@Test
public void testCategoricalProstate() throws InterruptedException, ExecutionException {
GLRM job = null;
GLRMModel model = null;
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
try {
Scope.enter();
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._k = 8;
parms._gamma_x = parms._gamma_y = 0.1;
parms._regularization_x = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._regularization_y = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._init = GLRM.Initialization.PlusPlus;
parms._transform = DataInfo.TransformType.STANDARDIZE;
parms._recover_svd = false;
parms._max_iterations = 200;
try {
job = new GLRM(parms);
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (train != null) train.delete();
if (model != null) model.delete();
Scope.exit();
}
}
@Test
public void testGroupbyTableSpeed() {
Frame ids = parse_test_file(Key.make("cov"), "smalldata/junit/id_cols.csv");
ids.replace(0, ids.anyVec().toCategoricalVec()).remove();
System.out.println(ids.toString(0, 10));
long start = System.currentTimeMillis();
Val v_gb = Exec.exec("(GB cov [0] nrow 0 \"all\")");
System.out.println("GB Time= " + (System.currentTimeMillis() - start) + "msec");
System.out.println(v_gb.toString());
((ValFrame) v_gb)._fr.delete();
long start2 = System.currentTimeMillis();
Val v_tb = Exec.exec("(table cov FALSE)");
System.out.println("Table Time= " + (System.currentTimeMillis() - start2) + "msec");
System.out.println(v_tb.toString());
((ValFrame) v_tb)._fr.delete();
ids.delete();
}
/**
* This method trains a stacked autoencoder
*
* @param trainData Training dataset as a JavaRDD
* @param batchSize Size of a training mini-batch
* @param layerSizes Number of neurons for each layer
* @param epochs Number of epochs to train
* @param responseColumn Name of the response column
* @param modelName Name of the model
* @return DeepLearningModel
*/
public DeepLearningModel train(
JavaRDD<LabeledPoint> trainData,
int batchSize,
int[] layerSizes,
String activationType,
int epochs,
String responseColumn,
String modelName,
MLModel mlModel,
long modelID) {
// build stacked autoencoder by training the model with training data
double trainingFraction = 1;
try {
Scope.enter();
if (trainData != null) {
int numberOfFeatures = mlModel.getFeatures().size();
List<Feature> features = mlModel.getFeatures();
String[] names = new String[numberOfFeatures + 1];
for (int i = 0; i < numberOfFeatures; i++) {
names[i] = features.get(i).getName();
}
names[numberOfFeatures] = mlModel.getResponseVariable();
Frame frame = DeeplearningModelUtils.javaRDDToFrame(names, trainData);
// H2O uses default C<x> for column header
// String classifColName = "C" + frame.numCols();
String classifColName = mlModel.getResponseVariable();
// Convert response to categorical (digits 1 to <num of columns>)
int ci = frame.find(classifColName);
Scope.track(frame.replace(ci, frame.vecs()[ci].toEnum())._key);
// Splitting train file to train, validation and test
// Using FrameSplitter (instead of SuffleSplitFrame) gives a weird exception
// barrier onExCompletion for hex.deeplearning.DeepLearning$DeepLearningDriver@78ec854
double[] ratios = new double[] {trainingFraction, 1 - trainingFraction};
@SuppressWarnings("unchecked")
Frame[] splits =
ShuffleSplitFrame.shuffleSplitFrame(
frame, generateNumKeys(frame._key, ratios.length), ratios, 123456789);
Frame trainFrame = splits[0];
Frame vframe = splits[1];
if (log.isDebugEnabled()) {
log.debug("Creating Deeplearning parameters");
}
DeepLearningParameters deeplearningParameters = new DeepLearningParameters();
// convert model name
String dlModelName = modelName.replace('.', '_').replace('-', '_');
// populate model parameters
deeplearningParameters._model_id = Key.make(dlModelName + "_dl");
deeplearningParameters._train = trainFrame._key;
deeplearningParameters._valid = vframe._key;
deeplearningParameters._response_column = classifColName; // last column is the response
// This is causin all the predictions to be 0.0
// p._autoencoder = true;
deeplearningParameters._activation = getActivationType(activationType);
deeplearningParameters._hidden = layerSizes;
deeplearningParameters._train_samples_per_iteration = batchSize;
deeplearningParameters._input_dropout_ratio = 0.2;
deeplearningParameters._l1 = 1e-5;
deeplearningParameters._max_w2 = 10;
deeplearningParameters._epochs = epochs;
// speed up training
deeplearningParameters._adaptive_rate =
true; // disable adaptive per-weight learning rate -> default
// settings for learning rate and momentum are probably
// not ideal (slow convergence)
deeplearningParameters._replicate_training_data =
true; // avoid extra communication cost upfront, got
// enough data on each node for load balancing
deeplearningParameters._overwrite_with_best_model =
true; // no need to keep the best model around
deeplearningParameters._diagnostics =
false; // no need to compute statistics during training
deeplearningParameters._classification_stop = -1;
deeplearningParameters._score_interval =
60; // score and print progress report (only) every 20 seconds
deeplearningParameters._score_training_samples =
batchSize / 10; // only score on a small sample of the
// training set -> don't want to spend
// too much time scoring (note: there
// will be at least 1 row per chunk)
DKV.put(trainFrame);
DKV.put(vframe);
deeplearning = new DeepLearning(deeplearningParameters);
if (log.isDebugEnabled()) {
log.debug("Start training deeplearning model ....");
}
try {
dlModel = deeplearning.trainModel().get();
if (log.isDebugEnabled()) {
log.debug("Successfully finished Training deeplearning model.");
}
} catch (RuntimeException ex) {
log.error("Error in training Stacked Autoencoder classifier model", ex);
}
} else {
log.error("Train file not found!");
}
} catch (RuntimeException ex) {
log.error("Failed to train the deeplearning model [id] " + modelID + ". " + ex.getMessage());
} finally {
Scope.exit();
}
return dlModel;
}
public Vec setWeights(String name, Vec vec) {
if (_weights) return _adaptedFrame.replace(weightChunkId(), vec);
_adaptedFrame.insertVec(weightChunkId(), name, vec);
_weights = true;
return null;
}
@Test
public void testExpandCatsProstate() throws InterruptedException, ExecutionException {
double[][] prostate =
ard(
ard(0, 71, 1, 0, 0, 4.8, 14.0, 7),
ard(1, 70, 1, 1, 0, 8.4, 21.8, 5),
ard(0, 73, 1, 3, 0, 10.0, 27.4, 6),
ard(1, 68, 1, 0, 0, 6.7, 16.7, 6));
double[][] pros_expandR =
ard(
ard(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 71, 4.8, 14.0, 7),
ard(0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 70, 8.4, 21.8, 5),
ard(0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 73, 10.0, 27.4, 6),
ard(1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 68, 6.7, 16.7, 6));
String[] pros_cols =
new String[] {"Capsule", "Age", "Race", "Dpros", "Dcaps", "PSA", "Vol", "Gleason"};
String[][] pros_domains =
new String[][] {
new String[] {"No", "Yes"},
null,
new String[] {"Other", "White", "Black"},
new String[] {"None", "UniLeft", "UniRight", "Bilobar"},
new String[] {"No", "Yes"},
null,
null,
null
};
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
Frame fr = null;
try {
Scope.enter();
fr = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(fr.replace(cats[i], fr.vec(cats[i]).toCategoricalVec())._key);
fr.remove("ID").remove();
DKV.put(fr._key, fr);
DataInfo dinfo =
new DataInfo(
Key.make(),
fr,
null,
0,
true,
DataInfo.TransformType.NONE,
DataInfo.TransformType.NONE,
false,
false,
false, /* weights */
false, /* offset */
false, /* fold */
false);
Log.info("Original matrix:\n" + colFormat(pros_cols, "%8.7s") + ArrayUtils.pprint(prostate));
double[][] pros_perm = ArrayUtils.permuteCols(prostate, dinfo._permutation);
Log.info(
"Permuted matrix:\n"
+ colFormat(pros_cols, "%8.7s", dinfo._permutation)
+ ArrayUtils.pprint(pros_perm));
double[][] pros_exp = GLRM.expandCats(pros_perm, dinfo);
Log.info(
"Expanded matrix:\n"
+ colExpFormat(pros_cols, pros_domains, "%8.7s", dinfo._permutation)
+ ArrayUtils.pprint(pros_exp));
Assert.assertArrayEquals(pros_expandR, pros_exp);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (fr != null) fr.delete();
Scope.exit();
}
}
@Test
public void testSetColumnLossCats() throws InterruptedException, ExecutionException {
GLRM job = null;
GLRMModel model = null;
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
Scope.enter();
try {
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._k = 12;
parms._loss = GLRMParameters.Loss.Quadratic;
parms._multi_loss = GLRMParameters.Loss.Categorical;
parms._loss_by_col =
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Ordinal, GLRMParameters.Loss.Poisson, GLRMParameters.Loss.Absolute
};
parms._loss_by_col_idx = new int[] {3 /* DPROS */, 1 /* AGE */, 6 /* VOL */};
parms._init = GLRM.Initialization.PlusPlus;
parms._min_step_size = 1e-5;
parms._recover_svd = false;
parms._max_iterations = 2000;
try {
job = new GLRM(parms);
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
GLRMTest.checkLossbyCol(parms, model);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (train != null) train.delete();
if (model != null) model.delete();
Scope.exit();
}
}
@Test
public void testLosses() throws InterruptedException, ExecutionException {
long seed = 0xDECAF;
Random rng = new Random(seed);
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
final GLRMParameters.Regularizer[] regs =
new GLRMParameters.Regularizer[] {
GLRMParameters.Regularizer.Quadratic,
GLRMParameters.Regularizer.L1,
GLRMParameters.Regularizer.NonNegative,
GLRMParameters.Regularizer.OneSparse,
GLRMParameters.Regularizer.UnitOneSparse,
GLRMParameters.Regularizer.Simplex
};
Scope.enter();
try {
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
for (GLRMParameters.Loss loss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Quadratic,
GLRMParameters.Loss.Absolute,
GLRMParameters.Loss.Huber,
GLRMParameters.Loss.Poisson,
GLRMParameters.Loss.Hinge,
GLRMParameters.Loss.Logistic
}) {
for (GLRMParameters.Loss multiloss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Categorical, GLRMParameters.Loss.Ordinal
}) {
GLRMModel model = null;
try {
Scope.enter();
long myseed = rng.nextLong();
Log.info("GLRM using seed = " + myseed);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._transform = DataInfo.TransformType.NONE;
parms._k = 5;
parms._loss = loss;
parms._multi_loss = multiloss;
parms._init = GLRM.Initialization.SVD;
parms._regularization_x = regs[rng.nextInt(regs.length)];
parms._regularization_y = regs[rng.nextInt(regs.length)];
parms._gamma_x = Math.abs(rng.nextDouble());
parms._gamma_y = Math.abs(rng.nextDouble());
parms._recover_svd = false;
parms._seed = myseed;
parms._verbose = false;
parms._max_iterations = 500;
GLRM job = new GLRM(parms);
try {
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
throw t;
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (model != null) model.delete();
Scope.exit();
}
}
}
} finally {
if (train != null) train.delete();
Scope.exit();
}
}
@Test
@Ignore
public void run() {
Scope.enter();
try {
File file = find_test_file("bigdata/laptop/mnist/train.csv.gz");
File valid = find_test_file("bigdata/laptop/mnist/test.csv.gz");
if (file != null) {
NFSFileVec trainfv = NFSFileVec.make(file);
Frame frame = ParseDataset.parse(Key.make(), trainfv._key);
NFSFileVec validfv = NFSFileVec.make(valid);
Frame vframe = ParseDataset.parse(Key.make(), validfv._key);
DeepLearningParameters p = new DeepLearningParameters();
// populate model parameters
p._model_id = Key.make("dl_mnist_model");
p._train = frame._key;
// p._valid = vframe._key;
p._response_column = "C785"; // last column is the response
p._activation = DeepLearningParameters.Activation.RectifierWithDropout;
// p._activation = DeepLearningParameters.Activation.MaxoutWithDropout;
p._hidden = new int[] {800, 800};
p._input_dropout_ratio = 0.2;
p._mini_batch_size = 1;
p._train_samples_per_iteration = 50000;
p._score_duty_cycle = 0;
// p._shuffle_training_data = true;
// p._l1= 1e-5;
// p._max_w2= 10;
p._epochs = 10 * 5. / 6;
// Convert response 'C785' to categorical (digits 1 to 10)
int ci = frame.find("C785");
Scope.track(frame.replace(ci, frame.vecs()[ci].toEnum())._key);
Scope.track(vframe.replace(ci, vframe.vecs()[ci].toEnum())._key);
DKV.put(frame);
DKV.put(vframe);
// speed up training
p._adaptive_rate =
true; // disable adaptive per-weight learning rate -> default settings for learning rate
// and momentum are probably not ideal (slow convergence)
p._replicate_training_data =
true; // avoid extra communication cost upfront, got enough data on each node for load
// balancing
p._overwrite_with_best_model = true; // no need to keep the best model around
p._classification_stop = -1;
p._score_interval = 60; // score and print progress report (only) every 20 seconds
p._score_training_samples =
10000; // only score on a small sample of the training set -> don't want to spend too
// much time scoring (note: there will be at least 1 row per chunk)
DeepLearning dl = new DeepLearning(p);
DeepLearningModel model = null;
try {
model = dl.trainModel().get();
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
dl.remove();
if (model != null) {
model.delete();
}
}
} else {
Log.info("Please run ./gradlew syncBigDataLaptop in the top-level directory of h2o-3.");
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
Scope.exit();
}
}
