def predict(self):
self.set_conf()
# Load Model from HDFS.
tmp_path = tempfile.gettempdir()
self.conf["gbdt.split.feature"] = tmp_path + "/out/xxx"
self.conf["gbdt.split.value"] = tmp_path + "/out/xxx"
runner = GBDTRunner()
runner.predict(self.conf)
def predict(self):
self.set_conf()
# Load Model from HDFS. You can replace “/out/feature” and “out/value” with your prefer path
tmp_path = tempfile.gettempdir()
self.conf["gbdt.split.feature"] = tmp_path + "/out/feature"
self.conf["gbdt.split.value"] = tmp_path + "/out/value"
runner = GBDTRunner()
runner.predict(conf)
def predict(self):
self.set_conf()
# Load Model from HDFS.
TMP_PATH = tempfile.gettempdir()
self.conf["gbdt.split.feature"] = TMP_PATH + "/out/xxx"
self.conf["gbdt.split.value"] = TMP_PATH + "/out/xxx"
runner = GBDTRunner()
runner.predict(conf)
def train(self):
self.set_conf()
local_fs = LocalFileSystem.DEFAULT_FS
tmp_path = tempfile.gettempdir()
save_path = local_fs + tmp_path + "/model"
log_path = local_fs + tmp_path + "/GBDTlog"
input_path = "data/exampledata/GBDTLocalExampleData/agaricus.txt.train"
output_path = "data/output"
self.conf[AngelConf.ANGEL_TRAIN_DATA_PATH] = input_path
self.conf[AngelConf.ANGEL_SAVE_MODEL_PATH] = output_path
self.conf[AngelConf.ANGEL_SAVE_MODEL_PATH] = save_path
# Set log path
self.conf[AngelConf.ANGEL_LOG_PATH] = log_path
# Set actionType train
self.conf[AngelConf.ANGEL_ACTION_TYPE] = MLConf.ANGEL_ML_TRAIN
runner = GBDTRunner()
runner.train(self.conf)
def train(self):
self.set_conf()
runner = GBDTRunner()
runner.train(self.conf)
def train(self):
self.set_conf()
runner = GBDTRunner()
runner.train(self.conf)