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# daal4py SGD (Stochastic Gradient Descent) example for shared memory systems
# using Logisitc Loss objective function

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'):
    infile   = "./data/batch/custom.csv"
    # Read the data, let's have 4 independent variables
    data     = readcsv(infile, range(4))
    dep_data = readcsv(infile, range(4,5))
    nVectors = data.shape[0]

    # configure a logistic loss object
    ll_algo = d4p.optimization_solver_logistic_loss(nVectors, interceptFlag=True)
    ll_algo.setup(data, dep_data)

    # configure a SGD object
    lrs = np.array([[0.01]], dtype=np.double)
    niters = 1000
    sgd_algo = d4p.optimization_solver_sgd(ll_algo,
                                           learningRateSequence=lrs,
                                           accuracyThreshold=0.02,
                                           nIterations=niters)

    # finally do the computation
    inp = np.array([[1], [1], [1], [1], [1]], dtype=np.double)
    res = sgd_algo.compute(inp)

    # The SGD result provides minimum and nIterations
    assert res.minimum.shape == inp.shape and res.nIterations[0][0] <= niters

    return res


if __name__ == "__main__":
    res = main()
    print("\nMinimum:\n", res.minimum)
    print("\nNumber of iterations performed:\n", res.nIterations[0][0])
    print('All looks good!')
