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univariate_outlier.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 outlier detection univariate example for shared memory systems
from pathlib import Path
import numpy as np
from readcsv import pd_read_csv
import daal4py as d4p
def main(readcsv=pd_read_csv):
# Input file
data_path = Path(__file__).parent / "data" / "batch"
infile = data_path / "outlierdetection.csv"
# Retrieve the data from the input file
data = readcsv(infile, range(3))
# Create an algorithm to detect outliers (univariate)
algorithm = d4p.univariate_outlier_detection()
# Compute outliers and get the computed results
res = algorithm.compute(data, None, None, None)
# result provides weights
assert res.weights.shape == (data.shape[0], 3)
return (data, res)
if __name__ == "__main__":
(data, res) = main()
print("\nInput data\n", data)
print("\nOutlier detection result (univariate) weights:\n", res.weights)
print("All looks good!")