/**
* Initialize the ModelBuilder, validating all arguments and preparing the training frame. This
* call is expected to be overridden in the subclasses and each subclass will start with
* "super.init();". This call is made by the front-end whenever the GUI is clicked, and needs to
* be fast; heavy-weight prep needs to wait for the trainModel() call.
*/
@Override
public void init(boolean expensive) {
super.init(expensive);
// Initialize local variables
if (!(0.0 < _parms._sample_rate && _parms._sample_rate <= 1.0))
throw new IllegalArgumentException(
"Sample rate should be interval (0,1> but it is " + _parms._sample_rate);
if (_parms._mtries < 1 && _parms._mtries != -1)
error(
"_mtries",
"mtries must be -1 (converted to sqrt(features)), or >= 1 but it is " + _parms._mtries);
if (_train != null) {
int ncols = _train.numCols();
if (_parms._mtries != -1 && !(1 <= _parms._mtries && _parms._mtries < ncols))
error(
"_mtries",
"Computed mtries should be -1 or in interval <1,#cols> but it is " + _parms._mtries);
}
if (_parms._sample_rate == 1f && _valid == null)
error(
"_sample_rate",
"Sample rate is 100% and no validation dataset is specified. There are no OOB data to compute out-of-bag error estimation!");
if (hasOffset()) error("_offset_column", "Offsets are not yet supported for DRF.");
if (hasOffset() && isClassifier()) {
error("_offset_column", "Offset is only supported for regression.");
}
}
/**
* Initialize the ModelBuilder, validating all arguments and preparing the training frame. This
* call is expected to be overridden in the subclasses and each subclass will start with
* "super.init();". This call is made by the front-end whenever the GUI is clicked, and needs to
* be fast; heavy-weight prep needs to wait for the trainModel() call.
*/
@Override
public void init(boolean expensive) {
super.init(expensive);
// Initialize local variables
if (!(0.0 < _parms._sample_rate && _parms._sample_rate <= 1.0))
throw new IllegalArgumentException(
"Sample rate should be interval [0,1] but it is " + _parms._sample_rate);
if (_parms._mtries < 1 && _parms._mtries != -1)
error(
"_mtries",
"mtries must be -1 (converted to sqrt(features)), or >= 1 but it is " + _parms._mtries);
if (_train != null) {
int ncols = _train.numCols();
if (_parms._mtries != -1 && !(1 <= _parms._mtries && _parms._mtries < ncols))
error(
"_mtries",
"Computed mtries should be -1 or in interval [1,"
+ ncols
+ "] but it is "
+ _parms._mtries);
}
if (_parms._distribution == Distribution.Family.AUTO) {
if (_nclass == 1) _parms._distribution = Distribution.Family.gaussian;
if (_nclass >= 2) _parms._distribution = Distribution.Family.multinomial;
}
if (expensive) {
_initialPrediction = isClassifier() ? 0 : getInitialValue();
}
if (_parms._sample_rate == 1f && _valid == null)
error(
"_sample_rate",
"Sample rate is 100% and no validation dataset is specified. There are no OOB data to compute out-of-bag error estimation!");
if (hasOffsetCol()) error("_offset_column", "Offsets are not yet supported for DRF.");
if (hasOffsetCol() && isClassifier()) {
error("_offset_column", "Offset is only supported for regression.");
}
}
