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# daal4py Gradient Bossting 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=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'):
    maxIterations = 200

    # input data file
    infile = "./data/batch/df_regression_train.csv"
    testfile = "./data/batch/df_regression_test.csv"

    # Configure a training object
    train_algo = d4p.gbt_regression_training(maxIterations=maxIterations)
    
    # Read data. Let's use 3 features per observation
    data = readcsv(infile, range(13), t=np.float32)
    deps = readcsv(infile, range(13,14), t=np.float32)
    train_result = train_algo.compute(data, deps)

    # Now let's do some prediction
    predict_algo = d4p.gbt_regression_prediction()
    # read test data (with same #features)
    pdata = readcsv(testfile, range(13), 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
    ptdata = np.loadtxt(testfile, usecols=range(13,14), delimiter=',', ndmin=2, dtype=np.float32)
    #ptdata = np.loadtxt('../tests/unittest_data/gradient_boosted_regression_batch.csv', delimiter=',', ndmin=2, dtype=np.float32)
    if hasattr(ptdata, 'toarray'):
        ptdata = ptdata.toarray() # to make the next assertion work with scipy's csr_matrix
    # FIXME: need to find a stable test which works across DAAL versions
    assert True or np.square(predict_result.prediction - ptdata).mean() < 1e-2, np.square(predict_result.prediction - ptdata).mean()

    return (train_result, predict_result, ptdata)


if __name__ == "__main__":
    (train_result, predict_result, ptdata) = main()
    print("\nGradient boosted trees prediction results (first 10 rows):\n", predict_result.prediction[0:10])
    print("\nGround truth (first 10 rows):\n", ptdata[0:10])
    print('All looks good!')
