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# daal4py implicit_als 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=None, 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=None, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)


def main(readcsv=read_csv, method='defaultDense'):
    nFactors = 2
    infile = "./data/batch/implicit_als_dense.csv"
    # We load the data
    data = readcsv(infile)

    # configure a implicit_als init object
    algo1 = d4p.implicit_als_training_init(nFactors=nFactors, method=method)
    # and compute initial model
    result1 = algo1.compute(data)

    # configure a implicit_als training object
    algo2 = d4p.implicit_als_training(nFactors=nFactors, method=method)
    # and compute model using initial model
    result2 = algo2.compute(data, result1.model)

    # Now do some prediction; first get prediction algorithm object
    algo3 = d4p.implicit_als_prediction_ratings(nFactors=nFactors)
    # and compute
    result3 = algo3.compute(result2.model)

    # implicit als prediction result objects provide prediction
    assert(result3.prediction.shape == data.shape)

    return result3


if __name__ == "__main__":
    res = main()
    print("Predicted ratings:\n", res.prediction[:10])
    print('All looks good!')

numpy.loadtxt