class MFLocalExample(object):
def __init__(self):
self.conf = Configuration()
self.MLConf = MLConf()
def set_conf(self):
inputPath = "../../data/exampledata/MFLocalExampleData"
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic self.configuration keys
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(
AngelConf.ANGEL_INPUTFORMAT_CLASS,
'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST,
True)
# set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# Set trainning data, and save model path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH,
LOCAL_FS + TMP_PATH + "/model")
self.conf.set(AngelConf.ANGEL_LOG_PATH, LOCAL_FS + TMP_PATH + "/log")
# Set actionType train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
# Set MF algorithm parameters
self.conf.set(MLConf.ML_MF_RANK, "200")
self.conf.set(MLConf.ML_EPOCH_NUM, "8")
self.conf.set(MLConf.ML_MF_ROW_BATCH_NUM, "2")
self.conf.set(MLConf.ML_MF_ITEM_NUM, "1683")
self.conf.set(MLConf.ML_MF_LAMBDA, "0.01")
self.conf.set(MLConf.ML_MF_ETA, "0.0054")
def train(self):
self.set_conf()
runner = MatrixFactorizationRunner()
runner.train(self.conf)
class MFLocalExample(object):
def __init__(self):
self.conf= Configuration()
self.MLConf = MLConf()
def set_conf(self):
inputPath = "../../data/exampledata/MFLocalExampleData"
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic self.configuration keys
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(AngelConf.ANGEL_INPUTFORMAT_CLASS, 'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST, True)
# set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# Set trainning data, and save model path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, LOCAL_FS + TMP_PATH + "/model")
self.conf.set(AngelConf.ANGEL_LOG_PATH, LOCAL_FS + TMP_PATH + "/log")
# Set actionType train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
# Set MF algorithm parameters
self.conf.set(MLConf.ML_MF_RANK, "200")
self.conf.set(MLConf.ML_EPOCH_NUM, "8")
self.conf.set(MLConf.ML_MF_ROW_BATCH_NUM, "2")
self.conf.set(MLConf.ML_MF_ITEM_NUM, "1683")
self.conf.set(MLConf.ML_MF_LAMBDA, "0.01")
self.conf.set(MLConf.ML_MF_ETA, "0.0054")
def train(self):
self.set_conf()
runner = MatrixFactorizationRunner()
runner.train(self.conf)
class SGDLRLocalExample(object):
def __init__(self):
self.conf = Configuration()
def set_conf(self):
# Feature number of train data
feature_num = 124
# Total iteration number
epoch_num = 20
# Validation sample Ratio
v_ratio = 0.1
# Data format, libsvm or dummy
data_fmt = "libsvm"
# Train batch number per epoch.
sp_ratio = 1.0
# Batch number
batch_num = 10
# Learning rate
learn_rate = 1.0
# Decay of learning rate
decay = 0.1
# Regularization coefficient
reg = 0.2
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic configuration keys
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(AngelConf.ANGEL_INPUTFORMAT_CLASS, 'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST, True)
# Set data format
self.conf.set(MLConf.ML_DATAFORMAT, data_fmt)
# set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
# set sgd LR algorithm parameters #feature #epoch
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.ML_BATCH_SAMPLE_Ratio, str(sp_ratio))
self.conf.set(MLConf.ML_VALIDATE_RATIO, str(v_ratio))
self.conf.set(MLConf.ML_LEARN_RATE, str(learn_rate))
self.conf.set(MLConf.ML_LEARN_DECAY, str(decay))
self.conf.set(MLConf.ML_REG_L2, str(reg))
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.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH, load_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 incremental train
self.conf.set(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)
class KmeansLocalExample(object):
def __init__(self):
self.conf = Configuration()
self.MLConf = MLConf()
def set_conf(self):
data_fmt = "libsvm"
# Cluster center number
center_num = 3
# Feature number of train data
feature_num = 4
# Total iteration number
epoch_num = 20
# Sample ratio per mini-batch
sample_ratio = 1.0
# C
c = 0.15
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic self.configuration key
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(
AngelConf.ANGEL_INPUTFORMAT_CLASS,
'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST,
True)
# Set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
# Set Kmeans algorithm parameters #cluster #feature #epoch
self.conf.set(MLConf.KMEANS_CENTER_NUM, str(center_num))
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.KMEANS_SAMPLE_RATIO_PERBATCH, str(sample_ratio))
self.conf.set(MLConf.kMEANS_C, str(c))
# Set data format
self.conf.set(MLConf.ML_DATAFORMAT, data_fmt)
def train(self):
self.set_conf()
input_path = "data/exampledata/clusteringLocalExampleData/iris"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# 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,
LOCAL_FS + TMP_PATH + "/model")
# Set log sava path
self.conf.set(AngelConf.ANGEL_LOG_PATH,
LOCAL_FS + TMP_PATH + "/kmeansLog/log")
# Set actionType train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
runner = KMeansRunner()
runner.train(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
def predict_onLocal_cluster(self):
self.set_conf()
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# Set trainning data path
self.conf.set(AngelConf.ANGEL_PREDICT_DATA_PATH, input_path)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH,
LOCAL_FS + TMP_PATH + "/model")
# Set predict result path
self.conf.set(AngelConf.ANGEL_PREDICT_PATH,
LOCAL_FS + TMP_PATH + "/predict")
# Set actionType prediction
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_PREDICT)
runner = KMeansRunner()
runner.predict(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
from pyangel.ml.conf import MLConf
from pyangel.conf import AngelConf
try:
Configuration._init()
except RuntimeError:
print("Oops!PyAngel failed to initialize")
# To Do
# Modify the way to get current Angel version
conf = Configuration()
conf[AngelConf.ANGEL_INPUTFORMAT_CLASS] = 'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat'
conf.set_boolean("mapred.mapper.new-api", True)
conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST, True)
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
conf[AngelConf.ANGEL_DEPLOY_MODE] = "LOCAL"
conf[AngelConf.ANGEL_SAVE_MODEL_PATH] = LOCAL_FS + TMP_PATH + "/out"
conf[AngelConf.ANGEL_LOG_PATH] = LOCAL_FS + TMP_PATH + "/log"
conf[AngelConf.ANGEL_WORKERGROUP_NUMBER] = 1
conf[AngelConf.ANGEL_WORKER_TASK_NUMBER] = 1
conf[AngelConf.ANGEL_PS_NUMBER] = 1
print("""Welcome to
___ __
class LinearRegLocalExample(object):
"""
Linear Regression Example used for user test, similar to "com.tencent.angel.example.ml.LinearRegLocalExample".
"""
def __init__(self):
self.conf= Configuration()
def set_conf(self):
"""
Set up self.configuration for runtime environment.
"""
# Feature number of train data
feature_num = 101
# Total iteration number
epoch_num = 20
# Validation sample ratio
v_ratio = 0.5
# Data format,libsvm or dummy
data_fmt = 'libsvm'
# Train batch number per epoch
sp_ratio = 1
# Learning rate
learn_rate = 0.1
# Decay of learning rate
decay = 0.01
# Regularization coefficient
reg = 0
# Set job queue, if you use YARN deploy mode, you can set job queue by
# self.conf.set('mapreduce.job.queue.name', 'default')
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, 'LOCAL')
# Set basic self.configuration keys
self.conf.set_boolean('mapred.mapper.new-api', True)
self.conf.set(AngelConf.ANGEL_INPUTFORMAT_CLASS, 'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST, True)
# Set data format
self.conf.set(MLConf.ML_DATAFORMAT, data_fmt)
# set angel resource parameters #worker, #tast, #ps
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 2)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 10)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 2)
# set sgd LR algorithim parameters # feature # epoch
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.ML_BATCH_SAMPLE_Ratio, str(sp_ratio))
self.conf.set(MLConf.ML_VALIDATE_RATIO, str(v_ratio))
self.conf.set(MLConf.ML_LEARN_RATE, str(learn_rate))
self.conf.set(MLConf.ML_LEARN_DECAY, str(decay))
self.conf.set(MLConf.ML_REG_L2, str(reg))
def train_on_local_cluster(self):
"""
Train model on local cluster
"""
self.set_conf()
input_path = '../data/exampledata/LinearRegression'
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
log_path = ".src/test/log"
model_path = 'file:///tmp/angel/model'
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, model_path)
self.conf.set(AngelConf.ANGEL_LOG_PATH, log_path)
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
self.conf.set("fs.defaultFS", LOCAL_FS + TMP_PATH)
runner = LinearRegRunner()
runner.train(self.conf)
def inc_train(self):
self.set_conf()
input_path = "../data/exampledata/LinearRegression/LinearReg100.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
log_path = "./src/test/log"
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH, LOCAL_FS + TMP_PATH + "/model")
# Set save model path
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, LOCAL_FS + TMP_PATH + "/newmodel")
# Set actionType incremental train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_INC_TRAIN())
# Set log path
self.conf.set(AngelConf.ANGEL_LOG_PATH, logPath)
runner = LinearRegRunner()
runner.incTrain(self.conf)
def predict(self):
self.set_conf()
input_path = "../data/exampledata/LinearRegression/LinearReg100.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH, LOCAL_FS + TMP_PATH + "/model")
# Set predict result path
self.conf.set(AngelConf.ANGEL_PREDICT_PATH, LOCAL_FS + TMP_PATH + "/predict")
# Set actionType prediction
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_PREDICT())
runner = LinearRegRunner()
runner.predict(self.conf)
class FMLocalExample(oject):
def __init__(self):
self.conf = Configuration()
def set_conf(self):
"""
set parameter values of self.conf
"""
# Feature number of train data
feature_num = 236
# Total iteration number
epoch_num = 20
# Rank
rank = 5
# Regularization parameters
reg0 = 0.0
reg1 = 0.0
reg2 = 0.001
# Learn rage
lr = 0.001
stev = 0.1
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic self.configuration keys
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(
AngelConf.ANGEL_INPUTFORMAT_CLASS,
'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST,
True)
#set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
#set FM algorithm parameters #feature #epoch
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.ML_FM_RANK, str(rank))
self.conf.set(MLConf.ML_LEARN_RATE, str(lr))
self.conf.set(MLConf.ML_FM_REG0, str(reg0))
self.conf.set(MLConf.ML_FM_REG1, str(reg1))
self.conf.set(MLConf.ML_FM_REG2, str(reg2))
self.conf.set(MLConf.ML_FM_V_STDDEV, str(stev))
def train_on_local_cluster(self):
self.set_conf()
input_path = "./src/test/data/fm/food_fm_libsvm"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/LRlog"
# 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 = FMRunner()
runner.train(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
def fm_classification(self):
input_path = "./src/test/data/fm/a9a.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/LRlog"
# 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)
# Set learnType
self.conf.set(MLConf.ML_FM_LEARN_TYPE, "c")
# Set feature number
self.conf.set(MLConf.ML_FEATURE_NUM, str(124))
runner = FMRunner()
runner.train(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
class FMLocalExample(oject):
def __init__(self):
self.conf = Configuration()
def set_conf(self):
"""
set parameter values of self.conf
"""
# Feature number of train data
feature_num = 236
# Total iteration number
epoch_num = 20
# Rank
rank = 5
# Regularization parameters
reg0 = 0.0
reg1 = 0.0
reg2 = 0.001
# Learn rage
lr = 0.001
stev = 0.1
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, "LOCAL")
# Set basic self.configuration keys
self.conf.set_boolean("mapred.mapper.new-api", True)
self.conf.set(AngelConf.ANGEL_INPUTFORMAT_CLASS, 'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST, True)
#set angel resource parameters #worker, #task, #PS
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 1)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 1)
#set FM algorithm parameters #feature #epoch
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.ML_FM_RANK, str(rank))
self.conf.set(MLConf.ML_LEARN_RATE, str(lr))
self.conf.set(MLConf.ML_FM_REG0, str(reg0))
self.conf.set(MLConf.ML_FM_REG1, str(reg1))
self.conf.set(MLConf.ML_FM_REG2, str(reg2))
self.conf.set(MLConf.ML_FM_V_STDDEV, str(stev))
def train_on_local_cluster(self):
self.set_conf()
input_path = "./src/test/data/fm/food_fm_libsvm"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/LRlog"
# 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 = FMRunner()
runner.train(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
def fm_classification(self):
input_path = "./src/test/data/fm/a9a.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
save_path = LOCAL_FS + TMP_PATH + "/model"
log_path = LOCAL_FS + TMP_PATH + "/LRlog"
# 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)
# Set learnType
self.conf.set(MLConf.ML_FM_LEARN_TYPE, "c")
# Set feature number
self.conf.set(MLConf.ML_FEATURE_NUM, str(124))
runner = FMRunner()
runner.train(self.conf)
angel_client = AngelClientFactory.get(self.conf)
angel_client.stop()
class LinearRegLocalExample(object):
"""
Linear Regression Example used for user test, similar to "com.tencent.angel.example.ml.LinearRegLocalExample".
"""
def __init__(self):
self.conf = Configuration()
def set_conf(self):
"""
Set up self.configuration for runtime environment.
"""
# Feature number of train data
feature_num = 101
# Total iteration number
epoch_num = 20
# Validation sample ratio
v_ratio = 0.5
# Data format,libsvm or dummy
data_fmt = 'libsvm'
# Train batch number per epoch
sp_ratio = 1
# Learning rate
learn_rate = 0.1
# Decay of learning rate
decay = 0.01
# Regularization coefficient
reg = 0
# Set job queue, if you use YARN deploy mode, you can set job queue by
# self.conf.set('mapreduce.job.queue.name', 'default')
# Set local deploy mode
self.conf.set(AngelConf.ANGEL_DEPLOY_MODE, 'LOCAL')
# Set basic self.configuration keys
self.conf.set_boolean('mapred.mapper.new-api', True)
self.conf.set(
AngelConf.ANGEL_INPUTFORMAT_CLASS,
'org.apache.hadoop.mapreduce.lib.input.CombineTextInputFormat')
self.conf.set_boolean(AngelConf.ANGEL_JOB_OUTPUT_PATH_DELETEONEXIST,
True)
# Set data format
self.conf.set(MLConf.ML_DATAFORMAT, data_fmt)
# set angel resource parameters #worker, #tast, #ps
self.conf.set_int(AngelConf.ANGEL_WORKERGROUP_NUMBER, 2)
self.conf.set_int(AngelConf.ANGEL_WORKER_TASK_NUMBER, 10)
self.conf.set_int(AngelConf.ANGEL_PS_NUMBER, 2)
# set sgd LR algorithim parameters # feature # epoch
self.conf.set(MLConf.ML_FEATURE_NUM, str(feature_num))
self.conf.set(MLConf.ML_EPOCH_NUM, str(epoch_num))
self.conf.set(MLConf.ML_BATCH_SAMPLE_Ratio, str(sp_ratio))
self.conf.set(MLConf.ML_VALIDATE_RATIO, str(v_ratio))
self.conf.set(MLConf.ML_LEARN_RATE, str(learn_rate))
self.conf.set(MLConf.ML_LEARN_DECAY, str(decay))
self.conf.set(MLConf.ML_REG_L2, str(reg))
def train_on_local_cluster(self):
"""
Train model on local cluster
"""
self.set_conf()
input_path = '../data/exampledata/LinearRegression'
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
log_path = ".src/test/log"
model_path = 'file:///tmp/angel/model'
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, input_path)
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH, model_path)
self.conf.set(AngelConf.ANGEL_LOG_PATH, log_path)
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_TRAIN)
self.conf.set("fs.defaultFS", LOCAL_FS + TMP_PATH)
runner = LinearRegRunner()
runner.train(self.conf)
def inc_train(self):
self.set_conf()
input_path = "../data/exampledata/LinearRegression/LinearReg100.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
log_path = "./src/test/log"
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH,
LOCAL_FS + TMP_PATH + "/model")
# Set save model path
self.conf.set(AngelConf.ANGEL_SAVE_MODEL_PATH,
LOCAL_FS + TMP_PATH + "/newmodel")
# Set actionType incremental train
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_INC_TRAIN())
# Set log path
self.conf.set(AngelConf.ANGEL_LOG_PATH, logPath)
runner = LinearRegRunner()
runner.incTrain(self.conf)
def predict(self):
self.set_conf()
input_path = "../data/exampledata/LinearRegression/LinearReg100.train"
LOCAL_FS = LocalFileSystem.DEFAULT_FS
TMP_PATH = tempfile.gettempdir()
# Set trainning data path
self.conf.set(AngelConf.ANGEL_TRAIN_DATA_PATH, inputPath)
# Set load model path
self.conf.set(AngelConf.ANGEL_LOAD_MODEL_PATH,
LOCAL_FS + TMP_PATH + "/model")
# Set predict result path
self.conf.set(AngelConf.ANGEL_PREDICT_PATH,
LOCAL_FS + TMP_PATH + "/predict")
# Set actionType prediction
self.conf.set(AngelConf.ANGEL_ACTION_TYPE, MLConf.ANGEL_ML_PREDICT())
runner = LinearRegRunner()
runner.predict(self.conf)