Path to file where to save readable model.
`quiet`	[bool] Do not print anything to the console.
`passes`	[int] Number of times the algorithm will cycle over the data (epochs).
`cache`	[bool] Use a cache for a data file.
`raw`	[bool] Output unnormalized predictions. Default is FALSE.
`progress`	[int/real] Progress update frequency. int: additive, real: multiplicative
`namespaces`	[list or yaml file] For `df2vw`. Name of each namespace and each variable for each namespace can be a R list, or a YAML file example namespace with the IRIS database: namespaces = list(sepal = list('Sepal.Length', 'Sepal.Width'), petal = list('Petal.Length', 'Petal.Width') this creates 2 namespaces (sepal and petal) containing the features defined by elements of this lists.
`keep_space`	[string vector] For `df2vw`. Keep spaces for this features Example:"FERRARI 4Si" With `keep_space` will be "FERRARI 4Si" and will be treated as two features Without `keep_space` will be "FERRARI_4Si" and will be treated as one feature
`fixed`	[string vector] fixed parsing for this features Similar to `keep_space`, but parse features exactly without replacement of special characters ("(", ")", "\|", ":", "'"). Can be used for LDA ("word_1:2 word_2:3" will stay the same), but should be used carefully, because special characters can ruin final VW format file.
`targets`	[string or string vector] For `df2vw`. If `[string]` then will be treated as vector with real number labels for regular VW input format. If `[string vector]` then will be treated as vectors with class costs for wap and csoaa multi-class classification algorithms or as vectors with actions for Contextual Bandit algorithm.
`probabilities`	[string vector] For `df2vw`. Vectors with action probabilities for Contextual Bandit algorithm.
`weight`	[string] For `df2vw`. Weight (importance) of each line of the dataset.
`base`	[string] For `df2vw`. Base of each line of the dataset. Used for residual regression.
`tag`	[string] For `df2vw`. Tag of each line of the dataset.
`multiline`	[integer] Number of labels (separate lines) for multilines example
`object`	Model of vw class to train for `predict.vw`
`...`	Parameters passed to `predict.vw`

Numerical vector containing predictions

ext_train_data <- system.file("extdata", "binary_train.vw", package = "rvw")
ext_test_data <- system.file("extdata", "binary_valid.vw", package = "rvw") 
test_vwmodel <- vwsetup()
vwtrain(test_vwmodel, data = ext_train_data)
vwtest(test_vwmodel, data = ext_test_data)