提交 729d5e33 编写于 作者: Z zhangwenhui03

add esmm infer

上级 52d3c0a5
......@@ -12,6 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/esmm_infer_reader.py"
test_data_path: "{workspace}/data/train"
train:
trainer:
# for cluster training
......
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
from collections import defaultdict
import numpy as np
class EvaluateReader(Reader):
def init(self):
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
'205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
self.all_field_id_dict = defaultdict(int)
for i,field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False,i]
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.strip().split(',')
ctr = int(features[1])
cvr = int(features[2])
padding = 0
output = [(field_id,[]) for field_id in self.all_field_id_dict]
for elem in features[4:]:
field_id,feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict:
visited,index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
return reader
......@@ -53,7 +53,7 @@ class Model(ModelBase):
return inputs
def net(self, inputs):
def net(self, inputs, is_infer=False):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
embed_size = envs.get_global_env("hyper_parameters.embed_size", None, self._namespace)
......@@ -90,13 +90,20 @@ class Model(ModelBase):
ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
if is_infer:
self._infer_results["AUC_ctr"] = auc_ctr
self._infer_results["AUC_ctcvr"] = auc_ctcvr
return
loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
cost = loss_ctr + loss_ctcvr
avg_cost = fluid.layers.mean(cost)
auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
self._cost = avg_cost
self._metrics["AUC_ctr"] = auc_ctr
......@@ -111,4 +118,7 @@ class Model(ModelBase):
def infer_net(self):
pass
self._infer_data_var = self.input_data()
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self.net(self._infer_data_var, is_infer=True)
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