armetadata.py

'''
Copyright (C) e2its - All Rights Reserved
 * Unauthorized copying of this file, via any medium is strictly prohibited
 * Proprietary and confidential
 *
 * This file is part of gDayF project.
 *
 * Written by Jose L. Sanchez <e2its.es@gmail.com>, 2016-2019
'''

from collections import OrderedDict
from copy import deepcopy
from json import dumps
from gdayf.common.utils import get_model_fw



class ArMetadata (OrderedDict):

    
    def __init__(self, type_='train'):
        OrderedDict.__init__(self)
        self['model_id'] = None
        self['version'] = None
        self['type'] = type_
        self['objective_column'] = None
        self['workflow_id'] = None
        self['user_id'] = None
        self['timestamp'] = None
        self['round'] = 1
        self['execution_seconds'] = 0
        self['load_path'] = None
        self['metrics'] = OrderedDict()
        self['normalizations_set'] = None
        self['dataset'] = None
        self['dataset_hash_value'] = None
        self['data_initial'] = None
        self['data_normalized'] = None
        self['model_parameters'] = None
        self['ignored_parameters'] = None
        self['full_parameters_stack'] = None
        self['log_path'] = None
        self['json_path'] = None
        self['prediction_path'] = None
        self['predecessor'] = None
        self['status'] = -1

    
    def get_json(self):
        return dumps(self, indent=4)
    
    
    def copy_template(self, increment=1):
        new_model = ArMetadata()
        new_model['model_id'] = deepcopy(self['model_id'])
        new_model['version'] = deepcopy(self['version'])
        new_model['workflow_id'] = deepcopy(self['workflow_id'])
        new_model['user_id'] = deepcopy(self['user_id'])
        new_model['type'] = deepcopy(self['type'])
        new_model['objective_column'] = deepcopy(self['objective_column'])
        new_model['timestamp'] = deepcopy(self['timestamp'])
        new_model['round'] = self['round'] + increment
        new_model['execution_seconds'] = 0.0
        new_model['tolerance'] = 0.0
        new_model['predecessor'] = self['model_parameters'][get_model_fw(self)]['parameters']['model_id']['value']
        new_model['normalizations_set'] = deepcopy(self['normalizations_set'])
        new_model['dataset'] = deepcopy(self['dataset'])
        new_model['dataset_hash_value'] = deepcopy(self['dataset_hash_value'])
        new_model['data_initial'] = deepcopy(self['data_initial'])
        new_model['data_normalized'] = deepcopy(self['data_normalized'])
        new_model['model_parameters'] = deepcopy(self['model_parameters'])
        new_model['ignored_parameters'] = deepcopy(self['ignored_parameters'])
        return new_model

    
    def pop(self, key, default=None):
        return 1

    
    def popitem(self, last=True):
        return 1



def deep_ordered_copy(armetadata):
    new_model = ArMetadata()
    for key, value in armetadata.items():
        if key not in ['load_path', 'json_path', 'load_path', 'prediction_path']:
            new_model[key] = deepcopy(value)
        else:
            if value is not None:
                new_model[key] = list()
                for each_storage in armetadata[key]:
                    new_model[key].append(OrderedDict(
                        {'value': each_storage['value'],
                         'type': each_storage['type'],
                         'hash_value': each_storage['hash_value'],
                         'hash_type': each_storage['hash_type']
                         }
                    ))
            else:
                new_model[key] = None
    return new_model


'''## Main block only for testing issues
if __name__ == "__main__":
    ## Varible for testinf propouses
    m = ArMetadata()
    print(m.get_json())'''