apache-ignite

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// Licensed to the Apache Software Foundation (ASF) under one or more
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// contributor license agreements.  See the NOTICE file distributed with
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// this work for additional information regarding copyright ownership.
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// The ASF licenses this file to You under the Apache License, Version 2.0
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// (the "License"); you may not use this file except in compliance with
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// the License.  You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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= Pipelines API
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Apache Ignite ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Pipelines API, where the pipeline concept is mostly inspired by the scikit-learn and Apache Spark projects.
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* **Preprocessor Model **- This is an algorithm which can transform one DataSet into another DataSet.
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* **Preprocessor Trainer**- This is an algorithm which can be fit on a DataSet to produce a PreprocessorModel.
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* **Pipeline **-  A Pipeline chains multiple Trainers and Preprocessors together to specify an ML workflow.
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* **Parameter **- All ML Trainers and Preprocessor Trainers now share a common API for specifying parameters.
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CAUTION: The Pipeline API is experimental and could be changed in the next releases.
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The Pipeline could replace the pieces of code with .fit() method calls as in the next examples:
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[tabs]
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--
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tab:Without Pipeline API[]
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[source, java]
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----
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final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 3, 4, 5, 6, 8, 10).labeled(1);
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TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>()
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  .split(0.75);
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Preprocessor<Integer, Vector> imputingPreprocessor = new ImputerTrainer<Integer, Vector>()
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  .fit(ignite,
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       dataCache,
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       vectorizer
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      );
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Preprocessor<Integer, Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Vector>()
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  .fit(ignite,
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       dataCache,
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       imputingPreprocessor
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      );
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Preprocessor<Integer, Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Vector>()
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  .withP(1)
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  .fit(ignite,
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       dataCache,
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       minMaxScalerPreprocessor
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      );
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// Tune hyper-parameters with K-fold Cross-Validation on the split training set.
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DecisionTreeClassificationTrainer trainerCV = new DecisionTreeClassificationTrainer();
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CrossValidation<DecisionTreeModel, Integer, Vector> scoreCalculator = new CrossValidation<>();
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ParamGrid paramGrid = new ParamGrid()
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  .addHyperParam("maxDeep", trainerCV::withMaxDeep, new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 10.0})
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  .addHyperParam("minImpurityDecrease", trainerCV::withMinImpurityDecrease, new Double[] {0.0, 0.25, 0.5});
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scoreCalculator
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  .withIgnite(ignite)
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  .withUpstreamCache(dataCache)
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  .withTrainer(trainerCV)
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  .withMetric(MetricName.ACCURACY)
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  .withFilter(split.getTrainFilter())
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  .isRunningOnPipeline(false)
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  .withPreprocessor(normalizationPreprocessor)
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  .withAmountOfFolds(3)
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  .withParamGrid(paramGrid);
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CrossValidationResult crossValidationRes = scoreCalculator.tuneHyperParameters();
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----
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tab:With Pipeline API[]
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[source, java]
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----
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final Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>(0, 4, 5, 6, 8).labeled(1);
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TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>()
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  .split(0.75);
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DecisionTreeClassificationTrainer trainerCV = new DecisionTreeClassificationTrainer();
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Pipeline<Integer, Vector, Integer, Double> pipeline = new Pipeline<Integer, Vector, Integer, Double>()
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  .addVectorizer(vectorizer)
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  .addPreprocessingTrainer(new ImputerTrainer<Integer, Vector>())
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  .addPreprocessingTrainer(new MinMaxScalerTrainer<Integer, Vector>())
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  .addTrainer(trainer);
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CrossValidation<DecisionTreeModel, Integer, Vector> scoreCalculator = new CrossValidation<>();
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ParamGrid paramGrid = new ParamGrid()
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  .addHyperParam("maxDeep", trainer::withMaxDeep, new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 10.0})
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  .addHyperParam("minImpurityDecrease", trainer::withMinImpurityDecrease, new Double[] {0.0, 0.25, 0.5});
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scoreCalculator
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  .withIgnite(ignite)
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  .withUpstreamCache(dataCache)
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  .withPipeline(pipeline)
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  .withMetric(MetricName.ACCURACY)
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  .withFilter(split.getTrainFilter())
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  .withAmountOfFolds(3)
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  .withParamGrid(paramGrid);
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CrossValidationResult crossValidationRes = scoreCalculator.tuneHyperParameters();
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----
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--
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The full code could be found in the https://github.com/apache/ignite/blob/master/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_8_CV_with_Param_Grid_and_pipeline.java[Titanic tutorial].
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