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Posted to commits@ignite.apache.org by sb...@apache.org on 2017/11/21 12:10:23 UTC
[29/47] ignite git commit: IGNITE-5846 Add support of distributed
matrices for OLS regression. This closes #3030.
http://git-wip-us.apache.org/repos/asf/ignite/blob/b0a86018/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java
new file mode 100644
index 0000000..e3c2979
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedBlockOLSMultipleLinearRegressionTest.java
@@ -0,0 +1,926 @@
+/*
+ * 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.apache.ignite.ml.regressions;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.TestUtils;
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException;
+import org.apache.ignite.ml.math.exceptions.NullArgumentException;
+import org.apache.ignite.ml.math.exceptions.SingularMatrixException;
+import org.apache.ignite.ml.math.impls.matrix.SparseBlockDistributedMatrix;
+import org.apache.ignite.ml.math.impls.vector.SparseBlockDistributedVector;
+import org.apache.ignite.ml.math.util.MatrixUtil;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+import org.apache.ignite.testframework.junits.common.GridCommonTest;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Tests for {@link OLSMultipleLinearRegression}.
+ */
+
+@GridCommonTest(group = "Distributed Models")
+public class DistributedBlockOLSMultipleLinearRegressionTest extends GridCommonAbstractTest {
+ /** */
+ private double[] y;
+
+ /** */
+ private double[][] x;
+
+ private AbstractMultipleLinearRegression regression;
+
+ /** Number of nodes in grid */
+ private static final int NODE_COUNT = 3;
+
+ private static final double PRECISION = 1E-12;
+
+ /** Grid instance. */
+ private Ignite ignite;
+
+ public DistributedBlockOLSMultipleLinearRegressionTest() {
+
+ super(false);
+
+
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void beforeTestsStarted() throws Exception {
+ for (int i = 1; i <= NODE_COUNT; i++)
+ startGrid(i);
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void afterTestsStopped() throws Exception {
+ stopAllGrids();
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override protected void beforeTest() throws Exception {
+ ignite = grid(NODE_COUNT);
+
+ ignite.configuration().setPeerClassLoadingEnabled(true);
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+
+ y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
+ x = new double[6][];
+ x[0] = new double[]{0, 0, 0, 0, 0};
+ x[1] = new double[]{2.0, 0, 0, 0, 0};
+ x[2] = new double[]{0, 3.0, 0, 0, 0};
+ x[3] = new double[]{0, 0, 4.0, 0, 0};
+ x[4] = new double[]{0, 0, 0, 5.0, 0};
+ x[5] = new double[]{0, 0, 0, 0, 6.0};
+
+ regression = createRegression();
+ }
+
+ /** */
+ protected OLSMultipleLinearRegression createRegression() {
+ OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
+ regression.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
+ return regression;
+ }
+
+ /** */
+ @Test
+ public void testPerfectFit() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[] betaHat = regression.estimateRegressionParameters();
+ System.out.println("Beta hat is " + betaHat);
+ TestUtils.assertEquals(new double[]{11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0},
+ betaHat,
+ 1e-13);
+ double[] residuals = regression.estimateResiduals();
+ TestUtils.assertEquals(new double[]{0d, 0d, 0d, 0d, 0d, 0d}, residuals,
+ 1e-13);
+ Matrix errors = regression.estimateRegressionParametersVariance();
+ final double[] s = {1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0};
+ Matrix refVar = new SparseBlockDistributedMatrix(s.length, s.length);
+ for (int i = 0; i < refVar.rowSize(); i++)
+ for (int j = 0; j < refVar.columnSize(); j++) {
+ if (i == 0) {
+ refVar.setX(i, j, s[j]);
+ continue;
+ }
+ double x = s[i] * s[j];
+ refVar.setX(i, j, (i == j) ? 2 * x : x);
+ }
+ Assert.assertEquals(0.0,
+ TestUtils.maximumAbsoluteRowSum(errors.minus(refVar)),
+ 5.0e-16 * TestUtils.maximumAbsoluteRowSum(refVar));
+ Assert.assertEquals(1, ((OLSMultipleLinearRegression) regression).calculateRSquared(), 1E-12);
+ }
+
+ /**
+ * Test Longley dataset against certified values provided by NIST.
+ * Data Source: J. Longley (1967) "An Appraisal of Least Squares
+ * Programs for the Electronic Computer from the Point of View of the User"
+ * Journal of the American Statistical Association, vol. 62. September,
+ * pp. 819-841.
+ *
+ * Certified values (and data) are from NIST:
+ * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
+ */
+ @Test
+ public void testLongly() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ // Y values are first, then independent vars
+ // Each row is one observation
+ double[] design = new double[]{
+ 60323, 83.0, 234289, 2356, 1590, 107608, 1947,
+ 61122, 88.5, 259426, 2325, 1456, 108632, 1948,
+ 60171, 88.2, 258054, 3682, 1616, 109773, 1949,
+ 61187, 89.5, 284599, 3351, 1650, 110929, 1950,
+ 63221, 96.2, 328975, 2099, 3099, 112075, 1951,
+ 63639, 98.1, 346999, 1932, 3594, 113270, 1952,
+ 64989, 99.0, 365385, 1870, 3547, 115094, 1953,
+ 63761, 100.0, 363112, 3578, 3350, 116219, 1954,
+ 66019, 101.2, 397469, 2904, 3048, 117388, 1955,
+ 67857, 104.6, 419180, 2822, 2857, 118734, 1956,
+ 68169, 108.4, 442769, 2936, 2798, 120445, 1957,
+ 66513, 110.8, 444546, 4681, 2637, 121950, 1958,
+ 68655, 112.6, 482704, 3813, 2552, 123366, 1959,
+ 69564, 114.2, 502601, 3931, 2514, 125368, 1960,
+ 69331, 115.7, 518173, 4806, 2572, 127852, 1961,
+ 70551, 116.9, 554894, 4007, 2827, 130081, 1962
+ };
+
+ final int nobs = 16;
+ final int nvars = 6;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
+
+ // Check expected beta values from NIST
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ -3482258.63459582, 15.0618722713733,
+ -0.358191792925910E-01, -2.02022980381683,
+ -1.03322686717359, -0.511041056535807E-01,
+ 1829.15146461355}, 2E-6); //
+
+ // Check expected residuals from R
+ double[] residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[]{
+ 267.340029759711, -94.0139423988359, 46.28716775752924,
+ -410.114621930906, 309.7145907602313, -249.3112153297231,
+ -164.0489563956039, -13.18035686637081, 14.30477260005235,
+ 455.394094551857, -17.26892711483297, -39.0550425226967,
+ -155.5499735953195, -85.6713080421283, 341.9315139607727,
+ -206.7578251937366},
+ 1E-7);
+
+ // Check standard errors from NIST
+ double[] errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[]{
+ 890420.383607373,
+ 84.9149257747669,
+ 0.334910077722432E-01,
+ 0.488399681651699,
+ 0.214274163161675,
+ 0.226073200069370,
+ 455.478499142212}, errors, 1E-6);
+
+ // Check regression standard error against R
+ Assert.assertEquals(304.8540735619638, mdl.estimateRegressionStandardError(), 1E-8);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.995479004577296, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.992465007628826, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ // TODO: IGNITE-5826, uncomment.
+ // checkVarianceConsistency(model);
+
+ // Estimate model without intercept
+ mdl.setNoIntercept(true);
+ mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
+
+ // Check expected beta values from R
+ betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ -52.99357013868291, 0.07107319907358,
+ -0.42346585566399, -0.57256866841929,
+ -0.41420358884978, 48.41786562001326}, 1E-8);
+
+ // Check standard errors from R
+ errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[]{
+ 129.54486693117232, 0.03016640003786,
+ 0.41773654056612, 0.27899087467676, 0.32128496193363,
+ 17.68948737819961}, errors, 1E-11);
+
+ // Check expected residuals from R
+ residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[]{
+ 279.90274927293092, -130.32465380836874, 90.73228661967445, -401.31252201634948,
+ -440.46768772620027, -543.54512853774793, 201.32111639536299, 215.90889365977932,
+ 73.09368242049943, 913.21694494481869, 424.82484953610174, -8.56475876776709,
+ -361.32974610842876, 27.34560497213464, 151.28955976355002, -492.49937355336846},
+ 1E-8);
+
+ // Check regression standard error against R
+ Assert.assertEquals(475.1655079819517, mdl.estimateRegressionStandardError(), 1E-10);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.9999670130706, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.999947220913, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ }
+
+ /**
+ * Test R Swiss fertility dataset against R.
+ * Data Source: R datasets package
+ */
+ @Test
+ public void testSwissFertility() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[] design = new double[]{
+ 80.2, 17.0, 15, 12, 9.96,
+ 83.1, 45.1, 6, 9, 84.84,
+ 92.5, 39.7, 5, 5, 93.40,
+ 85.8, 36.5, 12, 7, 33.77,
+ 76.9, 43.5, 17, 15, 5.16,
+ 76.1, 35.3, 9, 7, 90.57,
+ 83.8, 70.2, 16, 7, 92.85,
+ 92.4, 67.8, 14, 8, 97.16,
+ 82.4, 53.3, 12, 7, 97.67,
+ 82.9, 45.2, 16, 13, 91.38,
+ 87.1, 64.5, 14, 6, 98.61,
+ 64.1, 62.0, 21, 12, 8.52,
+ 66.9, 67.5, 14, 7, 2.27,
+ 68.9, 60.7, 19, 12, 4.43,
+ 61.7, 69.3, 22, 5, 2.82,
+ 68.3, 72.6, 18, 2, 24.20,
+ 71.7, 34.0, 17, 8, 3.30,
+ 55.7, 19.4, 26, 28, 12.11,
+ 54.3, 15.2, 31, 20, 2.15,
+ 65.1, 73.0, 19, 9, 2.84,
+ 65.5, 59.8, 22, 10, 5.23,
+ 65.0, 55.1, 14, 3, 4.52,
+ 56.6, 50.9, 22, 12, 15.14,
+ 57.4, 54.1, 20, 6, 4.20,
+ 72.5, 71.2, 12, 1, 2.40,
+ 74.2, 58.1, 14, 8, 5.23,
+ 72.0, 63.5, 6, 3, 2.56,
+ 60.5, 60.8, 16, 10, 7.72,
+ 58.3, 26.8, 25, 19, 18.46,
+ 65.4, 49.5, 15, 8, 6.10,
+ 75.5, 85.9, 3, 2, 99.71,
+ 69.3, 84.9, 7, 6, 99.68,
+ 77.3, 89.7, 5, 2, 100.00,
+ 70.5, 78.2, 12, 6, 98.96,
+ 79.4, 64.9, 7, 3, 98.22,
+ 65.0, 75.9, 9, 9, 99.06,
+ 92.2, 84.6, 3, 3, 99.46,
+ 79.3, 63.1, 13, 13, 96.83,
+ 70.4, 38.4, 26, 12, 5.62,
+ 65.7, 7.7, 29, 11, 13.79,
+ 72.7, 16.7, 22, 13, 11.22,
+ 64.4, 17.6, 35, 32, 16.92,
+ 77.6, 37.6, 15, 7, 4.97,
+ 67.6, 18.7, 25, 7, 8.65,
+ 35.0, 1.2, 37, 53, 42.34,
+ 44.7, 46.6, 16, 29, 50.43,
+ 42.8, 27.7, 22, 29, 58.33
+ };
+
+ final int nobs = 47;
+ final int nvars = 4;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
+
+ // Check expected beta values from R
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 91.05542390271397,
+ -0.22064551045715,
+ -0.26058239824328,
+ -0.96161238456030,
+ 0.12441843147162}, 1E-12);
+
+ // Check expected residuals from R
+ double[] residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[]{
+ 7.1044267859730512, 1.6580347433531366,
+ 4.6944952770029644, 8.4548022690166160, 13.6547432343186212,
+ -9.3586864458500774, 7.5822446330520386, 15.5568995563859289,
+ 0.8113090736598980, 7.1186762732484308, 7.4251378771228724,
+ 2.6761316873234109, 0.8351584810309354, 7.1769991119615177,
+ -3.8746753206299553, -3.1337779476387251, -0.1412575244091504,
+ 1.1186809170469780, -6.3588097346816594, 3.4039270429434074,
+ 2.3374058329820175, -7.9272368576900503, -7.8361010968497959,
+ -11.2597369269357070, 0.9445333697827101, 6.6544245101380328,
+ -0.9146136301118665, -4.3152449403848570, -4.3536932047009183,
+ -3.8907885169304661, -6.3027643926302188, -7.8308982189289091,
+ -3.1792280015332750, -6.7167298771158226, -4.8469946718041754,
+ -10.6335664353633685, 11.1031134362036958, 6.0084032641811733,
+ 5.4326230830188482, -7.2375578629692230, 2.1671550814448222,
+ 15.0147574652763112, 4.8625103516321015, -7.1597256413907706,
+ -0.4515205619767598, -10.2916870903837587, -15.7812984571900063},
+ 1E-12);
+
+ // Check standard errors from R
+ double[] errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[]{
+ 6.94881329475087,
+ 0.07360008972340,
+ 0.27410957467466,
+ 0.19454551679325,
+ 0.03726654773803}, errors, 1E-10);
+
+ // Check regression standard error against R
+ Assert.assertEquals(7.73642194433223, mdl.estimateRegressionStandardError(), 1E-12);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.649789742860228, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.6164363850373927, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ // TODO: IGNITE-5826, uncomment.
+ // checkVarianceConsistency(model);
+
+ // Estimate the model with no intercept
+ mdl = new OLSMultipleLinearRegression();
+ mdl.setNoIntercept(true);
+ mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
+
+ // Check expected beta values from R
+ betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 0.52191832900513,
+ 2.36588087917963,
+ -0.94770353802795,
+ 0.30851985863609}, 1E-12);
+
+ // Check expected residuals from R
+ residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[]{
+ 44.138759883538249, 27.720705122356215, 35.873200836126799,
+ 34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814,
+ 1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165,
+ -9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336,
+ -17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924,
+ -10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944,
+ -13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898,
+ -1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603,
+ -16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384,
+ -17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695,
+ -0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976,
+ 2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615},
+ 1E-12);
+
+ // Check standard errors from R
+ errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[]{
+ 0.10470063765677, 0.41684100584290,
+ 0.43370143099691, 0.07694953606522}, errors, 1E-10);
+
+ // Check regression standard error against R
+ Assert.assertEquals(17.24710630547, mdl.estimateRegressionStandardError(), 1E-10);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.946350722085, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.9413600915813, mdl.calculateAdjustedRSquared(), 1E-12);
+ }
+
+ /**
+ * Test hat matrix computation
+ */
+ @Test
+ public void testHat() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ /*
+ * This example is from "The Hat Matrix in Regression and ANOVA",
+ * David C. Hoaglin and Roy E. Welsch,
+ * The American Statistician, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
+ *
+ */
+ double[] design = new double[]{
+ 11.14, .499, 11.1,
+ 12.74, .558, 8.9,
+ 13.13, .604, 8.8,
+ 11.51, .441, 8.9,
+ 12.38, .550, 8.8,
+ 12.60, .528, 9.9,
+ 11.13, .418, 10.7,
+ 11.7, .480, 10.5,
+ 11.02, .406, 10.5,
+ 11.41, .467, 10.7
+ };
+
+ int nobs = 10;
+ int nvars = 2;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseBlockDistributedMatrix());
+
+ Matrix hat = mdl.calculateHat();
+
+ // Reference data is upper half of symmetric hat matrix
+ double[] refData = new double[]{
+ .418, -.002, .079, -.274, -.046, .181, .128, .222, .050, .242,
+ .242, .292, .136, .243, .128, -.041, .033, -.035, .004,
+ .417, -.019, .273, .187, -.126, .044, -.153, .004,
+ .604, .197, -.038, .168, -.022, .275, -.028,
+ .252, .111, -.030, .019, -.010, -.010,
+ .148, .042, .117, .012, .111,
+ .262, .145, .277, .174,
+ .154, .120, .168,
+ .315, .148,
+ .187
+ };
+
+ // Check against reference data and verify symmetry
+ int k = 0;
+ for (int i = 0; i < 10; i++) {
+ for (int j = i; j < 10; j++) {
+ Assert.assertEquals(refData[k], hat.getX(i, j), 10e-3);
+ Assert.assertEquals(hat.getX(i, j), hat.getX(j, i), 10e-12);
+ k++;
+ }
+ }
+
+ /*
+ * Verify that residuals computed using the hat matrix are close to
+ * what we get from direct computation, i.e. r = (I - H) y
+ */
+ double[] residuals = mdl.estimateResiduals();
+ Matrix id = MatrixUtil.identityLike(hat, 10);
+ double[] hatResiduals = id.minus(hat).times(mdl.getY()).getStorage().data();
+ TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
+ }
+
+ /**
+ * test calculateYVariance
+ */
+ @Test
+ public void testYVariance() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ // assumes: y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
+ TestUtils.assertEquals(mdl.calculateYVariance(), 3.5, 0);
+ }
+
+ /**
+ * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
+ */
+ @Test
+ public void testNewSample2() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] y = new double[]{1, 2, 3, 4};
+ double[][] x = new double[][]{
+ {19, 22, 33},
+ {20, 30, 40},
+ {25, 35, 45},
+ {27, 37, 47}
+ };
+ OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
+ regression.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
+ Matrix combinedX = regression.getX().copy();
+ Vector combinedY = regression.getY().copy();
+ regression.newXSampleData(new SparseBlockDistributedMatrix(x));
+ regression.newYSampleData(new SparseBlockDistributedVector(y));
+ for (int i = 0; i < combinedX.rowSize(); i++) {
+ for (int j = 0; j < combinedX.columnSize(); j++)
+ Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
+
+ }
+ for (int i = 0; i < combinedY.size(); i++)
+ Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
+
+
+ // No intercept
+ regression.setNoIntercept(true);
+ regression.newSampleData(new SparseBlockDistributedVector(y), new SparseBlockDistributedMatrix(x));
+ combinedX = regression.getX().copy();
+ combinedY = regression.getY().copy();
+ regression.newXSampleData(new SparseBlockDistributedMatrix(x));
+ regression.newYSampleData(new SparseBlockDistributedVector(y));
+
+ for (int i = 0; i < combinedX.rowSize(); i++) {
+ for (int j = 0; j < combinedX.columnSize(); j++)
+ Assert.assertEquals(combinedX.get(i, j), regression.getX().get(i, j), PRECISION);
+
+ }
+ for (int i = 0; i < combinedY.size(); i++)
+ Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
+
+ }
+
+ /** */
+ @Test(expected = NullArgumentException.class)
+ public void testNewSampleDataYNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ createRegression().newSampleData(null, new SparseBlockDistributedMatrix(new double[][]{{1}}));
+ fail("NullArgumentException");
+ } catch (NullArgumentException e) {
+ return;
+ }
+ fail("NullArgumentException");
+ }
+
+ /** */
+ public void testNewSampleDataXNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ createRegression().newSampleData(new SparseBlockDistributedVector(new double[]{1}), null);
+ fail("NullArgumentException");
+ } catch (NullArgumentException e) {
+ return;
+ }
+ fail("NullArgumentException");
+
+
+ }
+
+ /**
+ * This is a test based on the Wampler1 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
+ */
+ @Test
+ public void testWampler1() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[]{
+ 1, 0,
+ 6, 1,
+ 63, 2,
+ 364, 3,
+ 1365, 4,
+ 3906, 5,
+ 9331, 6,
+ 19608, 7,
+ 37449, 8,
+ 66430, 9,
+ 111111, 10,
+ 177156, 11,
+ 271453, 12,
+ 402234, 13,
+ 579195, 14,
+ 813616, 15,
+ 1118481, 16,
+ 1508598, 17,
+ 2000719, 18,
+ 2613660, 19,
+ 3368421, 20};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0, 1.0,
+ 1.0, 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{
+ 0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+
+ TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
+ }
+
+ /**
+ * This is a test based on the Wampler2 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
+ */
+ @Test
+ public void testWampler2() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[]{
+ 1.00000, 0,
+ 1.11111, 1,
+ 1.24992, 2,
+ 1.42753, 3,
+ 1.65984, 4,
+ 1.96875, 5,
+ 2.38336, 6,
+ 2.94117, 7,
+ 3.68928, 8,
+ 4.68559, 9,
+ 6.00000, 10,
+ 7.71561, 11,
+ 9.92992, 12,
+ 12.75603, 13,
+ 16.32384, 14,
+ 20.78125, 15,
+ 26.29536, 16,
+ 33.05367, 17,
+ 41.26528, 18,
+ 51.16209, 19,
+ 63.00000, 20};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0e-1,
+ 1.0e-2,
+ 1.0e-3, 1.0e-4,
+ 1.0e-5}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{
+ 0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+ TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
+ }
+
+ /**
+ * This is a test based on the Wampler3 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
+ */
+ @Test
+ public void testWampler3() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[]{
+ 760, 0,
+ -2042, 1,
+ 2111, 2,
+ -1684, 3,
+ 3888, 4,
+ 1858, 5,
+ 11379, 6,
+ 17560, 7,
+ 39287, 8,
+ 64382, 9,
+ 113159, 10,
+ 175108, 11,
+ 273291, 12,
+ 400186, 13,
+ 581243, 14,
+ 811568, 15,
+ 1121004, 16,
+ 1506550, 17,
+ 2002767, 18,
+ 2611612, 19,
+ 3369180, 20};
+
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{
+ 2152.32624678170,
+ 2363.55173469681, 779.343524331583,
+ 101.475507550350, 5.64566512170752,
+ 0.112324854679312}, 1E-8); //
+
+ TestUtils.assertEquals(.999995559025820, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(5570284.53333333, mdl.estimateErrorVariance(), 1.0e-6);
+ TestUtils.assertEquals(83554268.0000000, mdl.calculateResidualSumOfSquares(), 1.0e-5);
+ }
+
+ /**
+ * This is a test based on the Wampler4 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
+ */
+ @Test
+ public void testWampler4() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[]{
+ 75901, 0,
+ -204794, 1,
+ 204863, 2,
+ -204436, 3,
+ 253665, 4,
+ -200894, 5,
+ 214131, 6,
+ -185192, 7,
+ 221249, 8,
+ -138370, 9,
+ 315911, 10,
+ -27644, 11,
+ 455253, 12,
+ 197434, 13,
+ 783995, 14,
+ 608816, 15,
+ 1370781, 16,
+ 1303798, 17,
+ 2205519, 18,
+ 2408860, 19,
+ 3444321, 20};
+
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseBlockDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[]{
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-6);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[]{
+ 215232.624678170,
+ 236355.173469681, 77934.3524331583,
+ 10147.5507550350, 564.566512170752,
+ 11.2324854679312}, 1E-8);
+
+ TestUtils.assertEquals(.957478440825662, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(55702845333.3333, mdl.estimateErrorVariance(), 1.0e-4);
+ TestUtils.assertEquals(835542680000.000, mdl.calculateResidualSumOfSquares(), 1.0e-3);
+ }
+
+ /**
+ * Anything requiring beta calculation should advertise SME.
+ */
+ public void testSingularCalculateBeta() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression(1e-15);
+ mdl.newSampleData(new double[]{1, 2, 3, 1, 2, 3, 1, 2, 3}, 3, 2, new SparseBlockDistributedMatrix());
+
+ try {
+ mdl.calculateBeta();
+ fail("SingularMatrixException");
+ } catch (SingularMatrixException e) {
+ return;
+ }
+ fail("SingularMatrixException");
+
+ }
+
+ /** */
+ public void testNoDataNPECalculateBeta() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateBeta();
+ fail("java.lang.NullPointerException");
+ } catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+
+ }
+
+ /** */
+ public void testNoDataNPECalculateHat() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateHat();
+ fail("java.lang.NullPointerException");
+ } catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+ }
+
+ /** */
+ public void testNoDataNPESSTO() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateTotalSumOfSquares();
+ fail("java.lang.NullPointerException");
+ } catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+
+
+ }
+
+ /** */
+ public void testMathIllegalArgumentException() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+
+ try {
+ mdl.validateSampleData(new SparseBlockDistributedMatrix(1, 2), new SparseBlockDistributedVector(1));
+ fail("MathIllegalArgumentException");
+ } catch (MathIllegalArgumentException e) {
+ return;
+ }
+ fail("MathIllegalArgumentException");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b0a86018/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
new file mode 100644
index 0000000..764340c
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/DistributedOLSMultipleLinearRegressionTest.java
@@ -0,0 +1,934 @@
+/*
+ * 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.apache.ignite.ml.regressions;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.TestUtils;
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException;
+import org.apache.ignite.ml.math.exceptions.NullArgumentException;
+import org.apache.ignite.ml.math.exceptions.SingularMatrixException;
+import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
+import org.apache.ignite.ml.math.impls.vector.SparseDistributedVector;
+import org.apache.ignite.ml.math.util.MatrixUtil;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+import org.apache.ignite.testframework.junits.common.GridCommonTest;
+import org.junit.Assert;
+import org.junit.Test;
+
+/**
+ * Tests for {@link OLSMultipleLinearRegression}.
+ */
+
+@GridCommonTest(group = "Distributed Models")
+public class DistributedOLSMultipleLinearRegressionTest extends GridCommonAbstractTest {
+ /** */
+ private double[] y;
+
+ /** */
+ private double[][] x;
+
+ private AbstractMultipleLinearRegression regression;
+
+ /** Number of nodes in grid */
+ private static final int NODE_COUNT = 3;
+
+ private static final double PRECISION = 1E-12;
+
+ /** Grid instance. */
+ private Ignite ignite;
+
+ public DistributedOLSMultipleLinearRegressionTest(){
+
+ super(false);
+
+
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void beforeTestsStarted() throws Exception {
+ for (int i = 1; i <= NODE_COUNT; i++)
+ startGrid(i);
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void afterTestsStopped() throws Exception {
+ stopAllGrids();
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override protected void beforeTest() throws Exception {
+ ignite = grid(NODE_COUNT);
+
+ ignite.configuration().setPeerClassLoadingEnabled(true);
+
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+
+ y = new double[] {11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
+ x = new double[6][];
+ x[0] = new double[] {0, 0, 0, 0, 0};
+ x[1] = new double[] {2.0, 0, 0, 0, 0};
+ x[2] = new double[] {0, 3.0, 0, 0, 0};
+ x[3] = new double[] {0, 0, 4.0, 0, 0};
+ x[4] = new double[] {0, 0, 0, 5.0, 0};
+ x[5] = new double[] {0, 0, 0, 0, 6.0};
+
+ regression = createRegression();
+ }
+
+ /** */
+ protected OLSMultipleLinearRegression createRegression() {
+ OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
+ regression.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
+ return regression;
+ }
+
+ /** */
+ @Test
+ public void testPerfectFit() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[] betaHat = regression.estimateRegressionParameters();
+ System.out.println("Beta hat is " + betaHat);
+ TestUtils.assertEquals(new double[] {11.0, 1.0 / 2.0, 2.0 / 3.0, 3.0 / 4.0, 4.0 / 5.0, 5.0 / 6.0},
+ betaHat,
+ 1e-13);
+ double[] residuals = regression.estimateResiduals();
+ TestUtils.assertEquals(new double[] {0d, 0d, 0d, 0d, 0d, 0d}, residuals,
+ 1e-13);
+ Matrix errors = regression.estimateRegressionParametersVariance();
+ final double[] s = {1.0, -1.0 / 2.0, -1.0 / 3.0, -1.0 / 4.0, -1.0 / 5.0, -1.0 / 6.0};
+ Matrix refVar = new SparseDistributedMatrix(s.length, s.length);
+ for (int i = 0; i < refVar.rowSize(); i++)
+ for (int j = 0; j < refVar.columnSize(); j++) {
+ if (i == 0) {
+ refVar.setX(i, j, s[j]);
+ continue;
+ }
+ double x = s[i] * s[j];
+ refVar.setX(i, j, (i == j) ? 2 * x : x);
+ }
+ Assert.assertEquals(0.0,
+ TestUtils.maximumAbsoluteRowSum(errors.minus(refVar)),
+ 5.0e-16 * TestUtils.maximumAbsoluteRowSum(refVar));
+ Assert.assertEquals(1, ((OLSMultipleLinearRegression)regression).calculateRSquared(), 1E-12);
+ }
+
+ /**
+ * Test Longley dataset against certified values provided by NIST.
+ * Data Source: J. Longley (1967) "An Appraisal of Least Squares
+ * Programs for the Electronic Computer from the Point of View of the User"
+ * Journal of the American Statistical Association, vol. 62. September,
+ * pp. 819-841.
+ *
+ * Certified values (and data) are from NIST:
+ * http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
+ */
+ @Test
+ public void testLongly() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ // Y values are first, then independent vars
+ // Each row is one observation
+ double[] design = new double[] {
+ 60323, 83.0, 234289, 2356, 1590, 107608, 1947,
+ 61122, 88.5, 259426, 2325, 1456, 108632, 1948,
+ 60171, 88.2, 258054, 3682, 1616, 109773, 1949,
+ 61187, 89.5, 284599, 3351, 1650, 110929, 1950,
+ 63221, 96.2, 328975, 2099, 3099, 112075, 1951,
+ 63639, 98.1, 346999, 1932, 3594, 113270, 1952,
+ 64989, 99.0, 365385, 1870, 3547, 115094, 1953,
+ 63761, 100.0, 363112, 3578, 3350, 116219, 1954,
+ 66019, 101.2, 397469, 2904, 3048, 117388, 1955,
+ 67857, 104.6, 419180, 2822, 2857, 118734, 1956,
+ 68169, 108.4, 442769, 2936, 2798, 120445, 1957,
+ 66513, 110.8, 444546, 4681, 2637, 121950, 1958,
+ 68655, 112.6, 482704, 3813, 2552, 123366, 1959,
+ 69564, 114.2, 502601, 3931, 2514, 125368, 1960,
+ 69331, 115.7, 518173, 4806, 2572, 127852, 1961,
+ 70551, 116.9, 554894, 4007, 2827, 130081, 1962
+ };
+
+ final int nobs = 16;
+ final int nvars = 6;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
+
+ // Check expected beta values from NIST
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ -3482258.63459582, 15.0618722713733,
+ -0.358191792925910E-01, -2.02022980381683,
+ -1.03322686717359, -0.511041056535807E-01,
+ 1829.15146461355}, 2E-6); //
+
+ // Check expected residuals from R
+ double[] residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[] {
+ 267.340029759711, -94.0139423988359, 46.28716775752924,
+ -410.114621930906, 309.7145907602313, -249.3112153297231,
+ -164.0489563956039, -13.18035686637081, 14.30477260005235,
+ 455.394094551857, -17.26892711483297, -39.0550425226967,
+ -155.5499735953195, -85.6713080421283, 341.9315139607727,
+ -206.7578251937366},
+ 1E-7);
+
+ // Check standard errors from NIST
+ double[] errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[] {
+ 890420.383607373,
+ 84.9149257747669,
+ 0.334910077722432E-01,
+ 0.488399681651699,
+ 0.214274163161675,
+ 0.226073200069370,
+ 455.478499142212}, errors, 1E-6);
+
+ // Check regression standard error against R
+ Assert.assertEquals(304.8540735619638, mdl.estimateRegressionStandardError(), 1E-8);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.995479004577296, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.992465007628826, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ // TODO: IGNITE-5826, uncomment.
+ // checkVarianceConsistency(model);
+
+ // Estimate model without intercept
+ mdl.setNoIntercept(true);
+ mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
+
+ // Check expected beta values from R
+ betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ -52.99357013868291, 0.07107319907358,
+ -0.42346585566399, -0.57256866841929,
+ -0.41420358884978, 48.41786562001326}, 1E-8);
+
+ // Check standard errors from R
+ errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[] {
+ 129.54486693117232, 0.03016640003786,
+ 0.41773654056612, 0.27899087467676, 0.32128496193363,
+ 17.68948737819961}, errors, 1E-11);
+
+ // Check expected residuals from R
+ residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[] {
+ 279.90274927293092, -130.32465380836874, 90.73228661967445, -401.31252201634948,
+ -440.46768772620027, -543.54512853774793, 201.32111639536299, 215.90889365977932,
+ 73.09368242049943, 913.21694494481869, 424.82484953610174, -8.56475876776709,
+ -361.32974610842876, 27.34560497213464, 151.28955976355002, -492.49937355336846},
+ 1E-8);
+
+ // Check regression standard error against R
+ Assert.assertEquals(475.1655079819517, mdl.estimateRegressionStandardError(), 1E-10);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.9999670130706, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.999947220913, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ }
+
+ /**
+ * Test R Swiss fertility dataset against R.
+ * Data Source: R datasets package
+ */
+ @Test
+ public void testSwissFertility() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ double[] design = new double[] {
+ 80.2, 17.0, 15, 12, 9.96,
+ 83.1, 45.1, 6, 9, 84.84,
+ 92.5, 39.7, 5, 5, 93.40,
+ 85.8, 36.5, 12, 7, 33.77,
+ 76.9, 43.5, 17, 15, 5.16,
+ 76.1, 35.3, 9, 7, 90.57,
+ 83.8, 70.2, 16, 7, 92.85,
+ 92.4, 67.8, 14, 8, 97.16,
+ 82.4, 53.3, 12, 7, 97.67,
+ 82.9, 45.2, 16, 13, 91.38,
+ 87.1, 64.5, 14, 6, 98.61,
+ 64.1, 62.0, 21, 12, 8.52,
+ 66.9, 67.5, 14, 7, 2.27,
+ 68.9, 60.7, 19, 12, 4.43,
+ 61.7, 69.3, 22, 5, 2.82,
+ 68.3, 72.6, 18, 2, 24.20,
+ 71.7, 34.0, 17, 8, 3.30,
+ 55.7, 19.4, 26, 28, 12.11,
+ 54.3, 15.2, 31, 20, 2.15,
+ 65.1, 73.0, 19, 9, 2.84,
+ 65.5, 59.8, 22, 10, 5.23,
+ 65.0, 55.1, 14, 3, 4.52,
+ 56.6, 50.9, 22, 12, 15.14,
+ 57.4, 54.1, 20, 6, 4.20,
+ 72.5, 71.2, 12, 1, 2.40,
+ 74.2, 58.1, 14, 8, 5.23,
+ 72.0, 63.5, 6, 3, 2.56,
+ 60.5, 60.8, 16, 10, 7.72,
+ 58.3, 26.8, 25, 19, 18.46,
+ 65.4, 49.5, 15, 8, 6.10,
+ 75.5, 85.9, 3, 2, 99.71,
+ 69.3, 84.9, 7, 6, 99.68,
+ 77.3, 89.7, 5, 2, 100.00,
+ 70.5, 78.2, 12, 6, 98.96,
+ 79.4, 64.9, 7, 3, 98.22,
+ 65.0, 75.9, 9, 9, 99.06,
+ 92.2, 84.6, 3, 3, 99.46,
+ 79.3, 63.1, 13, 13, 96.83,
+ 70.4, 38.4, 26, 12, 5.62,
+ 65.7, 7.7, 29, 11, 13.79,
+ 72.7, 16.7, 22, 13, 11.22,
+ 64.4, 17.6, 35, 32, 16.92,
+ 77.6, 37.6, 15, 7, 4.97,
+ 67.6, 18.7, 25, 7, 8.65,
+ 35.0, 1.2, 37, 53, 42.34,
+ 44.7, 46.6, 16, 29, 50.43,
+ 42.8, 27.7, 22, 29, 58.33
+ };
+
+ final int nobs = 47;
+ final int nvars = 4;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
+
+ // Check expected beta values from R
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 91.05542390271397,
+ -0.22064551045715,
+ -0.26058239824328,
+ -0.96161238456030,
+ 0.12441843147162}, 1E-12);
+
+ // Check expected residuals from R
+ double[] residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[] {
+ 7.1044267859730512, 1.6580347433531366,
+ 4.6944952770029644, 8.4548022690166160, 13.6547432343186212,
+ -9.3586864458500774, 7.5822446330520386, 15.5568995563859289,
+ 0.8113090736598980, 7.1186762732484308, 7.4251378771228724,
+ 2.6761316873234109, 0.8351584810309354, 7.1769991119615177,
+ -3.8746753206299553, -3.1337779476387251, -0.1412575244091504,
+ 1.1186809170469780, -6.3588097346816594, 3.4039270429434074,
+ 2.3374058329820175, -7.9272368576900503, -7.8361010968497959,
+ -11.2597369269357070, 0.9445333697827101, 6.6544245101380328,
+ -0.9146136301118665, -4.3152449403848570, -4.3536932047009183,
+ -3.8907885169304661, -6.3027643926302188, -7.8308982189289091,
+ -3.1792280015332750, -6.7167298771158226, -4.8469946718041754,
+ -10.6335664353633685, 11.1031134362036958, 6.0084032641811733,
+ 5.4326230830188482, -7.2375578629692230, 2.1671550814448222,
+ 15.0147574652763112, 4.8625103516321015, -7.1597256413907706,
+ -0.4515205619767598, -10.2916870903837587, -15.7812984571900063},
+ 1E-12);
+
+ // Check standard errors from R
+ double[] errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[] {
+ 6.94881329475087,
+ 0.07360008972340,
+ 0.27410957467466,
+ 0.19454551679325,
+ 0.03726654773803}, errors, 1E-10);
+
+ // Check regression standard error against R
+ Assert.assertEquals(7.73642194433223, mdl.estimateRegressionStandardError(), 1E-12);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.649789742860228, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.6164363850373927, mdl.calculateAdjustedRSquared(), 1E-12);
+
+ // TODO: IGNITE-5826, uncomment.
+ // checkVarianceConsistency(model);
+
+ // Estimate the model with no intercept
+ mdl = new OLSMultipleLinearRegression();
+ mdl.setNoIntercept(true);
+ mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
+
+ // Check expected beta values from R
+ betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 0.52191832900513,
+ 2.36588087917963,
+ -0.94770353802795,
+ 0.30851985863609}, 1E-12);
+
+ // Check expected residuals from R
+ residuals = mdl.estimateResiduals();
+ TestUtils.assertEquals(residuals, new double[] {
+ 44.138759883538249, 27.720705122356215, 35.873200836126799,
+ 34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814,
+ 1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165,
+ -9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336,
+ -17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924,
+ -10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944,
+ -13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898,
+ -1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603,
+ -16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384,
+ -17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695,
+ -0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976,
+ 2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615},
+ 1E-12);
+
+ // Check standard errors from R
+ errors = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(new double[] {
+ 0.10470063765677, 0.41684100584290,
+ 0.43370143099691, 0.07694953606522}, errors, 1E-10);
+
+ // Check regression standard error against R
+ Assert.assertEquals(17.24710630547, mdl.estimateRegressionStandardError(), 1E-10);
+
+ // Check R-Square statistics against R
+ Assert.assertEquals(0.946350722085, mdl.calculateRSquared(), 1E-12);
+ Assert.assertEquals(0.9413600915813, mdl.calculateAdjustedRSquared(), 1E-12);
+ }
+
+ /**
+ * Test hat matrix computation
+ */
+ @Test
+ public void testHat() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ /*
+ * This example is from "The Hat Matrix in Regression and ANOVA",
+ * David C. Hoaglin and Roy E. Welsch,
+ * The American Statistician, Vol. 32, No. 1 (Feb., 1978), pp. 17-22.
+ *
+ */
+ double[] design = new double[] {
+ 11.14, .499, 11.1,
+ 12.74, .558, 8.9,
+ 13.13, .604, 8.8,
+ 11.51, .441, 8.9,
+ 12.38, .550, 8.8,
+ 12.60, .528, 9.9,
+ 11.13, .418, 10.7,
+ 11.7, .480, 10.5,
+ 11.02, .406, 10.5,
+ 11.41, .467, 10.7
+ };
+
+ int nobs = 10;
+ int nvars = 2;
+
+ // Estimate the model
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(design, nobs, nvars, new SparseDistributedMatrix());
+
+ Matrix hat = mdl.calculateHat();
+
+ // Reference data is upper half of symmetric hat matrix
+ double[] refData = new double[] {
+ .418, -.002, .079, -.274, -.046, .181, .128, .222, .050, .242,
+ .242, .292, .136, .243, .128, -.041, .033, -.035, .004,
+ .417, -.019, .273, .187, -.126, .044, -.153, .004,
+ .604, .197, -.038, .168, -.022, .275, -.028,
+ .252, .111, -.030, .019, -.010, -.010,
+ .148, .042, .117, .012, .111,
+ .262, .145, .277, .174,
+ .154, .120, .168,
+ .315, .148,
+ .187
+ };
+
+ // Check against reference data and verify symmetry
+ int k = 0;
+ for (int i = 0; i < 10; i++) {
+ for (int j = i; j < 10; j++) {
+ Assert.assertEquals(refData[k], hat.getX(i, j), 10e-3);
+ Assert.assertEquals(hat.getX(i, j), hat.getX(j, i), 10e-12);
+ k++;
+ }
+ }
+
+ /*
+ * Verify that residuals computed using the hat matrix are close to
+ * what we get from direct computation, i.e. r = (I - H) y
+ */
+ double[] residuals = mdl.estimateResiduals();
+ Matrix id = MatrixUtil.identityLike(hat, 10);
+ double[] hatResiduals = id.minus(hat).times(mdl.getY()).getStorage().data();
+ TestUtils.assertEquals(residuals, hatResiduals, 10e-12);
+ }
+
+ /**
+ * test calculateYVariance
+ */
+ @Test
+ public void testYVariance() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ // assumes: y = new double[]{11.0, 12.0, 13.0, 14.0, 15.0, 16.0};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ mdl.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
+ TestUtils.assertEquals(mdl.calculateYVariance(), 3.5, 0);
+ }
+
+ /**
+ * Verifies that setting X and Y separately has the same effect as newSample(X,Y).
+ */
+ @Test
+ public void testNewSample2() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] y = new double[] {1, 2, 3, 4};
+ double[][] x = new double[][] {
+ {19, 22, 33},
+ {20, 30, 40},
+ {25, 35, 45},
+ {27, 37, 47}
+ };
+ OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
+ regression.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
+ Matrix combinedX = regression.getX().copy();
+ Vector combinedY = regression.getY().copy();
+ regression.newXSampleData(new SparseDistributedMatrix(x));
+ regression.newYSampleData(new SparseDistributedVector(y));
+ for (int i = 0; i < combinedX.rowSize(); i++) {
+ for (int j = 0; j < combinedX.columnSize(); j++)
+ Assert.assertEquals(combinedX.get(i,j), regression.getX().get(i,j), PRECISION);
+
+ }
+ for (int i = 0; i < combinedY.size(); i++)
+ Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
+
+
+
+ // No intercept
+ regression.setNoIntercept(true);
+ regression.newSampleData(new SparseDistributedVector(y), new SparseDistributedMatrix(x));
+ combinedX = regression.getX().copy();
+ combinedY = regression.getY().copy();
+ regression.newXSampleData(new SparseDistributedMatrix(x));
+ regression.newYSampleData(new SparseDistributedVector(y));
+
+ for (int i = 0; i < combinedX.rowSize(); i++) {
+ for (int j = 0; j < combinedX.columnSize(); j++)
+ Assert.assertEquals(combinedX.get(i,j), regression.getX().get(i,j), PRECISION);
+
+ }
+ for (int i = 0; i < combinedY.size(); i++)
+ Assert.assertEquals(combinedY.get(i), regression.getY().get(i), PRECISION);
+
+ }
+
+ /** */
+ @Test(expected = NullArgumentException.class)
+ public void testNewSampleDataYNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ createRegression().newSampleData(null, new SparseDistributedMatrix(new double[][] {{1}}));
+ fail("NullArgumentException");
+ }
+ catch (NullArgumentException e) {
+ return;
+ }
+ fail("NullArgumentException");
+ }
+
+ /** */
+ public void testNewSampleDataXNull() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+
+ try {
+ createRegression().newSampleData(new SparseDistributedVector(new double[] {1}), null);
+ fail("NullArgumentException");
+ }
+ catch (NullArgumentException e) {
+ return;
+ }
+ fail("NullArgumentException");
+
+
+ }
+
+ /**
+ * This is a test based on the Wampler1 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler1.shtml
+ */
+ @Test
+ public void testWampler1() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[] {
+ 1, 0,
+ 6, 1,
+ 63, 2,
+ 364, 3,
+ 1365, 4,
+ 3906, 5,
+ 9331, 6,
+ 19608, 7,
+ 37449, 8,
+ 66430, 9,
+ 111111, 10,
+ 177156, 11,
+ 271453, 12,
+ 402234, 13,
+ 579195, 14,
+ 813616, 15,
+ 1118481, 16,
+ 1508598, 17,
+ 2000719, 18,
+ 2613660, 19,
+ 3368421, 20};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 1.0,
+ 1.0, 1.0,
+ 1.0, 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[] {
+ 0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+
+ TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
+ }
+
+ /**
+ * This is a test based on the Wampler2 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler2.shtml
+ */
+ @Test
+ public void testWampler2() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[] {
+ 1.00000, 0,
+ 1.11111, 1,
+ 1.24992, 2,
+ 1.42753, 3,
+ 1.65984, 4,
+ 1.96875, 5,
+ 2.38336, 6,
+ 2.94117, 7,
+ 3.68928, 8,
+ 4.68559, 9,
+ 6.00000, 10,
+ 7.71561, 11,
+ 9.92992, 12,
+ 12.75603, 13,
+ 16.32384, 14,
+ 20.78125, 15,
+ 26.29536, 16,
+ 33.05367, 17,
+ 41.26528, 18,
+ 51.16209, 19,
+ 63.00000, 20};
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 1.0,
+ 1.0e-1,
+ 1.0e-2,
+ 1.0e-3, 1.0e-4,
+ 1.0e-5}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[] {
+ 0.0,
+ 0.0, 0.0,
+ 0.0, 0.0,
+ 0.0}, 1E-8);
+ TestUtils.assertEquals(1.0, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(0, mdl.estimateErrorVariance(), 1.0e-7);
+ TestUtils.assertEquals(0.00, mdl.calculateResidualSumOfSquares(), 1.0e-6);
+ }
+
+ /**
+ * This is a test based on the Wampler3 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler3.shtml
+ */
+ @Test
+ public void testWampler3() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[] {
+ 760, 0,
+ -2042, 1,
+ 2111, 2,
+ -1684, 3,
+ 3888, 4,
+ 1858, 5,
+ 11379, 6,
+ 17560, 7,
+ 39287, 8,
+ 64382, 9,
+ 113159, 10,
+ 175108, 11,
+ 273291, 12,
+ 400186, 13,
+ 581243, 14,
+ 811568, 15,
+ 1121004, 16,
+ 1506550, 17,
+ 2002767, 18,
+ 2611612, 19,
+ 3369180, 20};
+
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-8);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[] {
+ 2152.32624678170,
+ 2363.55173469681, 779.343524331583,
+ 101.475507550350, 5.64566512170752,
+ 0.112324854679312}, 1E-8); //
+
+ TestUtils.assertEquals(.999995559025820, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(5570284.53333333, mdl.estimateErrorVariance(), 1.0e-6);
+ TestUtils.assertEquals(83554268.0000000, mdl.calculateResidualSumOfSquares(), 1.0e-5);
+ }
+
+ /**
+ * This is a test based on the Wampler4 data set
+ * http://www.itl.nist.gov/div898/strd/lls/data/Wampler4.shtml
+ */
+ @Test
+ public void testWampler4() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ double[] data = new double[] {
+ 75901, 0,
+ -204794, 1,
+ 204863, 2,
+ -204436, 3,
+ 253665, 4,
+ -200894, 5,
+ 214131, 6,
+ -185192, 7,
+ 221249, 8,
+ -138370, 9,
+ 315911, 10,
+ -27644, 11,
+ 455253, 12,
+ 197434, 13,
+ 783995, 14,
+ 608816, 15,
+ 1370781, 16,
+ 1303798, 17,
+ 2205519, 18,
+ 2408860, 19,
+ 3444321, 20};
+
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+ final int nvars = 5;
+ final int nobs = 21;
+ double[] tmp = new double[(nvars + 1) * nobs];
+ int off = 0;
+ int off2 = 0;
+ for (int i = 0; i < nobs; i++) {
+ tmp[off2] = data[off];
+ tmp[off2 + 1] = data[off + 1];
+ tmp[off2 + 2] = tmp[off2 + 1] * tmp[off2 + 1];
+ tmp[off2 + 3] = tmp[off2 + 1] * tmp[off2 + 2];
+ tmp[off2 + 4] = tmp[off2 + 1] * tmp[off2 + 3];
+ tmp[off2 + 5] = tmp[off2 + 1] * tmp[off2 + 4];
+ off2 += (nvars + 1);
+ off += 2;
+ }
+ mdl.newSampleData(tmp, nobs, nvars, new SparseDistributedMatrix());
+ double[] betaHat = mdl.estimateRegressionParameters();
+ TestUtils.assertEquals(betaHat,
+ new double[] {
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0,
+ 1.0}, 1E-6);
+
+ double[] se = mdl.estimateRegressionParametersStandardErrors();
+ TestUtils.assertEquals(se,
+ new double[] {
+ 215232.624678170,
+ 236355.173469681, 77934.3524331583,
+ 10147.5507550350, 564.566512170752,
+ 11.2324854679312}, 1E-8);
+
+ TestUtils.assertEquals(.957478440825662, mdl.calculateRSquared(), 1.0e-10);
+ TestUtils.assertEquals(55702845333.3333, mdl.estimateErrorVariance(), 1.0e-4);
+ TestUtils.assertEquals(835542680000.000, mdl.calculateResidualSumOfSquares(), 1.0e-3);
+ }
+
+ /**
+ * Anything requiring beta calculation should advertise SME.
+ */
+ public void testSingularCalculateBeta() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression(1e-15);
+ mdl.newSampleData(new double[] {1, 2, 3, 1, 2, 3, 1, 2, 3}, 3, 2, new SparseDistributedMatrix());
+
+ try {
+ mdl.calculateBeta();
+ fail("SingularMatrixException");
+ }
+ catch (SingularMatrixException e) {
+ return;
+ }
+ fail("SingularMatrixException");
+
+ }
+
+ /** */
+ public void testNoDataNPECalculateBeta() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateBeta();
+ fail("java.lang.NullPointerException");
+ }
+ catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+
+ }
+
+ /** */
+ public void testNoDataNPECalculateHat() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateHat();
+ fail("java.lang.NullPointerException");
+ }
+ catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+ }
+
+
+ public void testNoDataNPESSTO() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+ try {
+ mdl.calculateTotalSumOfSquares();
+ fail("java.lang.NullPointerException");
+ }
+ catch (NullPointerException e) {
+ return;
+ }
+ fail("java.lang.NullPointerException");
+
+
+ }
+
+ /** */
+ public void testMathIllegalArgumentException() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ OLSMultipleLinearRegression mdl = new OLSMultipleLinearRegression();
+
+
+ try {
+ mdl.validateSampleData(new SparseDistributedMatrix(1, 2), new SparseDistributedVector(1));
+ fail("MathIllegalArgumentException");
+ }
+ catch (MathIllegalArgumentException e) {
+ return;
+ }
+ fail("MathIllegalArgumentException");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b0a86018/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
index 4be7336..2774028 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/OLSMultipleLinearRegressionTest.java
@@ -418,6 +418,7 @@ public class OLSMultipleLinearRegressionTest extends AbstractMultipleLinearRegre
Matrix hat = mdl.calculateHat();
+
// Reference data is upper half of symmetric hat matrix
double[] refData = new double[] {
.418, -.002, .079, -.274, -.046, .181, .128, .222, .050, .242,
http://git-wip-us.apache.org/repos/asf/ignite/blob/b0a86018/modules/ml/src/test/java/org/apache/ignite/ml/regressions/RegressionsTestSuite.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/RegressionsTestSuite.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/RegressionsTestSuite.java
index a54a4e3..2a0b111 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/RegressionsTestSuite.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/RegressionsTestSuite.java
@@ -25,7 +25,7 @@ import org.junit.runners.Suite;
*/
@RunWith(Suite.class)
@Suite.SuiteClasses({
- OLSMultipleLinearRegressionTest.class
+ OLSMultipleLinearRegressionTest.class, DistributedOLSMultipleLinearRegressionTest.class, DistributedBlockOLSMultipleLinearRegressionTest.class
})
public class RegressionsTestSuite {
// No-op.