forked from mindspore-Ecosystem/mindspore
add wide_and_deep net
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Area under cure metric
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"""
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from mindspore.nn.metrics import Metric
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from sklearn.metrics import roc_auc_score
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class AUCMetric(Metric):
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"""
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Area under cure metric
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"""
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def __init__(self):
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super(AUCMetric, self).__init__()
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self.clear()
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def clear(self):
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"""Clear the internal evaluation result."""
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self.true_labels = []
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self.pred_probs = []
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def update(self, *inputs): # inputs
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all_predict = inputs[1].asnumpy() # predict
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all_label = inputs[2].asnumpy() # label
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self.true_labels.extend(all_label.flatten().tolist())
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self.pred_probs.extend(all_predict.flatten().tolist())
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def eval(self):
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if len(self.true_labels) != len(self.pred_probs):
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raise RuntimeError(
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'true_labels.size is not equal to pred_probs.size()')
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auc = roc_auc_score(self.true_labels, self.pred_probs)
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print("====" * 20 + " auc_metric end")
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print("====" * 20 + " auc: {}".format(auc))
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return auc
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_training """
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import os
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from mindspore import Model, context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from wide_deep.models.WideDeep import PredictWithSigmoid, TrainStepWrap, NetWithLossClass, WideDeepModel
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from wide_deep.utils.callbacks import LossCallBack, EvalCallBack
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from wide_deep.data.datasets import create_dataset
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from wide_deep.utils.metrics import AUCMetric
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from tools.config import Config_WideDeep
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context.set_context(mode=context.GRAPH_MODE, device_target="Davinci",
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save_graphs=True)
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def get_WideDeep_net(config):
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WideDeep_net = WideDeepModel(config)
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loss_net = NetWithLossClass(WideDeep_net, config)
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train_net = TrainStepWrap(loss_net)
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eval_net = PredictWithSigmoid(WideDeep_net)
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return train_net, eval_net
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class ModelBuilder():
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"""
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Wide and deep model builder
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"""
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def __init__(self):
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pass
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def get_hook(self):
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pass
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def get_train_hook(self):
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hooks = []
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callback = LossCallBack()
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hooks.append(callback)
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if int(os.getenv('DEVICE_ID')) == 0:
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pass
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return hooks
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def get_net(self, config):
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return get_WideDeep_net(config)
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def test_eval(config):
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"""
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test evaluate
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"""
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data_path = config.data_path
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batch_size = config.batch_size
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ds_eval = create_dataset(data_path, train_mode=False, epochs=2,
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batch_size=batch_size)
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print("ds_eval.size: {}".format(ds_eval.get_dataset_size()))
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net_builder = ModelBuilder()
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train_net, eval_net = net_builder.get_net(config)
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param_dict = load_checkpoint(config.ckpt_path)
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load_param_into_net(eval_net, param_dict)
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auc_metric = AUCMetric()
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model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
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eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
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model.eval(ds_eval, callbacks=eval_callback)
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if __name__ == "__main__":
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widedeep_config = Config_WideDeep()
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widedeep_config.argparse_init()
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test_eval(widedeep_config.widedeep)
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