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[MNT] Code formatting
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‎LICENSE

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@@ -14,48 +14,50 @@ documentation files (the "Software").
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The copyright holder hereby grants you a non-exclusive, non-transferable, free
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of charge right to copy, modify, merge, publish, distribute, and sublicense
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the Software for the sole purpose of performing non-commercial scientific
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research, non-commercial education, or non-commercial artistic projects provided
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that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this list
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of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice, this list
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of conditions and the following disclaimer in the documentation and/or other
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materials provided with the distribution.
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* All advertising materials mentioning features or use of this software must display
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the following information in the acknowledgement part: This product includes software
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developed by the LAMDA, Nanjing University, China.
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* In academic research publications and other publications where full and partial
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implementations of the code or binary distribution are used, presented, compared
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evaluated etc., a description or citation to the relevant articles ''Z.-H. Zhou and
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J. Feng, Deep Forest, National Science Review, vol. 6, no. 1, pp. 74-86, 2018'' and
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''Z.-H. Zhou and J. Feng, Deep Forest: Towards an alternative to deep neural networks,
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in IJCAI, 2017'' should be listed explicitly. In addition, this distribution should
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be given in the acknowledgement part in the following format: This work includes
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software developed by the LAMDA, Naning University, China.
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Any other use, in particular any use for commercial purposes, requires the grant
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from the LAMDA, Nanjing University, China. This includes, without limitation,
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incorporation in a commercial product, use in a commercial service, or production
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of other artefacts for commercial purposes.
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For all use above, including the non-commercial and commercial ones, neither the name
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of the LAMDA, Nanjing University, China nor the names of its contributors may be used to
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endorse or promote products derived from this software without specific prior
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written permission.
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THIS SOFTWARE IS PROVIDED BY LAMDA, NANJING UNIVERSITY, CHINA ''AS IS'' AND ANY EXPRESS
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OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
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THE LAMDA, NANJING UNIVERSITY, CHINA BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
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OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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research, non-commercial education, or non-commercial artistic projects
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provided that the following conditions are met:
19+
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* Redistributions of source code must retain the above copyright notice, this
21+
list of conditions and the following disclaimer.
22+
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* Redistributions in binary form must reproduce the above copyright notice,
24+
this list of conditions and the following disclaimer in the documentation
25+
and/or other materials provided with the distribution.
26+
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* All advertising materials mentioning features or use of this software must
28+
display the following information in the acknowledgement part: This product
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includes software developed by the LAMDA, Nanjing University, China.
30+
31+
* In academic research publications and other publications where full and
32+
partial implementations of the code or binary distribution are used,
33+
presented, compared evaluated etc., a description or citation to the relevant
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articles ''Z.-H. Zhou and J. Feng, Deep Forest, National Science Review,
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vol. 6, no. 1, pp. 74-86, 2018'' and ''Z.-H. Zhou and J. Feng, Deep Forest:
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Towards an alternative to deep neural networks, in IJCAI, 2017'' should be
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listed explicitly. In addition, this distribution should be given in the
38+
acknowledgement part in the following format: This work includes software
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developed by the LAMDA, Naning University, China.
40+
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Any other use, in particular any use for commercial purposes, requires the
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grant from the LAMDA, Nanjing University, China. This includes, without
43+
limitation, incorporation in a commercial product, use in a commercial service,
44+
or production of other artefacts for commercial purposes.
45+
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For all use above, including the non-commercial and commercial ones, neither
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the name of the LAMDA, Nanjing University, China nor the names of its
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contributors may be used to endorse or promote products derived from this
49+
software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY LAMDA, NANJING UNIVERSITY, CHINA ''AS IS'' AND ANY
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EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE LAMDA, NANJING UNIVERSITY, CHINA BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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From Scikit-Learn
@@ -124,8 +126,8 @@ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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From Numpy:
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-----------
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From Numpy
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----------
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BSD 3-Clause License
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@@ -160,8 +162,8 @@ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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From Scipy:
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-----------
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From Scipy
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----------
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BSD 3-Clause License
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@@ -374,4 +376,4 @@ From Cython
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incurred by, or claims asserted against, such Contributor by reason
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of your accepting any such warranty or additional liability.
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END OF TERMS AND CONDITIONS
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END OF TERMS AND CONDITIONS

‎deepforest/_binner.py

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"""
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Implementation of the Binner in Deep Forest.
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Implementation of the Binner class in Deep Forest.
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This class is modified from:
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https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_hist_gradient_boosting/binning.py

‎deepforest/_estimator.py

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"""A wrapper on base estimator."""
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"""A wrapper on the base estimator for naming consistency."""
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__all__ = ["Estimator"]

‎deepforest/_io.py

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class is designed to support the partial mode in deep forest.
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"""
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__all__ = ["Buffer"]
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import os

‎deepforest/_layer.py

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"""Implementation of forest-based Cascade Layer."""
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"""Implementation of the forest-based cascade layer."""
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__all__ = ["Layer"]

‎deepforest/forest.py

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"""
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Implementation of the forest for classification in Deep Forest.
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Implementation of the forest model for classification in Deep Forest.
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This class is modified from:
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https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/_forest.py

‎deepforest/tree/_criterion.pxd

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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Brian Holt <[email protected]>
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# Joel Nothman <[email protected]>
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# Arnaud Joly <[email protected]>
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# Jacob Schreiber <[email protected]>
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#
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# License: BSD 3 clause
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# This header file is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_criterion.pxd
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# See _criterion.pyx for implementation details.
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import numpy as np
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cimport numpy as np

‎deepforest/tree/_criterion.pyx

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# cython: boundscheck=False
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# cython: wraparound=False
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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Brian Holt <[email protected]>
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# Noel Dawe <[email protected]>
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# Satrajit Gosh <[email protected]>
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# Lars Buitinck
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# Arnaud Joly <[email protected]>
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# Joel Nothman <[email protected]>
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# Fares Hedayati <[email protected]>
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# Jacob Schreiber <[email protected]>
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# Nelson Liu <[email protected]>
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#
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# License: BSD 3 clause
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# This class is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_criterion.pyx
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from libc.stdlib cimport calloc
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from libc.stdlib cimport free

‎deepforest/tree/_splitter.pxd

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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Brian Holt <[email protected]>
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# Joel Nothman <[email protected]>
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# Arnaud Joly <[email protected]>
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# Jacob Schreiber <[email protected]>
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#
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# License: BSD 3 clause
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# See _splitter.pyx for details.
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# This header file is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_splitter.pxd
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import numpy as np
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cimport numpy as np

‎deepforest/tree/_splitter.pyx

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# cython: boundscheck=False
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# cython: wraparound=False
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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Brian Holt <[email protected]>
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# Noel Dawe <[email protected]>
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# Satrajit Gosh <[email protected]>
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# Lars Buitinck
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# Arnaud Joly <[email protected]>
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# Joel Nothman <[email protected]>
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# Fares Hedayati <[email protected]>
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# Jacob Schreiber <[email protected]>
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#
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# License: BSD 3 clause
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# This class is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_splitter.pyx
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from ._criterion cimport Criterion
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‎deepforest/tree/_tree.pxd

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# The header file for `_tree.pyx`
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#
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# Author: Yi-Xuan Xu <[email protected]>
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# Last Modified: December, 2020
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# Modified from the implementation of decision tree in Scikit-Learn.
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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Brian Holt <[email protected]>
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# Joel Nothman <[email protected]>
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# Arnaud Joly <[email protected]>
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# Jacob Schreiber <[email protected]>
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# Nelson Liu <[email protected]>
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#
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# License: BSD 3 clause
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# This header file is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_tree.pxd
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import numpy as np

‎deepforest/tree/_tree.pyx

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from libc.stdlib cimport free
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from libc.string cimport memcpy
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# This class is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_tree.pyx
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from libc.string cimport memset
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from libc.stdint cimport SIZE_MAX
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‎deepforest/tree/_utils.pxd

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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Arnaud Joly <[email protected]>
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# Jacob Schreiber <[email protected]>
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# Nelson Liu <[email protected]>
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#
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# License: BSD 3 clause
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# See _utils.pyx for details.
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# This header file is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_utils.pxd
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import numpy as np
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cimport numpy as np

‎deepforest/tree/_utils.pyx

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# cython: boundscheck=False
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# cython: wraparound=False
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# Authors: Gilles Louppe <[email protected]>
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# Peter Prettenhofer <[email protected]>
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# Arnaud Joly <[email protected]>
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# Jacob Schreiber <[email protected]>
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# Nelson Liu <[email protected]>
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#
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#
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# License: BSD 3 clause
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# This header file is modified from:
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# https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_utils.pyx
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from libc.stdlib cimport free
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from libc.stdlib cimport malloc

‎pyproject.toml

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[build-system]
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# Minimum requirements for the build system to execute.
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requires = [
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"setuptools",
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"wheel",

‎setup.py

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from numpy.distutils.core import setup
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DISTNAME = 'deepforest'
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# Package Info
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DISTNAME = "deep-forest"
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DESCRIPTION = "Deep Forest"
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with open('README.rst') as f:
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with open("README.rst") as f:
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LONG_DESCRIPTION = f.read()
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MAINTAINER = 'Yi-Xuan Xu'
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MAINTAINER_EMAIL = '[email protected]'
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URL = 'https://github.com/LAMDA-NJU/Deep-Forest'
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VERSION = '0.1.0'
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MAINTAINER = "Yi-Xuan Xu"
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MAINTAINER_EMAIL = "[email protected]"
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URL = "https://github.com/LAMDA-NJU/Deep-Forest"
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VERSION = "0.1.0"
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def configuration(parent_package='', top_path=None):
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if os.path.exists('MANIFEST'):
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os.remove('MANIFEST')
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def configuration(parent_package="", top_path=None):
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if os.path.exists("MANIFEST"):
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os.remove("MANIFEST")
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from numpy.distutils.misc_util import Configuration
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config = Configuration(None, parent_package, top_path)
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config.add_subpackage('deepforest')
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config.add_subpackage("deepforest")
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return config
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long_description=LONG_DESCRIPTION,
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zip_safe=False,
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classifiers=[
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'Intended Audience :: Science/Research',
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'Intended Audience :: Developers',
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'Programming Language :: C',
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'Programming Language :: Python',
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'Topic :: Software Development',
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'Topic :: Scientific/Engineering',
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'Operating System :: Microsoft :: Windows',
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'Operating System :: POSIX',
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'Operating System :: Unix',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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"Intended Audience :: Science/Research",
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"Intended Audience :: Developers",
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"Programming Language :: C",
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"Programming Language :: Python",
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"Topic :: Software Development",
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"Topic :: Scientific/Engineering",
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"Operating System :: Microsoft :: Windows",
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"Operating System :: Unix",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.6",
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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: 3.8",
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],
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python_requires=">=3.6",
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install_requires=[

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