hex.ScoringInfo Maven / Gradle / Ivy
package hex;
import org.joda.time.format.DateTimeFormat;
import org.joda.time.format.DateTimeFormatter;
import water.AutoBuffer;
import water.H2O;
import water.Iced;
import water.util.PrettyPrint;
import water.util.TwoDimTable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Comparator;
import java.util.List;
/**
* Lightweight scoring history snapshot, for things like displaying the scoring history.
*/
public class ScoringInfo extends Iced {
public long time_stamp_ms; //absolute time the model metrics were computed
public long total_training_time_ms; //total training time until this scoring event (including checkpoints)
public long total_scoring_time_ms; //total scoring time until this scoring event (including checkpoints)
public long total_setup_time_ms; //total setup time until this scoring event (including checkpoints)
public long this_scoring_time_ms; //scoring time for this scoring event (only)
public boolean is_classification;
public boolean is_autoencoder;
public AUC2 training_AUC;
public AUC2 validation_AUC;
public boolean validation;
public boolean cross_validation;
public ScoreKeeper scored_train = new ScoreKeeper();
public ScoreKeeper scored_valid = new ScoreKeeper();
public ScoreKeeper scored_xval = new ScoreKeeper();
public VarImp variable_importances;
public interface HasEpochs{ public double epoch_counter(); }
public interface HasSamples { public double training_samples(); public long score_training_samples(); public long score_validation_samples(); }
public interface HasIterations { public int iterations(); }
/**
* Add a new ScoringInfo to the given array and return the new array. Note: this has no side effects.
* @param scoringInfo
*/
public static ScoringInfo[] prependScoringInfo(ScoringInfo scoringInfo, ScoringInfo[] scoringInfos) {
if (scoringInfos == null) {
return new ScoringInfo[]{ scoringInfo };
} else {
ScoringInfo[] bigger = new ScoringInfo[scoringInfos.length + 1];
System.arraycopy(scoringInfos, 0, bigger, 0, scoringInfos.length);
bigger[bigger.length - 1] = scoringInfo;
return bigger;
}
}
/** For a given array of ScoringInfo return an array of the cross-validation, validation or training ScoreKeepers, as available. */
public static ScoreKeeper[] scoreKeepers(ScoringInfo[] scoring_history) {
ScoreKeeper[] sk = new ScoreKeeper[scoring_history.length];
for (int i=0;i comparator(final ScoreKeeper.StoppingMetric criterion) {
return new Comparator() {
@Override
public int compare(ScoringInfo o1, ScoringInfo o2) {
boolean moreIsBetter = ScoreKeeper.moreIsBetter(criterion);
if (!moreIsBetter)
return (int)Math.signum(o2.metric(criterion) - o1.metric(criterion));
else
return (int)Math.signum(o1.metric(criterion) - o2.metric(criterion));
}
};
}
/**
* Sort an array of ScoringInfo based on a stopping criterion / metric. Uses
* cross-validation or validation metrics if available, otherwise falls back to training
* metrics. Understands whether more is better for the given criterion and will order
* the array so that the best models are last
* @param scoringInfos array of ScoringInfo to sort
* @param criterion scalar model metric / stopping criterion by which to sort
*/
public static void sort(ScoringInfo[] scoringInfos, ScoreKeeper.StoppingMetric criterion) {
if (null == scoringInfos) return;
if (scoringInfos.length == 0) return;
// handle StoppingMetric.AUTO
if (criterion == ScoreKeeper.StoppingMetric.AUTO)
criterion = scoringInfos[0].is_classification ? ScoreKeeper.StoppingMetric.logloss : scoringInfos[0].is_autoencoder ? ScoreKeeper.StoppingMetric.MSE : ScoreKeeper.StoppingMetric.deviance;
Arrays.sort(scoringInfos, ScoringInfo.comparator(criterion));
}
/**
* Create a TwoDimTable to display the scoring history from an array of scoringInfo.
* @param scoringInfos array of ScoringInfo to render
* @param hasValidation do we have validation metrics?
* @param hasCrossValidation do we have cross-validation metrics?
* @param modelCategory the category for the model or models
* @param isAutoencoder is the model or are the models autoencoders?
* @return
*/
public static TwoDimTable createScoringHistoryTable(ScoringInfo[] scoringInfos, boolean hasValidation, boolean hasCrossValidation, ModelCategory modelCategory, boolean isAutoencoder) {
boolean hasEpochs = (scoringInfos instanceof HasEpochs[]);
boolean hasSamples = (scoringInfos instanceof HasSamples[]);
boolean hasIterations = (scoringInfos instanceof HasIterations[]);
boolean isClassifier = (modelCategory == ModelCategory.Binomial || modelCategory == ModelCategory.Multinomial);
List colHeaders = new ArrayList<>();
List colTypes = new ArrayList<>();
List colFormat = new ArrayList<>();
colHeaders.add("Timestamp"); colTypes.add("string"); colFormat.add("%s");
colHeaders.add("Duration"); colTypes.add("string"); colFormat.add("%s");
if (hasSamples) { colHeaders.add("Training Speed"); colTypes.add("string"); colFormat.add("%s"); }
if (hasEpochs) { colHeaders.add("Epochs"); colTypes.add("double"); colFormat.add("%.5f"); }
if (hasIterations) { colHeaders.add("Iterations"); colTypes.add("int"); colFormat.add("%d"); }
if (hasSamples) { colHeaders.add("Samples"); colTypes.add("double"); colFormat.add("%f"); }
colHeaders.add("Training MSE"); colTypes.add("double"); colFormat.add("%.5f");
if (modelCategory == ModelCategory.Regression) {
colHeaders.add("Training Deviance"); colTypes.add("double"); colFormat.add("%.5f");
}
if (!isAutoencoder) {
colHeaders.add("Training R^2"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Training LogLoss"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Training AUC"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Training Lift"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Training Classification Error"); colTypes.add("double"); colFormat.add("%.5f");
}
if (hasValidation) {
colHeaders.add("Validation MSE"); colTypes.add("double"); colFormat.add("%.5f");
if (modelCategory == ModelCategory.Regression) {
colHeaders.add("Validation Deviance"); colTypes.add("double"); colFormat.add("%.5f");
}
if (!isAutoencoder) {
colHeaders.add("Validation R^2"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Validation LogLoss"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Validation AUC"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Validation Lift"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Validation Classification Error"); colTypes.add("double"); colFormat.add("%.5f");
}
} // (hasValidation)
if (hasCrossValidation) {
colHeaders.add("Cross-Validation MSE"); colTypes.add("double"); colFormat.add("%.5f");
if (modelCategory == ModelCategory.Regression) {
colHeaders.add("Cross-Validation Deviance"); colTypes.add("double"); colFormat.add("%.5f");
}
if (!isAutoencoder) {
colHeaders.add("Cross-Validation R^2"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Cross-Validation LogLoss"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Cross-Validation AUC"); colTypes.add("double"); colFormat.add("%.5f");
}
if (modelCategory == ModelCategory.Binomial) {
colHeaders.add("Cross-Validation Lift"); colTypes.add("double"); colFormat.add("%.5f");
}
if (isClassifier) {
colHeaders.add("Cross-Validation Classification Error"); colTypes.add("double"); colFormat.add("%.5f");
}
} // (hasCrossValidation)
final int rows = scoringInfos == null ? 0 : scoringInfos.length;
String[] s = new String[0];
TwoDimTable table = new TwoDimTable(
"Scoring History", null,
new String[rows],
colHeaders.toArray(s),
colTypes.toArray(s),
colFormat.toArray(s),
"");
int row = 0;
if (null == scoringInfos)
return table;
for (ScoringInfo si : scoringInfos) {
int col = 0;
assert (row < table.getRowDim());
assert (col < table.getColDim());
DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
table.set(row, col++, fmt.print(si.time_stamp_ms));
table.set(row, col++, PrettyPrint.msecs(si.total_training_time_ms, true));
if (hasSamples) {
// Log.info("1st speed: (samples: " + si.training_samples + ", total_run_time: " + si.total_training_time_ms + ", total_scoring_time: " + si.total_scoring_time_ms + ", total_setup_time: " + si.total_setup_time_ms + ")");
int speed = (int) (((HasSamples)si).training_samples() / ((si.total_training_time_ms - si.total_scoring_time_ms - si.total_setup_time_ms) / 1e3));
assert (speed >= 0) : "Speed should not be negative! " + speed + " = (int)(" + ((HasSamples)si).training_samples() + "/((" + si.total_training_time_ms + "-" + si.total_scoring_time_ms + "-" + si.total_setup_time_ms + ")/1e3)";
table.set(row, col++, si.total_training_time_ms == 0 ? null : (String.format("%d", speed) + " rows/sec"));
}
if (hasEpochs) table.set(row, col++, ((HasEpochs)si).epoch_counter());
if (hasIterations) table.set(row, col++, ((HasIterations)si).iterations());
if (hasSamples) table.set(row, col++, ((HasSamples)si).training_samples());
table.set(row, col++, si.scored_train != null ? si.scored_train._mse : Double.NaN);
if (modelCategory == ModelCategory.Regression) {
table.set(row, col++, si.scored_train != null ? si.scored_train._mean_residual_deviance : Double.NaN);
}
if (!isAutoencoder) {
table.set(row, col++, si.scored_train != null ? si.scored_train._r2 : Double.NaN);
}
if (isClassifier) {
table.set(row, col++, si.scored_train != null ? si.scored_train._logloss : Double.NaN);
}
if (modelCategory == ModelCategory.Binomial) {
table.set(row, col++, si.training_AUC != null ? si.training_AUC._auc : Double.NaN);
table.set(row, col++, si.scored_train != null ? si.scored_train._lift : Double.NaN);
}
if (isClassifier) {
table.set(row, col++, si.scored_train != null ? si.scored_train._classError : Double.NaN);
}
if (hasValidation) {
table.set(row, col++, si.scored_valid != null ? si.scored_valid._mse : Double.NaN);
if (modelCategory == ModelCategory.Regression) {
table.set(row, col++, si.scored_valid != null ? si.scored_valid._mean_residual_deviance : Double.NaN);
}
if (!isAutoencoder) {
table.set(row, col++, si.scored_valid != null ? si.scored_valid._r2 : Double.NaN);
}
if (isClassifier) {
table.set(row, col++, si.scored_valid != null ? si.scored_valid._logloss : Double.NaN);
}
if (modelCategory == ModelCategory.Binomial) {
table.set(row, col++, si.validation_AUC != null ? si.validation_AUC._auc : Double.NaN);
table.set(row, col++, si.scored_valid != null ? si.scored_valid._lift : Double.NaN);
}
if (isClassifier) {
table.set(row, col, si.scored_valid != null ? si.scored_valid._classError : Double.NaN);
}
} // hasValidation
if (hasCrossValidation) {
table.set(row, col++, si.scored_xval != null ? si.scored_xval._mse : Double.NaN);
if (modelCategory == ModelCategory.Regression) {
table.set(row, col++, si.scored_xval != null ? si.scored_xval._mean_residual_deviance : Double.NaN);
}
if (!isAutoencoder) {
table.set(row, col++, si.scored_xval != null ? si.scored_xval._r2 : Double.NaN);
}
if (isClassifier) {
table.set(row, col++, si.scored_xval != null ? si.scored_xval._logloss : Double.NaN);
}
if (modelCategory == ModelCategory.Binomial) {
table.set(row, col++, si.validation_AUC != null ? si.validation_AUC._auc : Double.NaN);
table.set(row, col++, si.scored_xval != null ? si.scored_xval._lift : Double.NaN);
}
if (isClassifier) {
table.set(row, col, si.scored_xval != null ? si.scored_xval._classError : Double.NaN);
}
} // hasCrossValidation
row++;
}
return table;
}
public ScoringInfo deep_clone() {
AutoBuffer ab = new AutoBuffer();
this.write(ab);
ab.flipForReading();
return (ScoringInfo) new ScoringInfo().read(ab);
}
}
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