/** * 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."); } }