/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package hivemall.model;
import hivemall.model.WeightValue.WeightValueParamsF1;
import hivemall.model.WeightValue.WeightValueParamsF2;
import hivemall.model.WeightValue.WeightValueWithCovar;
import hivemall.utils.collections.IMapIterator;
import hivemall.utils.hadoop.HiveUtils;
import hivemall.utils.lang.Copyable;
import hivemall.utils.math.MathUtils;
import java.util.Arrays;
import javax.annotation.Nonnull;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
public final class DenseModel extends AbstractPredictionModel {
private static final Log logger = LogFactory.getLog(DenseModel.class);
private int size;
private float[] weights;
private float[] covars;
// optional values for adagrad
private float[] sum_of_squared_gradients;
// optional value for adadelta
private float[] sum_of_squared_delta_x;
// optional value for adagrad+rda
private float[] sum_of_gradients;
// optional value for MIX
private short[] clocks;
private byte[] deltaUpdates;
public DenseModel(int ndims) {
this(ndims, false);
}
public DenseModel(int ndims, boolean withCovar) {
super();
int size = ndims + 1;
this.size = size;
this.weights = new float[size];
if (withCovar) {
float[] covars = new float[size];
Arrays.fill(covars, 1f);
this.covars = covars;
} else {
this.covars = null;
}
this.sum_of_squared_gradients = null;
this.sum_of_squared_delta_x = null;
this.sum_of_gradients = null;
this.clocks = null;
this.deltaUpdates = null;
}
@Override
protected boolean isDenseModel() {
return true;
}
@Override
public boolean hasCovariance() {
return covars != null;
}
@Override
public void configureParams(boolean sum_of_squared_gradients, boolean sum_of_squared_delta_x,
boolean sum_of_gradients) {
if (sum_of_squared_gradients) {
this.sum_of_squared_gradients = new float[size];
}
if (sum_of_squared_delta_x) {
this.sum_of_squared_delta_x = new float[size];
}
if (sum_of_gradients) {
this.sum_of_gradients = new float[size];
}
}
@Override
public void configureClock() {
if (clocks == null) {
this.clocks = new short[size];
this.deltaUpdates = new byte[size];
}
}
@Override
public boolean hasClock() {
return clocks != null;
}
@Override
public void resetDeltaUpdates(int feature) {
deltaUpdates[feature] = 0;
}
private void ensureCapacity(final int index) {
if (index >= size) {
int bits = MathUtils.bitsRequired(index);
int newSize = (1 << bits) + 1;
int oldSize = size;
logger.info("Expands internal array size from " + oldSize + " to " + newSize + " ("
+ bits + " bits)");
this.size = newSize;
this.weights = Arrays.copyOf(weights, newSize);
if (covars != null) {
this.covars = Arrays.copyOf(covars, newSize);
Arrays.fill(covars, oldSize, newSize, 1.f);
}
if (sum_of_squared_gradients != null) {
this.sum_of_squared_gradients = Arrays.copyOf(sum_of_squared_gradients, newSize);
}
if (sum_of_squared_delta_x != null) {
this.sum_of_squared_delta_x = Arrays.copyOf(sum_of_squared_delta_x, newSize);
}
if (sum_of_gradients != null) {
this.sum_of_gradients = Arrays.copyOf(sum_of_gradients, newSize);
}
if (clocks != null) {
this.clocks = Arrays.copyOf(clocks, newSize);
this.deltaUpdates = Arrays.copyOf(deltaUpdates, newSize);
}
}
}
@SuppressWarnings("unchecked")
@Override
public <T extends IWeightValue> T get(Object feature) {
final int i = HiveUtils.parseInt(feature);
if (i >= size) {
return null;
}
if (sum_of_squared_gradients != null) {
if (sum_of_squared_delta_x != null) {
return (T) new WeightValueParamsF2(weights[i], sum_of_squared_gradients[i],
sum_of_squared_delta_x[i]);
} else if (sum_of_gradients != null) {
return (T) new WeightValueParamsF2(weights[i], sum_of_squared_gradients[i],
sum_of_gradients[i]);
} else {
return (T) new WeightValueParamsF1(weights[i], sum_of_squared_gradients[i]);
}
} else if (covars != null) {
return (T) new WeightValueWithCovar(weights[i], covars[i]);
} else {
return (T) new WeightValue(weights[i]);
}
}
@Override
public <T extends IWeightValue> void set(Object feature, T value) {
int i = HiveUtils.parseInt(feature);
ensureCapacity(i);
float weight = value.get();
weights[i] = weight;
float covar = 1.f;
boolean hasCovar = value.hasCovariance();
if (hasCovar) {
covar = value.getCovariance();
covars[i] = covar;
}
if (sum_of_squared_gradients != null) {
sum_of_squared_gradients[i] = value.getSumOfSquaredGradients();
}
if (sum_of_squared_delta_x != null) {
sum_of_squared_delta_x[i] = value.getSumOfSquaredDeltaX();
}
if (sum_of_gradients != null) {
sum_of_gradients[i] = value.getSumOfGradients();
}
short clock = 0;
int delta = 0;
if (clocks != null && value.isTouched()) {
clock = (short) (clocks[i] + 1);
clocks[i] = clock;
delta = deltaUpdates[i] + 1;
assert (delta > 0) : delta;
deltaUpdates[i] = (byte) delta;
}
onUpdate(i, weight, covar, clock, delta, hasCovar);
}
@Override
public void delete(@Nonnull Object feature) {
final int i = HiveUtils.parseInt(feature);
if (i >= size) {
return;
}
weights[i] = 0.f;
if (covars != null) {
covars[i] = 1.f;
}
if (sum_of_squared_gradients != null) {
sum_of_squared_gradients[i] = 0.f;
}
if (sum_of_squared_delta_x != null) {
sum_of_squared_delta_x[i] = 0.f;
}
if (sum_of_gradients != null) {
sum_of_gradients[i] = 0.f;
}
// avoid clock/delta
}
@Override
public float getWeight(Object feature) {
int i = HiveUtils.parseInt(feature);
if (i >= size) {
return 0f;
}
return weights[i];
}
@Override
public float getCovariance(Object feature) {
int i = HiveUtils.parseInt(feature);
if (i >= size) {
return 1f;
}
return covars[i];
}
@Override
protected void _set(Object feature, float weight, short clock) {
int i = ((Integer) feature).intValue();
ensureCapacity(i);
weights[i] = weight;
clocks[i] = clock;
deltaUpdates[i] = 0;
}
@Override
protected void _set(Object feature, float weight, float covar, short clock) {
int i = ((Integer) feature).intValue();
ensureCapacity(i);
weights[i] = weight;
covars[i] = covar;
clocks[i] = clock;
deltaUpdates[i] = 0;
}
@Override
public int size() {
return size;
}
@Override
public boolean contains(Object feature) {
int i = HiveUtils.parseInt(feature);
if (i >= size) {
return false;
}
float w = weights[i];
return w != 0.f;
}
@SuppressWarnings("unchecked")
@Override
public <K, V extends IWeightValue> IMapIterator<K, V> entries() {
return (IMapIterator<K, V>) new Itr();
}
private final class Itr implements IMapIterator<Number, IWeightValue> {
private int cursor;
private final WeightValueWithCovar tmpWeight;
private Itr() {
this.cursor = -1;
this.tmpWeight = new WeightValueWithCovar();
}
@Override
public boolean hasNext() {
return cursor < size;
}
@Override
public int next() {
++cursor;
if (!hasNext()) {
return -1;
}
return cursor;
}
@Override
public Integer getKey() {
return cursor;
}
@Override
public IWeightValue getValue() {
if (covars == null) {
float w = weights[cursor];
WeightValue v = new WeightValue(w);
v.setTouched(w != 0f);
return v;
} else {
float w = weights[cursor];
float cov = covars[cursor];
WeightValueWithCovar v = new WeightValueWithCovar(w, cov);
v.setTouched(w != 0.f || cov != 1.f);
return v;
}
}
@Override
public <T extends Copyable<IWeightValue>> void getValue(T probe) {
float w = weights[cursor];
tmpWeight.value = w;
float cov = 1.f;
if (covars != null) {
cov = covars[cursor];
tmpWeight.setCovariance(cov);
}
tmpWeight.setTouched(w != 0.f || cov != 1.f);
probe.copyFrom(tmpWeight);
}
}
}