// Test kaggle/creditsample-test data
@org.junit.Test
public void kaggle_credit() {
Key okey = loadAndParseFile("credit.hex", "smalldata/kaggle/creditsample-training.csv.gz");
UKV.remove(Key.make("smalldata/kaggle/creditsample-training.csv.gz_UNZIPPED"));
UKV.remove(Key.make("smalldata\\kaggle\\creditsample-training.csv.gz_UNZIPPED"));
ValueArray val = DKV.get(okey).get();
// Check parsed dataset
final int n = new int[] {4, 2, 1}[ValueArray.LOG_CHK - 20];
assertEquals("Number of chunks", n, val.chunks());
assertEquals("Number of rows", 150000, val.numRows());
assertEquals("Number of cols", 12, val.numCols());
// setup default values for DRF
int ntrees = 3;
int depth = 30;
int gini = StatType.GINI.ordinal();
int seed = 42;
StatType statType = StatType.values()[gini];
final int cols[] =
new int[] {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 1}; // ignore column 6, classify column 1
// Start the distributed Random Forest
final Key modelKey = Key.make("model");
DRFJob result =
hex.rf.DRF.execute(
modelKey,
cols,
val,
ntrees,
depth,
1024,
statType,
seed,
true,
null,
-1,
Sampling.Strategy.RANDOM,
1.0f,
null,
0,
0,
false);
// Wait for completion on all nodes
RFModel model = result.get();
assertEquals("Number of classes", 2, model.classes());
assertEquals("Number of trees", ntrees, model.size());
model.deleteKeys();
UKV.remove(modelKey);
UKV.remove(okey);
}
/*@org.junit.Test*/ public void covtype() {
// Key okey = loadAndParseFile("covtype.hex", "smalldata/covtype/covtype.20k.data");
// Key okey = loadAndParseFile("covtype.hex", "../datasets/UCI/UCI-large/covtype/covtype.data");
// Key okey = loadAndParseFile("covtype.hex", "/home/0xdiag/datasets/standard/covtype.data");
Key okey = loadAndParseFile("mnist.hex", "smalldata/mnist/mnist8m.10k.csv.gz");
// Key okey = loadAndParseFile("mnist.hex", "/home/0xdiag/datasets/mnist/mnist8m.csv");
ValueArray val = UKV.get(okey);
// setup default values for DRF
int ntrees = 8;
int depth = 999;
int gini = StatType.ENTROPY.ordinal();
int seed = 42;
StatType statType = StatType.values()[gini];
final int cols[] = new int[val.numCols()];
for (int i = 1; i < cols.length; i++) cols[i] = i - 1;
cols[cols.length - 1] = 0; // Class is in column 0 for mnist
// Start the distributed Random Forest
final Key modelKey = Key.make("model");
DRFJob result =
hex.rf.DRF.execute(
modelKey,
cols,
val,
ntrees,
depth,
1024,
statType,
seed,
true,
null,
-1,
Sampling.Strategy.RANDOM,
1.0f,
null,
0,
0,
false);
// Wait for completion on all nodes
RFModel model = result.get();
assertEquals("Number of classes", 10, model.classes());
assertEquals("Number of trees", ntrees, model.size());
model.deleteKeys();
UKV.remove(modelKey);
UKV.remove(okey);
}
@Override
protected Response serve() {
int tasks = 0;
int finished = 0;
RFModel model = _modelKey.value();
double[] weights = _weights.value();
// Finish refresh after rf model is done and confusion matrix for all trees is computed
boolean done = false;
int classCol = _classCol.specified() ? _classCol.value() : findResponseIdx(model);
tasks = model._totalTrees;
finished = model.size();
// Handle cancelled/aborted jobs
if (_job.value() != null) {
Job jjob = Job.findJob(_job.value());
if (jjob != null && jjob.isCancelled())
return Response.error(
jjob.exception == null ? "Job was cancelled by user!" : jjob.exception);
}
JsonObject response = defaultJsonResponse();
// CM return and possible computation is requested
if (!_noCM.value() && (finished == tasks || _iterativeCM.value()) && finished > 0) {
// Compute the highest number of trees which is less then a threshold
int modelSize = tasks * _refreshThresholdCM.value() / 100;
modelSize =
modelSize == 0 || finished == tasks ? finished : modelSize * (finished / modelSize);
// Get the computing the matrix - if no job is computing, then start a new job
Job cmJob =
ConfusionTask.make(
model, modelSize, _dataKey.value()._key, classCol, weights, _oobee.value());
// Here the the job is running - it saved a CM which can be already finished or in invalid
// state.
CMFinal confusion = UKV.get(cmJob.dest());
// if the matrix is valid, report it in the JSON
if (confusion != null && confusion.valid() && modelSize > 0) {
// finished += 1;
JsonObject cm = new JsonObject();
JsonArray cmHeader = new JsonArray();
JsonArray matrix = new JsonArray();
cm.addProperty(JSON_CM_TYPE, _oobee.value() ? "OOB error estimate" : "full scoring");
cm.addProperty(JSON_CM_CLASS_ERR, confusion.classError());
cm.addProperty(JSON_CM_ROWS_SKIPPED, confusion.skippedRows());
cm.addProperty(JSON_CM_ROWS, confusion.rows());
// create the header
for (String s : cfDomain(confusion, 1024)) cmHeader.add(new JsonPrimitive(s));
cm.add(JSON_CM_HEADER, cmHeader);
// add the matrix
final int nclasses = confusion.dimension();
JsonArray classErrors = new JsonArray();
for (int crow = 0; crow < nclasses; ++crow) {
JsonArray row = new JsonArray();
int classHitScore = 0;
for (int ccol = 0; ccol < nclasses; ++ccol) {
row.add(new JsonPrimitive(confusion.matrix(crow, ccol)));
if (crow != ccol) classHitScore += confusion.matrix(crow, ccol);
}
// produce infinity members in case of 0.f/0
classErrors.add(
new JsonPrimitive(
(float) classHitScore / (classHitScore + confusion.matrix(crow, crow))));
matrix.add(row);
}
cm.add(JSON_CM_CLASSES_ERRORS, classErrors);
cm.add(JSON_CM_MATRIX, matrix);
cm.addProperty(JSON_CM_TREES, modelSize);
response.add(JSON_CM, cm);
// Signal end only and only if all trees were generated and confusion matrix is valid
done = finished == tasks;
}
} else if (_noCM.value() && finished == tasks) done = true;
// Trees
JsonObject trees = new JsonObject();
trees.addProperty(Constants.TREE_COUNT, model.size());
if (model.size() > 0) {
trees.add(Constants.TREE_DEPTH, model.depth().toJson());
trees.add(Constants.TREE_LEAVES, model.leaves().toJson());
}
response.add(Constants.TREES, trees);
// Build a response
Response r;
if (done) {
r = jobDone(response);
r.addHeader(
"<div class='alert'>"
+ /*RFScore.link(MODEL_KEY, model._key, "Use this model for scoring.") */ GeneratePredictionsPage
.link(model._key, "Predict!")
+ " </div>");
} else {
r = Response.poll(response, finished, tasks);
}
r.setBuilder(JSON_CM, new ConfusionMatrixBuilder());
r.setBuilder(TREES, new TreeListBuilder());
return r;
}