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# daal4py logistic regression 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=t)
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main(readcsv=read_csv, method='defaultDense'):
    nClasses = 2
    nFeatures = 20

    # read training data from file with 20 features per observation and 1 class label
    trainfile = "./data/batch/binary_cls_train.csv"
    train_data = readcsv(trainfile, range(nFeatures))
    train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))

    # set parameters and train
    train_alg = d4p.logistic_regression_training(nClasses=nClasses, interceptFlag=True)
    train_result = train_alg.compute(train_data, train_labels)

    # read testing data from file with 20 features per observation
    testfile = "./data/batch/binary_cls_test.csv"
    predict_data = readcsv(testfile, range(nFeatures))
    predict_labels = readcsv(testfile, range(nFeatures, nFeatures + 1))

    # set parameters and compute predictions
    predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
    predict_result = predict_alg.compute(predict_data, train_result.model)

    # the prediction result provides prediction
    assert predict_result.prediction.shape == (predict_data.shape[0], train_labels.shape[1])

    return (train_result, predict_result, predict_labels)


if __name__ == "__main__":
    (train_result, predict_result, predict_labels) = main()
    print("\nLogistic Regression coefficients:\n", train_result.model.Beta)
    print("\nLogistic regression prediction results (first 10 rows):\n", predict_result.prediction[0:10])
    print("\nGround truth (first 10 rows):\n", predict_labels[0:10])
    print('All looks good!')
