# ==============================================================================

# 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

#

# 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.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

# daal4py Linear Regression example for distributed memory systems; SPMD mode

# run like this:

# mpirun -n 4 python ./linreg_spmd.py

from numpy import loadtxt

import daal4py as d4p

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()