Class focused on execute A* based analysis on three modalities of working Fast: 1 level analysis over default parameters Normal: One A* analysis for all models based until max_deep with early_stopping Paranoiac: One A* algorithm per model analysis until max_deep without early stoping. More...

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Public Member Functions
def	__init__ (self, e_c, deep_impact=5, metric='accuracy', dataframe_name='', hash_dataframe='')
	Constructor. More...

def	set_recommendations (self, dataframe_metadata, objective_column, amode=POC, atype=None)
	Main method oriented to execute smart analysis. More...

def	analysisnormal (self, dataframe_metadata, objective_column, amode)
	Method oriented to execute smart normal and fast analysis. More...

def	analysispoc (self, dataframe_metadata, objective_column, amode)
	Method oriented to execute poc analysis. More...

def	analysis_specific (self, dataframe_metadata, list_ar_metadata)
	Method oriented to execute new analysis. More...

def	analysisparanoiac (self, dataframe_metadata, objective_column, amode)
	Method oriented to execute smart normal and fast analysis. More...

def	analysisanomalies (self, dataframe_metadata, objective_column, amode)
	Method oriented to execute unsupervised anomalies models. More...

def	analysisclustering (self, dataframe_metadata, objective_column, amode)
	Method oriented to execute unsupervised clustering models. More...

def	base_specific (self, dataframe_metadata, list_ar_metadata)
	Method oriented to generate specific candidate metadata. More...

def	base_iteration (self, amode, dataframe_metadata, objective_column)
	Method oriented to select initial candidate models. More...

def	load_frameworks (self)
	Method oriented to get frameworks default values from config. More...

def	get_analysis_objective (self, dataframe_metadata, objective_column, atype=None)
	Method oriented to analyze DFmetadata and select analysis objective. More...

def	get_size_increment (self, df_metadata)
	Method oriented to analyze get increments on effort based on DF_metadata structure. More...

def	get_candidate_models (self, atype, amode, increment=1.0)
	Method oriented to analyze choose models candidate and select analysis objective. More...

def	applicability (self, model_list, nrows, ncols)
	Method oriented to select applicability of models over min_rows_limit. More...

def	priorize_models (self, model_list)
	Method managing scoring algorithm results params: results for Handlers (gdayf.handlers) More...

def	generate_vectors (self, model, normalization_set)
	Store executed model base parameters to check past executions. More...

def	is_executed (self, vector)
	Check if model has benn executed or is planned to execute. More...

def	compare_vectors (vector1, vector2)
	Compare to execution vectors. More...

def	safe_append (self, model_list, model)
	Check if model is previously executed. More...

Static Public Member Functions
def	get_train_accuracy (model)
	Method get train accuracy for generic model. More...

def	get_test_accuracy (model)
	Method get test accuracy for generic model. More...

def	get_combined_accuracy (model)
	Method get averaged train and test accuracy for generic model. More...

def	get_train_rmse (model)
	Method get rmse for generic model. More...

def	get_test_rmse (model)
	Method get test rmse for generic model. More...

def	get_cdistance (model)
	Method get clustering distance for generic model. More...

def	get_train_r2 (model)
	Method get train accuracy for generic model. More...

def	get_test_r2 (model)
	Method get test accuracy for generic model. More...

Public Attributes
	timestamp

	an_objective

	deep_impact

	analysis_recommendation_order

	analyzed_models

	excluded_models

	next_analysis_list

	metric

	dataframe_name

	hash_dataframe

	deepness

Static Public Attributes
int	deepness = 1

Detailed Description

Class focused on execute A* based analysis on three modalities of working Fast: 1 level analysis over default parameters Normal: One A* analysis for all models based until max_deep with early_stopping Paranoiac: One A* algorithm per model analysis until max_deep without early stoping.

Definition at line 36 of file adviserbase.py.

Constructor & Destructor Documentation

◆ init()

def gdayf.core.adviserbase.Adviser.__init__	(	self,
		e_c,
		deep_impact = `5`,
		metric = `'accuracy'`,
		dataframe_name = `''`,
		hash_dataframe = `''`
	)

Constructor.

Parameters

self	object pointer
e_c	context pointer
deep_impact	A* max_deep
metric	metrict for priorizing models ['accuracy', 'rmse', 'test_accuracy', 'combined'] on train
dataframe_name	dataframe_name or id
hash_dataframe	MD5 hash value

Definition at line 47 of file adviserbase.py.

Member Function Documentation

◆ analysis_specific()

def gdayf.core.adviserbase.Adviser.analysis_specific	(	self,
		dataframe_metadata,
		list_ar_metadata
	)

Method oriented to execute new analysis.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()j
list_ar_metadata	List of ar json compatible model's descriptors

Returns: analysis_id, Ordered[(algorithm_metadata.json, normalizations_sets.json)]

Definition at line 199 of file adviserbase.py.

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◆ analysisanomalies()

def gdayf.core.adviserbase.Adviser.analysisanomalies	(	self,
		dataframe_metadata,
		objective_column,
		amode
	)

Method oriented to execute unsupervised anomalies models.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
amode	[ANOMALIES]
objective_column	string indicating objective column

Returns: analysis_id,(framework, Ordered[(algorithm_metadata.json, normalizations_sets.json)])

Definition at line 282 of file adviserbase.py.

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◆ analysisclustering()

def gdayf.core.adviserbase.Adviser.analysisclustering	(	self,
		dataframe_metadata,
		objective_column,
		amode
	)

Method oriented to execute unsupervised clustering models.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
amode	[CLUSTERING]
objective_column	string indicating objective column

Returns: analysis_id,(framework, Ordered[(algorithm_metadata.json, normalizations_sets.json)])

Definition at line 323 of file adviserbase.py.

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◆ analysisnormal()

def gdayf.core.adviserbase.Adviser.analysisnormal	(	self,
		dataframe_metadata,
		objective_column,
		amode
	)

Method oriented to execute smart normal and fast analysis.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
objective_column	string indicating objective column
amode	[POC, NORMAL, FAST, PARANOIAC, FAST_PARANOIAC]

Returns: analysis_id, Ordered[(algorithm_metadata.json, normalizations_sets.json)]

Definition at line 104 of file adviserbase.py.

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◆ analysisparanoiac()

def gdayf.core.adviserbase.Adviser.analysisparanoiac	(	self,
		dataframe_metadata,
		objective_column,
		amode
	)

Method oriented to execute smart normal and fast analysis.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
amode	[POC, NORMAL, FAST, PARANOIAC, FAST_PARANOIAC]
objective_column	string indicating objective column

Returns: analysis_id,(framework, Ordered[(algorithm_metadata.json, normalizations_sets.json)])

Definition at line 241 of file adviserbase.py.

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◆ analysispoc()

def gdayf.core.adviserbase.Adviser.analysispoc	(	self,
		dataframe_metadata,
		objective_column,
		amode
	)

Method oriented to execute poc analysis.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
objective_column	string indicating objective column
amode	[POC, NORMAL, FAST, PARANOIAC, FAST_PARANOIAC]

Returns: analysis_id, Ordered[(algorithm_metadata.json, normalizations_sets.json)]

Definition at line 172 of file adviserbase.py.

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◆ applicability()

def gdayf.core.adviserbase.Adviser.applicability	(	self,
		model_list,
		nrows,
		ncols
	)

Method oriented to select applicability of models over min_rows_limit.

Parameters

self	object pointer
model_list	List[ArMetadata]
nrows	number of rows of dataframe
ncols	number of cols of dataframe

Returns: implicit List[ArMetadata]

Definition at line 561 of file adviserbase.py.

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◆ base_iteration()

def gdayf.core.adviserbase.Adviser.base_iteration	(	self,
		amode,
		dataframe_metadata,
		objective_column
	)

Method oriented to select initial candidate models.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
amode	[POC, NORMAL, FAST, PARANOIAC, FAST_PARANOIAC]
objective_column	string indicating objective column

Definition at line 397 of file adviserbase.py.

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◆ base_specific()

def gdayf.core.adviserbase.Adviser.base_specific	(	self,
		dataframe_metadata,
		list_ar_metadata
	)

Method oriented to generate specific candidate metadata.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
list_ar_metadata

Definition at line 361 of file adviserbase.py.

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◆ compare_vectors()

def gdayf.core.adviserbase.Adviser.compare_vectors	(	vector1,
		vector2
	)

Compare to execution vectors.

Parameters

vector1	- model_execution vector
vector2	- model_execution vector

Returns: True if equal False if inequity

Definition at line 795 of file adviserbase.py.

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◆ generate_vectors()

def gdayf.core.adviserbase.Adviser.generate_vectors	(	self,
		model,
		normalization_set
	)

Store executed model base parameters to check past executions.

Parameters

model	- ArMetadata to be stored as executed
normalization_set

Returns: model_vector (fw, model_id, vector, normalizaton_set)

Definition at line 764 of file adviserbase.py.

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◆ get_analysis_objective()

def gdayf.core.adviserbase.Adviser.get_analysis_objective	(	self,
		dataframe_metadata,
		objective_column,
		atype = `None`
	)

Method oriented to analyze DFmetadata and select analysis objective.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
objective_column	string indicating objective column
atype	atypes constats or None

Returns: ArType or None if objective_column not found

Definition at line 473 of file adviserbase.py.

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◆ get_candidate_models()

def gdayf.core.adviserbase.Adviser.get_candidate_models	(	self,
		atype,
		amode,
		increment = `1.0`
	)

Method oriented to analyze choose models candidate and select analysis objective.

Parameters

self	object pointer
atype	ATypesMetadata
amode	Analysismode
increment	increment x size

Returns: FrameworkMetadata()

Definition at line 535 of file adviserbase.py.

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◆ get_cdistance()

def gdayf.core.adviserbase.Adviser.get_cdistance ( model )

static

Method get clustering distance for generic model.

Parameters

model

Returns: The Total Within Cluster Sum-of-Square Error metric, inverse The Between Cluster Sum-of-Square Error, objective or 10e+308, 0.0, objective if not exists

Definition at line 684 of file adviserbase.py.

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◆ get_combined_accuracy()

def gdayf.core.adviserbase.Adviser.get_combined_accuracy ( model )

static

Method get averaged train and test accuracy for generic model.

Parameters

model

Returns: accuracy metric, inverse rmse, objective or 0.0, 10e+308, objective if not exists

Definition at line 621 of file adviserbase.py.

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◆ get_size_increment()

def gdayf.core.adviserbase.Adviser.get_size_increment	(	self,
		df_metadata
	)

Method oriented to analyze get increments on effort based on DF_metadata structure.

Parameters

self	object pointer
df_metadata	DfMetada

Returns: float increment

Definition at line 518 of file adviserbase.py.

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◆ get_test_accuracy()

def gdayf.core.adviserbase.Adviser.get_test_accuracy ( model )

static

Method get test accuracy for generic model.

Parameters

model

Returns: accuracy metric, inverse rmse, objective or 0.0, 10e+308, objective if not exists

Definition at line 603 of file adviserbase.py.

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◆ get_test_r2()

def gdayf.core.adviserbase.Adviser.get_test_r2 ( model )

static

Method get test accuracy for generic model.

Parameters

model

Returns: r2 metric, inverse rmse, objective or 0.0, 10e+308, objective if not exists

Definition at line 722 of file adviserbase.py.

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◆ get_test_rmse()

def gdayf.core.adviserbase.Adviser.get_test_rmse ( model )

static

Method get test rmse for generic model.

Parameters

model

Returns: rsme metric, inverse combined accuracy, objective or 10e+308, 0.0, objective if not exists

Definition at line 661 of file adviserbase.py.

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◆ get_train_accuracy()

def gdayf.core.adviserbase.Adviser.get_train_accuracy ( model )

static

Method get train accuracy for generic model.

Parameters

model

Returns: accuracy metric, inverse rmse, objective or 0.0, 10e+8, objective if not exists

Definition at line 585 of file adviserbase.py.

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◆ get_train_r2()

def gdayf.core.adviserbase.Adviser.get_train_r2 ( model )

static

Method get train accuracy for generic model.

Parameters

model

Returns: r2 metric, inverse rmse, objective or 0.0, 10e+8, objective if not exists

Definition at line 704 of file adviserbase.py.

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◆ get_train_rmse()

def gdayf.core.adviserbase.Adviser.get_train_rmse ( model )

static

Method get rmse for generic model.

Parameters

model

Returns: rsme metric, inverse combined accuracy, objective or 10e+308, 0.0, objective if not exists

Definition at line 639 of file adviserbase.py.

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◆ is_executed()

def gdayf.core.adviserbase.Adviser.is_executed	(	self,
		vector
	)

Check if model has benn executed or is planned to execute.

Parameters

vector - model vector

Returns: True if executed False in other case

Definition at line 783 of file adviserbase.py.

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◆ load_frameworks()

def gdayf.core.adviserbase.Adviser.load_frameworks ( self )

Method oriented to get frameworks default values from config.

Parameters

self	object pointer

Returns: FrameWorkMetadata

Definition at line 464 of file adviserbase.py.

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◆ priorize_models()

def gdayf.core.adviserbase.Adviser.priorize_models	(	self,
		model_list
	)

Method managing scoring algorithm results params: results for Handlers (gdayf.handlers)

Parameters

model_list for models analyzed

Returns: (fw,model_list) (ArMetadata, normalization_set)

Definition at line 740 of file adviserbase.py.

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◆ safe_append()

def gdayf.core.adviserbase.Adviser.safe_append	(	self,
		model_list,
		model
	)

Check if model is previously executed.

If it not append to list

Parameters

model_list
model	json compatible

Definition at line 802 of file adviserbase.py.

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◆ set_recommendations()

def gdayf.core.adviserbase.Adviser.set_recommendations	(	self,
		dataframe_metadata,
		objective_column,
		amode = `POC`,
		atype = `None`
	)

Main method oriented to execute smart analysis.

Parameters

self	object pointer
dataframe_metadata	DFMetadata()
amode	[POC, NORMAL, FAST, PARANOIAC, FAST_PARANOIAC]
objective_column	string indicating objective column
atype	atypes constats or None

Returns: ArMetadata()'s Prioritized queue

Definition at line 72 of file adviserbase.py.

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

D:/OneDrive/DayF/git/gdayf-core/src/gdayf/core/adviserbase.py

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ analysis_specific()

◆ analysisanomalies()

◆ analysisclustering()

◆ analysisnormal()

◆ analysisparanoiac()

◆ analysispoc()

◆ applicability()

◆ base_iteration()

◆ base_specific()

◆ compare_vectors()

◆ generate_vectors()

◆ get_analysis_objective()

◆ get_candidate_models()

◆ get_cdistance()

◆ get_combined_accuracy()

◆ get_size_increment()

◆ get_test_accuracy()

◆ get_test_r2()

◆ get_test_rmse()

◆ get_train_accuracy()

◆ get_train_r2()

◆ get_train_rmse()

◆ is_executed()

◆ load_frameworks()

◆ priorize_models()

◆ safe_append()

◆ set_recommendations()

◆ init()