private TwoDimTable createScoringHistoryTable(SharedTreeModel.SharedTreeOutput _output) {
List<String> colHeaders = new ArrayList<>();
List<String> colTypes = new ArrayList<>();
List<String> colFormat = new ArrayList<>();
colHeaders.add("Timestamp");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Duration");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Number of Trees");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Training MSE");
colTypes.add("double");
colFormat.add("%.5f");
if (_output.isClassifier()) {
colHeaders.add("Training LogLoss");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial) {
colHeaders.add("Training AUC");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial
|| _output.getModelCategory() == ModelCategory.Multinomial) {
colHeaders.add("Training Classification Error");
colTypes.add("double");
colFormat.add("%.5f");
}
if (valid() != null) {
colHeaders.add("Validation MSE");
colTypes.add("double");
colFormat.add("%.5f");
if (_output.isClassifier()) {
colHeaders.add("Validation LogLoss");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial) {
colHeaders.add("Validation AUC");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.isClassifier()) {
colHeaders.add("Validation Classification Error");
colTypes.add("double");
colFormat.add("%.5f");
}
}
int rows = 0;
for (int i = 1; i < _output._scored_train.length; i++) {
if (!Double.isNaN(_output._scored_train[i]._mse)) ++rows;
}
TwoDimTable table =
new TwoDimTable(
"Scoring History",
null,
new String[rows],
colHeaders.toArray(new String[0]),
colTypes.toArray(new String[0]),
colFormat.toArray(new String[0]),
"");
int row = 0;
for (int i = 1; i < _output._scored_train.length; i++) {
if (Double.isNaN(_output._scored_train[i]._mse)) continue;
int col = 0;
assert (row < table.getRowDim());
assert (col < table.getColDim());
DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
table.set(row, col++, fmt.print(_output._training_time_ms[i]));
table.set(row, col++, PrettyPrint.msecs(_output._training_time_ms[i] - _start_time, true));
table.set(row, col++, i);
ScoreKeeper st = _output._scored_train[i];
table.set(row, col++, st._mse);
if (_output.isClassifier()) table.set(row, col++, st._logloss);
if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
if (_output.isClassifier()) table.set(row, col++, st._classError);
if (_valid != null) {
st = _output._scored_valid[i];
table.set(row, col++, st._mse);
if (_output.isClassifier()) table.set(row, col++, st._logloss);
if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
if (_output.isClassifier()) table.set(row, col++, st._classError);
}
row++;
}
return table;
}
protected double doScoringAndSaveModel(
boolean finalScoring, boolean oob, boolean build_tree_one_node) {
double training_r2 = Double.NaN; // Training R^2 value, if computed
long now = System.currentTimeMillis();
if (_firstScore == 0) _firstScore = now;
long sinceLastScore = now - _timeLastScoreStart;
boolean updated = false;
new ProgressUpdate(
"Built " + _model._output._ntrees + " trees so far (out of " + _parms._ntrees + ").")
.fork(_progressKey);
// Now model already contains tid-trees in serialized form
if (_parms._score_each_iteration
|| finalScoring
|| (now - _firstScore < 4000)
|| // Score every time for 4 secs
// Throttle scoring to keep the cost sane; limit to a 10% duty cycle & every 4 secs
(sinceLastScore > 4000
&& // Limit scoring updates to every 4sec
(double) (_timeLastScoreEnd - _timeLastScoreStart) / sinceLastScore
< 0.1)) { // 10% duty cycle
checkMemoryFootPrint();
// If validation is specified we use a model for scoring, so we need to
// update it! First we save model with trees (i.e., make them available
// for scoring) and then update it with resulting error
_model.update(_key);
updated = true;
Log.info("============================================================== ");
SharedTreeModel.SharedTreeOutput out = _model._output;
_timeLastScoreStart = now;
// Score on training data
new ProgressUpdate("Scoring the model.").fork(_progressKey);
Score sc =
new Score(this, true, oob, _model._output.getModelCategory())
.doAll(train(), build_tree_one_node);
ModelMetrics mm = sc.makeModelMetrics(_model, _parms.train());
out._training_metrics = mm;
if (oob)
out._training_metrics._description = "Metrics reported on Out-Of-Bag training samples";
out._scored_train[out._ntrees].fillFrom(mm);
if (out._ntrees > 0) Log.info("Training " + out._scored_train[out._ntrees].toString());
// Score again on validation data
if (_parms._valid != null) {
Score scv =
new Score(this, false, false, _model._output.getModelCategory())
.doAll(valid(), build_tree_one_node);
ModelMetrics mmv = scv.makeModelMetrics(_model, _parms.valid());
out._validation_metrics = mmv;
out._scored_valid[out._ntrees].fillFrom(mmv);
if (out._ntrees > 0) Log.info("Validation " + out._scored_valid[out._ntrees].toString());
}
if (out._ntrees > 0) { // Compute variable importances
out._model_summary = createModelSummaryTable(out);
out._scoring_history = createScoringHistoryTable(out);
out._varimp = new hex.VarImp(_improvPerVar, out._names);
out._variable_importances = hex.ModelMetrics.calcVarImp(out._varimp);
Log.info(out._model_summary.toString());
// For Debugging:
// Log.info(out._scoring_history.toString());
// Log.info(out._variable_importances.toString());
}
ConfusionMatrix cm = mm.cm();
if (cm != null) {
if (cm._cm.length <= _parms._max_confusion_matrix_size) {
Log.info(cm.toASCII());
} else {
Log.info(
"Confusion Matrix is too large (max_confusion_matrix_size="
+ _parms._max_confusion_matrix_size
+ "): "
+ _nclass
+ " classes.");
}
}
_timeLastScoreEnd = System.currentTimeMillis();
}
// Double update - after either scoring or variable importance
if (updated) _model.update(_key);
return training_r2;
}