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#*******************************************************************************
# Copyright 2014-2019 Intel Corporation
# All Rights Reserved.
#
# This software is licensed under the Apache License, Version 2.0 (the
# "License"), the following terms apply:
#
# 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 logistic regression example for shared memory systems
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'):
nClasses = 2
nFeatures = 20
# read training data from file with 20 features per observation and 1 class label
trainfile = "./data/batch/binary_cls_train.csv"
train_data = readcsv(trainfile, range(nFeatures))
train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses, interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# read testing data from file with 20 features per observation
testfile = "./data/batch/binary_cls_test.csv"
predict_data = readcsv(testfile, range(nFeatures))
predict_labels = readcsv(testfile, range(nFeatures, nFeatures + 1))
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction
assert predict_result.prediction.shape == (predict_data.shape[0], train_labels.shape[1])
return (train_result, predict_result, predict_labels)
if __name__ == "__main__":
(train_result, predict_result, predict_labels) = main()
print("\nLogistic Regression coefficients:\n", train_result.model.Beta)
print("\nLogistic regression prediction results (first 10 rows):\n", predict_result.prediction[0:10])
print("\nGround truth (first 10 rows):\n", predict_labels[0:10])
print('All looks good!')
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