-
Notifications
You must be signed in to change notification settings - Fork 1.9k
Input format
arielf edited this page Aug 17, 2012
·
42 revisions
The raw (plain text) input data for VW should have one example per line. Each example should be formatted as follows.
[Label] [Importance [Tag]]|Namespace Features |Namespace Features ... |Namespace Features
where
Namespace=String[:Value]
and
Features=(String[:Value] )*
-
Label
is the real number that we are trying to predict for this example. If the label is omitted, then no training will be performed with the corresponding example, although VW will still compute a prediction. -
Importance
(importance weight) is a non-negative real number indicating the relative importance of this example over the others. Omitting this gives a default importance of 1 to the example. -
Tag
is a string that serves as an identifier for the example. It is reported back when predictions are made. It doesn't have to be unique. The default value if it is not provided is the empty string. If you provide a tag, you must also provide an importance. If you don't provide a tag, put a space before the vertical bar. -
Namespace
is an identifier of a source of information for the example optionally followed by a float (e.g.,MetricFeatures:3.28
), which acts as a global scaling of all the values of the features in this namespace. If value is omitted, the default is 1. -
Features
is a sequence of whitespace separated strings, each of which is optionally followed by a float (e.g.,NumberOfLegs:4.0 HasStripes
). Each string is a feature and the value is the feature value for that example. Omitting a feature means that its value is zero. Including a feature but omitting its value means that its value is 1.
Currently, the only characters that can't be used in feature names are vertical bar, colon, space, and newline.
1 1.0 |MetricFeatures:3.28 height:1.5 length:2.0 |Says black with white stripes |OtherFeatures NumberOfLegs:4.0 HasStripes
1 1.0 zebra|MetricFeatures:3.28 height:1.5 length:2.0 |Says black with white stripes |OtherFeatures NumberOfLegs:4.0 HasStripes
When using logistic or hinge loss, the labels need to be from the set {+1,-1} (documented in the V6.1 tutorial slide deck, but not elsewhere)
The spacing around the | characters is important and significant:
- Around the 1st | if there's no space preceding it, the string that touches the | is considered a tag (id of example)
After any |
- If there's a space, the next non-space token is considered a regular feature name
- If there's no space, the next non-space token is considered a name-space
name-spaces are considered as feature name prefixes, they are prepended to all feature names in the name-space
- Home
- First Steps
- Input
- Command line arguments
- Model saving and loading
- Controlling VW's output
- Audit
- Algorithm details
- Awesome Vowpal Wabbit
- Learning algorithm
- Learning to Search subsystem
- Loss functions
- What is a learner?
- Docker image
- Model merging
- Evaluation of exploration algorithms
- Reductions
- Contextual Bandit algorithms
- Contextual Bandit Exploration with SquareCB
- Contextual Bandit Zeroth Order Optimization
- Conditional Contextual Bandit
- Slates
- CATS, CATS-pdf for Continuous Actions
- Automl
- Epsilon Decay
- Warm starting contextual bandits
- Efficient Second Order Online Learning
- Latent Dirichlet Allocation
- VW Reductions Workflows
- Interaction Grounded Learning
- CB with Large Action Spaces
- CB with Graph Feedback
- FreeGrad
- Marginal
- Active Learning
- Eigen Memory Trees (EMT)
- Element-wise interaction
- Bindings
-
Examples
- Logged Contextual Bandit example
- One Against All (oaa) multi class example
- Weighted All Pairs (wap) multi class example
- Cost Sensitive One Against All (csoaa) multi class example
- Multiclass classification
- Error Correcting Tournament (ect) multi class example
- Malicious URL example
- Daemon example
- Matrix factorization example
- Rcv1 example
- Truncated gradient descent example
- Scripts
- Implement your own joint prediction model
- Predicting probabilities
- murmur2 vs murmur3
- Weight vector
- Matching Label and Prediction Types Between Reductions
- Zhen's Presentation Slides on enhancements to vw
- EZExample Archive
- Design Documents
- Contribute: