mirror of https://github.com/google/oss-fuzz.git
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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#!/usr/bin/python3
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# Copyright 2022 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Targets sklearn.preprocessing"""
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import os
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import sys
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import atheris
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import numpy as np
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.preprocessing import OrdinalEncoder
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def get_random_arr(fdp):
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l1 = list()
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for i in range(fdp.ConsumeIntInRange(1, 1000)):
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l1.append(
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[
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fdp.ConsumeIntInRange(1, 1000),
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fdp.ConsumeIntInRange(1, 1000),
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fdp.ConsumeIntInRange(1, 1000)
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]
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)
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return l1
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def TestOneInput(data):
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fdp = atheris.FuzzedDataProvider(data)
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oh_x1 = np.array(get_random_arr(fdp))
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oh_x2 = np.array(get_random_arr(fdp))
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# Test that one hot encoder raises error for unknown features
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# present during transform.
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oh = OneHotEncoder(handle_unknown="error")
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oh.fit(oh_x1)
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try:
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oh.transform(oh_x2)
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except ValueError:
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pass
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oe_x1 = np.array(get_random_arr(fdp))
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oe_x2 = np.array(get_random_arr(fdp))
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oe = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)
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oe.fit(oe_x1)
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try:
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oe.transform(oe_x2)
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except ValueError:
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pass
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def main():
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atheris.instrument_all()
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atheris.Setup(sys.argv, TestOneInput, enable_python_coverage=True)
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atheris.Fuzz()
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if __name__ == "__main__":
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main()
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