1 files changed, 0 insertions, 242 deletions
diff --git a/test/de/lmu/ifi/dbs/elki/algorithm/AbstractSimpleAlgorithmTest.java b/test/de/lmu/ifi/dbs/elki/algorithm/AbstractSimpleAlgorithmTest.java
deleted file mode 100644
index 3e260e8e..00000000
--- a/test/de/lmu/ifi/dbs/elki/algorithm/AbstractSimpleAlgorithmTest.java
+++ /dev/null
@@ -1,242 +0,0 @@
-package de.lmu.ifi.dbs.elki.algorithm;
-
-/*
- This file is part of ELKI:
- Environment for Developing KDD-Applications Supported by Index-Structures
-
- Copyright (C) 2012
- Ludwig-Maximilians-Universität München
- Lehr- und Forschungseinheit für Datenbanksysteme
- ELKI Development Team
-
- This program is free software: you can redistribute it and/or modify
- it under the terms of the GNU Affero General Public License as published by
- the Free Software Foundation, either version 3 of the License, or
- (at your option) any later version.
-
- This program is distributed in the hope that it will be useful,
- but WITHOUT ANY WARRANTY; without even the implied warranty of
- MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
- GNU Affero General Public License for more details.
-
- You should have received a copy of the GNU Affero General Public License
- along with this program.  If not, see <http://www.gnu.org/licenses/>.
- */
-
-import static org.junit.Assert.assertTrue;
-import static org.junit.Assert.fail;
-
-import java.io.File;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collection;
-import java.util.Collections;
-import java.util.List;
-
-import de.lmu.ifi.dbs.elki.algorithm.clustering.trivial.ByLabelClustering;
-import de.lmu.ifi.dbs.elki.data.Cluster;
-import de.lmu.ifi.dbs.elki.data.Clustering;
-import de.lmu.ifi.dbs.elki.data.model.Model;
-import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
-import de.lmu.ifi.dbs.elki.database.Database;
-import de.lmu.ifi.dbs.elki.database.StaticArrayDatabase;
-import de.lmu.ifi.dbs.elki.database.ids.DBID;
-import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
-import de.lmu.ifi.dbs.elki.database.relation.Relation;
-import de.lmu.ifi.dbs.elki.datasource.FileBasedDatabaseConnection;
-import de.lmu.ifi.dbs.elki.datasource.filter.FixedDBIDsFilter;
-import de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable;
-import de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierROCCurve;
-import de.lmu.ifi.dbs.elki.logging.Logging;
-import de.lmu.ifi.dbs.elki.result.Result;
-import de.lmu.ifi.dbs.elki.result.ResultUtil;
-import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
-import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.ListParameterization;
-
-/**
- * Abstract base class useful for testing various algorithms.
- * 
- * @author Erich Schubert
- */
-public abstract class AbstractSimpleAlgorithmTest {
-  /**
-   * Base path for unit test files.
-   */
-  public final static String UNITTEST = "data/testdata/unittests/";
-
-  /**
-   * Notice: this is okay for tests - don't use this for frequently used
-   * objects, use a static instance instead!
-   */
-  protected Logging logger = Logging.getLogger(this.getClass());
-
-  /**
-   * Validate that parameterization succeeded: no parameters left, no
-   * parameterization errors.
-   * 
-   * @param config Parameterization to test
-   */
-  protected void testParameterizationOk(ListParameterization config) {
-    if (config.hasUnusedParameters()) {
-      fail("Unused parameters: " + config.getRemainingParameters());
-    }
-    if (config.hasErrors()) {
-      config.logAndClearReportedErrors();
-      fail("Parameterization errors.");
-    }
-  }
-
-  /**
-   * Generate a simple DoubleVector database from a file.
-   * 
-   * @param filename File to load
-   * @param expectedSize Expected size in records
-   * @param params Extra parameters
-   * @return Database
-   */
-  protected <T> Database makeSimpleDatabase(String filename, int expectedSize, ListParameterization params, Class<?>[] filters) {
-    org.junit.Assert.assertTrue("Test data set not found: " + filename, (new File(filename)).exists());
-    params.addParameter(FileBasedDatabaseConnection.Parameterizer.INPUT_ID, filename);
-
-    List<Class<?>> filterlist = new ArrayList<>();
-    filterlist.add(FixedDBIDsFilter.class);
-    if (filters != null) {
-      for (Class<?> filter : filters) {
-        filterlist.add(filter);
-      }
-    }
-    params.addParameter(FileBasedDatabaseConnection.Parameterizer.FILTERS_ID, filterlist);
-    params.addParameter(FixedDBIDsFilter.Parameterizer.IDSTART_ID, 1);
-    Database db = ClassGenericsUtil.parameterizeOrAbort(StaticArrayDatabase.class, params);
-
-    testParameterizationOk(params);
-
-    db.initialize();
-    Relation<?> rel = db.getRelation(TypeUtil.ANY);
-    org.junit.Assert.assertEquals("Database size does not match.", expectedSize, rel.size());
-    return db;
-  }
-
-  /**
-   * Generate a simple DoubleVector database from a file.
-   * 
-   * @param filename File to load
-   * @param expectedSize Expected size in records
-   * @return Database
-   */
-  protected <T> Database makeSimpleDatabase(String filename, int expectedSize) {
-    return makeSimpleDatabase(filename, expectedSize, new ListParameterization(), null);
-  }
-
-  /**
-   * Find a clustering result, fail if there is more than one or none.
-   * 
-   * @param result Base result
-   * @return Clustering
-   */
-  protected Clustering<?> findSingleClustering(Result result) {
-    List<Clustering<? extends Model>> clusterresults = ResultUtil.getClusteringResults(result);
-    assertTrue("No unique clustering found in result.", clusterresults.size() == 1);
-    Clustering<? extends Model> clustering = clusterresults.get(0);
-    return clustering;
-  }
-
-  /**
-   * Test the clustering result by comparing the score with an expected value.
-   * 
-   * @param database Database to test
-   * @param clustering Clustering result
-   * @param expected Expected score
-   */
-  protected <O> void testFMeasure(Database database, Clustering<?> clustering, double expected) {
-    // Run by-label as reference
-    ByLabelClustering bylabel = new ByLabelClustering();
-    Clustering<Model> rbl = bylabel.run(database);
-
-    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
-    ct.process(clustering, rbl);
-    double score = ct.getPaircount().f1Measure();
-    if (logger.isVerbose()) {
-      logger.verbose(this.getClass().getSimpleName() + " score: " + score + " expect: " + expected);
-    }
-    org.junit.Assert.assertEquals(this.getClass().getSimpleName() + ": Score does not match.", expected, score, 0.0001);
-  }
-
-  /**
-   * Validate the cluster sizes with an expected result.
-   * 
-   * @param clustering Clustering to test
-   * @param expected Expected cluster sizes
-   */
-  protected void testClusterSizes(Clustering<?> clustering, int[] expected) {
-    List<Integer> sizes = new java.util.ArrayList<>();
-    for (Cluster<?> cl : clustering.getAllClusters()) {
-      sizes.add(cl.size());
-    }
-    // Sort both
-    Collections.sort(sizes);
-    Arrays.sort(expected);
-    // Report
-    // if(logger.isVerbose()) {
-    StringBuilder buf = new StringBuilder();
-    buf.append("Cluster sizes: [");
-    for (int i = 0; i < sizes.size(); i++) {
-      if (i > 0) {
-        buf.append(", ");
-      }
-      buf.append(sizes.get(i));
-    }
-    buf.append("]");
-    // }
-    // Test
-    org.junit.Assert.assertEquals("Number of clusters does not match expectations. " + buf.toString(), expected.length, sizes.size());
-    for (int i = 0; i < expected.length; i++) {
-      org.junit.Assert.assertEquals("Cluster size does not match at position " + i + " in " + buf.toString(), expected[i], (int) sizes.get(i));
-    }
-  }
-
-  /**
-   * Test the AUC value for an outlier result.
-   * 
-   * @param db Database
-   * @param positive Positive class name
-   * @param result Outlier result to process
-   * @param expected Expected AUC value
-   */
-  protected void testAUC(Database db, String positive, OutlierResult result, double expected) {
-    ListParameterization params = new ListParameterization();
-    params.addParameter(OutlierROCCurve.POSITIVE_CLASS_NAME_ID, positive);
-    OutlierROCCurve rocCurve = ClassGenericsUtil.parameterizeOrAbort(OutlierROCCurve.class, params);
-
-    // Ensure the result has been added to the hierarchy:
-    if (db.getHierarchy().numParents(result) < 1) {
-      db.getHierarchy().add(db, result);
-    }
-
-    // Compute ROC and AUC:
-    rocCurve.processNewResult(db, result);
-    // Find the ROC results
-    Collection<OutlierROCCurve.ROCResult> rocs = ResultUtil.filterResults(result, OutlierROCCurve.ROCResult.class);
-    org.junit.Assert.assertTrue("No ROC result found.", !rocs.isEmpty());
-    double auc = rocs.iterator().next().getAUC();
-    org.junit.Assert.assertFalse("More than one ROC result found.", rocs.size() > 1);
-    org.junit.Assert.assertEquals("ROC value does not match.", expected, auc, 0.0001);
-  }
-
-  /**
-   * Test the outlier score of a single object.
-   * 
-   * @param result Result object to use
-   * @param id Object ID
-   * @param expected expected value
-   */
-  protected void testSingleScore(OutlierResult result, int id, double expected) {
-    org.junit.Assert.assertNotNull("No outlier result", result);
-    org.junit.Assert.assertNotNull("No score result.", result.getScores());
-    final DBID dbid = DBIDUtil.importInteger(id);
-    org.junit.Assert.assertNotNull("No result for ID " + id, result.getScores().get(dbid));
-    double actual = result.getScores().get(dbid);
-    org.junit.Assert.assertEquals("Outlier score of object " + id + " doesn't match.", expected, actual, 0.0001);
-  }
-}