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org.codelibs.elasticsearch.taste.similarity.AbstractSimilarity Maven / Gradle / Ivy
/**
* 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 org.codelibs.elasticsearch.taste.similarity;
import java.util.Collection;
import java.util.concurrent.Callable;
import org.codelibs.elasticsearch.taste.common.RefreshHelper;
import org.codelibs.elasticsearch.taste.common.Refreshable;
import org.codelibs.elasticsearch.taste.common.Weighting;
import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.model.PreferenceArray;
import com.google.common.base.Preconditions;
/** Abstract superclass encapsulating functionality that is common to most implementations in this package. */
abstract class AbstractSimilarity extends AbstractItemSimilarity implements
UserSimilarity {
private PreferenceInferrer inferrer;
private final boolean weighted;
private final boolean centerData;
private int cachedNumItems;
private int cachedNumUsers;
private final RefreshHelper refreshHelper;
/**
*
* Creates a possibly weighted {@link AbstractSimilarity}.
*
*/
AbstractSimilarity(final DataModel dataModel, final Weighting weighting,
final boolean centerData) {
super(dataModel);
weighted = weighting == Weighting.WEIGHTED;
this.centerData = centerData;
cachedNumItems = dataModel.getNumItems();
cachedNumUsers = dataModel.getNumUsers();
refreshHelper = new RefreshHelper(new Callable() {
@Override
public Object call() {
cachedNumItems = dataModel.getNumItems();
cachedNumUsers = dataModel.getNumUsers();
return null;
}
});
}
final PreferenceInferrer getPreferenceInferrer() {
return inferrer;
}
@Override
public final void setPreferenceInferrer(final PreferenceInferrer inferrer) {
Preconditions.checkArgument(inferrer != null, "inferrer is null");
refreshHelper.addDependency(inferrer);
refreshHelper.removeDependency(this.inferrer);
this.inferrer = inferrer;
}
final boolean isWeighted() {
return weighted;
}
/**
*
* Several subclasses in this package implement this method to actually compute the similarity from figures
* computed over users or items. Note that the computations in this class "center" the data, such that X and
* Y's mean are 0.
*
*
*
* Note that the sum of all X and Y values must then be 0. This value isn't passed down into the standard
* similarity computations as a result.
*
*
* @param n
* total number of users or items
* @param sumXY
* sum of product of user/item preference values, over all items/users preferred by both
* users/items
* @param sumX2
* sum of the square of user/item preference values, over the first item/user
* @param sumY2
* sum of the square of the user/item preference values, over the second item/user
* @param sumXYdiff2
* sum of squares of differences in X and Y values
* @return similarity value between -1.0 and 1.0, inclusive, or {@link Double#NaN} if no similarity can be
* computed (e.g. when no items have been rated by both users
*/
abstract double computeResult(int n, double sumXY, double sumX2,
double sumY2, double sumXYdiff2);
@Override
public double userSimilarity(final long userID1, final long userID2) {
final DataModel dataModel = getDataModel();
final PreferenceArray xPrefs = dataModel
.getPreferencesFromUser(userID1);
final PreferenceArray yPrefs = dataModel
.getPreferencesFromUser(userID2);
final int xLength = xPrefs.length();
final int yLength = yPrefs.length();
if (xLength == 0 || yLength == 0) {
return Double.NaN;
}
long xIndex = xPrefs.getItemID(0);
long yIndex = yPrefs.getItemID(0);
int xPrefIndex = 0;
int yPrefIndex = 0;
double sumX = 0.0;
double sumX2 = 0.0;
double sumY = 0.0;
double sumY2 = 0.0;
double sumXY = 0.0;
double sumXYdiff2 = 0.0;
int count = 0;
final boolean hasInferrer = inferrer != null;
while (true) {
final int compare = xIndex < yIndex ? -1 : xIndex > yIndex ? 1 : 0;
if (hasInferrer || compare == 0) {
double x;
double y;
if (xIndex == yIndex) {
// Both users expressed a preference for the item
x = xPrefs.getValue(xPrefIndex);
y = yPrefs.getValue(yPrefIndex);
} else {
// Only one user expressed a preference, but infer the other one's preference and tally
// as if the other user expressed that preference
if (compare < 0) {
// X has a value; infer Y's
x = xPrefs.getValue(xPrefIndex);
y = inferrer.inferPreference(userID2, xIndex);
} else {
// compare > 0
// Y has a value; infer X's
x = inferrer.inferPreference(userID1, yIndex);
y = yPrefs.getValue(yPrefIndex);
}
}
sumXY += x * y;
sumX += x;
sumX2 += x * x;
sumY += y;
sumY2 += y * y;
final double diff = x - y;
sumXYdiff2 += diff * diff;
count++;
}
if (compare <= 0) {
if (++xPrefIndex >= xLength) {
if (hasInferrer) {
// Must count other Ys; pretend next X is far away
if (yIndex == Long.MAX_VALUE) {
// ... but stop if both are done!
break;
}
xIndex = Long.MAX_VALUE;
} else {
break;
}
} else {
xIndex = xPrefs.getItemID(xPrefIndex);
}
}
if (compare >= 0) {
if (++yPrefIndex >= yLength) {
if (hasInferrer) {
// Must count other Xs; pretend next Y is far away
if (xIndex == Long.MAX_VALUE) {
// ... but stop if both are done!
break;
}
yIndex = Long.MAX_VALUE;
} else {
break;
}
} else {
yIndex = yPrefs.getItemID(yPrefIndex);
}
}
}
// "Center" the data. If my math is correct, this'll do it.
double result;
if (centerData) {
final double meanX = sumX / count;
final double meanY = sumY / count;
// double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;
final double centeredSumXY = sumXY - meanY * sumX;
// double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;
final double centeredSumX2 = sumX2 - meanX * sumX;
// double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;
final double centeredSumY2 = sumY2 - meanY * sumY;
result = computeResult(count, centeredSumXY, centeredSumX2,
centeredSumY2, sumXYdiff2);
} else {
result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);
}
if (!Double.isNaN(result)) {
result = normalizeWeightResult(result, count, cachedNumItems);
}
return result;
}
@Override
public final double itemSimilarity(final long itemID1, final long itemID2) {
final DataModel dataModel = getDataModel();
final PreferenceArray xPrefs = dataModel.getPreferencesForItem(itemID1);
final PreferenceArray yPrefs = dataModel.getPreferencesForItem(itemID2);
final int xLength = xPrefs.length();
final int yLength = yPrefs.length();
if (xLength == 0 || yLength == 0) {
return Double.NaN;
}
long xIndex = xPrefs.getUserID(0);
long yIndex = yPrefs.getUserID(0);
int xPrefIndex = 0;
int yPrefIndex = 0;
double sumX = 0.0;
double sumX2 = 0.0;
double sumY = 0.0;
double sumY2 = 0.0;
double sumXY = 0.0;
double sumXYdiff2 = 0.0;
int count = 0;
// No, pref inferrers and transforms don't apply here. I think.
while (true) {
final int compare = xIndex < yIndex ? -1 : xIndex > yIndex ? 1 : 0;
if (compare == 0) {
// Both users expressed a preference for the item
final double x = xPrefs.getValue(xPrefIndex);
final double y = yPrefs.getValue(yPrefIndex);
sumXY += x * y;
sumX += x;
sumX2 += x * x;
sumY += y;
sumY2 += y * y;
final double diff = x - y;
sumXYdiff2 += diff * diff;
count++;
}
if (compare <= 0) {
if (++xPrefIndex == xLength) {
break;
}
xIndex = xPrefs.getUserID(xPrefIndex);
}
if (compare >= 0) {
if (++yPrefIndex == yLength) {
break;
}
yIndex = yPrefs.getUserID(yPrefIndex);
}
}
double result;
if (centerData) {
// See comments above on these computations
final double n = count;
final double meanX = sumX / n;
final double meanY = sumY / n;
// double centeredSumXY = sumXY - meanY * sumX - meanX * sumY + n * meanX * meanY;
final double centeredSumXY = sumXY - meanY * sumX;
// double centeredSumX2 = sumX2 - 2.0 * meanX * sumX + n * meanX * meanX;
final double centeredSumX2 = sumX2 - meanX * sumX;
// double centeredSumY2 = sumY2 - 2.0 * meanY * sumY + n * meanY * meanY;
final double centeredSumY2 = sumY2 - meanY * sumY;
result = computeResult(count, centeredSumXY, centeredSumX2,
centeredSumY2, sumXYdiff2);
} else {
result = computeResult(count, sumXY, sumX2, sumY2, sumXYdiff2);
}
if (!Double.isNaN(result)) {
result = normalizeWeightResult(result, count, cachedNumUsers);
}
return result;
}
@Override
public double[] itemSimilarities(final long itemID1, final long[] itemID2s) {
final int length = itemID2s.length;
final double[] result = new double[length];
for (int i = 0; i < length; i++) {
result[i] = itemSimilarity(itemID1, itemID2s[i]);
}
return result;
}
final double normalizeWeightResult(final double result, final int count,
final int num) {
double normalizedResult = result;
if (weighted) {
final double scaleFactor = 1.0 - (double) count
/ (double) (num + 1);
if (normalizedResult < 0.0) {
normalizedResult = -1.0 + scaleFactor
* (1.0 + normalizedResult);
} else {
normalizedResult = 1.0 - scaleFactor * (1.0 - normalizedResult);
}
}
// Make sure the result is not accidentally a little outside [-1.0, 1.0] due to rounding:
if (normalizedResult < -1.0) {
normalizedResult = -1.0;
} else if (normalizedResult > 1.0) {
normalizedResult = 1.0;
}
return normalizedResult;
}
@Override
public final void refresh(final Collection alreadyRefreshed) {
super.refresh(alreadyRefreshed);
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public final String toString() {
return this.getClass().getSimpleName() + "[dataModel:" + getDataModel()
+ ",inferrer:" + inferrer + ']';
}
}
