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# daal4py Linear Regression example for distributed memory systems; SPMD mode
# run like this:
#    mpirun -n 4 python ./linreg_spmd.py

import daal4py as d4p
from numpy import loadtxt, allclose

if __name__ == "__main__":
    # Initialize SPMD mode
    d4p.daalinit()

    # Each process gets its own data
    infile = "./data/distributed/linear_regression_train_" + str(d4p.my_procid()+1) + ".csv"

    # Configure a Linear regression training object
    train_algo = d4p.linear_regression_training(distributed=True)
    
    # Read data. Let's have 10 independent, and 2 dependent variables (for each observation)
    indep_data = loadtxt(infile, delimiter=',', usecols=range(10))
    dep_data   = loadtxt(infile, delimiter=',', usecols=range(10,12))
    # Now train/compute, the result provides the model for prediction
    train_result = train_algo.compute(indep_data, dep_data)

    # Now let's do some prediction
    # It run only on a single node
    if d4p.my_procid() == 0:
        predict_algo = d4p.linear_regression_prediction()
        # read test data (with same #features)
        pdata = loadtxt("./data/distributed/linear_regression_test.csv", delimiter=',', usecols=range(10))
        # now predict using the model from the training above
        predict_result = d4p.linear_regression_prediction().compute(pdata, train_result.model)
        
        # The prediction result provides prediction
        assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])

    print('All looks good!')
    d4p.daalfini()
