logisticregressiontest.java

一个自然语言处理的Java开源工具包。LingPipe目前已有很丰富的功能

package com.aliasi.test.unit.stats;

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 com.aliasi.test.unit.BaseTestCase;

import com.aliasi.util.AbstractExternalizable;

import java.io.IOException;

public class LogisticRegressionTest extends BaseTestCase {

    // 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
    };


    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());
    }

    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 {
        LogisticRegression regression
            = LogisticRegression.estimate(data_matrix,
                                          WALLET_OUTCOME_VECTOR,
                                          RegressionPrior.noninformative(),
                                          AnnealingSchedule.inverse(0.05,100),
                                          0.00000001, // min improve
                                          10, // min epochs
                                          500000, // max epochs
                                          null);  // no print feedback

        Vector[] vs = regression.weightVectors();
        for (int i = 0; i < vs.length; ++i) {
            // System.out.println("residuals " + i);
            for (int j = 0; j < vs[i].numDimensions(); ++j) {
                assertEquals(WALLET_EXPECTED_FEATURES[i][j],vs[i].value(j),0.1);
                // System.out.printf("%8.5f ", vs[i].value(j) - WALLET_EXPECTED_FEATURES[i][j]);
            }
            // System.out.println();
        }

        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]);

    }
}