/* file: ridge_reg_norm_eq_dense_batch.cpp */
/*******************************************************************************
* Copyright 2014 Intel Corporation
*
* 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.
*******************************************************************************/

/*
!  Content:
!    C++ example of ridge regression in the batch processing mode.
!
!    The program trains the ridge regression model on a training
!    datasetFileName with the normal equations method and computes regression
!    for the test data.
!******************************************************************************/

/**
 * <a name="DAAL-EXAMPLE-CPP-RIDGE_REGRESSION_NORM_EQ_BATCH"></a>
 * \example ridge_reg_norm_eq_dense_batch.cpp
 */

#include "daal.h"
#include "service.h"

using namespace daal;
using namespace daal::data_management;
using namespace daal::algorithms::ridge_regression;

/* Input data set parameters */
std::string trainDatasetFileName = "../data/batch/linear_regression_train.csv";
std::string testDatasetFileName = "../data/batch/linear_regression_test.csv";

const size_t nFeatures = 10; /* Number of features in training and testing data sets */
const size_t nDependentVariables =
    2; /* Number of dependent variables that correspond to each observation */

void trainModel();
void testModel();

training::ResultPtr trainingResult;
prediction::ResultPtr predictionResult;

int main(int argc, char* argv[]) {
    checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);

    trainModel();
    testModel();

    return 0;
}

void trainModel() {
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
    FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileName,
                                                      DataSource::notAllocateNumericTable,
                                                      DataSource::doDictionaryFromContext);

    /* Create Numeric Tables for training data and dependent variables */
    NumericTablePtr trainData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
    NumericTablePtr trainDependentVariables(
        new HomogenNumericTable<>(nDependentVariables, 0, NumericTable::doNotAllocate));
    NumericTablePtr mergedData(new MergedNumericTable(trainData, trainDependentVariables));

    /* Retrieve the data from input file */
    trainDataSource.loadDataBlock(mergedData.get());

    /* Create an algorithm object to train the ridge regression model with the normal equations method */
    training::Batch<> algorithm;

    /* Pass a training data set and dependent values to the algorithm */
    algorithm.input.set(training::data, trainData);
    algorithm.input.set(training::dependentVariables, trainDependentVariables);

    /* Build the ridge regression model */
    algorithm.compute();

    /* Retrieve the algorithm results */
    trainingResult = algorithm.getResult();
    printNumericTable(trainingResult->get(training::model)->getBeta(),
                      "Ridge Regression coefficients:");
}

void testModel() {
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
    FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,
                                                     DataSource::doAllocateNumericTable,
                                                     DataSource::doDictionaryFromContext);

    /* Create Numeric Tables for testing data and ground truth values */
    NumericTablePtr testData(new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
    NumericTablePtr testGroundTruth(
        new HomogenNumericTable<>(nDependentVariables, 0, NumericTable::doNotAllocate));
    NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));

    /* Load the data from the data file */
    testDataSource.loadDataBlock(mergedData.get());

    /* Create an algorithm object to predict values of ridge regression */
    prediction::Batch<> algorithm;

    /* Pass a testing data set and the trained model to the algorithm */
    algorithm.input.set(prediction::data, testData);
    algorithm.input.set(prediction::model, trainingResult->get(training::model));

    /* Predict values of ridge regression */
    algorithm.compute();

    /* Retrieve the algorithm results */
    predictionResult = algorithm.getResult();
    printNumericTable(predictionResult->get(prediction::prediction),
                      "Ridge Regression prediction results: (first 10 rows):",
                      10);
    printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10);
}
