sequifier 0.3.0

Train a transformer model with the command line

easy sequence classification training and inference with transformers

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Overview

The sequifier package enables:

• the extraction of sequences for training
• the configuration and training of a transformer classification model
• inference on data with a trained model

Other materials

If you want to first get a more specific understanding of the transformer architecture, have a look at the Wikipedia article.

If you want to see a benchmark on a small synthetic dataset with 10k cases, agains a random forest, an xgboost model and a logistic regression, check out this notebook.

Complete example how to build and apply a transformer sequence classifier with sequifier

1. create a conda environment with python 3.9.12, activate and run

pip install sequifier

2. create a new project folder (at a path referred to as PROJECT PATH later) and a configs subfolder
3. copy default configs from repository for preprocessing, training and inference
4. adapt preprocess config to take the path to the data you want to preprocess and set project_path toPROJECT PATH
5. run

sequifier --preprocess --config_path=[PROJECT PATH]/configs/preprocess.yaml

6. the preprocessing step outputs a "data driven config" at [PROJECT PATH]/configs/ddconfigs/[FILE NAME]. It contains the number of classes found in the data, a map of classes to indices and the oaths to train, validation and test splits of data. Adapt the dd_config parameter in train-on-preprocessed.yaml and infer.yaml in to the path [PROJECT PATH]/configs/ddconfigs/[FILE NAME]and set project_path to PROJECT PATH in both configs
7. run

sequifier --train --on-preprocessed --config_path=[PROJECT PATH]/configs/train-on-preprocessed.yaml

8. adapt inference_data_path in infer.yaml
9. run

sequifier --infer --config_path=[PROJECT PATH]/configs/infer.yaml

10. find your predictions at [PROJECT PATH]/outputs/predictions/sequifier-default-best_predictions.csv

More detailed explanations of the three steps

Preprocessing of data into sequences for training

The preprocessing step is designed for scenarios where for long series of events, the prediction of the next event from the previous N events is of interest. In cases of sequences where only the last item is a valid target, the preprocessing step should not be executed.

This step presupposes input data with three columns: "sequenceId", "itemId" and "timesort". "sequenceId" and "itemId" identify sequence and item, and the timesort column must provide values that enable sequential sorting. Often this will simply be a timestamp. You can find an example of the preprocessing input data at documentation/example_inputs/preprocessing_input.csv

The data can then be processed and split into training, validation and testing datasets of all valid subsequences in the original data with the command:

sequifier --preprocess --config_path=[CONFIG PATH]

The config path specifies the path to the preprocessing config and the project path the path to the (preferably empty) folder the output files of the different steps are written to.

The default config can be found on this path:

configs/preprocess.yaml

Configuring and training the sequence classification model

The training step is executed with the command:

sequifier --train --config_path=[CONFIG PATH]

If the data on which the model is trained comes from the preprocessing step, the flag

--on-preprocessed

should also be added.

If the training data does not come from the preprocessing step, both train and validation data have to take the form of a csv file with the columns "sequenceId", [SEQ LENGTH], [SEQ LENGTH - 1],...,"1", "target". You can find an example of the preprocessing input data at documentation/example_inputs/training_input.csv

The training step is configured using the config. The two default configs can be found here:

configs/train.yaml

configs/train-on-preprocessed.yaml

depending on whether the preprocessing step was executed.

Inferring on test data using the trained model

Inference is done using the command:

sequifier --infer --config_path=[CONFIG PATH]

and configured using a config file. The default version can be found here:

configs/infer.yaml