{% set name = "xgboost" %}
{% set version = "1.1.1" %}
{% set buildnumber = 0 %}

package:
  name: {{ name|lower }}
  version: {{ version }}

source:
    git_url: https://github.com/dmlc/xgboost.git
    git_rev: cd3d14ad0e4e851cc855d045db1800855347cd76

build:
  number: {{buildnumber}}
  string: {{ GIT_BUILD_STR[:-2] }}py{{ CONDA_PY }}_{{ buildnumber }}

requirements:
  build:
    - python
    - setuptools
    - wheel
    - cmake >=3.13

  run:
    - python
    - numpy
    - scipy

test:
  requires:
    - scikit-learn >=0.18.0
    - numpy
    - scipy

  imports:
    - xgboost

about:
  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
  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.
  doc_url: https://xgboost.readthedocs.io/
  dev_url: https://github.com/dmlc/xgboost/
