def __init__(self, **kwargs):
''' Create instance of the db management model
Input:
kwargs: various parameters
'''
# Call super class init first
if (kwargs.get("default_name") == None):
kwargs["default_name"] = "attribute_indexer"
if (kwargs.get("model_type") == None):
kwargs["model_type"] = attribute_index
if (kwargs.get("index_uuid") == None):
index_uuid = True
else:
index_uuid = kwargs.get("index_uuid")
if (kwargs.get("update_index") == None):
update_index = False
else:
update_index = kwargs.get("update_index")
modeling_worker.__init__(self, **kwargs)
self._model = kwargs.get("model")
self._model_observations_query_info = kwargs.get("model_observations_query_info")
self._value_index = {}
self._processed_count = 0
self._index_uuid = index_uuid
self._update_index = update_index
self.loadModel()
def __init__(self, **kwargs):
# Call super class init first
if (kwargs.get("default_name") == None):
kwargs["default_name"] = "multinomialnb_worker"
if (kwargs.get("model_type") == None):
kwargs["model_type"] = sklearn_utils.multinomial_nb_model
modeling_worker.__init__(self, **kwargs)
self._model_type = kwargs.get("model_type")
self._model = kwargs.get("model")
self._model_observations_query_info = kwargs.get(
"model_observations_query_info")
self._feature_vector_paths = kwargs.get("feature_vectors")
self._truth_vector_paths = kwargs.get("truth_vectors")
def __init__(self, **kwargs):
# Call super class init first
if (kwargs.get("default_name") == None):
kwargs["default_name"] = "decision_tree_regressor"
if (kwargs.get("model_type") == None):
kwargs["model_type"] = sklearn_utils.decision_tree_regressor_model
modeling_worker.__init__(self, **kwargs)
self._model_type = kwargs.get("model_type")
self._model = kwargs.get("model")
self._model_observations_query_info = kwargs.get(
"model_observations_query_info")
self._model_observations_manager_name = kwargs.get(
"model_observations_manager_name")
self._feature_vector_paths = kwargs.get("feature_vectors")
self._truth_vector_paths = kwargs.get("truth_vectors")
def __init__(self, **kwargs):
"""
this is the first worker called in our pipline
"""
if (kwargs.get("default_name") == None):
kwargs["default_name"] = "example_worker"
if (kwargs.get("model_type") == None):
print("Making an exampleModel")
kwargs["model_type"] = ExampleModel
# Call super class init
modeling_worker.__init__(self, **kwargs)
if (kwargs.get("pattern") == None):
pattern = []
self._model_type = kwargs.get("model_type")
self._model = kwargs.get("model")
self._model_observations_manager_name = kwargs.get("model_observations_manager_name")
self._model_observations_query_info = kwargs.get("model_observations_query_info")
model = self.loadModel()
def __init__(self, **kwargs):
''' Create instance of the db management model
Input:
kwargs: various parameters
'''
# Call super class init first
if kwargs.get("default_name") is None:
kwargs["default_name"] = "attribute_count_analyzer"
if kwargs.get("model_type") is None:
kwargs["model_type"] = attribute_count_analysis
modeling_worker.__init__(self, **kwargs)
self._report_aggregation_max = kwargs.get("report_aggregation_max")
self._report_aggregation_min = kwargs.get("report_aggregation_max")
self._report_aggregation_avg = kwargs.get("report_aggregation_max")
self._model = kwargs.get("model")
self.loadModel()