tech.tablesaw.api.NumericColumn Maven / Gradle / Ivy
package tech.tablesaw.api;
import static tech.tablesaw.aggregate.AggregateFunctions.geometricMean;
import static tech.tablesaw.aggregate.AggregateFunctions.kurtosis;
import static tech.tablesaw.aggregate.AggregateFunctions.max;
import static tech.tablesaw.aggregate.AggregateFunctions.mean;
import static tech.tablesaw.aggregate.AggregateFunctions.median;
import static tech.tablesaw.aggregate.AggregateFunctions.min;
import static tech.tablesaw.aggregate.AggregateFunctions.populationVariance;
import static tech.tablesaw.aggregate.AggregateFunctions.product;
import static tech.tablesaw.aggregate.AggregateFunctions.quadraticMean;
import static tech.tablesaw.aggregate.AggregateFunctions.quartile1;
import static tech.tablesaw.aggregate.AggregateFunctions.quartile3;
import static tech.tablesaw.aggregate.AggregateFunctions.range;
import static tech.tablesaw.aggregate.AggregateFunctions.skewness;
import static tech.tablesaw.aggregate.AggregateFunctions.stdDev;
import static tech.tablesaw.aggregate.AggregateFunctions.sum;
import static tech.tablesaw.aggregate.AggregateFunctions.sumOfLogs;
import static tech.tablesaw.aggregate.AggregateFunctions.sumOfSquares;
import static tech.tablesaw.aggregate.AggregateFunctions.variance;
import static tech.tablesaw.columns.numbers.NumberPredicates.isMissing;
import static tech.tablesaw.columns.numbers.NumberPredicates.isNotMissing;
import java.util.Arrays;
import java.util.Optional;
import java.util.function.BiPredicate;
import java.util.function.DoubleBinaryOperator;
import java.util.function.DoubleFunction;
import java.util.function.DoublePredicate;
import org.apache.commons.math3.exception.NotANumberException;
import org.apache.commons.math3.stat.correlation.KendallsCorrelation;
import org.apache.commons.math3.stat.correlation.PearsonsCorrelation;
import org.apache.commons.math3.stat.correlation.SpearmansCorrelation;
import it.unimi.dsi.fastutil.doubles.DoubleComparator;
import it.unimi.dsi.fastutil.doubles.DoubleRBTreeSet;
import tech.tablesaw.aggregate.AggregateFunctions;
import tech.tablesaw.aggregate.NumericAggregateFunction;
import tech.tablesaw.columns.Column;
import tech.tablesaw.columns.numbers.NumberFilters;
import tech.tablesaw.columns.numbers.NumberMapFunctions;
import tech.tablesaw.columns.numbers.NumberRollingColumn;
import tech.tablesaw.columns.numbers.Stats;
import tech.tablesaw.filtering.predicates.DoubleBiPredicate;
import tech.tablesaw.filtering.predicates.DoubleRangePredicate;
import tech.tablesaw.selection.BitmapBackedSelection;
import tech.tablesaw.selection.Selection;
public interface NumericColumn extends Column, NumberMapFunctions, NumberFilters {
@Override
default boolean isEmpty() {
return size() == 0;
}
@Override
default double[] asDoubleArray() {
final double[] output = new double[size()];
for (int i = 0; i < size(); i++) {
output[i] = getDouble(i);
}
return output;
}
@Override
default Selection eval(final DoublePredicate predicate) {
final Selection bitmap = new BitmapBackedSelection();
for (int idx = 0; idx < size(); idx++) {
final double next = getDouble(idx);
if (predicate.test(next)) {
bitmap.add(idx);
}
}
return bitmap;
}
@Override
default Selection eval(final DoubleBiPredicate predicate, final DoubleColumn otherColumn) {
final Selection selection = new BitmapBackedSelection();
for (int idx = 0; idx < size(); idx++) {
if (predicate.test(getDouble(idx), otherColumn.getDouble(idx))) {
selection.add(idx);
}
}
return selection;
}
@Override
default Selection eval(final DoubleBiPredicate predicate, final Number number) {
final double value = number.doubleValue();
final Selection bitmap = new BitmapBackedSelection();
for (int idx = 0; idx < size(); idx++) {
final double next = getDouble(idx);
if (predicate.test(next, value)) {
bitmap.add(idx);
}
}
return bitmap;
}
@Override
default Selection eval(final BiPredicate predicate, final Number number) {
final double value = number.doubleValue();
final Selection bitmap = new BitmapBackedSelection();
for (int idx = 0; idx < size(); idx++) {
final double next = getDouble(idx);
if (predicate.test(next, value)) {
bitmap.add(idx);
}
}
return bitmap;
}
@Override
default Selection eval(final DoubleRangePredicate predicate, final Number rangeStart, final Number rangeEnd) {
final double start = rangeStart.doubleValue();
final double end = rangeEnd.doubleValue();
final Selection bitmap = new BitmapBackedSelection();
for (int idx = 0; idx < size(); idx++) {
final double next = getDouble(idx);
if (predicate.test(next, start, end)) {
bitmap.add(idx);
}
}
return bitmap;
}
@Override
default Selection isIn(final Number... numbers) {
return isIn(Arrays.stream(numbers).mapToDouble(Number::doubleValue).toArray());
}
@Override
default Selection isIn(final double... doubles) {
final Selection results = new BitmapBackedSelection();
final DoubleRBTreeSet doubleSet = new DoubleRBTreeSet(doubles);
for (int i = 0; i < size(); i++) {
if (doubleSet.contains(getDouble(i))) {
results.add(i);
}
}
return results;
}
@Override
default Selection isNotIn(final Number... numbers) {
final Selection results = new BitmapBackedSelection();
results.addRange(0, size());
results.andNot(isIn(numbers));
return results;
}
@Override
default Selection isNotIn(final double... doubles) {
final Selection results = new BitmapBackedSelection();
results.addRange(0, size());
results.andNot(isIn(doubles));
return results;
}
@Override
default Selection isMissing() {
return eval(isMissing);
}
@Override
default Selection isNotMissing() {
return eval(isNotMissing);
}
/**
* Counts the number of rows satisfying predicate
*
* @param test the predicate
* @return the number of rows satisfying the predicate
*/
default int count(DoublePredicate test) {
return count(test, size());
}
/**
* Counts the number of rows satisfying predicate, but only upto the max value
*
* @param test the predicate
* @param max the maximum number of rows to count
* @return the number of rows satisfying the predicate
*/
default int count(DoublePredicate test, int max) {
int count = 0;
for (int i = 0; i < size(); i++) {
if (test.test(getDouble(i))) {
count++;
if (count >= max) {
return count;
}
}
}
return count;
}
/**
* Returns the maximum row according to the provided Comparator
*
* @param comp
* @return the maximum row
*/
default Optional max(DoubleComparator comp) {
boolean first = true;
double d1 = 0.0;
for (int i = 0; i < size(); i++) {
double d2 = getDouble(i);
if (first) {
d1 = d2;
first = false;
} else if (comp.compare(d1, d2) < 0) {
d1 = d2;
}
}
return (first ? Optional.empty() : Optional.of(d1));
}
/**
* Returns the minimum row according to the provided Comparator
*
* @param comp
* @return the minimum row
*/
default Optional min(DoubleComparator comp) {
boolean first = true;
double d1 = 0.0;
for (int i = 0; i < size(); i++) {
double d2 = getDouble(i);
if (first) {
d1 = d2;
first = false;
} else if (comp.compare(d1, d2) > 0) {
d1 = d2;
}
}
return (first ? Optional.empty() : Optional.of(d1));
}
/**
* Reduction with binary operator and initial value
*
* @param initial initial value
* @param op the operator
* @return the result of reducing initial value and all rows with operator
*/
default double reduce(double initial, DoubleBinaryOperator op) {
double acc = initial;
for (int i = 0; i < size(); i++) {
acc = op.applyAsDouble(acc, getDouble(i));
}
return acc;
}
/**
* Reduction with binary operator
*
* @param op the operator
* @return Optional with the result of reducing all rows with operator
*/
default Optional reduce(DoubleBinaryOperator op) {
boolean first = true;
double acc = 0.0;
for (int i = 0; i < size(); i++) {
double d = getDouble(i);
if (first) {
acc = d;
first = false;
} else {
acc = op.applyAsDouble(acc, d);
}
}
return (first ? Optional.empty() : Optional.of(acc));
}
/**
* Maps the function across all rows, appending the results to the provided Column
*
* @param fun function to map
* @param into Column to which results are appended
* @return the provided Column, to which results are appended
*/
default Column mapInto(DoubleFunction fun, Column into) {
for (int i = 0; i < size(); i++) {
try {
into.append(fun.apply(getDouble(i)));
} catch (Exception e) {
into.appendMissing();
}
}
return into;
}
@Override
default NumericColumn where(final Selection selection) {
return (NumericColumn) subset(selection.toArray());
}
/**
* Summarizes the data in this column for all rows where the current value matches the selection criteria
*
* Example:
* myColumn.summarize(myColumn.isLessThan(100), AggregateFunctions.count);
*/
default Double summarize(Selection selection, NumericAggregateFunction function) {
NumericColumn column = where(selection);
return function.summarize(column);
}
// Reduce functions applied to the whole column
default double sum() {
return sum.summarize(this);
}
default double product() {
return product.summarize(this);
}
default double mean() {
return mean.summarize(this);
}
default double median() {
return median.summarize(this);
}
default double quartile1() {
return quartile1.summarize(this);
}
default double quartile3() {
return quartile3.summarize(this);
}
default double percentile(double percentile) {
return AggregateFunctions.percentile(this, percentile);
}
default double range() {
return range.summarize(this);
}
default double max() {
return max.summarize(this);
}
default double min() {
return min.summarize(this);
}
default double variance() {
return variance.summarize(this);
}
default double populationVariance() {
return populationVariance.summarize(this);
}
default double standardDeviation() {
return stdDev.summarize(this);
}
default double sumOfLogs() {
return sumOfLogs.summarize(this);
}
default double sumOfSquares() {
return sumOfSquares.summarize(this);
}
default double geometricMean() {
return geometricMean.summarize(this);
}
/**
* Returns the quadraticMean, aka the root-mean-square, for all values in this column
*/
default double quadraticMean() {
return quadraticMean.summarize(this);
}
default double kurtosis() {
return kurtosis.summarize(this);
}
default double skewness() {
return skewness.summarize(this);
}
/**
* Returns the pearson's correlation between the receiver and the otherColumn
**/
default double pearsons(NumericColumn otherColumn) {
double[] x = asDoubleArray();
double[] y = otherColumn.asDoubleArray();
return new PearsonsCorrelation().correlation(x, y);
}
/**
* Returns the Spearman's Rank correlation between the receiver and the otherColumn
*
* @param otherColumn A NumberColumn with no missing values
* @throws NotANumberException if either column contains any missing values
**/
default double spearmans(NumericColumn otherColumn) {
double[] x = asDoubleArray();
double[] y = otherColumn.asDoubleArray();
return new SpearmansCorrelation().correlation(x, y);
}
/**
* Returns the Kendall's Tau Rank correlation between the receiver and the otherColumn
**/
default double kendalls(NumericColumn otherColumn) {
double[] x = asDoubleArray();
double[] y = otherColumn.asDoubleArray();
return new KendallsCorrelation().correlation(x, y);
}
default Table summary() {
return stats().asTable();
}
default Stats stats() {
return Stats.create(this);
}
default NumberRollingColumn rolling(final int windowSize) {
return new NumberRollingColumn(this, windowSize);
}
@Override
default NumericColumn lead(final int n) {
final NumericColumn numberColumn = lag(-n);
numberColumn.setName(name() + " lead(" + n + ")");
return numberColumn;
}
NumericColumn lag(final int n);
double getDouble(int index);
/**
* Returns a new LongColumn containing a value for each value in this column
*
* The exact behavior when overridden depends on the type of the receiver (LongColumn, FloatColumn, etc.)
*
* In this version, the result is a copy of the original
*/
default LongColumn asLongColumn() {
return (LongColumn) this.copy();
}
/**
* Returns a new IntColumn containing a value for each value in this column
*
* The exact behavior when overridden depends on the type of the receiver (LongColumn, FloatColumn, etc.)
*
* In this version, the result is a copy of the original
*/
default IntColumn asIntColumn() {
return (IntColumn) this.copy();
}
/**
* Returns a new FloatColumn containing a value for each value in this column
*
* The exact behavior when overridden depends on the type of the receiver (LongColumn, FloatColumn, etc.)
*
* In this version, the result is a copy of the original
*/
default FloatColumn asFloatColumn() {
return (FloatColumn) this.copy();
}
/**
* Returns a new DoubleColumn containing a value for each value in this column
*
* The exact behavior when overridden depends on the type of the receiver (LongColumn, FloatColumn, etc.)
*
* In this version, the result is a copy of the original
*/
default DoubleColumn asDoubleColumn() {
return (DoubleColumn) this.copy();
}
default ShortColumn asShortColumn() {
return (ShortColumn) this.copy();
}
StringColumn asStringColumn();
}