# file: adaboost_dense_batch.py
#===============================================================================
# Copyright 2014-2019 Intel Corporation.
#
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# the related documents without Intel's prior written permission.
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#===============================================================================

## <a name="DAAL-EXAMPLE-PY-ADABOOST_BATCH"></a>
## \example adaboost_dense_batch.py

import os
import sys

from daal.algorithms.adaboost import prediction, training
from daal.algorithms import classifier
from daal.data_management import (
    FileDataSource, DataSourceIface, 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 printNumericTables

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

# Input data set parameters
trainDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'adaboost_train.csv')

testDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'adaboost_test.csv')

nFeatures = 20

trainingResult = None
predictionResult = None
testGroundTruth = None


def trainModel():
    global trainingResult

    # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
    trainDataSource = FileDataSource(
        trainDatasetFileName, DataSourceIface.notAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Create Numeric Tables for training data and labels
    trainData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
    trainGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
    mergedData = MergedNumericTable(trainData, trainGroundTruth)

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

    # Create an algorithm object to train the AdaBoost model
    algorithm = training.Batch()

    # Pass the training data set and dependent values to the algorithm
    algorithm.input.set(classifier.training.data, trainData)
    algorithm.input.set(classifier.training.labels, trainGroundTruth)

    # Train the AdaBoost model and retrieve the results of the training algorithm
    trainingResult = algorithm.compute()


def testModel():
    global predictionResult, testGroundTruth

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

    # Create Numeric Tables for testing data and labels
    testData = HomogenNumericTable(nFeatures, 0, NumericTableIface.doNotAllocate)
    testGroundTruth = HomogenNumericTable(1, 0, NumericTableIface.doNotAllocate)
    mergedData = MergedNumericTable(testData, testGroundTruth)

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

    # Create algorithm objects for AdaBoost prediction with the default method
    algorithm = prediction.Batch()

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

    # Compute prediction results and retrieve algorithm results
    # (Result class from classifier.prediction)
    predictionResult = algorithm.compute()

def printResults():
    printNumericTables(
        testGroundTruth, predictionResult.get(classifier.prediction.prediction),
        "Ground truth", "Classification results",
        "AdaBoost classification results (first 20 observations):", 20, flt64=False
    )

if __name__ == "__main__":

    trainModel()
    testModel()
    printResults()