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Posted to dev@systemds.apache.org by GitBox <gi...@apache.org> on 2021/01/15 08:23:46 UTC
[GitHub] [systemds] Baunsgaard commented on a change in pull request #1153: Gaussian Classifier
Baunsgaard commented on a change in pull request #1153:
URL: https://github.com/apache/systemds/pull/1153#discussion_r558011418
##########
File path: scripts/builtin/gaussianClassifier.dml
##########
@@ -0,0 +1,127 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+#
+# Computes the parameters needed for Gaussian Classification.
+# Thus it computes the following per class: the prior probability,
+# the inverse covariance matrix, the mean per feature and the determinant
+# of the covariance matrix. Furthermore (if not explicitely defined), it
+# adds some small smoothing value along the variances, to prevent
+# numerical errors / instabilities.
+#
+#
+# INPUT PARAMETERS:
+# -------------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# -------------------------------------------------------------------------------------------------
+# D Matrix[Double] --- Input matrix (training set)
+# C Matrix[Double] --- Target vector
+# varSmoothing Double 1e-9 Smoothing factor for variances
+# verbose Boolean TRUE Print accuracy of the training set
+# ---------------------------------------------------------------------------------------------
+# OUTPUT:
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# classPriors Matrix[Double] --- Vector storing the class prior probabilities
+# classMeans Matrix[Double] --- Matrix storing the means of the classes
+# classInvCovariances List[Unknown] --- List of inverse covariance matrices
+# determinants Matrix[Double] --- Vector storing the determinants of the classes
+# ---------------------------------------------------------------------------------------------
+#
+
+
+m_gaussianClassifier = function(Matrix[Double] D, Matrix[Double] C, Double varSmoothing=1e-9, Boolean verbose = TRUE)
+ return (Matrix[Double] classPriors, Matrix[Double] classMeans,
+ List[Unknown] classInvCovariances, Matrix[Double] determinants)
+{
+ #Retrieve number of samples, classes and features
+ nSamples = nrow(D)
+ nClasses = max(C)
+ nFeats = ncol(D)
+
+ #Set varSmoothing (to prevent numerical errors)
+ varSmoothing = 1e-9
Review comment:
here you overwrite the input parameter
##########
File path: scripts/builtin/gaussianClassifier.dml
##########
@@ -0,0 +1,127 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+#
+# Computes the parameters needed for Gaussian Classification.
+# Thus it computes the following per class: the prior probability,
+# the inverse covariance matrix, the mean per feature and the determinant
+# of the covariance matrix. Furthermore (if not explicitely defined), it
+# adds some small smoothing value along the variances, to prevent
+# numerical errors / instabilities.
+#
+#
+# INPUT PARAMETERS:
+# -------------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# -------------------------------------------------------------------------------------------------
+# D Matrix[Double] --- Input matrix (training set)
+# C Matrix[Double] --- Target vector
+# varSmoothing Double 1e-9 Smoothing factor for variances
+# verbose Boolean TRUE Print accuracy of the training set
+# ---------------------------------------------------------------------------------------------
+# OUTPUT:
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# classPriors Matrix[Double] --- Vector storing the class prior probabilities
+# classMeans Matrix[Double] --- Matrix storing the means of the classes
+# classInvCovariances List[Unknown] --- List of inverse covariance matrices
+# determinants Matrix[Double] --- Vector storing the determinants of the classes
+# ---------------------------------------------------------------------------------------------
+#
+
+
+m_gaussianClassifier = function(Matrix[Double] D, Matrix[Double] C, Double varSmoothing=1e-9, Boolean verbose = TRUE)
+ return (Matrix[Double] classPriors, Matrix[Double] classMeans,
+ List[Unknown] classInvCovariances, Matrix[Double] determinants)
+{
+ #Retrieve number of samples, classes and features
+ nSamples = nrow(D)
+ nClasses = max(C)
+ nFeats = ncol(D)
+
+ #Set varSmoothing (to prevent numerical errors)
+ varSmoothing = 1e-9
+
+ #Compute means, variances and priors
+ classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(nClasses));
+ classMeans = aggregate(target=D, groups=C, fn="mean", ngroups=as.integer(nClasses));
+ classVars = aggregate(target=D, groups=C, fn="variance", ngroups=as.integer(nClasses));
+ classPriors = classCounts / nSamples
+
+ smoothedVar = diag(matrix(1.0, rows=nFeats, cols=1)) * max(classVars) * varSmoothing
+
+ classInvCovariances = list()
+ determinants = matrix(0, rows=nClasses, cols=1)
+
+ #Compute determinants and inverseCovariances
+ for (class in 1:nClasses)
Review comment:
consider if it can be parallelized, if so use a parfor
##########
File path: src/test/scripts/functions/builtin/GaussianClassifier.dml
##########
@@ -0,0 +1,37 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+X = read($1);
+y = read($2);
+
+[prior, means, covs, det] = gaussianClassifier(D=X, C=y, varSmoothing=$3);
+
+#Cbind the inverse covariance matrices, to make them comparable in the unit tests
+invcovs = as.matrix(covs[1])
+for (i in 2:max(y))
+{
+ invcovs = cbind(invcovs, as.matrix(covs[i]))
+}
+
+write(prior, $4);
+write(means, $5);
+write(det, $6);
+write(invcovs, $7);
Review comment:
newline
##########
File path: src/test/scripts/functions/builtin/GaussianClassifier.R
##########
@@ -0,0 +1,111 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+args <- commandArgs(TRUE)
+library("Matrix")
+
+D <- as.matrix(readMM(paste(args[1], "X.mtx", sep="")))
+c <- as.matrix(readMM(paste(args[1], "Y.mtx", sep="")))
+
+nClasses <- as.integer(max(c))
+varSmoothing <- as.double(args[2])
+
+nSamples <- nrow(D)
+nFeatures <- ncol(D)
+
+classInvCovariances <- list()
+
+classMeans <- aggregate(D, by=list(c), FUN= mean)
+classMeans <- classMeans[1:nFeatures+1]
+
+classVars <- aggregate(D, by=list(c), FUN=var)
+classVars[is.na(classVars)] <- 0
+smoothedVar <- varSmoothing * max(classVars) * diag(nFeatures)
+
+classCounts <- aggregate(c, by=list(c), FUN=length)
+classCounts <- classCounts[2]
+classPriors <- classCounts / nSamples
+
+determinants <- matrix(0, nrow=nClasses, ncol=1)
+
+for (i in 1:nClasses)
+{
+ classMatrix <- subset(D, c==i)
+ covMatrix <- cov(x=classMatrix, use="all.obs")
+ covMatrix[is.na(covMatrix)] <- 0
+ covMatrix <- covMatrix + smoothedVar
+ #determinant <- det(covMatrix)
+ #determinants[i] <- det(covMatrix)
+
+ ev <- eigen(covMatrix)
+ vecs <- ev$vectors
+ vals <- ev$values
+ lam <- diag(vals^(-1))
+ determinants[i] <- prod(vals)
+
+ invCovMatrix <- vecs %*% lam %*% t(vecs)
+ invCovMatrix[is.na(invCovMatrix)] <- 0
+ classInvCovariances[[i]] <- invCovMatrix
+}
+
+
+#Calc accuracy
+results <- matrix(0, nrow=nSamples, ncol=nClasses)
+for (class in 1:nClasses)
+{
+ for (i in 1:nSamples)
+ {
+ intermediate <- 0
+ meanDiff <- (D[i,] - classMeans[class,])
+ intermediate <- -1/2 * log((2*pi)^nFeatures * determinants[class,])
+ intermediate <- intermediate - 1/2 * (as.matrix(meanDiff) %*% as.matrix(classInvCovariances[[class]]) %*% t(as.matrix(meanDiff)))
+ intermediate <- log(classPriors[class,]) + intermediate
+ results[i, class] <- intermediate
+ }
+}
+
+pred <- max.col(results)
+acc <- sum(pred == c) / nSamples * 100
+print(paste("Training Accuracy (%): ", acc, sep=""))
+
+classPriors <- data.matrix(classPriors)
+classMeans <- data.matrix(classMeans)
+
+#Cbind the inverse covariance matrices, to make them comparable in the unit tests
+stackedInvCovs <- classInvCovariances[[1]]
+for (i in 2:nClasses)
+{
+ stackedInvCovs <- cbind(stackedInvCovs, classInvCovariances[[i]])
+}
+
+writeMM(as(classPriors, "CsparseMatrix"), paste(args[3], "priors", sep=""));
+writeMM(as(classMeans, "CsparseMatrix"), paste(args[3], "means", sep=""));
+writeMM(as(determinants, "CsparseMatrix"), paste(args[3], "determinants", sep=""));
+writeMM(as(stackedInvCovs, "CsparseMatrix"), paste(args[3], "invcovs", sep=""));
+
+
+
+
Review comment:
to many new lines
##########
File path: scripts/builtin/gaussianClassifier.dml
##########
@@ -0,0 +1,127 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+#
+# Computes the parameters needed for Gaussian Classification.
+# Thus it computes the following per class: the prior probability,
+# the inverse covariance matrix, the mean per feature and the determinant
+# of the covariance matrix. Furthermore (if not explicitely defined), it
+# adds some small smoothing value along the variances, to prevent
+# numerical errors / instabilities.
+#
+#
+# INPUT PARAMETERS:
+# -------------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# -------------------------------------------------------------------------------------------------
+# D Matrix[Double] --- Input matrix (training set)
+# C Matrix[Double] --- Target vector
+# varSmoothing Double 1e-9 Smoothing factor for variances
+# verbose Boolean TRUE Print accuracy of the training set
+# ---------------------------------------------------------------------------------------------
+# OUTPUT:
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# classPriors Matrix[Double] --- Vector storing the class prior probabilities
+# classMeans Matrix[Double] --- Matrix storing the means of the classes
+# classInvCovariances List[Unknown] --- List of inverse covariance matrices
+# determinants Matrix[Double] --- Vector storing the determinants of the classes
+# ---------------------------------------------------------------------------------------------
+#
+
+
+m_gaussianClassifier = function(Matrix[Double] D, Matrix[Double] C, Double varSmoothing=1e-9, Boolean verbose = TRUE)
+ return (Matrix[Double] classPriors, Matrix[Double] classMeans,
+ List[Unknown] classInvCovariances, Matrix[Double] determinants)
+{
+ #Retrieve number of samples, classes and features
+ nSamples = nrow(D)
+ nClasses = max(C)
+ nFeats = ncol(D)
+
+ #Set varSmoothing (to prevent numerical errors)
+ varSmoothing = 1e-9
+
+ #Compute means, variances and priors
+ classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(nClasses));
+ classMeans = aggregate(target=D, groups=C, fn="mean", ngroups=as.integer(nClasses));
+ classVars = aggregate(target=D, groups=C, fn="variance", ngroups=as.integer(nClasses));
+ classPriors = classCounts / nSamples
+
+ smoothedVar = diag(matrix(1.0, rows=nFeats, cols=1)) * max(classVars) * varSmoothing
+
+ classInvCovariances = list()
+ determinants = matrix(0, rows=nClasses, cols=1)
+
+ #Compute determinants and inverseCovariances
+ for (class in 1:nClasses)
+ {
+ covMatrix = matrix(0, rows=nFeats, cols=nFeats)
+ classMatrix = removeEmpty(target=D, margin="rows", select=(C==class))
+
+ for (i in 1:nFeats)
+ {
+ for (j in 1:nFeats)
+ {
+ if (j == i)
+ covMatrix[i,j] = classVars[class, j]
+ else if (j < i)
+ covMatrix[i,j] = covMatrix[j,i]
+ else
+ covMatrix[i,j] = cov(classMatrix[,i], classMatrix[,j])
+ }
+ }
+
+ #Apply smoothing of the variances, to avoid numerical errors
+ covMatrix = covMatrix + smoothedVar
+
+ #Compute inverse
+ [eVals, eVecs] = eigen(covMatrix)
+ lam = diag(eVals^(-1))
+ invCovMatrix = eVecs %*% lam %*% t(eVecs)
+
+ #Compute determinant
+ det = prod(eVals)
+
+ determinants[class, 1] = det
+ classInvCovariances = append(classInvCovariances, invCovMatrix)
+ }
+
+ #Compute accuracy on the training set
+ if (verbose)
+ {
+ results = matrix(0, rows=nSamples, cols=nClasses)
+ for (class in 1:nClasses)
Review comment:
again maybe parfor
##########
File path: scripts/builtin/gaussianClassifier.dml
##########
@@ -0,0 +1,127 @@
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+#
+# Computes the parameters needed for Gaussian Classification.
+# Thus it computes the following per class: the prior probability,
+# the inverse covariance matrix, the mean per feature and the determinant
+# of the covariance matrix. Furthermore (if not explicitely defined), it
+# adds some small smoothing value along the variances, to prevent
+# numerical errors / instabilities.
+#
+#
+# INPUT PARAMETERS:
+# -------------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# -------------------------------------------------------------------------------------------------
+# D Matrix[Double] --- Input matrix (training set)
+# C Matrix[Double] --- Target vector
+# varSmoothing Double 1e-9 Smoothing factor for variances
+# verbose Boolean TRUE Print accuracy of the training set
+# ---------------------------------------------------------------------------------------------
+# OUTPUT:
+# ---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+# ---------------------------------------------------------------------------------------------
+# classPriors Matrix[Double] --- Vector storing the class prior probabilities
+# classMeans Matrix[Double] --- Matrix storing the means of the classes
+# classInvCovariances List[Unknown] --- List of inverse covariance matrices
+# determinants Matrix[Double] --- Vector storing the determinants of the classes
+# ---------------------------------------------------------------------------------------------
+#
+
+
+m_gaussianClassifier = function(Matrix[Double] D, Matrix[Double] C, Double varSmoothing=1e-9, Boolean verbose = TRUE)
+ return (Matrix[Double] classPriors, Matrix[Double] classMeans,
+ List[Unknown] classInvCovariances, Matrix[Double] determinants)
+{
+ #Retrieve number of samples, classes and features
+ nSamples = nrow(D)
+ nClasses = max(C)
+ nFeats = ncol(D)
+
+ #Set varSmoothing (to prevent numerical errors)
+ varSmoothing = 1e-9
+
+ #Compute means, variances and priors
+ classCounts = aggregate(target=C, groups=C, fn="count", ngroups=as.integer(nClasses));
+ classMeans = aggregate(target=D, groups=C, fn="mean", ngroups=as.integer(nClasses));
+ classVars = aggregate(target=D, groups=C, fn="variance", ngroups=as.integer(nClasses));
+ classPriors = classCounts / nSamples
+
+ smoothedVar = diag(matrix(1.0, rows=nFeats, cols=1)) * max(classVars) * varSmoothing
+
+ classInvCovariances = list()
+ determinants = matrix(0, rows=nClasses, cols=1)
+
+ #Compute determinants and inverseCovariances
+ for (class in 1:nClasses)
+ {
+ covMatrix = matrix(0, rows=nFeats, cols=nFeats)
+ classMatrix = removeEmpty(target=D, margin="rows", select=(C==class))
+
+ for (i in 1:nFeats)
+ {
+ for (j in 1:nFeats)
+ {
+ if (j == i)
+ covMatrix[i,j] = classVars[class, j]
+ else if (j < i)
+ covMatrix[i,j] = covMatrix[j,i]
+ else
+ covMatrix[i,j] = cov(classMatrix[,i], classMatrix[,j])
+ }
+ }
+
+ #Apply smoothing of the variances, to avoid numerical errors
+ covMatrix = covMatrix + smoothedVar
+
+ #Compute inverse
+ [eVals, eVecs] = eigen(covMatrix)
+ lam = diag(eVals^(-1))
+ invCovMatrix = eVecs %*% lam %*% t(eVecs)
+
+ #Compute determinant
+ det = prod(eVals)
+
+ determinants[class, 1] = det
+ classInvCovariances = append(classInvCovariances, invCovMatrix)
+ }
+
+ #Compute accuracy on the training set
+ if (verbose)
+ {
+ results = matrix(0, rows=nSamples, cols=nClasses)
+ for (class in 1:nClasses)
+ {
+ for (i in 1:nSamples)
+ {
+ intermediate = 0
+ meanDiff = (D[i,] - classMeans[class,])
+ intermediate = -1/2 * log((2*pi)^nFeats * determinants[class,])
+ intermediate = intermediate - 1/2 * (meanDiff %*% as.matrix(classInvCovariances[class]) %*% t(meanDiff))
+ intermediate = log(classPriors[class,]) + intermediate
+ results[i, class] = intermediate
+ }
+ }
+ acc = sum(rowIndexMax(results) == C) / nSamples * 100
+ print("Training Accuracy (%): " + acc)
+ }
+}
Review comment:
newline in the end of the scripts to make github happy.
##########
File path: src/test/java/org/apache/sysds/test/functions/builtin/BuiltinGaussianClassifierTest.java
##########
@@ -0,0 +1,142 @@
+/*
+ * 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.sysds.test.functions.builtin;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+
+import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.apache.sysds.test.TestUtils;
+import org.junit.Test;
+
+public class BuiltinGaussianClassifierTest extends AutomatedTestBase
+{
+ private final static String TEST_NAME = "GaussianClassifier";
+ private final static String TEST_DIR = "functions/builtin/";
+ private final static String TEST_CLASS_DIR = TEST_DIR + BuiltinGaussianClassifierTest.class.getSimpleName() + "/";
+
+
+ @Override
+ public void setUp() {
+ addTestConfiguration(TEST_NAME,new TestConfiguration(TEST_CLASS_DIR, TEST_NAME,new String[]{"B"}));
+ }
+
+
+ @Test
+ public void testSmallDenseFiveClasses() {
+ testGaussianClassifier(80, 30, 0.9, 5);
+ }
+
+ @Test
+ public void testSmallDenseTenClasses() {
+ testGaussianClassifier(80, 30, 0.9, 10);
+ }
+
+ @Test
+ public void testBiggerDenseFiveClasses() {
+ testGaussianClassifier(200, 50, 0.9, 5);
+ }
+
+ @Test
+ public void testBiggerDenseTenClasses() {
+ testGaussianClassifier(200, 50, 0.9, 10);
+ }
+
+ @Test
+ public void testBiggerSparseFiveClasses() {
+ testGaussianClassifier(200, 50, 0.3, 5);
+ }
+
+ @Test
+ public void testBiggerSparseTenClasses() {
+ testGaussianClassifier(200, 50, 0.3, 10);
+ }
+
+ @Test
+ public void testSmallSparseFiveClasses() {
+ testGaussianClassifier(80, 30, 0.3, 5);
+ }
+
+ @Test
+ public void testSmallSparseTenClasses() {
+ testGaussianClassifier(80, 30, 0.3, 10);
+ }
+
+ public void testGaussianClassifier(int rows, int cols, double sparsity, int classes)
+ {
+ loadTestConfiguration(getTestConfiguration(TEST_NAME));
+ String HOME = SCRIPT_DIR + TEST_DIR;
+ fullDMLScriptName = HOME + TEST_NAME + ".dml";
+ ;
+ double varSmoothing = 1e-9;
+
+ List<String> proArgs = new ArrayList<>();
+ proArgs.add("-args");
+ proArgs.add(input("X"));
+ proArgs.add(input("Y"));
+ proArgs.add(String.valueOf(varSmoothing));
+ proArgs.add(output("priors"));
+ proArgs.add(output("means"));
+ proArgs.add(output("determinants"));
+ proArgs.add(output("invcovs"));
+
+ programArgs = proArgs.toArray(new String[proArgs.size()]);
+
+ rCmd = getRCmd(inputDir(), Double.toString(varSmoothing), expectedDir());
+
+ double[][] X = getRandomMatrix(rows, cols, 0, 100, sparsity, -1);
+ double[][] Y = getRandomMatrix(rows, 1, 0, 1, 1, -1);
+ for(int i=0; i<rows; i++){
+ Y[i][0] = (int)(Y[i][0]*classes) + 1;
+ Y[i][0] = (Y[i][0] > classes) ? classes : Y[i][0];
+ }
+
+ writeInputMatrixWithMTD("X", X, true);
+ writeInputMatrixWithMTD("Y", Y, true);
+
+ runTest(true, EXCEPTION_NOT_EXPECTED, null, -1);
+
+ runRScript(true);
+
+ HashMap<CellIndex, Double> priorR = readRMatrixFromExpectedDir("priors");
+ HashMap<CellIndex, Double> priorSYSTEMDS= readDMLMatrixFromOutputDir("priors");
+ HashMap<CellIndex, Double> meansRtemp = readRMatrixFromExpectedDir("means");
+ HashMap<CellIndex, Double> meansSYSTEMDStemp = readDMLMatrixFromOutputDir("means");
+ HashMap<CellIndex, Double> determinantsRtemp = readRMatrixFromExpectedDir("determinants");
+ HashMap<CellIndex, Double> determinantsSYSTEMDStemp = readDMLMatrixFromOutputDir("determinants");
+ HashMap<CellIndex, Double> invcovsRtemp = readRMatrixFromExpectedDir("invcovs");
+ HashMap<CellIndex, Double> invcovsSYSTEMDStemp = readDMLMatrixFromOutputDir("invcovs");
+
+ double[][] meansR = TestUtils.convertHashMapToDoubleArray(meansRtemp);
+ double[][] meansSYSTEMDS = TestUtils.convertHashMapToDoubleArray(meansSYSTEMDStemp);
+ double[][] determinantsR = TestUtils.convertHashMapToDoubleArray(determinantsRtemp);
+ double[][] determinantsSYSTEMDS = TestUtils.convertHashMapToDoubleArray(determinantsSYSTEMDStemp);
+ double[][] invcovsR = TestUtils.convertHashMapToDoubleArray(invcovsRtemp);
+ double[][] invcovsSYSTEMDS = TestUtils.convertHashMapToDoubleArray(invcovsSYSTEMDStemp);
+
+ TestUtils.compareMatrices(priorR, priorSYSTEMDS, Math.pow(10, -5.0), "priorR", "priorSYSTEMDS");
+ TestUtils.compareMatricesBitAvgDistance(meansR, meansSYSTEMDS, 5L,5L, this.toString());
+ TestUtils.compareMatricesBitAvgDistance(determinantsR, determinantsSYSTEMDS, (long)2E+12,(long)2E+12, this.toString());
+ TestUtils.compareMatricesBitAvgDistance(invcovsR, invcovsSYSTEMDS, (long)2E+20,(long)2E+20, this.toString());
Review comment:
i would probably add the execution of the inverse multiplied with the result,
just as you stated in the pr it gives the original.
if you add this to the tests, would it still make sense to run the R script?
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