Add BuiltinGloVeTest.java

- This test first runs the DML script to generate the top K most similar words for each word in the GloVe word embedding.  
- Then, it computes the accuracy of the DML results based on the hits of the most similar words for the entire vocabulary, comparing the expected results with the DML output.  
- To validate the correctness of our GloVe word embedding implementation, we employ a Controlled Overfitting Validation approach.  
- This methodology addresses the inherent challenge of testing stochastic algorithms, where random initialization typically prevents direct output comparison between different runs or implementations.

Author: saminbassiri

src/test/java/org/apache/sysds/test/functions/builtin/part1/BuiltinGloVeTest.java

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+/*
+ * 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.part1;
+
+import java.io.IOException;
+import java.util.Objects;
+
+import org.apache.sysds.common.Types;
+import org.apache.sysds.common.Types.FileFormat;
+import org.apache.sysds.runtime.frame.data.FrameBlock;
+import org.apache.sysds.test.AutomatedTestBase;
+import org.apache.sysds.test.TestConfiguration;
+import org.junit.Test;
+
+public class BuiltinGloVeTest extends AutomatedTestBase {
+
+    private static final String TEST_NAME = "glove";
+    private static final String TEST_DIR = "functions/builtin/";
+    private static final String RESOURCE_DIRECTORY = "./src/test/resources/datasets/";
+    private static final String TEST_CLASS_DIR = TEST_DIR + BuiltinGloVeTest.class.getSimpleName() + "/";
+
+    private final static Types.ValueType[] schema = {Types.ValueType.STRING};
+
+    private static final int TOP_K = 5;
+    private static final double ACCURACY_THRESHOLD = 0.85;
+    
+    private static final double seed = 45;
+    private static final double vector_size = 100;
+    private static final double alpha = 0.75;
+    private static final double eta = 0.05;
+    private static final double x_max = 100;
+    private static final double tol = 1e-4;
+    private static final double iterations = 10000;
+    private static final double print_loss_it  = 100;
+    private static final double maxTokens = 2600;
+    private static final double windowSize = 15;
+    private static final String distanceWeighting = "TRUE";
+    private static final String symmetric = "TRUE";
+
+    @Override
+    public void setUp() {
+        addTestConfiguration(TEST_NAME,
+                new TestConfiguration(TEST_CLASS_DIR, TEST_NAME, new String[] {"out_result"}));
+    }
+
+    @Test
+    public void gloveTest() throws IOException{
+        // Using top-5 words for similarity comparison
+        runGloVe(TOP_K); 
+
+        // Read the computed similarity results from SystemDS
+        FrameBlock computedSimilarity = readDMLFrameFromHDFS("out_result", FileFormat.CSV);
+
+        // Load expected results (precomputed in Python)
+        FrameBlock expectedSimilarity = readDMLFrameFromHDFS(RESOURCE_DIRECTORY + "/GloVe/gloveExpectedTop10.csv", FileFormat.CSV, false);
+
+        // Compute accuracy by comparing computed and expected results
+        double accuracy = computeAccuracy(computedSimilarity, expectedSimilarity, TOP_K);
+
+        System.out.println("Computed Accuracy: " + accuracy);
+
+        // Ensure accuracy is above a reasonable threshold
+        assert accuracy > ACCURACY_THRESHOLD : "Accuracy too low! Expected > 85% match.";
+    }
+
+    public void runGloVe(int topK) {
+        // Load test configuration
+        Types.ExecMode platformOld = setExecMode(Types.ExecType.CP);
+        try {
+            loadTestConfiguration(getTestConfiguration(TEST_NAME));
+
+            String HOME = SCRIPT_DIR + TEST_DIR;
+
+            fullDMLScriptName = HOME + TEST_NAME + ".dml";
+
+            programArgs = new String[] {
+                    "-nvargs",
+                    "input=" + RESOURCE_DIRECTORY + "20news/20news_subset_untokenized.csv",
+                    "seed=" + seed, 
+                    "vector_size=" + vector_size, 
+                    "alpha=" + alpha, 
+                    "eta=" + eta, 
+                    "x_max=" + x_max, 
+                    "tol=" + tol, 
+                    "iterations=" + iterations, 
+                    "print_loss_it=" + print_loss_it, 
+                    "maxTokens=" + maxTokens, 
+                    "windowSize=" + windowSize, 
+                    "distanceWeighting=" + distanceWeighting,
+                    "symmetric=" + symmetric,
+                    "topK=" + topK,
+                    "out_result=" + output("out_result")
+            };
+
+            System.out.println("Running DML script...");
+            runTest(true, false, null, -1);
+            System.out.println("Test execution completed.");
+        } finally {
+            rtplatform = platformOld;
+        }
+    }
+
+    /**
+     * Computes accuracy by comparing top-K similar words between computed and expected results.
+     */
+    private double computeAccuracy(FrameBlock computed, FrameBlock expected, int k) {
+        int count = 0;
+        for (int i = 0; i < computed.getNumRows(); i++) {
+            int matchCount = 0;
+            for (int j = 1; j < k; j++) {
+                String word1 = computed.getString(i, j);
+                for (int m = 0; m < k; m++) {
+                    if (Objects.equals(word1, expected.getString(i, m))) {
+                        matchCount++;
+                        break;
+                    }
+                }
+            }
+            if (matchCount > 0) {
+                count++;
+            }
+        }
+        return (double) count / computed.getNumRows();
+    }
+}