Function save_model

alphapy/model.py:1257–1331

r"""Save the results in the model file. Parameters ---------- model : alphapy.Model The model object to save. tag : str A unique identifier for the output files, e.g., a date stamp. partition : alphapy.Partition Reference to the dataset. Returns

(model, tag, partition)

#

def save_model(model, tag, partition):
    r"""Save the results in the model file.

    Parameters
    ----------
    model : alphapy.Model
        The model object to save.
    tag : str
        A unique identifier for the output files, e.g., a date stamp.
    partition : alphapy.Partition
        Reference to the dataset.

    Returns
    -------
    None : None

    Notes
    -----

    The following components are extracted from the model object
    and saved to disk:

    * Model predictor (via joblib/pickle)
    * Predictions
    * Probabilities (classification only)
    * Rankings
    * Submission File (optional)

    """

    logger.info('='*80)

    # Extract model parameters.

    directory = model.specs['directory']
    extension = model.specs['extension']
    model_type = model.specs['model_type']
    submission_file = model.specs['submission_file']
    submit_probas = model.specs['submit_probas']

    # Get date stamp to record file creation

    d = datetime.now()
    f = "%Y%m%d"
    timestamp = d.strftime(f)

    # Save the model predictor
    save_predictor(model, timestamp)

    # Save the feature map
    save_feature_map(model, timestamp)

    # Specify input and output directories

    input_dir = SSEP.join([directory, 'input'])
    output_dir = SSEP.join([directory, 'output'])

    # Save predictions