autoBOTLib CLI =============== To streamline the experiments, it makes a lot of sense to directly use the *autoBOTLib* as a CLI tool. The library itself implements wrappers for main functions, and can be executed as follows. If you installed the package, the `autobot-cli` tool was also built as part of the installation. By running .. code-block:: text autobot-cli --help The arguments are defined as follows. .. code-block:: text usage: autobot-cli [-h] [--time TIME] [--job_id JOB_ID] [--popsize POPSIZE] [--output_folder OUTPUT_FOLDER] [--learner_preset LEARNER_PRESET] [--hof_size HOF_SIZE] [--representation_type REPRESENTATION_TYPE] [--train_data TRAIN_DATA] [--mutation_rate MUTATION_RATE] [--crossover_rate CROSSOVER_RATE] [--predict_data PREDICT_DATA] [--load_model LOAD_MODEL] [--num_cpu NUM_CPU] [--upsample UPSAMPLE] [--mode MODE] optional arguments: -h, --help show this help message and exit --time TIME --job_id JOB_ID --popsize POPSIZE --output_folder OUTPUT_FOLDER --learner_preset LEARNER_PRESET --hof_size HOF_SIZE --representation_type REPRESENTATION_TYPE --train_data TRAIN_DATA --mutation_rate MUTATION_RATE --crossover_rate CROSSOVER_RATE --predict_data PREDICT_DATA --load_model LOAD_MODEL --num_cpu NUM_CPU --upsample UPSAMPLE --mode MODE Hence, as a minimal example, we can consider running .. code-block:: text autobot-cli --train_data ./data/insults/train.tsv --output_folder CLI --learner_preset mini-l1 Here, the `train.tsv` file needs to have two attributes; `text_a` - the documents field, and `label`, the label field. Once the run finishes, you will have a trained model with a report in the `CLI` folder. To make predictions on unseen data, simply .. code-block:: text autobot-cli --mode prediction --predict_data ./data/insults/test.tsv --load_model ./CLI/autoBOTmodel.pickle --output_folder CLI See `https://github.com/skblaz/autobot/cli_example.sh` for a full example.