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PyAngel快速入门

编写和编译

  1. PyAngel的编写，推荐使用Atom或者pyCharm，高手请自备Vim或者Emacs
  2. 编译代码：请参考Angel编译指南，注意Python版本需要是3.6

运行任务

PyAngel有2种提交任务的模式

  • 交互式

    • local模式

       bin/pyangel local

    • Yarn模式

       bin/pyangel

  • 脚本式

    • local模式

       	bin/angel-local-submit --angel.pyangel.pyfile ${ANGEL_HOME}/python/examples/gbdt_local_example/py

    • Yarn模式

       	bin/angel-submit --angel.pyangel.pyfile ${ANGEL_HOME}/python/examples/gbdt_example.py

可以看出，PyAngel也支持2种运行模式：local & Yarn，Yarn模式依赖Hadoop，需要在机器上将Hadoop安装好，并且保证HADOOP_HOME设置正确，具体可以参考Yarn运行模式

  • 提交的样例命令
	bin/angel-local-submit    --angel.pyangel.pyfile ${ANGEL_HOME}/python/examples/gbdt_local_example.py \
        			     	  --angel.train.data.path "file:///${ANGEL_HOME}/data/exampledata/GBDTLocalExampleData/agaricus.txt.train" \
        			     	  --angel.log.path "file:///${ANGEL_HOME}/data/log" \
        			     	  --angel.save.model.path "file:///${ANGEL_HOME}/data/output" \

Example Code

  • PyAngel版本的GBDT

     from pyangel.ml.gbdt.runner import GBDTRunner

 # Trainning data input path
 input_path = "file:///${YOUR_ANGEL_HOME}/data/exampledata/GBDTLocalExampleData/agaricus.txt.train"
 
 # Algo param
 feature_num = 127
 feature_nzz = 25
 tree_num = 2
 tree_depth = 2
 split_num = 10
 sample_ratio = 1.0

 # Data format
 data_fmt = "libsvm"

 # Learning rate
 learn_rate = 0.01

 # Set GBDT trainning data path
 conf[AngelConf.ANGEL_TRAIN_DATA_PATH] = input_path
 # Set GBDT algorithm parameters
 conf[MLConf.ML_FEATURE_NUM] = str(feature_num)
 conf[MLConf.ML_FEATURE_NNZ] = str(feature_nzz)
 conf[MLConf.ML_GBDT_TREE_NUM] = str(tree_num)
 conf[MLConf.ML_GBDT_TREE_DEPTH] = str(tree_depth)
 conf[MLConf.ML_GBDT_SPLIT_NUM] = str(split_num)
 conf[MLConf.ML_GBDT_SAMPLE_RATIO] = str(sample_ratio)
 conf[MLConf.ML_LEARN_RATE] = str(learn_rate)

 runner = GBDTRunner()
 runner.train(conf)

  • Demo实例