pip install -U pip
pip install -r requirements.txt

usage: LITMUS Tool [-h] [--scores_file SCORES_FILE]
                   [--train_format {json,csv}] [--save_state SAVE_STATE]
                   [--load_state LOAD_STATE]
                   [--precomputed_features PRECOMPUTED_FEATURES]
                   [--pivot_features {none,all,data_only}] [--use_all_langs]
                   [--common_scaling] [--training_algorithm {xgboost,mlp}]
                   [--error_method {LOO,LOTO,split,kfold,manual_split}]
                   [--data_sizes DATA_SIZES] [--mode MODE [MODE ...]]
                   [--output_dir OUTPUT_DIR]
                   [--heatmap_targets HEATMAP_TARGETS]
                   [--suggestions_budget SUGGESTIONS_BUDGET]
                   [--suggestions_langbudget SUGGESTIONS_LANGBUDGET]
                   [--suggestions_targets SUGGESTIONS_TARGETS]
                   [--suggestions_weights SUGGESTIONS_WEIGHTS]
                   [--suggestions_pivots SUGGESTIONS_PIVOTS]
                   [--suggestions_augmentable SUGGESTIONS_AUGMENTABLE]
                   [--suggestions_grid {exponential,linear}]
                   [--suggestions_objective {avg,min}]
                   [--suggestions_minperf SUGGESTIONS_MINPERF]
                   [--suggestions_minlangperf SUGGESTIONS_MINLANGPERF]
                   [--suggestions_verbose]
                   {mbert,xlmr}

positional arguments:
  {mbert,xlmr}          name of model to use

optional arguments:
  -h, --help            show this help message and exit
  --scores_file SCORES_FILE
                        path of json file containing scores to train on
  --train_format {json,csv}
                        Format of the training data
  --save_state SAVE_STATE
                        Save state of training of model to pickle file
  --load_state LOAD_STATE
                        Load trained model from pickle file
  --precomputed_features PRECOMPUTED_FEATURES
                        Path to precomputed-features file.
  --pivot_features {none,all,data_only}
                        What features based on pivot langs to use
  --use_all_langs       Add features based on all langs the tool supports
                        (Needed for transfer)
  --common_scaling      Common min max scaling params that are pvt
                        dependent(data size, type overlap, distance)
  --training_algorithm {xgboost,mlp}
                        which regressor to use
  --error_method {LOO,LOTO,split,kfold,manual_split}

  --data_sizes DATA_SIZES
                        Pivot data-size configs (semi-colon separated configs,
                        each config itself being comma-separated key-value
                        pairs)

  --mode MODE [MODE ...]
                        Output modes (comma-separated). Choose from following:
                        {heatmap, suggestions}.
  --output_dir OUTPUT_DIR
                        Overrride output directory
  --heatmap_targets HEATMAP_TARGETS
                        Targets for heatmap. Overrides suggestions_targets
                        (which is used by deafult)

  --suggestions_budget SUGGESTIONS_BUDGET
                        Budget for finding suggestions of which languages to
                        add data for (0 to disable)
  --suggestions_langbudget SUGGESTIONS_LANGBUDGET
                        Language-specific budget for finding suggestions
                        (overrrides suggestions_budget for these langs, comma-
                        separated list of key:value pairs)
  --suggestions_targets SUGGESTIONS_TARGETS
                        Targets being considered (comma-separated)
  --suggestions_weights SUGGESTIONS_WEIGHTS
                        Target weights for avg perf objective (comma-separated
                        list of key:value pairs, default wt=1)
  --suggestions_pivots SUGGESTIONS_PIVOTS
                        Index of desired row in data_sizes
  --suggestions_augmentable SUGGESTIONS_AUGMENTABLE
                        Set of augmentable languages (comma-separated)
  --suggestions_grid {exponential,linear}
                        Search space grid to use for suggestions
  --suggestions_objective {avg,min}
                        Objective function to be used for finding suggestions
  --suggestions_minperf SUGGESTIONS_MINPERF
                        Minimum acceptable average performance across tgts
  --suggestions_minlangperf SUGGESTIONS_MINLANGPERF
                        Minimum acceptable performance for given tgts (comma-
                        separated list of key:value pairs)
  --suggestions_verbose
                        Verbose logging of search

python3 litmus_mixing.py xlmr --scores_file training_observations.json --common_scaling --error_method split --mode heatmap --data_sizes "en:1000,hi:1000;en:1000,ar:1000" --use_all_langs --heatmap_targets en,fr,de,hi,ar,ru

from litmus import litmus_mixing

data_file = "" # Location of train data file
args = litmus_mixing.parse_args([
    "xlmr", data_file,
    "--common_scaling",
    "--error_method", "kfold",
    "--training_algorithm", "xgboost"
])
res = litmus_mixing.litmus_main(args)