Run inference

Input

To run inference, users need to provide:

a trained model
a GAISHI configuration file in YAML format

Feature-vector models

Feature-vector inference is used for models trained with:

logistic_regression
extra_trees_classifier

Run inference with:

gaishi infer \
    --model examples/results/models/example.lr.joblib \
    --config examples/configs/lr.config.yaml \
    --output examples/results/infer/example.lr.pred.tsv

UNet++ models

UNet++ inference uses genotype matrices stored in HDF5 format.

Run inference with:

gaishi infer \
    --model examples/results/models/example.unet.safetensors \
    --config examples/configs/unet.config.yaml \
    --output examples/results/infer/example.unet.pred.tsv

Output

For feature-vector models, the output table contains one row per window and sample:

Column	Description
`Chromosome`	Chromosome name.
`Start`	Window start position.
`End`	Window end position.
`Sample`	Target sample or haplotype name.
`Non_Intro_Prob`	Probability of the non-introgressed class.
`Intro_Prob`	Probability of the introgressed class.

For UNet++ models, the output table contains one row per site and sample:

Column	Description
`Chromosome`	Chromosome name.
`Position`	Site position.
`Sample`	Target sample or haplotype name.
`Non_Intro_Prob`	Probability of the non-introgressed class.
`Intro_Prob`	Probability of the introgressed class.

Settings

Argument	Description
`--model`	Path to the trained model file.
`--config`	Path to the `GAISHI` configuration YAML file.
`--output`	Path to the inference output file.