GenoLearn Train

Fits Machine Learning models to their genome sequence data. Users are initially prompted to select a feature selection file to use, which has an associated metadata file, and a model config file before being prompted for further information. An example prompt is shown below.

Prompted Information

Genolearn ({VERSION}) Command Line Interface

GenoLearn is designed to enable researchers to perform Machine Learning on their genome
sequence data such as fsm-lite or unitig files.

See https://genolearn.readthedocs.io for documentation.

Working directory: {WORKING_DIRECTORY}

Command: train

Train parameters for metadata "default" with feature-selection "default-fisher" and model config "random-forest"

output_dir [default-fisher-random-forest]           :
num_features* [1000]                                :
min_count [0]                                       :
target_subset [None]                                :
metric [f1_score]                                   :
aggregate_func {mean, weighted_mean} [weighted_mean]:

where

  • output_dir is the subdirectory name to output contents to within <working directory>/train. By default this is <feature-selection>-<model-config>.

  • num_features* is a sequence of comma seperated integers indicating how many sequence features to use when training. For example, if num_features is set to 100, 1000, GenoLearn will perform a grid parameter search when using the 100 highest scoring genome sequences (according) to the Fisher Score and then again using 1000 genome sequences.

  • min_count states the number of times a class label has to appear in the train set of the metadata for instances of that class label to be modelled. For example, setting min_count to 10 results in instances being ignored of the associated target label appears less than 10 times.

  • target_subset is a comma seperated string of target class labels to model. For example. if in your metadata, your class labels are a, b, c, d, and e, and we only want to model a, b, c then we would set target_subset as a, b, c

  • metric is the objective function to evaluate goodness-of-fit to select the best performing model in your parameter gridsearch. See Metrics for more details.

  • aggregate_func is set to either mean or weighted_mean to best aggregate the previously set metric for each class label.