# This file created by conda-build 3.18.9
# ------------------------------------------------

package:
    name: xgboost
    version: 1.1.1
source:
    git_rev: cd3d14ad0e4e851cc855d045db1800855347cd76
    git_url: https://github.com/dmlc/xgboost.git
build:
    number: '0'
    string: 497_gcd3d14apy35_0
requirements:
    build:
        - bzip2 1.0.8 1
        - certifi 2018.8.24 py35_1
        - cmake 3.16.0 hf94ab9c_0
        - curl 7.62.0 h646f8bb_1000
        - expat 2.2.9 he1b5a44_2
        - intelpython 2020.1 0
        - krb5 1.16.1 hc83ff2d_6
        - libcurl 7.62.0 h01ee5af_1000
        - libedit 3.1.20181209 hc058e9b_0
        - libffi 3.2.1 11
        - libgcc-ng 9.1.0 hdf63c60_0
        - libssh2 1.8.0 h9cfc8f7_4
        - libstdcxx-ng 9.1.0 hdf63c60_0
        - libuv 1.34.0 h516909a_0
        - ncurses 6.1 hf484d3e_1002
        - openssl 1.0.2s 0
        - python 3.5.6 hc3d631a_0
        - readline 7.0 h7b6447c_5
        - rhash 1.3.6 h14c3975_1001
        - setuptools 40.2.0 py35_0
        - sqlite 3.30.1 6
        - tk 8.6.9 29
        - wheel 0.31.1 py35_0
        - xz 5.2.4 h14c3975_7
        - zlib 1.2.11.1 h14c3975_1
    run:
        - numpy
        - python >=3.5,<3.6.0a0
        - scipy
test:
    imports:
        - xgboost
    requires:
        - numpy
        - scikit-learn >=0.18.0
        - scipy
about:
    description: 'XGBoost is an optimized distributed gradient boosting library designed
        to be highly efficient,

        flexible and portable. It implements machine learning algorithms under the
        Gradient Boosting

        framework. XGBoost provides a parallel tree boosting (also known as GBDT,
        GBM) that solve many

        data science problems in a fast and accurate way. The same code runs on major
        distributed

        environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

        '
    dev_url: https://github.com/dmlc/xgboost/
    doc_url: https://xgboost.readthedocs.io/
    home: https://github.com/dmlc/xgboost
    license: Apache-2.0
    license_file:
        - dmlc-core/LICENSE
        - rabit/LICENSE
    summary: 'Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or
        GBM) Library, for

        Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark,
        Flink

        and DataFlow

        '
extra:
    copy_test_source_files: true
    final: true
