Command Line Basics#

This tutorial introduces Vowpal Wabbit command line basics with a quick introduction to training and testing your model with Vowpal Wabbit. We explore passing some data to Vowpal Wabbit to learn a model and get a prediction.

For more advanced Vowpal Wabbit tutorials, including how to format data and understand results, see Tutorials.

Prerequisites

To install Vowpal Wabbit see Get Started.

Training scenario and dataset#

For this tutorial scenario, we want to use Vowpal Wabbit to help us predict whether or not a house will require a new roof in the next 10 years.

First, create a file train.txt and copy the following dataset:

| price:.23 sqft:.25 age:.05 2006
| price:.18 sqft:.15 age:.35 1976
| price:.53 sqft:.32 age:.87 1924

Note: If the format of this sample dataset looks unfamiliar and you want more details see the Vowpal Wabbit Linear Regression Tutorial for information on input format and feature hashing techniques.

Train a model#

Next, we train a model, and save it to a file:

vw -d train.txt -f model.vw

This tells Vowpal Wabbit to:

Use the -d data file train.txt.
Write the -f final model to model.vw.

With Vowpal Wabbit, the output includes more than a few statistics and statuses. The Linear Regression Tutorial and Contextual Bandit Reinforcement Learning Tutorial covers this format in more detail:

Output:

final_regressor = model.vw
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = train.txt
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
0.000000 0.000000            1            1.0   0.0000   0.0000        5
0.500000 1.000000            2            2.0   1.0000   0.0000        5

finished run
number of examples = 3
weighted example sum = 3.000000s
weighted label sum = 1.000000
average loss = 0.666667
best constant = 0.333333
best constant's loss = 0.222222
total feature number = 15

Test a model#

Now, create a file called test.txt and copy this data:

| price:.46 sqft:.4 age:.10 1924

We get a prediction by loading the model and supplying our test data:

vw -d test.txt -i model.vw -p predictions.txt

This tells Vowpal Wabbit to:

Use the -d data file test.txt.
Use the -i input model model.vw.
Write -p predictions to predictions.txt.

Output:

predictions = predictions.txt
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading datafile = test.txt
num sources = 1
average  since         example        example  current  current  current
loss     last          counter         weight    label  predict features
    n.a.     n.a.            1            1.0  unknown   0.0000        5

finished run
number of examples = 1
weighted example sum = 1.000000
weighted label sum = 0.000000
average loss = n.a.

cat predictions.txt shows:

Vowpal Wabbit results#

The model predicted a value of 0. This result means our house will not need a new roof in the next 10 years (based on just three examples we used in our training dataset).

More to explore#

See Python tutorial for a quick introduction to the basics of training and testing your model.
To learn more about how to approach a contextual bandits problem using tVowpal Wabbit — including how to work with different contextual bandits approaches, how to format data, and understand the results — see the Contextual Bandit Reinforcement Learning Tutorial.
For more on the contextual bandits approach to reinforcement learning, including a content personalization scenario, see the Contextual Bandit Simulation Tutorial.
See the Linear Regression Tutorial for a different look at the roof replacement problem and learn more about Vowpal Wabbit’s format and understanding the results.