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# daal4py Decision Tree Classification example for shared memory systems

import daal4py as d4p
import numpy as np

# let's try to use pandas' fast csv reader
try:
    import pandas
    read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=np.float32)
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=np.float32)


def main(readcsv=read_csv, method='defaultDense'):
    # input data file
    infile = "./data/batch/decision_tree_train.csv"
    prunefile = "./data/batch/decision_tree_prune.csv"
    testfile = "./data/batch/decision_tree_test.csv"

    # Configure a training object (5 classes)
    train_algo = d4p.decision_tree_classification_training(5)
    
    # Read data. Let's use 5 features per observation
    data   = readcsv(infile, range(5), t=np.float32)
    labels = readcsv(infile, range(5,6), t=np.float32)
    prunedata = readcsv(prunefile, range(5), t=np.float32)
    prunelabels = readcsv(prunefile, range(5,6), t=np.float32)
    train_result = train_algo.compute(data, labels, prunedata, prunelabels)

    # Now let's do some prediction
    predict_algo = d4p.decision_tree_classification_prediction()
    # read test data (with same #features)
    pdata = readcsv(testfile, range(5), t=np.float32)
    plabels = readcsv(testfile, range(5,6), t=np.float32)
    # now predict using the model from the training above
    predict_result = predict_algo.compute(pdata, train_result.model)

    # Prediction result provides prediction
    assert(predict_result.prediction.shape == (pdata.shape[0], 1))

    return (train_result, predict_result, plabels)


if __name__ == "__main__":
    (train_result, predict_result, plabels) = main()
    print("\nDecision tree prediction results (first 20 rows):\n", predict_result.prediction[0:20])
    print("\nGround truth (first 20 rows):\n", plabels[0:20])
    print('All looks good!')
