MDABivariateStatsTest.java

/**
 * (C) Copyright IBM Corp. 2010, 2015
 *
 * Licensed 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 com.ibm.bi.dml.test.integration.applications;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.List;

import org.junit.runners.Parameterized.Parameters;

import com.ibm.bi.dml.runtime.matrix.MatrixCharacteristics;
import com.ibm.bi.dml.runtime.matrix.data.MatrixValue.CellIndex;
import com.ibm.bi.dml.test.integration.AutomatedTestBase;
import com.ibm.bi.dml.test.utils.TestUtils;

public abstract class MDABivariateStatsTest extends AutomatedTestBase 
{

	protected final static String TEST_DIR = "applications/mdabivar/";
	protected final static String TEST_NAME = "MDABivariateStats";
	
	protected int n, m, label_index, label_measurement_level;
	
	public MDABivariateStatsTest(int n, int m, int li, int lml) {
		this.n = n; 
		this.m = m; 
		this.label_index = li;
		this.label_measurement_level = lml;
	}
	
	@Parameters
	 public static Collection<Object[]> data() {
	   Object[][] data = new Object[][] { { 10000, 100, 1, 1 }, { 10000, 100, 100, 0}, 
			                              { 100000, 100, 1, 1 }, { 100000, 100, 100, 0}
			   							  };
	   return Arrays.asList(data);
	 }
	 
	@Override
	public void setUp() {
		addTestConfiguration(TEST_DIR, TEST_NAME);
	}
	
	protected void testMDABivariateStats(ScriptType scriptType) {
		System.out.println("------------ BEGIN " + TEST_NAME + " " + scriptType + " TEST WITH {" + n + ", " + m
				+ ", " + label_index + ", " + label_measurement_level + "} ------------");
		this.scriptType = scriptType;
		
		getAndLoadTestConfiguration(TEST_NAME);
		
		List<String> proArgs = new ArrayList<String>();
		if (scriptType == ScriptType.PYDML) {
			proArgs.add("-python");
		}
		proArgs.add("-args");
		proArgs.add(input("X"));
		proArgs.add(Integer.toString(label_index));
		proArgs.add(input("feature_indices"));
		proArgs.add(Integer.toString(label_measurement_level));
		proArgs.add(input("feature_measurement_levels"));
		proArgs.add(output("stats"));
		proArgs.add(output("tests"));
		proArgs.add(output("covariances"));
		proArgs.add(output("standard_deviations"));
		proArgs.add(output("contingency_tables_counts"));
		proArgs.add(output("contingency_tables_label_values"));
		proArgs.add(output("contingency_tables_feature_values"));
		proArgs.add(output("feature_values"));
		proArgs.add(output("feature_counts"));
		proArgs.add(output("feature_means"));
		proArgs.add(output("feature_standard_deviations"));
		programArgs = proArgs.toArray(new String[proArgs.size()]);
        
		fullDMLScriptName = getScript();
		
		rCmd = getRCmd(inputDir(), Integer.toString(label_index), Integer.toString(label_measurement_level), expectedDir());

		double[][] X = getRandomMatrix(n, m, 0, 1, 1, System.currentTimeMillis());
		for(int i=0; i<X.length; i++)
			for(int j=m/2; j<X[i].length; j++){
				//generating a 5-valued categorical random variable
				if(X[i][j] < 0.2) X[i][j] = 1;
				else if(X[i][j] < 0.4) X[i][j] = 2; 
				else if(X[i][j] < 0.6) X[i][j] = 3; 
				else if(X[i][j] < 0.8) X[i][j] = 4;
				else X[i][j] = 5;	
			}
		
		double[][] feature_indices = new double[m-1][1];
		double[][] feature_measurement_levels = new double[m-1][1];
		int pos = 0;
		for(int i=1; i<=m; i++)
			if(i != label_index){
				feature_indices[pos][0] = i;
				feature_measurement_levels[pos][0] = (i > m/2) ? 0 : 1;
				pos++;
			}
		
		MatrixCharacteristics mcX = new MatrixCharacteristics(n, m, -1, -1);
		writeInputMatrixWithMTD("X", X, true, mcX);
		
		MatrixCharacteristics mc_features = new MatrixCharacteristics(m-1, 1, -1, -1);
		writeInputMatrixWithMTD("feature_indices", feature_indices, true, mc_features);
		writeInputMatrixWithMTD("feature_measurement_levels", feature_measurement_levels, true, mc_features);
		
		int expectedNumberOfJobs = -1;
		runTest(true, EXCEPTION_NOT_EXPECTED, null, expectedNumberOfJobs); 
		
		runRScript(true);

		HashMap<CellIndex, Double> statsSYSTEMML = readDMLMatrixFromHDFS("stats");
		HashMap<CellIndex, Double> statsR = readRMatrixFromFS("stats");
		
		TestUtils.compareMatrices(statsSYSTEMML, statsR, 0.000001, "statsSYSTEMML", "statsR");
	}
}