commands.py

@classification.command()
@click.option('--input_train', default='train')
@click.option('--input_validation', default='validation')
@click.option('--output_model', default='train_model.bin')
@click.option('--output_parameters', default='parameters.json')
@click.option('--metric', default='f1')
@click.option('--k', default=1)
@click.option('--duration', default=1200)
@click.option('--model_size', default='2000M')
def autotune(input_train, input_validation, output_model, output_parameters,
    metric, k, duration, model_size):
    input_train_path = get_input_path(input_train)
    input_validation_path = get_input_path(input_validation)
    output_model_path = get_output_path(output_model)
    output_parameters_path = get_output_path(output_parameters)

    # Autotune model
    model = fasttext.train_supervised(
        input=input_train_path,
        autotuneValidationFile=input_validation_path,
        autotuneMetric=metric,
        autotuneDuration=duration,
        autotuneModelSize=model_size,
        verbose=VERBOSE)

    # Log best model metrics
    n, p, r = model.test(input_validation_path, k=k)
    print(json.dumps(
        {'n': n, 'precision': p, 'recall': r, 'k': k}))

    # Save best parameters
    with open(output_parameters_path, 'w') as f:
        json.dump(get_model_parameters(model), f)

    # Save best model
    model.save_model(output_model_path)