RLoadModel()
Loads an R model.
Synopsis
int RLoadModel( const string filename, string& model);
Parameters
| Parameter | Description |
|---|---|
| filename |
Model file name. The file can be saved in a directory of your choice. You can also choose the file extension. The default extension is "RData". You could, for example, also use: string out = "D:/Test/myNewModel1.myExtens"; |
| model | Return parameter for the model name. |
| userData | User data of the function call. The user data variable can be set to an integer value and be used to detect errors when calling R functions. Set the variable to an integer value and when the function is called and an error occurs, the specified integer value is returned. |
Return Value
The function returns 0 if it was successfully executed.
Description
Loads an R model. For information on random forest models, see https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Example
The example loads the model "D:/Test/myNewModel2.RData"; First the model is created via the RRandomForest_Train function. The function REvalExp evaluates an R expression to save the model in an output file. The model that was saved as a file can then be loaded via the RLoadModel function.
#uses "CtrlR"
main()
{
bool classification = TRUE;
int ntree = 5;
int mtry = 5;
float err_rate;
dyn_dyn_float confusion; //Return parameter of confusion matrix - see chapter Classification Wizard - Quality
dyn_dyn_float importance; //Return parameter of importance matrix - see chapter Classification Wizard - Quality
//Add data
dyn_float df1 = makeDynFloat(31,31,33,32,34,35,36); /* The size of the arrays must be identical. Each 7 items*/
dyn_float df2 = makeDynFloat(401,381,382,392,406,410,408); /* The size of the arrays must correspond to the header size */
dyn_float df3 = makeDynFloat(89,85,90,90,99,98,97);
dyn_float df4 = makeDynFloat(63,68,71,73,200,300,350);
dyn_float df5 = makeDynFloat(4,10,10,-4,8,7,8);
dyn_float labels = makeDynFloat(0,0,1,1,2,3,3); /*The number of labels must correspond to the number of array entries */
dyn_string headers = makeDynString ("Current", "Voltage", "Load","T_Increase", "T_Ambient");
/* The size of the headers must correspond to the number of array entries df1, df2.. */
string H_LINE = "***************************************************************************";
dyn_dyn_float ddf1;
dynAppend(ddf1,df1);
dynAppend(ddf1,df2);
dynAppend(ddf1,df3);
dynAppend(ddf1,df4);
dynAppend(ddf1,df5);
string rModelVar = "myModel2"; //r model variable
string err_desc; //Description variable for error handling
int UserData; /* See the description of the user data. This is the return parameter for the RGetLastErr - see below */
//Create a model via the function "RRandomForest_Train
int retV = RRandomForest_Train(rModelVar, headers, ddf1, labels, classification, ntree, mtry, err_rate, confusion, importance);
string out = "D:/Test/myNewModel2.RData"; //The concrete file for the model
REvalExp(0, "save(%var%, file = %var%)", rModelVar, out);
/*Evaluates the expression and saves the rModelVar in the out parameter */
DebugN("Value of the out parameter:", out);
string ModelName;
int h = RLoadModel(out, ModelName); /* Load the model. The function returns the name of the model. ModelName contains the name of the model */
DebugN("ModelName:",ModelName, "loaded:", h);
DebugN("Error rate:", err_rate, "Confusion matrix:", confusion, "Importance matrix:", importance);
//For information on confusion and importance matrices, see chapter Classification Wizard - Quality
blob at = RGetVarSerialized(rModelVar); //Retrieves the deserialized value of the R variable rModelVar.
if (RGetLastErr(err_desc, UserData, true) != 0) //Error handling
{
DebugN("Error occurred: " + err_desc);
return;
}
DebugN("Rmodelvar:", at);
DebugN(H_LINE);
int row_to_predict = 5;
int row_len = 5;
dyn_float values;
for(int i = 1; i <= row_len; i++)
{
int ret = dynAppend(values, ddf1[i][row_to_predict]); /*Add data to "values"-> Prepare values for the prediction function */
if( ret == -1 )
{
DebugN("Error! dynAppend to labels failed!");
return;
}
}
int prediction = RPredict("myModel2", headers, values); /* Calls the function "predict" of a loaded model and returns the prediction result.*/
//error handling
if (RGetLastErr(err_desc, UserData, true) != 0)
{
DebugTN("Error occurred: " + err_desc);
return;
}
DebugN("prediction=" + prediction);
DebugN("RPredict_test finished!");
DebugN(H_LINE);
}Assignment
Availability
See also
