# file: lin_reg_qr_dense_distr.py
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## <a name="DAAL-EXAMPLE-PY-LINEAR_REGRESSION_QR_DISTRIBUTED"></a>
## \example lin_reg_qr_dense_distr.py

import os
import sys

from daal import step1Local, step2Master
from daal.algorithms.linear_regression import training, prediction
from daal.data_management import (
    DataSourceIface, FileDataSource, HomogenNumericTable, MergedNumericTable, NumericTableIface
)

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = os.path.join('..', 'data')

# Input data set parameters
trainDatasetFileNames = [
    os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_1.csv'),
    os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_2.csv'),
    os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_3.csv'),
    os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_train_4.csv')
]

testDatasetFileName = os.path.join(DAAL_PREFIX, 'distributed', 'linear_regression_test.csv')

nBlocks = 4

nFeatures           = 10    # Number of features in training and testing data sets
nDependentVariables = 2     # Number of dependent variables that correspond to each observation

trainingResult = None
predictionResult = None


def trainModel():
    global trainingResult

    # Create an algorithm object to build the final multiple linear regression model on the master node
    masterAlgorithm = training.Distributed(step2Master, method=training.qrDense)

    for i in range(nBlocks):
        # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
        trainDataSource = FileDataSource(
            trainDatasetFileNames[i], DataSourceIface.notAllocateNumericTable,
            DataSourceIface.doDictionaryFromContext
        )

        # Create Numeric Tables for training data and dependent variables
        trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
        trainDependentVariables = HomogenNumericTable(
            nDependentVariables, 0, NumericTableIface.doNotAllocate
        )
        mergedData = MergedNumericTable(trainData, trainDependentVariables)

        # Retrieve the data from input file
        trainDataSource.loadDataBlock(mergedData)

        # Create an algorithm object to train the multiple linear regression model based on the local-node data
        localAlgorithm = training.Distributed(step1Local, method=training.qrDense)

        # Pass a training data set and dependent values to the algorithm
        localAlgorithm.input.set(training.data, trainData)
        localAlgorithm.input.set(training.dependentVariables, trainDependentVariables)

        # Train the multiple linear regression model on the local-node data
        # Set the local multiple linear regression model as input for the master-node algorithm
        masterAlgorithm.input.add(training.partialModels, localAlgorithm.compute())

    # Merge and finalize the multiple linear regression model on the master node
    masterAlgorithm.compute()

    # Retrieve the algorithm results
    trainingResult = masterAlgorithm.finalizeCompute()
    printNumericTable(trainingResult.get(training.model).getBeta(), "Linear Regression coefficients:")


def testModel():

    # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
    testDataSource = FileDataSource(
        testDatasetFileName, DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Create Numeric Tables for testing data and ground truth values
    testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
    testGroundTruth = HomogenNumericTable(nDependentVariables, 0, NumericTableIface.doNotAllocate)
    mergedData = MergedNumericTable(testData, testGroundTruth)

    # Retrieve the data from the input file
    testDataSource.loadDataBlock(mergedData)

    # Create an algorithm object to predict values of multiple linear regression
    algorithm = prediction.Batch()

    # Pass a testing data set and the trained model to the algorithm
    algorithm.input.setTable(prediction.data, testData)
    algorithm.input.setModel(prediction.model, trainingResult.get(training.model))

    # Predict values of multiple linear regression and retrieve the algorithm results
    predictionResult = algorithm.compute()
    printNumericTable(predictionResult.get(prediction.prediction), "Linear Regression prediction results: (first 10 rows):", 10)
    printNumericTable(testGroundTruth, "Ground truth (first 10 rows):", 10)

if __name__ == "__main__":

    trainModel()
    testModel()
