def train_on_local_cluster(self):
self.set_conf()
input_path = "../data/exampledata/LRLocalExampleData/a9a.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/log"
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
# Set save model path
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, save_path)
# Set log path
self.conf.set(AngelConf.ANGEL_LOG_PATH, log_path)
# Set actionType train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
runner = LRRunner()
runner.train(self.conf)
def train_on_local_cluster(self):
self.set_conf()
input_path = "../data/exampledata/LRLocalExampleData/a9a.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/log"
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
# Set save model path
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, save_path)
# Set log path
self.conf.set(AngelConf.ANGEL_LOG_PATH, log_path)
# Set actionType train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
runner = LRRunner()
runner.train(self.conf)
def inc_train(self):
self.set_conf()
input_path = 'data/exampledata/LRLocalExampleData/a9a.train'
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
load_path = LOCAL_FS + TMP_PATH + "/model"
save_path = LOCAL_FS + TMP_PATH + "/newmodel"
log_path = LOCAL_FS + TMP_PATH + "/log"
# Set trainning data path
self.conf[AngelConf.ANGEL_TRAIN_DATA_PATH] = input_path
# Set load model path
self.conf[AngelConf.ANGEL_LOAD_MODEL_PATH] = load_path
# Set save model path
self.conf[AngelConf.ANGEL_SAVE_MODEL_PATH] = save_path
# Set log path
self.conf[AngelConf.ANGEL_LOG_PATH] = log_path
# Set actionType incremental train
self.conf[AngelConf.ANGEL_ACTION_TYPE] = MLConf.ANGEL_ML_INC_TRAIN
runner = LRRunner()
runner.inc_train(self.conf)
def inc_train(self):
self.set_conf()
input_path = 'data/exampledata/LRLocalExampleData/a9a.train'
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
load_path = LOCAL_FS + TMP_PATH + "/model"
save_path = LOCAL_FS + TMP_PATH + "/newmodel"
log_path = LOCAL_FS + TMP_PATH + "/log"
# Set trainning data path
self.conf[AngelConf.ANGEL_TRAIN_DATA_PATH] = input_path
# Set load model path
self.conf[AngelConf.ANGEL_LOAD_MODEL_PATH] = load_path
# Set save model path
self.conf[AngelConf.ANGEL_SAVE_MODEL_PATH] = save_path
# Set log path
self.conf[AngelConf.ANGEL_LOG_PATH] = log_path
# Set actionType incremental train
self.conf[AngelConf.ANGEL_ACTION_TYPE] = MLConf.ANGEL_ML_INC_TRAIN
runner = LRRunner()
runner.inc_train(self.conf)
def predict(self):
self.set_conf()
input_path = "../data/exampledata/LRLocalExampleData/a9a.test"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = System.getProperty("java.io.tmpdir", "/tmp")
load_path = LOCAL_FS + TMP_PATH + "/model"
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/log"
predict_path = LOCAL_FS + TMP_PATH + "/predict"
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH, load_path)
# Set predict result path
self.conf.set(AngelConf.ANGEL_PREDICT_PATH, predict_path)
# Set log path
self.conf.set(AngelConf.ANGEL_LOG_PATH, log_path)
# Set actionType prediction
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_PREDICT())
runner = LRRunner()
runner.predict(self.conf)