com.linkedin.dagli.dag.DAG1x5 Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of core Show documentation
Show all versions of core Show documentation
DAG-oriented machine learning framework for bug-resistant, readable, efficient, maintainable and trivially deployable models in Java and other JVM languages
// AUTOGENERATED CODE. DO NOT MODIFY DIRECTLY! Instead, please modify the dag/DAGXxY.ftl file.
// See the README in the module's src/template directory for details.
package com.linkedin.dagli.dag;
import java.util.Collection;
import java.util.Collections;
import com.linkedin.dagli.annotation.equality.IgnoredByValueEquality;
import com.linkedin.dagli.annotation.equality.ValueEquality;
import com.linkedin.dagli.objectio.ObjectIterator;
import com.linkedin.dagli.objectio.ObjectReader;
import com.linkedin.dagli.preparer.Preparer1;
import com.linkedin.dagli.preparer.AbstractBatchPreparer1;
import com.linkedin.dagli.preparer.PreparerContext;
import com.linkedin.dagli.preparer.PreparerResultMixed;
import com.linkedin.dagli.producer.Producer;
import com.linkedin.dagli.reducer.Reducer;
import com.linkedin.dagli.transformer.AbstractPreparableTransformer1;
import com.linkedin.dagli.transformer.AbstractPreparedTransformer1;
import com.linkedin.dagli.transformer.PreparedTransformer1;
import com.linkedin.dagli.util.array.AutoCloseableArray;
import com.linkedin.dagli.tuple.Tuple5;
/**
* DAGs, directed acyclic graphs, contain root nodes ({@link com.linkedin.dagli.placeholder.Placeholder}s and {@link com.linkedin.dagli.generator.Generator}s) and child nodes
* ({@link com.linkedin.dagli.transformer.PreparedTransformer}s,
* {@link com.linkedin.dagli.transformer.PreparableTransformer}s, and {@link com.linkedin.dagli.view.TransformerView}s).
*
* DAG1x5 is a preparable DAG.
*
* Before it can be used to transform new examples, it must be prepared with training data (e.g. by calling the
* prepare() method). The prepareAndApply() method both trains the DAG and applies it to the training data, yielding
* both the prepared DAG and the results of applying the DAG to the training data (this can be useful in autoevaluation,
* e.g. how well a classification model performs when predicting labels for its own training data).
*
* DAG1x5 is also a {@link com.linkedin.dagli.transformer.PreparableTransformer} and can be used as
* a transformer within other DAGs.
*
* @param the type of the first input
* @param the type of the first result
* @param the type of the second result
* @param the type of the third result
* @param the type of the fourth result
* @param the type of the fifth result
*/
@ValueEquality
public class DAG1x5
extends
AbstractPreparableTransformer1, DAG1x5.Prepared, DAG1x5>
implements
PreparableDAGTransformer, DAG1x5.Prepared, DAG1x5> {
private static final long serialVersionUID = 1;
// the DAGStructure stores the actual graph
private DAGStructure> _dag;
// keep track of what level of reduction we used to construct this DAG; this will be carried over to the prepared DAG
@IgnoredByValueEquality
// irrelevant given _dag and not (directly) externally observable
private Reducer.Level _reductionLevel = null; // null -> no reductions applied
// remember what executor should be used to prepare the DAG; this will also be "inherited" by the prepared DAG and
// used for inference
@IgnoredByValueEquality
// not a factor in the semantics of the DAG and not (directly) externally observable
private DAGExecutor _executor;
/**
* Creates a new DAG instance from the specified {@link DAGStructure} graph with the specified input. DAG objects
* have inputs because they can also serve as transformers within other DAGs. In the common case where the DAG is
* used directly and not embedded within another DAG, these inputs don't matter (and can simply be left as
* {@link com.linkedin.dagli.producer.MissingInput}s).
*
* By default, this DAG will use a {@link LocalDAGExecutor}; to use a different executor, use the
* {@link #withExecutor(DAGExecutor)} method. When this DAG is prepared, the DAG executor will be "inherited" by the
* resultant prepared DAG.
*
* @param dag the {@link DAGStructure} representing the actual graph of nodes and directed (parent/child) edges
* @param input1 the {@link Producer} providing the first input to the transformer
*/
DAG1x5(DAGStructure> dag, Producer input1) {
super(input1);
_dag = dag;
_executor = new LocalDAGExecutor();
}
/**
* Returns a DAG derived from this one that has been reduced to at least the specified level.
*
* If level is less than this DAG's current reduction level, {@code this} DAG is returned.
*
* Otherwise, this DAG is reduced, and the new, reduced DAG is returned.
*
* @param level the level of reduction desired; all reducers at or above this level will be applied
* @return a DAG that has been reduced to at least the specified level (possibly this same DAG)
*/
@Override
public DAG1x5 withReduction(Reducer.Level level) {
if (Reducer.Level.compare(level, _reductionLevel) >= 0) {
return this;
}
DeduplicatedDAG reduced = DAGReducer.reduce(new DeduplicatedDAG(_dag), level);
return clone(c -> {
c._dag = new DAGStructure<>(reduced);
c._reductionLevel = level;
});
}
@Override
public InternalAPI internalAPI() {
return new InternalAPI();
}
public class InternalAPI
extends
AbstractPreparableTransformer1, DAG1x5.Prepared, DAG1x5>.InternalAPI
implements
PreparableDAGTransformer.InternalAPI, DAG1x5.Prepared, DAG1x5> {
@Override
public DAGStructure> getDAGStructure() {
return _dag;
}
@Override
public Reducer.Level getReductionLevel() {
return _reductionLevel;
}
@Override
public DAGExecutor getDAGExecutor() {
return _executor;
}
@Override
public DAG1x5 getInstance() {
return DAG1x5.this;
}
}
@Override
protected Collection>> getGraphReducers() {
return Collections.singletonList(DAGTransformerReducer.INSTANCE);
}
@Override
protected boolean hasAlwaysConstantResult() {
return _dag._isAlwaysConstant;
}
/**
* Returns a copy of this DAG that will use the given {@link DAGExecutor} that will be used to execute this DAG.
*
* This executor will also be "inherited" by the resultant prepared DAG.
*
* @param executor the {@link DAGExecutor} to use
* @return a copy of this instance that will use the provided executor
*/
public DAG1x5 withExecutor(DAGExecutor executor) {
return clone(r -> r._executor = executor);
}
@Override
protected boolean hasIdempotentPreparer() {
return _dag._hasIdempotentPreparer;
}
/**
* Sets the parent of this transformer. The results of the parent node will be the input to the transformer.
*
* @param input1 the {@link Producer} providing the first input to the transformer
* @return a copy of this instance that will have the specified parent
*/
public DAG1x5 withInput(Producer input1) {
return super.withAllInputs(input1);
}
/**
* The {@link com.linkedin.dagli.preparer.Preparer} that prepares (trains) the DAG.
*
* @param the type of the first input
* @param the type of the first result
* @param the type of the second result
* @param the type of the third result
* @param the type of the fourth result
* @param the type of the fifth result
*/
private static class Preparer extends
AbstractBatchPreparer1, DAG1x5.Prepared> {
// the DAG being prepared
private final DAG1x5 _dag;
/**
* Creates a new instance.
*
* @param dag the DAG that will be prepared
*/
Preparer(DAG1x5 dag) {
_dag = dag;
}
@Override
@SuppressWarnings("unchecked")
// we know the returned type is correct by the semantics of executors
public PreparerResultMixed>, DAG1x5.Prepared> finish(
ObjectReader inputs) {
return (PreparerResultMixed) _dag._executor.internalAPI().prepareUnsafe(_dag, new ObjectReader[] { inputs });
}
@Override
public void process(A value1) {
// noop
}
}
@Override
protected Preparer1, DAG1x5.Prepared> getPreparer(
PreparerContext context) {
return new Preparer<>(this);
}
/**
* Prepares (trains) the DAG on the provided input data (examples), and applies it to this same data. The returned
* {@link Result} instance contains both the prepared DAG as well as the results from applying the DAG on the
* preparation data.
*
* The inputs to this method take the form of parallel sequences of values. Each sequence will provide the values
* for a particular {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG (the ordering of the
* {@link com.linkedin.dagli.placeholder.Placeholder}s in a DAG is determined by their
* ordering in the call to "DAG.withPlaceholders(...)...." that created it). They are "parallel" because, e.g. all the
* values corresponding to the fourth example will be the fourth value in their respective input sequences (all
* sequences must, of course, be equal in size).
*
* Note that the results from applying the DAG to the preparation data with this method are not necessarily the same
* results that would be obtained from calling the apply(...) method on prepared DAG with the same data. This is
* because certain transformers (e.g. KFoldCrossTrained and PreparedByGroup) intentionally transform training data in
* a different way than inference data. Please see the documentation of these transformers for details.
*
* @param values1 the sequence of values for the first
* {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG.
* @return a {@link Result} instance containing both the prepared DAG and the result of applying the DAG on the
* preparation data
*/
public Result prepareAndApply(Iterable values1) {
DAGExecutionResult, DAG1x5.Prepared> res =
_executor.internalAPI().prepareAndApplyUnsafe(this, new ObjectReader[] { ObjectReader.wrap(values1) });
return new Result<>((DAG1x5.Prepared) res.getPreparerResult()
.getPreparedTransformerForNewData(), res.getOutputs());
}
/**
* Prepares (trains) the DAG on the provided input data (examples). The resultant prepared (trained) DAG can then be
* applied to new examples (inference).
*
* The inputs to this method take the form of parallel sequences of values. Each sequence will provide the values
* for a particular {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG (the ordering of the
* {@link com.linkedin.dagli.placeholder.Placeholder}s in a DAG is determined by their
* ordering in the call to "DAG.withPlaceholders(...)...." that created it). They are "parallel" because, e.g. all the
* values corresponding to the fourth example will be the fourth value in their respective input sequences (all
* sequences must, of course, be equal in size).
*
* @param values1 the sequence of values for the first
* {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG.
* @return a prepared (trained) DAG that can be applied to new examples (inference)
*/
public DAG1x5.Prepared prepare(Iterable values1) {
return (DAG1x5.Prepared) _executor.internalAPI()
.prepareUnsafe(this, new ObjectReader[] { ObjectReader.wrap(values1) }).getPreparedTransformerForNewData();
}
/**
* {@link Result}s are returned by the DAG's prepareAndApply(...) methods and contain both the prepared (trained) DAG
* and the results of applying that DAG to the preparation (training) data.
*
* @param the type of the first input
* @param the type of the first result
* @param the type of the second result
* @param the type of the third result
* @param the type of the fourth result
* @param the type of the fifth result
*/
public static final class Result extends AbstractDAGResult5 {
// the prepared DAG
private final DAG1x5.Prepared _preparedDAG;
/**
* @return the {@link Prepared} DAG that was prepared (trained) on the provided preparation (training) data.
*/
public DAG1x5.Prepared getPreparedDAG() {
return _preparedDAG;
}
/**
* Creates a new instance.
*
* @param preparedDAG the prepared DAG to be stored in this instance
* @param results an array of {@link ObjectReader}s that contain each resultant output value of applying the DAG to
* the preparation (training) data
*/
Result(DAG1x5.Prepared preparedDAG, ObjectReader[] results) {
super(results);
_preparedDAG = preparedDAG;
}
}
/**
* Prepared DAGs (directed acyclic graphs) contain root nodes ({@link com.linkedin.dagli.placeholder.Placeholder}s and
* {@link com.linkedin.dagli.generator.Generator}s) and child nodes
* ({@link com.linkedin.dagli.transformer.PreparedTransformer}s) Unlike preparable DAGs, prepared DAGs do not contain
* {@link com.linkedin.dagli.transformer.PreparableTransformer}s nor {@link com.linkedin.dagli.view.TransformerView}s
* and can be applied to examples to transform them to some desired result (often an inference, such as a
* classification or regression) using the apply(...) (for single examples) or applyAll(...) (for multiple examples)
* methods.
*
* Prepared DAGs are also themselves {@link com.linkedin.dagli.transformer.PreparedTransformer}s, and can be used as
* such within another DAG.
*
* @param the type of the first input
* @param the type of the first result
* @param the type of the second result
* @param the type of the third result
* @param the type of the fourth result
* @param the type of the fifth result
*/
@ValueEquality
public static class Prepared extends
AbstractPreparedTransformer1, DAG1x5.Prepared> implements
PreparedDAGTransformer, DAG1x5.Prepared> {
private static final long serialVersionUID = 1;
// the DAGStructure stores the actual graph
private DAGStructure> _dag;
// keep track of what level of reduction we used to construct this DAG
@IgnoredByValueEquality
// irrelevant given _dag and not (directly) externally observable
private Reducer.Level _reductionLevel = null; // null -> no reductions applied
// the executor that will be used to apply the DAG to new examples
@IgnoredByValueEquality
// not a factor in the semantics of the DAG and not (directly) externally observable
private PreparedDAGExecutor _executor;
/**
* Creates a new DAG instance from the specified {@link DAGStructure} graph with the specified input. DAG objects
* have inputs because they can also serve as transformers within other DAGs. In the common case where the DAG is
* used directly and not embedded within another DAG, these inputs don't matter (and can simply be left as
* {@link com.linkedin.dagli.producer.MissingInput}s).
*
* By default, this DAG will use a {@link LocalDAGExecutor}; to use a different executor, use the
* {@link #withExecutor(DAGExecutor)} method.
*
* @param dag the {@link DAGStructure} representing the actual graph of nodes and directed (parent/child) edges
* @param input1 the {@link Producer} providing the first input to the transformer
*/
Prepared(DAGStructure> dag, Producer input1) {
super(input1);
_dag = dag;
_executor = new LocalDAGExecutor();
}
/**
* Returns a DAG derived from this one that has been reduced to at least the specified level.
*
* If level is less than this DAG's current reduction level, {@code this} DAG is returned.
*
* Otherwise, this DAG is reduced, and the new, reduced DAG is returned.
*
* @param level the level of reduction desired; all reducers at or above this level will be applied
* @return a DAG that has been reduced to at least the specified level (possibly this same DAG)
*/
@Override
public DAG1x5.Prepared withReduction(Reducer.Level level) {
if (Reducer.Level.compare(level, _reductionLevel) >= 0) {
return this;
}
DeduplicatedDAG reduced = DAGReducer.reduce(new DeduplicatedDAG(_dag), level);
return clone(c -> {
c._dag = new DAGStructure<>(reduced);
c._reductionLevel = level;
});
}
@Override
public InternalAPI internalAPI() {
return new InternalAPI();
}
public class InternalAPI extends
AbstractPreparedTransformer1, DAG1x5.Prepared>.InternalAPI
implements
PreparedDAGTransformer.InternalAPI, DAG1x5.Prepared> {
@Override
public DAGStructure> getDAGStructure() {
return _dag;
}
@Override
public Reducer.Level getReductionLevel() {
return _reductionLevel;
}
@Override
public PreparedDAGExecutor getDAGExecutor() {
return _executor;
}
@Override
public DAG1x5.Prepared getInstance() {
return DAG1x5.Prepared.this;
}
}
@Override
protected Collection>> getGraphReducers() {
return Collections.singletonList(DAGTransformerReducer.INSTANCE);
}
@Override
protected boolean hasAlwaysConstantResult() {
return _dag._isAlwaysConstant;
}
/**
* Returns a copy of this DAG that will use the given {@link PreparedDAGExecutor} that will be used to execute this DAG.
*
* @param executor the {@link DAGExecutor} to use
* @return a copy of this instance that will use the provided executor
*/
public DAG1x5.Prepared withExecutor(PreparedDAGExecutor executor) {
return clone(r -> r._executor = executor);
}
/**
* Sets the parent of this transformer. The results of the parent node will be the input to the transformer.
*
* @param input1 the {@link Producer} providing the first input to the transformer
* @return a copy of this instance that will have the specified parent
*/
public DAG1x5.Prepared withInput(Producer input1) {
return super.withAllInputs(input1);
}
/**
* Applies the DAG to the provided input data (examples), running the model on the data and producing the results as a
* {@link Result} instance.
*
* The inputs to this method take the form of parallel sequences of values. Each sequence will provide the values
* for a particular {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG (the ordering of the
* {@link com.linkedin.dagli.placeholder.Placeholder}s in a DAG is determined by their
* ordering in the call to "DAG.withPlaceholders(...)...." that created it). They are "parallel" because, e.g. all the
* values corresponding to the fourth example will be the fourth value in their respective input sequences (all
* sequences must, of course, be equal in size).
*
* @param values1 the sequence of values for the first
* {@link com.linkedin.dagli.placeholder.Placeholder} in the DAG.
* @return a {@link Result} instance containing the results of applying the DAG on the provided data
*/
public Result applyAll(Iterable values1) {
return new Result(_executor.internalAPI().applyUnsafe(this,
new ObjectReader[] { ObjectReader.wrap(values1) }));
}
@Override
public Tuple5 apply(A value1) {
try (
AutoCloseableArray> res =
new AutoCloseableArray<>(_executor.internalAPI().applyUnsafe(this,
new ObjectReader[] { ObjectReader.singleton(value1) }));
ObjectIterator resultIterator0 = res.get(0).iterator();
ObjectIterator resultIterator1 = res.get(1).iterator();
ObjectIterator resultIterator2 = res.get(2).iterator();
ObjectIterator resultIterator3 = res.get(3).iterator();
ObjectIterator resultIterator4 = res.get(4).iterator()) {
return (Tuple5) Tuple5.of(resultIterator0.next(), resultIterator1.next(),
resultIterator2.next(), resultIterator3.next(), resultIterator4.next());
}
}
/**
* Contains the results of applying the DAG to one or more examples, as produced by a call to applyAll(...).
*
* @param the type of the first result
* @param the type of the second result
* @param the type of the third result
* @param the type of the fourth result
* @param the type of the fifth result
*/
public static final class Result extends AbstractDAGResult5 {
/**
* Creates a new instance.
*
* @param results an array of {@link ObjectReader}s that contain each resultant output value of applying the DAG to
* the provided examples
*/
Result(ObjectReader[] results) {
super(results);
}
}
}
}
© 2015 - 2024 Weber Informatics LLC | Privacy Policy