turi::supervised::logistic_regression Class Reference

#include <toolkits/supervised_learning/logistic_regression.hpp>

Public Member Functions
	~logistic_regression ()
void	set_default_evaluation_metric () override
void	set_default_tracking_metric () override
void	model_specific_init (const ml_data &data, const ml_data &valid_data) override
bool	is_classifier () const override
void	init_options (const std::map< std::string, flexible_type > &_options) override
size_t	get_version () const override
void	train () override
void	set_coefs (const DenseVector &_coefs) override
void	save_impl (turi::oarchive &oarc) const override
void	load_version (turi::iarchive &iarc, size_t version) override
flexible_type	predict_single_example (const DenseVector &x, const prediction_type_enum &output_type=prediction_type_enum::NA) override
gl_sframe	fast_predict_topk (const std::vector< flexible_type > &rows, const std::string &missing_value_action="error", const std::string &output_type="", const size_t topk=5) override
flexible_type	predict_single_example (const SparseVector &x, const prediction_type_enum &output_type=prediction_type_enum::NA) override
void	get_coefficients (DenseVector &_coefs) const
std::shared_ptr< coreml::MLModelWrapper >	export_to_coreml () override
std::vector< std::vector< flexible_type > >	get_metadata_mapping ()
virtual flexible_type	predict_single_example (const ml_data_iterator &it, const prediction_type_enum &output_type=prediction_type_enum::NA)
virtual std::map< std::string, variant_type >	evaluate (const ml_data &test_data, const std::string &evaluation_type="", bool with_prediction=false)
virtual std::map< std::string, variant_type >	evaluate (const sframe &X, const sframe &y, const std::string &evaluation_type="", bool with_prediction=false)
virtual std::shared_ptr< sarray< flexible_type > >	predict (const ml_data &test_data, const std::string &output_type="")
virtual std::shared_ptr< sarray< flexible_type > >	predict (const sframe &X, const std::string &output_type="")
virtual std::shared_ptr< sarray< flexible_type > >	extract_features (const sframe &X, ml_missing_value_action missing_value_action)
virtual sframe	predict_topk (const sframe &test_data, const std::string &output_type="", size_t topk=5)
virtual sframe	predict_topk (const ml_data &test_data, const std::string &output_type="", size_t topk=5)
virtual sframe	classify (const ml_data &test_data, const std::string &output_type="")
virtual sframe	classify (const sframe &X, const std::string &output_type="")
virtual gl_sarray	fast_predict (const std::vector< flexible_type > &rows, const std::string &missing_value_action="error", const std::string &output_type="")
virtual gl_sframe	fast_classify (const std::vector< flexible_type > &rows, const std::string &missing_value_action="error")
virtual void	init (const sframe &X, const sframe &y, const sframe &valid_X=sframe(), const sframe &valid_y=sframe(), ml_missing_value_action mva=ml_missing_value_action::ERROR)
void	set_evaluation_metric (std::vector< std::string > _metrics)
void	set_tracking_metric (std::vector< std::string > _metrics)
void	set_more_warnings (bool more_warnings)
std::map< std::string, flexible_type >	get_train_stats () const
sframe	impute_missing_columns_using_current_metadata (const sframe &X) const
ml_data	construct_ml_data_using_current_metadata (const sframe &X, const sframe &y, ml_missing_value_action mva=ml_missing_value_action::ERROR) const
ml_data	construct_ml_data_using_current_metadata (const sframe &X, ml_missing_value_action mva=ml_missing_value_action::ERROR) const
size_t	num_features () const
size_t	num_examples () const
size_t	num_unpacked_features () const
std::vector< std::string >	get_feature_names () const
std::string	get_target_name () const
std::shared_ptr< ml_metadata >	get_ml_metadata () const
bool	is_dense ()
std::vector< std::string >	get_metrics () const
std::vector< std::string >	get_tracking_metrics () const
std::string	get_metric_display_name (const std::string &metric) const
void	display_regression_training_summary (std::string model_display_name) const
void	display_classifier_training_summary (std::string model_display_name, bool simple_mode=false) const
virtual bool	support_missing_value () const
void	api_train (gl_sframe data, const std::string &target, const variant_type &validation_data, const std::map< std::string, flexible_type > &_options)
gl_sarray	api_predict (gl_sframe data, std::string missing_value_action, std::string output_type)
gl_sframe	api_predict_topk (gl_sframe data, std::string missing_value_action, std::string output_type, size_t topk=5)
gl_sframe	api_classify (gl_sframe data, std::string missing_value_action, std::string output_type)
variant_map_type	api_evaluate (gl_sframe data, std::string missing_value_action, std::string metric, gl_sarray predictions=gl_sarray(), bool with_prediction=false)
gl_sarray	api_extract_features (gl_sframe data, std::string missing_value_action)
std::vector< std::string >	list_fields ()
const variant_type &	get_value_from_state (std::string key)
const std::map< std::string, flexible_type > &	get_current_options () const
std::map< std::string, flexible_type >	get_default_options () const
const flexible_type &	get_option_value (const std::string &name) const
const std::map< std::string, variant_type > &	get_state () const
bool	is_trained () const
void	set_options (const std::map< std::string, flexible_type > &_options)
void	add_or_update_state (const std::map< std::string, variant_type > &dict)
const std::vector< option_handling::option_info > &	get_option_info () const
virtual const char *	name ()=0
virtual const std::string &	uid ()=0
void	save_to_url (const std::string &url, const variant_map_type &side_data={})
void	save_model_to_data (std::ostream &out)
const std::map< std::string, std::vector< std::string > > &	list_functions ()
const std::vector< std::string > &	list_get_properties ()
const std::vector< std::string > &	list_set_properties ()
variant_type	call_function (const std::string &function, variant_map_type argument)
variant_type	get_property (const std::string &property)
variant_type	set_property (const std::string &property, variant_map_type argument)
const std::string &	get_docstring (const std::string &symbol)
virtual void	perform_registration ()

Protected Member Functions
void	register_defaults (const std::string &fnname, const variant_map_type &arguments)
void	register_function (std::string fnname, const std::vector< std::string > &arguments, impl_fn fn)
void	register_setter (const std::string &propname, impl_fn setfn)
void	register_getter (const std::string &propname, impl_fn getfn)
void	register_docstring (const std::pair< std::string, std::string > &fnname_docstring)

Protected Attributes
Eigen::Matrix< double, Eigen::Dynamic, 1 >	coefs
size_t	num_classes = 0
size_t	num_coefficients = 0
std::map< std::string, variant_type >	state

Detailed Description

Logistic regression model class definition.

Definition at line 35 of file logistic_regression.hpp.

Constructor & Destructor Documentation

~logistic_regression()

turi::supervised::logistic_regression::~logistic_regression

(

)

Destructor. Make sure bad things don't happen

Member Function Documentation

add_or_update_state()

void turi::ml_model_base::add_or_update_state ( const std::map< std::string, variant_type > &  dict )

inherited

Append the key value store of the model.

Parameters

[in]

dict

Options (Key-Value pairs) to set

api_classify()

gl_sframe turi::supervised::supervised_learning_model_base::api_classify ( gl_sframe  data, std::string  missing_value_action, std::string  output_type  )

inherited

API interface through the unity server.

Run classification.

api_evaluate()

variant_map_type turi::supervised::supervised_learning_model_base::api_evaluate ( gl_sframe  data, std::string  missing_value_action, std::string  metric, gl_sarray  predictions = gl_sarray(), bool  with_prediction = false  )

inherited

API interface through the unity server.

Evaluate the model

api_extract_features()

gl_sarray turi::supervised::supervised_learning_model_base::api_extract_features ( gl_sframe  data, std::string  missing_value_action  )

inherited

API interface through the unity server.

Extract features!

api_predict()

gl_sarray turi::supervised::supervised_learning_model_base::api_predict ( gl_sframe  data, std::string  missing_value_action, std::string  output_type  )

inherited

API interface through the unity server.

Run prediction.

api_predict_topk()

gl_sframe turi::supervised::supervised_learning_model_base::api_predict_topk ( gl_sframe  data, std::string  missing_value_action, std::string  output_type, size_t  topk = 5  )

inherited

API interface through the unity server.

Run multiclass prediction.

api_train()

void turi::supervised::supervised_learning_model_base::api_train ( gl_sframe  data, const std::string &  target, const variant_type &  validation_data, const std::map< std::string, flexible_type > &  _options  )

inherited

API interface through the unity server.

Train the model

call_function()

variant_type turi::model_base::call_function ( const std::string &  function, variant_map_type  argument  )

inherited

Calls a user defined function.

classify() [1/2]

virtual sframe turi::supervised::supervised_learning_model_base::classify ( const ml_data &  test_data, const std::string &  output_type = ""  )

virtualinherited

Make classification using a trained supervised_learning model.

Parameters

[in]	X	Test data (only independent variables)
[in]	output_type	Type of classifcation (future proof).

Returns

ret SFrame with "class" and probability (if applicable)

Note

Already assumes that data is of the right shape.

Reimplemented in turi::supervised::linear_svm.

classify() [2/2]

virtual sframe turi::supervised::supervised_learning_model_base::classify ( const sframe &  X, const std::string &  output_type = ""  )

inlinevirtualinherited

Same as classify(ml_data), but takes SFrame as input.

Definition at line 361 of file supervised_learning.hpp.

construct_ml_data_using_current_metadata() [1/2]

ml_data turi::supervised::supervised_learning_model_base::construct_ml_data_using_current_metadata ( const sframe &  X, const sframe &  y, ml_missing_value_action  mva = ml_missing_value_action::ERROR  ) const

inherited

Construct ml-data from the predictors and target using the current value of the metadata.

Parameters

[in]	X	Predictors
[in]	y	target
[in]	new_opts	Additional options.

Returns

A constructed ml_data object

construct_ml_data_using_current_metadata() [2/2]

ml_data turi::supervised::supervised_learning_model_base::construct_ml_data_using_current_metadata ( const sframe &  X, ml_missing_value_action  mva = ml_missing_value_action::ERROR  ) const

inherited

Construct ml-data from the predictors using the current value of the metadata.

Parameters

[in]	X	Predictors
[in]	new_opts	Additional options.

Returns

A constructed ml_data object

display_classifier_training_summary()

void turi::supervised::supervised_learning_model_base::display_classifier_training_summary ( std::string  model_display_name, bool  simple_mode = false  ) const

inherited

Display model training data summary for classifier.

Parameters

[in]

model_display_name

Name to be displayed

display_regression_training_summary()

void turi::supervised::supervised_learning_model_base::display_regression_training_summary ( std::string  model_display_name ) const

inherited

Display model training data summary for regression.

Parameters

[in]

model_display_name

Name to be displayed

evaluate() [1/2]

virtual std::map<std::string, variant_type> turi::supervised::supervised_learning_model_base::evaluate ( const ml_data &  test_data, const std::string &  evaluation_type = "", bool  with_prediction = false  )

virtualinherited

Evaluate the model.

Parameters

[in]	test_data	Test data.
[in]	evaluation_type	Evalution type.

Note

Already assumes that data is of the right shape. Test data must contain target column also.

Reimplemented in turi::supervised::xgboost::xgboost_model.

evaluate() [2/2]

virtual std::map<std::string, variant_type> turi::supervised::supervised_learning_model_base::evaluate ( const sframe &  X, const sframe &  y, const std::string &  evaluation_type = "", bool  with_prediction = false  )

inlinevirtualinherited

Same as evaluate(ml_data), but take SFrame as input.

Definition at line 278 of file supervised_learning.hpp.

export_to_coreml()

std::shared_ptr<coreml::MLModelWrapper> turi::supervised::logistic_regression::export_to_coreml ( )

overridevirtual

Export to CoreML.

Implements turi::supervised::supervised_learning_model_base.

extract_features()

virtual std::shared_ptr<sarray<flexible_type> > turi::supervised::supervised_learning_model_base::extract_features ( const sframe &  X, ml_missing_value_action  missing_value_action  )

inlinevirtualinherited

Extract features!

Reimplemented in turi::supervised::xgboost::xgboost_model.

Definition at line 309 of file supervised_learning.hpp.

fast_classify()

virtual gl_sframe turi::supervised::supervised_learning_model_base::fast_classify ( const std::vector< flexible_type > &  rows, const std::string &  missing_value_action = "error"  )

virtualinherited

Fast path predictions given a row of flexible_types

Parameters

[in]	rows	List of rows (each row is a flex_dict)
[in]	output_type	Output type.

Reimplemented in turi::supervised::linear_svm.

fast_predict()

virtual gl_sarray turi::supervised::supervised_learning_model_base::fast_predict ( const std::vector< flexible_type > &  rows, const std::string &  missing_value_action = "error", const std::string &  output_type = ""  )

virtualinherited

Fast path predictions given a row of flexible_types.

Parameters

[in]	rows	List of rows (each row is a flex_dict)
[in]	missing_value_action	Missing value action string
[in]	output_type	Output type.

Reimplemented in turi::supervised::xgboost::xgboost_model.

fast_predict_topk()

gl_sframe turi::supervised::logistic_regression::fast_predict_topk ( const std::vector< flexible_type > &  rows, const std::string &  missing_value_action = "error", const std::string &  output_type = "", const size_t  topk = 5  )

overridevirtual

Fast path predictions given a row of flexible_types.

Parameters

[in]	rows	List of rows (each row is a flex_dict)
[in]	output_type	Output type.

Reimplemented from turi::supervised::supervised_learning_model_base.

get_coefficients()

void turi::supervised::logistic_regression::get_coefficients ( DenseVector &  _coefs ) const

inline

Get coefficients for a trained model.

Definition at line 168 of file logistic_regression.hpp.

get_current_options()

const std::map<std::string, flexible_type>& turi::ml_model_base::get_current_options ( ) const

inherited

Get current options.

Returns

Dictionary containing current options.

Python side interface

Interfaces with the get_current_options function in the Python side.

get_default_options()

std::map<std::string, flexible_type> turi::ml_model_base::get_default_options ( ) const

inherited

Get default options.

Returns

Dictionary with default options.

Python side interface

Interfaces with the get_default_options function in the Python side.

get_docstring()

const std::string& turi::model_base::get_docstring ( const std::string &  symbol )

inherited

Returns the toolkit documentation for a function or property.

get_feature_names()

std::vector<std::string> turi::supervised::supervised_learning_model_base::get_feature_names ( ) const

inherited

Get names of predictor variables.

Returns

Names of features (Vector of string names).

get_metadata_mapping()

std::vector<std::vector<flexible_type> > turi::supervised::supervised_learning_model_base::get_metadata_mapping ( )

inherited

Get metadata mapping.

get_metric_display_name()

std::string turi::supervised::supervised_learning_model_base::get_metric_display_name ( const std::string &  metric ) const

inherited

Get metric display name.

get_metrics()

std::vector<std::string> turi::supervised::supervised_learning_model_base::get_metrics ( ) const

inherited

Get metrics strings.

get_ml_metadata()

std::shared_ptr<ml_metadata> turi::supervised::supervised_learning_model_base::get_ml_metadata ( ) const

inlineinherited

Get ml_metadata.

Returns

Get the ml_metadata.

Definition at line 564 of file supervised_learning.hpp.

get_option_info()

const std::vector<option_handling::option_info>& turi::ml_model_base::get_option_info ( ) const

inherited

Returns the option information struct for each of the set parameters.

get_option_value()

const flexible_type& turi::ml_model_base::get_option_value ( const std::string &  name ) const

inherited

Returns the value of an option. Throws an error if the option does not exist.

Parameters

[in]

name

Name of the option to get.

get_property()

variant_type turi::model_base::get_property ( const std::string &  property )

inherited

Reads a property.

get_state()

const std::map<std::string, variant_type>& turi::ml_model_base::get_state ( ) const

inherited

Get model.

Returns

Model map.

get_target_name()

std::string turi::supervised::supervised_learning_model_base::get_target_name ( ) const

inherited

Get name of the target column.

Returns

Names of target.

get_tracking_metrics()

std::vector<std::string> turi::supervised::supervised_learning_model_base::get_tracking_metrics ( ) const

inherited

Get tracking metrics strings.

get_train_stats()

std::map<std::string, flexible_type> turi::supervised::supervised_learning_model_base::get_train_stats ( ) const

inherited

Get training stats.

Returns

The training stats map.

Python side interface

The dictionary returned to the user can be transfered as is to the python side. You MUST use this to return a dictionary to the object.

get_value_from_state()

const variant_type& turi::ml_model_base::get_value_from_state ( std::string  key )

inherited

Returns the value of a particular key from the state.

Returns

Value of a key model_base for details.

Python side interface

From the python side, this is interfaced with the get() function or the [] operator in python.

get_version()

size_t turi::supervised::logistic_regression::get_version ( ) const

overridevirtual

Gets the model version number

Reimplemented from turi::model_base.

impute_missing_columns_using_current_metadata()

sframe turi::supervised::supervised_learning_model_base::impute_missing_columns_using_current_metadata ( const sframe &  X ) const

inherited

Impute missing columns with 'None' values.

Parameters

[in]

X

Predictors

Returns

An SFrame with 'None' written to all columns that are missing.

init()

virtual void turi::supervised::supervised_learning_model_base::init ( const sframe &  X, const sframe &  y, const sframe &  valid_X = sframe(), const sframe &  valid_y = sframe(), ml_missing_value_action  mva = ml_missing_value_action::ERROR  )

virtualinherited

Methods with already meaningful default implementations.

Init the model with the data.

Parameters

[in]	X	Predictors
[in]	y	target

Python side interface

NA.

init_options()

void turi::supervised::logistic_regression::init_options ( const std::map< std::string, flexible_type > &  _options )

overridevirtual

Initialize the options.

Parameters

[in]

_options

Options to set

Reimplemented from turi::ml_model_base.

is_classifier()

bool turi::supervised::logistic_regression::is_classifier ( ) const

inlineoverridevirtual

Returns true if the model is a classifier.

Implements turi::supervised::supervised_learning_model_base.

Definition at line 92 of file logistic_regression.hpp.

is_dense()

bool turi::supervised::supervised_learning_model_base::is_dense ( )

inlineinherited

Returns true if the model is a classifier.

Definition at line 576 of file supervised_learning.hpp.

is_trained()

bool turi::ml_model_base::is_trained ( ) const

inherited

Is this model trained.

Returns

True if already trained.

list_fields()

std::vector<std::string> turi::ml_model_base::list_fields ( )

inherited

Methods with already meaningful default implementations.

Lists all the keys accessible in the "model" map.

Returns

List of keys in the model map. model_base for details.

Python side interface

This is the function that the list_fields should call in python.

list_functions()

const std::map<std::string, std::vector<std::string> >& turi::model_base::list_functions ( )

inherited

Lists all the registered functions. Returns a map of function name to array of argument names for the function.

list_get_properties()

const std::vector<std::string>& turi::model_base::list_get_properties ( )

inherited

Lists all the get-table properties of the class.

list_set_properties()

const std::vector<std::string>& turi::model_base::list_set_properties ( )

inherited

Lists all the set-table properties of the class.

load_version()

void turi::supervised::logistic_regression::load_version ( turi::iarchive &  iarc, size_t  version  )

overridevirtual

Load the object

Reimplemented from turi::model_base.

model_specific_init()

void turi::supervised::logistic_regression::model_specific_init ( const ml_data &  data, const ml_data &  valid_data  )

overridevirtual

Initialize things that are specific to your model.

Parameters

[in]

data

ML-Data object created by the init function.

Reimplemented from turi::supervised::supervised_learning_model_base.

name()

virtual const char* turi::model_base::name ( )

pure virtualinherited

Returns the name of the toolkit class, as exposed to client code. For example, the Python proxy for this instance will have a type with this name.

Note: this function is typically overridden using the BEGIN_CLASS_MEMBER_REGISTRATION macro.

num_examples()

size_t turi::supervised::supervised_learning_model_base::num_examples ( ) const

inherited

Get the number of examples in the model

Returns

Number of examples.

num_features()

size_t turi::supervised::supervised_learning_model_base::num_features ( ) const

inherited

Get the number of feature columns in the model

Returns

Number of features.

num_unpacked_features()

size_t turi::supervised::supervised_learning_model_base::num_unpacked_features ( ) const

inherited

Get the number of features in the model (unpacked)

Returns

Number of features.

perform_registration()

virtual void turi::model_base::perform_registration ( )

virtualinherited

Declare the base registration function. This class has to be handled specially; the macros don't work here due to the override declarations.

Reimplemented in turi::model_proxy.

predict() [1/2]

virtual std::shared_ptr<sarray<flexible_type> > turi::supervised::supervised_learning_model_base::predict ( const ml_data &  test_data, const std::string &  output_type = ""  )

virtualinherited

Make predictions using a trained supervised_learning model.

Parameters

[in]	test_X	Test data (only independent variables)
[in]	output_type	Type of prediction.

Returns

ret Shared pointer to an SArray containing predicions.

Note

Already assumes that data is of the right shape.

Reimplemented in turi::supervised::xgboost::xgboost_model.

predict() [2/2]

virtual std::shared_ptr<sarray<flexible_type> > turi::supervised::supervised_learning_model_base::predict ( const sframe &  X, const std::string &  output_type = ""  )

inlinevirtualinherited

Same as predict(ml_data), but takes SFrame as input.

Definition at line 299 of file supervised_learning.hpp.

predict_single_example() [1/3]

flexible_type turi::supervised::logistic_regression::predict_single_example ( const DenseVector &  x, const prediction_type_enum &  output_type = prediction_type_enum::NA  )

overridevirtual

Predict for a single example.

Parameters

[in]	x	Single example.
[in]	output_type	Type of prediction.

Returns

Prediction for a single example.

Reimplemented from turi::supervised::supervised_learning_model_base.

predict_single_example() [2/3]

flexible_type turi::supervised::logistic_regression::predict_single_example ( const SparseVector &  x, const prediction_type_enum &  output_type = prediction_type_enum::NA  )

overridevirtual

Predict for a single example.

Parameters

[in]	x	Single example.
[in]	output_type	Type of prediction.

Returns

Prediction for a single example.

Reimplemented from turi::supervised::supervised_learning_model_base.

predict_single_example() [3/3]

virtual flexible_type turi::supervised::supervised_learning_model_base::predict_single_example ( const ml_data_iterator &  it, const prediction_type_enum &  output_type = prediction_type_enum::NA  )

inlinevirtualinherited

Methods with default implementations but are in-flux during the

Trees and NeuralNetworks integration

Predict for a single example.

Parameters

[in]	x	Single example.
[in]	output_type	Type of prediction.

Returns

Prediction for a single example.

Definition at line 226 of file supervised_learning.hpp.

predict_topk() [1/2]

virtual sframe turi::supervised::supervised_learning_model_base::predict_topk ( const sframe &  test_data, const std::string &  output_type = "", size_t  topk = 5  )

inlinevirtualinherited

Make multiclass predictions using a trained supervised_learning model.

Parameters

[in]	test_X	Test data (only independent variables)
[in]	output_type	Type of prediction.
[in]	topk	Number of classes to return.

Returns

ret SFrame containing {row_id, class, output_type}.

Note

Already assumes that data is of the right shape.

Default throws error, model supporting this method should override this function.

Definition at line 326 of file supervised_learning.hpp.

predict_topk() [2/2]

virtual sframe turi::supervised::supervised_learning_model_base::predict_topk ( const ml_data &  test_data, const std::string &  output_type = "", size_t  topk = 5  )

virtualinherited

Make multiclass predictions using a trained supervised_learning model.

Parameters

[in]	test_X	Test data (only independent variables)
[in]	output_type	Type of prediction.
[in]	topk	Number of classes to return.

Returns

ret SFrame containing {row_id, class, output_type}.

Note

Already assumes that data is of the right shape.

Reimplemented in turi::supervised::xgboost::xgboost_model.

register_defaults()

void turi::model_base::register_defaults ( const std::string &  fnname, const variant_map_type &  arguments  )

protectedinherited

Registers default argument values

register_docstring()

void turi::model_base::register_docstring ( const std::pair< std::string, std::string > &  fnname_docstring )

protectedinherited

Adds a docstring for the specified function or property name.

register_function()

void turi::model_base::register_function ( std::string  fnname, const std::vector< std::string > &  arguments, impl_fn  fn  )

protectedinherited

Adds a function with the specified name, and argument list.

register_getter()

void turi::model_base::register_getter ( const std::string &  propname, impl_fn  getfn  )

protectedinherited

Adds a property getter with the specified name.

register_setter()

void turi::model_base::register_setter ( const std::string &  propname, impl_fn  setfn  )

protectedinherited

Adds a property setter with the specified name.

save_impl()

void turi::supervised::logistic_regression::save_impl ( turi::oarchive &  oarc ) const

overridevirtual

Serialize the object.

Reimplemented from turi::model_base.

save_model_to_data()

void turi::model_base::save_model_to_data ( std::ostream &  out )

inherited

Save a toolkit class to a data stream.

save_to_url()

void turi::model_base::save_to_url ( const std::string &  url, const variant_map_type &  side_data = {}  )

inherited

Save a toolkit class to disk.

Parameters

url	The destination url to store the class.
sidedata	Any additional side information

set_coefs()

void turi::supervised::logistic_regression::set_coefs ( const DenseVector &  _coefs )

overridevirtual

Setter for model coefficieints.

Reimplemented from turi::supervised::supervised_learning_model_base.

set_default_evaluation_metric()

void turi::supervised::logistic_regression::set_default_evaluation_metric ( )

inlineoverridevirtual

Set the default evaluation metric during model evaluation..

Reimplemented from turi::supervised::supervised_learning_model_base.

Definition at line 62 of file logistic_regression.hpp.

set_default_tracking_metric()

void turi::supervised::logistic_regression::set_default_tracking_metric ( )

inlineoverridevirtual

Set the default evaluation metric for progress tracking.

Reimplemented from turi::supervised::supervised_learning_model_base.

Definition at line 78 of file logistic_regression.hpp.

set_evaluation_metric()

void turi::supervised::supervised_learning_model_base::set_evaluation_metric ( std::vector< std::string >  _metrics )

inlineinherited

Set the evaluation metric. Set to RMSE by default.

Definition at line 439 of file supervised_learning.hpp.

set_more_warnings()

void turi::supervised::supervised_learning_model_base::set_more_warnings ( bool  more_warnings )

inlineinherited

Set the Extra Warnings output. These warnings include telling the user about low-variance features, etc...

Definition at line 455 of file supervised_learning.hpp.

set_options()

void turi::ml_model_base::set_options ( const std::map< std::string, flexible_type > &  _options )

inherited

Set one of the options in the algorithm.

The value are checked with the requirements given by the option instance.

Parameters

[in]	name	Name of the option.
[in]	value	Value for the option.

set_property()

variant_type turi::model_base::set_property ( const std::string &  property, variant_map_type  argument  )

inherited

Sets a property. The new value of the property should appear in the argument map under the key "value".

set_tracking_metric()

void turi::supervised::supervised_learning_model_base::set_tracking_metric ( std::vector< std::string >  _metrics )

inlineinherited

Set the evaluation metric. Set to RMSE by default.

Definition at line 447 of file supervised_learning.hpp.

support_missing_value()

virtual bool turi::supervised::supervised_learning_model_base::support_missing_value ( ) const

inlinevirtualinherited

Returns true if the model can handle missing value

Reimplemented in turi::supervised::xgboost::xgboost_model.

Definition at line 628 of file supervised_learning.hpp.

train()

void turi::supervised::logistic_regression::train ( )

overridevirtual

Train a regression model.

Implements turi::supervised::supervised_learning_model_base.

uid()

virtual const std::string& turi::model_base::uid ( )

pure virtualinherited

Returns a unique identifier for the toolkit class. It can be any unique ID. The UID is only used at runtime (to determine the concrete type of an arbitrary model_base instance) and is never stored.

Note: this function is typically overridden using the BEGIN_CLASS_MEMBER_REGISTRATION macro.

Implemented in turi::model_proxy.

Member Data Documentation

coefs

Eigen::Matrix<double, Eigen::Dynamic,1> turi::supervised::logistic_regression::coefs

protected

Coefs

Definition at line 42 of file logistic_regression.hpp.

num_classes

size_t turi::supervised::logistic_regression::num_classes = 0

protected

fast access: num classes

Definition at line 45 of file logistic_regression.hpp.

num_coefficients

size_t turi::supervised::logistic_regression::num_coefficients = 0

protected

fast access: num coefs

Definition at line 46 of file logistic_regression.hpp.

state

std::map<std::string, variant_type> turi::ml_model_base::state

protectedinherited

All things python

Definition at line 206 of file ml_model.hpp.

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

• toolkits/supervised_learning/logistic_regression.hpp