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lbfgs_mse.py
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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
#
# 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 LBFGS (limited memory Broyden-Fletcher-Goldfarb-Shanno)
# example for shared memory systems
# using Mean Squared Error objective function
from pathlib import Path
import numpy as np
from readcsv import pd_read_csv
import daal4py as d4p
def main(readcsv=pd_read_csv):
data_path = Path(__file__).parent / "data" / "batch"
infile = data_path / "lbfgs.csv"
# Read the data, let's have 10 independent variables
data = readcsv(infile, range(10))
dep_data = readcsv(infile, range(10, 11))
nVectors = data.shape[0]
# configure a MSE object
mse_algo = d4p.optimization_solver_mse(nVectors)
mse_algo.setup(data, dep_data)
# configure an LBFGS object
sls = np.array([[1.0e-4]], dtype=np.double)
niters = 1000
lbfgs_algo = d4p.optimization_solver_lbfgs(
mse_algo, stepLengthSequence=sls, nIterations=niters
)
# finally do the computation
inp = np.array([[100]] * 11, dtype=np.double)
res = lbfgs_algo.compute(inp)
# The LBFGS 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(
"\nExpected coefficients:\n",
np.array(
[[11], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]], dtype=np.double
),
)
print("\nResulting coefficients:\n", res.minimum)
print("\nNumber of iterations performed:\n", res.nIterations[0][0])
print("All looks good!")