elastic_net.sql_in File Reference

SQL functions for elastic net regularization. More...

Functions
void	elastic_net_train (text tbl_source, text tbl_result, text col_dep_var, text col_ind_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardize, text grouping_col, text optimizer, text optimizer_params, text excluded, integer max_iter, float8 tolerance)
	Interface for elastic net. More...
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization, text grouping_columns, text optimizer, text optimizer_params, text excluded, integer max_iter)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization, text grouping_columns, text optimizer, text optimizer_params, text excluded)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization, text grouping_columns, text optimizer, text optimizer_params)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization, text grouping_columns, text optimizer)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization, text grouping_columns)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value, boolean standardization)
void	elastic_net_train (text tbl_source, text tbl_result, text col_ind_var, text col_dep_var, text regress_family, float8 alpha, float8 lambda_value)
text	elastic_net_train ()
	Help function, to print out the supported families. More...
text	elastic_net_train (text family_or_optimizer)
	Help function, to print out the supported optimizer for a family or print out the parameter list for an optimizer. More...
void	elastic_net_predict (text tbl_model, text tbl_new_source, text col_id, text tbl_predict)
	Prediction and put the result in a table can be used together with General-CV. More...
float8	elastic_net_predict (text regress_family, float8[] coefficients, float8 intercept, float8[] ind_var)
	Prediction use learned coefficients for a given example. More...
float8	elastic_net_gaussian_predict (float8[] coefficients, float8 intercept, float8[] ind_var)
	Prediction for linear models use learned coefficients for a given example. More...
boolean	elastic_net_binomial_predict (float8[] coefficients, float8 intercept, float8[] ind_var)
	Prediction for logistic models use learned coefficients for a given example. More...
float8	elastic_net_binomial_prob (float8[] coefficients, float8 intercept, float8[] ind_var)
	Compute the probability of belonging to the True class for a given observation. More...
float8	__elastic_net_binomial_loglikelihood (float8[] coefficients, float8 intercept, boolean dep_var, float8[] ind_var)
float8 []	__gaussian_igd_transition (float8[] state, float8[] ind_var, float8 dep_var, float8[] pre_state, float8 lambda, float8 alpha, integer dimension, float8 stepsize, integer total_rows, float8[] xmean, float8 ymean, float8 step_decay)
float8 []	__gaussian_igd_merge (float8[] state1, float8[] state2)
float8 []	__gaussian_igd_final (float8[] state)
aggregate float8 []	__gaussian_igd_step (float8[], float8, float8[], float8, float8, integer, float8, integer, float8[], float8, float8)
aggregate float8 []	__gaussian_igd_step_single_seg (float8[], float8, float8[], float8, float8, integer, float8, integer, float8[], float8, float8)
float8	__gaussian_igd_state_diff (float8[] state1, float8[] state2)
__elastic_net_result	__gaussian_igd_result (float8[] in_state, float8[] feature_sq, float8 threshold, float8 tolerance)
float8 []	__gaussian_fista_transition (float8[] state, float8[] ind_var, float8 dep_var, float8[] pre_state, float8 lambda, float8 alpha, integer dimension, integer total_rows, float8 max_stepsize, float8 eta, integer use_active_set, integer is_active, integer random_stepsize)
float8 []	__gaussian_fista_merge (float8[] state1, float8[] state2)
float8 []	__gaussian_fista_final (float8[] state)
aggregate float8 []	__gaussian_fista_step (float8[], float8, float8[], float8, float8, integer, integer, float8, float8, integer, integer, integer)
float8	__gaussian_fista_state_diff (float8[] state1, float8[] state2)
__elastic_net_result	__gaussian_fista_result (float8[] in_state)
float8 []	__binomial_igd_transition (float8[] state, float8[] ind_var, boolean dep_var, float8[] pre_state, float8 lambda, float8 alpha, integer dimension, float8 stepsize, integer total_rows, float8[] xmean, float8 ymean, float8 step_decay)
float8 []	__binomial_igd_merge (float8[] state1, float8[] state2)
float8 []	__binomial_igd_final (float8[] state)
aggregate float8 []	__binomial_igd_step (float8[], boolean, float8[], float8, float8, integer, float8, integer, float8[], float8, float8)
aggregate float8 []	__binomial_igd_step_single_seg (float8[], boolean, float8[], float8, float8, integer, float8, integer, float8[], float8, float8)
float8	__binomial_igd_state_diff (float8[] state1, float8[] state2)
__elastic_net_result	__binomial_igd_result (float8[] in_state, float8[] feature_sq, float8 threshold, float8 tolerance)
float8 []	__binomial_fista_transition (float8[] state, float8[] ind_var, boolean dep_var, float8[] pre_state, float8 lambda, float8 alpha, integer dimension, integer total_rows, float8 max_stepsize, float8 eta, integer use_active_set, integer is_active, integer random_stepsize)
float8 []	__binomial_fista_merge (float8[] state1, float8[] state2)
float8 []	__binomial_fista_final (float8[] state)
aggregate float8 []	__binomial_fista_step (float8[], boolean, float8[], float8, float8, integer, integer, float8, float8, integer, integer, integer)
float8	__binomial_fista_state_diff (float8[] state1, float8[] state2)
__elastic_net_result	__binomial_fista_result (float8[] in_state)

Detailed Description

Date

July 2012

See also

For a brief introduction to elastic net, see the module description Elastic Net Regularization.

Function Documentation

__binomial_fista_final()

float8 [] __binomial_fista_final

(

float8 []

state

)

__binomial_fista_merge()

float8 [] __binomial_fista_merge	(	float8 []	state1,
		float8 []	state2
	)

__binomial_fista_result()

__elastic_net_result __binomial_fista_result

(

float8 []

in_state

)

__binomial_fista_state_diff()

float8 __binomial_fista_state_diff	(	float8 []	state1,
		float8 []	state2
	)

__binomial_fista_step()

aggregate float8 [] __binomial_fista_step	(	float8	[],
		boolean	,
		float8	[],
		float8	,
		float8	,
		integer	,
		integer	,
		float8	,
		float8	,
		integer	,
		integer	,
		integer
	)

__binomial_fista_transition()

float8 [] __binomial_fista_transition	(	float8 []	state,
		float8 []	ind_var,
		boolean	dep_var,
		float8 []	pre_state,
		float8	lambda,
		float8	alpha,
		integer	dimension,
		integer	total_rows,
		float8	max_stepsize,
		float8	eta,
		integer	use_active_set,
		integer	is_active,
		integer	random_stepsize
	)

__binomial_igd_final()

float8 [] __binomial_igd_final

(

float8 []

state

)

__binomial_igd_merge()

float8 [] __binomial_igd_merge	(	float8 []	state1,
		float8 []	state2
	)

__binomial_igd_result()

__elastic_net_result __binomial_igd_result	(	float8 []	in_state,
		float8 []	feature_sq,
		float8	threshold,
		float8	tolerance
	)

__binomial_igd_state_diff()

float8 __binomial_igd_state_diff	(	float8 []	state1,
		float8 []	state2
	)

__binomial_igd_step()

aggregate float8 [] __binomial_igd_step	(	float8	[],
		boolean	,
		float8	[],
		float8	,
		float8	,
		integer	,
		float8	,
		integer	,
		float8	[],
		float8	,
		float8
	)

__binomial_igd_step_single_seg()

aggregate float8 [] __binomial_igd_step_single_seg	(	float8	[],
		boolean	,
		float8	[],
		float8	,
		float8	,
		integer	,
		float8	,
		integer	,
		float8	[],
		float8	,
		float8
	)

__binomial_igd_transition()

float8 [] __binomial_igd_transition	(	float8 []	state,
		float8 []	ind_var,
		boolean	dep_var,
		float8 []	pre_state,
		float8	lambda,
		float8	alpha,
		integer	dimension,
		float8	stepsize,
		integer	total_rows,
		float8 []	xmean,
		float8	ymean,
		float8	step_decay
	)

__elastic_net_binomial_loglikelihood()

float8 __elastic_net_binomial_loglikelihood	(	float8 []	coefficients,
		float8	intercept,
		boolean	dep_var,
		float8 []	ind_var
	)

__gaussian_fista_final()

float8 [] __gaussian_fista_final

(

float8 []

state

)

__gaussian_fista_merge()

float8 [] __gaussian_fista_merge	(	float8 []	state1,
		float8 []	state2
	)

__gaussian_fista_result()

__elastic_net_result __gaussian_fista_result

(

float8 []

in_state

)

__gaussian_fista_state_diff()

float8 __gaussian_fista_state_diff	(	float8 []	state1,
		float8 []	state2
	)

__gaussian_fista_step()

aggregate float8 [] __gaussian_fista_step	(	float8	[],
		float8	,
		float8	[],
		float8	,
		float8	,
		integer	,
		integer	,
		float8	,
		float8	,
		integer	,
		integer	,
		integer
	)

__gaussian_fista_transition()

float8 [] __gaussian_fista_transition	(	float8 []	state,
		float8 []	ind_var,
		float8	dep_var,
		float8 []	pre_state,
		float8	lambda,
		float8	alpha,
		integer	dimension,
		integer	total_rows,
		float8	max_stepsize,
		float8	eta,
		integer	use_active_set,
		integer	is_active,
		integer	random_stepsize
	)

__gaussian_igd_final()

float8 [] __gaussian_igd_final

(

float8 []

state

)

__gaussian_igd_merge()

float8 [] __gaussian_igd_merge	(	float8 []	state1,
		float8 []	state2
	)

__gaussian_igd_result()

__elastic_net_result __gaussian_igd_result	(	float8 []	in_state,
		float8 []	feature_sq,
		float8	threshold,
		float8	tolerance
	)

__gaussian_igd_state_diff()

float8 __gaussian_igd_state_diff	(	float8 []	state1,
		float8 []	state2
	)

__gaussian_igd_step()

aggregate float8 [] __gaussian_igd_step	(	float8	[],
		float8	,
		float8	[],
		float8	,
		float8	,
		integer	,
		float8	,
		integer	,
		float8	[],
		float8	,
		float8
	)

__gaussian_igd_step_single_seg()

aggregate float8 [] __gaussian_igd_step_single_seg	(	float8	[],
		float8	,
		float8	[],
		float8	,
		float8	,
		integer	,
		float8	,
		integer	,
		float8	[],
		float8	,
		float8
	)

__gaussian_igd_transition()

float8 [] __gaussian_igd_transition	(	float8 []	state,
		float8 []	ind_var,
		float8	dep_var,
		float8 []	pre_state,
		float8	lambda,
		float8	alpha,
		integer	dimension,
		float8	stepsize,
		integer	total_rows,
		float8 []	xmean,
		float8	ymean,
		float8	step_decay
	)

elastic_net_binomial_predict()

boolean elastic_net_binomial_predict	(	float8 []	coefficients,
		float8	intercept,
		float8 []	ind_var
	)

Parameters

coefficients	Logistic fitting coefficients
intercept	Logistic fitting intercept
ind_var	Features (independent variables)

returns a boolean value

elastic_net_binomial_prob()

float8 elastic_net_binomial_prob	(	float8 []	coefficients,
		float8	intercept,
		float8 []	ind_var
	)

Parameters

coefficients	Logistic fitting coefficients
intercept	Logistic fitting intercept
ind_var	Features (independent variables)

returns a double value, which is the probability of this data point being True class

elastic_net_gaussian_predict()

float8 elastic_net_gaussian_predict	(	float8 []	coefficients,
		float8	intercept,
		float8 []	ind_var
	)

Parameters

coefficients	Linear fitting coefficients
intercept	Linear fitting intercept
ind_var	Features (independent variables)

returns a double value

elastic_net_predict() [1/2]

void elastic_net_predict	(	text	tbl_model,
		text	tbl_new_source,
		text	col_id,
		text	tbl_predict
	)

Parameters

tbl_model	The result from elastic_net_train
tbl_new_source	Data table
col_id	Unique ID associated with each row
tbl_predict	Prediction result

elastic_net_predict() [2/2]

float8 elastic_net_predict	(	text	regress_family,
		float8 []	coefficients,
		float8	intercept,
		float8 []	ind_var
	)

Parameters

regress_family	model family
coefficients	The fitting coefficients
intercept	The fitting intercept
ind_var	Features (independent variables)

Returns

float8

Note: Please use function elastic_net_gaussian_predict() or elastic_net_binomial_predict() instead if you could. This may be deprecated in the future, as users are confused between this function and the table function with the same name.

When regress_family is 'binomial' or 'logistic', this function returns 1 for True and 0 for False

elastic_net_train() [1/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_dep_var,
		text	col_ind_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardize,
		text	grouping_col,
		text	optimizer,
		text	optimizer_params,
		text	excluded,
		integer	max_iter,
		float8	tolerance
	)

Parameters

tbl_source	Name of data source table
tbl_result	Name of the table to store the results
col_ind_var	Name of independent variable column, independent variable is an array
col_dep_var	Name of dependent variable column
regress_family	Response type (gaussian or binomial)
alpha	The elastic net parameter, [0, 1]
lambda_value	The regularization parameter
standardize	Whether to normalize the variables (default True)
grouping_col	List of columns on which to apply grouping (currently only a placeholder)
optimizer	The optimization algorithm, 'fista' or 'igd'. Default is 'fista'
optimizer_params	Parameters of the above optimizer, the format is 'arg = value, ...'. Default is NULL
excluded	Which columns to exclude? Default is NULL (applicable only if col_ind_var is set as * or a column of array, column names as 'col1, col2, ...' if col_ind_var is '*'; element indices as '1,2,3, ...' if col_ind_var is a column of array)
max_iter	Maximum number of iterations to run the algorithm (default value of 1000)
tolerance	Iteration stopping criteria. Default is 1e-6

elastic_net_train() [2/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization,
		text	grouping_columns,
		text	optimizer,
		text	optimizer_params,
		text	excluded,
		integer	max_iter
	)

elastic_net_train() [3/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization,
		text	grouping_columns,
		text	optimizer,
		text	optimizer_params,
		text	excluded
	)

elastic_net_train() [4/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization,
		text	grouping_columns,
		text	optimizer,
		text	optimizer_params
	)

elastic_net_train() [5/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization,
		text	grouping_columns,
		text	optimizer
	)

elastic_net_train() [6/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization,
		text	grouping_columns
	)

elastic_net_train() [7/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value,
		boolean	standardization
	)

elastic_net_train() [8/10]

void elastic_net_train	(	text	tbl_source,
		text	tbl_result,
		text	col_ind_var,
		text	col_dep_var,
		text	regress_family,
		float8	alpha,
		float8	lambda_value
	)

elastic_net_train() [9/10]

text elastic_net_train

(

)

elastic_net_train() [10/10]

text elastic_net_train

(

text

family_or_optimizer

)

Parameters

family_or_optimizer

Response type, 'gaussian' or 'binomial', or optimizer type