add wide_and_deep net

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lirongzhen1 2020-05-28 16:47:40 +08:00
parent 1a4abefa9a
commit 4add766655
2 changed files with 145 additions and 0 deletions

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Area under cure metric
"""
from mindspore.nn.metrics import Metric
from sklearn.metrics import roc_auc_score
class AUCMetric(Metric):
"""
Area under cure metric
"""
def __init__(self):
super(AUCMetric, self).__init__()
self.clear()
def clear(self):
"""Clear the internal evaluation result."""
self.true_labels = []
self.pred_probs = []
def update(self, *inputs): # inputs
all_predict = inputs[1].asnumpy() # predict
all_label = inputs[2].asnumpy() # label
self.true_labels.extend(all_label.flatten().tolist())
self.pred_probs.extend(all_predict.flatten().tolist())
def eval(self):
if len(self.true_labels) != len(self.pred_probs):
raise RuntimeError(
'true_labels.size is not equal to pred_probs.size()')
auc = roc_auc_score(self.true_labels, self.pred_probs)
print("====" * 20 + " auc_metric end")
print("====" * 20 + " auc: {}".format(auc))
return auc

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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" test_training """
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
context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
save_graphs=True)
def get_WideDeep_net(config):
WideDeep_net = WideDeepModel(config)
loss_net = NetWithLossClass(WideDeep_net, config)
train_net = TrainStepWrap(loss_net)
eval_net = PredictWithSigmoid(WideDeep_net)
return train_net, eval_net
class ModelBuilder():
"""
Wide and deep model builder
"""
def __init__(self):
pass
def get_hook(self):
pass
def get_train_hook(self):
hooks = []
callback = LossCallBack()
hooks.append(callback)
if int(os.getenv('DEVICE_ID')) == 0:
pass
return hooks
def get_net(self, config):
return get_WideDeep_net(config)
def test_eval(config):
"""
test evaluate
"""
data_path = config.data_path
batch_size = config.batch_size
ds_eval = create_dataset(data_path, train_mode=False, epochs=2,
batch_size=batch_size)
print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
net_builder = ModelBuilder()
train_net, eval_net = net_builder.get_net(config)
param_dict = load_checkpoint(config.ckpt_path)
load_param_into_net(eval_net, param_dict)
auc_metric = AUCMetric()
model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
model.eval(ds_eval, callbacks=eval_callback)
if __name__ == "__main__":
widedeep_config = Config_WideDeep()
widedeep_config.argparse_init()
test_eval(widedeep_config.widedeep)