order-learning

Dataset Assumptions

For a problem that requires an order to be learnt, availability of the following two datasets is assumed:

• $D_{main}$ : A dataset of $x_i$ which ultimately we would like to predict the order between any two members.
• $D_{comp}$ : A dataset consisting of ordered tuples $(x_i, x_j, o)$, where $o \in {\geq, \leq, \sim }$

Ranking Color Gradient

A simple toy example. $D_{main}$ is made up of 40 different gradients of green, rank 1 is the darkest and rank 40 is the lightest.

Rank = 1	Rank = 10	Rank = 20	Rank = 30	Rank = 40

For $D_{comp}$ we create two different sampling methods. EXTREMES: greater than has rank > 30 and lesser than has rank < 10 CENTER: greater than has 30 > rank > 20 and lesser than has 10 < rank < 20

code for these datasets is here

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A Comparator model is used to learn the ranking using $D_{comp}$. This is treated as a supervised learning method.

Code for the models can be found here

The training script is here

After training with the EXTREME sampling scheme the model learns the following (Column names are: $x_i$, $x_j$, target, prediction)

• 0 means $x_i \leq x_j$
• 1 means $x_i \sim x_j$
• 2 means $x_i \geq x_j$