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package com.aliasi.test.unit.stats;
import com.aliasi.corpus.ObjectHandler;
import com.aliasi.io.Reporter;
import com.aliasi.io.Reporters;
import com.aliasi.io.LogLevel;
import com.aliasi.stats.AnnealingSchedule;
import com.aliasi.stats.LogisticRegression;
import com.aliasi.stats.RegressionPrior;
import com.aliasi.matrix.DenseVector;
import com.aliasi.matrix.SparseFloatVector;
import com.aliasi.matrix.Vector;
import org.junit.Test;
import static junit.framework.Assert.assertEquals;
import com.aliasi.util.AbstractExternalizable;
import java.io.IOException;
public class LogisticRegressionTest {
// WALLET example from:
// Paul David Allison. Logistic Regression Using the SAS System: Theory and Application.
// p. 117. http://books.google.com/books?id=AcHB61vd-1UC
// 0 = KEEP_BOTH, 1=KEEP_MONEY, 2=RETURN_BOTH
static final int[] WALLET_OUTCOME_VECTOR
= new int[] {
1,
1,
2,
2,
0,
2,
2,
2,
2,
2,
1,
2,
2,
2,
2,
2,
2,
1,
0,
1,
1,
2,
2,
2,
2,
1,
1,
0,
2,
2,
2,
2,
0,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
1,
2,
2,
2,
2,
2,
2,
1,
2,
2,
2,
2,
2,
0,
2,
2,
0,
2,
1,
0,
0,
2,
2,
1,
1,
1,
2,
2,
2,
2,
2,
2,
2,
2,
1,
2,
2,
1,
2,
2,
2,
2,
2,
2,
2,
0,
0,
1,
0,
1,
0,
1,
0,
2,
2,
1,
2,
0,
2,
1,
2,
2,
1,
2,
2,
0,
1,
1,
0,
0,
2,
2,
2,
2,
2,
2,
2,
2,
2,
1,
1,
2,
1,
2,
1,
2,
2,
0,
2,
2,
2,
2,
1,
2,
1,
2,
1,
2,
2,
2,
2,
1,
2,
2,
1,
2,
2,
1,
2,
1,
2,
0,
2,
1,
0,
1,
2,
1,
2,
1,
1,
0,
1,
1,
0,
1,
1,
2,
2,
1,
0,
1,
2,
1,
2,
0,
1,
2,
1,
2,
2,
2,
2,
2,
1, };
// INTERCEPT, MALE, BUSINESS, PUNISH, EXPLAIN
static final double[][] WALLET_DATA_MATRIX
= new double[][] {
{ 1, 0, 0, 2, 0 },
{ 1, 0, 0, 2, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 2, 1 },
{ 1, 0, 1, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 0, 2, 1 },
{ 1, 0, 0, 3, 0 },
{ 1, 1, 1, 3, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 2, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 1, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 1, 0, 3, 0 },
{ 1, 1, 0, 2, 0 },
{ 1, 1, 0, 2, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 2, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 1, 0, 1, 0 },
{ 1, 1, 1, 2, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 1, 3, 0 },
{ 1, 1, 0, 2, 0 },
{ 1, 0, 0, 2, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 0 },
{ 1, 1, 1, 1, 0 },
{ 1, 1, 0, 1, 0 },
{ 1, 1, 1, 3, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 3, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 1, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 0 },
{ 1, 1, 0, 3, 1 },
{ 1, 1, 0, 3, 1 },
{ 1, 1, 1, 2, 1 },
{ 1, 1, 0, 2, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 3, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 1, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 2, 1 },
{ 1, 1, 1, 1, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 1, 1, 1 },
{ 1, 0, 0, 2, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 2, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 3, 0 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 1, 3, 1 },
{ 1, 0, 0, 3, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 1, 3, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 3, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 1, 1, 1 },
{ 1, 0, 0, 3, 0 },
{ 1, 0, 1, 2, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 2, 0 },
{ 1, 1, 0, 1, 0 },
{ 1, 1, 0, 1, 0 },
{ 1, 0, 0, 2, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 3, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 0, 1, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 0 },
{ 1, 0, 0, 1, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 2, 0 },
{ 1, 1, 1, 2, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 1, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 0 },
{ 1, 0, 1, 2, 1 },
{ 1, 1, 1, 2, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 3, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 0 },
{ 1, 1, 0, 3, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 1, 2, 0 },
{ 1, 1, 0, 2, 0 },
{ 1, 0, 0, 1, 1 },
{ 1, 0, 1, 3, 0 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 2, 0 },
{ 1, 0, 1, 2, 1 },
{ 1, 0, 0, 2, 0 },
{ 1, 1, 1, 1, 1 },
{ 1, 0, 1, 2, 1 },
{ 1, 0, 0, 3, 0 },
{ 1, 1, 1, 1, 0 },
{ 1, 0, 0, 3, 1 },
{ 1, 1, 0, 2, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 0, 3, 1 },
{ 1, 0, 0, 1, 1 },
{ 1, 1, 1, 1, 1 },
{ 1, 1, 0, 1, 1 },
{ 1, 1, 0, 1, 1 }
};
static final double[][] WALLET_EXPECTED_FEATURES
= new double[][] {
// INTERCEPT, MALE, BUSINESS, PUNISH, EXPLAIN
{ -3.4712, 1.2673, 1.1804, 1.0817, -1.6006 }, // CAT 0=KEEP_BOTH
{ -1.2917, 1.1699, 0.4179, 0.1957, -0.8040 }, // CAT 1=KEEP_MONEY
{ 0.0, 0.0, 0.0, 0.0, 0.0 } // CAT 2=RETURN_BOTH
};
@Test
public void testClass() {
Vector[] weightVectors = new Vector[2];
weightVectors[0] = new DenseVector(new double[] { 1, 2, 3 });
weightVectors[1] = new DenseVector(new double[] { -2, 1, -1 });
LogisticRegression regression = new LogisticRegression(weightVectors);
Vector testCase = new DenseVector(new double[] { 1, -1, 2 });
double prod1 = (1*1) + (-1 * 2) + (2 * 3);
double prod2 = (1 * -2) + (-1 * 1) + (2 * -1);
double prod3 = 0;
double prop1 = Math.exp(prod1);
double prop2 = Math.exp(prod2);
double prop3 = Math.exp(prod3);
assertEquals(1.0,prop3,0.0001);
double p1 = prop1 / (prop1 + prop2 + prop3);
double p2 = prop2 / (prop1 + prop2 + prop3);
double p3 = prop3 / (prop1 + prop2 + prop3);
double[] expected = new double[] { p1, p2, p3};
double[] estimated = regression.classify(testCase);
assertEquals(expected.length, estimated.length);
for (int i = 0; i < expected.length; ++i)
assertEquals(expected[i],estimated[i],0.0000001);
}
static Vector[] sparseCopy(Vector[] matrix) {
Vector[] result = new Vector[matrix.length];
for (int i = 0; i < matrix.length; ++i)
result[i] = sparseCopy(matrix[i]);
return result;
}
static Vector sparseCopy(Vector v) {
int[] dims = new int[v.numDimensions()];
float[] vals = new float[v.numDimensions()];
for (int i = 0; i < dims.length; ++i) {
dims[i] = i;
vals[i] = (float) v.value(i);
}
return new SparseFloatVector(dims,vals,v.numDimensions());
}
@Test
public void testEstimation() throws IOException, ClassNotFoundException {
Vector[] data_matrix = new Vector[WALLET_DATA_MATRIX.length];
for (int i = 0; i < data_matrix.length; ++i)
data_matrix[i] = new DenseVector(WALLET_DATA_MATRIX[i]);
Vector[] sparse_data_matrix = sparseCopy(data_matrix);
assertCorrectRegression(data_matrix);
assertCorrectRegression(sparse_data_matrix);
}
void assertCorrectRegression(Vector[] data_matrix) throws IOException, ClassNotFoundException {
Reporter reporter = null; // Reporters.stdOut().setLevel(LogLevel.DEBUG); // null;
LogisticRegression hotStart = null;
ObjectHandler handler = null;
int priorBlockSize = 3;
LogisticRegression regression
= LogisticRegression.estimate(data_matrix,
WALLET_OUTCOME_VECTOR,
RegressionPrior.noninformative(),
priorBlockSize,
hotStart,
AnnealingSchedule.inverse(0.05,100),
0.00001, // min improve
5, // rolling avg size
10, // min epochs
500000, // max epochs
handler, // handler for each epoch's regression
reporter); // no print feedback
Vector[] vs = regression.weightVectors();
for (int i = 0; i < vs.length; ++i) {
for (int j = 0; j < vs[i].numDimensions(); ++j) {
assertEquals(WALLET_EXPECTED_FEATURES[i][j],vs[i].value(j),0.1);
}
}
LogisticRegression regression2
= (LogisticRegression) AbstractExternalizable.compile(regression);
assertEquals(regression.numOutcomes(),
regression2.numOutcomes());
assertEquals(regression.numInputDimensions(),
regression.numInputDimensions());
Vector[] vs1 = regression.weightVectors();
Vector[] vs2 = regression2.weightVectors();
assertEquals(vs1.length,vs2.length);
assertEquals(vs1.length,vs2.length);
for (int i = 0; i < vs1.length; ++i)
assertEquals(vs1[i],vs2[i]);
hotStart = regression;
priorBlockSize = 2;
LogisticRegression regression3
= LogisticRegression.estimate(data_matrix,
WALLET_OUTCOME_VECTOR,
RegressionPrior.noninformative(),
priorBlockSize,
hotStart,
AnnealingSchedule.inverse(0.05,100),
0.0000001, // min improve
5, // rolling avg size
10, // min epochs
500000, // max epochs
handler, // handler for epoch's regression
reporter); // no print feedback
vs = regression3.weightVectors();
for (int i = 0; i < vs.length; ++i) {
for (int j = 0; j < vs[i].numDimensions(); ++j) {
assertEquals(WALLET_EXPECTED_FEATURES[i][j],vs[i].value(j),0.1);
}
}
}
}
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