cargo install omikuji --features cli --locked

cargo install --git https://github.com/tomtung/omikuji.git --features cli --locked

omikuji train eurlex_train.txt --model_path ./model
omikuji test ./model eurlex_test.txt --out_path predictions.txt

pip install omikuji

pip install git+https://github.com/tomtung/omikuji.git -v

$ omikuji train --help
Train a new omikuji model

USAGE:
    omikuji train [OPTIONS] <TRAINING_DATA_PATH>

ARGS:
    <TRAINING_DATA_PATH>
            Path to training dataset file

            The dataset file is expected to be in the format of the Extreme Classification
            Repository.

OPTIONS:
        --centroid_threshold <THRESHOLD>
            Threshold for pruning label centroid vectors

            [default: 0]

        --cluster.eps <CLUSTER_EPS>
            Epsilon value for determining linear classifier convergence

            [default: 0.0001]

        --cluster.k <K>
            Number of clusters

            [default: 2]

        --cluster.min_size <MIN_SIZE>
            Labels in clusters with sizes smaller than this threshold are reassigned to other
            clusters instead

            [default: 2]

        --cluster.unbalanced
            Perform regular k-means clustering instead of balanced k-means clustering

        --collapse_every_n_layers <N_LAYERS>
            Number of adjacent layers to collapse

            This increases tree arity and decreases tree depth.

            [default: 0]

    -h, --help
            Print help information

        --linear.c <C>
            Cost coefficient for regularizing linear classifiers

            [default: 1]

        --linear.eps <LINEAR_EPS>
            Epsilon value for determining linear classifier convergence

            [default: 0.1]

        --linear.loss <LOSS>
            Loss function used by linear classifiers

            [default: hinge]
            [possible values: hinge, log]

        --linear.max_iter <M>
            Max number of iterations for training each linear classifier

            [default: 20]

        --linear.weight_threshold <MIN_WEIGHT>
            Threshold for pruning weight vectors of linear classifiers

            [default: 0.1]

        --max_depth <DEPTH>
            Maximum tree depth

            [default: 20]

        --min_branch_size <SIZE>
            Number of labels below which no further clustering & branching is done

            [default: 100]

        --model_path <MODEL_PATH>
            Optional path of the directory where the trained model will be saved if provided

            If an model with compatible settings is already saved in the given directory, the newly
            trained trees will be added to the existing model")

        --n_threads <N_THREADS>
            Number of worker threads

            If 0, the number is selected automatically.

            [default: 0]

        --n_trees <N_TREES>
            Number of trees

            [default: 3]

        --train_trees_1_by_1
            Finish training each tree before start training the next

            This limits initial parallelization but saves memory.

        --tree_structure_only
            Build the trees without training classifiers

            Might be useful when a downstream user needs the tree structures only.

$ omikuji test --help
Test an existing omikuji model

USAGE:
    omikuji test [OPTIONS] <MODEL_PATH> <TEST_DATA_PATH>

ARGS:
    <MODEL_PATH>
            Path of the directory where the trained model is saved

    <TEST_DATA_PATH>
            Path to test dataset file

            The dataset file is expected to be in the format of the Extreme Classification
            Repository.

OPTIONS:
        --beam_size <BEAM_SIZE>
            Beam size for beam search

            [default: 10]

    -h, --help
            Print help information

        --k_top <K>
            Number of top predictions to write out for each test example

            [default: 5]

        --max_sparse_density <DENSITY>
            Density threshold above which sparse weight vectors are converted to dense format

            Lower values speed up prediction at the cost of more memory usage.

            [default: 0.1]

        --n_threads <N_THREADS>
            Number of worker threads

            If 0, the number is selected automatically.

            [default: 0]

        --out_path <OUT_PATH>
            Path to the which predictions will be written, if provided

label1,label2,...labelk ft1:ft1_val ft2:ft2_val ft3:ft3_val .. ftd:ftd_val