def train(config, trainfile, testfile):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
keys = get_norm_keys(config['input'].get('conf'))
train_dirs = trainfile.split(',')
train_files = [[] for _ in range(5)]
for train_dir in train_dirs:
for f in os.listdir(train_dir):
if f != "_SUCCESS":
ind = int(int(f.split('-')[-1]) / 40)
train_files[ind].append(os.path.join(train_dir, f))
#train_files = [os.path.join(train_dir, f) for train_dir in train_dirs for f in os.listdir(train_dir) if f != "_SUCCESS"]
#train_files = tf.random_shuffle(tf.train.match_filenames_once([os.path.join(train_dir, f) for f in os.listdir(train_dir) if f != "_SUCCESS"]))
#train_files = tf.random_shuffle(tf.train.match_filenames_once(['%s/%s/part-r-*' % (data_path, dt) for dt in date_list]))
#logging.info('train directory: {}'.format(train_dirs))
#logging.info('train files: {}'.format(reprlib.repr(train_files)))
dev_dirs = testfile.split(',')
dev_files = [
os.path.join(dev_dir, f) for dev_dir in dev_dirs
for f in os.listdir(dev_dir) if f != "_SUCCESS"
]
#logging.info('dev directory: {}'.format(dev_dirs))
#logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#特征的配置文件 在input 这个section的spec这个key
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec']) #特征配置文件 有boundaries等信息
columns, spec = FCGen.GetFeatureSpec(
feature_config) #按特征列对特征进行处理，不同类型处理会不一样，比如数值、embed等
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth = True
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
run_config = tf.estimator.RunConfig().replace(
model_dir=config['train'].get('model_dir', 'model_dir'),
save_checkpoints_secs=3600,
session_config=conf)
dynamic = config['train']['dynamic'] == 'true'
print("dynamic:", dynamic)
logging.info("Creating model...")
# Define the model
hidden_units = [int(n) for n in config['model']['hidden_units'].split(',')]
learning_rate = float(config['model']['learning_rate'])
ctr_reg = float(config['model'].get('ctr_reg', '1e-4'))
cvr_reg = float(config['model'].get('cvr_reg', '1e-4'))
ctcvr_loss_weight = float(config['model'].get('ctcvr_loss_weight', '1.0'))
model = tf.estimator.Estimator(model_fn=esmm_model_fn,
params={
'cat_columns':
columns['cat'],
'val_columns':
columns['val'],
'dnn_columns':
list(columns['dnn'].values()),
'weight_columns':
list(columns['weight'].values()),
'column_to_field': {},
'hidden_units':
hidden_units,
'learning_rate':
learning_rate,
'ctr_reg':
ctr_reg,
'cvr_reg':
cvr_reg,
'reg':
1e-4,
'ctcvr_loss_weight':
ctcvr_loss_weight,
'model':
config['model']['model'],
'embed_dim':
int(config['model']['embedding_dim']),
'dynamic':
dynamic
},
config=run_config)
# Train and evaluate
max_steps = config['train'].get('max_step', '')
if max_steps == '':
max_steps = None
else:
max_steps = int(max_steps)
#for variable_name in model.get_variable_names():
# print(variable_name)
logging.info("training...")
epochs = int(config['train'].get('epochs', '1'))
#train_input_fn = lambda: input_fn(train_files, spec, True, batch_size, mt=True)
eval_input_fn = lambda: input_fn(
dev_files, spec, False, batch_size, mt=True)
for i in range(epochs):
logging.info("{}th training...".format(i + 1))
for j in range(len(train_files)):
model.train(input_fn=lambda: input_fn(
train_files[j], spec, True, batch_size, mt=True),
steps=max_steps)
results = model.evaluate(input_fn=eval_input_fn)
logging.info("{}th test results...".format(i + j + 1))
for key in sorted(results):
print('%s: %s' % (key, results[key]))
model.export_savedmodel(
export_dir_base=config['train'].get('export_dir', 'export_dir'),
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
def train(config, trainfile, testfile):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
prefix = "/data/home/graywang/esmm/tfrecords/rt_mt"
train_dirs = trainfile.split(',')
cluster = "hdfs://ss-sng-dc-v2/stage/outface/SNG/g_sng_qqmusic_develop/g_sng_qqmusic_develop/timmili/gray_temp/"
if config['train']['source'] == 'hdfs':
train_files = [[] for _ in range(5)]
for train_dir in train_dirs:
for i in range(5):
train_files[i].append(cluster + train_dir + "/part-r-00" +
str(i) + "*")
else:
train_files = [[] for _ in range(4)]
for train_dir in train_dirs:
for f in os.listdir(prefix + "/" + train_dir):
if len(os.listdir(prefix + "/" + train_dir)) > 250:
div = 125
else:
div = 50
if f != "_SUCCESS":
ind = int(int(f.split('-')[-1]) / div)
train_files[ind].append(os.path.join(prefix, train_dir, f))
logging.info('train directory: {}'.format(train_dirs))
logging.info('train files: {}'.format(reprlib.repr(train_files)))
dev_dirs = testfile.split(',')
dev_files = [
os.path.join(prefix, dev_dir, f) for dev_dir in dev_dirs
for f in os.listdir(prefix + "/" + dev_dir) if f != "_SUCCESS"
]
logging.info('dev directory: {}'.format(dev_dirs))
logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#logging.info('dev directory: {}'.format(dev_dirs))
#logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#特征的配置文件 在input 这个section的spec这个key
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec']) #特征配置文件 有boundaries等信息
columns, spec, dimension_config = FCGen.GetFeatureSpec(
feature_config) #按特征列对特征进行处理，不同类型处理会不一样，比如数值、embed等
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth = True
dimension_config = {}
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
run_config = tf.estimator.RunConfig().replace(
model_dir=config['train'].get('model_dir', 'model_dir'),
save_checkpoints_secs=3600,
session_config=conf)
dynamic = config['train']['dynamic'] == 'true'
warm_dir = config['train'].get('warm_dir', '')
if len(warm_dir) > 1:
ws = tf.estimator.WarmStartSettings(ckpt_to_initialize_from=warm_dir,
vars_to_warm_start=".*")
else:
ws = None
print("dynamic:", dynamic)
logging.info("Creating model...")
# Define the model
hidden_units = [int(n) for n in config['model']['hidden_units'].split(',')]
learning_rate = float(config['model']['learning_rate'])
ctr_reg = float(config['model'].get('ctr_reg', '1e-4'))
cvr_reg = float(config['model'].get('cvr_reg', '1e-4'))
ctcvr_loss_weight = float(config['model'].get('ctcvr_loss_weight', '1.0'))
model = tf.estimator.Estimator(model_fn=esmm_model_fn,
params={
'cat_columns':
columns['cat'],
'val_columns':
columns['val'],
'dnn_columns':
list(columns['dnn'].values()),
'weight_columns':
list(columns['weight'].values())[0],
'column_to_field': {},
'hidden_units':
hidden_units,
'learning_rate':
learning_rate,
'ctr_reg':
ctr_reg,
'cvr_reg':
cvr_reg,
'reg':
1e-4,
'dimension_config':
dimension_config,
'ctcvr_loss_weight':
ctcvr_loss_weight,
'model':
config['model']['model'],
'embed_dim':
int(config['model']['embedding_dim']),
'expert_num':
int(config['model']['expert_num']),
'expert_unit':
int(config['model']['expert_unit']),
'dynamic':
dynamic
},
config=run_config,
warm_start_from=ws)
# Train and evaluate
max_steps = config['train'].get('max_step', '')
if max_steps == '':
max_steps = None
else:
max_steps = int(max_steps)
#for variable_name in model.get_variable_names():
# print(variable_name)
logging.info("training...")
epochs = int(config['train'].get('epochs', '1'))
#train_input_fn = lambda: input_fn(train_files, spec, True, batch_size, mt=True)
if config['train']['source'] == 'hdfs':
input_func = input_fn_pattern
else:
input_func = input_fn
eval_input_fn = lambda: input_fn(
dev_files, spec, False, batch_size, mt=True)
for i in range(epochs):
logging.info("{}th training...".format(i + 1))
for j in range(len(train_files)):
model.train(input_fn=lambda: input_func(
train_files[j], spec, True, batch_size, mt=True))
results = model.evaluate(input_fn=eval_input_fn)
logging.info("{}th test results...".format(i + j + 1))
for key in sorted(results):
print('%s: %s' % (key, results[key]))
model.export_savedmodel(
export_dir_base=config['train'].get('export_dir', 'export_dir'),
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
def train(config , trainfile, testfile):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
keys = get_norm_keys(config['input'].get('conf'))
train_dirs = trainfile.split(',')
train_files = [[] for _ in range(5)]
for train_dir in train_dirs:
for f in os.listdir(train_dir):
if f != "_SUCCESS":
ind = int(int(f.split('-')[-1]) / 40)
train_files[ind].append(os.path.join(train_dir, f))
#train_files = [os.path.join(train_dir, f) for train_dir in train_dirs for f in os.listdir(train_dir) if f != "_SUCCESS"]
#train_files = tf.random_shuffle(tf.train.match_filenames_once([os.path.join(train_dir, f) for f in os.listdir(train_dir) if f != "_SUCCESS"]))
#train_files = tf.random_shuffle(tf.train.match_filenames_once(['%s/%s/part-r-*' % (data_path, dt) for dt in date_list]))
logging.info('train directory: {}'.format(train_dirs))
logging.info('train files: {}'.format(reprlib.repr(train_files)))
dev_dirs = testfile.split(',')
dev_files = [os.path.join(dev_dir, f) for dev_dir in dev_dirs for f in os.listdir(dev_dir) if f != "_SUCCESS"]
logging.info('dev directory: {}'.format(dev_dirs))
logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#特征的配置文件 在input 这个section的spec这个key
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec'])#特征配置文件 有boundaries等信息
columns, spec = FCGen.GetFeatureSpec(feature_config)#按特征列对特征进行处理，不同类型处理会不一样，比如数值、embed等
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth=True
os.environ["CUDA_VISIBLE_DEVICES"] = "5"
run_config = tf.estimator.RunConfig().replace(
model_dir=config['train'].get('model_dir', 'model_dir'),
session_config=conf)
logging.info("Creating model...")
# Define the model
hidden_units = [int(n) for n in config['model']['hidden_units'].split(',')]
learning_rate = float(config['model']['learning_rate'])
ctr_reg = float(config['model'].get('ctr_reg', '1e-6'))
cvr_reg = float(config['model'].get('cvr_reg', '1e-4'))
ctcvr_loss_weight = float(config['model'].get('ctcvr_loss_weight', '1.0'))
model = tf.estimator.Estimator(
model_fn=esmm_model_fn,
params={
'dnn_columns': list(columns['dnn'].values()),
'linear_columns': list(columns['linear'].values()),
'weight_columns': list(columns['weight'].values()),
'hidden_units': hidden_units,
'learning_rate': learning_rate,
'ctr_reg': ctr_reg,
'cvr_reg': cvr_reg,
'ctcvr_loss_weight': ctcvr_loss_weight,
'model': config['model']['model']
},
config = run_config
)
print(model.evaluate(input_fn=lambda: input_fn(dev_files[0:1], spec, False, batch_size, mt=True)))
model.export_savedmodel(export_dir_base=config['train'].get('export_dir', 'export_dir'),
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
def train(config, trainfile, testfile):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
train_dirs = trainfile.split(',')
train_files = [
os.path.join(train_dir, f) for train_dir in train_dirs
for f in os.listdir(train_dir) if f != "_SUCCESS"
]
#train_files = tf.random_shuffle(tf.train.match_filenames_once([os.path.join(train_dir, f) for f in os.listdir(train_dir) if f != "_SUCCESS"]))
#train_files = tf.random_shuffle(tf.train.match_filenames_once(['%s/%s/part-r-*' % (data_path, dt) for dt in date_list]))
logging.info('train directory: {}'.format(train_dirs))
logging.info('train files: {}'.format(reprlib.repr(train_files)))
dev_dirs = testfile.split(',')
dev_files = [
os.path.join(dev_dir, f) for dev_dir in dev_dirs
for f in os.listdir(dev_dir) if f != "_SUCCESS"
]
logging.info('dev directory: {}'.format(dev_dirs))
logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#特征的配置文件 在input 这个section的spec这个key
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec']) #特征配置文件 有boundaries等信息
columns, spec = FCGen.GetFeatureSpec(
feature_config) #按特征列对特征进行处理，不同类型处理会不一样，比如数值、embed等
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth = True
os.environ["CUDA_VISIBLE_DEVICES"] = "5"
run_config = tf.estimator.RunConfig().replace(session_config=conf)
logging.info("Creating model...")
# Define the model
hidden_units = [
int(n) for n in config['dnn_model']['hidden_units'].split(',')
]
dropout = config['dnn_model'].get('dropout', '')
if dropout == '':
dropout = None
else:
dropout = float(dropout)
#print(columns['weight'][0])#如果有weight这个就不能注释
model = tf.estimator.DNNLinearCombinedClassifier(
config=run_config,
model_dir=config['train'].get('model_dir', 'model_dir'),
linear_feature_columns=columns['linear'],
linear_optimizer=tf.train.FtrlOptimizer(
learning_rate=float(config['linear_model']['learning_rate']),
#l1_regularization_strength=float(config['linear_model']['l1_reg']),
#l2_regularization_strength=float(config['linear_model']['l2_reg'])),
l1_regularization_strength=0.01,
l2_regularization_strength=0.01),
dnn_feature_columns=columns['dnn'], #没有dnn的话这个就注销
dnn_hidden_units=hidden_units,
weight_column=columns['weight'][0], #如果有weight这个就不能注释
#dnn_optimizer=tf.train.AdamOptimizer(
# learning_rate=float(config['dnn_model']['learning_rate'])),
dnn_optimizer=tf.train.AdagradOptimizer(learning_rate=float(
config['dnn_model']['learning_rate']),
initial_accumulator_value=0.1,
use_locking=False),
batch_norm=True,
#dnn_dropout=dropout,
#dnn_dropout=None,
loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE)
# Train and evaluate
max_steps = config['train'].get('max_step', '')
if max_steps == '':
max_steps = None
else:
max_steps = int(max_steps)
epochs = int(config['train']['epochs'])
for i in range(epochs):
logging.info("training...")
model.train(input_fn=lambda: input_fn(
train_files, spec, shuffle=True, batch_size=batch_size),
steps=max_steps)
results = model.evaluate(input_fn=lambda: input_fn(
dev_files, spec, shuffle=False, batch_size=batch_size))
logging.info("results...")
for key in sorted(results):
print('{}th {}: {}'.format(i + 1, key, results[key]))
model.export_savedmodel(
export_dir_base=config['train'].get('export_dir', 'export_dir'),
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
def train(config, hdfs_prefix, ftime, gap, ckpt_dir, export_dir, metric_dir):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
train_files = []
dev_files = []
cur_date = datetime.datetime.strptime(ftime, "%Y%m%d")
for i in range(1, gap + 1):
dest_date = (cur_date + datetime.timedelta(days=-i)).strftime("%Y%m%d")
train_files.append(hdfs_prefix + "/" + dest_date + "/train/part-r-*")
dev_files.append(hdfs_prefix + "/" + dest_date + "/test/part-r-*")
logging.info('train files: {}'.format(reprlib.repr(train_files)))
logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
#特征的配置文件 在input 这个section的spec这个key
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec']) #特征配置文件 有boundaries等信息
columns, spec = FCGen.GetFeatureSpec(
feature_config) #按特征列对特征进行处理，不同类型处理会不一样，比如数值、embed等
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth = True
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
run_config = tf.estimator.RunConfig(save_checkpoints_secs=1800).replace(
session_config=conf)
logging.info("Creating model...")
# Define the model
model = tf.estimator.BoostedTreesClassifier(
config=run_config,
n_batches_per_layer=1000,
n_trees=100,
learning_rate=0.2,
l1_regularization=0.01,
l2_regularization=0.01,
max_depth=10,
model_dir=ckpt_dir,
feature_columns=list(columns['linear'].values()),
weight_column=list(columns['weight'].values())[0] #如果有weight这个就不能注释
)
#model = tf.estimator.add_metrics(model, metric_auc)
# Train and evaluate
epochs = int(config['train']['epochs'])
for i in range(epochs):
logging.info("training...")
model.train(input_fn=lambda: input_fn(
train_files, spec, shuffle=True, batch_size=batch_size))
results = model.evaluate(input_fn=lambda: input_fn(
dev_files, spec, shuffle=False, batch_size=batch_size))
auc = float(results["auc"])
logloss = float(results["loss"])
index = [{
"name": "auc",
"type": "float",
"value": str(auc)
}, {
"name": "logloss",
"type": "float",
"value": str(logloss)
}]
file_name = metric_dir + "/metrics_info.json"
with open(file_name, 'w') as file_obj:
json.dump(index, file_obj)
model.export_savedmodel(
export_dir_base=export_dir,
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
def train(config, trainfile, testfile):
"""Entry for trainig
Args:
config: (configparser) All the hyperparameters for training
"""
train_dir = trainfile
train_files = [
os.path.join(train_dir, f) for f in os.listdir(train_dir)
if f != "_SUCCESS"
]
logging.info('train directory: {}'.format(train_dir))
logging.info('train files: {}'.format(reprlib.repr(train_files)))
dev_dir = testfile
dev_files = [
os.path.join(dev_dir, f) for f in os.listdir(dev_dir)
if f != "_SUCCESS"
]
logging.info('dev directory: {}'.format(dev_dir))
logging.info('dev files: {}'.format(reprlib.repr(dev_files)))
feature_config = configparser.ConfigParser()
feature_config.read(config['input']['spec'])
columns, spec = FCGen.GetFeatureSpec(feature_config)
batch_size = int(config['train']['batch_size'])
conf = tf.ConfigProto()
conf.gpu_options.allow_growth = True
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
run_config = tf.estimator.RunConfig().replace(session_config=conf)
logging.info("Creating model...")
# Define the model
hidden_units = [
int(n) for n in config['dnn_model']['hidden_units'].split(',')
]
dropout = config['dnn_model'].get('dropout', '')
if dropout == '':
dropout = None
else:
dropout = float(dropout)
print(columns['weight'][0])
model = tf.estimator.DNNLinearCombinedClassifier(
config=run_config,
model_dir=config['train'].get('model_dir', 'model_dir'),
linear_feature_columns=columns['linear'],
linear_optimizer=tf.train.FtrlOptimizer(
learning_rate=float(config['linear_model']['learning_rate']),
l1_regularization_strength=float(config['linear_model']['l1_reg']),
l2_regularization_strength=float(
config['linear_model']['l2_reg'])),
dnn_feature_columns=columns['dnn'],
dnn_hidden_units=hidden_units,
weight_column=columns['weight'][0],
dnn_optimizer=tf.train.AdamOptimizer(
learning_rate=float(config['dnn_model']['learning_rate'])),
dnn_dropout=dropout,
loss_reduction=tf.losses.Reduction.SUM_OVER_BATCH_SIZE)
# Train and evaluate
max_steps = config['train'].get('max_step', '')
if max_steps == '':
max_steps = None
else:
max_steps = int(max_steps)
logging.info("training...")
model.train(input_fn=lambda: input_fn(
train_files, spec, shuffle=True, batch_size=batch_size),
steps=max_steps)
results = model.evaluate(input_fn=lambda: input_fn(
dev_files, spec, shuffle=False, batch_size=batch_size))
logging.info("results...")
for key in sorted(results):
print('%s: %s' % (key, results[key]))
model.export_savedmodel(
export_dir_base=config['train'].get('export_dir', 'export_dir'),
serving_input_receiver_fn=lambda: input_receiver(spec),
strip_default_attrs=True)
