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.

矩阵是在数据科学和人工智能中表示和处理数据的一种非常常见的方式。编写一小段代码以进行简单的矩阵转换常常令人沮丧，特别是对于那些没有广泛编程专业知识的人而言。我们介绍AUTOMAT [R]IX，一种能够从用户提供的单个（可能是部分）矩阵变换示例中得出R程序片段的系统。我们的学习算法能够通过组合库中的基元来得出正确的矩阵流水线片段。由于难解的搜索空间-库大小和摘要中要组合的原语的数量成指数关系，我们使用（1）一种类型化的系统来加快此过程，该系统排除输入与输入和输出之间映射不一致的原语的所有组合输出矩阵的维数；以及（2）概率模型，用于根据原始图元的使用频率和用户提供的文本提示来估计每个原始图元的概率。我们验证AUTOMAT [R]IX带有一组涉及Stack Overflow中矩阵的实际编程查询，表明我们可以仅通过一个部分示例就可以高效地学习转换。