AUTOMAT[R]IX: learning simple matrix pipelines

Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOM...

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Bibliographic Details
Published in:Machine learning 2021-04, Vol.110 (4), p.779-799
Main Authors: Contreras-Ochando, Lidia, Ferri, Cèsar, Hernández-Orallo, José
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Control
Data science
Libraries
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Probabilistic models
Robotics
Simulation and Modeling
Special issue on Learning and Reasoning
Statistical analysis
Transformations (mathematics)
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Summary:Matrices are a very common way of representing and working with data in data science and artificial intelligence. Writing a small snippet of code to make a simple matrix transformation is frequently frustrating, especially for those people without an extensive programming expertise. We present AUTOMAT[R]IX, a system that is able to induce R program snippets from a single (and possibly partial) matrix transformation example provided by the user. Our learning algorithm is able to induce the correct matrix pipeline snippet by composing primitives from a library. Because of the intractable search space—exponential on the size of the library and the number of primitives to be combined in the snippet, we speed up the process with (1) a typed system that excludes all combinations of primitives with inconsistent mapping between input and output matrix dimensions, and (2) a probabilistic model to estimate the probability of each sequence of primitives from their frequency of use and a text hint provided by the user. We validate AUTOMAT[R]IX with a set of real programming queries involving matrices from Stack Overflow, showing that we can learn the transformations efficiently, from just one partial example.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-021-05950-7