gdayf.optimizers.spark_optimizer.Optimizer Class Reference

Inheritance diagram for gdayf.optimizers.spark_optimizer.Optimizer:

Collaboration diagram for gdayf.optimizers.spark_optimizer.Optimizer:

Public Member Functions
def	__init__ (self, e_c)
	Constructor Initialize all framework variables and starts or connect to spark cluster Aditionally starts PersistenceHandler and logsHandler. More...
def	optimize_models (self, armetadata, metric_value, objective, deepness, deep_impact)
	Method manging generation of possible optimized H2O models loadded dinamically on adviserclass params: results for Handlers (gdayf.handlers) More...

Detailed Description

Definition at line 10 of file spark_optimizer.py.

Constructor & Destructor Documentation

__init__()

def gdayf.optimizers.spark_optimizer.Optimizer.__init__	(		self,
			e_c
	)

Constructor Initialize all framework variables and starts or connect to spark cluster Aditionally starts PersistenceHandler and logsHandler.

Parameters

self	object pointer
e_c	context pointer

Definition at line 17 of file spark_optimizer.py.

Member Function Documentation

optimize_models()

def gdayf.optimizers.spark_optimizer.Optimizer.optimize_models	(		self,
			armetadata,
			metric_value,
			objective,
			deepness,
			deep_impact
	)

Method manging generation of possible optimized H2O models loadded dinamically on adviserclass params: results for Handlers (gdayf.handlers)

Parameters

armetadata	ArMetadata Model
metric_value	metrict for priorizing models ['train_accuracy', 'test_rmse', 'train_rmse', 'test_accuracy', 'combined_accuracy', ...]
objective	objective for analysis ['regression, binomial, ...]
deepness	currrent deeepnes of the analysis
deep_impact	max deepness of analysis

Returns

list of possible optimized models to execute return None if nothing to do

Definition at line 30 of file spark_optimizer.py.

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The documentation for this class was generated from the following file:

• D:/OneDrive/DayF/git/gdayf-core/src/gdayf/optimizers/spark_optimizer.py