hanaml.NaiveBayes.Rd
hanaml.NaiveBayes is a R wrapper for SAP HANA PAL Naive Bayes.
hanaml.NaiveBayes(
data = NULL,
key = NULL,
features = NULL,
label = NULL,
formula = NULL,
alpha = NULL,
discretization = NULL,
model.format = NULL,
categorical.variable = NULL,
thread.ratio = NULL,
resampling.method = NULL,
evaluation.metric = NULL,
fold.num = NULL,
repeat.times = NULL,
param.search.strategy = NULL,
random.search.times = NULL,
random.state = NULL,
timeout = NULL,
progress.indicator.id = NULL,
parameter.range = NULL,
parameter.values = NULL,
reduction.rate = NULL,
aggressive.elimination = NULL
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
If not provided, the data is assumed to have no ID column.
No default value.
character of list of characters, optional
Name of feature columns.
If not provided, it defaults all non-key, non-label columns of data.
character, optional
Name of the column which specifies the dependent variable.
Defaults to the last column of data if not provided.
formula type, optional
Formula to be used for model generation.
format = label~<feature_list>
e.g.: formula=CATEGORY~V1+V2+V3
You can either give the formula,
or a feature and label combination, but do not provide both.
Defaults to NULL.
double, optional
Laplace smoothing value. Set a positive value to enable Laplace smoothing
for categorical variables and use that value as the smoothing parameter.
Set value 0 to disable Laplace smoothing.
Defaults to 0.
c("no", "supervised"), optional
Discretize continuous attributes.
"no": disable discretization.
"supervised": use supervised discretization on all the continuous attributes.
Defaults to "no".
c("json", "pmml"), optional
Controls whether to output the model in JSON format or PMML format.
"json": JSON format.
"pmml": PMML format. Defaults to json.
Defaults to "json".
character or list/vector of characters, optional
Indicates features should be treated as categorical variable.
The default behavior is dependent on what input is given:
"VARCHAR" and "NVARCHAR": categorical
"INTEGER" and "DOUBLE": continuous.
VALID only for variables of "INTEGER" type, omitted otherwise.
No default value.
double, optional
Controls the proportion of available threads that can be used by this
function.
The value range is from 0 to 1, where 0 indicates a single thread,
and 1 indicates all available threads.
Values between 0 and 1 will use up to
that percentage of available threads.Values outside this
range are ignored.
Defaults to 0.
character, optional
Specifies the resampling method for model evaluation or parameter selection.
Valid options are listed as follows:
"cv", "stratified_cv", "bootstrap", "stratified_bootstrap",
"cv_sha", "stratified_cv_sha", "bootstrap_sha", "stratified_bootstrap_sha",
"cv_hyperband", "stratified_cv_hyperband", "bootstrap_hyperband",
"stratified_bootstrap_hyperband".
Note that resampling methods with suffix "sha" or "hyperband" are only applicable
to parameter selection, not model evaluation.
If no value is specified, neither model evaluation
nor parameter selection is activated.
No default value.
character, optional
Specifies the evaluation metric for model evaluation or parameter selection.
Must be specified together with resampling.method
to activate model evaluation
or parameter selection.
Currently valid evaluation metrics include: "accuracy", "f1_score", "auc".
Must be specified together with resampling.method
to activate
model evaluation or parameter selection.
No default value.
integer, optional
Specifies the fold number for the cross-validation(cv).
Mandatory and valid only when resampling.method
is specified and contains
"cv" as substring, e.g. "stratified_cv", "cv_hyperband".
numeric, optional
Specifies the number of repeat times for resampling.
Defaults to 1.
c("grid", "random"), optional
Specifies the method to activate parameter selection.
If not specified, model parameter selection shall not be triggered.
Defaults to "random" and cannot be changed if resampling.method
is set as one of the following: "cv_hyperband", "bootstrap_hyperband",
"stratified_cv_hyperband", "stratified_bootstrap_hyperband"; otherwise no
default value.
integer, optional
Specifies the number of times to randomly select candidate parameters for selection.
Mandatory and valid only when param.search.strategy
is set as "random", or when
resampling.method
is set as one of the following: "cv_hyperband", "bootstrap_hyperband",
"stratified_cv_hyperband", "stratified_bootstrap_hyperband".
numeric, optional
Specifies the seed for random generation.
Use system time when 0 is specified.
integer, optional
Specifies maximum running time for model evaluation or parameter selection in seconds.
No timeout when 0 is specified.
character, optional
Sets an ID of progress indicator for model evaluation or parameter selection.
No progress indicator is active if no value is provided.
list, optional
Specifies range of the following parameter for parameter selection:alpha
.
Parameter range should be specified by 3 numbers in the form of c(start, step, end).
Examples:
, which means taking
alpha
values from 0.01 to 0.1 with 0.01 being the step size, i.e.
0.01, 0.02, 0.03, ..., 0.09, 0.1.
If param.search.strategy
is 'random', then the middle term,
i.e. step has no effect and thus can be omitted.
list, optional
Specifies values of the following parameter for parameter selection:alpha
.
Example:
.
numeric, optional
Specifies the reduction rate of available size of hyper-parameter candidates.
For each round, the available parameter candidate size will be divided by value of this parameter.
Thus valid value for this parameter must be greater than 1.0
Defaults to 3.0.
logical, optional
Specifies whether to perform aggressive elimination behavior for successive-halving algorithm or not.
When set to TRUE, it will eliminate more parameter candidates than
expected(defined via reduction.rate
).
This can enhance the run-time performance but could result in sub-optimal hyper-parameter candidate.
Valid only when resampling.method
is specified with suffix "sha".
Defaults to FALSE.
Returns an R6 object of class "NaiveBayes", with following attributes and methods:
Attributes
model: DataFrame
Naive Bayes model information.
statistics: DataFrame
Statistics information.
optim.param: DataFrame
Selected optimal parameters.
Methods
CreateModelState(model=NULL, algorithm=NULL, func=NULL, state.description="ModelState", force=FALSE)
Usage:
> nb <- hanaml.NaiveBayes(data=df)
> nb$CreateModelState()
Arguments:
model: DataFrame
DataFrame containing the model for parsing.
Defaults to self$model
.
algorithm: character
Specifies the PAL algorithm associated with model
.
Defaults to self$pal.algorithm
.
func: character
Specifies the functionality for Unified Classification/Regression.
Valid only for object instance of R6Class "UnifiedClassification" or "UnifiedRegression".
Defaults to self$func
.
state.description: character
A summary string for the generated model state.
Defaults to "ModelState".
force: logic
Specifies whether or not the replace existing state for model
.
Defaults to FALSE.
After calling this method, an attribute state
that contains the parsed info for model
shall be assigned
to the corresponding R6 object.
DeleteModelState(state=NULL)
Usage:
Assuming we have trained a hanaml
model and created its model state, like the following:
> nb <- hanaml.NaiveBayes(data=df)
> nb$CreateModelState()
After using the model state for real-time scoring, we can delete the state by calling:
> nb$DelateModelState()
Arguments:
state: DataFrame
DataFrame containing the state info.
Defaults to self$state
.
After calling this method, the specified model state shall be cleaned up and associated memory be released.
Naive Bayes is a classification algorithm based on Bayes theorem. It estimates the class-conditional probability by assuming that the attributes are conditionally independent of one another.
The Laplace value (alpha) is only stored by JSON format models. If the PMML format is chosen, you may need to set the Laplace value (alpha) again in predict() and score().
Input DataFrame df:
> df$Collect()
ID HOMEOWNER MARITALSTATUS ANNUALINCOME DEFAULTEDBORROWER
1 0 YES Single 125.0 NO
2 1 NO Married 100.0 NO
3 2 NO Single 70.0 NO
4 3 YES Married 120.0 NO
5 4 NO Divorced 95.0 YES
6 5 NO Married 60.0 NO
7 6 YES Divorced 220.0 NO
8 7 NO Single 85.0 YES
9 8 NO Married 75.0 NO
10 9 NO Single 90.0 YES
Call the function:
> nb <- hanaml.NaiveBayes(data = df, alpha = 1.0,
model.format = "pmml", thread.ratio = 0.2,
features = list("HOMEOWNER", "MARITALSTATUS", "ANNUALINCOME"),
label = "DEFAULTEDBORROWER")