Factorization Machines Algorithm - Amazon SageMaker
Input/Output Interface for the Factorization Machines AlgorithmEC2 Instance Recommendation for the Factorization Machines AlgorithmSample Notebooks

Factorization Machines Algorithm

The Factorization Machines algorithm is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the Factorization Machines model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation.

Note

The Amazon SageMaker implementation of the Factorization Machines algorithm considers only pair-wise (2nd order) interactions between features.

Topics

Input/Output Interface for the Factorization Machines Algorithm

The Factorization Machines algorithm can be run in either in binary classification mode or regression mode. In each mode, a dataset can be provided to the test channel along with the train channel dataset. The scoring depends on the mode used. In regression mode, the testing dataset is scored using Root Mean Square Error (RMSE). In binary classification mode, the test dataset is scored using Binary Cross Entropy (Log Loss), Accuracy (at threshold=0.5) and F1 Score (at threshold =0.5).

For training, the Factorization Machines algorithm currently supports only the recordIO-protobuf format with Float32 tensors. Because their use case is predominantly on sparse data, CSV is not a good candidate. Both File and Pipe mode training are supported for recordIO-wrapped protobuf.

For inference, the Factorization Machines algorithm supports the application/json and x-recordio-protobuf formats.

  • For the binary classification problem, the algorithm predicts a score and a label. The label is a number and can be either 0 or 1. The score is a number that indicates how strongly the algorithm believes that the label should be 1. The algorithm computes score first and then derives the label from the score value. If the score is greater than or equal to 0.5, the label is 1.

  • For the regression problem, just a score is returned and it is the predicted value. For example, if Factorization Machines is used to predict a movie rating, score is the predicted rating value.

Please see Factorization Machines Sample Notebooks for more details on training and inference file formats.

EC2 Instance Recommendation for the Factorization Machines Algorithm

The Amazon SageMaker Factorization Machines algorithm is highly scalable and can train across distributed instances. We recommend training and inference with CPU instances for both sparse and dense datasets. In some circumstances, training with one or more GPUs on dense data might provide some benefit. Training with GPUs is available only on dense data. Use CPU instances for sparse data. The Factorization Machines algorithm supports P2, P3, G4dn, and G5 instances for training and inference.

Factorization Machines Sample Notebooks

For a sample notebook that uses the SageMaker Factorization Machines algorithm to analyze the images of handwritten digits from zero to nine in the MNIST dataset, see An Introduction to Factorization Machines with MNIST. For instructions how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Amazon SageMaker Notebook Instances. Once you have created a notebook instance and opened it, select the SageMaker Examples tab to see a list of all the SageMaker samples. Example notebooks that use Factorization Machines algorithm are located in the Introduction to Amazon algorithms section. To open a notebook, click on its Use tab and select Create copy.