# file: df_reg_traverse_model.py
#===============================================================================
# Copyright 2014-2019 Intel Corporation.
#
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# provided to you (License).  Unless the License provides otherwise, you may not
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# the related documents without Intel's prior written permission.
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# or implied  warranties,  other  than those  that are  expressly stated  in the
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#===============================================================================

#
# !  Content:
# !    Python example of decision forest regression model traversal.
# !
# !    The program trains the decision forest regression model on a training
# !    datasetFileName and prints the trained model by its depth-first traversing.
# !*****************************************************************************

#
## <a name = "DAAL-EXAMPLE-PY-DF_REG_TRAVERSE_MODEL"></a>
## \example df_reg_traverse_model.py
#
from __future__ import print_function

from daal import algorithms
from daal.algorithms import decision_forest
import daal.algorithms.decision_forest.regression
import daal.algorithms.decision_forest.regression.training

from daal.data_management import (
    FileDataSource, DataSourceIface, NumericTableIface, HomogenNumericTable, MergedNumericTable, features
)

# Input data set parameters
trainDatasetFileName = "../data/batch/df_regression_train.csv"
categoricalFeaturesIndices = [3]
nFeatures = 13  # Number of features in training and testing data sets

# Decision forest parameters
nTrees = 2


def trainModel():

    # Create Numeric Tables for training data and dependent variables
    trainData, trainDependentVariable = loadData(trainDatasetFileName)

    # Create an algorithm object to train the decision forest regression model with the default method
    algorithm = decision_forest.regression.training.Batch()

    # Pass a training data set and dependent values to the algorithm
    algorithm.input.set(decision_forest.regression.training.data, trainData)
    algorithm.input.set(decision_forest.regression.training.dependentVariable, trainDependentVariable)

    algorithm.parameter.nTrees = nTrees

    # Build the decision forest regression model and return the result
    return algorithm.compute()


def loadData(fileName):

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

    # Create Numeric Tables for training data and dependent variables
    data = HomogenNumericTable(nFeatures, 0, NumericTableIface.notAllocate)
    dependentVar = HomogenNumericTable(1, 0, NumericTableIface.notAllocate)
    mergedData = MergedNumericTable(data, dependentVar)

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

    dictionary = data.getDictionary()
    for i in range(len(categoricalFeaturesIndices)):
        dictionary[categoricalFeaturesIndices[i]].featureType = features.DAAL_CATEGORICAL

    return data, dependentVar


# Visitor class implementing NodeVisitor interface, prints out tree nodes of the model when it is called back by model traversal method
class PrintNodeVisitor(algorithms.regression.TreeNodeVisitor):

    def __init__(self):
        super(PrintNodeVisitor, self).__init__()

    def onLeafNode(self, level, response):

        for i in range(level):
            print("  ", end='')
        print("Level {}, leaf node. Response value = {:.4g}".format(level, response))
        return True


    def onSplitNode(self, level, featureIndex, featureValue):

        for i in range(level):
            print("  ", end='')
        print("Level {}, split node. Feature index = {}, feature value = {:.4g}".format(level, featureIndex, featureValue))
        return True


def printModel(m):
    visitor = PrintNodeVisitor()
    print("Number of trees: {}".format(m.getNumberOfTrees()))
    for i in range(m.getNumberOfTrees()):
        print("Tree #{}".format(i))
        m.traverseDF(i, visitor)

if __name__ == "__main__":

    trainingResult = trainModel()
    printModel(trainingResult.get(decision_forest.regression.training.model))
