model.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import math

import paddle.fluid as fluid

from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase


class Model(ModelBase):
    def __init__(self, config):
        ModelBase.__init__(self, config)

    def _init_hyper_parameters(self):
        self.sparse_feature_number = envs.get_global_env(
            "hyper_parameters.sparse_feature_number", None)
        self.num_field = envs.get_global_env("hyper_parameters.num_field",
                                             None)
        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)

    def net(self, inputs, is_infer=False):
        init_value_ = 0.1
        is_distributed = True if envs.get_trainer() == "CtrTrainer" else False

        # ------------------------- network input --------------------------

        raw_feat_idx = self._sparse_data_var[1]
        raw_feat_value = self._dense_data_var[0]
        self.label = self._sparse_data_var[0]

        feat_idx = raw_feat_idx
        feat_value = fluid.layers.reshape(
            raw_feat_value, [-1, self.num_field])  # None * num_field * 1

        first_weights_re = fluid.embedding(
            input=feat_idx,
            is_sparse=True,
            is_distributed=is_distributed,
            dtype='float32',
            size=[self.sparse_feature_number + 1, 1],
            padding_idx=0,
            param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.TruncatedNormalInitializer(
                    loc=0.0, scale=init_value_),
                regularizer=fluid.regularizer.L1DecayRegularizer(self.reg)))
        first_weights = fluid.layers.reshape(
            first_weights_re,
            shape=[-1, self.num_field])  # None * num_field * 1
        y_first_order = fluid.layers.reduce_sum(
            first_weights * feat_value, 1, keep_dim=True)

        b_linear = fluid.layers.create_parameter(
            shape=[1],
            dtype='float32',
            default_initializer=fluid.initializer.ConstantInitializer(value=0))

        self.predict = fluid.layers.sigmoid(y_first_order + b_linear)
        cost = fluid.layers.log_loss(
            input=self.predict, label=fluid.layers.cast(self.label, "float32"))
        avg_cost = fluid.layers.reduce_sum(cost)

        self._cost = avg_cost

        predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
        label_int = fluid.layers.cast(self.label, 'int64')
        auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
                                                     label=label_int,
                                                     slide_steps=0)
        self._metrics["AUC"] = auc_var
        self._metrics["BATCH_AUC"] = batch_auc_var
        if is_infer:
            self._infer_results["AUC"] = auc_var