Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/167#discussion_r226243032
--- Diff: core/src/main/java/hivemall/mf/CofactorModel.java ---
@@ -0,0 +1,638 @@
+/*
+ * 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.mf;
+
+import hivemall.fm.Feature;
+import hivemall.utils.math.MathUtils;
+import hivemall.utils.math.MatrixUtils;
+import org.apache.commons.math3.linear.ArrayRealVector;
+import org.apache.commons.math3.linear.Array2DRowRealMatrix;
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.linear.RealVector;
+import org.apache.commons.math3.linear.SingularValueDecomposition;
+
+import javax.annotation.Nonnegative;
+import javax.annotation.Nonnull;
+import javax.annotation.Nullable;
+import java.util.*;
+
+public class CofactorModel {
+
+ public enum RankInitScheme {
+ random /* default */, gaussian;
+
+ @Nonnegative
+ protected float maxInitValue;
+ @Nonnegative
+ protected double initStdDev;
+
+ @Nonnull
+ public static CofactorModel.RankInitScheme resolve(@Nullable
String opt) {
+ if (opt == null) {
+ return random;
+ } else if ("gaussian".equalsIgnoreCase(opt)) {
+ return gaussian;
+ } else if ("random".equalsIgnoreCase(opt)) {
+ return random;
+ }
+ return random;
+ }
+
+ public void setMaxInitValue(float maxInitValue) {
+ this.maxInitValue = maxInitValue;
+ }
+
+ public void setInitStdDev(double initStdDev) {
+ this.initStdDev = initStdDev;
+ }
+
+ }
+
+ private static final int EXPECTED_SIZE = 136861;
+ @Nonnegative
+ protected final int factor;
+
+ // rank matrix initialization
+ protected final RankInitScheme initScheme;
+
+ @Nonnull
+ private double globalBias;
+
+ // storing trainable latent factors and weights
+ private Map<String, RealVector> theta;
+ private Map<String, RealVector> beta;
+ private Map<String, Double> betaBias;
+ private Map<String, RealVector> gamma;
+ private Map<String, Double> gammaBias;
+
+ // precomputed identity matrix
+ private RealMatrix identity;
+
+ protected final Random[] randU, randI;
+
+ // hyperparameters
+ private final float c0, c1;
+ private final float lambdaTheta, lambdaBeta, lambdaGamma;
+
+ public CofactorModel(@Nonnegative int factor, @Nonnull RankInitScheme
initScheme,
+ @Nonnull float c0, @Nonnull float c1, float
lambdaTheta,
+ float lambdaBeta, float lambdaGamma) {
+
+ // rank init scheme is gaussian
+ //
https://github.com/dawenl/cofactor/blob/master/src/cofacto.py#L98
+ this.factor = factor;
+ this.initScheme = initScheme;
+ this.globalBias = 0.d;
+ this.lambdaTheta = lambdaTheta;
+ this.lambdaBeta = lambdaBeta;
+ this.lambdaGamma = lambdaGamma;
+
+ this.theta = new HashMap<>();
+ this.beta = new HashMap<>();
+ this.betaBias = new HashMap<>();
+ this.gamma = new HashMap<>();
+ this.gammaBias = new HashMap<>();
+
+ this.randU = newRandoms(factor, 31L);
+ this.randI = newRandoms(factor, 41L);
+
+ checkHyperparameterC(c0);
+ checkHyperparameterC(c1);
+ this.c0 = c0;
+ this.c1 = c1;
+
+ }
+
+ private void initFactorVector(String key, Map<String, RealVector>
weights) {
+ if (weights.containsKey(key)) {
+ return;
+ }
+ RealVector v = new ArrayRealVector(factor);
+ switch (initScheme) {
+ case random:
+ uniformFill(v, randI[0], initScheme.maxInitValue);
+ break;
+ case gaussian:
+ gaussianFill(v, randI, initScheme.initStdDev);
+ break;
+ default:
+ throw new IllegalStateException(
+ "Unsupported rank initialization scheme: " +
initScheme);
+
+ }
+ weights.put(key, v);
+ }
+
+ private static RealVector getFactorVector(String key, Map<String,
RealVector> weights) {
+ return weights.get(key);
+ }
+
+ private static void setFactorVector(String key, Map<String,
RealVector> weights, RealVector factorVector) {
+ assert weights.containsKey(key);
+ weights.put(key, factorVector);
+ }
+
+ private static double getBias(String key, Map<String, Double> biases) {
+ if (!biases.containsKey(key)) {
--- End diff --
three hash lookup for worse case...
```
final Double v = biases.get(key);
if(v == null) {
return 0.d;
}
return v.doubleValue();
```
Or,
```java
private static double getBias(String key, Object2DoubleMap<String>
biases) {
// biases.defaultReturnValue(0.f); -- set in initialization
return biases.get(key);
}
```
---
