Cost Sensitive One Against All (csoaa) multi class example

Overview

CSOAA stands for "Cost Sensitive One Against All".

Purpose:

The option --csoaa <K> where is the number of distinct classes directs vw to perform cost-sensitive K multi-class (as opposed to binary) classification. It extends --oaa <K> to support multiple labels per input example, and costs associated with classifying these labels.

Notes:

• Data-set labels must be in the natural number set {1 .. <K>}
• <K> is the maximum label value, and must be passed as an argument to --csoaa
• The input/training format for --csoaa <K> is different than the traditional VW format:
  • It supports multiple labels on the same line
  • Each label has a trailing optional cost (default cost is 1.0)
  • Cost syntax looks just like weight syntax: a colon followed by a floating-point number. For example: 4:3.2 means the class-label 4 with a cost of 3.2, but means the opposite of weights.
  • It is critical to note that costs are not weights. They are the inverse of weights. A label with a lower cost is prefered over a label with a higher cost on the same line. That's why they are called 'costs'.

Example

Assume we have a 3-class classfication problem. We label our 3 classes {1,2,3}

Our data set csoaa.dat may look like this:

1:1.0 a1_expect_1| a
2:1.0 b1_expect_2| b
3:1.0 c1_expect_3| c
1:2.0 2:1.0 ab1_expect_2| a b
2:1.0 3:3.0 bc1_expect_2| b c
1:3.0 3:1.0 ac1_expect_3| a c
2:3.0 d1_expect_2| d

Notes:

• The first 3 examples (lines) have only one label (with costs) each, and the next 3 examples have multiple labels on the same line. Any number of class-labels between {1 .. <K>} (1..3 in this case) is allowed on each line.
• Each label belongs to one of the {1..<K>} classes, i.e. a natural number between 1 and <K>.
• We assign a lower cost to the label we want to be preferred. e.g. in line 4 (tagged ab1_expect_2) we have a cost of 1.0, for class-label 2; and a higher cost 2.0, for class-label 1.
• You may add a cost to each label. If you don't, the default cost of 1.0 is used.
• The input feature section following the '|' is the same as in traditional VW: you may have multiple name-spaces, numeric features, and optional weights for features and/or name-spaces (Note in this section the weights are weights, not costs, so they are positively correlated with chosen labels)

We train:

vw --csoaa 3 csoaa.dat -f csoaa.model

Which gives us this progress output:

final_regressor = csoaa.model
Num weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
using no cache
Reading from csoaa.dat
num sources = 1
average    since         example     example  current  current current
loss       last          counter      weight    label  predict features
0.000000   0.000000          3           3.0    known        3        2
0.833333   1.666667          6           6.0    known        1        3

finished run
number of examples = 7
weighted example sum = 7
weighted label sum = 0
average loss = 0.7143
best constant = 0
total feature number = 17

Now we can predict, loading the model csoaa.model and using the same data-set csoaa.predict as our test-set:

vw -t -i csoaa.model csoaa.dat -p csoaa.predict

Similar to what we do in vanilla classification or regression.

The resulting csoaa.predict file has contents:

1.000000 a1_expect_1
2.000000 b1_expect_2
3.000000 c1_expect_3
2.000000 ab1_expect_2
2.000000 bc1_expect_2
3.000000 ac1_expect_3
2.000000 d1_expect_2

Which is a perfect classification:

• all the expect_1 lines have a predicted class of 1,
• all the expect_2 lines have a predicted class of 2,
• and all the expect_3 lines have a predicted class of 3.

QED