Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

increase the default epochs to 1000 for linear regression #394

Merged
merged 2 commits into from
Aug 8, 2022
Merged

increase the default epochs to 1000 for linear regression #394

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
97f09dd
increase the default epochs to 1000 for linear regression
Zhangxunmt Aug 5, 2022
6a69747
make more parameters configuration in linear regression
Zhangxunmt Aug 8, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 14 additions & 2 deletions ...main/java/org/opensearch/ml/common/input/parameter/regression/LinearRegressionParams.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@ public class LinearRegressionParams implements MLAlgoParams {
public static final String DECAY_RATE_FIELD = "decay_rate";
public static final String EPOCHS_FIELD = "epochs";
public static final String BATCH_SIZE_FIELD = "batch_size";
public static final String LOGGING_INTERVAL_FIELD = "logging_interval";
public static final String SEED_FIELD = "seed";
public static final String TARGET_FIELD = "target";

Expand All @@ -58,11 +59,12 @@ public class LinearRegressionParams implements MLAlgoParams {
private Double decayRate;
private Integer epochs;
private Integer batchSize;
private Integer loggingInterval;
private Long seed;
private String target;

@Builder(toBuilder = true)
public LinearRegressionParams(ObjectiveType objectiveType, OptimizerType optimizerType, Double learningRate, MomentumType momentumType, Double momentumFactor, Double epsilon, Double beta1, Double beta2, Double decayRate, Integer epochs, Integer batchSize, Long seed, String target) {
public LinearRegressionParams(ObjectiveType objectiveType, OptimizerType optimizerType, Double learningRate, MomentumType momentumType, Double momentumFactor, Double epsilon, Double beta1, Double beta2, Double decayRate, Integer epochs, Integer batchSize, Integer loggingInterval, Long seed, String target) {
this.objectiveType = objectiveType;
this.optimizerType = optimizerType;
this.learningRate = learningRate;
Expand All @@ -74,6 +76,7 @@ public LinearRegressionParams(ObjectiveType objectiveType, OptimizerType optimiz
this.decayRate = decayRate;
this.epochs = epochs;
this.batchSize = batchSize;
this.loggingInterval = loggingInterval;
this.seed = seed;
this.target = target;
}
Expand All @@ -96,6 +99,7 @@ public LinearRegressionParams(StreamInput in) throws IOException {
this.decayRate = in.readOptionalDouble();
this.epochs = in.readOptionalInt();
this.batchSize = in.readOptionalInt();
this.loggingInterval = in.readOptionalInt();
this.seed = in.readOptionalLong();
this.target = in.readOptionalString();
}
Expand All @@ -112,6 +116,7 @@ public static MLAlgoParams parse(XContentParser parser) throws IOException {
Double decayRate = null;
Integer epochs = null;
Integer batchSize = null;
Integer loggingInterval = null;
Long seed = null;
String target = null;

Expand Down Expand Up @@ -154,6 +159,9 @@ public static MLAlgoParams parse(XContentParser parser) throws IOException {
case BATCH_SIZE_FIELD:
batchSize = parser.intValue(false);
break;
case LOGGING_INTERVAL_FIELD:
loggingInterval = parser.intValue(false);
break;
case SEED_FIELD:
seed = parser.longValue(false);
break;
Expand All @@ -165,7 +173,7 @@ public static MLAlgoParams parse(XContentParser parser) throws IOException {
break;
}
}
return new LinearRegressionParams(objective, optimizerType, learningRate, momentumType, momentumFactor, epsilon, beta1, beta2,decayRate, epochs, batchSize, seed, target);
return new LinearRegressionParams(objective, optimizerType, learningRate, momentumType, momentumFactor, epsilon, beta1, beta2,decayRate, epochs, batchSize, loggingInterval, seed, target);
}

@Override
Expand Down Expand Up @@ -201,6 +209,7 @@ public void writeTo(StreamOutput out) throws IOException {
out.writeOptionalDouble(decayRate);
out.writeOptionalInt(epochs);
out.writeOptionalInt(batchSize);
out.writeOptionalInt(loggingInterval);
out.writeOptionalLong(seed);
out.writeOptionalString(target);
}
Expand Down Expand Up @@ -241,6 +250,9 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
if (batchSize != null) {
builder.field(BATCH_SIZE_FIELD, batchSize);
}
if (loggingInterval != null) {
builder.field(LOGGING_INTERVAL_FIELD, loggingInterval);
}
if (seed != null) {
builder.field(SEED_FIELD, seed);
}
Expand Down
18 changes: 15 additions & 3 deletions ...rithms/src/main/java/org/opensearch/ml/engine/algorithms/regression/LinearRegression.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
import org.opensearch.ml.engine.utils.TribuoUtil;
import org.tribuo.MutableDataset;
import org.tribuo.Prediction;
import org.tribuo.Trainer;
import org.tribuo.math.StochasticGradientOptimiser;
import org.tribuo.math.optimisers.AdaDelta;
import org.tribuo.math.optimisers.AdaGrad;
Expand Down Expand Up @@ -58,15 +59,18 @@ public class LinearRegression implements Trainable, Predictable {
//RMSProp
private static final double DEFAULT_DECAY_RATE = 0.9;

private static final int DEFAULT_EPOCHS = 10;
private static final int DEFAULT_EPOCHS = 1000;
private static final int DEFAULT_INTERVAL = -1;
private static final int DEFAULT_BATCH_SIZE = 1;
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Can you help fix the hard coded issue in line 203

LinearSGDTrainer linearSGDTrainer = new LinearSGDTrainer(objective, optimiser, epochs, DEFAULT_INTERVAL, DEFAULT_BATCH_SIZE, seed);

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Sure, do you mean DEFAULT_INTERVAL, DEFAULT_BATCH_SIZE, seed these 3 hard coded ones?

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

yes

private static final Long DEFAULT_SEED = Trainer.DEFAULT_SEED;

private LinearRegressionParams parameters;
private StochasticGradientOptimiser optimiser;
private RegressionObjective objective;

private long seed = System.currentTimeMillis();
private int loggingInterval;
private int minibatchSize;
private long seed;

public LinearRegression() {}

Expand Down Expand Up @@ -177,6 +181,14 @@ private void validateParameters() {
if (parameters.getBatchSize() != null && parameters.getBatchSize() < 0) {
throw new IllegalArgumentException("MiniBatchSize should not be negative.");
}

if (parameters.getLoggingInterval() != null && parameters.getLoggingInterval() < -1) {
throw new IllegalArgumentException("Invalid Logging intervals");
}

loggingInterval = Optional.ofNullable(parameters.getLoggingInterval()).orElse(DEFAULT_INTERVAL);
minibatchSize = Optional.ofNullable(parameters.getBatchSize()).orElse(DEFAULT_BATCH_SIZE);
seed = Optional.ofNullable(parameters.getSeed()).orElse(DEFAULT_SEED);
}

@Override
Expand All @@ -200,7 +212,7 @@ public Model train(DataFrame dataFrame) {
MutableDataset<Regressor> trainDataset = TribuoUtil.generateDatasetWithTarget(dataFrame, new RegressionFactory(),
"Linear regression training data from opensearch", TribuoOutputType.REGRESSOR, parameters.getTarget());
Integer epochs = Optional.ofNullable(parameters.getEpochs()).orElse(DEFAULT_EPOCHS);
LinearSGDTrainer linearSGDTrainer = new LinearSGDTrainer(objective, optimiser, epochs, DEFAULT_INTERVAL, DEFAULT_BATCH_SIZE, seed);
LinearSGDTrainer linearSGDTrainer = new LinearSGDTrainer(objective, optimiser, epochs, loggingInterval, minibatchSize, seed);
org.tribuo.Model<Regressor> regressionModel = linearSGDTrainer.train(trainDataset);
Model model = new Model();
model.setName(FunctionName.LINEAR_REGRESSION.name());
Expand Down