diff --git a/h2o-admissibleml/src/main/java/hex/Infogram/Infogram.java b/h2o-admissibleml/src/main/java/hex/Infogram/Infogram.java index bf17ce7a5bc7..ccd0faa36e1d 100644 --- a/h2o-admissibleml/src/main/java/hex/Infogram/Infogram.java +++ b/h2o-admissibleml/src/main/java/hex/Infogram/Infogram.java @@ -1,9 +1,6 @@ package hex.Infogram; import hex.*; -import hex.Infogram.InfogramModel.InfogramModelOutput; -import hex.Infogram.InfogramModel.InfogramParameters; -import hex.ModelMetrics.MetricBuilder; import water.*; import water.exceptions.H2OModelBuilderIllegalArgumentException; import water.fvec.Frame; @@ -21,8 +18,8 @@ import static water.util.ArrayUtils.sort; import static water.util.ArrayUtils.sum; -public class Infogram extends ModelBuilder { +public class Infogram extends ModelBuilder { static final double NORMALIZE_ADMISSIBLE_INDEX = 1.0/Math.sqrt(2.0); boolean _buildCore; // true to find core predictors, false to find admissible predictors String[] _topKPredictors; // contain the names of top predictors to consider for infogram @@ -48,14 +45,14 @@ public class Infogram extends ModelBuilder key) { + public Infogram(hex.Infogram.InfogramModel.InfogramParameters parms, Key key) { super(parms, key); init(false); } @@ -74,23 +71,18 @@ protected int nModelsInParallel(int folds) { * This is called before cross-validation is carried out */ @Override - protected void cv_init() { - super.cv_init(); + public void computeCrossValidation() { info("cross-validation", "cross-validation infogram information is stored in frame with key" + " labeled as admissible_score_key_cv and the admissible features in admissible_features_cv."); if (error_count() > 0) { throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(Infogram.this); } - } - - @Override - protected MetricBuilder makeCVMetricBuilder(ModelBuilder cvModelBuilder, Futures fs) { - return null; //infogram does not support scoring + super.computeCrossValidation(); } // find the best alpha/lambda values used to build the main model moving forward by looking at the devianceValid @Override - protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { + public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { int nBuilders = cvModelBuilders.length; double[][] cmiRaw = new double[nBuilders][]; List> columns = new ArrayList<>(); @@ -111,12 +103,7 @@ protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) } _cvDone = true; // cv is done and we are going to build main model next } - - @Override - protected void cv_mainModelScores(int N, MetricBuilder[] mbs, ModelBuilder[] cvModelBuilders) { - //infogram does not support scoring - } - + public void calculateMeanInfogramInfo(double[][] cmiRaw, List> columns, long[] nObs) { int nFolds = cmiRaw.length; @@ -317,7 +304,7 @@ public final void buildModel() { try { boolean validPresent = _parms.valid() != null; prepareModelTrainingFrame(); // generate training frame with predictors and sensitive features (if specified) - InfogramModel model = new hex.Infogram.InfogramModel(dest(), _parms, new InfogramModelOutput(Infogram.this)); + InfogramModel model = new hex.Infogram.InfogramModel(dest(), _parms, new hex.Infogram.InfogramModel.InfogramModelOutput(Infogram.this)); _model = model.delete_and_lock(_job); _model._output._start_time = System.currentTimeMillis(); _cmiRaw = new double[_numModels]; @@ -372,7 +359,7 @@ public final void buildModel() { * relevance >= relevance_threshold. Derive _admissible_index as distance from point with cmi = 1 and * relevance = 1. In addition, all arrays are sorted on _admissible_index. */ - private void copyCMIRelevance(InfogramModelOutput modelOutput) { + private void copyCMIRelevance(InfogramModel.InfogramModelOutput modelOutput) { modelOutput._cmi_raw = new double[_cmi.length]; System.arraycopy(_cmiRaw, 0, modelOutput._cmi_raw, 0, modelOutput._cmi_raw.length); modelOutput._admissible_index = new double[_cmi.length]; @@ -388,7 +375,7 @@ private void copyCMIRelevance(InfogramModelOutput modelOutput) { modelOutput._admissible_index, modelOutput._admissible, modelOutput._all_predictor_names); } - public void copyCMIRelevanceValid(InfogramModelOutput modelOutput) { + public void copyCMIRelevanceValid(InfogramModel.InfogramModelOutput modelOutput) { modelOutput._cmi_raw_valid = new double[_cmiValid.length]; System.arraycopy(_cmiRawValid, 0, modelOutput._cmi_raw_valid, 0, modelOutput._cmi_raw_valid.length); modelOutput._admissible_index_valid = new double[_cmiValid.length]; diff --git a/h2o-algos/src/main/java/hex/deeplearning/DeepLearning.java b/h2o-algos/src/main/java/hex/deeplearning/DeepLearning.java index 6f77c5f5b879..9093bac63225 100755 --- a/h2o-algos/src/main/java/hex/deeplearning/DeepLearning.java +++ b/h2o-algos/src/main/java/hex/deeplearning/DeepLearning.java @@ -165,7 +165,7 @@ static DataInfo makeDataInfo(Frame train, Frame valid, DeepLearningParameters pa } } - @Override protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { + @Override public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { _parms._overwrite_with_best_model = false; if( _parms._stopping_rounds == 0 && _parms._max_runtime_secs == 0) return; // No exciting changes to stopping conditions diff --git a/h2o-algos/src/main/java/hex/ensemble/StackedEnsembleModel.java b/h2o-algos/src/main/java/hex/ensemble/StackedEnsembleModel.java index d09f8712919c..a49202eda204 100644 --- a/h2o-algos/src/main/java/hex/ensemble/StackedEnsembleModel.java +++ b/h2o-algos/src/main/java/hex/ensemble/StackedEnsembleModel.java @@ -580,7 +580,7 @@ public ModelMetrics makeModelMetrics(Frame fr, Frame adaptFrm) { @Override public ModelMetrics.MetricBuilder getMetricBuilder() { throw new UnsupportedOperationException("Stacked Ensemble model doesn't implement MetricBuilder infrastructure code, " + - "retrieve your metrics by calling makeModelMetrics method."); + "retrieve your metrics by calling getOrMakeMetrics method."); } } diff --git a/h2o-algos/src/main/java/hex/glm/GLM.java b/h2o-algos/src/main/java/hex/glm/GLM.java index 7c7a9d835a64..433f8c058dc5 100644 --- a/h2o-algos/src/main/java/hex/glm/GLM.java +++ b/h2o-algos/src/main/java/hex/glm/GLM.java @@ -26,6 +26,7 @@ import hex.util.LinearAlgebraUtils; import hex.util.LinearAlgebraUtils.BMulTask; import hex.util.LinearAlgebraUtils.FindMaxIndex; +import jsr166y.CountedCompleter; import org.joda.time.format.DateTimeFormat; import org.joda.time.format.DateTimeFormatter; import water.*; @@ -118,8 +119,7 @@ public boolean isSupervised() { public ModelCategory[] can_build() { return new ModelCategory[]{ ModelCategory.Regression, - ModelCategory.Binomial, - ModelCategory.Multinomial + ModelCategory.Binomial, }; } @@ -148,12 +148,13 @@ public ModelCategory[] can_build() { * (builds N+1 models, all have train+validation metrics, the main model has N-fold cross-validated validation metrics) */ @Override - protected void cv_init() { + public void computeCrossValidation() { // init computes global list of lambdas init(true); _cvRuns = true; if (error_count() > 0) throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(GLM.this); + super.computeCrossValidation(); } @@ -292,7 +293,7 @@ private double[] alignSubModelsAcrossCVModels(ModelBuilder[] cvModelBuilders) { * 4. unlock the n-folds models (they are changed here, so the unlocking happens here) */ @Override - protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { + public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { setMaxRuntimeSecsForMainModel(); double bestTestDev = Double.POSITIVE_INFINITY; double[] alphasAndLambdas = alignSubModelsAcrossCVModels(cvModelBuilders); @@ -371,6 +372,12 @@ protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) break; } } + for (int i = 0; i < cvModelBuilders.length; ++i) { + GLM g = (GLM) cvModelBuilders[i]; + if (g._toRemove != null) + for (Key k : g._toRemove) + Keyed.remove(k); + } for (int i = 0; i < cvModelBuilders.length; ++i) { GLM g = (GLM) cvModelBuilders[i]; @@ -1536,11 +1543,11 @@ private void buildModel() { protected static final long WORK_TOTAL = 1000000; - @Override - protected void cleanUp() { - if (_parms._lambda_search && _parms._is_cv_model) - keepUntilCompletion(_dinfo.getWeightsVec()._key); - super.cleanUp(); + transient Key [] _toRemove; + + private Key[] removeLater(Key ...k){ + _toRemove = _toRemove == null?k:ArrayUtils.append(_toRemove,k); + return k; } @Override protected GLMDriver trainModelImpl() { return _driver = new GLMDriver(); } @@ -1569,6 +1576,23 @@ public final class GLMDriver extends Driver implements ProgressMonitor { private transient GLMTask.GLMIterationTask _gramInfluence; private transient double[][] _cholInvInfluence; + private void doCleanup() { + try { + if (_parms._lambda_search && _parms._is_cv_model) + Scope.untrack(removeLater(_dinfo.getWeightsVec()._key)); + if (_parms._HGLM) { + Key[] vecKeys = _toRemove; + for (int index = 0; index < vecKeys.length; index++) { + Vec tempVec = DKV.getGet(vecKeys[index]); + tempVec.remove(); + } + } + } catch (Exception e) { + Log.err("Error while cleaning up GLM " + _result); + Log.err(e); + } + } + private transient Cholesky _chol; private transient L1Solver _lslvr; @@ -3540,8 +3564,9 @@ private Vec[] genGLMVectors(DataInfo dinfo, double[] nb) { sumExp += Math.exp(nb[i * N + P] - maxRow); } Vec[] vecs = dinfo._adaptedFrame.anyVec().makeDoubles(2, new double[]{sumExp, maxRow}); - if (_parms._lambda_search) { - track(vecs[0]); track(vecs[1]); + if (_parms._lambda_search && _parms._is_cv_model) { + Scope.untrack(vecs[0]._key, vecs[1]._key); + removeLater(vecs[0]._key, vecs[1]._key); } return vecs; } @@ -3823,7 +3848,7 @@ private void checkCoeffsBounds() { * - column 2: zi, intermediate values * - column 3: eta = X*beta, intermediate values */ - private void addWdataZiEtaOld2Response() { // attach wdata, zi, eta to response for HGLM + public void addWdataZiEtaOld2Response() { // attach wdata, zi, eta to response for HGLM int moreColnum = 3 + _parms._random_columns.length; Vec[] vecs = _dinfo._adaptedFrame.anyVec().makeZeros(moreColnum); String[] colNames = new String[moreColnum]; @@ -3836,11 +3861,25 @@ private void addWdataZiEtaOld2Response() { // attach wdata, zi, eta to response vecs[index] = _parms.train().vec(randColIndices[index - 3]).makeCopy(); } _dinfo.addResponse(colNames, vecs); - Frame wdataZiEta = new Frame(Key.make("wdataZiEta"+Key.rand()), colNames, vecs); - DKV.put(wdataZiEta); - track(wdataZiEta); + for (int index = 0; index < moreColnum; index++) { + Scope.untrack(vecs[index]._key); + removeLater(vecs[index]._key); + } + } + + @Override + public void onCompletion(CountedCompleter caller) { + doCleanup(); + super.onCompletion(caller); } + @Override + public boolean onExceptionalCompletion(Throwable t, CountedCompleter caller) { + doCleanup(); + return super.onExceptionalCompletion(t, caller); + } + + @Override public boolean progress(double[] beta, GradientInfo ginfo) { _state._iter++; diff --git a/h2o-algos/src/main/java/hex/kmeans/KMeans.java b/h2o-algos/src/main/java/hex/kmeans/KMeans.java index 55a5b0db3c3c..4c69246bb41b 100755 --- a/h2o-algos/src/main/java/hex/kmeans/KMeans.java +++ b/h2o-algos/src/main/java/hex/kmeans/KMeans.java @@ -109,7 +109,7 @@ else if( user_points.numRows() != _parms._k) if (expensive && error_count() == 0) checkMemoryFootPrint(); } - protected void cv_makeAggregateModelMetrics(ModelMetrics.MetricBuilder[] mbs){ + public void cv_makeAggregateModelMetrics(ModelMetrics.MetricBuilder[] mbs){ super.cv_makeAggregateModelMetrics(mbs); ((ModelMetricsClustering.MetricBuilderClustering) mbs[0])._within_sumsqe = null; ((ModelMetricsClustering.MetricBuilderClustering) mbs[0])._size = null; diff --git a/h2o-algos/src/main/java/hex/tree/SharedTree.java b/h2o-algos/src/main/java/hex/tree/SharedTree.java index 68e716654533..43ea630ae210 100755 --- a/h2o-algos/src/main/java/hex/tree/SharedTree.java +++ b/h2o-algos/src/main/java/hex/tree/SharedTree.java @@ -1203,7 +1203,7 @@ public double initialValue() { return _parms._parallel_main_model_building; } - @Override protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { + @Override public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { // Extract stopping conditions from each CV model, and compute the best stopping answer if (!cv_initStoppingParameters()) return; // No exciting changes to stopping conditions diff --git a/h2o-bindings/bin/custom.py b/h2o-bindings/bin/custom.py index 496fad3946c3..ec1c81fa29e0 100644 --- a/h2o-bindings/bin/custom.py +++ b/h2o-bindings/bin/custom.py @@ -25,9 +25,8 @@ def get_customizations_for(language, algo, property=None, default=None): tokens = property.split('.') value = customizations for token in tokens: - if token in value: - value = value.get(token) - else: + value = value.get(token) + if value is None: return default return value else: diff --git a/h2o-bindings/bin/custom/R/gen_pipeline.py b/h2o-bindings/bin/custom/R/gen_pipeline.py deleted file mode 100644 index a887be01831d..000000000000 --- a/h2o-bindings/bin/custom/R/gen_pipeline.py +++ /dev/null @@ -1,28 +0,0 @@ - -extensions = dict( - required_params=[], - frame_params=[], - validate_required_params="", - set_required_params="", - module=""" -.h2o.fill_pipeline <- function(model, parameters, allparams) { - if (!is.null(model$estimator)) { - model$estimator_model <- h2o.getModel(model$estimator$name) - } else { - model$estimator_model <- NULL - } - model$transformers <- unlist(lapply(model$transformers, function(dt) new("H2ODataTransformer", id=dt$id, description=dt$description))) - # class(model) <- "H2OPipeline" - return(model) -} -""" -) - -doc = dict( - preamble=""" -Build a pipeline model given a list of transformers and a final model. - -Currently R model pipelines, as produced by AutoML for example, -are only available as read-only models that can not be constructed and trained directly by the end-user. -""", -) diff --git a/h2o-bindings/bin/custom/python/gen_pipeline.py b/h2o-bindings/bin/custom/python/gen_pipeline.py deleted file mode 100644 index 8fb647c18f86..000000000000 --- a/h2o-bindings/bin/custom/python/gen_pipeline.py +++ /dev/null @@ -1,56 +0,0 @@ -supervised_learning = None # actually depends on the estimator model in the pipeline, leave it to None for now as it is needed only for training and we don't support pipeline as input yet - - -# in future update, we'll want to expose parameters applied to each transformer -def module_extensions(): - class H2ODataTransformer(H2ODisplay): - @classmethod - def make(cls, kvs): - dt = H2ODataTransformer(**{k: v for k, v in kvs if k not in H2OSchema._ignored_schema_keys_}) - dt._json = kvs - return dt - - def __init__(self, id=None, description=None): - self._json = None - self.id = id - self.description = description - - def _repr_(self): - return repr(self._json) - - def _str_(self, verbosity=None): - return repr_def(self) - - - # self-register transformer class: done as soon as `h2o.estimators` is loaded, which means as soon as h2o.h2o is... - register_schema_handler("DataTransformerV3", H2ODataTransformer) - - -def class_extensions(): - @property - def transformers(self): - return self._model_json['output']['transformers'] - - @property - def estimator_model(self): - m_json = self._model_json['output']['estimator'] - return None if (m_json is None or m_json['name'] is None) else h2o.get_model(m_json['name']) - - def transform(self, fr): - """ - Applies all the pipeline transformers to the given input frame. - :return: the transformed frame, as it would be passed to `estimator_model`, if calling `predict` instead. - """ - return H2OFrame._expr(expr=ExprNode("transform", ASTId(self.key), ASTId(fr.key)))._frame(fill_cache=True) - - -extensions = dict( - __imports__=""" -import h2o -from h2o.display import H2ODisplay, repr_def -from h2o.expr import ASTId, ExprNode -from h2o.schemas import H2OSchema, register_schema_handler -""", - __class__=class_extensions, - __module__=module_extensions, -) diff --git a/h2o-bindings/bin/gen_R.py b/h2o-bindings/bin/gen_R.py index 7143f6051377..fdb0150d4d28 100644 --- a/h2o-bindings/bin/gen_R.py +++ b/h2o-bindings/bin/gen_R.py @@ -145,8 +145,8 @@ def get_schema_params(pname): "verbose", "destination_key"] # destination_key is only for SVD bulk_params = list(zip(*filter(lambda t: not t[0] in bulk_pnames_skip, zip(sig_pnames, sig_params)))) - bulk_pnames = list(bulk_params[0]) if bulk_params else [] - sig_bulk_params = list(bulk_params[1]) if bulk_params else [] + bulk_pnames = list(bulk_params[0]) + sig_bulk_params = list(bulk_params[1]) sig_bulk_params.append("segment_columns = NULL") sig_bulk_params.append("segment_models_id = NULL") sig_bulk_params.append("parallelism = 1") diff --git a/h2o-bindings/bin/gen_python.py b/h2o-bindings/bin/gen_python.py index 9ebdaf9a8215..6fa044f77d0e 100755 --- a/h2o-bindings/bin/gen_python.py +++ b/h2o-bindings/bin/gen_python.py @@ -314,19 +314,16 @@ def extend_schema_params(param): yield " self._parms[\"%s\"] = %s" % (sname, pname) yield "" - if deprecated_params: - for old, new in deprecated_params.items(): - new_name = new[0] if isinstance(new, tuple) else new - yield " %s = deprecated_property('%s', %s)" % (old, old, new) - yield "" - + for old, new in deprecated_params.items(): + new_name = new[0] if isinstance(new, tuple) else new + yield " %s = deprecated_property('%s', %s)" % (old, old, new) + + yield "" if class_extras: yield reformat_block(code_as_str(class_extras), 4) - yield "" if module_extras: yield "" yield reformat_block(code_as_str(module_extras)) - yield "" def algo_to_classname(algo): @@ -355,7 +352,6 @@ def algo_to_classname(algo): if algo == "modelselection": return "H2OModelSelectionEstimator" if algo == "isotonicregression": return "H2OIsotonicRegressionEstimator" if algo == "adaboost": return "H2OAdaBoostEstimator" - if algo == "pipeline": return "H2OPipeline" return "H2O" + algo.capitalize() + "Estimator" @@ -460,10 +456,8 @@ def main(): modelselection="model_selection" ) algo_to_category = dict( - generic="Miscellaneous", - pipeline=None, svd="Miscellaneous", - word2vec="Miscellaneous", + word2vec="Miscellaneous" ) for name, mb in builders: module = name @@ -471,9 +465,9 @@ def main(): module = algo_to_module[name] bi.vprint("Generating model: " + name) bi.write_to_file("%s.py" % module, gen_module(mb, name)) - category = (algo_to_category[name] if name in algo_to_category - else "Supervised" if mb["supervised"] - else "Unsupervised") + category = algo_to_category[name] if name in algo_to_category \ + else "Supervised" if mb["supervised"] \ + else "Unsupervised" full_module = '.'.join(["h2o.estimators", module]) modules.append((full_module, module, algo_to_classname(name), category)) diff --git a/h2o-core/src/main/java/hex/CVModelBuilder.java b/h2o-core/src/main/java/hex/CVModelBuilder.java index 550332604e67..3287b62636df 100644 --- a/h2o-core/src/main/java/hex/CVModelBuilder.java +++ b/h2o-core/src/main/java/hex/CVModelBuilder.java @@ -43,49 +43,46 @@ public void bulkBuildModels() { stopAll(submodel_tasks); throw new Job.JobCancelledException(job); } - LOG.info("Building "+modelBuilders[i]._desc+"."); + LOG.info("Building cross-validation model " + (i + 1) + " / " + N + "."); prepare(modelBuilders[i]); modelBuilders[i].startClock(); submodel_tasks[i] = modelBuilders[i].submitTrainModelTask(); if (++nRunning == parallelization) { //piece-wise advance in training the models while (nRunning > 0) { final int waitForTaskIndex = i + 1 - nRunning; - final String modelDesc = modelBuilders[waitForTaskIndex]._desc; try { submodel_tasks[waitForTaskIndex].join(); finished(modelBuilders[waitForTaskIndex]); } catch (RuntimeException t) { if (rt == null) { - LOG.info("Exception from "+ modelDesc + " will be reported as main exception."); + LOG.info("Exception from CV model #" + waitForTaskIndex + " will be reported as main exception."); rt = t; } else { - LOG.warn(modelDesc + " failed, the exception will not be reported", t); + LOG.warn("CV model #" + waitForTaskIndex + " failed, the exception will not be reported", t); } } finally { - LOG.info("Completed "+modelDesc+"."); + LOG.info("Completed cross-validation model " + waitForTaskIndex + " / " + N + "."); nRunning--; // need to decrement regardless even if there is an exception, otherwise looping... } } if (rt != null) throw rt; } } - for (int i = 0; i < N; ++i) { //all sub-models must be completed before the main model can be built - final String modelDesc = modelBuilders[i]._desc; + for (int i = 0; i < N; ++i) //all sub-models must be completed before the main model can be built try { final TrainModelTaskController task = submodel_tasks[i]; assert task != null; task.join(); } catch (RuntimeException t) { if (rt == null) { - LOG.info("Exception from "+ modelDesc + " will be reported as main exception."); + LOG.info("Exception from CV model #" + i + " will be reported as main exception."); rt = t; } else { - LOG.warn(modelDesc + " failed, the exception will not be reported", t); + LOG.warn("CV model #" + i + " failed, the exception will not be reported", t); } } finally { - LOG.info("Completed "+modelDesc+"."); + LOG.info("Completed cross-validation model " + i + " / " + N + "."); } - } if (rt != null) throw rt; } diff --git a/h2o-core/src/main/java/hex/Model.java b/h2o-core/src/main/java/hex/Model.java index 026ed7d85ace..c246e5675f05 100755 --- a/h2o-core/src/main/java/hex/Model.java +++ b/h2o-core/src/main/java/hex/Model.java @@ -349,7 +349,7 @@ public static class GridSortBy { // intentionally not an enum to allow 3rd party * WARNING: Model Parameters is not immutable object and ModelBuilder can modify * them! */ - public abstract static class Parameters extends Iced implements AdaptFrameParameters, Parameterizable, Checksumable { + public abstract static class Parameters extends Iced implements AdaptFrameParameters { /** Maximal number of supported levels in response. */ public static final int MAX_SUPPORTED_LEVELS = 1<<20; @@ -620,7 +620,6 @@ public void read_unlock_frames(Job job) { public boolean hasCustomMetricFunc() { return _custom_metric_func != null; } - @Override public long checksum() { return checksum(null); } @@ -635,7 +634,66 @@ public long checksum() { * @return checksum A 64-bit long representing the checksum of the {@link Parameters} object */ public long checksum(final Set ignoredFields) { - long xs = Checksum.checksum(this, ignoredFields); + long xs = 0x600DL; + int count = 0; + Field[] fields = Weaver.getWovenFields(this.getClass()); + Arrays.sort(fields, Comparator.comparing(Field::getName)); + for (Field f : fields) { + if (ignoredFields != null && ignoredFields.contains(f.getName())) { + // Do not include ignored fields in the final hash + continue; + } + final long P = MathUtils.PRIMES[count % MathUtils.PRIMES.length]; + Class c = f.getType(); + if (c.isArray()) { + try { + f.setAccessible(true); + if (f.get(this) != null) { + if (c.getComponentType() == Integer.TYPE){ + int[] arr = (int[]) f.get(this); + xs = xs * P + (long) Arrays.hashCode(arr); + } else if (c.getComponentType() == Float.TYPE) { + float[] arr = (float[]) f.get(this); + xs = xs * P + (long) Arrays.hashCode(arr); + } else if (c.getComponentType() == Double.TYPE) { + double[] arr = (double[]) f.get(this); + xs = xs * P + (long) Arrays.hashCode(arr); + } else if (c.getComponentType() == Long.TYPE){ + long[] arr = (long[]) f.get(this); + xs = xs * P + (long) Arrays.hashCode(arr); + } else if (c.getComponentType() == Boolean.TYPE){ + boolean[] arr = (boolean[]) f.get(this); + xs = xs * P + (long) Arrays.hashCode(arr); + } else { + Object[] arr = (Object[]) f.get(this); + xs = xs * P + (long) Arrays.deepHashCode(arr); + } //else lead to ClassCastException + } else { + xs = xs * P; + } + } catch (IllegalAccessException e) { + throw new RuntimeException(e); + } catch (ClassCastException t) { + throw H2O.fail("Failed to calculate checksum for the parameter object", t); //no support yet for int[][] etc. + } + } else { + try { + f.setAccessible(true); + Object value = f.get(this); + if (value instanceof Enum) { + // use string hashcode for enums, otherwise the checksum would be different each run + xs = xs * P + (long)(value.toString().hashCode()); + } else if (value != null) { + xs = xs * P + (long)(value.hashCode()); + } else { + xs = xs * P + P; + } + } catch (IllegalAccessException e) { + throw new RuntimeException(e); + } + } + count++; + } xs ^= (train() == null ? 43 : train().checksum()) * (valid() == null ? 17 : valid().checksum()); return xs; } @@ -754,54 +812,6 @@ public void setDistributionFamily(DistributionFamily distributionFamily){ public DistributionFamily getDistributionFamily() { return _distribution; } - - @Override - public boolean hasParameter(String name) { - try { - getParameter(name); - return true; - } catch (Exception e) { - return false; - } - } - - @Override - public Object getParameter(String name) { - return PojoUtils.getFieldValue(this, name); - } - - @Override - public void setParameter(String name, Object value) { - PojoUtils.setField(this, name, value); - } - - @Override - public boolean isParameterSetToDefault(String name) { - Object val = getParameter(name); - Object defaultVal = getParameterDefaultValue(name); - return Objects.deepEquals(val, defaultVal); - } - - @Override - public Object getParameterDefaultValue(String name) { - return getDefaults().getParameter(name); - } - - @Override - public boolean isParameterAssignable(String name) { - return "_seed".equals(name) || isParameterSetToDefault(name); - } - - /** private use only to avoid this getting mutated. */ - private transient Parameters _defaults; - - /** private use only to avoid this getting mutated. */ - private Parameters getDefaults() { - if (_defaults == null) { - _defaults = ModelBuilder.makeParameters(algoName()); - } - return _defaults; - } } public ModelMetrics addModelMetrics(final ModelMetrics mm) { @@ -1682,7 +1692,7 @@ public static String[] adaptTestForTrain(final Frame test, final String[] origNa String[] names, String[][] domains, final AdaptFrameParameters parms, final boolean expensive, final boolean computeMetrics, final InteractionBuilder interactionBldr, final ToEigenVec tev, - final Map toDelete, final boolean catEncoded) + final IcedHashMap toDelete, final boolean catEncoded) throws IllegalArgumentException { String[] msg = new String[0]; if (test == null) return msg; @@ -1720,7 +1730,7 @@ public static String[] adaptTestForTrain(final Frame test, final String[] origNa // As soon as the test frame contains at least one original pre-encoding predictor, // then we consider the frame as valid for predictions, and we'll later fill missing columns with NA Set required = new HashSet<>(Arrays.asList(origNames)); - required.removeAll(Arrays.asList(parms.getNonPredictors())); + required.removeAll(Arrays.asList(response, weights, fold, treatment)); for (String name : test.names()) { if (required.contains(name)) { match = true; @@ -1905,7 +1915,7 @@ public Frame result() { } public Frame transform(Frame fr) { - throw new UnsupportedOperationException("this model doesn't support frame transformation"); + throw new UnsupportedOperationException("this model doesn't support constant frame results"); } /** Bulk score the frame {@code fr}, producing a Frame result; the 1st @@ -1984,7 +1994,8 @@ public Frame score(Frame fr, String destination_key, Job j, boolean computeMetri try (Scope.Safe s = Scope.safe(fr)) { // Adapt frame, clean up the previous score warning messages _warningsP = new String[0]; - computeMetrics = computeMetrics && canComputeMetricsForFrame(fr); + computeMetrics = computeMetrics && + (!_output.hasResponse() || (fr.vec(_output.responseName()) != null && !fr.vec(_output.responseName()).isBad())); Frame adaptFr = adaptFrameForScore(fr, computeMetrics); // Predict & Score @@ -2073,34 +2084,14 @@ public void map(Chunk[] cs, NewChunk[] nc) { }.doAll(Vec.T_NUM, predictions).outputFrame(Key.make(outputName), new String[]{"deviance"}, null); } - protected boolean canComputeMetricsForFrame(Frame fr) { - return !_output.hasResponse() || (fr.vec(_output.responseName()) != null && !fr.vec(_output.responseName()).isBad()); - } - protected String[] makeScoringNames(){ return makeScoringNames(_output); } - - /** - * ???: something fishy here! - * I suspect a bug not discovered yet, as there is a surprising similarity with the implementation taking a `names` parameter, - * but usages are only very slightly different and only one version is overridden by algos, which means that one may be incorrect in some cases. - */ - protected String[][] makeScoringDomains(Frame adaptFrm, boolean computeMetrics) { - String[][] domains = new String[1][]; - Vec response = adaptFrm.lastVec(); - domains[0] = _output.nclasses() == 1 ? null : !computeMetrics ? _output._domains[_output._domains.length-1] : response.domain(); - if (_parms._distribution == DistributionFamily.quasibinomial) { - domains[0] = new VecUtils.CollectDoubleDomain(null,2).doAll(response).stringDomain(response.isInt()); - } - return domains; - } - protected String[][] makeScoringDomains(Frame adaptFrm, boolean computeMetrics, String[] names) { String[][] domains = new String[names.length][]; Vec response = adaptFrm.lastVec(); - domains[0] = names.length == 1 || _output.hasTreatment() ? null : !computeMetrics ? _output._domains[_output._domains.length - 1] : response.domain(); + domains[0] = names.length == 1 || _output.hasTreatment() ? null : ! computeMetrics ? _output._domains[_output._domains.length - 1] : response.domain(); if (_parms._distribution == DistributionFamily.quasibinomial) { domains[0] = new VecUtils.CollectDoubleDomain(null,2).doAll(response).stringDomain(response.isInt()); } @@ -2223,10 +2214,16 @@ protected Frame postProcessPredictions(Frame adaptFrm, Frame predictFr, Job j) { * @return MetricBuilder */ protected ModelMetrics.MetricBuilder scoreMetrics(Frame adaptFrm) { - final boolean computeMetrics = canComputeMetricsForFrame(adaptFrm); + final boolean computeMetrics = (!isSupervised() || (adaptFrm.vec(_output.responseName()) != null && !adaptFrm.vec(_output.responseName()).isBad())); // Build up the names & domains. -// String[] names = makeScoringNames(); - String[][] domains = makeScoringDomains(adaptFrm, computeMetrics); + //String[] names = makeScoringNames(); + String[][] domains = new String[1][]; + Vec response = adaptFrm.lastVec(); + domains[0] = _output.nclasses() == 1 ? null : !computeMetrics ? _output._domains[_output._domains.length-1] : response.domain(); + if (_parms._distribution == DistributionFamily.quasibinomial) { + domains[0] = new VecUtils.CollectDoubleDomain(null,2).doAll(response).stringDomain(response.isInt()); + } + // Score the dataset, building the class distribution & predictions BigScore bs = makeBigScoreTask(domains, null, adaptFrm, computeMetrics, false, null, CFuncRef.from(_parms._custom_metric_func)).doAll(adaptFrm); return bs._mb; @@ -2250,11 +2247,7 @@ public BigScore(String[] domain, int ncols, double[] mean, boolean testHasWeight boolean computeMetrics, boolean makePreds, Job j, CFuncRef customMetricFunc) { super(customMetricFunc); _j = j; - _domain = domain; - _npredcols = ncols; - _mean = mean; - _computeMetrics = computeMetrics; - _makePreds = makePreds; + _domain = domain; _npredcols = ncols; _mean = mean; _computeMetrics = computeMetrics; _makePreds = makePreds; if(_output._hasWeights && _computeMetrics && !testHasWeights) throw new IllegalArgumentException("Missing weights when computing validation metrics."); _hasWeights = testHasWeights; @@ -2435,7 +2428,7 @@ public double score(double[] data){ deleteCrossValidationPreds(); deleteCrossValidationModels(); } - cleanUp(_toDelete == null ? null : _toDelete.keySet()); + cleanUp(_toDelete); return super.remove_impl(fs, cascade); } diff --git a/h2o-core/src/main/java/hex/ModelBuilder.java b/h2o-core/src/main/java/hex/ModelBuilder.java index 6cbeff59daeb..cbf1e301542d 100644 --- a/h2o-core/src/main/java/hex/ModelBuilder.java +++ b/h2o-core/src/main/java/hex/ModelBuilder.java @@ -23,14 +23,6 @@ * Model builder parent class. Contains the common interfaces and fields across all model builders. */ abstract public class ModelBuilder, P extends Model.Parameters, O extends Model.Output> extends Iced { - - public static final String CV_WEIGHTS_COLUMN = "__internal_cv_weights__"; - - private ModelBuilderCallbacks _callbacks; - - public void setCallbacks(ModelBuilderCallbacks callbacks) { - this._callbacks = callbacks; - } public ToEigenVec getToEigenVec() { return null; } public boolean shouldReorder(Vec v) { return _parms._categorical_encoding.needsResponse() && isSupervised(); } @@ -157,7 +149,7 @@ public static

P makeParameters(String algo) { /** Factory method to create a ModelBuilder instance for given the algo name. * Shallow clone of both the default ModelBuilder instance and a Parameter. */ - public static > B make(String algo, Job job, K result) { + public static B make(String algo, Job job, Key result) { return getRegisteredBuilder(algo) .map(prototype -> { @SuppressWarnings("unchecked") @@ -185,8 +177,8 @@ public static B make(MP pa return make(parms, mKey); } - public static > B make(MP parms, K mKey) { - Job mJob = new Job<>(mKey, parms.javaName(), parms.algoName()); + public static B make(MP parms, Key mKey) { + Job mJob = new Job<>(mKey, parms.javaName(), parms.algoName()); B newMB = ModelBuilder.make(parms.algoName(), mJob, mKey); newMB._parms = parms.clone(); newMB._input_parms = parms.clone(); @@ -244,17 +236,17 @@ abstract protected class Driver extends H2O.H2OCountedCompleter { protected Driver(){ super(); } - @Override - public void compute2() { - if (_callbacks != null) _callbacks.wrapCompute(ModelBuilder.this, this::compute3); - else this.compute3(); + private ModelBuilderListener _callback; + + public void setCallback(ModelBuilderListener callback) { + this._callback = callback; } - + // Pull the boilerplate out of the computeImpl(), so the algo writer doesn't need to worry about the following: // 1) Scope (unless they want to keep data, then they must call Scope.untrack(Key[])) // 2) Train/Valid frame locking and unlocking // 3) calling tryComplete() - public void compute3() { + public void compute2() { try { Scope.enter(); _parms.read_lock_frames(_job); // Fetch & read-lock input frames @@ -266,10 +258,7 @@ public void compute3() { _parms.read_unlock_frames(_job); if (_parms._is_cv_model) { // CV models get completely cleaned up when the main model is fully trained. - Key[] keep = new Key[0]; - try { - keep = _workspace.getToDelete(true).keySet().toArray(new Key[0]); - } catch (Exception ignored) {} + Key[] keep = _workspace == null ? new Key[0] : _workspace.getToDelete(true).keySet().toArray(new Key[0]); Scope.exit(keep); } else { cleanUp(); @@ -282,16 +271,16 @@ public void compute3() { @Override public void onCompletion(CountedCompleter caller) { setFinalState(); - if (_callbacks != null) { - _callbacks.onModelSuccess(_result); + if (_callback != null) { + _callback.onModelSuccess(_result.get()); } } @Override public boolean onExceptionalCompletion(Throwable ex, CountedCompleter caller) { setFinalState(); - if (_callbacks != null) { - _callbacks.onModelFailure(_result, ex, _parms); + if (_callback != null) { + _callback.onModelFailure(ex, _parms); } return true; } @@ -390,11 +379,16 @@ public void run() { /** Method to launch training of a Model, based on its parameters. */ final public Job trainModel() { + return trainModel(null); + } + + final public Job trainModel(final ModelBuilderListener callback) { if (error_count() > 0) throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(this); startClock(); if (!nFoldCV()) { Driver driver = trainModelImpl(); + driver.setCallback(callback); return _job.start(driver, _parms.progressUnits(), _parms._max_runtime_secs); } else { // cross-validation needs to be forked off to allow continuous (non-blocking) progress bar @@ -405,10 +399,15 @@ public void compute2() { tryComplete(); } + @Override + public void onCompletion(CountedCompleter caller) { + if (callback != null) callback.onModelSuccess(_result.get()); + } + @Override public boolean onExceptionalCompletion(Throwable ex, CountedCompleter caller) { Log.warn("Model training job " + _job._description + " completed with exception: " + ex); - if (_callbacks != null) _callbacks.onModelFailure(_result, ex, _parms); + if (callback != null) callback.onModelFailure(ex, _parms); try { Keyed.remove(_job._result); // ensure there's no incomplete model left for manipulation after crash or cancellation } catch (Exception logged) { @@ -607,8 +606,7 @@ public void updateParameters() { cv_updateOptimalParameters(_cvModelBuilders); } } - - + /** * Default naive (serial) implementation of N-fold cross-validation * (builds N+1 models, all have train+validation metrics, the main model has N-fold cross-validated validation metrics) @@ -620,8 +618,7 @@ public void computeCrossValidation() { ModelBuilder[] cvModelBuilders = null; try { Scope.enter(); - // Step 0: custom preparation for CV - cv_init(); // ensures that this initialization is done in the current Scope to avoid key leakage. + init(false); // Step 1: Assign each row to a fold final FoldAssignment foldAssignment = cv_AssignFold(N); @@ -664,7 +661,8 @@ public void computeCrossValidation() { // Step 7: Combine cross-validation scores; compute main model x-val // scores; compute gains/lifts - cv_mainModelScores(N, mbs, cvModelBuilders); + if (!cvModelBuilders[0].getName().equals("infogram")) // infogram does not support scoring + cv_mainModelScores(N, mbs, cvModelBuilders); _job.setReadyForView(true); DKV.put(_job); @@ -693,14 +691,9 @@ public void computeCrossValidation() { Scope.exit(); } } - - // Step 0: Algos can override this if additional preparation is required before starting CV. - protected void cv_init() { - init(false); - } // Step 1: Assign each row to a fold - protected FoldAssignment cv_AssignFold(int N) { + FoldAssignment cv_AssignFold(int N) { assert(N>=2); Vec fold = train().vec(_parms._fold_column); if (fold != null) { @@ -727,7 +720,7 @@ protected FoldAssignment cv_AssignFold(int N) { } // Step 2: Make 2*N binary weight vectors - protected Vec[] cv_makeWeights(final int N, FoldAssignment foldAssignment) { + Vec[] cv_makeWeights(final int N, FoldAssignment foldAssignment) { String origWeightsName = _parms._weights_column; Vec origWeight = origWeightsName != null ? train().vec(origWeightsName) : train().anyVec().makeCon(1.0); Frame folds_and_weights = new Frame(foldAssignment.getAdaptedFold(), origWeight); @@ -759,11 +752,12 @@ protected Vec[] cv_makeWeights(final int N, FoldAssignment foldAssignment) { } // Step 3: Build N train & validation frames; build N ModelBuilders; error check them all - protected ModelBuilder[] cv_makeFramesAndBuilders( int N, Vec[] weights) { + private ModelBuilder[] cv_makeFramesAndBuilders( int N, Vec[] weights ) { final long old_cs = _parms.checksum(); final String origDest = _result.toString(); - if (train().find(CV_WEIGHTS_COLUMN) != -1) throw new H2OIllegalArgumentException("Frame cannot contain a Vec called '"+CV_WEIGHTS_COLUMN+"'."); + final String weightName = "__internal_cv_weights__"; + if (train().find(weightName) != -1) throw new H2OIllegalArgumentException("Frame cannot contain a Vec called '" + weightName + "'."); Frame cv_fr = new Frame(train().names(),train().vecs()); if( _parms._weights_column!=null ) cv_fr.remove( _parms._weights_column ); // The CV frames will have their own private weight column @@ -776,32 +770,32 @@ protected ModelBuilder[] cv_makeFramesAndBuilders( int N, Vec[] weights // Training/Validation share the same data, but will have exclusive weights Frame cvTrain = new Frame(Key.make(identifier + "_train"), cv_fr.names(), cv_fr.vecs()); cvTrain.write_lock(_job); - cvTrain.add(CV_WEIGHTS_COLUMN, weights[2*i]); + cvTrain.add(weightName, weights[2*i]); cvTrain.update(_job); Frame cvValid = new Frame(Key.make(identifier + "_valid"), cv_fr.names(), cv_fr.vecs()); cvValid.write_lock(_job); - cvValid.add(CV_WEIGHTS_COLUMN, weights[2*i+1]); + cvValid.add(weightName, weights[2*i+1]); cvValid.update(_job); // Shallow clone - not everything is a private copy!!! ModelBuilder cv_mb = (ModelBuilder)this.clone(); - cv_mb._desc = "Cross-Validation model " + (i + 1) + " / " + N; + cv_mb.setTrain(cvTrain); cv_mb._result = Key.make(identifier); // Each submodel gets its own key cv_mb._parms = (P) _parms.clone(); // Fix up some parameters of the clone cv_mb._parms._is_cv_model = true; cv_mb._parms._cv_fold = i; - cv_mb._parms._weights_column = CV_WEIGHTS_COLUMN;// All submodels have a weight column, which the main model does not + cv_mb._parms._weights_column = weightName;// All submodels have a weight column, which the main model does not cv_mb._parms.setTrain(cvTrain._key); // All submodels have a weight column, which the main model does not cv_mb._parms._valid = cvValid._key; cv_mb._parms._fold_assignment = Model.Parameters.FoldAssignmentScheme.AUTO; cv_mb._parms._nfolds = 0; // Each submodel is not itself folded cv_mb._parms._max_runtime_secs = cv_max_runtime_secs; - + cv_mb.clearValidationErrors(); // each submodel gets its own validation messages and error_count() cv_mb._input_parms = (P) _parms.clone(); + cv_mb._desc = "Cross-Validation model " + (i + 1) + " / " + N; + // Error-check all the cross-validation Builders before launching any - cv_mb.clearValidationErrors(); // each submodel gets its own validation messages and error_count() - cv_mb.setTrain(null); cv_mb.setValid(null); // reset before validating cv_mb.init(false); if( cv_mb.error_count() > 0 ) { // Gather all submodel error messages Log.info("Marking frame for failed cv model for removal: " + cvTrain._key); @@ -818,7 +812,7 @@ protected ModelBuilder[] cv_makeFramesAndBuilders( int N, Vec[] weights if( error_count() > 0 ) { // Found an error in one or more submodels Futures fs = new Futures(); for (Frame cvf : cvFramesForFailedModels) { - cvf.vec(CV_WEIGHTS_COLUMN).remove(fs); // delete the Vec's chunks + cvf.vec(weightName).remove(fs); // delete the Vec's chunks DKV.remove(cvf._key, fs); // delete the Frame from the DKV, leaving its vecs Log.info("Removing frame for failed cv model: " + cvf._key); } @@ -831,7 +825,7 @@ protected ModelBuilder[] cv_makeFramesAndBuilders( int N, Vec[] weights } // Step 4: Run all the CV models and launch the main model - protected void cv_buildModels(int N, ModelBuilder[] cvModelBuilders ) { + public void cv_buildModels(int N, ModelBuilder[] cvModelBuilders ) { makeCVModelBuilder(cvModelBuilders, nModelsInParallel(N)).bulkBuildModels(); cv_computeAndSetOptimalParameters(cvModelBuilders); } @@ -840,35 +834,9 @@ protected CVModelBuilder makeCVModelBuilder(ModelBuilder[] modelBuilder return new CVModelBuilder(_job, modelBuilders, parallelization); } - protected ModelMetrics.MetricBuilder makeCVMetricBuilder(ModelBuilder cvModelBuilder, Futures fs) { - Frame cvValid = cvModelBuilder.valid(); - Frame preds = null; - try (Scope.Safe s = Scope.safe(cvValid)) { - ModelMetrics.MetricBuilder mb; - Frame adaptFr = new Frame(cvValid); - M cvModel = cvModelBuilder.dest().get(); - cvModel.adaptTestForTrain(adaptFr, true, !isSupervised()); - if (nclasses() == 2 /* need holdout predictions for gains/lift table */ - || _parms._keep_cross_validation_predictions - || (cvModel.isDistributionHuber() /*need to compute quantiles on abs error of holdout predictions*/)) { - String predName = cvModelBuilder.getPredictionKey(); - Model.PredictScoreResult result = cvModel.predictScoreImpl(cvValid, adaptFr, predName, _job, true, CFuncRef.NOP); - preds = result.getPredictions(); - Scope.untrack(preds); - result.makeModelMetrics(cvValid, adaptFr); - mb = result.getMetricBuilder(); - DKV.put(cvModel); - } else { - mb = cvModel.scoreMetrics(adaptFr); - } - return mb; - } finally { - Scope.track(preds); - } - } // Step 5: Score the CV models - protected ModelMetrics.MetricBuilder[] cv_scoreCVModels(int N, Vec[] weights, ModelBuilder[] cvModelBuilders) { + public ModelMetrics.MetricBuilder[] cv_scoreCVModels(int N, Vec[] weights, ModelBuilder[] cvModelBuilders) { if (_job.stop_requested()) { Log.info("Skipping scoring of CV models"); throw new Job.JobCancelledException(_job); @@ -884,17 +852,44 @@ protected ModelMetrics.MetricBuilder[] cv_scoreCVModels(int N, Vec[] weights, Mo Log.info("Skipping scoring for last "+(N-i)+" out of "+N+" CV models"); throw new Job.JobCancelledException(_job); } - mbs[i] = makeCVMetricBuilder(cvModelBuilders[i], fs); - + Frame cvValid = cvModelBuilders[i].valid(); + Frame preds = null; + try (Scope.Safe s = Scope.safe(cvValid)) { + Frame adaptFr = new Frame(cvValid); + if (makeCVMetrics(cvModelBuilders[i])) { + M cvModel = cvModelBuilders[i].dest().get(); + cvModel.adaptTestForTrain(adaptFr, true, !isSupervised()); + if (nclasses() == 2 /* need holdout predictions for gains/lift table */ + || _parms._keep_cross_validation_predictions + || (cvModel.isDistributionHuber() /*need to compute quantiles on abs error of holdout predictions*/)) { + String predName = cvModelBuilders[i].getPredictionKey(); + Model.PredictScoreResult result = cvModel.predictScoreImpl(cvValid, adaptFr, predName, _job, true, CFuncRef.from(_parms._custom_metric_func)); + preds = result.getPredictions(); + Scope.untrack(preds); + result.makeModelMetrics(cvValid, adaptFr); + mbs[i] = result.getMetricBuilder(); + DKV.put(cvModel); + } else { + mbs[i] = cvModel.scoreMetrics(adaptFr); + } + } + } finally { + Scope.track(preds); + } DKV.remove(cvModelBuilders[i]._parms._train,fs); DKV.remove(cvModelBuilders[i]._parms._valid,fs); weights[2*i ].remove(fs); weights[2*i+1].remove(fs); } + fs.blockForPending(); return mbs; } + protected boolean makeCVMetrics(ModelBuilder cvModelBuilder) { + return !cvModelBuilder.getName().equals("infogram"); + } + private boolean useParallelMainModelBuilding(int nFolds) { int parallelizationLevel = nModelsInParallel(nFolds); return parallelizationLevel > 1 && _parms._parallelize_cross_validation && cv_canBuildMainModelInParallel(); @@ -913,7 +908,7 @@ protected boolean cv_initStoppingParameters() { } // Step 6: build the main model - protected void buildMainModel(long max_runtime_millis) { + private void buildMainModel(long max_runtime_millis) { if (_job.stop_requested()) { Log.info("Skipping main model"); throw new Job.JobCancelledException(_job); @@ -927,7 +922,7 @@ protected void buildMainModel(long max_runtime_millis) { } // Step 7: Combine cross-validation scores; compute main model x-val scores; compute gains/lifts - protected void cv_mainModelScores(int N, ModelMetrics.MetricBuilder[] mbs, ModelBuilder[] cvModelBuilders) { + public void cv_mainModelScores(int N, ModelMetrics.MetricBuilder mbs[], ModelBuilder cvModelBuilders[]) { //never skipping CV main scores: we managed to reach last step and this should not be an expensive one, so let's offer this model M mainModel = _result.get(); @@ -1013,7 +1008,7 @@ protected void cv_mainModelScores(int N, ModelMetrics.MetricBuilder[] mbs, Model DKV.put(mainModel); } - protected void cv_makeAggregateModelMetrics(ModelMetrics.MetricBuilder[] mbs){ + public void cv_makeAggregateModelMetrics(ModelMetrics.MetricBuilder[] mbs){ for (int i = 1; i < mbs.length; ++i) { mbs[0].reduceForCV(mbs[i]); } @@ -1047,7 +1042,7 @@ protected void setMaxRuntimeSecsForMainModel() { * Also allow the cv models to be modified after all of them have been built. * For example, the model might need to be told to not do early stopping. CV models might have their lambda value modified, etc. */ - protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { } + public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { } /** @return Whether n-fold cross-validation is done */ public boolean nFoldCV() { @@ -1454,7 +1449,7 @@ public void init(boolean expensive) { } else { hide("_nfolds", "nfolds is ignored when a fold column is specified."); } - if (_parms._fold_assignment != Model.Parameters.FoldAssignmentScheme.AUTO && _parms._fold_assignment != null) { + if (_parms._fold_assignment != Model.Parameters.FoldAssignmentScheme.AUTO && _parms._fold_assignment != null && _parms != null) { error("_fold_assignment", "Fold assignment is not allowed in conjunction with a fold column."); } } @@ -1630,8 +1625,9 @@ else if (_parms._weights_column != null && _weights != null && !_weights.isBinar } if (expensive) { - Frame newtrain = applyPreprocessors(_train, true); - newtrain = encodeFrameCategoricals(newtrain); //we could turn this into a preprocessor later + boolean scopeTrack = !_parms._is_cv_model; + Frame newtrain = applyPreprocessors(_train, true, scopeTrack); + newtrain = encodeFrameCategoricals(newtrain, scopeTrack); //we could turn this into a preprocessor later if (newtrain != _train) { _origTrain = _train; _origNames = _train.names(); @@ -1642,8 +1638,8 @@ else if (_parms._weights_column != null && _weights != null && !_weights.isBinar _origTrain = null; } if (_valid != null) { - Frame newvalid = applyPreprocessors(_valid, false); - newvalid = encodeFrameCategoricals(newvalid /* for CV, need to score one more time in outer loop */); + Frame newvalid = applyPreprocessors(_valid, false, scopeTrack); + newvalid = encodeFrameCategoricals(newvalid, scopeTrack /* for CV, need to score one more time in outer loop */); setValid(newvalid); } boolean restructured = false; @@ -1749,7 +1745,7 @@ protected void checkCustomMetricForEarlyStopping() { public Frame init_adaptFrameToTrain(Frame fr, String frDesc, String field, boolean expensive) { Frame adapted = adaptFrameToTrain(fr, frDesc, field, expensive, false); if (expensive) - adapted = encodeFrameCategoricals(adapted); + adapted = encodeFrameCategoricals(adapted, true); return adapted; } @@ -1786,7 +1782,7 @@ private Frame adaptFrameToTrain(Frame fr, String frDesc, String field, boolean e return adapted; } - private Frame applyPreprocessors(Frame fr, boolean isTraining) { + private Frame applyPreprocessors(Frame fr, boolean isTraining, boolean scopeTrack) { if (_parms._preprocessors == null) return fr; for (Key key : _parms._preprocessors) { @@ -1797,14 +1793,14 @@ private Frame applyPreprocessors(Frame fr, boolean isTraining) { for (Key key : _parms._preprocessors) { ModelPreprocessor preprocessor = key.get(); encoded = isTraining ? preprocessor.processTrain(result, _parms) : preprocessor.processValid(result, _parms); - if (encoded != result) Scope.track(encoded); + if (encoded != result) trackEncoded(encoded, scopeTrack); result = encoded; } - track(result); // otherwise encoded frame is fully removed on CV model completion, raising exception when computing CV scores. + if (!scopeTrack) Scope.untrack(result); // otherwise encoded frame is fully removed on CV model completion, raising exception when computing CV scores. return result; } - private Frame encodeFrameCategoricals(Frame fr) { + private Frame encodeFrameCategoricals(Frame fr, boolean scopeTrack) { Frame encoded = FrameUtils.categoricalEncoder( fr, _parms.getNonPredictors(), @@ -1812,49 +1808,17 @@ private Frame encodeFrameCategoricals(Frame fr) { getToEigenVec(), _parms._max_categorical_levels ); - if (encoded != fr) track(encoded); + if (encoded != fr) trackEncoded(encoded, scopeTrack); return encoded; } - - protected void track(Frame... frames) { - for (Frame fr : frames) track(fr, _parms._is_cv_model); - } - - protected void track(Frame fr, boolean keepUntilCompletion) { - if (fr == null || fr._key == null) return; - if (keepUntilCompletion) { - keepUntilCompletion(fr._key); - Scope.untrack(fr); - } - else { + + private void trackEncoded(Frame fr, boolean scopeTrack) { + assert fr._key != null; + if (scopeTrack) Scope.track(fr); - } - } - - protected void track(Vec... vecs) { - for (Vec vec : vecs) track(vec, _parms._is_cv_model); - } - - protected void track(Vec vec, boolean keepUntilCompletion) { - if (vec == null || vec._key == null) return; - if (keepUntilCompletion) { - keepUntilCompletion(vec._key); - Scope.untrack(vec._key); - } else { - Scope.track(vec); - } - } - - /** - * Track keys to be removed only once the model is fully trained. - * Especially useful for keys created during CV model training that may be needed after the CV model is trained (e.g. CV scoring). - * @param key - */ - protected void keepUntilCompletion(Key key) { - assert key != null; - _workspace.getToDelete(true).put(key, Arrays.toString(Thread.currentThread().getStackTrace())); + else + _workspace.getToDelete(true).put(fr._key, Arrays.toString(Thread.currentThread().getStackTrace())); } - /** * Rebalance a frame for load balancing @@ -2182,7 +2146,7 @@ public String getName() { return getClass().getSimpleName().toLowerCase(); } - protected void cleanUp() { + private void cleanUp() { _workspace.cleanUp(); } diff --git a/h2o-core/src/main/java/hex/ModelBuilderCallbacks.java b/h2o-core/src/main/java/hex/ModelBuilderCallbacks.java deleted file mode 100644 index 9bc641b77d82..000000000000 --- a/h2o-core/src/main/java/hex/ModelBuilderCallbacks.java +++ /dev/null @@ -1,54 +0,0 @@ -package hex; - -import water.Iced; -import water.Key; -import water.util.ArrayUtils; - -public abstract class ModelBuilderCallbacks extends Iced { - - private static class HandledException extends Exception { - @Override - public String toString() { - return ""; - } - - @Override - public synchronized Throwable fillInStackTrace() { - return this; - } - } - - private static final HandledException HANDLED = new HandledException(); - - public void wrapCompute(ModelBuilder builder, Runnable compute) { compute.run(); } - - /** - * Callback for successfully finished model builds - * - * @param modelKey key of built Model. - */ - public void onModelSuccess(Key modelKey) {} - - /** - * Callback for failed model builds - * - * @param modelKey Key of the model that was attempted at being built. - * @param cause An instance of {@link Throwable} - cause of failure - * @param parameters An instance of Model.Parameters used in the attempt to build the model - */ - public void onModelFailure(Key modelKey, Throwable cause, Model.Parameters parameters) {} - - /** - * subclasses may want to call this before processing exceptions on model failure. - * @param cause - * @return true if the exception is considered as having already been handled and can be ignored. - * false otherwise: in this case the exception is automatically marked as handled for future checks, - * but current code is supposed to handle it immediately. - */ - protected boolean checkExceptionHandled(Throwable cause) { - if (ArrayUtils.contains(cause.getSuppressed(), HANDLED)) return true; - cause.addSuppressed(HANDLED); - return false; - } - -} diff --git a/h2o-core/src/main/java/hex/ModelBuilderListener.java b/h2o-core/src/main/java/hex/ModelBuilderListener.java new file mode 100644 index 000000000000..96cfc1a47a96 --- /dev/null +++ b/h2o-core/src/main/java/hex/ModelBuilderListener.java @@ -0,0 +1,22 @@ +package hex; + +import water.Iced; + +public abstract class ModelBuilderListener extends Iced { + /** + * Callback for successfully finished model builds + * + * @param model Model built + */ + abstract void onModelSuccess(Model model); + + /** + * Callback for failed model builds + * + * @param cause An instance of {@link Throwable} - cause of failure + * @param parameters An instance of Model.Parameters used in the attempt to build the model + */ + abstract void onModelFailure(Throwable cause, Model.Parameters parameters); + + +} diff --git a/h2o-core/src/main/java/hex/ModelMetrics.java b/h2o-core/src/main/java/hex/ModelMetrics.java index 70001292315c..0e0e3624da9f 100755 --- a/h2o-core/src/main/java/hex/ModelMetrics.java +++ b/h2o-core/src/main/java/hex/ModelMetrics.java @@ -475,8 +475,7 @@ public void postGlobal(CustomMetric customMetric) { * @param m Model * @param f Scored Frame * @param adaptedFrame Adapted Frame - * @param preds Predictions of m on f (optional) - * @return Filled Model Metrics object + *@param preds Predictions of m on f (optional) @return Filled Model Metrics object */ public abstract ModelMetrics makeModelMetrics(Model m, Frame f, Frame adaptedFrame, Frame preds); diff --git a/h2o-core/src/main/java/hex/ParallelModelBuilder.java b/h2o-core/src/main/java/hex/ParallelModelBuilder.java index 34e365621e5a..98bb175a165e 100644 --- a/h2o-core/src/main/java/hex/ParallelModelBuilder.java +++ b/h2o-core/src/main/java/hex/ParallelModelBuilder.java @@ -3,10 +3,10 @@ import jsr166y.ForkJoinTask; import org.apache.log4j.Logger; import water.Iced; -import water.Key; import water.util.IcedAtomicInt; import java.util.*; +import java.util.concurrent.atomic.AtomicBoolean; /** * Dispatcher for parallel model building. Starts building models every time the run method is invoked. @@ -29,12 +29,12 @@ public static abstract class ParallelModelBuilderCallback modelBuilders) { if (LOG.isTraceEnabled()) LOG.trace("run with " + modelBuilders.size() + " models"); for (final ModelBuilder modelBuilder : modelBuilders) { _modelInProgressCounter.incrementAndGet(); - modelBuilder.setCallbacks(_modelBuildersCallbacks); - modelBuilder.trainModel(); + modelBuilder.trainModel(_parallelModelBuiltListener); } } - private class EachBuilderCallbacks extends ModelBuilderCallbacks { + private class ParallelModelBuiltListener extends ModelBuilderListener { @Override - public void onModelSuccess(Key modelKey) { - Model model = modelKey.get(); - if (model._parms._is_cv_model) return; // not interested in CV models here + public void onModelSuccess(Model model) { try { _callback.onBuildSuccess(model, ParallelModelBuilder.this); } finally { @@ -67,9 +64,7 @@ public void onModelSuccess(Key modelKey) { } @Override - public void onModelFailure(Key modelKey, Throwable cause, Model.Parameters parameters) { - if (checkExceptionHandled(cause)) return; - if (parameters._is_cv_model) return; // not interested in CV models here + public void onModelFailure(Throwable cause, Model.Parameters parameters) { try { final ModelBuildFailure modelBuildFailure = new ModelBuildFailure(cause, parameters); _callback.onBuildFailure(modelBuildFailure, ParallelModelBuilder.this); diff --git a/h2o-core/src/main/java/hex/Parameterizable.java b/h2o-core/src/main/java/hex/Parameterizable.java deleted file mode 100644 index 839d31af81c0..000000000000 --- a/h2o-core/src/main/java/hex/Parameterizable.java +++ /dev/null @@ -1,48 +0,0 @@ -package hex; - -/** - * Must be implemented by classes that can be configured dynamically during HyperParameter-Optimization. - */ -public interface Parameterizable { - - /** - * @param name hyperparameter name. - * @return true if this hyperparameter is generally supported. - */ - boolean hasParameter(String name); - - /** - * - * @param name hyperparameter name. - * @return the current value for the given hyperparameter. - */ - Object getParameter(String name); - - /** - * - * @param name hyperparameter name. - * @param value the new value to assign for the given hyperparameter. - */ - void setParameter(String name, Object value); - - /** - * - * @param name hyperparameter name. - * @return true iff the hyperparameter is currently set to its default value. - */ - boolean isParameterSetToDefault(String name); - - /** - * - * @param name hyperparameter name. - * @return the default value for the given hyperparameter. - */ - Object getParameterDefaultValue(String name); - - /** - * - * @param name hyperparameter name. - * @return true iff the given hyperparameter is allowed to be modified/reassigned on this instance. - */ - boolean isParameterAssignable(String name); -} diff --git a/h2o-core/src/main/java/hex/SubModelBuilder.java b/h2o-core/src/main/java/hex/SubModelBuilder.java index 7b1c7890c2a7..c5681c69510f 100644 --- a/h2o-core/src/main/java/hex/SubModelBuilder.java +++ b/h2o-core/src/main/java/hex/SubModelBuilder.java @@ -4,6 +4,7 @@ import water.H2O; import water.Job; import water.ParallelizationTask; +import water.util.Log; /** * Execute build of a collection of sub-models (CV models, main model) in parallel diff --git a/h2o-core/src/main/java/hex/pipeline/DataTransformer.java b/h2o-core/src/main/java/hex/pipeline/DataTransformer.java deleted file mode 100644 index a56c3e20e74d..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/DataTransformer.java +++ /dev/null @@ -1,195 +0,0 @@ -package hex.pipeline; - -import hex.Parameterizable; -import hex.pipeline.TransformerChain.Completer; -import water.Checksumable; -import water.Futures; -import water.Iced; -import water.Key; -import water.fvec.Frame; -import water.util.Checksum; -import water.util.PojoUtils; - -import java.util.Objects; -import java.util.concurrent.atomic.AtomicInteger; - -public abstract class DataTransformer extends Iced implements Parameterizable, Checksumable { - - public enum FrameType { - Training, - Validation, - Test - } - - public boolean _enabled = true; // flag allowing to enable/disable transformers dynamically esp. in pipelines (can be used as a pipeline hyperparam in grids). - private String _id; - private String _description; - private AtomicInteger refCount = new AtomicInteger(0); - - protected DataTransformer() { - this(null); - } - - public DataTransformer(String id) { - this(id, null); - } - - public DataTransformer(String id, String description) { - _id = id == null ? getClass().getSimpleName().toLowerCase()+Key.rand() : id; - _description = description == null ? getClass().getSimpleName().toLowerCase() : description; - } - - @SuppressWarnings("unchecked") - public SELF id(String id) { - _id = id; - return (SELF) this; - } - - public String id() { - return _id; - } - - @SuppressWarnings("unchecked") - public SELF description(String description) { - _description = description; - return (SELF) this; - } - - public String description() { - return _description; - } - - @SuppressWarnings("unchecked") - public SELF enable(boolean enabled) { - _enabled = enabled; - return (SELF) this; - } - - public boolean enabled() { return _enabled; } - - @Override - public boolean hasParameter(String name) { - try { - getParameter(name); - return true; - } catch (Exception e) { - return false; - } - } - - @Override - public Object getParameter(String name) { - return PojoUtils.getFieldValue(this, name); - } - - @Override - public void setParameter(String name, Object value) { - PojoUtils.setField(this, name, value); - } - - @Override - public boolean isParameterSetToDefault(String name) { - Object val = getParameter(name); - Object defaultVal = getParameterDefaultValue(name); - return Objects.deepEquals(val, defaultVal); - } - - @Override - public Object getParameterDefaultValue(String name) { - return getDefaults().getParameter(name); - } - - @Override - public boolean isParameterAssignable(String name) { - return isParameterSetToDefault(name); - } - - /** private use only to avoid this getting mutated. */ - private transient DataTransformer _defaults; - - /** private use only to avoid this getting mutated. */ - private DataTransformer getDefaults() { - if (_defaults == null) { - _defaults = makeDefaults(); - } - return _defaults; - } - - protected DataTransformer makeDefaults() { - try { - return getClass().newInstance(); - } catch (Exception e) { - throw new RuntimeException(e); - } - } - - /** - * @return true iff the transformer needs to be applied in a specific way to training/validation frames during cross-validation. - */ - public boolean isCVSensitive() { - return false; - } - - public final void prepare(PipelineContext context) { - if (_enabled) doPrepare(context); - refCount.incrementAndGet(); - } - - protected void doPrepare(PipelineContext context) {} - - final void prepare(PipelineContext context, TransformerChain chain) { - assert chain != null; - prepare(context); - chain.nextPrepare(context); - } - - public final void cleanup() { - cleanup(new Futures()); - } - - public final void cleanup(Futures futures) { - if (refCount.decrementAndGet() <= 0) doCleanup(futures); - } - protected void doCleanup(Futures futures) {} - - public final Frame transform(Frame fr) { - return transform(fr, FrameType.Test, null); - } - - public final Frame transform(Frame fr, FrameType type, PipelineContext context) { - if (!_enabled || fr == null) return fr; - Frame trfr = doTransform(fr, type, context); - if (context != null && context._tracker != null) { - context._tracker.apply(trfr, fr, type, context, this); - } - return trfr; - } - - /** - * Transformers must implement this method. They can ignore type and context if they're not context-sensitive. - * @param fr - * @param type - * @param context - * @return - */ - protected abstract Frame doTransform(Frame fr, FrameType type, PipelineContext context); - - final R transform(Frame[] frames, FrameType[] types, PipelineContext context, Completer completer, TransformerChain chain) { - assert frames != null; - assert types != null; - assert frames.length == types.length; - assert chain != null; - Frame[] transformed = new Frame[frames.length]; - for (int i=0; i { - - public Pipeline(PipelineParameters parms) { - super(parms); - init(false); - } - - public Pipeline(PipelineParameters parms, Key key) { - super(parms, key); - } - - public Pipeline(boolean startup_once) { -// super(new PipelineParameters(), startup_once, null); // no schema directory to completely disable schema lookup for now. - super(new PipelineParameters(), startup_once); - } - - @Override - public void init(boolean expensive) { - if (expensive) { - earlyValidateParams(); - if (_parms._transformers == null) _parms._transformers = new DataTransformer[0]; - _parms._transformers = Arrays.stream(_parms._transformers).filter(DataTransformer::enabled).toArray(DataTransformer[]::new); - } - super.init(expensive); - } - - protected void earlyValidateParams() { - if (_parms._categorical_encoding != Model.Parameters.CategoricalEncodingScheme.AUTO) { - // we need to ensure that no transformation occurs before the transformers in the pipeline - hide("_categorical_encoding", - "Pipeline supports only AUTO categorical encoding: custom categorical encoding should be applied either as a transformer or directly to the final estimator of the pipeline."); - _parms._categorical_encoding = Model.Parameters.CategoricalEncodingScheme.AUTO; - } - if (_parms._estimatorParams == null && nFoldCV()) { - error("_estimator", "Pipeline can use cross validation only if provided with an estimator."); - } - } - - @Override - protected PipelineDriver trainModelImpl() { - return new PipelineDriver(); - } - - @Override - public ModelCategory[] can_build() { - ModelBuilder finalBuilder = getFinalBuilder(); -// return finalBuilder == null ? new ModelCategory[] {ModelCategory.Unknown} : finalBuilder.can_build(); - return finalBuilder == null ? ModelCategory.values() : finalBuilder.can_build(); - } - - @Override - public boolean isSupervised() { - ModelBuilder finalBuilder = getFinalBuilder(); - return finalBuilder != null && finalBuilder.isSupervised(); - } - - private ModelBuilder getFinalBuilder() { - return _parms._estimatorParams == null ? null : ModelBuilder.make(_parms._estimatorParams.algoName(), null, null); - } - - - public class PipelineDriver extends Driver { - @Override - public void computeImpl() { - init(true); //also protects original train+valid frames - PipelineOutput output = new PipelineOutput(Pipeline.this); - PipelineModel model = new PipelineModel(dest(), _parms, output); - model.delete_and_lock(_job); - - try { - PipelineContext context = newContext(); - TransformerChain chain = newChain(context); - setTrain(context.getTrain()); - setValid(context.getValid()); - Scope.track(train(), valid()); //chain preparation may have provided extended/modified train/valid frames, so better track the current ones. - output._transformers = _parms._transformers.clone(); - if (_parms._estimatorParams == null) return; - try (Scope.Safe inner = Scope.safe(train(), valid())) { - output._estimator = chain.transform( - new Frame[]{train(), valid()}, - new FrameType[]{FrameType.Training, FrameType.Validation}, - context, - (frames, ctxt) -> { - // use params from the context as they may have been modified during chain preparation - ModelBuilder mb = makeEstimatorBuilder(_parms._estimatorKeyGen.make(_result), ctxt._params._estimatorParams, ctxt._params, frames[0], frames[1]); - Keyed res = mb.trainModelNested(null); - return res == null ? null : res.getKey(); - } - ); - } - } finally { - model.syncOutput(); - model.update(_job); - model.unlock(_job); - } - } - } - - @Override - public void computeCrossValidation() { - assert _parms._estimatorParams != null; // no CV if pipeline used as a pure transformer (see params validation) - PipelineModel model = null; - try { - Scope.enter(); - init(true); //also protects original train+valid frames - PipelineOutput output = new PipelineOutput(Pipeline.this); - model = new PipelineModel(dest(), _parms, output); - model.delete_and_lock(_job); - - PipelineContext context = newContext(); - TransformerChain chain = newChain(context); - setTrain(context.getTrain()); - setValid(context.getValid()); - Scope.track(train(), valid()); //chain preparation may have provided extended/modified train/valid frames. - try (Scope.Safe mainModelScope = Scope.safe(train(), valid())) { - output._transformers = _parms._transformers.clone(); - output._estimator = chain.transform( - new Frame[]{train(), valid()}, - new FrameType[]{FrameType.Training, FrameType.Validation}, - context, - (frames, ctxt) -> { - ModelBuilder mb = makeEstimatorBuilder(_parms._estimatorKeyGen.make(_result), ctxt._params._estimatorParams, ctxt._params, frames[0], frames[1]); - mb.setCallbacks(new ModelBuilderCallbacks() { - /** - * Using this callback, the transformations are applied at the time the CV model training is triggered, - * we don't have to stack up all transformed frames in memory BEFORE starting the CV-training. - */ - @Override - public void wrapCompute(ModelBuilder builder, Runnable compute) { - Model.Parameters params = builder._parms; - if (!params._is_cv_model || !chain.isCVSensitive()) { - compute.run(); - return; - } - - try (Scope.Safe cvModelComputeScope = Scope.safe(train(), params.train(), params.valid())) { - PipelineContext cvContext = newCVContext(context, params); - Scope.track(cvContext.getTrain(), cvContext.getValid()); - TransformerChain cvChain = chain.clone(); // as cv models can be trained in parallel - cvChain.transform( - new Frame[]{cvContext.getTrain(), cvContext.getValid()}, - new FrameType[]{FrameType.Training, FrameType.Validation}, - cvContext, - (cvFrames, ctxt) -> { - // ensure that generated vecs, that will be used to train+score this CV model, get deleted at the end of the pipeline training - track(cvFrames[0], true); - track(cvFrames[1], true); - reassign(cvFrames[0], params._train, _job.getKey()); - reassign(cvFrames[1], params._valid, _job.getKey()); - // re-init & re-validate the builder in case we produced a bad frame - // (although this should have been detected earlier as a similar transformation was already applied to main training frame) - builder._input_parms = params.clone(); - builder.setTrain(null); - builder.setValid(null); - builder.init(false); - if (builder.error_count() > 0) - throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(builder); - return null; - } - ); - compute.run(); - } - } - }); - mb.trainModelNested(null); - return mb.dest(); - } - ); - } - model.setInputParms(_input_parms); - } finally { - if (model != null) { - model.syncOutput(); - model.update(_job); - model.unlock(_job); - } - cleanUp(); - Scope.exit(); - } - } - - private PipelineContext newCVContext(PipelineContext context, Model.Parameters cvParams) { - PipelineContext cvContext = new PipelineContext(context._params, context._tracker); - PipelineParameters pparams = cvContext._params; - pparams._is_cv_model = cvParams._is_cv_model; - pparams._cv_fold = cvParams._cv_fold; - Frame baseFrame = new Frame(Key.make(_result.toString()+"_cv_"+(pparams._cv_fold+1)), train().names(), train().vecs()); - if ( pparams._weights_column != null ) baseFrame.remove( pparams._weights_column ); - Frame cvTrainOld = cvParams.train(); - Frame cvValidOld = cvParams.valid(); - String cvWeights = cvParams._weights_column; - Frame cvTrain = new Frame(baseFrame); - cvTrain.add(cvWeights, cvTrainOld.vec(cvWeights)); - DKV.put(cvTrain); - Frame cvValid = new Frame(baseFrame); - cvValid.add(cvWeights, cvValidOld.vec(cvWeights)); - DKV.put(cvValid); - cvContext.setTrain(cvTrain); - cvContext.setValid(cvValid); - return cvContext; - } - - private PipelineContext newContext() { - return new PipelineContext(_parms, new CompositeFrameTracker( - new FrameTracker() { // propagates training cancellation requests as early as possible - @Override - public void apply(Frame transformed, Frame original, FrameType type, PipelineContext context, DataTransformer transformer) { - if (stop_requested()) throw new Job.JobCancelledException(_job); - } - }, - new ConsistentKeyTracker(), - new ScopeTracker() - )); - } - - private TransformerChain newChain(PipelineContext context) { - TransformerChain chain = new TransformerChain(_parms._transformers); - chain.prepare(context); - return chain; - } - - private ModelBuilder makeEstimatorBuilder(Key eKey, Model.Parameters eParams, PipelineParameters pParams, Frame train, Frame valid) { - eParams._train = train == null ? null : train.getKey(); - eParams._valid = valid == null ? null : valid.getKey(); - eParams._response_column = pParams._response_column; - eParams._weights_column = pParams._weights_column; - eParams._offset_column = pParams._offset_column; - eParams._ignored_columns = pParams._ignored_columns; - eParams._fold_column = pParams._fold_column; - eParams._fold_assignment = pParams._fold_assignment; - eParams._nfolds= pParams._nfolds; - eParams._max_runtime_secs = pParams._max_runtime_secs > 0 ? remainingTimeSecs() : pParams._max_runtime_secs; - - ModelBuilder mb = ModelBuilder.make(eParams, eKey); - mb._job = _job; - return mb; - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/PipelineAlgoRegistration.java b/h2o-core/src/main/java/hex/pipeline/PipelineAlgoRegistration.java deleted file mode 100644 index d53c903610dc..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/PipelineAlgoRegistration.java +++ /dev/null @@ -1,15 +0,0 @@ -package hex.pipeline; - -import water.api.AlgoAbstractRegister; -import water.api.RestApiContext; -import water.api.SchemaServer; - -public class PipelineAlgoRegistration extends AlgoAbstractRegister { - - @Override - public void registerEndPoints(RestApiContext context) { - Pipeline builder = new Pipeline(true); - registerModelBuilder(context, builder, SchemaServer.getStableVersion()); - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/PipelineContext.java b/h2o-core/src/main/java/hex/pipeline/PipelineContext.java deleted file mode 100644 index a0296e20fb89..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/PipelineContext.java +++ /dev/null @@ -1,50 +0,0 @@ -package hex.pipeline; - -import hex.pipeline.PipelineModel.PipelineParameters; -import water.fvec.Frame; - -/** - * A context object passed to the {@link DataTransformer}s (usually through a {@link TransformerChain} - * and providing useful information, especially to help some transformers - * to configure themselves/initialize during the {@link DataTransformer#prepare(PipelineContext)} phase. - */ -public class PipelineContext implements Cloneable { - - public final PipelineParameters _params; - - public final FrameTracker _tracker; - - private Frame _train; - private Frame _valid; - - public PipelineContext(PipelineParameters params) { - this(params, null); - } - - public PipelineContext(PipelineParameters params, FrameTracker tracker) { - assert params != null; - _params = (PipelineParameters) params.clone(); // cloning this here as those _params can be mutated during transformers' preparation. - _tracker = tracker; - } - - public Frame getTrain() { - return _train != null ? _train - : _params != null ? _params.train() - : null; - } - - public void setTrain(Frame train) { - _train = train; - } - - public Frame getValid() { - return _valid != null ? _valid - : _params != null ? _params.valid() - : null; - } - - public void setValid(Frame valid) { - _valid = valid; - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/PipelineHelper.java b/h2o-core/src/main/java/hex/pipeline/PipelineHelper.java deleted file mode 100644 index 6b956a591ea6..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/PipelineHelper.java +++ /dev/null @@ -1,36 +0,0 @@ -package hex.pipeline; - -import water.DKV; -import water.Job; -import water.Key; -import water.Scope; -import water.fvec.Frame; - -public final class PipelineHelper { - - private PipelineHelper() {} - - public static Frame reassign(Frame fr, Key key) { - return reassign(fr, key, null); - } - - public static Frame reassign(Frame fr, Key key, Key job) { - Frame copy = new Frame(fr); - DKV.remove(key); - copy._key = key; - copy.write_lock(job); - copy.update(job); - return copy; - } - - public static void reassignInplace(Frame fr, Key key) { - reassignInplace(fr, key, null); - } - - public static void reassignInplace(Frame fr, Key key, Key job) { - assert DKV.get(key) == null || DKV.getGet(key) == null; // inplace reassignment only to fresh/unassigned keys - if (fr.getKey() != null) DKV.remove(fr.getKey()); - fr._key = key; - DKV.put(fr); - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/PipelineModel.java b/h2o-core/src/main/java/hex/pipeline/PipelineModel.java deleted file mode 100644 index 3e5747b88b4b..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/PipelineModel.java +++ /dev/null @@ -1,341 +0,0 @@ -package hex.pipeline; - -import hex.Model; -import hex.ModelBuilder; -import hex.ModelCategory; -import hex.ModelMetrics; -import hex.pipeline.DataTransformer.FrameType; -import hex.pipeline.trackers.CompositeFrameTracker; -import hex.pipeline.TransformerChain.UnaryCompleter; -import hex.pipeline.trackers.ConsistentKeyTracker; -import hex.pipeline.trackers.ScopeTracker; -import org.apache.commons.lang.StringUtils; -import water.*; -import water.KeyGen.PatternKeyGen; -import water.fvec.Frame; -import water.udf.CFuncRef; -import water.util.ArrayUtils; - -import java.util.HashSet; -import java.util.Set; -import java.util.regex.Matcher; -import java.util.regex.Pattern; -import java.util.stream.Stream; - -/** - * A {@link PipelineModel} encapsulates in a single model a collection of transformations applied to train/validation - * before training a final delegate model (here called `estimator` to avoid confusion). - * For scoring, the same transformations are first applied to the test data and the result is used by the estimator model to provide the final score. - */ -public class PipelineModel extends Model { - - private static final KeyGen TRANSFORM_KEY_GEN = new PatternKeyGen("{0}_trf_by_{1}"); - - public PipelineModel(Key selfKey, PipelineParameters parms, PipelineOutput output) { - super(selfKey, parms, output); - } - - @Override - public boolean havePojo() { - return false; - } - - @Override - public boolean haveMojo() { - return false; - } - - @Override - public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) { - throw new UnsupportedOperationException("PipelineModel.makeMetricBuilder should never be called!"); - } - - @Override - protected double[] score0(double[] data, double[] preds) { - throw H2O.unimpl("Pipeline can not score on raw data"); - } - - /* - @Override - protected PipelinePredictScoreResult predictScoreImpl(Frame fr, Frame adaptFrm, String destination_key, Job j, boolean computeMetrics, CFuncRef customMetricFunc) { - Frame preds = doScore(adaptFrm, destination_key, j, computeMetrics, customMetricFunc); - ModelMetrics mm = null; - Model finalModel = _output.getFinalModel(); - if (computeMetrics && finalModel != null) { - // obtaining the model metrics from the final model - Key[] mms = finalModel._output.getModelMetrics(); - ModelMetrics lastComputedMetric = mms[mms.length - 1].get(); - mm = lastComputedMetric.deepCloneWithDifferentModelAndFrame(this, adaptFrm); - this.addModelMetrics(mm); - //now that we have the metric set on the pipeline model, removing the one we just computed on the delegate model (otherwise it leaks in client mode) - for (Key kmm : finalModel._output.clearModelMetrics(true)) { - DKV.remove(kmm); - } - } - String[] names = makeScoringNames(); - String[][] domains = makeScoringDomains(adaptFrm, computeMetrics, names); - ModelMetrics.MetricBuilder mb = makeMetricBuilder(domains[0]); - return new PipelinePredictScoreResult(mb, preds, mm); - } - */ - - @Override - public Frame score(Frame fr, String destination_key, Job j, boolean computeMetrics, CFuncRef customMetricFunc) throws IllegalArgumentException { - return doScore(fr, destination_key, j, computeMetrics, customMetricFunc); - } - - private Frame doScore(Frame fr, String destination_key, Job j, boolean computeMetrics, CFuncRef customMetricFunc) throws IllegalArgumentException { - if (fr == null) return null; - try (Scope.Safe s = Scope.safe(fr)) { - PipelineContext context = newContext(fr); - Frame result = newChain().transform(fr, FrameType.Test, context, new UnaryCompleter() { - @Override - public Frame apply(Frame frame, PipelineContext context) { - if (_output._estimator == null) { - return new Frame(Key.make(destination_key), frame.names(), frame.vecs()); - } - Frame result = _output._estimator.get().score(frame, destination_key, j, computeMetrics, customMetricFunc); - if (computeMetrics) { - ModelMetrics mm = ModelMetrics.getFromDKV(_output._estimator.get(), frame); - if (mm != null) addModelMetrics(mm.deepCloneWithDifferentModelAndFrame(PipelineModel.this, fr)); - } - return result; - } - }); - Scope.untrack(result); - DKV.put(result); - return result; - } - } - - /** - * applies all the pipeline transformers to the input frame. - * @param fr - * @return the transformed frame, as it would be passed to the estimator model if fr was used for predict/scoring. - */ - @Override - public Frame transform(Frame fr) { - if (fr == null) return null; - try (Scope.Safe s = Scope.safe(fr)) { - PipelineContext context = newContext(fr); - Frame result = newChain().transform(fr, FrameType.Test, context, new UnaryCompleter() { - @Override - public Frame apply(Frame frame, PipelineContext context) { - return new Frame(TRANSFORM_KEY_GEN.make(fr.getKey(), getKey()), frame.names(), frame.vecs()); - } - }); - Scope.untrack(result); - DKV.put(result); - return result; - } - } - - private TransformerChain newChain() { - //no need to call `prepare` on this chain as we're using the output transformers, which have been prepared during training. - return new TransformerChain(_output._transformers); - } - - private PipelineContext newContext(Frame fr) { - return new PipelineContext(_parms, new CompositeFrameTracker( - new ConsistentKeyTracker(fr), - new ScopeTracker() - )); - } - - @Override - protected Futures remove_impl(Futures fs, boolean cascade) { - if (cascade) { - if (_output._transformers != null) { - for (DataTransformer dt : _output._transformers) { - dt.cleanup(fs); - } - } - if (_output._estimator != null) { - Keyed.remove(_output._estimator, fs, cascade); - } - } - return super.remove_impl(fs, cascade); - } - - void syncOutput() { - PipelineModel.PipelineOutput pmo = this._output; - if (pmo.getEstimatorModel() == null) return; - Model.Output mo = pmo.getEstimatorModel()._output; - if (mo._training_metrics != null) pmo._training_metrics = addModelMetrics(mo._training_metrics.deepCloneWithDifferentModelAndFrame(this, this._parms.train())); - if (mo._validation_metrics != null) pmo._validation_metrics = addModelMetrics(mo._validation_metrics.deepCloneWithDifferentModelAndFrame(this, this._parms.valid())); - if (mo._cross_validation_metrics != null) pmo._cross_validation_metrics = addModelMetrics(mo._cross_validation_metrics.deepCloneWithDifferentModelAndFrame(this, this._parms.train())); - pmo._cross_validation_metrics_summary = mo._cross_validation_metrics_summary; - pmo._cross_validation_fold_assignment_frame_id = mo._cross_validation_fold_assignment_frame_id; - pmo._cross_validation_holdout_predictions_frame_id = mo._cross_validation_holdout_predictions_frame_id; - pmo._cross_validation_predictions = mo._cross_validation_predictions; - pmo._cross_validation_models = mo._cross_validation_models; // FIXME: ideally, should be PipelineModels (build pipeline output pointing at CV model, use it for new pipeline model, etc.) - //...??? - } - - - /* - public class PipelinePredictScoreResult extends PredictScoreResult { - - private final ModelMetrics _modelMetrics; - public PipelinePredictScoreResult(ModelMetrics.MetricBuilder metricBuilder, Frame preds, ModelMetrics modelMetrics) { - super(metricBuilder, preds, preds); - _modelMetrics = modelMetrics; - } - - @Override - public ModelMetrics makeModelMetrics(Frame fr, Frame adaptFr) { - return _modelMetrics; - } - } - */ - - - public static class PipelineParameters extends Model.Parameters { - - static String ALGO = "Pipeline"; - - // think about Grids: we should be able to slightly modify grids to set nested hyperparams, for example "_transformers[1]._my_param", "_estimator._my_param" - // this doesn't have to work for all type of transformers, but for example for those wrapping a model (see ModelAsFeatureTransformer) and for the final estimator. - // as soon as we can do this, then we will be able to train pipelines in grids like any other model. - - public DataTransformer[] _transformers; - public Model.Parameters _estimatorParams; - public KeyGen _estimatorKeyGen = new PatternKeyGen("{0}_estimator"); - - @Override - public String algoName() { - return ALGO; - } - - @Override - public String fullName() { - return ALGO; - } - - @Override - public String javaName() { - return PipelineModel.class.getName(); - } - - @Override - public long progressUnits() { - return 0; - } - - @Override - public Object getParameter(String name) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - if ("estimator".equals(tok0)) return _estimatorParams == null ? null : _estimatorParams.getParameter(tokens[1]); - DataTransformer dt = getTransformer(tok0); - return dt == null ? null : dt.getParameter(tokens[1]); - } - return super.getParameter(name); - } - - @Override - public void setParameter(String name, Object value) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - if ("estimator".equals(tok0)) { - _estimatorParams.setParameter(tokens[1], value); - return; - } - DataTransformer dt = getTransformer(tok0); - if (dt != null) dt.setParameter(tokens[1], value); - return; - } - super.setParameter(name, value); - } - - @Override - public boolean isParameterAssignable(String name) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - if ("estimator".equals(tok0)) return _estimatorParams == null ? null : _estimatorParams.isParameterAssignable(tokens[1]); - DataTransformer dt = getTransformer(tok0); - // for now allow transformers hyper params on non-defaults - return dt != null && dt.hasParameter(tokens[1]); -// return dt != null && dt.isValidHyperParameter(tokens[1]); - } - return super.isParameterAssignable(name); - } - - private static final Pattern TRANSFORMER_PAT = Pattern.compile("transformers\\[(\\w+)]"); - private String[] parseParameterName(String name) { - String[] tokens = name.split("\\.", 2); - if (tokens.length == 1) return tokens; - String tok0 = StringUtils.stripStart(tokens[0], "_"); - if ("estimator".equals(tok0) || getTransformer(tok0) != null) { - return new String[]{tok0, tokens[1]} ; - } else { - Matcher m = TRANSFORMER_PAT.matcher(tok0); - if (m.matches()) { - String id = m.group(1); - try { - int idx = Integer.parseInt(id); - assert idx >=0 && idx < _transformers.length; - return new String[]{_transformers[idx].id(), tokens[1]}; - } catch(NumberFormatException nfe) { - if (getTransformer(id) != null) return new String[] {id, tokens[1]}; - throw new IllegalArgumentException("Unknown pipeline transformer: "+tok0); - } - } else { - throw new IllegalArgumentException("Unknown pipeline parameter: "+name); - } - } - } - - private DataTransformer getTransformer(String id) { - if (_transformers == null) return null; - return Stream.of(_transformers).filter(t -> t.id().equals(id)).findFirst().orElse(null); - } - - @Override - protected Parameters cloneImpl() throws CloneNotSupportedException { - PipelineParameters clone = (PipelineParameters) super.cloneImpl(); - clone._transformers = _transformers == null ? null : ArrayUtils.deepClone(_transformers); - clone._estimatorParams = _estimatorParams == null ? null : _estimatorParams.clone(); - return clone; - } - - @Override - public long checksum(Set ignoredFields) { - Set ignored = ignoredFields == null ? new HashSet<>() : new HashSet<>(ignoredFields); - ignored.add("_transformers"); -// ignored.add("_estimatorParams"); - long xs = super.checksum(ignoredFields); -// xs ^= (_transformers == null ? 47 : _transformers.checksum()); -// xs ^= (_estimatorParams == null ? 47 : _estimatorParams.checksum()); - return xs; - } - } - - public static class PipelineOutput extends Model.Output { - - DataTransformer[] _transformers; - Key _estimator; - - public PipelineOutput(ModelBuilder b) { - super(b); - } - - public DataTransformer[] getTransformers() { - return _transformers == null ? null : _transformers.clone(); - } - - public Model getEstimatorModel() { - return _estimator == null ? null : _estimator.get(); - } - - @Override - public ModelCategory getModelCategory() { - Model em = getEstimatorModel(); - return em == null ? super.getModelCategory() : em._output.getModelCategory(); - } - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/TransformerChain.java b/h2o-core/src/main/java/hex/pipeline/TransformerChain.java deleted file mode 100644 index e42c76bcc7db..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/TransformerChain.java +++ /dev/null @@ -1,135 +0,0 @@ -package hex.pipeline; - -import water.fvec.Frame; - -import java.util.Arrays; - -/** - * A {@link DataTransformer} that calls multiple transformers as a chain of transformations, - * that can be optionally completed by a {@link Completer} whose result will be the result of the chain. - *
- * The chain accepts one or multiple {@link Frame}s as input, all going through the transformations at the same time, - * and all transformed frames can feed the final {@link Completer}. - *
- * The chain logic also allows transformers to create temporary resources and close/clean them only after the {@link Completer} has been evaluated. - */ -public class TransformerChain extends DataTransformer { - - /** - * The last operation applied to all the transformed frames. - * @param type of the final result - */ - @FunctionalInterface - interface Completer { - R apply(Frame[] frames, PipelineContext context); - } - - /** - * A {@link Completer} accepting a single {@link Frame}. - * @param type of the final result - */ - public static abstract class UnaryCompleter implements Completer { - @Override - public R apply(Frame[] frames, PipelineContext context) { - assert frames.length == 1; - return apply(frames[0], context); - } - - public abstract R apply(Frame frame, PipelineContext context); - } - - /** - * A default {@link Completer} simply returning all transformed frames. - */ - public static class AsFramesCompleter implements Completer { - public static final AsFramesCompleter INSTANCE = new AsFramesCompleter(); - - @Override - public Frame[] apply(Frame[] frames, PipelineContext context) { - return frames; - } - } - - /** - * A default {@link Completer} simply returning the transformed frame (when used in context of a single transformed frame). - */ - public static class AsSingleFrameCompleter implements Completer { - public static final AsSingleFrameCompleter INSTANCE = new AsSingleFrameCompleter(); - - public Frame apply(Frame[] frames, PipelineContext context) { - assert frames.length == 1; - return frames[0]; - } - } - - private final DataTransformer[] _transformers; - - private int _index; - - public TransformerChain(DataTransformer[] transformers) { - assert transformers != null; - _transformers = transformers.clone(); - } - - @Override - public boolean isCVSensitive() { - return Arrays.stream(_transformers).anyMatch(DataTransformer::isCVSensitive); - } - - @Override - protected DataTransformer makeDefaults() { - return new TransformerChain(new DataTransformer[0]); - } - - @Override - protected void doPrepare(PipelineContext context) { - resetIteration(); - nextPrepare(context); - } - - final void nextPrepare(PipelineContext context) { - DataTransformer dt = next(); - if (dt != null) { - dt.prepare(context, this); - } - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - return transform(fr, type, context, AsSingleFrameCompleter.INSTANCE); - } - - Frame[] doTransform(Frame[] frames, FrameType[] types, PipelineContext context) { - return transform(frames, types, context, AsFramesCompleter.INSTANCE); - } - - final R transform(Frame fr, FrameType type, PipelineContext context, Completer completer) { - return transform(new Frame[] { fr }, new FrameType[] { type }, context, completer); - } - - final R transform(Frame[] frames, FrameType[] types, PipelineContext context, Completer completer) { - resetIteration(); - return nextTransform(frames, types, context, completer); - } - - final R nextTransform(Frame[] frames, FrameType[] types, PipelineContext context, Completer completer) { - DataTransformer dt = next(); - if (dt != null) { - return dt.transform(frames, types, context, completer, this); - } else if (completer != null) { - return completer.apply(frames, context); - } else { - return null; - } - } - - private DataTransformer next() { - if (_index >= _transformers.length) return null; - return _transformers[_index++]; - } - - private void resetIteration() { - _index = 0; - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/trackers/CompositeFrameTracker.java b/h2o-core/src/main/java/hex/pipeline/trackers/CompositeFrameTracker.java deleted file mode 100644 index 04cb4d85fe52..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/trackers/CompositeFrameTracker.java +++ /dev/null @@ -1,25 +0,0 @@ -package hex.pipeline.trackers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.FrameTracker; -import hex.pipeline.PipelineContext; -import water.fvec.Frame; - -/** - * A {@link FrameTracker} applying multiple trackers sequentially. - */ -public class CompositeFrameTracker implements FrameTracker { - - private final FrameTracker[] _trackers; - - public CompositeFrameTracker(FrameTracker... trackers) { - _trackers = trackers; - } - - @Override - public void apply(Frame transformed, Frame original, DataTransformer.FrameType type, PipelineContext context, DataTransformer transformer) { - for (FrameTracker tracker : _trackers) { - tracker.apply(transformed, original, type, context, transformer); - } - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/trackers/ConsistentKeyTracker.java b/h2o-core/src/main/java/hex/pipeline/trackers/ConsistentKeyTracker.java deleted file mode 100644 index 18e0d92397d6..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/trackers/ConsistentKeyTracker.java +++ /dev/null @@ -1,64 +0,0 @@ -package hex.pipeline.trackers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.FrameTracker; -import hex.pipeline.PipelineContext; -import water.KeyGen; -import water.fvec.Frame; - -import static hex.pipeline.PipelineHelper.reassignInplace; - -/** - * A {@link FrameTracker} ensuring that all transformed frames in the pipeline are named consistently}, - * facilitating debugging and obtaining the origin of frames in the DKV. - */ -public class ConsistentKeyTracker implements FrameTracker { - - private static final String SEP = "@@"; // anything that doesn't contain Key.MAGIC_CHAR - private static final KeyGen DEFAULT_FRAME_KEY_GEN = new KeyGen.PatternKeyGen("{0}"+SEP+"{1}_trf_by_{2}_{rstr}"); - private final KeyGen _frameKeyGen; - - private final Frame _refFrame; - - public ConsistentKeyTracker() { - this(null, DEFAULT_FRAME_KEY_GEN); - } - - public ConsistentKeyTracker(Frame origin) { - this(origin, DEFAULT_FRAME_KEY_GEN); - } - - public ConsistentKeyTracker(Frame origin, KeyGen frameKeyGen) { - _refFrame = origin; - _frameKeyGen = frameKeyGen; - } - - private Frame getReference(DataTransformer.FrameType type, PipelineContext context) { - if (_refFrame != null) return _refFrame; - switch (type) { - case Training: - return context.getTrain(); - case Validation: - return context.getValid(); - case Test: - default: - return null; - } - } - - @Override - public void apply(Frame transformed, Frame original, DataTransformer.FrameType type, PipelineContext context, DataTransformer transformer) { - if (transformed == null) return; - Frame ref = getReference(type, context); - if (ref == null) return; - String refName = ref.getKey().toString(); - String frName = original.getKey().toString(); - if (!frName.startsWith(refName)) - return; // all successive frames must have the same naming pattern when using this tracker -> doesn't apply to this frame. - - if (transformed != original) { - String baseName = frName.contains(SEP) ? frName.substring(0, frName.lastIndexOf(SEP)) : frName; - reassignInplace(transformed, _frameKeyGen.make(baseName, type, transformer.id())); - } - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/trackers/ScopeTracker.java b/h2o-core/src/main/java/hex/pipeline/trackers/ScopeTracker.java deleted file mode 100644 index c3e7e230d220..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/trackers/ScopeTracker.java +++ /dev/null @@ -1,18 +0,0 @@ -package hex.pipeline.trackers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.FrameTracker; -import hex.pipeline.PipelineContext; -import water.Scope; -import water.fvec.Frame; - -/** - * a {@link FrameTracker} ensuring that all transformed framed are added to current {@link Scope}. - */ -public class ScopeTracker implements FrameTracker { - @Override - public void apply(Frame transformed, Frame original, DataTransformer.FrameType type, PipelineContext context, DataTransformer transformer) { - if (transformed == null) return; - Scope.track(transformed); - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/CachingTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/CachingTransformer.java deleted file mode 100644 index 9f45c6278c1c..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/CachingTransformer.java +++ /dev/null @@ -1,83 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import water.DKV; -import water.Key; -import water.fvec.Frame; -import water.nbhm.NonBlockingHashMap; -import water.util.FrameUtils; - -import java.util.Collection; - -/** - * WIP: not ready for production usage for now due to memory + frame lifecycle issues. - * If a Frame is cached, then returning a shallow copy is not enough as the individual Vecs could then be removed from DKV. - * Deep copy would however increase the memory cost of caching. - */ -public class CachingTransformer extends DelegateTransformer { - - boolean _cacheEnabled = true; - private final NonBlockingHashMap> _cache = new NonBlockingHashMap<>(); - - protected CachingTransformer() {} - - public CachingTransformer(T transformer) { - super(transformer); - } - - void enableCache(boolean enabled) { - _cacheEnabled = enabled; - } - - boolean isCacheEnabled() { - return _cacheEnabled; - } - - private Object makeCachingKey(Frame fr, FrameType type, PipelineContext context) { - // this way, works only for simple transformations, not if it is type/context-sensitive. - // The most important is to have something that would prevent transformation again and again: - // - for each model using the main training frame in AutoML. - // - for each model scoring the validation or leaderboard frame in AutoML. - // - for each cv-training frame based on the same main training frame for each model in AutoML. - // this makes about max 3+nfolds frame to cache, which is not much. - // We can probably rely on the frame checksum instead of the frame key as a caching key. - // -// return fr.getKey(); - return fr.checksum(); - } - - private Frame copy(Frame fr, Key newKey) { - Frame cp = new Frame(fr); - cp._key = newKey; - return cp; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - if (!isCacheEnabled()) return super.doTransform(fr, type, context); - - final Object cachingKey = makeCachingKey(fr, type, context); - if (_cache.containsKey(cachingKey)) { - Frame cached = _cache.get(cachingKey).get(); - if (cached == null) { - _cache.remove(cachingKey); - } else { - return copy(cached, Key.make(cached.getKey()+".copy")); - } - } - Frame transformed = super.doTransform(fr, type, context); - Frame cached = copy(transformed, Key.make(fr.getKey()+".cached")); - DKV.put(cached); //??? how can we guarantee that cached Frame(s)/Vec(s) won't be deleted at the end of a single model training? - // we want those to remain accessible as long as the cache is "alive", - // they should only be removed from DKV when cache (or DKV) is cleared explicitly. - // Could we flag some objects in DKV as "cached"/"persistent" and can be removed from DKV only through a special call to `DKV.remove`? Use special key? - _cache.put(cachingKey, cached.getKey()); - return transformed; - } - - public void clearCache() { - FrameUtils.cleanUp((Collection)_cache.values()); - _cache.clear(); - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/DelegateTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/DelegateTransformer.java deleted file mode 100644 index 7a7a8980ac5a..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/DelegateTransformer.java +++ /dev/null @@ -1,60 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import water.fvec.Frame; - -public abstract class DelegateTransformer extends DataTransformer { - - T _transformer; - - protected DelegateTransformer() {} - - public DelegateTransformer(T transformer) { - _transformer = transformer; - } - - @Override - public Object getParameter(String name) { - try { - return _transformer.getParameter(name); - } catch (IllegalArgumentException iae) { - return super.getParameter(name); - } - } - - @Override - public void setParameter(String name, Object value) { - try { - _transformer.setParameter(name, value); - } catch (IllegalArgumentException iae) { - super.setParameter(name, value); - } - } - - @Override - public Object getParameterDefaultValue(String name) { - try { - return _transformer.getParameterDefaultValue(name); - } catch (IllegalArgumentException iae) { - return super.getParameterDefaultValue(name); - } - } - - @Override - protected void doPrepare(PipelineContext context) { - _transformer.prepare(context); - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - return _transformer.transform(fr, type, context); - } - - @Override - protected S cloneImpl() throws CloneNotSupportedException { - S clone = super.cloneImpl(); - clone._transformer = (T)_transformer.clone(); - return clone; - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/FeatureTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/FeatureTransformer.java deleted file mode 100644 index 3e33131d42c5..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/FeatureTransformer.java +++ /dev/null @@ -1,15 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; - -/** - * a DataTransformer that never modifies the response column - */ -public abstract class FeatureTransformer extends DataTransformer { - - private String[] _excluded_columns; - - public void excludeColumns(String[] columns) { - _excluded_columns = columns; - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/FilteringTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/FilteringTransformer.java deleted file mode 100644 index 488d26a53da1..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/FilteringTransformer.java +++ /dev/null @@ -1,24 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import water.fvec.Frame; - -/** - * WiP: not used for now. - * An abstract transformer to sample/filter the input the frame. - */ -public abstract class FilteringTransformer extends DataTransformer { - - boolean _filterEnabled = true; - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - if (_filterEnabled) { - return filterRows(fr); - } - return fr; - } - - public abstract Frame filterRows(Frame fr); -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/KFoldColumnGenerator.java b/h2o-core/src/main/java/hex/pipeline/transformers/KFoldColumnGenerator.java deleted file mode 100644 index a94b15a00829..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/KFoldColumnGenerator.java +++ /dev/null @@ -1,115 +0,0 @@ -package hex.pipeline.transformers; - -import hex.Model.Parameters.FoldAssignmentScheme; -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import water.DKV; -import water.KeyGen; -import water.Scope; -import water.fvec.Frame; -import water.fvec.Vec; -import water.rapids.ast.prims.advmath.AstKFold; - -public class KFoldColumnGenerator extends DataTransformer { - - private static final int DEFAULT_NFOLDS = 5; - - static String FOLD_COLUMN_PREFIX = "__fold__"; - - private String _fold_column; - private FoldAssignmentScheme _scheme; - - private int _nfolds; - private long _seed; - - private String _response_column; - - private final KeyGen _trainWFoldKeyGen = new KeyGen.PatternKeyGen("{0}_wfoldc"); - - public KFoldColumnGenerator() { - this(null); - } - - public KFoldColumnGenerator(String foldColumn) { - this(foldColumn, null, -1, -1); - } - - public KFoldColumnGenerator(String foldColumn, FoldAssignmentScheme scheme, int nfolds, long seed) { - super(); - _fold_column = foldColumn; - _scheme = scheme; - _nfolds = nfolds; - _seed = seed; - } - - @Override - protected void doPrepare(PipelineContext context) { - assert context != null; - assert context._params != null; - if (_fold_column == null) _fold_column = context._params._fold_column; - if (_fold_column == null) _fold_column = FOLD_COLUMN_PREFIX+context._params._response_column; - - if (_scheme == null) _scheme = context._params._fold_assignment; - if (_scheme == null) _scheme = FoldAssignmentScheme.AUTO; - - if (_nfolds <= 0) _nfolds = context._params._nfolds; - if (_nfolds <= 0) _nfolds = DEFAULT_NFOLDS; - - if (_seed < 0) _seed = context._params.getOrMakeRealSeed(); - - if (_response_column == null) _response_column = context._params._response_column; - assert !(_response_column == null && _scheme == FoldAssignmentScheme.Stratified); - - if (context.getTrain() != null && context.getTrain().find(_fold_column) < 0) { - Frame withFoldC = doTransform(context.getTrain(), FrameType.Training, context); - withFoldC._key = _trainWFoldKeyGen.make(context.getTrain()._key); - DKV.put(withFoldC); - Scope.track(withFoldC); - context.setTrain(withFoldC); - } - // now that we have a fold column, reassign cv params to avoid confusion - context._params._fold_column = _fold_column; - context._params._nfolds = 0; - context._params._fold_assignment = FoldAssignmentScheme.AUTO; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - if (type == FrameType.Training && fr.find(_fold_column) < 0) { - Vec foldColumn = createFoldColumn( - fr, - _scheme, - _nfolds, - _response_column, - _seed - ); - Frame withFoldc = new Frame(fr); - withFoldc.add(_fold_column, foldColumn); - return withFoldc; - } - return fr; - } - - static Vec createFoldColumn(Frame fr, - FoldAssignmentScheme fold_assignment, - int nfolds, - String responseColumn, - long seed) { - Vec foldColumn; - switch (fold_assignment) { - default: - case AUTO: - case Random: - foldColumn = AstKFold.kfoldColumn(fr.anyVec().makeZero(), nfolds, seed); - break; - case Modulo: - foldColumn = AstKFold.moduloKfoldColumn(fr.anyVec().makeZero(), nfolds); - break; - case Stratified: - foldColumn = AstKFold.stratifiedKFoldColumn(fr.vec(responseColumn), nfolds, seed); - break; - } - return foldColumn; - } - -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/ModelAsFeatureTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/ModelAsFeatureTransformer.java deleted file mode 100644 index dc083ea5bf4c..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/ModelAsFeatureTransformer.java +++ /dev/null @@ -1,147 +0,0 @@ -package hex.pipeline.transformers; - -import hex.Model; -import hex.ModelBuilder; -import hex.pipeline.PipelineContext; -import water.*; -import water.KeyGen.PatternKeyGen; -import water.fvec.Frame; -import water.nbhm.NonBlockingHashMap; - -import java.util.Map; - -public class ModelAsFeatureTransformer, MP extends Model.Parameters> extends FeatureTransformer { - - protected MP _params; - private Key _modelKey; - private final KeyGen _modelKeyGen; - - private final int _model_type; - - private final transient Map> _modelsCache = new NonBlockingHashMap<>(); - - protected ModelAsFeatureTransformer() { - this(null); - } - - public ModelAsFeatureTransformer(MP params) { - this(params, null); - } - - public ModelAsFeatureTransformer(MP params, Key modelKey) { - _params = params; - _modelKey = modelKey; - _modelKeyGen = modelKey == null - ? new PatternKeyGen("{0}_{n}_model") // if no modelKey provided, then a new key and its corresponding model is trained for each - : new KeyGen.ConstantKeyGen(modelKey); // if modelKey provided, only use that one - _model_type = params == null ? TypeMap.NULL : TypeMap.getIcedId(params.javaName()); - } - - public M getModel() { - return _modelKey == null ? null : _modelKey.get(); - } - - @SuppressWarnings("unchecked") - protected Key lookupModel(MP params) { - long cs = params.checksum(); - Key k = _modelsCache.get(cs); - if (k != null && k.get() != null) return k; - return KeySnapshot.globalSnapshot().findFirst(ki -> { - if (ki._type != _model_type) return false; - M m = (M) ki._key.get(); - return m != null && m._parms != null && m._output != null && m._output._end_time > 0 && cs == m._parms.checksum(); - }); - } - - - @Override - public Object getParameter(String name) { - try { - return _params.getParameter(name); - } catch (IllegalArgumentException iae) { - return super.getParameter(name); - } - } - - @Override - public void setParameter(String name, Object value) { - try { - _modelKey = null; //consider this as a completely new transformer as soon as we're trying new hyper-parameters. - _params.setParameter(name, value); - } catch (IllegalArgumentException iae) { - super.setParameter(name, value); - } - } - - @Override - public Object getParameterDefaultValue(String name) { - try { - return _params.getParameterDefaultValue(name); - } catch (IllegalArgumentException iae) { - return super.getParameterDefaultValue(name); - } - } - - protected void doPrepare(PipelineContext context) { - if (getModel() != null) return; // if modelKey was provided, use it immediately. - prepareModelParams(context); - excludeColumns(_params.getNonPredictors()); - Key km = lookupModel(_params); - if (km == null || (_modelKey != null && !km.equals(_modelKey))) { - if (_modelKey == null) _modelKey = _modelKeyGen.make(id()); - ModelBuilder mb = ModelBuilder.make(_params, _modelKey); - mb.trainModel().get(); - _modelsCache.put(_params.checksum(), _modelKey); - } else { - _modelKey = km; - } - } - - protected void prepareModelParams(PipelineContext context) { - assert context != null; - // to train the model, we use the train frame from context by default - - if (_params._train == null) { // do not propagate params from context otherwise as they were defined for the default training frame - assert context.getTrain() != null; - _params._train = context.getTrain().getKey(); - if (_params._valid == null && context.getValid() != null) _params._valid = context.getValid().getKey(); - if (_params._response_column == null) _params._response_column = context._params._response_column; - if (_params._weights_column == null) _params._weights_column = context._params._weights_column; - if (_params._offset_column == null) _params._offset_column = context._params._offset_column; - if (_params._ignored_columns == null) _params._ignored_columns = context._params._ignored_columns; - if (isCVSensitive()) { - MP defaults = ModelBuilder.makeParameters(_params.algoName()); - if (_params._fold_column == null) _params._fold_column = context._params._fold_column; - if (_params._fold_assignment == defaults._fold_assignment) - _params._fold_assignment = context._params._fold_assignment; - if (_params._nfolds == defaults._nfolds) _params._nfolds = context._params._nfolds; - } - } - } - - @Override - protected void doCleanup(Futures fs) { - Keyed.removeQuietly(_modelKey); - for (Key k : _modelsCache.values()) { - Keyed.removeQuietly(k); - } - _modelsCache.clear(); - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - validateTransform(); - return fr == null ? null : getModel().transform(fr); - } - - protected void validateTransform() { - assert getModel() != null; - } - - @Override - protected S cloneImpl() throws CloneNotSupportedException { - ModelAsFeatureTransformer clone = super.cloneImpl(); - clone._params = (MP) _params.clone(); - return (S) clone; - } -} diff --git a/h2o-core/src/main/java/hex/pipeline/transformers/UnionTransformer.java b/h2o-core/src/main/java/hex/pipeline/transformers/UnionTransformer.java deleted file mode 100644 index cc7675c42a46..000000000000 --- a/h2o-core/src/main/java/hex/pipeline/transformers/UnionTransformer.java +++ /dev/null @@ -1,117 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import org.apache.commons.lang.StringUtils; -import water.fvec.Frame; - -import java.util.regex.Matcher; -import java.util.regex.Pattern; -import java.util.stream.Stream; - -/** - * This transformer applies several independent transformers (possibly in parallel) to the same input frame. - * The results of those transformations to the input frame are then concatenated to produce the result frame, - * or possibly appended to the input frame. - */ -public class UnionTransformer extends DataTransformer { - - public enum UnionStrategy { - append, - replace - } - - private DataTransformer[] _transformers; - private UnionStrategy _strategy; - - - protected UnionTransformer() {} - - public UnionTransformer(UnionStrategy strategy, DataTransformer... transformers) { - _strategy = strategy; - _transformers = transformers; - } - - @Override - public Object getParameter(String name) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - DataTransformer dt = getTransformer(tok0); - return dt == null ? null : dt.getParameter(tokens[1]); - } - return super.getParameter(name); - } - - @Override - public void setParameter(String name, Object value) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - DataTransformer dt = getTransformer(tok0); - if (dt != null) dt.setParameter(tokens[1], value); - return; - } - super.setParameter(name, value); - } - - @Override - public boolean isParameterAssignable(String name) { - String[] tokens = parseParameterName(name); - if (tokens.length > 1) { - String tok0 = tokens[0]; - DataTransformer dt = getTransformer(tok0); - return dt != null && dt.hasParameter(tokens[1]); - } - return super.isParameterAssignable(name); - } - - //TODO similar logic as in PipelineParameters: delegate to some kind of ModelParametersAccessor? - private static final Pattern TRANSFORMER_PAT = Pattern.compile("transformers\\[(\\w+)]"); - private String[] parseParameterName(String name) { - String[] tokens = name.split("\\.", 2); - if (tokens.length == 1) return tokens; - String tok0 = StringUtils.stripStart(tokens[0], "_"); - if (getTransformer(tok0) != null) { - return new String[]{tok0, tokens[1]} ; - } else { - Matcher m = TRANSFORMER_PAT.matcher(tok0); - if (m.matches()) { - String id = m.group(1); - try { - int idx = Integer.parseInt(id); - assert idx >=0 && idx < _transformers.length; - return new String[]{_transformers[idx].id(), tokens[1]}; - } catch(NumberFormatException nfe) { - if (getTransformer(id) != null) return new String[] {id, tokens[1]}; - throw new IllegalArgumentException("Unknown transformer: "+tok0); - } - } else { - throw new IllegalArgumentException("Unknown parameter: "+name); - } - } - } - - private DataTransformer getTransformer(String id) { - if (_transformers == null) return null; - return Stream.of(_transformers).filter(t -> t.id().equals(id)).findFirst().orElse(null); - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - Frame result = null; - switch (_strategy) { - case append: - result = new Frame(fr); - break; - case replace: - result = new Frame(); - break; - } - for (DataTransformer dt : _transformers) { - result.add(dt.transform(fr, type, context)); - } - return result; - } - -} diff --git a/h2o-core/src/main/java/hex/schemas/DataTransformerV3.java b/h2o-core/src/main/java/hex/schemas/DataTransformerV3.java deleted file mode 100644 index 2b1ea5f612c9..000000000000 --- a/h2o-core/src/main/java/hex/schemas/DataTransformerV3.java +++ /dev/null @@ -1,15 +0,0 @@ -package hex.schemas; - -import hex.pipeline.DataTransformer; -import water.api.API; -import water.api.schemas3.SchemaV3; - -public class DataTransformerV3> extends SchemaV3 { - - @API(help="Transformer identifier (must be unique in the pipeline)", direction=API.Direction.OUTPUT) - public String id; - - @API(help="A short description of this transformer", direction=API.Direction.OUTPUT) - public String description; - -} diff --git a/h2o-core/src/main/java/hex/schemas/PipelineModelV3.java b/h2o-core/src/main/java/hex/schemas/PipelineModelV3.java deleted file mode 100644 index 219aad75e752..000000000000 --- a/h2o-core/src/main/java/hex/schemas/PipelineModelV3.java +++ /dev/null @@ -1,40 +0,0 @@ -package hex.schemas; - -import hex.pipeline.PipelineModel; -import hex.pipeline.PipelineModel.PipelineOutput; -import hex.pipeline.PipelineModel.PipelineParameters; -import water.api.API; -import water.api.schemas3.KeyV3; -import water.api.schemas3.ModelOutputSchemaV3; -import water.api.schemas3.ModelSchemaV3; - -public class PipelineModelV3 extends ModelSchemaV3< - PipelineModel, PipelineModelV3, - PipelineParameters, PipelineV3.PipelineParametersV3, - PipelineOutput, PipelineModelV3.PipelineModelOutputV3 - > { - - @Override - public PipelineV3.PipelineParametersV3 createParametersSchema() { - return new PipelineV3.PipelineParametersV3(); - } - - @Override - public PipelineModelOutputV3 createOutputSchema() { - return new PipelineModelOutputV3(); - } - - public static final class PipelineModelOutputV3 extends ModelOutputSchemaV3 { - - @API(help="Sequence of transformers applied to input data.", direction = API.Direction.OUTPUT) - public DataTransformerV3[] transformers; - - @API(help="Estimator model trained and/or applied after transformations.", direction = API.Direction.OUTPUT) - public KeyV3.ModelKeyV3 estimator; - - @Override - public PipelineModelOutputV3 fillFromImpl(PipelineOutput impl) { - return super.fillFromImpl(impl); - } - } -} diff --git a/h2o-core/src/main/java/hex/schemas/PipelineV3.java b/h2o-core/src/main/java/hex/schemas/PipelineV3.java deleted file mode 100644 index ed3796894086..000000000000 --- a/h2o-core/src/main/java/hex/schemas/PipelineV3.java +++ /dev/null @@ -1,20 +0,0 @@ -package hex.schemas; - -import hex.pipeline.Pipeline; -import hex.pipeline.PipelineModel; -import water.api.schemas3.ModelParametersSchemaV3; - -public class PipelineV3 extends ModelBuilderSchema { - - public static final class PipelineParametersV3 extends ModelParametersSchemaV3 { - static public String[] fields = new String[] { - "model_id", -// "training_frame", -// "validation_frame", -// "response_column", -// "nfolds", -// "fold_column", - }; - - } -} diff --git a/h2o-core/src/main/java/water/Checksumable.java b/h2o-core/src/main/java/water/Checksumable.java deleted file mode 100644 index 179e4de03dc6..000000000000 --- a/h2o-core/src/main/java/water/Checksumable.java +++ /dev/null @@ -1,6 +0,0 @@ -package water; - -public interface Checksumable { - - long checksum(); -} diff --git a/h2o-core/src/main/java/water/Freezable.java b/h2o-core/src/main/java/water/Freezable.java index 7eb4b522249a..b9f0b86a9c9c 100644 --- a/h2o-core/src/main/java/water/Freezable.java +++ b/h2o-core/src/main/java/water/Freezable.java @@ -111,5 +111,5 @@ public interface Freezable extends Cloneable { T reloadFromBytes(byte [] ary); /** Make clone public, but without the annoying exception. * @return Returns this object cloned. */ - T clone(); + public T clone(); } diff --git a/h2o-core/src/main/java/water/Iced.java b/h2o-core/src/main/java/water/Iced.java index f36f6d87ed89..0c47556460f0 100644 --- a/h2o-core/src/main/java/water/Iced.java +++ b/h2o-core/src/main/java/water/Iced.java @@ -87,14 +87,9 @@ final public byte[] toJsonBytes() { @Override final public int frozenType() { return icer().frozenType(); } /** Clone, without the annoying exception */ @Override public final D clone() { - try { return cloneImpl(); } + try { return (D)super.clone(); } catch( CloneNotSupportedException e ) { throw water.util.Log.throwErr(e); } } - - /** override this if the class requires custom cloning logic */ - protected D cloneImpl() throws CloneNotSupportedException { - return (D)super.clone(); - } /** Copy over cloned instance 'src' over 'this', field by field. */ protected void copyOver( D src ) { icer().copyOver((D)this,src); } diff --git a/h2o-core/src/main/java/water/KeyGen.java b/h2o-core/src/main/java/water/KeyGen.java deleted file mode 100644 index e54eb4966b9f..000000000000 --- a/h2o-core/src/main/java/water/KeyGen.java +++ /dev/null @@ -1,115 +0,0 @@ -package water; - -import water.util.ArrayUtils; - -import java.util.Objects; -import java.util.concurrent.atomic.AtomicInteger; -import java.util.regex.Matcher; -import java.util.regex.Pattern; - -public abstract class KeyGen extends Iced { - - public abstract > Key make(Object... params); - - public static final class ConstantKeyGen extends KeyGen { - private final Key key; - - public ConstantKeyGen(Key key) { - this.key = key; - } - - @Override - public > Key make(Object... params) { - return key; - } - } - - public static final class RandomKeyGen extends KeyGen { - public RandomKeyGen() {} - - @Override - public > Key make(Object... params) { - return Key.make(Key.rand()); - } - } - - public static final class PatternKeyGen extends KeyGen { - - private static final String PIPE = "\\s*\\|\\s*"; - - - private enum Command { - SUBSTITUTE() { - private final Pattern CMD = Pattern.compile("s/(.*?)/(.*?)/?"); - - @Override - boolean matches(String cmd) { - return cmd.startsWith("s/"); - } - - @Override - String apply(String cmd, String str) { - Matcher m = CMD.matcher(cmd); - if (m.matches()) { - return str.replace(m.group(1), m.group(2)); - } - throw new IllegalArgumentException("invalid command `"+cmd+"` for "+name()); - } - }, - ; - - abstract boolean matches(String cmd); - - abstract String apply(String cmd, String str); - } - - private static final String RANDOM_STR = "{rstr}"; //this will be replaced by random string on each new key; - private static final String COUNTER = "{n}"; //this will be replaced by incremented integer on each new key; - - private final String pattern; - - private final String[] commands; - private final AtomicInteger count = new AtomicInteger(0); - - /** - * creates a key generator using the given pattern supporting the following placeholders: - *

    - *
  • {rstr} -> will be replaced by a randomly generated string
    • - *
  • {n} -> will be replaced by an integer incremented at each call to {@link #make(Object...)}
    • - *
  • {0}, {1}, {2}, ... -> dynamically replaced by positional parameters of {@link #make(Object...)}
    • - *
  • piped commands applied after the pattern
      - *
    • {0}_suffix | s/foo/bar/ -> adds a suffix to the first {@link #make(Object...)} param and then substitute occurrences of "foo" with "bar"
      • - *
    • - *
- * - * @param pattern - */ - public PatternKeyGen(String pattern) { - String[] tokens = pattern.split(PIPE); - this.pattern = tokens[0]; - this.commands = ArrayUtils.remove(tokens, 0); - } - - public > Key make(Object... params) { - String keyStr = pattern; - for (int i = 0; i < params.length; i++) { - keyStr = keyStr.replace("{"+i+"}", Objects.toString(params[i])); - } - keyStr = keyStr - .replace(RANDOM_STR, Key.rand()) - .replace(COUNTER, Integer.toString(count.incrementAndGet())); - for (String cmd : commands) { - keyStr = applyCommand(cmd, keyStr); - } - return Key.make(keyStr); - } - - private String applyCommand(String cmd, String str) { - for (Command c : Command.values()) { - if (c.matches(cmd)) - return c.apply(cmd, str); - } - throw new IllegalArgumentException("Invalid command: "+cmd); - } - } -} diff --git a/h2o-core/src/main/java/water/KeySnapshot.java b/h2o-core/src/main/java/water/KeySnapshot.java index d4979f0e0883..a03725fedb75 100644 --- a/h2o-core/src/main/java/water/KeySnapshot.java +++ b/h2o-core/src/main/java/water/KeySnapshot.java @@ -19,11 +19,10 @@ */ public class KeySnapshot { /** Class to filter keys from the snapshot. */ - @FunctionalInterface - public interface KVFilter { + public abstract static class KVFilter { /** @param k KeyInfo to be filtered * @return true if the key should be included in the new (filtered) set. */ - boolean filter(KeyInfo k); + public abstract boolean filter(KeyInfo k); } /** Class containing information about user keys. @@ -66,12 +65,6 @@ public KeySnapshot filter(KVFilter kvf){ if(kvf.filter(kinfo))res.add(kinfo); return new KeySnapshot(res.toArray(new KeyInfo[res.size()])); } - - public Key findFirst(KVFilter kvf) { - for (KeyInfo ki : _keyInfos) - if (kvf.filter(ki)) return ki._key; - return null; - } KeySnapshot(KeyInfo[] snapshot){ _keyInfos = snapshot; diff --git a/h2o-core/src/main/java/water/Scope.java b/h2o-core/src/main/java/water/Scope.java index 3bfc16c78275..32a4b398c53f 100644 --- a/h2o-core/src/main/java/water/Scope.java +++ b/h2o-core/src/main/java/water/Scope.java @@ -199,7 +199,6 @@ private static void track_impl(Level level, Key key) { * Use {@link #untrack(Frame...)} is you need a behaviour symmetrical to {@link #track(Frame...)}. * @param keys */ - @SafeVarargs public static void untrack(K... keys) { if (keys.length == 0) return; Level level = lget(); // Pay the price of T.L.S. lookup diff --git a/h2o-core/src/main/java/water/ScopeInspect.java b/h2o-core/src/main/java/water/ScopeInspect.java index 9404b2411f96..76552d7797eb 100644 --- a/h2o-core/src/main/java/water/ScopeInspect.java +++ b/h2o-core/src/main/java/water/ScopeInspect.java @@ -106,8 +106,7 @@ private static StringBuilder appendKey(StringBuilder sb, Key key, int numIndent, if (fr != null) { for (int i=0; i vk = fr.keys()[i]; - String name = fr.name(i); - appendKey(sb, vk, numIndent+1, i+":'"+name+"'", true, keyFilter); + appendKey(sb, vk, numIndent+1, "vec_"+i, true, keyFilter); } } } diff --git a/h2o-core/src/main/java/water/TypeMap.java b/h2o-core/src/main/java/water/TypeMap.java index 8d0fc85ca22d..64d755dcc33c 100644 --- a/h2o-core/src/main/java/water/TypeMap.java +++ b/h2o-core/src/main/java/water/TypeMap.java @@ -139,7 +139,7 @@ public static String[] bootstrapClasses() { // new code: leader sets string->ID mapping // printing: id -> string // deserial: id -> string -> Icer -> Iced (slow path) - // deserial: id -> Icer -> Iced (fast path) + // deserial: id -> Icer -> Iced (fath path) // lookup : id -> string (on leader) // diff --git a/h2o-core/src/main/java/water/api/ModelBuilderHandlerUtils.java b/h2o-core/src/main/java/water/api/ModelBuilderHandlerUtils.java index 89a47fee34d5..19155f083999 100644 --- a/h2o-core/src/main/java/water/api/ModelBuilderHandlerUtils.java +++ b/h2o-core/src/main/java/water/api/ModelBuilderHandlerUtils.java @@ -15,7 +15,6 @@ static , P extends M ) { String algoName = ModelBuilder.algoName(algoURLName); // gbm -> GBM; deeplearning -> DeepLearning String schemaDir = ModelBuilder.schemaDirectory(algoURLName); - if (schemaDir == null) return null; // this builder doesn't have any schema // Build a Model Schema and a ModelParameters Schema String schemaName = schemaDir + algoName + "V" + version; diff --git a/h2o-core/src/main/java/water/api/ModelBuildersHandler.java b/h2o-core/src/main/java/water/api/ModelBuildersHandler.java index eeb65c0e349c..876cb5498768 100644 --- a/h2o-core/src/main/java/water/api/ModelBuildersHandler.java +++ b/h2o-core/src/main/java/water/api/ModelBuildersHandler.java @@ -22,10 +22,8 @@ class ModelBuildersHandler extends Handler { public ModelBuildersV3 list(int version, ModelBuildersV3 m) { m.model_builders = new ModelBuilderSchema.IcedHashMapStringModelBuilderSchema(); for( String algo : ModelBuilder.algos() ) { - ModelBuilderSchema schema = makeSchema(algo, version); - if (schema != null) { - m.model_builders.put(algo.toLowerCase(), schema); - } + ModelBuilder builder = ModelBuilder.make(algo, null, null); + m.model_builders.put(algo.toLowerCase(), (ModelBuilderSchema)SchemaServer.schema(version, builder).fillFromImpl(builder)); } return m; } @@ -34,9 +32,8 @@ public ModelBuildersV3 list(int version, ModelBuildersV3 m) { @SuppressWarnings("unused") // called through reflection by RequestServer public ModelBuildersV3 fetch(int version, ModelBuildersV3 m) { m.model_builders = new ModelBuilderSchema.IcedHashMapStringModelBuilderSchema(); - ModelBuilderSchema schema = makeSchema(m.algo, version); - if (schema != null) - m.model_builders.put(m.algo.toLowerCase(), schema); + ModelBuilder builder = ModelBuilder.make(m.algo, null, null); + m.model_builders.put(m.algo.toLowerCase(), (ModelBuilderSchema)SchemaServer.schema(version, builder).fillFromImpl(builder)); return m; } @@ -74,11 +71,6 @@ public ModelsInfoV4 modelsInfo(int version, ListRequestV4 m) { return res; } - private ModelBuilderSchema makeSchema(String algo, int version) { - ModelBuilder builder = ModelBuilder.make(algo, null, null); - if (ModelBuilder.schemaDirectory(builder.getName()) == null) return null; // this builder disabled schema - return (ModelBuilderSchema)SchemaServer.schema(version, builder).fillFromImpl(builder); // use ModelBuilderHandlerUtils.makeBuilderSchema instead? - } private String detectMojoVersion(ModelBuilder builder) { Class modelClass = ReflectionUtils.findActualClassParameter(builder.getClass(), 0); diff --git a/h2o-core/src/main/java/water/util/Checksum.java b/h2o-core/src/main/java/water/util/Checksum.java deleted file mode 100644 index 2715e40b1e61..000000000000 --- a/h2o-core/src/main/java/water/util/Checksum.java +++ /dev/null @@ -1,107 +0,0 @@ -package water.util; - -import water.Checksumable; -import water.H2O; -import water.Weaver; - -import java.lang.reflect.Field; -import java.util.Arrays; -import java.util.Comparator; -import java.util.Set; - -public final class Checksum { - - private Checksum() {} - - public static long checksum(final T obj) { - return checksum(obj, null); - } - - public static long checksum(final T obj, final Set ignoredFields) { - return checksum(obj, ignoredFields, 0x600DL); - } - - /** - * Compute a checksum based on all non-transient non-static ice-able assignable fields (incl. inherited ones). - * Sort the fields first, since reflection gives us the fields in random order, and we don't want the checksum to be affected by the field order. - * - * NOTE: if a field is added to a class the checksum will differ even when all the previous parameters have the same value. If - * a client wants backward compatibility they will need to compare values explicitly. - * - * The method is motivated by standard hash implementation `hash = hash * P + value` but we use high prime numbers in random order. - * - * @param ignoredFields A {@link Set} of fields to ignore. Can be empty or null. - * @return checksum A 64-bit long representing the checksum of the object - */ - public static long checksum(final T obj, final Set ignoredFields, final long initVal) { - assert obj != null; - long xs = initVal; - int count = 0; - Field[] fields = Weaver.getWovenFields(obj.getClass()); - Arrays.sort(fields, Comparator.comparing(Field::getName)); - for (Field f : fields) { - if (ignoredFields != null && ignoredFields.contains(f.getName())) { - // Do not include ignored fields in the final hash - continue; - } - final long P = MathUtils.PRIMES[count % MathUtils.PRIMES.length]; - Class c = f.getType(); - Object fvalue; - try { - f.setAccessible(true); - fvalue = f.get(obj); - } catch (IllegalAccessException e) { - throw new RuntimeException(e); - } - if (c.isArray()) { - try { - if (fvalue != null) { - if (c.getComponentType() == Integer.TYPE){ - int[] arr = (int[]) fvalue; - xs = xs * P + (long) Arrays.hashCode(arr); - } else if (c.getComponentType() == Float.TYPE) { - float[] arr = (float[]) fvalue; - xs = xs * P + (long) Arrays.hashCode(arr); - } else if (c.getComponentType() == Double.TYPE) { - double[] arr = (double[]) fvalue; - xs = xs * P + (long) Arrays.hashCode(arr); - } else if (c.getComponentType() == Long.TYPE){ - long[] arr = (long[]) fvalue; - xs = xs * P + (long) Arrays.hashCode(arr); - } else if (c.getComponentType() == Boolean.TYPE){ - boolean[] arr = (boolean[]) fvalue; - xs = xs * P + (long) Arrays.hashCode(arr); - } else { - Object[] arr = (Object[]) fvalue; - if (Checksumable.class.isAssignableFrom(arr.getClass().getComponentType())) { - for (Checksumable cs : (Checksumable[])arr) { - xs = xs * P + cs.checksum(); - } - } else { - xs = xs * P + (long) Arrays.deepHashCode(arr); - } - } //else lead to ClassCastException - } else { - xs = xs * P; - } - } catch (ClassCastException t) { - throw H2O.fail("Failed to calculate checksum for the parameter object", t); //no support yet for int[][] etc. - } - } else { - if (fvalue instanceof Enum) { - // use string hashcode for enums, otherwise the checksum would be different each run - xs = xs * P + (long) (fvalue.toString().hashCode()); - } else if (fvalue instanceof Checksumable) { - xs = xs * P + ((Checksumable) fvalue).checksum(); - } else if (fvalue != null) { - xs = xs * P + (long)(fvalue.hashCode()); - } else { - xs = xs * P + P; - } - } - count++; - } - return xs; - } - -} diff --git a/h2o-core/src/main/java/water/util/FrameUtils.java b/h2o-core/src/main/java/water/util/FrameUtils.java index 82d9583a3c0a..5924b31b7f66 100644 --- a/h2o-core/src/main/java/water/util/FrameUtils.java +++ b/h2o-core/src/main/java/water/util/FrameUtils.java @@ -1091,12 +1091,12 @@ public Job exec() { } } - public static void cleanUp(Collection toDelete) { + static public void cleanUp(IcedHashMap toDelete) { if (toDelete == null) { return; } Futures fs = new Futures(); - for (Key k : toDelete) { + for (Key k : toDelete.keySet()) { Keyed.remove(k, fs, true); } fs.blockForPending(); diff --git a/h2o-core/src/main/java/water/util/PojoUtils.java b/h2o-core/src/main/java/water/util/PojoUtils.java index 17f5301e8cdf..b06b8dd41aa0 100644 --- a/h2o-core/src/main/java/water/util/PojoUtils.java +++ b/h2o-core/src/main/java/water/util/PojoUtils.java @@ -21,16 +21,16 @@ public class PojoUtils { public enum FieldNaming { CONSISTENT { - @Override public String toDest(String origin) { return origin; } - @Override public String toOrigin(String dest) { return dest; } + @Override String toDest(String origin) { return origin; } + @Override String toOrigin(String dest) { return dest; } }, DEST_HAS_UNDERSCORES { - @Override public String toDest(String origin) { return "_" + origin; } - @Override public String toOrigin(String dest) { return dest.substring(1); } + @Override String toDest(String origin) { return "_" + origin; } + @Override String toOrigin(String dest) { return dest.substring(1); } }, ORIGIN_HAS_UNDERSCORES { - @Override public String toDest(String origin) { return origin.substring(1); } - @Override public String toOrigin(String dest) { return "_" + dest; } + @Override String toDest(String origin) { return origin.substring(1); } + @Override String toOrigin(String dest) { return "_" + dest; } }; /** @@ -38,14 +38,14 @@ public enum FieldNaming { * @param origin name of origin argument * @return return a name of destination argument. */ - public abstract String toDest(String origin); + abstract String toDest(String origin); /** * Return name of origin parameter derived from name of origin parameter. * @param dest name of destination argument. * @return return a name of origin argument. */ - public abstract String toOrigin(String dest); + abstract String toOrigin(String dest); } @@ -625,28 +625,16 @@ public static Field getFieldEvenInherited(Object o, String name) throws NoSuchFi * @throws java.lang.IllegalArgumentException when o is null, or field is not found, * or field cannot be read. */ - public static Object getFieldValue(Object o, String name) { - return getFieldValue(o, name, false); - } - - public static Object getFieldValue(Object o, String name, boolean anyVisibility) { + public static Object getFieldValue(Object o, String name, FieldNaming fieldNaming) { if (o == null) throw new IllegalArgumentException("Cannot get the field from null object!"); + String destName = fieldNaming.toDest(name); try { - Field f = PojoUtils.getFieldEvenInherited(o, name); // failing with fields declared in superclasses - if (anyVisibility) f.setAccessible(true); + Field f = PojoUtils.getFieldEvenInherited(o, destName); // failing with fields declared in superclasses return f.get(o); } catch (NoSuchFieldException e) { - throw new IllegalArgumentException("Field not found: '"+name+"' on object " + o); + throw new IllegalArgumentException("Field not found: '" + name + "/" + destName + "' on object " + o); } catch (IllegalAccessException e) { - throw new IllegalArgumentException("Cannot get value of the field: '"+name+"' on object " + o); - } - } - public static Object getFieldValue(Object o, String name, FieldNaming fieldNaming) { - String dest = fieldNaming.toDest(name); - try { - return getFieldValue(o, dest); - } catch (IllegalArgumentException e) { - throw new IllegalArgumentException(e.getMessage().replace("'"+dest+"'", "'"+name+"/"+dest+"'")); + throw new IllegalArgumentException("Cannot get value of the field: '" + name + "/" + destName + "' on object " + o); } } diff --git a/h2o-core/src/main/java/water/util/TwoDimTable.java b/h2o-core/src/main/java/water/util/TwoDimTable.java index 7c751998817a..26de71836210 100644 --- a/h2o-core/src/main/java/water/util/TwoDimTable.java +++ b/h2o-core/src/main/java/water/util/TwoDimTable.java @@ -373,20 +373,11 @@ public String toString(final int pad, boolean full) { return sb.toString(); } public Frame asFrame(Key frameKey) { - return asFrame(frameKey, false); - } - - public Frame asFrame(Key frameKey, boolean withRowHeaders) { String[] colNames = new String[getColDim()]; System.arraycopy(getColHeaders(), 0, colNames, 0, getColDim()); - int colOffset = 0; - if (withRowHeaders) { - colNames = ArrayUtils.append(new String[]{"_"}, colNames); - colOffset++; - } Vec[] vecs = new Vec[colNames.length]; - if (withRowHeaders) vecs[0] = Vec.makeVec(getRowHeaders(), Vec.newKey()); + vecs[0] = Vec.makeVec(getRowHeaders(), Vec.newKey()); for (int j = 0; j < this.getColDim(); j++) { switch (getColTypes()[j]){ @@ -395,7 +386,7 @@ public Frame asFrame(Key frameKey, boolean withRowHeaders) { for (int i = 0; i < getRowDim(); i++) { strRow[i] = (String) get(i, j); } - vecs[j+colOffset] = Vec.makeVec(strRow, Vec.newKey()); + vecs[j] = Vec.makeVec(strRow, Vec.newKey()); break; case "int": case "long": @@ -403,7 +394,7 @@ public Frame asFrame(Key frameKey, boolean withRowHeaders) { for (int i = 0; i < getRowDim(); i++) { longRow[i] = ((Number) get(i, j)).longValue(); } - vecs[j+colOffset] = Vec.makeVec(longRow, Vec.newKey()); + vecs[j] = Vec.makeVec(longRow, Vec.newKey()); break; case "float": case "double": @@ -411,7 +402,7 @@ public Frame asFrame(Key frameKey, boolean withRowHeaders) { for (int i = 0; i < getRowDim(); i++) { dblRow[i] = (double) get(i, j); } - vecs[j+colOffset] = Vec.makeVec(dblRow, Vec.newKey()); + vecs[j] = Vec.makeVec(dblRow, Vec.newKey()); break; } } diff --git a/h2o-core/src/main/resources/META-INF/services/water.api.RestApiExtension b/h2o-core/src/main/resources/META-INF/services/water.api.RestApiExtension index e50525c5d638..f5e9d032750b 100644 --- a/h2o-core/src/main/resources/META-INF/services/water.api.RestApiExtension +++ b/h2o-core/src/main/resources/META-INF/services/water.api.RestApiExtension @@ -1,3 +1,2 @@ water.api.RegisterV3Api -water.api.RegisterV4Api -hex.pipeline.PipelineAlgoRegistration +water.api.RegisterV4Api \ No newline at end of file diff --git a/h2o-core/src/main/resources/META-INF/services/water.api.Schema b/h2o-core/src/main/resources/META-INF/services/water.api.Schema index 366310fb40d3..75286a3d4080 100644 --- a/h2o-core/src/main/resources/META-INF/services/water.api.Schema +++ b/h2o-core/src/main/resources/META-INF/services/water.api.Schema @@ -1,5 +1,4 @@ hex.schemas.ClusteringModelBuilderSchema -hex.schemas.DataTransformerV3 hex.schemas.GridSchemaV99 hex.schemas.GridSearchSchema hex.schemas.HyperSpaceSearchCriteriaV99 @@ -7,10 +6,6 @@ hex.schemas.HyperSpaceSearchCriteriaV99$CartesianSearchCriteriaV99 hex.schemas.HyperSpaceSearchCriteriaV99$RandomDiscreteValueSearchCriteriaV99 hex.schemas.HyperSpaceSearchCriteriaV99$SequentialSearchCriteriaV99 hex.schemas.ModelBuilderSchema -hex.schemas.PipelineV3 -hex.schemas.PipelineModelV3 -hex.schemas.PipelineV3$PipelineParametersV3 -hex.schemas.PipelineModelV3$PipelineModelOutputV3 hex.schemas.QuantileV3 hex.schemas.QuantileV3$QuantileParametersV3 water.api.ModelBuildersHandler$ModelIdV3 diff --git a/h2o-core/src/test/java/hex/ModelAdaptTest.java b/h2o-core/src/test/java/hex/ModelAdaptTest.java index 5eb454a36948..0461fe95f989 100644 --- a/h2o-core/src/test/java/hex/ModelAdaptTest.java +++ b/h2o-core/src/test/java/hex/ModelAdaptTest.java @@ -148,7 +148,7 @@ static class AOutput extends Model.Output { } Frame.deleteTempFrameAndItsNonSharedVecs(adapt, tst); tst.remove(); - FrameUtils.cleanUp(am._toDelete.keySet()); + FrameUtils.cleanUp(am._toDelete); } } diff --git a/h2o-core/src/test/java/hex/pipeline/DataTransformerTest.java b/h2o-core/src/test/java/hex/pipeline/DataTransformerTest.java deleted file mode 100644 index 63080b1d5652..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/DataTransformerTest.java +++ /dev/null @@ -1,216 +0,0 @@ -package hex.pipeline; - -import org.junit.Test; -import org.junit.runner.RunWith; -import water.Key; -import water.MRTask; -import water.Scope; -import water.fvec.*; -import water.logging.Logger; -import water.logging.LoggerFactory; -import water.runner.CloudSize; -import water.runner.H2ORunner; - -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import static org.junit.Assert.*; -import static water.TestUtil.*; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class DataTransformerTest { - - @Test - public void test_transform() { - try { - Scope.enter(); - DataTransformer dt = new AddRandomColumnTransformer("foo"); - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3)) - .withDataForCol(1, ard(1, 2, 3)) - .withDataForCol(2, ar("yes", "no", "yes")) - .build()); - - Frame transformed = Scope.track(dt.transform(fr)); - assertEquals(4, transformed.names().length); - assertArrayEquals(new String[]{"one", "two", "target", "foo"}, transformed.names()); - } finally { - Scope.exit(); - } - } - - public static class MultiplyNumericColumnTransformer extends DataTransformer { - - private final String colName; - - private final int multiplier; - - public MultiplyNumericColumnTransformer(String colName, int multiplier) { - this.colName = colName; - this.multiplier = multiplier; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - Frame tr = new Frame(fr); - final int colIdx = tr.find(colName); - Vec col = tr.vec(colIdx); - assert col.isNumeric(); - Vec multCol = new MRTask() { - @Override - public void map(Chunk[] cs, NewChunk[] ncs) { - for (int i = 0; i < cs[0]._len; i++) - ncs[0].addNum(multiplier * (cs[0].atd(i))); - } - }.doAll(Vec.T_NUM, new Frame(col)).outputFrame().vec(0); - tr.replace(colIdx, multCol); - return tr; - } - } - - - public static class AddRandomColumnTransformer extends DataTransformer { - - private final String colName; - private final long seed; - - public AddRandomColumnTransformer(String colName) { - this(colName, 0); - } - - public AddRandomColumnTransformer(String colName, long seed) { - this.colName = colName; - this.seed = seed; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - Frame tr = new Frame(fr); - tr.add(colName, tr.anyVec().makeRand(seed)); - return tr; - } - } - - public static class AddDummyCVColumnTransformer extends DataTransformer { - - private final String colName; - private final int vecType; - - public AddDummyCVColumnTransformer(String colName) { - this(colName, Vec.T_NUM); - } - - public AddDummyCVColumnTransformer(String colName, int vecType) { - this.colName = colName; - this.vecType = vecType; - } - - @Override - public boolean isCVSensitive() { - return true; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - if (type == FrameType.Training && context._params._is_cv_model) { - Frame tr = new Frame(fr); - Vec v = Vec.makeRepSeq(tr.anyVec().length(), context._params._cv_fold+2); - Vec tmp; - switch (vecType) { - case Vec.T_CAT: - tmp = v.toCategoricalVec(); v.remove(); v = tmp; break; - case Vec.T_STR: - tmp = v.toStringVec(); v.remove(); v = tmp; break; - case Vec.T_NUM: - default: - //already numeric by construct - break; - } - tr.add(colName, v); - return tr; - } - return fr; - } - } - - public static class RenameFrameTransformer extends DataTransformer { - - private final String frameName; - - public RenameFrameTransformer(String frameName) { - this.frameName = frameName; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - return new Frame(Key.make(frameName), fr.names(), fr.vecs()); - } - } - - public static class FrameTrackerAsTransformer extends DataTransformer { - - static final Logger logger = LoggerFactory.getLogger(FrameTrackerAsTransformer.class); - - public static class Transformation { - final String frameId; - final FrameType type; - final boolean is_cv; - - public Transformation(String frameId, FrameType type, boolean is_cv) { - this.frameId = frameId; - this.type = type; - this.is_cv = is_cv; - } - - @Override - public String toString() { - final StringBuilder sb = new StringBuilder("Transformation{"); - sb.append("frameId='").append(frameId).append('\''); - sb.append(", type=").append(type); - sb.append(", is_cv=").append(is_cv); - sb.append('}'); - return sb.toString(); - } - } - - transient List transformations = new ArrayList<>(); - - @Override - protected void doPrepare(PipelineContext context) { - transformations.clear(); - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - if (fr == null) return null; - logger.info(fr.getKey()+": columns="+Arrays.toString(fr.names())); - boolean is_cv = context != null && context._params._is_cv_model; - transformations.add(new Transformation(fr.getKey().toString(), type, is_cv)); - return fr; - } - } - - @FunctionalInterface - interface FrameChecker { - void check(Frame fr); - } - - public static class FrameCheckerAsTransformer extends DataTransformer { - - final transient FrameChecker checker; - - public FrameCheckerAsTransformer(FrameChecker checker) { - this.checker = checker; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - checker.check(fr); - return fr; - } - } -} diff --git a/h2o-core/src/test/java/hex/pipeline/PipelineHelperTest.java b/h2o-core/src/test/java/hex/pipeline/PipelineHelperTest.java deleted file mode 100644 index b0a7327b3ce7..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/PipelineHelperTest.java +++ /dev/null @@ -1,178 +0,0 @@ -package hex.pipeline; - -import org.junit.Rule; -import org.junit.Test; -import org.junit.rules.ExternalResource; -import org.junit.runner.RunWith; -import water.DKV; -import water.Key; -import water.Scope; -import water.fvec.Frame; -import water.fvec.TestFrameBuilder; -import water.fvec.Vec; -import water.junit.rules.DKVIsolation; -import water.junit.rules.ScopeTracker; -import water.runner.CloudSize; -import water.runner.H2ORunner; - -import java.io.Serializable; - -import static hex.pipeline.PipelineHelper.reassign; -import static hex.pipeline.PipelineHelper.reassignInplace; -import static org.junit.Assert.*; -import static water.TestUtil.ard; -import static water.TestUtil.assertFrameEquals; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class PipelineHelperTest { - - private static class ReferenceFrameProvider extends ExternalResource implements Serializable { - private static final Key refKey = Key.make("refFrame"); - - private Frame refFrame; - - @Override - protected void before() throws Throwable { - refFrame = new TestFrameBuilder() - .withName(refKey.toString()) - .withColNames("one", "two") - .withVecTypes(Vec.T_NUM, Vec.T_NUM) - .withDataForCol(0, ard(1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1)) - .build(); - } - - @Override - protected void after() { - assertNotNull(DKV.get(refKey)); - assertFrameEquals(DKV.getGet(refKey), refFrame, 0); // only comparing frame content as in multinode, can't guarantee that these will be the same objects - for (Key k : refFrame.keys()) assertNotNull(DKV.get(k)); - } - - Frame get() { - return refFrame; - } - } - - @Rule - public ScopeTracker scope = new ScopeTracker(); - - @Rule - public ReferenceFrameProvider refFrame = new ReferenceFrameProvider(); - - @Rule - public DKVIsolation isolation = new DKVIsolation(); - - private void addRndVec(Frame fr) { - int lockedStatus = fr._lockers == null ? 0 : fr._lockers.length; - if (lockedStatus == 0) fr.write_lock(); - fr.add("rndvec", fr.anyVec().makeRand(0)); - fr.update(); - if (lockedStatus == 0) fr.unlock(); - } - - @Test - public void test_reassign_frame_null_key_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(null, ref.names(), ref.vecs()); - Key copyKey = copy.getKey(); - assertNull(copyKey); - - Key reassigned = Key.make("reassigned"); - Frame cc = reassign(copy, reassigned); - addRndVec(cc); - Scope.track_generic(cc); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNotSame(copy, cc); - assertNull(copy.getKey()); //copy was not assigned any key - assertNotNull(DKV.get(reassigned)); - assertSame(cc, DKV.getGet(reassigned)); - } - - @Test - public void test_reassign_frame_not_in_DKV_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(ref); - Key copyKey = copy.getKey(); - assertNotNull(copyKey); - assertNull(DKV.get(copyKey)); - - Key reassigned = Key.make("reassigned"); - Frame cc = reassign(copy, reassigned); - addRndVec(cc); - Scope.track_generic(cc); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNotSame(copy, cc); - assertNull(DKV.get(copyKey)); - assertEquals(copyKey, copy.getKey()); //copy key was not modified - assertNotNull(DKV.get(reassigned)); - assertSame(cc, DKV.getGet(reassigned)); - } - - @Test - public void test_reassign_frame_in_DKV_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(ref); - Key copyKey = copy.getKey(); - DKV.put(Scope.track_generic(copy)); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNotNull(copyKey); - assertNotNull(DKV.get(copyKey)); - Key reassigned = Key.make("reassigned"); - Frame cc = reassign(copy, reassigned); - addRndVec(cc); - Scope.track_generic(cc); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNotNull(DKV.get(copyKey)); - assertSame(copy, DKV.getGet(copyKey)); // copy still assigned to previous key - assertNotNull(DKV.get(reassigned)); - assertSame(cc, DKV.getGet(reassigned)); - } - - @Test - public void test_reassign_inplace_frame_null_key_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(null, ref.names(), ref.vecs()); - Key copyKey = copy.getKey(); - assertNull(copyKey); - - Key reassigned = Key.make("reassigned"); - reassignInplace(copy, reassigned); - addRndVec(copy); - Scope.track_generic(copy); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNotNull(DKV.get(reassigned)); - assertSame(copy, DKV.getGet(reassigned)); - } - - @Test - public void test_reassign_inplace_frame_not_in_DKV_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(ref); - Key copyKey = copy.getKey(); - assertNotNull(copyKey); - assertNull(DKV.get(copyKey)); - - Key reassigned = Key.make("reassigned"); - reassignInplace(copy, reassigned); - addRndVec(copy); - Scope.track_generic(copy); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNull(DKV.get(copyKey)); - assertNotNull(DKV.get(reassigned)); - assertSame(copy, DKV.getGet(reassigned)); - } - - @Test - public void test_reassign_inplace_frame_in_DKV_with_fresh_key() { - Frame ref = refFrame.get(); - Frame copy = new Frame(ref); - Key copyKey = copy.getKey(); - DKV.put(copy); - assertNotNull(copyKey); - assertNotNull(DKV.get(copyKey)); - Key reassigned = Key.make("reassigned"); - reassignInplace(copy, reassigned); - addRndVec(copy); - Scope.track_generic(copy); // tracking the key only (instead of the frame) to better see what can potentially leak - assertNull(DKV.get(copyKey)); - assertNotNull(DKV.get(reassigned)); - assertSame(copy, DKV.getGet(reassigned)); - } - -} diff --git a/h2o-core/src/test/java/hex/pipeline/PipelineTest.java b/h2o-core/src/test/java/hex/pipeline/PipelineTest.java deleted file mode 100644 index 0721e99bf695..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/PipelineTest.java +++ /dev/null @@ -1,503 +0,0 @@ -package hex.pipeline; - -import hex.Model; -import hex.ModelBuilder; -import hex.pipeline.DataTransformerTest.*; -import hex.pipeline.PipelineModel.PipelineOutput; -import hex.pipeline.PipelineModel.PipelineParameters; -import org.junit.Rule; -import org.junit.Test; -import org.junit.runner.RunWith; -import water.*; -import water.fvec.Frame; -import water.fvec.TestFrameBuilder; -import water.fvec.Vec; -import water.junit.rules.DKVIsolation; -import water.junit.rules.ScopeTracker; -import water.runner.CloudSize; -import water.runner.H2ORunner; -import water.test.dummy.DummyModel; -import water.test.dummy.DummyModelParameters; -import water.util.ArrayUtils; - -import java.util.Arrays; -import java.util.stream.IntStream; - -import static org.junit.Assert.*; -import static water.TestUtil.*; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class PipelineTest { - - @Rule - public ScopeTracker scope = new ScopeTracker(); - - @Rule - public DKVIsolation isolation = new DKVIsolation(); - - private void checkFrameState(Frame fr) { - assertNotNull(fr.getKey()); - assertNotNull(DKV.get(fr.getKey())); - assertFrameEquals(fr, DKV.getGet(fr.getKey()), 1e-10); - for (int i=0; i k = fr.keys()[i]; - assertNotNull(k); - assertNotNull(k.get()); - assertVecEquals(fr.vec(i), k.get(), 1e-10); - } - } - - @Test - public void test_simple_transformation_pipeline() { - PipelineParameters pparams = new PipelineParameters(); - FrameTrackerAsTransformer tracker = new FrameTrackerAsTransformer(); - pparams._transformers = new DataTransformer[] { - new MultiplyNumericColumnTransformer("two", 5).id("mult_5"), - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - tracker.id("tracker") - }; - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1)) - .withDataForCol(2, ar("yes", "no", "yes")) - .build()); - - Vec notMult = fr.vec(1).makeCopy(); - Vec mult = fr.vec(1).makeZero(); mult.set(0, 3*5); mult.set(1, 2*5); mult.set(2, 1*5); // a 5-mult vec of column "two" hand-made for test assertions. - - pparams._train = fr._key; - - Pipeline pipeline = ModelBuilder.make(pparams); - PipelineModel pmodel = Scope.track_generic(pipeline.trainModel().get()); - - assertNotNull(pmodel); - PipelineOutput output = pmodel._output; - assertNotNull(output); - assertNull(output._estimator); - assertNotNull(output._transformers); - assertEquals(4, output._transformers.length); - assertEquals(0, tracker.transformations.size()); - checkFrameState(fr); - assertVecEquals(notMult, fr.vec(1), 0); - - Frame scored = Scope.track(pmodel.score(fr)); - assertNotNull(scored); - TestUtil.printOutFrameAsTable(scored); - assertEquals(1, tracker.transformations.size()); - assertArrayEquals(new String[] {"one", "two", "target", "foo", "bar"}, scored.names()); - checkFrameState(fr); - checkFrameState(scored); - assertVecEquals(notMult, fr.vec(1), 0); - assertVecEquals(mult, scored.vec(1), 0); - - Frame rescored = Scope.track(pmodel.score(fr)); - TestUtil.printOutFrameAsTable(rescored); - assertEquals(2, tracker.transformations.size()); - assertNotSame(scored, rescored); - assertFrameEquals(scored, rescored, 1.6); - checkFrameState(fr); - checkFrameState(rescored); - assertVecEquals(notMult, fr.vec(1), 0); - assertVecEquals(mult, rescored.vec(1), 0); - - Frame transformed = Scope.track(pmodel.transform(fr)); - TestUtil.printOutFrameAsTable(transformed); - assertEquals(3, tracker.transformations.size()); - assertNotSame(scored, transformed); - assertFrameEquals(scored, transformed, 1.6); - checkFrameState(fr); - checkFrameState(transformed); - assertVecEquals(notMult, fr.vec(1), 0); - assertVecEquals(mult, transformed.vec(1), 0); - } - - @Test - public void test_simple_classification_pipeline() { - PipelineParameters pparams = new PipelineParameters(); - FrameTrackerAsTransformer tracker = new FrameTrackerAsTransformer(); - pparams._transformers = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - tracker.id("tracker") - }; - DummyModelParameters eparams = new DummyModelParameters(); - eparams._makeModel = true; - pparams._estimatorParams = eparams; - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1)) - .withDataForCol(2, ar("yes", "no", "yes")) - .build()); - - pparams._train = fr._key; - pparams._response_column = "target"; - - Pipeline pipeline = ModelBuilder.make(pparams); - PipelineModel pmodel = Scope.track_generic(pipeline.trainModel().get()); - assertNotNull(pmodel); - PipelineOutput output = pmodel._output; - assertNotNull(output); - assertNotNull(output._estimator); - Model emodel = output._estimator.get(); - assertNotNull(emodel); - assertTrue(emodel instanceof DummyModel); - assertArrayEquals(new String[] {"one", "two", "foo", "bar", "target"}, emodel._output._names); - - assertEquals(1, tracker.transformations.size()); - checkFrameState(fr); - - Frame predictions = Scope.track(pmodel.score(fr)); - assertEquals(2, tracker.transformations.size()); - assertNotNull(predictions); - TestUtil.printOutFrameAsTable(predictions); - checkFrameState(fr); - checkFrameState(predictions); - - Frame transformed = Scope.track(pmodel.transform(fr)); - assertEquals(3, tracker.transformations.size()); - assertNotNull(transformed); - TestUtil.printOutFrameAsTable(transformed); - assertArrayEquals( - Arrays.stream(emodel._output._names).sorted().toArray(), //model reorders input columns to obtain this output - Arrays.stream(transformed.names()).sorted().toArray() - ); - checkFrameState(fr); - checkFrameState(transformed); - } - - @Test - public void test_simple_classification_pipeline_with_sensitive_cv() { - int nfolds = 3; - PipelineParameters pparams = new PipelineParameters(); - pparams._nfolds = nfolds; - FrameTrackerAsTransformer tracker = new FrameTrackerAsTransformer(); - pparams._transformers = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new AddDummyCVColumnTransformer("cv_fold", Vec.T_CAT).id("add_cv_fold"), - tracker.id("track"), - }; - DummyModelParameters eparams = new DummyModelParameters(); - eparams._makeModel = true; - eparams._keep_cross_validation_models = true; - - pparams._estimatorParams = eparams; - final Frame fr = Scope.track(new TestFrameBuilder() - .withName("train") - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 8, 7, 6, 5, 4, 3, 2, 1)) - .withDataForCol(1, ard(3, 2, 1, 6, 5, 4, 9, 8, 7, 2, 1, 0, 5, 4, 3, 8, 7, 6, 1)) - .withDataForCol(2, ar("y", "n", "y", "y", "y", "y", "n", "n", "n", "n", "y", "y", "n", "n", "y", "y", "y", "n", "n")) - .build()); - - pparams._train = fr._key; - pparams._response_column = "target"; - - Pipeline pipeline = ModelBuilder.make(pparams); - PipelineModel pmodel = Scope.track_generic(pipeline.trainModel().get()); - assertNotNull(pmodel); - PipelineOutput output = pmodel._output; - assertNotNull(output); - assertNotNull(output._estimator); - Model emodel = output._estimator.get(); - assertNotNull(emodel); - assertTrue(emodel instanceof DummyModel); - assertArrayEquals(new String[] {"one", "two", "foo", "bar", "target"}, emodel._output._names); - - System.out.println(tracker.transformations); - assertEquals(2*nfolds+1, tracker.transformations.size()); // nfolds * 2 [train+valid] + 1 [final model, train only] - assertNotEquals(fr.getKey().toString(), tracker.transformations.get(0).frameId); // training frame for final model transformed first - assertTrue(tracker.transformations.get(0).frameId.startsWith(fr.getKey().toString()+"@@Training_trf_by_add_bar")); - assertEquals(DataTransformer.FrameType.Training, tracker.transformations.get(0).type); - assertFalse(tracker.transformations.get(0).is_cv); - assertEquals(nfolds*2, tracker.transformations.stream().filter(t -> t.is_cv).count()); - assertEquals(nfolds, tracker.transformations.stream().filter(t -> t.is_cv && t.type == DataTransformer.FrameType.Training).count()); - assertEquals(nfolds, tracker.transformations.stream().filter(t -> t.is_cv && t.type == DataTransformer.FrameType.Validation).count()); - assertEquals(nfolds, emodel._output._cross_validation_models.length); - for (int i=0; i { -// TestUtil.printOutFrameAsTable(fr); - String[][] unencodedDomains = {{"a", "b", "c", "d"}, {"a", "b", "c", "d"}, {"n", "y"}}; - if (ArrayUtils.contains(fr.names(), ModelBuilder.CV_WEIGHTS_COLUMN)) { - assertEquals(ModelBuilder.CV_WEIGHTS_COLUMN, fr.name(fr.names().length-1)); - // weights col is numerical - assertArrayEquals(ArrayUtils.append(unencodedDomains, new String[][] { null }), fr.domains()); - } else { - assertArrayEquals(unencodedDomains, fr.domains()); - } - }); - FrameTrackerAsTransformer tracker = new FrameTrackerAsTransformer(); - pparams._transformers = new DataTransformer[]{ - checker.id("check_frame_not_encoded"), - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new AddDummyCVColumnTransformer("cv_fold").id("add_cv_fold"), - tracker.id("tracker") - }; - DummyModelParameters eparams = new DummyModelParameters(); - eparams._makeModel = true; - eparams._keep_cross_validation_models = true; - eparams._categorical_encoding = Model.Parameters.CategoricalEncodingScheme.LabelEncoder; - - pparams._estimatorParams = eparams; - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_CAT, Vec.T_CAT, Vec.T_CAT) - .withDataForCol(0, ar("a", "b", "c", "a", "b", "c", "a", "b", "c", "d", "c", "b", "a", "c", "b", "a", "c", "b", "a")) - .withDataForCol(1, ar("c", "b", "a", "c", "b", "a", "c", "b", "a", "c", "b", "d", "b", "a", "c", "b", "a", "c", "b")) - .withDataForCol(2, ar("y", "n", "y", "y", "y", "y", "n", "n", "n", "n", "y", "y", "n", "n", "y", "y", "y", "n", "n")) - .build()); - - pparams._train = fr._key; - pparams._response_column = "target"; - - Pipeline pipeline = ModelBuilder.make(pparams); - PipelineModel pmodel = Scope.track_generic(pipeline.trainModel().get()); - assertNotNull(pmodel); - PipelineOutput output = pmodel._output; - assertNotNull(output); - assertNotNull(output._estimator); - Model emodel = output._estimator.get(); - assertNotNull(emodel); - assertTrue(emodel instanceof DummyModel); - assertArrayEquals(new String[][] {{"a", "b", "c", "d"}, {"a", "b", "c", "d"}, {"n", "y"}}, pmodel._output._domains); - assertArrayEquals(new String[][] {{"a", "b", "c", "d"}, {"a", "b", "c", "d"}, null/*foo*/, null/*bar*/, {"n", "y"}}, emodel._output._origDomains); - assertArrayEquals(new String[][] {null/*encoded one*/, null/*encoded two*/, null/*foo*/, null/*bar*/, {"n", "y"}}, emodel._output._domains); - - assertEquals(nfolds, emodel._output._cross_validation_models.length); - for (Key km : emodel._output._cross_validation_models) { - DummyModel cvModel = km.get(); - assertNotNull(cvModel); - assertArrayEquals(new String[][] {{"a", "b", "c", "d"}, {"a", "b", "c", "d"}, null/*foo*/, null/*bar*/, null/*cv_fold*/, null/*cv_weights*/, {"n", "y"}}, cvModel._output._origDomains); - assertArrayEquals(new String[][] {null/*encoded one*/, null/*encoded two*/, null/*foo*/, null/*bar*/ , null/*cv_fold*/, null/*cv_weights*/, {"n", "y"}}, cvModel._output._domains); - } - checkFrameState(fr); - } -} diff --git a/h2o-core/src/test/java/hex/pipeline/TransformerChainTest.java b/h2o-core/src/test/java/hex/pipeline/TransformerChainTest.java deleted file mode 100644 index 2a7c9ad6ab24..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/TransformerChainTest.java +++ /dev/null @@ -1,269 +0,0 @@ -package hex.pipeline; - -import hex.pipeline.DataTransformer.FrameType; -import hex.pipeline.DataTransformerTest.AddRandomColumnTransformer; -import hex.pipeline.DataTransformerTest.FrameTrackerAsTransformer; -import hex.pipeline.PipelineModel.PipelineParameters; -import org.junit.Test; -import org.junit.runner.RunWith; -import water.Scope; -import water.fvec.Frame; -import water.fvec.TestFrameBuilder; -import water.fvec.Vec; -import water.runner.CloudSize; -import water.runner.H2ORunner; -import water.util.ArrayUtils; -import water.util.Pair; - -import java.util.Arrays; -import java.util.HashSet; -import java.util.List; -import java.util.Set; -import java.util.concurrent.atomic.AtomicInteger; -import java.util.stream.Collectors; - -import static hex.pipeline.DataTransformerTest.*; -import static org.junit.Assert.*; -import static water.TestUtil.*; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class TransformerChainTest { - - @Test - public void test_transformers_are_applied_in_sequential_order() { - try { - Scope.enter(); - final Frame fr = Scope.track(dummyFrame()); - FrameTrackerAsTransformer tracker = new FrameTrackerAsTransformer().id("tracker"); - DataTransformer[] dts = new DataTransformer[] { - tracker, - new FrameCheckerAsTransformer(f -> { - assertFalse(ArrayUtils.contains(f.names(), "foo")); - assertFalse(ArrayUtils.contains(f.names(), "bar")); - }), - new AddRandomColumnTransformer("foo").id("add_foo"), - new FrameCheckerAsTransformer(f -> { - assertTrue(ArrayUtils.contains(f.names(), "foo")); - assertFalse(ArrayUtils.contains(f.names(), "bar")); - }), - tracker, - new AddRandomColumnTransformer("bar").id("add_bar"), - new FrameCheckerAsTransformer(f -> { - assertTrue(ArrayUtils.contains(f.names(), "foo")); - assertTrue(ArrayUtils.contains(f.names(), "bar")); - }), - tracker, - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - tracker, - }; - TransformerChain chain = new TransformerChain(dts); - Frame transformed = chain.transform(fr); - assertEquals("dumdum", transformed.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr.names(), new String[]{"foo", "bar"}), transformed.names()); - assertEquals(4, tracker.transformations.size()); - } finally { - Scope.exit(); - } - } - - @Test - - public void test_a_failing_transformer_fails_the_entire_chain() { - try { - Scope.enter(); - final Frame fr = Scope.track(dummyFrame()); - DataTransformer[] dts = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new DataTransformer() { - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - assert false: "expected"; - return fr; - } - }, - new AddRandomColumnTransformer("bar").id("add_bar"), - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - }; - TransformerChain chain = new TransformerChain(dts); - AssertionError err = assertThrows(AssertionError.class, () -> chain.transform(fr)); - assertEquals("expected", err.getMessage()); - } finally { - Scope.exit(); - } - } - - - @Test - public void test_transformers_are_applied_in_sequential_order_using_context_tracker() { - try { - Scope.enter(); - final Frame fr = Scope.track(dummyFrame()); - DataTransformer[] dts = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - }; - List dtIds = Arrays.stream(dts).map(DataTransformer::id).collect(Collectors.toList()); - final AtomicInteger dtIdx = new AtomicInteger(0); - PipelineContext context = new PipelineContext(new PipelineParameters(), new FrameTracker() { - @Override - public void apply(Frame transformed, Frame original, FrameType type, PipelineContext context, DataTransformer transformer) { - assertTrue(dtIds.contains(transformer.id())); - switch (transformer.id()) { - case "add_foo": - assertEquals(0, dtIdx.getAndIncrement()); - assertEquals(fr, original); - assertArrayEquals(ArrayUtils.append(fr.names(), new String[]{"foo"}), transformed.names()); - break; - case "add_bar": - assertEquals(1, dtIdx.getAndIncrement()); - assertArrayEquals(ArrayUtils.append(fr.names(), new String[]{"foo", "bar"}), transformed.names()); - break; - case "rename_dumdum": - assertEquals(2, dtIdx.getAndIncrement()); - assertArrayEquals(ArrayUtils.append(fr.names(), new String[]{"foo", "bar"}), transformed.names()); - assertEquals("dumdum", transformed.getKey().toString()); - break; - } - } - }); - - TransformerChain chain = new TransformerChain(dts); - Frame transformed = chain.transform(fr); - assertEquals("dumdum", transformed.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr.names(), new String[]{"foo", "bar"}), transformed.names()); - } finally { - Scope.exit(); - } - } - - @Test - public void test_chain_can_be_applied_multiple_times() { - try { - Scope.enter(); - final Frame fr1 = Scope.track(dummyFrame()); - final Frame fr2 = Scope.track(oddFrame()); - DataTransformer[] dts = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - }; - TransformerChain chain = new TransformerChain(dts); - - Frame tr1 = chain.transform(fr1); - assertEquals("dumdum", tr1.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr1.names(), new String[]{"foo", "bar"}), tr1.names()); - - Frame tr1bis = chain.transform(fr1); - assertNotSame(tr1, tr1bis); - assertEquals(tr1.getKey(), tr1bis.getKey()); - assertArrayEquals(tr1.names(), tr1bis.names()); - assertFrameEquals(tr1, tr1bis, 1e-10); - - Frame tr2 = chain.transform(fr2); - assertEquals("dumdum", tr2.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr2.names(), new String[]{"foo", "bar"}), tr2.names()); - } finally { - Scope.exit(); - } - } - - - @Test - public void test_chain_can_be_finalized_by_completer() { - try { - Scope.enter(); - final Frame fr1 = Scope.track(dummyFrame()); - final Frame fr2 = Scope.track(oddFrame()); - DataTransformer[] dts = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - }; - TransformerChain chain = new TransformerChain(dts); - TransformerChain.Completer> dim = new TransformerChain.UnaryCompleter>() { - @Override - public Pair apply(Frame frame, PipelineContext context) { - return new Pair<>(frame.numRows(), (long)frame.numCols()); - } - }; - - assertEquals(new Pair<>(3L, 5L), chain.transform(fr1, FrameType.Test, null, dim)); - assertEquals(new Pair<>(3L, 7L), chain.transform(fr2, FrameType.Test, null, dim)); - } finally { - Scope.exit(); - } - - } - - @Test - public void test_multiple_frames_can_be_transformed_at_once_to_feed_a_completer() { - try { - Scope.enter(); - final Frame fr1 = Scope.track(dummyFrame()); - final Frame fr2 = Scope.track(oddFrame()); - DataTransformer[] dts = new DataTransformer[] { - new AddRandomColumnTransformer("foo").id("add_foo"), - new AddRandomColumnTransformer("bar").id("add_bar"), - new RenameFrameTransformer("dumdum").id("rename_dumdum"), - }; - TransformerChain chain = new TransformerChain(dts); - - Frame[] transformed = chain.transform( - new Frame[] {fr1, fr1, fr2}, - new FrameType[]{FrameType.Test, FrameType.Test, FrameType.Test}, - null, - (fs, c) -> fs - ); - Frame tr1 = transformed[0]; - assertEquals("dumdum", tr1.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr1.names(), new String[]{"foo", "bar"}), tr1.names()); - - Frame tr1bis = transformed[1]; - assertNotSame(tr1, tr1bis); - assertEquals(tr1.getKey(), tr1bis.getKey()); - assertArrayEquals(tr1.names(), tr1bis.names()); - assertFrameEquals(tr1, tr1bis, 1e-10); - - Frame tr2 = transformed[2]; - assertEquals("dumdum", tr2.getKey().toString()); - assertArrayEquals(ArrayUtils.append(fr2.names(), new String[]{"foo", "bar"}), tr2.names()); - - Set uniqueCols = chain.transform( - new Frame[] {fr1, fr1, fr2}, - new FrameType[]{FrameType.Test, FrameType.Test, FrameType.Test}, - null, - (Frame[] fs, PipelineContext c) -> - Arrays.stream(fs) - .flatMap(f -> Arrays.stream(f.names())) - .collect(Collectors.toSet()) - ); - assertEquals(new HashSet<>(Arrays.asList("one", "two", "three", "five", "seven", "nine", "foo", "bar")), uniqueCols); - } finally { - Scope.exit(); - } - } - - private Frame dummyFrame() { - return new TestFrameBuilder() - .withColNames("one", "two", "three") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_NUM) - .withDataForCol(0, ard(1, 1, 1)) - .withDataForCol(1, ard(2, 2, 2)) - .withDataForCol(2, ard(3, 3, 3)) - .build(); - } - private Frame oddFrame() { - return new TestFrameBuilder() - .withColNames("one", "three", "five", "seven", "nine") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_NUM, Vec.T_NUM, Vec.T_NUM) - .withDataForCol(0, ard(1, 1, 1)) - .withDataForCol(1, ard(3, 3, 3)) - .withDataForCol(2, ard(5, 5, 5)) - .withDataForCol(3, ard(7, 7, 7)) - .withDataForCol(4, ard(9, 9, 9)) - .build(); - } - -} diff --git a/h2o-core/src/test/java/hex/pipeline/transformers/KFoldColumnGeneratorTest.java b/h2o-core/src/test/java/hex/pipeline/transformers/KFoldColumnGeneratorTest.java deleted file mode 100644 index 0260a097cdb8..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/transformers/KFoldColumnGeneratorTest.java +++ /dev/null @@ -1,107 +0,0 @@ -package hex.pipeline.transformers; - -import hex.Model.Parameters.FoldAssignmentScheme; -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import hex.pipeline.PipelineModel.PipelineParameters; -import org.junit.Rule; -import org.junit.Test; -import org.junit.runner.RunWith; -import water.TestUtil; -import water.fvec.Frame; -import water.fvec.TestFrameBuilder; -import water.fvec.Vec; -import water.junit.rules.ScopeTracker; -import water.runner.CloudSize; -import water.runner.H2ORunner; -import water.util.ArrayUtils; - -import static hex.pipeline.DataTransformer.FrameType.Training; -import static hex.pipeline.DataTransformer.FrameType.Validation; -import static org.junit.Assert.*; -import static water.TestUtil.ar; -import static water.TestUtil.ard; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class KFoldColumnGeneratorTest { - - @Rule - public ScopeTracker scope = new ScopeTracker(); - - @Test - public void test_transformer_modifies_only_training_frames() { - String foldc = "foldc"; - int nfolds = 3; - DataTransformer kfold = new KFoldColumnGenerator(foldc, FoldAssignmentScheme.AUTO, nfolds, 0); - final Frame fr = scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1)) - .withDataForCol(2, ar("y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y")) - .build()); - - Frame transformed = kfold.transform(fr); - assertSame(fr, transformed); - - Frame validTransformed = kfold.transform(fr, Validation, null); - assertSame(fr, validTransformed); - - Frame trainTransformed = kfold.transform(fr, Training, null); - TestUtil.printOutFrameAsTable(trainTransformed); - assertArrayEquals(new String[] {"one", "two", "target", foldc}, trainTransformed.names()); - assertEquals(0, (int) trainTransformed.vec(3).min()); - assertEquals(nfolds-1, (int) trainTransformed.vec(3).max()); - } - - - @Test - public void test_transformer_autodetection_in_pipeline_context() { - DataTransformer kfold = new KFoldColumnGenerator(); - final Frame fr = scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1)) - .withDataForCol(2, ar("y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y")) - .build()); - - PipelineParameters params = new PipelineParameters(); - params._nfolds = 4; - params._fold_assignment = FoldAssignmentScheme.Stratified; - params._response_column = "target"; - PipelineContext context = new PipelineContext(params); - kfold.prepare(context); - assertFalse(ArrayUtils.contains(fr.names(), context._params._fold_column)); - Frame trainTransformed = kfold.transform(fr, Training, context); - TestUtil.printOutFrameAsTable(trainTransformed); - assertArrayEquals(new String[] {"one", "two", "target", context._params._fold_column}, trainTransformed.names()); - assertEquals(0, (int) trainTransformed.vec(3).min()); - assertEquals(params._nfolds-1, (int) trainTransformed.vec(3).max()); - //hard to verify that it's properly stratified on a smaller dataset given the algo being used, but trusting the utility method that is used is other places. - } - - @Test - public void test_transformer_transforms_context_train_frame_during_prepare() { - DataTransformer kfold = new KFoldColumnGenerator(); - final Frame fr = scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1)) - .withDataForCol(2, ar("y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y")) - .build()); - - PipelineParameters params = new PipelineParameters(); - params._nfolds = 3; - params._response_column = "target"; - PipelineContext context = new PipelineContext(params); - context.setTrain(fr); - kfold.prepare(context); - assertFalse(ArrayUtils.contains(fr.names(), context._params._fold_column)); - assertTrue(ArrayUtils.contains(context.getTrain().names(), context._params._fold_column)); - TestUtil.printOutFrameAsTable(context.getTrain()); - } - -} diff --git a/h2o-core/src/test/java/hex/pipeline/transformers/ModelAsFeatureTransformerTest.java b/h2o-core/src/test/java/hex/pipeline/transformers/ModelAsFeatureTransformerTest.java deleted file mode 100644 index d8e720caa001..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/transformers/ModelAsFeatureTransformerTest.java +++ /dev/null @@ -1,157 +0,0 @@ -package hex.pipeline.transformers; - -import hex.Model; -import hex.pipeline.PipelineContext; -import hex.pipeline.PipelineModel; -import org.junit.Rule; -import org.junit.Test; -import org.junit.runner.RunWith; -import water.Key; -import water.Scope; -import water.TestUtil; -import water.fvec.Frame; -import water.fvec.TestFrameBuilder; -import water.fvec.Vec; -import water.junit.rules.ScopeTracker; -import water.runner.CloudSize; -import water.runner.H2ORunner; -import water.test.dummy.DummyModel; -import water.test.dummy.DummyModelBuilder; -import water.test.dummy.DummyModelParameters; - -import static org.junit.Assert.*; -import static water.TestUtil.*; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class ModelAsFeatureTransformerTest { - - private static class DummyModelAsFeatureTransformer extends ModelAsFeatureTransformer { - - boolean cvSensitive = false; - - public DummyModelAsFeatureTransformer(DummyModelParameters params) { - super(params); - } - - public DummyModelAsFeatureTransformer(DummyModelParameters params, Key modelKey) { - super(params, modelKey); - } - - @Override - public boolean isCVSensitive() { - return cvSensitive; - } - } - - - @Rule - public ScopeTracker scope = new ScopeTracker(); - - @Test - public void test_delegate_model_trained_and_cached_in_prepare() { - DummyModelParameters params = makeModelParams(null); - DummyModelAsFeatureTransformer transformer = new DummyModelAsFeatureTransformer(params); - assertNull(transformer.getModel()); - - Frame fr = makeTrain(); - PipelineModel.PipelineParameters pParams = new PipelineModel.PipelineParameters(); - assignTrainingParams(pParams, fr); - PipelineContext context = new PipelineContext(pParams); - transformer.prepare(context); - DummyModel m1 = Scope.track_generic(transformer.getModel()); - assertNotNull(m1); - long m1Checksum = m1.checksum(); - - transformer.prepare(context); - DummyModel m2 = transformer.getModel(); - assertSame(m1, m2); - assertEquals("model shouldn't be modified during second prepare", m1Checksum, m2.checksum()); - } - - @Test - public void test_transform_delegates_to_provided_pretrained_model() { - Frame fr = makeTrain(); - DummyModelParameters params = makeModelParams(fr); - DummyModel model = Scope.track_generic(new DummyModelBuilder(params).trainModel().get()); - long oriChecksum = model.checksum(); - DummyModelAsFeatureTransformer transformer = new DummyModelAsFeatureTransformer(params, model._key); - assertNotNull(transformer.getModel()); - - PipelineContext context = new PipelineContext(new PipelineModel.PipelineParameters()); - transformer.prepare(context); - DummyModel m = transformer.getModel(); - assertSame(model, m); - assertEquals("model shouldn't be modified during prepare", oriChecksum, m.checksum()); - } - - - @Test - public void test_transform_delegates_to_internally_trained_model() { - Frame fr = makeTrain(); - DummyModelParameters params = makeModelParams(fr); - DummyModel model = Scope.track_generic(new DummyModelBuilder(params).trainModel().get()); - DummyModelAsFeatureTransformer transformer = new DummyModelAsFeatureTransformer(params, model._key); - assertNotNull(transformer.getModel()); - - PipelineContext context = new PipelineContext(new PipelineModel.PipelineParameters()); - transformer.prepare(context); - DummyModel m = transformer.getModel(); - assertSame(model, m); - assertEquals("model shouldn't be modified during prepare", model.checksum(), m.checksum()); - - Frame trans = transformer.transform(fr); - assertEquals(fr.getKey()+"_stats", trans.getKey().toString()); - } - - - @Test - public void test_data_input_model_params_are_detected_from_context() { - DummyModelParameters params = makeModelParams(null); - DummyModelAsFeatureTransformer transformer = new DummyModelAsFeatureTransformer(params); - assertNull(transformer.getModel()); - - PipelineModel.PipelineParameters pParams = new PipelineModel.PipelineParameters(); - pParams._response_column = "target"; - PipelineContext context = new PipelineContext(pParams); - Frame train = makeTrain(); - context.setTrain(train); - transformer.prepare(context); - DummyModel m = Scope.track_generic(transformer.getModel()); - assertNotNull(m); - assertEquals("target", m._parms._response_column); - assertEquals(train._key, m._parms._train); - - Frame trans = transformer.transform(train); - TestUtil.printOutFrameAsTable(trans); - assertEquals(train.getKey()+"_stats", trans.getKey().toString()); - } - - @Test - public void test_grid_like_scenario() { - - } - - private DummyModelParameters makeModelParams(Frame train) { - DummyModelParameters params = new DummyModelParameters(); - params._makeModel = true; - if (train != null) assignTrainingParams(params, train); - return params; - } - - private void assignTrainingParams(Model.Parameters params, Frame train) { - params._train = train.getKey(); - params._response_column = train.name(train._names.length - 1); - } - - private Frame makeTrain() { - return scope.track(new TestFrameBuilder() - .withColNames("one", "two", "target") - .withVecTypes(Vec.T_NUM, Vec.T_NUM, Vec.T_CAT) - .withDataForCol(0, ard(1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3)) - .withDataForCol(1, ard(3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1)) - .withDataForCol(2, ar("y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y", "n", "y")) - .build()); - } - -} diff --git a/h2o-core/src/test/java/hex/pipeline/transformers/UnionTransformerTest.java b/h2o-core/src/test/java/hex/pipeline/transformers/UnionTransformerTest.java deleted file mode 100644 index aadf4489b048..000000000000 --- a/h2o-core/src/test/java/hex/pipeline/transformers/UnionTransformerTest.java +++ /dev/null @@ -1,118 +0,0 @@ -package hex.pipeline.transformers; - -import hex.pipeline.DataTransformer; -import hex.pipeline.PipelineContext; -import org.junit.Rule; -import org.junit.Test; -import org.junit.runner.RunWith; -import water.MRTask; -import water.Scope; -import water.fvec.*; -import water.junit.rules.ScopeTracker; -import water.runner.CloudSize; -import water.runner.H2ORunner; -import water.util.StringUtils; - -import static water.TestUtil.ar; -import static water.TestUtil.assertFrameEquals; - -@RunWith(H2ORunner.class) -@CloudSize(1) -public class UnionTransformerTest { - - @Rule - public ScopeTracker scope = new ScopeTracker(); - - @Test - public void test_union_transform_append() { - DataTransformer plus = new StrConcatColumnsTransformer("+"); - DataTransformer minus = new StrConcatColumnsTransformer("-"); - - DataTransformer union = new UnionTransformer(UnionTransformer.UnionStrategy.append, plus, minus); - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two") - .withVecTypes(Vec.T_STR, Vec.T_STR) - .withDataForCol(0, ar("un", "eins", "jeden")) - .withDataForCol(1, ar("deux", "zwei", "dva")) - .build()); - - final Frame expected = Scope.track(new TestFrameBuilder() - .withColNames("one", "two", "one+two", "one-two") - .withVecTypes(Vec.T_STR, Vec.T_STR, Vec.T_STR, Vec.T_STR) - .withDataForCol(0, ar("un", "eins", "jeden")) - .withDataForCol(1, ar("deux", "zwei", "dva")) - .withDataForCol(2, ar("un+deux", "eins+zwei", "jeden+dva")) - .withDataForCol(3, ar("un-deux", "eins-zwei", "jeden-dva")) - .build()); - - Frame transformed = Scope.track(union.transform(fr)); - assertFrameEquals(expected, transformed, 0); - } - - @Test - public void test_union_transform_replace() { - DataTransformer plus = new StrConcatColumnsTransformer("+"); - DataTransformer minus = new StrConcatColumnsTransformer("-"); - - DataTransformer union = new UnionTransformer(UnionTransformer.UnionStrategy.replace, plus, minus); - final Frame fr = Scope.track(new TestFrameBuilder() - .withColNames("one", "two") - .withVecTypes(Vec.T_STR, Vec.T_STR) - .withDataForCol(0, ar("un", "eins", "jeden")) - .withDataForCol(1, ar("deux", "zwei", "dva")) - .build()); - - final Frame expected = Scope.track(new TestFrameBuilder() - .withColNames("one+two", "one-two") - .withVecTypes(Vec.T_STR, Vec.T_STR) - .withDataForCol(0, ar("un+deux", "eins+zwei", "jeden+dva")) - .withDataForCol(1, ar("un-deux", "eins-zwei", "jeden-dva")) - .build()); - - Frame transformed = Scope.track(union.transform(fr)); - assertFrameEquals(expected, transformed, 0); - } - - public static class StrConcatColumnsTransformer extends DataTransformer { - - private String separator; - - public StrConcatColumnsTransformer(String separator) { - this.separator = separator; - } - - @Override - protected Frame doTransform(Frame fr, FrameType type, PipelineContext context) { - return new VecsConcatenizer(separator).concat(fr); - } - - public static class VecsConcatenizer extends MRTask { - private final String _sep; - - public VecsConcatenizer(String separator) { - _sep = separator; - } - - @Override - public void map(Chunk[] cs, NewChunk nc) { - for (int row = 0; row < cs[0]._len; row++) { - StringBuilder tmpStr = new StringBuilder(); - for (int col = 0; col < cs.length; col++) { - Chunk chk = cs[col]; - if (chk.isNA(row)) continue; - String s = chk.stringAt(row); - tmpStr.append(s); - if (col+1 < cs.length) tmpStr.append(_sep); - } - nc.addStr(tmpStr.toString()); - } - } - - public Frame concat(Frame fr) { - String name = StringUtils.join(_sep, fr.names()); - return doAll(Vec.T_STR, fr).outputFrame(new String[]{name}, null); - } - } - } - -} diff --git a/h2o-core/src/test/java/water/util/Sort.java b/h2o-core/src/test/java/water/util/Sort.java index cded20c82a6f..9ea2aa9f0c61 100644 --- a/h2o-core/src/test/java/water/util/Sort.java +++ b/h2o-core/src/test/java/water/util/Sort.java @@ -5,7 +5,6 @@ import hex.ModelMetrics; import org.junit.Ignore; -import water.Futures; import water.rapids.Merge; @Ignore @@ -47,8 +46,8 @@ protected int nModelsInParallel(int folds) { } @Override - protected ModelMetrics.MetricBuilder makeCVMetricBuilder(ModelBuilder cvModelBuilder, Futures fs) { - return null; + protected boolean makeCVMetrics(ModelBuilder cvModelBuilder) { + return false; } @Override diff --git a/h2o-extensions/xgboost/src/main/java/hex/tree/xgboost/XGBoost.java b/h2o-extensions/xgboost/src/main/java/hex/tree/xgboost/XGBoost.java index 35fe91003cfc..9a7c32891c5d 100755 --- a/h2o-extensions/xgboost/src/main/java/hex/tree/xgboost/XGBoost.java +++ b/h2o-extensions/xgboost/src/main/java/hex/tree/xgboost/XGBoost.java @@ -809,7 +809,7 @@ protected CVModelBuilder makeCVModelBuilder(ModelBuilder[] modelBuilder } } - @Override protected void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { + @Override public void cv_computeAndSetOptimalParameters(ModelBuilder[] cvModelBuilders) { if( _parms._stopping_rounds == 0 && _parms._max_runtime_secs == 0) return; // No exciting changes to stopping conditions // Extract stopping conditions from each CV model, and compute the best stopping answer _parms._stopping_rounds = 0; diff --git a/h2o-py/docs/modeling.rst b/h2o-py/docs/modeling.rst index 722241b9185e..9ceecb83b361 100644 --- a/h2o-py/docs/modeling.rst +++ b/h2o-py/docs/modeling.rst @@ -144,6 +144,12 @@ Unsupervised :show-inheritance: :members: +:mod:`H2OGenericEstimator` +-------------------------- +.. autoclass:: h2o.estimators.generic.H2OGenericEstimator + :show-inheritance: + :members: + :mod:`H2OGeneralizedLowRankEstimator` ------------------------------------- .. autoclass:: h2o.estimators.glrm.H2OGeneralizedLowRankEstimator @@ -190,12 +196,6 @@ Miscellaneous :show-inheritance: :members: -:mod:`H2OGenericEstimator` --------------------------- -.. autoclass:: h2o.estimators.generic.H2OGenericEstimator - :show-inheritance: - :members: - :mod:`H2OSingularValueDecompositionEstimator` --------------------------------------------- .. autoclass:: h2o.estimators.svd.H2OSingularValueDecompositionEstimator diff --git a/h2o-py/h2o/__init__.py b/h2o-py/h2o/__init__.py index c9e8937ac5d1..70ffd42e142d 100644 --- a/h2o-py/h2o/__init__.py +++ b/h2o-py/h2o/__init__.py @@ -96,12 +96,10 @@ def _read_txt_from_whl(name, fallback): def _init_(): from .display import ReplHook, in_py_repl from .backend.connection import register_session_hook - from .schemas import register_schemas if in_py_repl(): replhook = ReplHook() register_session_hook('open', replhook.__enter__) register_session_hook('close', replhook.__exit__) - register_schemas() _init_() diff --git a/h2o-py/h2o/backend/connection.py b/h2o-py/h2o/backend/connection.py index 7167cce9a630..bc99c25f5b93 100644 --- a/h2o-py/h2o/backend/connection.py +++ b/h2o-py/h2o/backend/connection.py @@ -28,10 +28,12 @@ import requests from requests.auth import AuthBase -from h2o.backend import H2OLocalServer +from h2o.backend import H2OCluster, H2OLocalServer from h2o.display import print2 from h2o.exceptions import H2OConnectionError, H2OServerError, H2OResponseError, H2OValueError -from h2o.schemas import H2OErrorV3, define_classes_from_schema, get_schema_handler +from h2o.model.metrics import make_metrics +from h2o.schemas import H2OMetadataV3, H2OErrorV3, H2OModelBuilderErrorV3, define_classes_from_schema +from h2o.two_dim_table import H2OTwoDimTable from h2o.utils.metaclass import CallableString, backwards_compatibility, h2o_meta from h2o.utils.shared_utils import stringify_list, stringify_dict, as_resource from h2o.utils.typechecks import (assert_is_type, assert_matches, assert_satisfies, is_type, numeric) @@ -675,7 +677,7 @@ def _test_connection(self, max_retries=5, messages=None): if self._local_server and not self._local_server.is_running(): raise H2OServerError("Local server was unable to start") try: - define_classes_from_schema(self) + define_classes_from_schema(_classes_defined_from_schema_, self) cld = self.request("GET /3/Cloud") if self.name and cld.cloud_name != self.name: @@ -887,6 +889,7 @@ def __exit__(self, *args): class H2OResponse(dict): + """Temporary...""" def __new__(cls, keyvals): # This method is called by the simplejson.json(object_pairs_hook=) @@ -900,10 +903,15 @@ def __new__(cls, keyvals): if k == "__schema" and is_type(v, str): schema = v break - if schema is not None: - handler = get_schema_handler(schema) - if handler is not None: - return handler(keyvals) + if schema == "MetadataV3": return H2OMetadataV3.make(keyvals) + if schema == "CloudV3": return H2OCluster.make(keyvals) + if schema == "H2OErrorV3": return H2OErrorV3.make(keyvals) + if schema == "H2OModelBuilderErrorV3": return H2OModelBuilderErrorV3.make(keyvals) + if schema == "TwoDimTableV3": return H2OTwoDimTable.make(keyvals) + if schema and schema.startswith("ModelMetrics"): + metrics = make_metrics(schema, keyvals) + if metrics is not None: + return metrics return super(H2OResponse, cls).__new__(cls, keyvals) # def __getattr__(self, key): @@ -913,6 +921,9 @@ def __new__(cls, keyvals): # return None +_classes_defined_from_schema_ = [H2OCluster, H2OErrorV3, H2OModelBuilderErrorV3] + + # Find the exception that occurs on invalid JSON input JSONDecodeError, _r = None, None try: diff --git a/h2o-py/h2o/display.py b/h2o-py/h2o/display.py index 063c64ff273f..6815c19a1f79 100644 --- a/h2o-py/h2o/display.py +++ b/h2o-py/h2o/display.py @@ -14,10 +14,10 @@ import tabulate # noinspection PyUnresolvedReferences -from h2o.utils.compatibility import * # NOQA -from h2o.utils.compatibility import str2 as str, bytes2 as bytes -from h2o.utils.shared_utils import can_use_pandas -from h2o.utils.threading import local_context, local_context_safe, local_env +from .utils.compatibility import * # NOQA +from .utils.compatibility import str2 as str, bytes2 as bytes +from .utils.shared_utils import can_use_pandas +from .utils.threading import local_context, local_context_safe, local_env __no_export = set(dir()) # all variables defined above this are not exported diff --git a/h2o-py/h2o/estimators/__init__.py b/h2o-py/h2o/estimators/__init__.py index 6a55f7c90af5..766e1678b950 100644 --- a/h2o-py/h2o/estimators/__init__.py +++ b/h2o-py/h2o/estimators/__init__.py @@ -28,7 +28,6 @@ from .model_selection import H2OModelSelectionEstimator from .naive_bayes import H2ONaiveBayesEstimator from .pca import H2OPrincipalComponentAnalysisEstimator -from .pipeline import H2OPipeline from .psvm import H2OSupportVectorMachineEstimator from .random_forest import H2ORandomForestEstimator from .rulefit import H2ORuleFitEstimator @@ -67,8 +66,8 @@ def create_estimator(algo, **params): "H2OExtendedIsolationForestEstimator", "H2OGeneralizedAdditiveEstimator", "H2OGradientBoostingEstimator", "H2OGenericEstimator", "H2OGeneralizedLinearEstimator", "H2OGeneralizedLowRankEstimator", "H2OInfogram", "H2OIsolationForestEstimator", "H2OIsotonicRegressionEstimator", "H2OKMeansEstimator", "H2OModelSelectionEstimator", - "H2ONaiveBayesEstimator", "H2OPrincipalComponentAnalysisEstimator", "H2OPipeline", - "H2OSupportVectorMachineEstimator", "H2ORandomForestEstimator", "H2ORuleFitEstimator", - "H2OStackedEnsembleEstimator", "H2OSingularValueDecompositionEstimator", "H2OTargetEncoderEstimator", - "H2OUpliftRandomForestEstimator", "H2OWord2vecEstimator", "H2OXGBoostEstimator" + "H2ONaiveBayesEstimator", "H2OPrincipalComponentAnalysisEstimator", "H2OSupportVectorMachineEstimator", + "H2ORandomForestEstimator", "H2ORuleFitEstimator", "H2OStackedEnsembleEstimator", + "H2OSingularValueDecompositionEstimator", "H2OTargetEncoderEstimator", "H2OUpliftRandomForestEstimator", + "H2OWord2vecEstimator", "H2OXGBoostEstimator" ) diff --git a/h2o-py/h2o/estimators/adaboost.py b/h2o-py/h2o/estimators/adaboost.py index 5323d3ec84e9..467d4f07045d 100644 --- a/h2o-py/h2o/estimators/adaboost.py +++ b/h2o-py/h2o/estimators/adaboost.py @@ -264,3 +264,4 @@ def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed + diff --git a/h2o-py/h2o/estimators/aggregator.py b/h2o-py/h2o/estimators/aggregator.py index f95b1083ac23..274e84852536 100644 --- a/h2o-py/h2o/estimators/aggregator.py +++ b/h2o-py/h2o/estimators/aggregator.py @@ -408,6 +408,7 @@ def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir + @property def aggregated_frame(self): if (self._model_json is not None @@ -423,4 +424,3 @@ def mapping_frame(self): if mj.get("output", {}).get("mapping_frame", {}).get("name") is not None: mapping_frame_name = mj["output"]["mapping_frame"]["name"] return H2OFrame.get_frame(mapping_frame_name) - diff --git a/h2o-py/h2o/estimators/anovaglm.py b/h2o-py/h2o/estimators/anovaglm.py index ea01bac719c5..6fdb66c88f7c 100644 --- a/h2o-py/h2o/estimators/anovaglm.py +++ b/h2o-py/h2o/estimators/anovaglm.py @@ -819,6 +819,7 @@ def type(self, type): assert_is_type(type, None, int) self._parms["type"] = type + @property def Lambda(self): """DEPRECATED. Use ``self.lambda_`` instead""" @@ -835,4 +836,3 @@ def result(self): :return: the H2OFrame that contains information about the model building process like for modelselection and anovaglm. """ return H2OFrame._expr(expr=ExprNode("result", ASTId(self.key)))._frame(fill_cache=True) - diff --git a/h2o-py/h2o/estimators/coxph.py b/h2o-py/h2o/estimators/coxph.py index 9e88edb22ee2..c0771c142795 100644 --- a/h2o-py/h2o/estimators/coxph.py +++ b/h2o-py/h2o/estimators/coxph.py @@ -570,6 +570,7 @@ def single_node_mode(self, single_node_mode): assert_is_type(single_node_mode, None, bool) self._parms["single_node_mode"] = single_node_mode + @property def baseline_hazard_frame(self): if (self._model_json is not None @@ -583,4 +584,3 @@ def baseline_survival_frame(self): and self._model_json.get("output", {}).get("baseline_survival", {}).get("name") is not None): baseline_survival_name = self._model_json["output"]["baseline_survival"]["name"] return H2OFrame.get_frame(baseline_survival_name) - diff --git a/h2o-py/h2o/estimators/decision_tree.py b/h2o-py/h2o/estimators/decision_tree.py index 8fe69a97cf25..e598396b2a82 100644 --- a/h2o-py/h2o/estimators/decision_tree.py +++ b/h2o-py/h2o/estimators/decision_tree.py @@ -186,3 +186,4 @@ def min_rows(self, min_rows): assert_is_type(min_rows, None, int) self._parms["min_rows"] = min_rows + diff --git a/h2o-py/h2o/estimators/deeplearning.py b/h2o-py/h2o/estimators/deeplearning.py index e78ffc557676..8c71881eea64 100644 --- a/h2o-py/h2o/estimators/deeplearning.py +++ b/h2o-py/h2o/estimators/deeplearning.py @@ -3260,6 +3260,7 @@ def gainslift_bins(self, gainslift_bins): self._parms["gainslift_bins"] = gainslift_bins + class H2OAutoEncoderEstimator(H2ODeepLearningEstimator): """ :examples: @@ -3279,4 +3280,3 @@ class H2OAutoEncoderEstimator(H2ODeepLearningEstimator): def __init__(self, **kwargs): super(H2OAutoEncoderEstimator, self).__init__(**kwargs) self.autoencoder = True - diff --git a/h2o-py/h2o/estimators/extended_isolation_forest.py b/h2o-py/h2o/estimators/extended_isolation_forest.py index 8eb5f9d42f3f..5be8ae966918 100644 --- a/h2o-py/h2o/estimators/extended_isolation_forest.py +++ b/h2o-py/h2o/estimators/extended_isolation_forest.py @@ -333,3 +333,4 @@ def disable_training_metrics(self, disable_training_metrics): assert_is_type(disable_training_metrics, None, bool) self._parms["disable_training_metrics"] = disable_training_metrics + diff --git a/h2o-py/h2o/estimators/gam.py b/h2o-py/h2o/estimators/gam.py index bcba5dcf4a4c..7eb2f88fe072 100644 --- a/h2o-py/h2o/estimators/gam.py +++ b/h2o-py/h2o/estimators/gam.py @@ -1650,4 +1650,3 @@ def get_gam_knot_column_names(self): raise H2OValueError("Knot locations are not available. Please re-run with store_knot_locations=True") return self._model_json['output']['gam_knot_column_names'] - diff --git a/h2o-py/h2o/estimators/gbm.py b/h2o-py/h2o/estimators/gbm.py index 93a22a1bf3ff..2452b5136390 100644 --- a/h2o-py/h2o/estimators/gbm.py +++ b/h2o-py/h2o/estimators/gbm.py @@ -2239,3 +2239,4 @@ def auto_rebalance(self, auto_rebalance): assert_is_type(auto_rebalance, None, bool) self._parms["auto_rebalance"] = auto_rebalance + diff --git a/h2o-py/h2o/estimators/generic.py b/h2o-py/h2o/estimators/generic.py index 60483aee7239..37067ea0a6db 100644 --- a/h2o-py/h2o/estimators/generic.py +++ b/h2o-py/h2o/estimators/generic.py @@ -100,6 +100,7 @@ def path(self, path): assert_is_type(path, None, str) self._parms["path"] = path + @staticmethod def from_file(file=str, model_id=None): """ @@ -126,4 +127,3 @@ def from_file(file=str, model_id=None): model.train() return model - diff --git a/h2o-py/h2o/estimators/glm.py b/h2o-py/h2o/estimators/glm.py index e8302692feb1..fd4062c78b81 100644 --- a/h2o-py/h2o/estimators/glm.py +++ b/h2o-py/h2o/estimators/glm.py @@ -2599,4 +2599,3 @@ def makeGLMModel(model, coefs, threshold=.5): m = H2OGeneralizedLinearEstimator() m._resolve_model(model_json["model_id"]["name"], model_json) return m - diff --git a/h2o-py/h2o/estimators/glrm.py b/h2o-py/h2o/estimators/glrm.py index d79b9dbed0ac..8574c8a6f186 100644 --- a/h2o-py/h2o/estimators/glrm.py +++ b/h2o-py/h2o/estimators/glrm.py @@ -1054,6 +1054,7 @@ def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir + def transform_frame(self, fr): """ GLRM performs A=X*Y during training. When a new dataset is given, GLRM will perform Anew = Xnew*Y. When @@ -1061,4 +1062,3 @@ def transform_frame(self, fr): :return: an H2OFrame that contains Xnew. """ return H2OFrame._expr(expr=ExprNode("transform", ASTId(self.key), ASTId(fr.key)))._frame(fill_cache=True) - diff --git a/h2o-py/h2o/estimators/infogram.py b/h2o-py/h2o/estimators/infogram.py index 2b65c3bb54de..6fe54b08c1a7 100644 --- a/h2o-py/h2o/estimators/infogram.py +++ b/h2o-py/h2o/estimators/infogram.py @@ -860,6 +860,7 @@ def top_n_features(self, top_n_features): assert_is_type(top_n_features, None, int) self._parms["top_n_features"] = top_n_features + def _extract_x_from_model(self): """ extract admissible features from an Infogram model. @@ -1189,4 +1190,3 @@ def train_subset_models(self, model_class, y, training_frame, test_frame, protec if protected_columns is None or len(protected_columns) == 0: return make_leaderboard(models, leaderboard_frame=test_frame) return disparate_analysis(models, test_frame, protected_columns, reference, favorable_class) - diff --git a/h2o-py/h2o/estimators/isolation_forest.py b/h2o-py/h2o/estimators/isolation_forest.py index b6f931f30cf5..64f2ae58afc9 100644 --- a/h2o-py/h2o/estimators/isolation_forest.py +++ b/h2o-py/h2o/estimators/isolation_forest.py @@ -744,3 +744,4 @@ def validation_response_column(self, validation_response_column): assert_is_type(validation_response_column, None, str) self._parms["validation_response_column"] = validation_response_column + diff --git a/h2o-py/h2o/estimators/isotonicregression.py b/h2o-py/h2o/estimators/isotonicregression.py index d8f05f5189c1..d5d72eaee640 100644 --- a/h2o-py/h2o/estimators/isotonicregression.py +++ b/h2o-py/h2o/estimators/isotonicregression.py @@ -291,3 +291,4 @@ def fold_column(self, fold_column): assert_is_type(fold_column, None, str) self._parms["fold_column"] = fold_column + diff --git a/h2o-py/h2o/estimators/kmeans.py b/h2o-py/h2o/estimators/kmeans.py index 506f452d7dd5..c63249385eb4 100644 --- a/h2o-py/h2o/estimators/kmeans.py +++ b/h2o-py/h2o/estimators/kmeans.py @@ -741,3 +741,4 @@ def cluster_size_constraints(self, cluster_size_constraints): assert_is_type(cluster_size_constraints, None, [int]) self._parms["cluster_size_constraints"] = cluster_size_constraints + diff --git a/h2o-py/h2o/estimators/model_selection.py b/h2o-py/h2o/estimators/model_selection.py index ae7a1d42e370..8632b1a12fb9 100644 --- a/h2o-py/h2o/estimators/model_selection.py +++ b/h2o-py/h2o/estimators/model_selection.py @@ -1326,6 +1326,7 @@ def multinode_mode(self, multinode_mode): assert_is_type(multinode_mode, None, bool) self._parms["multinode_mode"] = multinode_mode + def get_regression_influence_diagnostics(self, predictor_size=None): """ Get the regression influence diagnostics frames for all models with different number of predictors. If a @@ -1549,4 +1550,3 @@ def get_best_model_predictors(self): :return: a list of best predictors subset """ return self._model_json["output"]["best_predictors_subset"] - diff --git a/h2o-py/h2o/estimators/naive_bayes.py b/h2o-py/h2o/estimators/naive_bayes.py index 8c43df51e7e7..31123f55087e 100644 --- a/h2o-py/h2o/estimators/naive_bayes.py +++ b/h2o-py/h2o/estimators/naive_bayes.py @@ -906,3 +906,4 @@ def auc_type(self, auc_type): assert_is_type(auc_type, None, Enum("auto", "none", "macro_ovr", "weighted_ovr", "macro_ovo", "weighted_ovo")) self._parms["auc_type"] = auc_type + diff --git a/h2o-py/h2o/estimators/pca.py b/h2o-py/h2o/estimators/pca.py index bc324cd7e60d..7ecca50f3795 100644 --- a/h2o-py/h2o/estimators/pca.py +++ b/h2o-py/h2o/estimators/pca.py @@ -508,6 +508,7 @@ def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir + def init_for_pipeline(self): """ Returns H2OPCA object which implements fit and transform method to be used in sklearn.Pipeline properly. @@ -532,4 +533,3 @@ def init_for_pipeline(self): var_names = list(dict(inspect.getmembers(H2OPCA.__init__.__code__))['co_varnames']) parameters = {k: v for k, v in self._parms.items() if k in var_names} return H2OPCA(**parameters) - diff --git a/h2o-py/h2o/estimators/pipeline.py b/h2o-py/h2o/estimators/pipeline.py deleted file mode 100644 index bbbcb868a1c9..000000000000 --- a/h2o-py/h2o/estimators/pipeline.py +++ /dev/null @@ -1,77 +0,0 @@ -#!/usr/bin/env python -# -*- encoding: utf-8 -*- -# -# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py -# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details) -# - -import h2o -from h2o.display import H2ODisplay, repr_def -from h2o.expr import ASTId, ExprNode -from h2o.schemas import H2OSchema, register_schema_handler -from h2o.estimators.estimator_base import H2OEstimator -from h2o.exceptions import H2OValueError -from h2o.frame import H2OFrame -from h2o.utils.typechecks import assert_is_type, Enum, numeric - - -class H2OPipeline(H2OEstimator): - """ - Pipeline - - """ - - algo = "pipeline" - supervised_learning = None - - def __init__(self, - model_id=None, # type: Optional[Union[None, str, H2OEstimator]] - ): - """ - :param model_id: Destination id for this model; auto-generated if not specified. - Defaults to ``None``. - :type model_id: Union[None, str, H2OEstimator], optional - """ - super(H2OPipeline, self).__init__() - self._parms = {} - self._id = self._parms['model_id'] = model_id - - @property - def transformers(self): - return self._model_json['output']['transformers'] - - @property - def estimator_model(self): - m_json = self._model_json['output']['estimator'] - return None if (m_json is None or m_json['name'] is None) else h2o.get_model(m_json['name']) - - def transform(self, fr): - """ - Applies all the pipeline transformers to the given input frame. - :return: the transformed frame, as it would be passed to `estimator_model`, if calling `predict` instead. - """ - return H2OFrame._expr(expr=ExprNode("transform", ASTId(self.key), ASTId(fr.key)))._frame(fill_cache=True) - - -class H2ODataTransformer(H2ODisplay): - @classmethod - def make(cls, kvs): - dt = H2ODataTransformer(**{k: v for k, v in kvs if k not in H2OSchema._ignored_schema_keys_}) - dt._json = kvs - return dt - - def __init__(self, id=None, description=None): - self._json = None - self.id = id - self.description = description - - def _repr_(self): - return repr(self._json) - - def _str_(self, verbosity=None): - return repr_def(self) - - -# self-register transformer class: done as soon as `h2o.estimators` is loaded, which means as soon as h2o.h2o is... -register_schema_handler("DataTransformerV3", H2ODataTransformer) - diff --git a/h2o-py/h2o/estimators/psvm.py b/h2o-py/h2o/estimators/psvm.py index 5bd3ece1ab3c..0261660f7652 100644 --- a/h2o-py/h2o/estimators/psvm.py +++ b/h2o-py/h2o/estimators/psvm.py @@ -553,3 +553,4 @@ def seed(self, seed): assert_is_type(seed, None, int) self._parms["seed"] = seed + diff --git a/h2o-py/h2o/estimators/rulefit.py b/h2o-py/h2o/estimators/rulefit.py index c7718e3b7933..529b371780ea 100644 --- a/h2o-py/h2o/estimators/rulefit.py +++ b/h2o-py/h2o/estimators/rulefit.py @@ -408,4 +408,3 @@ def predict_rules(self, frame, rule_ids): from h2o.expr import ExprNode assert_is_type(frame, H2OFrame) return H2OFrame._expr(expr=ExprNode("rulefit.predict.rules", self, frame, rule_ids)) - diff --git a/h2o-py/h2o/estimators/stackedensemble.py b/h2o-py/h2o/estimators/stackedensemble.py index bb2fb4c58946..6a2117c007b2 100644 --- a/h2o-py/h2o/estimators/stackedensemble.py +++ b/h2o-py/h2o/estimators/stackedensemble.py @@ -925,6 +925,7 @@ def gainslift_bins(self, gainslift_bins): assert_is_type(gainslift_bins, None, int) self._parms["gainslift_bins"] = gainslift_bins + def metalearner(self): """Print the metalearner of an H2OStackedEnsembleEstimator. @@ -1046,4 +1047,3 @@ def extend_parms(parms): raise H2OResponseError("Meta learner didn't get to be trained in time. " "Try increasing max_runtime_secs or setting it to 0 (unlimited).") return self - diff --git a/h2o-py/h2o/estimators/svd.py b/h2o-py/h2o/estimators/svd.py index 84d3bbaae3b6..996790e57af9 100644 --- a/h2o-py/h2o/estimators/svd.py +++ b/h2o-py/h2o/estimators/svd.py @@ -432,6 +432,7 @@ def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir + def init_for_pipeline(self): """ Returns H2OSVD object which implements fit and transform method to be used in sklearn.Pipeline properly. @@ -457,4 +458,3 @@ def init_for_pipeline(self): var_names = list(dict(inspect.getmembers(H2OSVD.__init__.__code__))['co_varnames']) parameters = {k: v for k, v in self._parms.items() if k in var_names} return H2OSVD(**parameters) - diff --git a/h2o-py/h2o/estimators/targetencoder.py b/h2o-py/h2o/estimators/targetencoder.py index 0345642d6512..e878dad4fb6e 100644 --- a/h2o-py/h2o/estimators/targetencoder.py +++ b/h2o-py/h2o/estimators/targetencoder.py @@ -452,4 +452,3 @@ def transform(self, frame, blending=None, inflection_point=None, smoothing=None, output = h2o.api("GET /3/TargetEncoderTransform", data=params) return h2o.get_frame(output["name"]) - diff --git a/h2o-py/h2o/estimators/uplift_random_forest.py b/h2o-py/h2o/estimators/uplift_random_forest.py index efa3bd71f347..7ebb1b13f133 100644 --- a/h2o-py/h2o/estimators/uplift_random_forest.py +++ b/h2o-py/h2o/estimators/uplift_random_forest.py @@ -668,3 +668,4 @@ def stopping_tolerance(self, stopping_tolerance): assert_is_type(stopping_tolerance, None, numeric) self._parms["stopping_tolerance"] = stopping_tolerance + diff --git a/h2o-py/h2o/estimators/word2vec.py b/h2o-py/h2o/estimators/word2vec.py index 828c9fa74c7d..f4930384ae8b 100644 --- a/h2o-py/h2o/estimators/word2vec.py +++ b/h2o-py/h2o/estimators/word2vec.py @@ -419,6 +419,7 @@ def export_checkpoints_dir(self, export_checkpoints_dir): assert_is_type(export_checkpoints_dir, None, str) self._parms["export_checkpoints_dir"] = export_checkpoints_dir + @staticmethod def from_external(external=H2OFrame): """ @@ -458,4 +459,3 @@ def _determine_vec_size(pre_trained): pre_trained.frame_id) return pre_trained.dim[1] - 1 - diff --git a/h2o-py/h2o/estimators/xgboost.py b/h2o-py/h2o/estimators/xgboost.py index 789042901e62..569181c9bd7f 100644 --- a/h2o-py/h2o/estimators/xgboost.py +++ b/h2o-py/h2o/estimators/xgboost.py @@ -2532,6 +2532,7 @@ def score_eval_metric_only(self, score_eval_metric_only): assert_is_type(score_eval_metric_only, None, bool) self._parms["score_eval_metric_only"] = score_eval_metric_only + @staticmethod def available(): """ @@ -2598,4 +2599,3 @@ def convert_H2OXGBoostParams_2_XGBoostParams(self): paramsSet = self.full_parameters return nativeXGBoostParams, paramsSet['ntrees']['actual_value'] - diff --git a/h2o-py/h2o/expr.py b/h2o-py/h2o/expr.py index 628ffa2a4b6c..6c6a4c340a6f 100644 --- a/h2o-py/h2o/expr.py +++ b/h2o-py/h2o/expr.py @@ -18,9 +18,10 @@ import tabulate import h2o -from h2o.exceptions import H2OConnectionError -from h2o.expr_optimizer import optimize +from h2o.backend.connection import H2OConnectionError from h2o.utils.shared_utils import _is_fr, _py_tmp_key +from h2o.model.model_base import ModelBase +from h2o.expr_optimizer import optimize class ExprNode(object): @@ -190,7 +191,7 @@ def _arg_to_expr(arg): return "[%d:%s]" % (start, str(stop - start)) else: return "[%d:%s:%d]" % (start, str((stop - start + step - 1) // step), step) - if isinstance(arg, h2o.model.ModelBase): + if isinstance(arg, ModelBase): return arg.model_id # Number representation without Py2 L suffix enforced if isinstance(arg, numbers.Integral): diff --git a/h2o-py/h2o/h2o.py b/h2o-py/h2o/h2o.py index 457a0394c258..542cb117d872 100644 --- a/h2o-py/h2o/h2o.py +++ b/h2o-py/h2o/h2o.py @@ -17,7 +17,14 @@ from .base import Keyed from .estimators import create_estimator from .estimators.generic import H2OGenericEstimator -from .exceptions import H2OError, H2OConnectionError, H2OValueError, H2ODependencyWarning, H2ODeprecationWarning +from .exceptions import H2OError, H2ODeprecationWarning +from .estimators.gbm import H2OGradientBoostingEstimator +from .estimators.glm import H2OGeneralizedLinearEstimator +from .estimators.xgboost import H2OXGBoostEstimator +from .estimators.infogram import H2OInfogram +from .estimators.deeplearning import H2OAutoEncoderEstimator, H2ODeepLearningEstimator +from .estimators.extended_isolation_forest import H2OExtendedIsolationForestEstimator +from .exceptions import H2OConnectionError, H2OValueError, H2ODependencyWarning from .expr import ExprNode from .frame import H2OFrame from .grid.grid_search import H2OGridSearch @@ -510,8 +517,8 @@ def load_grid(grid_file_path, load_params_references=False): :examples: >>> from collections import OrderedDict - >>> from h2o.grid import H2OGridSearch - >>> from h2o.estimators import H2OGradientBoostingEstimator + >>> from h2o.grid.grid_search import H2OGridSearch + >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> train = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") # Run GBM Grid Search >>> ntrees_opts = [1, 3] @@ -555,8 +562,8 @@ def save_grid(grid_directory, grid_id, save_params_references=False, export_cros :examples: >>> from collections import OrderedDict - >>> from h2o.grid import H2OGridSearch - >>> from h2o.estimators import H2OGradientBoostingEstimator + >>> from h2o.grid.grid_search import H2OGridSearch + >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> train = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv") # Run GBM Grid Search >>> ntrees_opts = [1, 3] @@ -1051,7 +1058,6 @@ def models(): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator, H2OXGBoostEstimator >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() @@ -1077,7 +1083,6 @@ def get_model(model_id): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> airlines["Year"]= airlines["Year"].asfactor() >>> airlines["Month"]= airlines["Month"].asfactor() @@ -1117,7 +1122,7 @@ def get_grid(grid_id): :examples: - >>> from h2o.grid import H2OGridSearch + >>> from h2o.grid.grid_search import H2OGridSearch >>> from h2o.estimators import H2OGradientBoostingEstimator >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> x = ["DayofMonth", "Month"] @@ -1187,7 +1192,6 @@ def no_progress(): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> h2o.no_progress() >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> x = ["DayofMonth", "Month"] @@ -1204,7 +1208,6 @@ def show_progress(): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> h2o.no_progress() >>> airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> x = ["DayofMonth", "Month"] @@ -1391,7 +1394,6 @@ def download_pojo(model, path="", get_jar=True, jar_name=""): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> h2o_df = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor() >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator @@ -1493,7 +1495,7 @@ def save_model(model, path="", force=False, export_cross_validation_predictions= :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator + >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> h2o_df = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> my_model = H2OGeneralizedLinearEstimator(family = "binomial") >>> my_model.train(y = "CAPSULE", @@ -1529,7 +1531,7 @@ def download_model(model, path="", export_cross_validation_predictions=False, fi :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator + >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator >>> h2o_df = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> my_model = H2OGeneralizedLinearEstimator(family = "binomial") >>> my_model.train(y = "CAPSULE", @@ -1575,7 +1577,6 @@ def load_model(path): :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> training_data = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip") >>> predictors = ["Origin", "Dest", "Year", "UniqueCarrier", ... "DayOfWeek", "Month", "Distance", "FlightNum"] @@ -1622,7 +1623,6 @@ def export_file(frame, path, force=False, sep=",", compression=None, parts=1, he :examples: - >>> from h2o.estimators import H2OGeneralizedLinearEstimator >>> h2o_df = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv") >>> h2o_df['CAPSULE'] = h2o_df['CAPSULE'].asfactor() >>> rand_vec = h2o_df.runif(1234) @@ -2026,7 +2026,6 @@ def make_metrics(predicted, actual, domain=None, distribution=None, weights=None the new thresholds from the predicted data. :examples: - >>> from h2o.estimators import H2OGradientBoostingEstimator >>> fr = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip") >>> fr["CAPSULE"] = fr["CAPSULE"].asfactor() >>> fr["RACE"] = fr["RACE"].asfactor() @@ -2386,7 +2385,7 @@ def print_mojo(mojo_path, format="json", tree_index=None): :example: >>> import json - >>> from h2o.estimators import H2OGradientBoostingEstimator + >>> from h2o.estimators.gbm import H2OGradientBoostingEstimator >>> prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv") >>> prostate["CAPSULE"] = prostate["CAPSULE"].asfactor() >>> gbm_h2o = H2OGradientBoostingEstimator(ntrees = 5, diff --git a/h2o-py/h2o/pipeline/__init__.py b/h2o-py/h2o/pipeline/__init__.py index 285e84550e68..082f127c5374 100644 --- a/h2o-py/h2o/pipeline/__init__.py +++ b/h2o-py/h2o/pipeline/__init__.py @@ -1,3 +1,9 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- +# +# This file is auto-generated by h2o-3/h2o-bindings/bin/gen_python.py +# Copyright 2016 H2O.ai; Apache License Version 2.0 (see LICENSE for details) +# from .mojo_pipeline import H2OMojoPipeline __all__ = ( diff --git a/h2o-py/h2o/schemas/__init__.py b/h2o-py/h2o/schemas/__init__.py index 2a645a046694..048943138f71 100644 --- a/h2o-py/h2o/schemas/__init__.py +++ b/h2o-py/h2o/schemas/__init__.py @@ -1,53 +1,6 @@ -from functools import partial - from .error import H2OErrorV3, H2OModelBuilderErrorV3 from .metadata import H2OMetadataV3 -from .schema import H2OSchema +from .schema import H2OSchema, define_classes_from_schema -__all__ = ['H2OSchema', +__all__ = ['H2OSchema', 'define_classes_from_schema', 'H2OErrorV3', 'H2OModelBuilderErrorV3', 'H2OMetadataV3'] - - -__schema_handlers = [] - - -def get_schema_handler(schema): - for s, h in __schema_handlers: - if s == schema: - return h - elif callable(s) and s(schema): - return partial(h, schema) - - -def register_schema_handler(schema, handler): - """ - :param Union[str, Callable] schema: a string representing a schema name, or a predicate on a schema name. - :param Callable handler: a function taking the schema payload as parameter, represented as a list or key-value tuples. - If the handler is a factory object with a `make` method, then that method will be used as the handler. - if the schema is a predicate, then the handler function must accept 2 parameters: the schema name + the schema payload. - """ - if hasattr(handler, "make") and callable(handler.make): - handler = handler.make - __schema_handlers.append((schema, handler)) - - -def register_schemas(): - from h2o.backend import H2OCluster - from h2o.model.metrics import make_metrics - from h2o.two_dim_table import H2OTwoDimTable - - for (schema, handler) in [ - ("MetadataV3", H2OMetadataV3), - ("CloudV3", H2OCluster), - ("H2OErrorV3", H2OErrorV3), - ("H2OModelBuilderErrorV3", H2OModelBuilderErrorV3), - ("TwoDimTableV3", H2OTwoDimTable), - (lambda s: s.startswith("ModelMetrics"), make_metrics), - ]: - register_schema_handler(schema, handler) - - -def define_classes_from_schema(conn): - from h2o.backend import H2OCluster - for cls in [H2OCluster, H2OErrorV3, H2OModelBuilderErrorV3]: - cls.define_from_schema(conn) diff --git a/h2o-py/h2o/schemas/schema.py b/h2o-py/h2o/schemas/schema.py index 19703a0c48f4..45fc047dc5df 100644 --- a/h2o-py/h2o/schemas/schema.py +++ b/h2o-py/h2o/schemas/schema.py @@ -11,6 +11,11 @@ from h2o.exceptions import H2OConnectionError, H2OServerError +def define_classes_from_schema(classes, connection): + for cls in classes: + cls.define_from_schema(connection) + + class H2OSchema(object): _ignored_schema_keys_ = {"__meta", "_exclude_fields", "__schema"} diff --git a/h2o-py/h2o/sklearn/__init__.py b/h2o-py/h2o/sklearn/__init__.py index 14f4f0d4ffef..dd5157884a7c 100644 --- a/h2o-py/h2o/sklearn/__init__.py +++ b/h2o-py/h2o/sklearn/__init__.py @@ -197,7 +197,6 @@ def h2o_connection(**init_args): _excluded_estimators = ( # e.g. abstract classes 'H2OEstimator', 'H2OTransformer', - 'H2OPipeline', 'H2OInfogram', 'H2OANOVAGLMEstimator', # fully disabled as it does not support `predict` method. 'H2OModelSelectionEstimator', # fully disabled as it does no support `predict` method. diff --git a/h2o-py/h2o/utils/shared_utils.py b/h2o-py/h2o/utils/shared_utils.py index b3e0358452c6..67b622fd62c6 100644 --- a/h2o-py/h2o/utils/shared_utils.py +++ b/h2o-py/h2o/utils/shared_utils.py @@ -44,6 +44,7 @@ def __subclasshook__(cls, C): return NotImplemented +from h2o.backend.server import H2OLocalServer from h2o.exceptions import H2OValueError from h2o.utils.typechecks import assert_is_type, is_type, numeric from h2o.utils.threading import local_env @@ -446,8 +447,6 @@ def mojo_predict_csv(input_csv_path, mojo_zip_path, output_csv_path=None, genmod :param extra_cmd_args: Optional, a list of additional arguments to append to genmodel.jar's command line. :return: List of computed predictions """ - from h2o.backend.server import H2OLocalServer - default_java_options = '-Xmx4g -XX:ReservedCodeCacheSize=256m' prediction_output_file = 'prediction.csv' diff --git a/h2o-py/tests/testdir_algos/grid/pyunit_grid_parallel_cv_error.py b/h2o-py/tests/testdir_algos/grid/pyunit_grid_parallel_cv_error.py index 562c7b57d9e9..79c5b6c66ca3 100644 --- a/h2o-py/tests/testdir_algos/grid/pyunit_grid_parallel_cv_error.py +++ b/h2o-py/tests/testdir_algos/grid/pyunit_grid_parallel_cv_error.py @@ -29,7 +29,6 @@ def grid_parallel(): gs.train(x=list(range(4)), y=4, training_frame=train, fold_column="fold_assignment") assert gs is not None # only six models are trained, since CV is not possible with min_rows=100 - print(gs.model_ids) assert len(gs.model_ids) == 6 diff --git a/h2o-py/tests_rest_smoke/testdir_multi_jvm/test_rest_api.py b/h2o-py/tests_rest_smoke/testdir_multi_jvm/test_rest_api.py index 6510800f0472..13c93693ae8e 100644 --- a/h2o-py/tests_rest_smoke/testdir_multi_jvm/test_rest_api.py +++ b/h2o-py/tests_rest_smoke/testdir_multi_jvm/test_rest_api.py @@ -23,7 +23,7 @@ algos = ['coxph', 'kmeans', 'deeplearning', 'drf', 'glm', 'gbm', 'pca', 'naivebayes', 'glrm', 'svd', 'isotonicregression', 'psvm', 'aggregator', 'word2vec', 'stackedensemble', 'xgboost', 'isolationforest', 'gam', 'generic', 'targetencoder', 'rulefit', 'extendedisolationforest', 'anovaglm', 'modelselection', - 'upliftdrf', 'infogram', 'dt', 'adaboost', 'pipeline'] + 'upliftdrf', 'infogram', 'dt', 'adaboost'] algo_additional_default_params = { 'grep' : { 'regex' : '.*' }, 'kmeans' : { 'k' : 2 }, diff --git a/h2o-r/h2o-package/R/classes.R b/h2o-r/h2o-package/R/classes.R index 9edad687c2fc..296105062f62 100755 --- a/h2o-r/h2o-package/R/classes.R +++ b/h2o-r/h2o-package/R/classes.R @@ -868,13 +868,6 @@ setClass("H2OSegmentModelsFuture", slots = c(job_key = "character", segment_mode #' @export setClass("H2OSegmentModels", slots = c(segment_models_id = "character")) -setClass("H2ODataTransformer", slots = c(id = "character", description = "character")) -setClass("H2OPipeline", contains="H2OModel", - slots = c( - transformers = "list", - estimator_model = "H2OModel" - )) - #' H2O Grid #' #' A class to contain the information about grid results diff --git a/h2o-r/h2o-package/R/kvstore.R b/h2o-r/h2o-package/R/kvstore.R index 55300d5c5045..aba10a750f82 100644 --- a/h2o-r/h2o-package/R/kvstore.R +++ b/h2o-r/h2o-package/R/kvstore.R @@ -239,7 +239,7 @@ h2o.getModel <- function(model_id) { names(model$random_coefficients) <- model$random_coefficients_table[,1] } } else { # with AnovaGLM - coefLen <- length(model$coefficients_table) + coefLen = length(model$coefficients_table) model$coefficients <- vector("list", coefLen) for (index in 1:coefLen) { model$coefficients[[index]] <- model$coefficients_table[[index]] diff --git a/h2o-r/h2o-package/R/models.R b/h2o-r/h2o-package/R/models.R index 2a344d173295..dcff26351981 100755 --- a/h2o-r/h2o-package/R/models.R +++ b/h2o-r/h2o-package/R/models.R @@ -682,17 +682,6 @@ setMethod("h2o.transform", signature("H2OWordEmbeddingModel"), function(model, w }) -#' -#' Transform the given data frame using the model if the latter supports transformations. -#' -#' @param model A trained model representing the transformation strategy (currently supported algorithms are `glrm` and `pipeline`). -#' @param data An H2OFrame on which the transformation is applied. -#' @return an H2OFrame object representing the transformed data. -#' @export -setMethod("h2o.transform", signature("H2OModel"), function(model, data) { - if (!model@algorithm %in% c("glrm", "pipeline")) stop("h2o.transform is not available for this type of model.") - return(.newExpr("transform", model@model_id, h2o.getId(data))) -}) #' #' @rdname predict.H2OModel diff --git a/h2o-r/tests/testdir_misc/runit_connect.R b/h2o-r/tests/testdir_misc/runit_connect.R index 2e76ed275a72..46495aa5b4dd 100644 --- a/h2o-r/tests/testdir_misc/runit_connect.R +++ b/h2o-r/tests/testdir_misc/runit_connect.R @@ -8,7 +8,7 @@ to_src <- c("aggregator.R", "classes.R", "connection.R","config.R", "constants.R "coxph.R", "coxphutils.R", "gbm.R", "glm.R", "gam.R", "glrm.R", "kmeans.R", "deeplearning.R", "randomforest.R", "generic.R", "naivebayes.R", "pca.R", "svd.R", "locate.R", "grid.R", "word2vec.R", "w2vutils.R", "stackedensemble.R", "rulefit.R", "predict.R", "xgboost.R", "isolationforest.R", "psvm.R", "segment.R", "tf-idf.R", "explain.R", "permutation_varimp.R", - "extendedisolationforest.R", "upliftrandomforest.R", "pipeline.R") + "extendedisolationforest.R", "upliftrandomforest.R") src_path <- paste(h2oRDir,"h2o-package","R",sep=.Platform$file.sep) invisible(lapply(to_src,function(x){source(paste(src_path, x, sep = .Platform$file.sep))})) diff --git a/h2o-test-support/src/main/java/water/test/dummy/DummyModel.java b/h2o-test-support/src/main/java/water/test/dummy/DummyModel.java index b418bab71fc9..35c8f146dee8 100644 --- a/h2o-test-support/src/main/java/water/test/dummy/DummyModel.java +++ b/h2o-test-support/src/main/java/water/test/dummy/DummyModel.java @@ -3,10 +3,8 @@ import hex.Model; import hex.ModelMetrics; import hex.ModelMetricsBinomial; -import hex.ModelMetricsRegression; import water.Futures; import water.Key; -import water.fvec.Frame; public class DummyModel extends Model { public DummyModel(Key selfKey, DummyModelParameters parms, DummyModelOutput output) { @@ -14,17 +12,10 @@ public DummyModel(Key selfKey, DummyModelParameters parms, DummyMode } @Override public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) { - if (domain == null) return new ModelMetricsRegression.MetricBuilderRegression(); return new ModelMetricsBinomial.MetricBuilderBinomial(domain); } @Override protected double[] score0(double[] data, double[] preds) { return preds; } - - @Override - public Frame transform(Frame fr) { - return fr == null ? null : fr.toTwoDimTable(0, 10, true).asFrame(Key.make(fr._key+"_stats"), true); - } - @Override protected Futures remove_impl(Futures fs, boolean cascade) { super.remove_impl(fs, cascade); diff --git a/h2o-test-support/src/main/java/water/test/dummy/DummyModelBuilder.java b/h2o-test-support/src/main/java/water/test/dummy/DummyModelBuilder.java index 59b5a41ceaf5..05fd08c0df91 100644 --- a/h2o-test-support/src/main/java/water/test/dummy/DummyModelBuilder.java +++ b/h2o-test-support/src/main/java/water/test/dummy/DummyModelBuilder.java @@ -30,7 +30,7 @@ public void computeImpl() { init(true); Model model = null; try { - model = new DummyModel(dest(), _parms, new DummyModelOutput(DummyModelBuilder.this, msg)); + model = new DummyModel(dest(), _parms, new DummyModelOutput(DummyModelBuilder.this, train(), msg)); model.delete_and_lock(_job); model.update(_job); } finally { diff --git a/h2o-test-support/src/main/java/water/test/dummy/DummyModelOutput.java b/h2o-test-support/src/main/java/water/test/dummy/DummyModelOutput.java index e7320ab84f9a..e8ee91998ee9 100644 --- a/h2o-test-support/src/main/java/water/test/dummy/DummyModelOutput.java +++ b/h2o-test-support/src/main/java/water/test/dummy/DummyModelOutput.java @@ -7,8 +7,8 @@ public class DummyModelOutput extends Model.Output { public final String _msg; - public DummyModelOutput(ModelBuilder b, String msg) { - super(b, b.train()); + public DummyModelOutput(ModelBuilder b, Frame train, String msg) { + super(b, train); _msg = msg; } @Override