/*
 * 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.applications;

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

import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;
import org.junit.runners.Parameterized.Parameters;
import org.apache.sysds.hops.OptimizerUtils;
import org.apache.sysds.runtime.matrix.data.MatrixValue.CellIndex;
import org.apache.sysds.runtime.meta.MatrixCharacteristics;
import org.apache.sysds.test.AutomatedTestBase;
import org.apache.sysds.test.TestUtils;

@RunWith(value = Parameterized.class)
@net.jcip.annotations.NotThreadSafe
public class GLMTest extends AutomatedTestBase
{
	protected final static String TEST_DIR = "applications/glm/";
	protected final static String TEST_NAME = "GLM";
	protected String TEST_CLASS_DIR = TEST_DIR + GLMTest.class.getSimpleName() + "/";

	protected int numRecords, numFeatures, distFamilyType, linkType;
	protected double distParam, linkPower, intercept, logFeatureVarianceDisbalance, avgLinearForm, stdevLinearForm, dispersion;
	
	public GLMTest (int numRecords_, int numFeatures_, int distFamilyType_, double distParam_,
		int linkType_, double linkPower_, double intercept_, double logFeatureVarianceDisbalance_, 
		double avgLinearForm_, double stdevLinearForm_, double dispersion_)
	{
		this.numRecords = numRecords_;
		this.numFeatures = numFeatures_;
		this.distFamilyType = distFamilyType_;
		this.distParam = distParam_;
		this.linkType = linkType_;
		this.linkPower = linkPower_;
		this.intercept = intercept_;
		this.logFeatureVarianceDisbalance = logFeatureVarianceDisbalance_;
		this.avgLinearForm = avgLinearForm_;
		this.stdevLinearForm = stdevLinearForm_;
		this.dispersion = dispersion_;
	}
	
	// SUPPORTED GLM DISTRIBUTION FAMILIES AND LINKS:
	// -----------------------------------------------
	// INPUT PARAMETERS:	MEANING:			Cano-
	// dfam vpow link lpow  Distribution.link   nical?
	// -----------------------------------------------
	//  1   0.0   1  -1.0   Gaussian.inverse
	//  1   0.0   1   0.0   Gaussian.log
	//  1   0.0   1   1.0   Gaussian.id		  Yes
	//  1   1.0   1   0.0   Poisson.log		  Yes
	//  1   1.0   1   0.5   Poisson.sqrt
	//  1   1.0   1   1.0   Poisson.id
	//  1   2.0   1  -1.0   Gamma.inverse		Yes
	//  1   2.0   1   0.0   Gamma.log
	//  1   2.0   1   1.0   Gamma.id
	//  1   3.0   1  -2.0   InvGaussian.1/mu^2   Yes
	//  1   3.0   1  -1.0   InvGaussian.inverse
	//  1   3.0   1   0.0   InvGaussian.log
	//  1   3.0   1   1.0   InvGaussian.id
	//  1	*	1	*	AnyVariance.AnyLink
	// -----------------------------------------------
	//  2	*	1   0.0   Binomial.log
	//  2	*	2	*	Binomial.logit	   Yes
	//  2	*	3	*	Binomial.probit
	//  2	*	4	*	Binomial.cloglog
	//  2	*	5	*	Binomial.cauchit
	// -----------------------------------------------

	@Parameters
	public static Collection<Object[]> data() {
		// SCHEMA: 
		// #RECORDS, #FEATURES, DISTRIBUTION_FAMILY, VARIANCE_POWER or BERNOULLI_NO, LINK_TYPE, LINK_POWER, 
		//	 INTERCEPT, LOG_FEATURE_VARIANCE_DISBALANCE, AVG_LINEAR_FORM, ST_DEV_LINEAR_FORM, DISPERSION
		Object[][] data = new Object[][] { 
				
		//	 #RECS  #FTRS DFM VPOW  LNK LPOW   ICPT  LFVD  AVGLT STDLT  DISP
				
		// Both DML and R work and compute close results:

/*				
		// THIS IS TO TEST "NO INTERCEPT" OPTION ("icpt=0"):
			{ 100000,   50,  1,  0.0,  1,  0.0,  0.0,  3.0,  10.0,  2.0,  2.5 },   // Gaussian.log
			{  10000,  100,  1,  0.0,  1,  1.0,  0.0,  3.0,   0.0,  2.0,  2.5 },   // Gaussian.id
			{  20000,  100,  1,  0.0,  1, -1.0,  0.0,  0.0,   0.2,  0.03, 2.5 },   // Gaussian.inverse
			{  10000,  100,  1,  1.0,  1,  0.0,  0.0,  3.0,   0.0,  1.0,  2.5 },   // Poisson.log
			{ 100000,   10,  1,  1.0,  1,  0.0,  0.0,  3.0,   0.0, 50.0,  2.5 },   // Poisson.log			 // Pr[0|x] gets near 1
			{  20000,  100,  1,  1.0,  1,  0.5,  0.0,  3.0,  10.0,  2.0,  2.5 },   // Poisson.sqrt
			{  20000,  100,  1,  1.0,  1,  1.0,  0.0,  3.0,  50.0, 10.0,  2.5 },   // Poisson.id
			{ 100000,   50,  1,  2.0,  1,  0.0,  0.0,  3.0,   0.0,  2.0,  2.5 },   // Gamma.log
			{ 100000,   50,  1,  2.0,  1, -1.0,  0.0,  1.0,   2.0,  0.4,  1.5 },   // Gamma.inverse
			{  10000,  100,  1,  3.0,  1, -2.0,  0.0,  3.0,  50.0,  7.0,  1.7 },   // InvGaussian.1/mu^2
			{  10000,  100,  1,  3.0,  1, -1.0,  0.0,  3.0,  10.0,  2.0,  2.5 },   // InvGaussian.inverse			
			{ 100000,   50,  1,  3.0,  1,  0.0,  0.0,  2.0,  -2.0,  1.0,  1.7 },   // InvGaussian.log
			{ 100000,   50,  1,  3.0,  1,  1.0,  0.0,  1.0,   0.2,  0.04, 1.7 },   // InvGaussian.id

			{ 100000,   50,  2, -1.0,  1,  0.0,  0.0,  3.0,  -5.0,  1.0,  1.0 },   // Bernoulli {-1, 1}.log	 // Note: Y is sparse
			{ 100000,   50,  2, -1.0,  1,  1.0,  0.0,  1.0,   0.6,  0.1,  1.0 },   // Bernoulli {-1, 1}.id
			{ 100000,   50,  2, -1.0,  1,  0.5,  0.0,  0.0,   0.4,  0.05, 1.0 },   // Bernoulli {-1, 1}.sqrt
			{  10000,  100,  2, -1.0,  2,  0.0,  0.0,  3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.logit
			{  10000,  100,  2, -1.0,  2,  0.0,  0.0,  3.0,   0.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.logit   // Pr[y|x] near 0, 1
			{  20000,  100,  2, -1.0,  3,  0.0,  0.0,  3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.probit
			{ 100000,   10,  2, -1.0,  3,  0.0,  0.0,  3.0,   0.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.probit  // Pr[y|x] near 0, 1
			{  10000,  100,  2, -1.0,  4,  0.0,  0.0,  3.0,  -2.0,  1.0,  1.0 },   // Bernoulli {-1, 1}.cloglog
			{  50000,   20,  2, -1.0,  4,  0.0,  0.0,  3.0,  -2.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.cloglog // Pr[y|x] near 0, 1
			{  50000,  100,  2, -1.0,  5,  0.0,  0.0,  3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.cauchit
				
			{ 100000,   50,  2,  1.0,  1,  0.0,  0.0,  3.0,  -5.0,  1.0,  2.5 },   // Binomial two-column.log   // Note: Y is sparse
			{  10000,  100,  2,  1.0,  1,  1.0,  0.0,  0.0,   0.4,  0.05, 2.5 },   // Binomial two-column.id
			{ 100000,   50,  2,  1.0,  1,  0.5,  0.0,  0.0,   0.4,  0.05, 2.5 },   // Binomial two-column.sqrt
			{  10000,  100,  2,  1.0,  2,  0.0,  0.0,  3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.logit
			{  20000,  100,  2,  1.0,  3,  0.0,  0.0,  3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.probit
			{  10000,  100,  2,  1.0,  4,  0.0,  0.0,  3.0,  -2.0,  1.0,  2.5 },   // Binomial two-column.cloglog
			{  20000,  100,  2,  1.0,  5,  0.0,  0.0,  3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.cauchit
*/				
				

		// THIS IS TO TEST "INTERCEPT AND SHIFT/SCALE" OPTION ("icpt=2"):
			// { 200000,   50,  1,  0.0,  1,  0.0,  0.01, 3.0,  10.0,  2.0,  2.5 },   // Gaussian.log	 // CHECK DEVIANCE !!!
			{  10000,  100,  1,  0.0,  1,  1.0,  0.01, 3.0,   0.0,  2.0,  2.5 },   // Gaussian.id
			{  20000,  100,  1,  0.0,  1, -1.0,  0.01, 0.0,   0.2,  0.03, 2.5 },   // Gaussian.inverse
			{  10000,  100,  1,  1.0,  1,  0.0,  0.01, 3.0,   0.0,  1.0,  2.5 },   // Poisson.log
			{ 100000,   10,  1,  1.0,  1,  0.0,  0.01, 3.0,   0.0, 50.0,  2.5 },   // Poisson.log			 // Pr[0|x] gets near 1
			{  20000,  100,  1,  1.0,  1,  0.5,  0.01, 3.0,  10.0,  2.0,  2.5 },   // Poisson.sqrt
			{  10000,  100,  1,  1.0,  1,  1.0,  0.01, 3.0,  50.0, 10.0,  2.5 },   // Poisson.id
			{  50000,  100,  1,  2.0,  1,  0.0,  0.01, 3.0,   0.0,  2.0,  2.5 },   // Gamma.log
			{  10000,  100,  1,  2.0,  1, -1.0,  0.01, 3.0,   2.0,  0.3,  2.0 },   // Gamma.inverse
			{  10000,  100,  1,  3.0,  1, -2.0,  1.0,  3.0,  50.0,  7.0,  1.7 },   // InvGaussian.1/mu^2
			{  10000,  100,  1,  3.0,  1, -1.0,  0.01, 3.0,  10.0,  2.0,  2.5 },   // InvGaussian.inverse
			{ 100000,   50,  1,  3.0,  1,  0.0,  0.5,  3.0,  -2.0,  1.0,  2.5 },   // InvGaussian.log
			{ 100000,  100,  1,  3.0,  1,  1.0,  0.01, 3.0,   0.2,  0.03, 2.5 },   // InvGaussian.id

			{ 100000,   50,  2, -1.0,  1,  0.0,  0.01, 3.0,  -5.0,  1.0,  1.0 },   // Bernoulli {-1, 1}.log	 // Note: Y is sparse
			{ 100000,   50,  2, -1.0,  1,  1.0,  0.01, 3.0,   0.4,  0.1,  1.0 },   // Bernoulli {-1, 1}.id
			{ 100000,   40,  2, -1.0,  1,  0.5,  0.1,  3.0,   0.4,  0.1,  1.0 },   // Bernoulli {-1, 1}.sqrt
			{  10000,  100,  2, -1.0,  2,  0.0,  0.01, 3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.logit
			{  10000,  100,  2, -1.0,  2,  0.0,  0.01, 3.0,   0.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.logit   // Pr[y|x] near 0, 1
			{  20000,  100,  2, -1.0,  3,  0.0,  0.01, 3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.probit
			{ 100000,   10,  2, -1.0,  3,  0.0,  0.01, 3.0,   0.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.probit  // Pr[y|x] near 0, 1
			{  10000,  100,  2, -1.0,  4,  0.0,  0.01, 3.0,  -2.0,  1.0,  1.0 },   // Bernoulli {-1, 1}.cloglog
			{  50000,   20,  2, -1.0,  4,  0.0,  0.01, 3.0,  -2.0, 50.0,  1.0 },   // Bernoulli {-1, 1}.cloglog // Pr[y|x] near 0, 1
			{  20000,  100,  2, -1.0,  5,  0.0,  0.01, 3.0,   0.0,  2.0,  1.0 },   // Bernoulli {-1, 1}.cauchit
		
			{  50000,  100,  2,  1.0,  1,  0.0,  0.01, 3.0,  -5.0,  1.0,  2.5 },   // Binomial two-column.log   // Note: Y is sparse
			{  10000,  100,  2,  1.0,  1,  1.0,  0.0,  0.0,   0.4,  0.05, 2.5 },   // Binomial two-column.id
			{ 100000,  100,  2,  1.0,  1,  0.5,  0.1,  3.0,   0.4,  0.05, 2.5 },   // Binomial two-column.sqrt
			{  10000,  100,  2,  1.0,  2,  0.0,  0.01, 3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.logit
			{  20000,  100,  2,  1.0,  3,  0.0,  0.01, 3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.probit
			{  10000,  100,  2,  1.0,  4,  0.0,  0.01, 3.0,  -2.0,  1.0,  2.5 },   // Binomial two-column.cloglog
			{  20000,  100,  2,  1.0,  5,  0.0,  0.01, 3.0,   0.0,  2.0,  2.5 },   // Binomial two-column.cauchit
			


		//  DML WORKS, BUT R FAILS:
				
		//	{  10000,  100,  1,  1.0,  1,  1.0,  0.0,  0.0,  10.0,  2.0,  2.5 },   // Poisson.id
		//	{  10000,  100,  1,  2.0,  1, -1.0,  0.0,  0.0,  10.0,  2.0,  2.5 },   // Gamma.inverse
		//	{  10000,  100,  1,  2.0,  1,  1.0,  0.0,  0.0,  10.0,  2.0,  2.5 },   // Gamma.id			 // Tried tweaking, cannot satisfy R
		//	{  10000,  100,  1,  3.0,  1, -2.0,  0.0,  0.0,  10.0,  2.0,  2.5 },   // InvGaussian.1/mu^2
		//	{  10000,  100,  1,  3.0,  1,  0.0,  0.0,  0.0,   0.0,  2.0,  2.5 },   // InvGaussian.log	  // R computes nonsense!
		//	{  10000,  100,  1,  3.0,  1,  1.0,  0.0,  0.0,   2.0,  0.2,  1.5 },   // InvGaussian.id
				
		//  BOTH R AND DML FAIL:
				
		//	{  10000,  100,  1,  0.0,  1, -1.0,  0.0,  0.0,  10.0,  2.0,  2.5 },   // Gaussian.inverse	 // R and DML compute nonsense
		
		
		};
		return Arrays.asList(data);
	}

	@Override
	public void setUp()
	{
		addTestConfiguration(TEST_CLASS_DIR, TEST_NAME);
	}
	
	@Test
	public void testGLM()
	{
		System.out.println("------------ BEGIN " + TEST_NAME + " TEST WITH {" + 
				numRecords + ", " +
				numFeatures + ", " +
				distFamilyType + ", " +
				distParam + ", " +
				linkType + ", " +
				linkPower + ", " +
				intercept + ", " +
				logFeatureVarianceDisbalance + ", " +
				avgLinearForm + ", " +
				stdevLinearForm + ", " +
				dispersion +
				"} ------------");
		
		int rows = numRecords;  // # of rows in the training data 
		int cols = numFeatures; // # of features in the training data
		
		TestUtils.GLMDist glmdist = new TestUtils.GLMDist (distFamilyType, distParam, linkType, linkPower);
		glmdist.set_dispersion (dispersion);
		
		getAndLoadTestConfiguration(TEST_NAME);

		// prepare training data set
		Random r = new Random(314159265);
		double[][] X = TestUtils.generateUnbalancedGLMInputDataX(rows, cols, logFeatureVarianceDisbalance);
		double[] beta = TestUtils.generateUnbalancedGLMInputDataB(X, cols, intercept, avgLinearForm, stdevLinearForm, r);
		double[][] y = TestUtils.generateUnbalancedGLMInputDataY(X, beta, rows, cols, glmdist, intercept, dispersion, r);

		int defaultBlockSize = OptimizerUtils.DEFAULT_BLOCKSIZE;

		MatrixCharacteristics mc_X = new MatrixCharacteristics(rows, cols, defaultBlockSize, -1);
		writeInputMatrixWithMTD ("X", X, true, mc_X);

		MatrixCharacteristics mc_y = new MatrixCharacteristics(rows, y[0].length, defaultBlockSize, -1);
		writeInputMatrixWithMTD ("Y", y, true, mc_y);
		
		List<String> proArgs = new ArrayList<>();
		proArgs.add("-nvargs");
		proArgs.add("dfam=" + String.format ("%d", distFamilyType));
		proArgs.add(((distFamilyType == 2 && distParam != 1.0) ? "yneg=" : "vpow=") + String.format ("%f", distParam));
		proArgs.add((distFamilyType == 2 && distParam != 1.0) ? "vpow=0.0" : "yneg=0.0");
		proArgs.add("link=" + String.format ("%d", linkType));
		proArgs.add("lpow=" + String.format ("%f", linkPower));
		proArgs.add("icpt=2"); // INTERCEPT - CHANGE THIS AS NEEDED
		proArgs.add("disp=0.0"); // DISPERSION (0.0: ESTIMATE)
		proArgs.add("reg=0.0"); // LAMBDA REGULARIZER
		proArgs.add("tol=0.000000000001"); // TOLERANCE (EPSILON)
		proArgs.add("moi=300");
		proArgs.add("mii=0");
		proArgs.add("X=" + input("X"));
		proArgs.add("Y=" + input("Y"));
		proArgs.add("B=" + output("betas_SYSTEMDS"));
		programArgs = proArgs.toArray(new String[proArgs.size()]);
		fullDMLScriptName = getScript();
		
		rCmd = getRCmd(input("X.mtx"), input("Y.mtx"), String.format ("%d", distFamilyType), String.format ("%f", distParam),
				String.format ("%d", linkType), String.format ("%f", linkPower), "1" /*intercept*/, "0.000000000001" /*tolerance (espilon)*/,
				expected("betas_R"));
		
		int expectedNumberOfJobs = -1; // 31;

		
		runTest(true, EXCEPTION_NOT_EXPECTED, null, expectedNumberOfJobs);

		double max_abs_beta = 0.0;
		HashMap<CellIndex, Double> wTRUE = new HashMap<> ();
		for (int j = 0; j < cols; j ++)
		{
			wTRUE.put (new CellIndex (j + 1, 1), Double.valueOf(beta [j]));
			max_abs_beta = (max_abs_beta >= Math.abs (beta[j]) ? max_abs_beta : Math.abs (beta[j]));
		}

		HashMap<CellIndex, Double> wSYSTEMDS_raw = readDMLMatrixFromOutputDir("betas_SYSTEMDS");
		HashMap<CellIndex, Double> wSYSTEMDS = new HashMap<> ();
		for (CellIndex key : wSYSTEMDS_raw.keySet())
			if (key.column == 1)
				wSYSTEMDS.put (key, wSYSTEMDS_raw.get (key));

		runRScript(true);

		HashMap<CellIndex, Double> wR   = readRMatrixFromExpectedDir("betas_R");
		
		double eps = 0.000001;
		if( (distParam==0 && linkType==1) ) { // Gaussian.*
			//NOTE MB: Gaussian.log was the only test failing when we introduced multi-threaded
			//matrix multplications (mmchain). After discussions with Sasha, we decided to change the eps
			//because accuracy is anyway affected by various rewrites like binary to unary (-1*x->-x),
			//transpose-matrixmult, and dot product sum. Disabling these rewrites led to a successful 
			//test result. Even without multi-threaded matrix mult this test was failing for different number 
			//of rows if these rewrites are enabled. Users can turn off rewrites if high accuracy is required. 
			//However, in the future we might also consider to use Kahan plus for aggregations in matrix mult 
			//(at least for the final aggregation of partial results from individual threads).
			
			//NOTE MB: similar issues occurred with other tests when moving to github action tests
			eps *=  (linkPower==-1) ? 4 : 2; //Gaussian.inverse vs Gaussian.*;
		}
		TestUtils.compareMatrices (wR, wSYSTEMDS, eps * max_abs_beta, "wR", "wSYSTEMDS");
	}
}
