Function save_predictions

alphapy/model.py:1157–1250

r"""Save the predictions to disk. 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_predictions(model, tag, partition):
    r"""Save the predictions to disk.

    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
    -------
    preds : numpy array
        The prediction vector.
    probas : numpy array
        The probability vector.

    """

    # Extract model parameters.

    directory = model.specs['directory']
    extension = model.specs['extension']
    model_type = model.specs['model_type']
    separator = model.specs['separator']

    # Get date stamp to record file creation
    timestamp = get_datestamp()

    # Specify input and output directories

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

    # Read the prediction frame
    file_spec = ''.join([datasets[partition], '*'])
    file_name = most_recent_file(input_dir, file_spec)
    file_name = file_name.split(SSEP)[-1].split(PSEP)[0]
    pf = read_frame(input_dir, file_name, extension, separator)

    # Cull records before the prediction date

    try:
        predict_date = model.specs['predict_date']
        found_pdate = True
    except:
        found_pdate = False

    if found_pdate:
        pd_indices = pf[pf.date >= predict_date].index.tolist()
        pf = pf.iloc[pd_indices]
    else:
        pd_indices = pf.index.tolist()

    # Save predictions for all projects