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# Copyright 2014 Intel Corporation

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

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# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

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# daal4py Naive Bayes Classification example for distributed memory systems; SPMD mode

# run like this:

# mpirun -n 4 python ./naive_bayes_spmd.py

from pathlib import Path

from numpy import loadtxt

import daal4py as d4p

if __name__ == "__main__":

# Initialize SPMD mode

d4p.daalinit()

# Each process gets its own data

data_path = Path(__file__).parent / "data" / "batch"

infile = data_path / "naivebayes_train_dense.csv"

# Configure a training object (20 classes)

talgo = d4p.multinomial_naive_bayes_training(20, distributed=True)

# Read data. Let's use 20 features per observation

data = loadtxt(infile, delimiter=",", usecols=range(20))

labels = loadtxt(infile, delimiter=",", usecols=range(20, 21))

labels.shape = (labels.size, 1) # must be a 2d array

tresult = talgo.compute(data, labels)

# Now let's do some prediction

# It runs only on a single node

if d4p.my_procid() == 0:

palgo = d4p.multinomial_naive_bayes_prediction(20)

# read test data (with same #features)

pdata = loadtxt(

"./data/batch/naivebayes_test_dense.csv", delimiter=",", usecols=range(20)

)

# now predict using the model from the training above

presult = palgo.compute(pdata, tresult.model)

# Prediction result provides prediction

assert presult.prediction.shape == (pdata.shape[0], 1)

print("All looks good!")

d4p.daalfini()