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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 Decision Tree 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'):
infile = "./data/batch/decision_tree_train.csv"
prunefile = "./data/batch/decision_tree_prune.csv"
testfile = "./data/batch/decision_tree_test.csv"
# Configure a Linear regression training object
train_algo = d4p.decision_tree_regression_training()
# Read data. Let's have 5 independent, and 1 dependent variables (for each observation)
indep_data = readcsv(infile, range(5))
dep_data = readcsv(infile, range(5,6))
prune_indep = readcsv(prunefile, range(5))
prune_dep = readcsv(prunefile, range(5,6))
# Now train/compute, the result provides the model for prediction
train_result = train_algo.compute(indep_data, dep_data, prune_indep, prune_dep)
# Now let's do some prediction
predict_algo = d4p.decision_tree_regression_prediction()
# read test data (with same #features)
pdata = readcsv(testfile, range(5))
ptdata = readcsv(testfile, range(5,6))
# now predict using the model from the training above
predict_result = predict_algo.compute(pdata, train_result.model)
# The prediction result provides prediction
assert predict_result.prediction.shape == (pdata.shape[0], dep_data.shape[1])
return (train_result, predict_result, ptdata)
if __name__ == "__main__":
(train_result, predict_result, ptdata) = main()
print("\nDecision tree prediction results (first 20 rows):\n", predict_result.prediction[0:20])
print("\nGround truth (first 10 rows):\n", ptdata[0:20])
print('All looks good!')
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