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93
model_zoo/wide_and_deep/README.md Normal file
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@ -0,0 +1,93 @@
recommendation Model
## Overview
This is an implementation of WideDeep as described in the [Wide & Deep Learning for Recommender System](https://arxiv.org/pdf/1606.07792.pdf) paper.
WideDeep model jointly trained wide linear models and deep neural network, which combined the benefits of memorization and generalization for recommender systems.
## Dataset
The [Criteo datasets](http://labs.criteo.com/2014/02/download-kaggle-display-advertising-challenge-dataset/) are used for model training and evaluation.
## Running Code
### Download and preprocess dataset
To download the dataset, please install Pandas package first. Then issue the following command:
```
bash download.sh
```
### Code Structure
The entire code structure is as following:
```
|--- wide_and_deep/
train_and_test.py "Entrance of Wide&Deep model training and evaluation"
test.py "Entrance of Wide&Deep model evaluation"
train.py "Entrance of Wide&Deep model training"
train_and_test_multinpu.py "Entrance of Wide&Deep model data parallel training and evaluation"
|--- src/ "entrance of training and evaluation"
config.py "parameters configuration"
dataset.py "Dataset loader class"
WideDeep.py "Model structure"
callbacks.py "Callback class for training and evaluation"
metrics.py "Metric class"
```
### Train and evaluate model
To train and evaluate the model, issue the following command:
```
python train_and_test.py
```
Arguments:
* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
* `--epochs`: Total train epochs.
* `--batch_size`: Training batch size.
* `--eval_batch_size`: Eval batch size.
* `--field_size`: The number of features.
* `--vocab_size`： The total features of dataset.
* `--emb_dim`： The dense embedding dimension of sparse feature.
* `--deep_layers_dim`： The dimension of all deep layers.
* `--deep_layers_act`： The activation of all deep layers.
* `--keep_prob`： The rate to keep in dropout layer.
* `--ckpt_path`：The location of the checkpoint file.
* `--eval_file_name` : Eval output file.
* `--loss_file_name` : Loss output file.
To train the model, issue the following command:
```
python train.py
```
Arguments:
* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
* `--epochs`: Total train epochs.
* `--batch_size`: Training batch size.
* `--eval_batch_size`: Eval batch size.
* `--field_size`: The number of features.
* `--vocab_size`： The total features of dataset.
* `--emb_dim`： The dense embedding dimension of sparse feature.
* `--deep_layers_dim`： The dimension of all deep layers.
* `--deep_layers_act`： The activation of all deep layers.
* `--keep_prob`： The rate to keep in dropout layer.
* `--ckpt_path`：The location of the checkpoint file.
* `--eval_file_name` : Eval output file.
* `--loss_file_name` : Loss output file.
To evaluate the model, issue the following command:
```
python test.py
```
Arguments:
* `--data_path`: This should be set to the same directory given to the data_download's data_dir argument.
* `--epochs`: Total train epochs.
* `--batch_size`: Training batch size.
* `--eval_batch_size`: Eval batch size.
* `--field_size`: The number of features.
* `--vocab_size`： The total features of dataset.
* `--emb_dim`： The dense embedding dimension of sparse feature.
* `--deep_layers_dim`： The dimension of all deep layers.
* `--deep_layers_act`： The activation of all deep layers.
* `--keep_prob`： The rate to keep in dropout layer.
* `--ckpt_path`：The location of the checkpoint file.
* `--eval_file_name` : Eval output file.
* `--loss_file_name` : Loss output file.
There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions.

146
model_zoo/wide_and_deep/src/callbacks.py
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@ -26,79 +26,79 @@ def add_write(file_path, out_str):
file_out.write(out_str + "\n")
class LossCallBack(Callback):
class LossCallBack(Callback):
"""
Monitor the loss in training.
If the loss is NAN or INF, terminate the training.
Note:
If per_print_times is 0, do NOT print loss.
Args:
per_print_times (int): Print loss every times. Default: 1.
"""
def __init__(self, config=None, per_print_times=1):
super(LossCallBack, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("per_print_times must be in and >= 0.")
self._per_print_times = per_print_times
self.config = config
def step_end(self, run_context):
cb_params = run_context.original_args()
wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
cur_num = cb_params.cur_step_num
print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)
# raise ValueError
if self._per_print_times != 0 and cur_num % self._per_print_times == 0 and config is not None:
loss_file = open(self.config.loss_file_name, "a+")
loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
loss_file.write("\n")
loss_file.close()
print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
class EvalCallBack(Callback):
"""
Monitor the loss in evaluating.
If the loss is NAN or INF, terminate evaluating.
Note:
If per_print_times is 0, do NOT print loss.
Args:
print_per_step (int): Print loss every times. Default: 1.
"""
def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1):
super(EvalCallBack, self).__init__()
if not isinstance(print_per_step, int) or print_per_step < 0:
raise ValueError("print_per_step must be int and >= 0.")
self.print_per_step = print_per_step
self.model = model
self.eval_dataset = eval_dataset
self.aucMetric = auc_metric
self.aucMetric.clear()
self.eval_file_name = config.eval_file_name
def epoch_name(self, run_context):
"""
Monitor the loss in training.
If the loss is NAN or INF, terminate the training.
Note:
If per_print_times is 0, do NOT print loss.
Args:
per_print_times (int): Print loss every times. Default: 1.
epoch name
"""
def __init__(self, config, per_print_times=1):
super(LossCallBack, self).__init__()
if not isinstance(per_print_times, int) or per_print_times < 0:
raise ValueError("per_print_times must be in and >= 0.")
self._per_print_times = per_print_times
self.config = config
self.aucMetric.clear()
context.set_auto_parallel_context(strategy_ckpt_save_file="",
strategy_ckpt_load_file="./strategy_train.ckpt")
start_time = time.time()
out = self.model.eval(self.eval_dataset)
end_time = time.time()
eval_time = int(end_time - start_time)
def step_end(self, run_context):
cb_params = run_context.original_args()
wide_loss, deep_loss = cb_params.net_outputs[0].asnumpy(), cb_params.net_outputs[1].asnumpy()
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
cur_num = cb_params.cur_step_num
print("===loss===", cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss)
# raise ValueError
if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
loss_file = open(self.config.loss_file_name, "a+")
loss_file.write("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
loss_file.write("\n")
loss_file.close()
print("epoch: %s, step: %s, wide_loss: %s, deep_loss: %s" %
(cb_params.cur_epoch_num, cur_step_in_epoch, wide_loss, deep_loss))
class EvalCallBack(Callback):
"""
Monitor the loss in evaluating.
If the loss is NAN or INF, terminate evaluating.
Note:
If per_print_times is 0, do NOT print loss.
Args:
print_per_step (int): Print loss every times. Default: 1.
"""
def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1):
super(EvalCallBack, self).__init__()
if not isinstance(print_per_step, int) or print_per_step < 0:
raise ValueError("print_per_step must be int and >= 0.")
self.print_per_step = print_per_step
self.model = model
self.eval_dataset = eval_dataset
self.aucMetric = auc_metric
self.aucMetric.clear()
self.eval_file_name = config.eval_file_name
def epoch_name(self, run_context):
"""
epoch name
"""
self.aucMetric.clear()
context.set_auto_parallel_context(strategy_ckpt_save_file="",
strategy_ckpt_load_file="./strategy_train.ckpt")
start_time = time.time()
out = self.model.eval(self.eval_dataset)
end_time = time.time()
eval_time = int(end_time - start_time)
time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime())
out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time)
print(out_str)
add_write(self.eval_file_name, out_str)
time_str = time.strftime("%Y-%m-%d %H:%M%S", time.localtime())
out_str = "{}==== EvalCallBack model.eval(): {}; eval_time: {}s".format(time_str, out.values(), eval_time)
print(out_str)
add_write(self.eval_file_name, out_str)

5
model_zoo/wide_and_deep/tools/config.py → model_zoo/wide_and_deep/src/config.py
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@ -38,9 +38,9 @@ def argparse_init():
return parser
class Config_WideDeep():
class WideDeepConfig():
"""
Config_WideDeep
WideDeepConfig
"""
def __init__(self):
self.data_path = "./test_raw_data/"
@ -70,6 +70,7 @@ class Config_WideDeep():
"""
parser = argparse_init()
args, _ = parser.parse_known_args()
self.data_path = args.data_path
self.epochs = args.epochs
self.batch_size = args.batch_size
self.eval_batch_size = args.eval_batch_size

0
model_zoo/wide_and_deep/metrics.py → model_zoo/wide_and_deep/src/metrics.py
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4
model_zoo/wide_and_deep/src/wide_and_deep.py
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@ -135,8 +135,8 @@ class WideDeepModel(nn.Cell):
self.field_size = config.field_size
self.vocab_size = config.vocab_size
self.emb_dim = config.emb_dim
self.deep_layer_args = config.deep_layer_args
self.deep_layer_dims_list, self.deep_layer_act = self.deep_layer_args
self.deep_layer_dims_list = config.deep_layer_dim
self.deep_layer_act = config.deep_layer_act
self.init_args = config.init_args
self.weight_init, self.bias_init = config.weight_bias_init
self.weight_bias_init = config.weight_bias_init

12
model_zoo/wide_and_deep/test.py
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@ -20,11 +20,11 @@ import os
from mindspore import Model, context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
from wide_deep.utils.callbacks import LossCallBack, EvalCallBack
from wide_deep.data.datasets import create_dataset
from wide_deep.utils.metrics import AUCMetric
from tools.config import Config_WideDeep
from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
from src.callbacks import LossCallBack, EvalCallBack
from src.datasets import create_dataset
from src.metrics import AUCMetric
from src.config import WideDeepConfig
context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
save_graphs=True)
@ -88,7 +88,7 @@ def test_eval(config):
if __name__ == "__main__":
widedeep_config = Config_WideDeep()
widedeep_config = WideDeepConfig()
widedeep_config.argparse_init()
test_eval(widedeep_config.widedeep)

16
model_zoo/wide_and_deep/train.py
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@ -16,19 +16,19 @@ import os
from mindspore import Model, context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWarp, NetWithLossClass, WideDeepModel
from wide_deep.utils.callbacks import LossCallBack
from wide_deep.data.datasets import create_dataset
from tools.config import Config_WideDeep
from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
from src.callbacks import LossCallBack
from src.datasets import create_dataset
from src.config import WideDeepConfig
context.set_context(model=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
def get_WideDeep_net(configure):
WideDeep_net = WideDeepModel(configure)
loss_net = NetWithLossClass(WideDeep_net, configure)
train_net = TrainStepWarp(loss_net)
train_net = TrainStepWrap(loss_net)
eval_net = PredictWithSigmoid(WideDeep_net)
return train_net, eval_net
@ -71,7 +71,7 @@ def test_train(configure):
train_net.set_train()
model = Model(train_net)
callback = LossCallBack(configure)
callback = LossCallBack(config=configure)
ckptconfig = CheckpointConfig(save_checkpoint_steps=1,
keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=configure.ckpt_path, config=ckptconfig)
@ -79,7 +79,7 @@ def test_train(configure):
if __name__ == "__main__":
config = Config_WideDeep()
config = WideDeepConfig()
config.argparse_init()
test_train(config)

14
model_zoo/wide_and_deep/tools/train_and_test.py → model_zoo/wide_and_deep/train_and_test.py
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@ -17,11 +17,11 @@ import os
from mindspore import Model, context
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
from wide_deep.utils.callbacks import LossCallBack, EvalCallBack
from wide_deep.data.datasets import create_dataset
from wide_deep.utils.metrics import AUCMetric
from tools.config import Config_WideDeep
from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
from src.callbacks import LossCallBack, EvalCallBack
from src.datasets import create_dataset
from src.metrics import AUCMetric
from src.config import WideDeepConfig
context.set_context(mode=context.GRAPH_MODE, device_target="Davinci")
@ -81,7 +81,7 @@ def test_train_eval(config):
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack()
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train', directory=config.ckpt_path, config=ckptconfig)
@ -91,7 +91,7 @@ def test_train_eval(config):
if __name__ == "__main__":
wide_deep_config = Config_WideDeep()
wide_deep_config = WideDeepConfig()
wide_deep_config.argparse_init()
test_train_eval(wide_deep_config)

8
model_zoo/wide_and_deep/train_and_test_multinpu.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""train_imagenet."""
"""train_multinpu."""
import os
@ -27,7 +27,7 @@ from src.wide_and_deep import PredictWithSigmoid, TrainStepWrap, NetWithLossClas
from src.callbacks import LossCallBack, EvalCallBack
from src.datasets import create_dataset
from src.metrics import AUCMetric
from src.config import Config_WideDeep
from src.config import WideDeepConfig
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
context.set_context(mode=GRAPH_MODE, device_target="Davinci", save_graph=True)
@ -71,7 +71,7 @@ def test_train_eval():
test_train_eval
"""
np.random.seed(1000)
config = Config_WideDeep
config = WideDeepConfig
data_path = Config.data_path
batch_size = config.batch_size
epochs = config.epochs
@ -93,7 +93,7 @@ def test_train_eval():
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
callback = LossCallBack(config)
callback = LossCallBack(config=config)
ckptconfig = CheckpointConfig(save_checkpoint_steps=1, keep_checkpoint_max=5)
ckpoint_cb = ModelCheckpoint(prefix='widedeep_train',
directory=config.ckpt_path, config=ckptconfig)