/** Score a frame with the given model and return the metrics AND the prediction frame. */
@SuppressWarnings("unused") // called through reflection by RequestServer
public JobV3 predict2(int version, final ModelMetricsListSchemaV3 s) {
// parameters checking:
if (null == s.model) throw new H2OIllegalArgumentException("model", "predict", s.model);
if (null == DKV.get(s.model.name))
throw new H2OKeyNotFoundArgumentException("model", "predict", s.model.name);
if (null == s.frame) throw new H2OIllegalArgumentException("frame", "predict", s.frame);
if (null == DKV.get(s.frame.name))
throw new H2OKeyNotFoundArgumentException("frame", "predict", s.frame.name);
final ModelMetricsList parms = s.createAndFillImpl();
// predict2 does not return modelmetrics, so cannot handle deeplearning: reconstruction_error
// (anomaly) or GLRM: reconstruct and archetypes
// predict2 can handle deeplearning: deepfeatures and predict
if (s.deep_features_hidden_layer > 0) {
if (null == parms._predictions_name)
parms._predictions_name =
"deep_features"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
} else if (null == parms._predictions_name)
parms._predictions_name =
"predictions"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
final Job<Frame> j =
new Job(Key.make(parms._predictions_name), Frame.class.getName(), "prediction");
H2O.H2OCountedCompleter work =
new H2O.H2OCountedCompleter() {
@Override
public void compute2() {
if (s.deep_features_hidden_layer < 0) {
parms._model.score(parms._frame, parms._predictions_name, j);
} else {
Frame predictions =
((Model.DeepFeatures) parms._model)
.scoreDeepFeatures(parms._frame, s.deep_features_hidden_layer, j);
predictions =
new Frame(
Key.make(parms._predictions_name), predictions.names(), predictions.vecs());
DKV.put(predictions._key, predictions);
}
tryComplete();
}
};
j.start(work, parms._frame.anyVec().nChunks());
return new JobV3().fillFromImpl(j);
}
@Override
public ModelMetricsHandler.ModelMetricsList fillImpl(ModelMetricsList mml) {
// TODO: check for type!
mml._model = (null == this.model || null == this.model.key() ? null : this.model.key().get());
mml._frame = (null == this.frame || null == this.frame.key() ? null : this.frame.key().get());
mml._predictions_name =
(null == this.predictions_frame || null == this.predictions_frame.key()
? null
: this.predictions_frame.key().toString());
mml._reconstruction_error = this.reconstruction_error;
mml._reconstruction_error_per_feature = this.reconstruction_error_per_feature;
mml._deep_features_hidden_layer = this.deep_features_hidden_layer;
mml._reconstruct_train = this.reconstruct_train;
mml._project_archetypes = this.project_archetypes;
mml._reverse_transform = this.reverse_transform;
mml._leaf_node_assignment = this.leaf_node_assignment;
if (null != model_metrics) {
mml._model_metrics = new ModelMetrics[model_metrics.length];
for (int i = 0; i < model_metrics.length; i++)
mml._model_metrics[i++] = (ModelMetrics) model_metrics[i].createImpl();
}
return mml;
}
/** Score a frame with the given model and return the metrics AND the prediction frame. */
@SuppressWarnings("unused") // called through reflection by RequestServer
public ModelMetricsListSchemaV3 predict(int version, ModelMetricsListSchemaV3 s) {
// parameters checking:
if (null == s.model) throw new H2OIllegalArgumentException("model", "predict", s.model);
if (null == DKV.get(s.model.name))
throw new H2OKeyNotFoundArgumentException("model", "predict", s.model.name);
if (null == s.frame) throw new H2OIllegalArgumentException("frame", "predict", s.frame);
if (null == DKV.get(s.frame.name))
throw new H2OKeyNotFoundArgumentException("frame", "predict", s.frame.name);
ModelMetricsList parms = s.createAndFillImpl();
Frame predictions;
if (!s.reconstruction_error
&& !s.reconstruction_error_per_feature
&& s.deep_features_hidden_layer < 0
&& !s.project_archetypes
&& !s.reconstruct_train
&& !s.leaf_node_assignment) {
if (null == parms._predictions_name)
parms._predictions_name =
"predictions"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
predictions = parms._model.score(parms._frame, parms._predictions_name);
} else {
if (Model.DeepFeatures.class.isAssignableFrom(parms._model.getClass())) {
if (s.reconstruction_error || s.reconstruction_error_per_feature) {
if (s.deep_features_hidden_layer >= 0)
throw new H2OIllegalArgumentException(
"Can only compute either reconstruction error OR deep features.", "");
if (null == parms._predictions_name)
parms._predictions_name =
"reconstruction_error"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
predictions =
((Model.DeepFeatures) parms._model)
.scoreAutoEncoder(
parms._frame,
Key.make(parms._predictions_name),
parms._reconstruction_error_per_feature);
} else {
if (s.deep_features_hidden_layer < 0)
throw new H2OIllegalArgumentException(
"Deep features hidden layer index must be >= 0.", "");
if (null == parms._predictions_name)
parms._predictions_name =
"deep_features"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
predictions =
((Model.DeepFeatures) parms._model)
.scoreDeepFeatures(parms._frame, s.deep_features_hidden_layer);
}
predictions =
new Frame(Key.make(parms._predictions_name), predictions.names(), predictions.vecs());
DKV.put(predictions._key, predictions);
} else if (Model.GLRMArchetypes.class.isAssignableFrom(parms._model.getClass())) {
if (s.project_archetypes) {
if (null == parms._predictions_name)
parms._predictions_name =
"reconstructed_archetypes_"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_of_"
+ parms._frame._key.toString();
predictions =
((Model.GLRMArchetypes) parms._model)
.scoreArchetypes(
parms._frame, Key.make(parms._predictions_name), s.reverse_transform);
} else {
assert s.reconstruct_train;
if (null == parms._predictions_name)
parms._predictions_name =
"reconstruction_"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_of_"
+ parms._frame._key.toString();
predictions =
((Model.GLRMArchetypes) parms._model)
.scoreReconstruction(
parms._frame, Key.make(parms._predictions_name), s.reverse_transform);
}
} else if (Model.LeafNodeAssignment.class.isAssignableFrom(parms._model.getClass())) {
assert (s.leaf_node_assignment);
if (null == parms._predictions_name)
parms._predictions_name =
"leaf_node_assignement"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
predictions =
((Model.LeafNodeAssignment) parms._model)
.scoreLeafNodeAssignment(parms._frame, Key.make(parms._predictions_name));
} else
throw new H2OIllegalArgumentException(
"Requires a Deep Learning, GLRM, DRF or GBM model.",
"Model must implement specific methods.");
}
ModelMetricsListSchemaV3 mm = this.fetch(version, s);
// TODO: for now only binary predictors write an MM object.
// For the others cons one up here to return the predictions frame.
if (null == mm) mm = new ModelMetricsListSchemaV3();
mm.predictions_frame = new KeyV3.FrameKeyV3(predictions._key);
if (parms._leaf_node_assignment) // don't show metrics in leaf node assignments are made
mm.model_metrics = null;
if (null == mm.model_metrics || 0 == mm.model_metrics.length) {
// There was no response in the test set -> cannot make a model_metrics object
} else {
mm.model_metrics[0].predictions =
new FrameV3(predictions, 0, 100); // TODO: Should call schema(version)
}
return mm;
}