private TwoDimTable createScoringHistoryTable(KMeansModel.KMeansOutput 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("Iteration");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Avg. Change of Std. Centroids");
colTypes.add("double");
colFormat.add("%.5f");
colHeaders.add("Within Cluster Sum Of Squares");
colTypes.add("double");
colFormat.add("%.5f");
final int rows = output._avg_centroids_chg.length;
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 = 0; i < rows; i++) {
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);
table.set(row, col++, output._avg_centroids_chg[i]);
table.set(row, col++, output._history_withinss[i]);
row++;
}
return table;
}
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;
}
@Override
public boolean toHTML(StringBuilder sb) {
if (jobs != null) {
DocGen.HTML.arrayHead(sb);
sb.append("<tr class='warning'>");
ArrayList<Argument> args = jobs[0].arguments();
// Filter some keys to simplify UI
args = (ArrayList<Argument>) args.clone();
filter(
args,
"destination_key",
"source",
"cols",
"ignored_cols_by_name",
"response",
"classification",
"validation");
for (int i = 0; i < args.size(); i++)
sb.append("<td><b>").append(args.get(i)._name).append("</b></td>");
sb.append("<td><b>").append("run time").append("</b></td>");
String perf = jobs[0].speedDescription();
if (perf != null) sb.append("<td><b>").append(perf).append("</b></td>");
sb.append("<td><b>").append("model key").append("</b></td>");
sb.append("<td><b>").append("prediction error").append("</b></td>");
sb.append("<td><b>").append("F1 score").append("</b></td>");
sb.append("</tr>");
ArrayList<JobInfo> infos = new ArrayList<JobInfo>();
for (Job job : jobs) {
JobInfo info = new JobInfo();
info._job = job;
Object value = UKV.get(job.destination_key);
info._model = value instanceof Model ? (Model) value : null;
if (info._model != null) info._cm = info._model.cm();
if (info._cm != null) info._error = info._cm.err();
infos.add(info);
}
Collections.sort(
infos,
new Comparator<JobInfo>() {
@Override
public int compare(JobInfo a, JobInfo b) {
return Double.compare(a._error, b._error);
}
});
for (JobInfo info : infos) {
sb.append("<tr>");
for (Argument a : args) {
try {
Object value = a._field.get(info._job);
String s;
if (value instanceof int[]) s = Utils.sampleToString((int[]) value, 20);
else s = "" + value;
sb.append("<td>").append(s).append("</td>");
} catch (Exception e) {
throw new RuntimeException(e);
}
}
String runTime = "Pending", speed = "";
if (info._job.start_time != 0) {
runTime = PrettyPrint.msecs(info._job.runTimeMs(), true);
speed = perf != null ? PrettyPrint.msecs(info._job.speedValue(), true) : "";
}
sb.append("<td>").append(runTime).append("</td>");
if (perf != null) sb.append("<td>").append(speed).append("</td>");
String link = info._job.destination_key.toString();
if (info._job.start_time != 0 && DKV.get(info._job.destination_key) != null) {
if (info._model instanceof GBMModel)
link = GBMModelView.link(link, info._job.destination_key);
else if (info._model instanceof NeuralNetModel)
link = NeuralNetProgress.link(info._job.self(), info._job.destination_key, link);
if (info._model instanceof KMeans2Model)
link = KMeans2ModelView.link(link, info._job.destination_key);
else link = Inspect.link(link, info._job.destination_key);
}
sb.append("<td>").append(link).append("</td>");
String pct = "", f1 = "";
if (info._cm != null) {
pct = String.format("%.2f", 100 * info._error) + "%";
if (info._cm._arr.length == 2)
f1 = String.format("%.2f", info._cm.precisionAndRecall());
}
sb.append("<td><b>").append(pct).append("</b></td>");
sb.append("<td><b>").append(f1).append("</b></td>");
sb.append("</tr>");
}
DocGen.HTML.arrayTail(sb);
}
return true;
}