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提交 522d4d4d 编写于 作者: X xujiaqi01
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slot reader for rank

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core/trainers/transpiler_trainer.py
浏览文件 @ 522d4d4d
......@@ -54,7 +54,6 @@ class TranspileTrainer(Trainer):
sparse_slots = envs.get_global_env("sparse_slots", None, namespace)
dense_slots = envs.get_global_env("dense_slots", None, namespace)
batch_size = envs.get_global_env("batch_size", None, namespace)
print("batch_size: {}".format(batch_size))
......
models/rank/criteo_reader.py 已删除 100755 → 0
浏览文件 @ 91dad673
# 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
class TrainReader(Reader):
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
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.rstrip('\n').split('\t')
dense_feature = []
sparse_feature = []
for idx in self.continuous_range_:
if features[idx] == "":
dense_feature.append(0.0)
else:
dense_feature.append(
(float(features[idx]) - self.cont_min_[idx - 1]) /
self.cont_diff_[idx - 1])
for idx in self.categorical_range_:
sparse_feature.append(
[hash(str(idx) + features[idx]) % self.hash_dim_])
label = [int(features[0])]
feature_name = ["D"]
for idx in self.categorical_range_:
feature_name.append("S" + str(idx - 13))
feature_name.append("label")
yield zip(feature_name, [dense_feature] + sparse_feature + [label])
return reader
models/rank/dcn/config.yaml
浏览文件 @ 522d4d4d
......@@ -22,9 +22,10 @@ train:
reader:
batch_size: 2
class: "{workspace}/criteo_reader.py"
train_data_path: "{workspace}/data/train"
train_data_path: "{workspace}/slot_data/train"
feat_dict_name: "{workspace}/data/vocab"
sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
model:
models: "{workspace}/model.py"
......@@ -34,7 +35,7 @@ train:
l2_reg_cross: 0.00005
dnn_use_bn: False
clip_by_norm: 100.0
cat_feat_num: "{workspace}/data/cat_feature_num.txt"
cat_feat_num: "{workspace}/slot_data/cat_feature_num.txt"
is_sparse: False
is_test: False
num_field: 39
......
models/rank/dcn/criteo_reader.py 已删除 100755 → 0
浏览文件 @ 91dad673
# 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
import math
import sys
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
try:
import cPickle as pickle
except ImportError:
import pickle
from collections import Counter
import os
class TrainReader(Reader):
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 11,
231, 4008, 7393
]
self.cont_diff_ = [
self.cont_max_[i] - self.cont_min_[i]
for i in range(len(self.cont_min_))
]
self.cont_idx_ = list(range(1, 14))
self.cat_idx_ = list(range(14, 40))
dense_feat_names = ['I' + str(i) for i in range(1, 14)]
sparse_feat_names = ['C' + str(i) for i in range(1, 27)]
target = ['label']
self.label_feat_names = target + dense_feat_names + sparse_feat_names
self.cat_feat_idx_dict_list = [{} for _ in range(26)]
# TODO: set vocabulary dictionary
vocab_dir = envs.get_global_env("feat_dict_name", None, "train.reader")
for i in range(26):
lookup_idx = 1 # remain 0 for default value
for line in open(
os.path.join(vocab_dir, 'C' + str(i + 1) + '.txt')):
self.cat_feat_idx_dict_list[i][line.strip()] = lookup_idx
lookup_idx += 1
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
label_feat_list = [[] for _ in range(40)]
for idx in self.cont_idx_:
if features[idx] == '':
label_feat_list[idx].append(0)
else:
# 0-1 minmax norm
# label_feat_list[idx].append((float(features[idx]) - self.cont_min_[idx - 1]) /
# self.cont_diff_[idx - 1])
# log transform
label_feat_list[idx].append(
math.log(4 + float(features[idx]))
if idx == 2 else math.log(1 + float(features[idx])))
for idx in self.cat_idx_:
if features[idx] == '' or features[
idx] not in self.cat_feat_idx_dict_list[idx - 14]:
label_feat_list[idx].append(0)
else:
label_feat_list[idx].append(self.cat_feat_idx_dict_list[
idx - 14][features[idx]])
label_feat_list[0].append(int(features[0]))
return label_feat_list
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
label_feat_list = self._process_line(line)
yield list(zip(self.label_feat_names, label_feat_list))
return data_iter
\ No newline at end of file
models/rank/dcn/model.py
浏览文件 @ 522d4d4d
......@@ -16,6 +16,11 @@ class Model(ModelBase):
self.dnn_use_bn = envs.get_global_env("hyper_parameters.dnn_use_bn", None, self._namespace)
self.clip_by_norm = envs.get_global_env("hyper_parameters.clip_by_norm", None, self._namespace)
cat_feat_num = envs.get_global_env("hyper_parameters.cat_feat_num", None, self._namespace)
self.sparse_inputs = self._sparse_data_var[1:]
self.dense_inputs = self._dense_data_var
self.target_input = self._sparse_data_var[0]
cat_feat_dims_dict = OrderedDict()
for line in open(cat_feat_num):
spls = line.strip().split()
......@@ -25,8 +30,8 @@ class Model(ModelBase):
)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse", None, self._namespace)
self.dense_feat_names = ['I' + str(i) for i in range(1, 14)]
self.sparse_feat_names = ['C' + str(i) for i in range(1, 27)]
self.dense_feat_names = [i.name for i in self.dense_inputs]
self.sparse_feat_names = [i.name for i in self.sparse_inputs]
# {feat_name: dims}
self.feat_dims_dict = OrderedDict(
......@@ -36,21 +41,17 @@ class Model(ModelBase):
self.net_input = None
self.loss = None
def _create_embedding_input(self, data_dict):
def _create_embedding_input(self):
# sparse embedding
sparse_emb_dict = OrderedDict((name, fluid.embedding(
input=fluid.layers.cast(
data_dict[name], dtype='int64'),
size=[
self.feat_dims_dict[name] + 1,
6 * int(pow(self.feat_dims_dict[name], 0.25))
],
is_sparse=self.is_sparse)) for name in self.sparse_feat_names)
sparse_emb_dict = OrderedDict()
for var in self.sparse_inputs:
sparse_emb_dict[var.name] = fluid.embedding(input=var,
size=[self.feat_dims_dict[var.name] + 1,
6 * int(pow(self.feat_dims_dict[var.name], 0.25))
],is_sparse=self.is_sparse)
# combine dense and sparse_emb
dense_input_list = [
data_dict[name] for name in data_dict if name.startswith('I')
]
dense_input_list = self.dense_inputs
sparse_emb_list = list(sparse_emb_dict.values())
sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1)
......@@ -97,14 +98,8 @@ class Model(ModelBase):
def train_net(self):
self.init_network()
self.target_input = fluid.data(
name='label', shape=[None, 1], dtype='float32')
data_dict = OrderedDict()
for feat_name in self.feat_dims_dict:
data_dict[feat_name] = fluid.data(
name=feat_name, shape=[None, 1], dtype='float32')
self.net_input = self._create_embedding_input(data_dict)
self.net_input = self._create_embedding_input()
deep_out = self._deep_net(self.net_input, self.dnn_hidden_units, self.dnn_use_bn, False)
......@@ -115,9 +110,6 @@ class Model(ModelBase):
logit = fluid.layers.fc(last_out, 1)
self.prob = fluid.layers.sigmoid(logit)
self._data_var = [self.target_input] + [
data_dict[dense_name] for dense_name in self.dense_feat_names
] + [data_dict[sparse_name] for sparse_name in self.sparse_feat_names]
# auc
prob_2d = fluid.layers.concat([1 - self.prob, self.prob], 1)
......@@ -127,9 +119,8 @@ class Model(ModelBase):
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
# logloss
logloss = fluid.layers.log_loss(self.prob, self.target_input)
logloss = fluid.layers.log_loss(self.prob, fluid.layers.cast(self.target_input, dtype='float32'))
self.avg_logloss = fluid.layers.reduce_mean(logloss)
# reg_coeff * l2_reg_cross
......
models/rank/dcn/slot_data/cat_feature_num.txt 0 → 100644
浏览文件 @ 522d4d4d
C1 139
C2 422
C3 1548
C4 1965
C5 54
C6 10
C7 3213
C8 81
C9 3
C10 2402
C11 2246
C12 1583
C13 1911
C14 24
C15 2011
C16 1731
C17 9
C18 1197
C19 584
C20 3
C21 1652
C22 8
C23 14
C24 1770
C25 40
C26 1349
models/rank/dcn/slot_data/test/sample_test.txt 0 → 100644
浏览文件 @ 522d4d4d
label:1 I1:0 I2:2.56494935746 I3:3.13549421593 I4:1.94591014906 I5:9.45100945016 I6:4.21950770518 I7:0.69314718056 I8:2.19722457734 I9:3.95124371858 I10:0 I11:0.69314718056 I12:0.0 I13:1.94591014906 C1:14 C2:265 C3:147 C4:1521 C5:36 C6:4 C7:344 C8:2 C9:3 C10:1084 C11:1191 C12:751 C13:1387 C14:11 C15:1204 C16:631 C17:4 C18:744 C19:226 C20:1 C21:1223 C22:0 C23:10 C24:1461 C25:5 C26:1146
label:1 I1:0 I2:2.8903717579 I3:2.7080502011 I4:2.07944154168 I5:8.83521046366 I6:3.58351893846 I7:1.79175946923 I8:2.63905732962 I9:4.2341065046 I10:0 I11:1.09861228867 I12:0 I13:2.07944154168 C1:14 C2:409 C3:0 C4:213 C5:53 C6:0 C7:1062 C8:2 C9:3 C10:245 C11:283 C12:0 C13:1100 C14:21 C15:664 C16:270 C17:5 C18:25 C19:0 C20:0 C21:0 C22:0 C23:10 C24:939 C25:0 C26:0
label:0 I1:0 I2:3.09104245336 I3:1.09861228867 I4:0 I5:0 I6:0 I7:0 I8:0.0 I9:0 I10:0 I11:0 I12:0 I13:0 C1:64 C2:2 C3:1086 C4:178 C5:53 C6:0 C7:3193 C8:2 C9:1 C10:245 C11:677 C12:1507 C13:1757 C14:6 C15:855 C16:1242 C17:7 C18:773 C19:226 C20:2 C21:1635 C22:0 C23:7 C24:1292 C25:13 C26:247
label:0 I1:0.0 I2:2.48490664979 I3:0 I4:3.63758615973 I5:8.26616443661 I6:6.17170059741 I7:0.69314718056 I8:3.66356164613 I9:4.94164242261 I10:0.0 I11:0.69314718056 I12:0.0 I13:3.63758615973 C1:14 C2:123 C3:991 C4:197 C5:36 C6:1 C7:2088 C8:2 C9:3 C10:1499 C11:1761 C12:1482 C13:642 C14:6 C15:625 C16:720 C17:9 C18:480 C19:0 C20:0 C21:1010 C22:0 C23:10 C24:720 C25:0 C26:0
label:0 I1:0.69314718056 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.09861228867 I8:0.0 I9:0.0 I10:0.69314718056 I11:1.09861228867 I12:0 I13:0.0 C1:14 C2:200 C3:26 C4:1015 C5:53 C6:1 C7:2503 C8:1 C9:3 C10:2243 C11:229 C12:385 C13:457 C14:6 C15:763 C16:400 C17:9 C18:959 C19:226 C20:2 C21:958 C22:0 C23:10 C24:1556 C25:4 C26:366
label:0 I1:0.0 I2:4.1743872699 I3:1.60943791243 I4:1.38629436112 I5:8.76311532962 I6:5.65599181082 I7:0.69314718056 I8:2.19722457734 I9:3.04452243772 I10:0.0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:95 C2:409 C3:1170 C4:1413 C5:53 C6:4 C7:1647 C8:2 C9:3 C10:29 C11:963 C12:48 C13:134 C14:6 C15:692 C16:1338 C17:9 C18:1186 C19:0 C20:0 C21:325 C22:0 C23:10 C24:697 C25:0 C26:0
label:0 I1:0.0 I2:4.54329478227 I3:1.09861228867 I4:1.60943791243 I5:7.75491027202 I6:3.63758615973 I7:2.07944154168 I8:3.09104245336 I9:6.16331480403 I10:0.0 I11:0.69314718056 I12:0.0 I13:1.60943791243 C1:14 C2:394 C3:0 C4:1692 C5:53 C6:10 C7:0 C8:2 C9:3 C10:0 C11:463 C12:784 C13:124 C14:6 C15:1237 C16:1274 C17:5 C18:794 C19:0 C20:0 C21:708 C22:0 C23:10 C24:355 C25:0 C26:0
label:1 I1:0.69314718056 I2:1.79175946923 I3:1.09861228867 I4:0.69314718056 I5:6.42971947804 I6:2.48490664979 I7:3.09104245336 I8:2.8903717579 I9:5.51342874616 I10:0.69314718056 I11:2.83321334406 I12:0 I13:1.09861228867 C1:14 C2:278 C3:0 C4:0 C5:53 C6:7 C7:39 C8:73 C9:3 C10:0 C11:1602 C12:0 C13:719 C14:8 C15:471 C16:0 C17:5 C18:659 C19:0 C20:0 C21:0 C22:0 C23:10 C24:0 C25:0 C26:0
label:0 I1:2.48490664979 I2:1.38629436112 I3:2.3978952728 I4:1.94591014906 I5:6.54678541076 I6:1.94591014906 I7:2.48490664979 I8:1.60943791243 I9:1.94591014906 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.94591014906 C1:14 C2:205 C3:5 C4:1659 C5:44 C6:1 C7:1727 C8:2 C9:3 C10:1964 C11:210 C12:1237 C13:1632 C14:8 C15:1228 C16:1045 C17:5 C18:227 C19:226 C20:1 C21:1102 C22:0 C23:8 C24:463 C25:4 C26:566
label:0 I1:0.0 I2:1.09861228867 I3:0 I4:0 I5:6.56385552653 I6:2.63905732962 I7:3.13549421593 I8:2.63905732962 I9:3.97029191355 I10:0.0 I11:1.09861228867 I12:0 I13:0 C1:14 C2:409 C3:0 C4:0 C5:53 C6:1 C7:1034 C8:73 C9:3 C10:2035 C11:1334 C12:0 C13:692 C14:6 C15:999 C16:0 C17:5 C18:234 C19:0 C20:0 C21:0 C22:0 C23:10 C24:0 C25:0 C26:0
label:0 I1:0 I2:2.7080502011 I3:1.09861228867 I4:1.09861228867 I5:8.19946419761 I6:0 I7:0.0 I8:3.76120011569 I9:4.2341065046 I10:0 I11:0.0 I12:0 I13:1.09861228867 C1:86 C2:409 C3:0 C4:0 C5:53 C6:0 C7:2567 C8:2 C9:3 C10:245 C11:310 C12:0 C13:1591 C14:6 C15:583 C16:0 C17:2 C18:78 C19:0 C20:0 C21:0 C22:0 C23:10 C24:1075 C25:0 C26:0
label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.0337225181 I6:0 I7:0.0 I8:2.07944154168 I9:4.15888308336 I10:0 I11:0.0 I12:0 I13:0 C1:96 C2:409 C3:0 C4:0 C5:53 C6:10 C7:844 C8:2 C9:3 C10:96 C11:1646 C12:0 C13:1731 C14:21 C15:0 C16:0 C17:6 C18:0 C19:0 C20:0 C21:0 C22:0 C23:10 C24:0 C25:0 C26:0
label:0 I1:0.0 I2:3.295836866 I3:1.09861228867 I4:1.09861228867 I5:8.82951909478 I6:0 I7:0.0 I8:3.04452243772 I9:1.94591014906 I10:0.0 I11:0.0 I12:0.0 I13:1.09861228867 C1:14 C2:394 C3:0 C4:1692 C5:36 C6:4 C7:3211 C8:24 C9:3 C10:163 C11:334 C12:784 C13:1325 C14:6 C15:1237 C16:1274 C17:4 C18:794 C19:0 C20:0 C21:708 C22:0 C23:13 C24:355 C25:0 C26:0
label:1 I1:0.0 I2:1.94591014906 I3:2.7080502011 I4:2.99573227355 I5:7.31121838442 I6:3.95124371858 I7:1.38629436112 I8:2.7080502011 I9:3.89182029811 I10:0.0 I11:0.69314718056 I12:0.0 I13:2.99573227355 C1:14 C2:292 C3:186 C4:1518 C5:53 C6:1 C7:191 C8:2 C9:3 C10:579 C11:1304 C12:857 C13:1124 C14:8 C15:1216 C16:1408 C17:1 C18:962 C19:226 C20:1 C21:1126 C22:0 C23:8 C24:501 C25:16 C26:288
label:1 I1:0.0 I2:2.56494935746 I3:1.38629436112 I4:1.09861228867 I5:8.32820949175 I6:5.1059454739 I7:0.69314718056 I8:1.09861228867 I9:3.97029191355 I10:0.0 I11:0.69314718056 I12:0 I13:1.09861228867 C1:14 C2:306 C3:868 C4:1301 C5:30 C6:10 C7:3194 C8:24 C9:3 C10:305 C11:738 C12:1364 C13:904 C14:14 C15:39 C16:864 C17:8 C18:1039 C19:431 C20:3 C21:1196 C22:0 C23:14 C24:1341 C25:35 C26:1075
label:1 I1:1.79175946923 I2:1.38629436112 I3:3.73766961828 I4:2.56494935746 I5:6.62936325344 I6:3.21887582487 I7:2.63905732962 I8:3.7135720667 I9:3.7135720667 I10:1.09861228867 I11:1.38629436112 I12:0.0 I13:2.56494935746 C1:95 C2:9 C3:474 C4:1750 C5:53 C6:0 C7:653 C8:24 C9:3 C10:245 C11:449 C12:1284 C13:838 C14:8 C15:826 C16:924 C17:5 C18:424 C19:361 C20:3 C21:1379 C22:1 C23:8 C24:950 C25:40 C26:764
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models/rank/dcn/slot_data/train/sample_train.txt 0 → 100644
浏览文件 @ 522d4d4d
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label:0 I1:0 I2:3.66356164613 I3:4.51085950652 I4:1.09861228867 I5:10.6269456796 I6:5.86646805693 I7:0.0 I8:1.79175946923 I9:3.80666248977 I10:0 I11:0.0 I12:0 I13:1.79175946923 C1:14 C2:185 C3:0 C4:0 C5:53 C6:1 C7:399 C8:71 C9:3 C10:1291 C11:234 C12:0 C13:466 C14:6 C15:1744 C16:0 C17:5 C18:819 C19:22 C20:1 C21:0 C22:0 C23:9 C24:1341 C25:4 C26:1075
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label:0 I1:0.0 I2:3.95124371858 I3:1.38629436112 I4:1.09861228867 I5:9.5807308268 I6:5.93489419562 I7:2.19722457734 I8:1.09861228867 I9:5.64897423816 I10:0.0 I11:1.09861228867 I12:0 I13:1.09861228867 C1:14 C2:137 C3:0 C4:0 C5:50 C6:0 C7:0 C8:2 C9:3 C10:1982 C11:705 C12:0 C13:545 C14:6 C15:1929 C16:0 C17:5 C18:230 C19:226 C20:1 C21:0 C22:0 C23:10 C24:0 C25:1 C26:0
models/rank/dcn/slot_data/vocab/C1.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C10.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C11.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C12.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C13.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C14.txt 0 → 100644
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models/rank/dcn/slot_data/vocab/C15.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C16.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C17.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C18.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C19.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C2.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C21.txt 0 → 100644
浏览文件 @ 522d4d4d
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浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C23.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C24.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C25.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C26.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C3.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C4.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C5.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C6.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C7.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dcn/slot_data/vocab/C8.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/deepfm/config.yaml
浏览文件 @ 522d4d4d
......@@ -22,9 +22,10 @@ train:
reader:
batch_size: 2
class: "{workspace}/criteo_reader.py"
train_data_path: "{workspace}/data/train_data"
feat_dict_name: "{workspace}/data/aid_data/feat_dict_10.pkl2"
train_data_path: "{workspace}/slot_data/train_data"
feat_dict_name: "{workspace}/slot_data/feat_dict_10.pkl2"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
model:
models: "{workspace}/model.py"
......
models/rank/deepfm/criteo_reader.py 已删除 100755 → 0
浏览文件 @ 91dad673
# 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
try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(Reader):
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 46,
231, 4008, 7393
]
self.cont_diff_ = [
self.cont_max_[i] - self.cont_min_[i]
for i in range(len(self.cont_min_))
]
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
# load preprocessed feature dict
self.feat_dict_name = envs.get_global_env("feat_dict_name", None, "train.reader")
self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
feat_idx = []
feat_value = []
for idx in self.continuous_range_:
if features[idx] == '':
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[idx])
feat_value.append(
(float(features[idx]) - self.cont_min_[idx - 1]) /
self.cont_diff_[idx - 1])
for idx in self.categorical_range_:
if features[idx] == '' or features[idx] not in self.feat_dict_:
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[features[idx]])
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
feat_idx, feat_value, label = self._process_line(line)
yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label', label)]
return data_iter
\ No newline at end of file
models/rank/deepfm/model.py
浏览文件 @ 522d4d4d
......@@ -18,20 +18,13 @@ class Model(ModelBase):
# ------------------------- network input --------------------------
num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field], dtype='int64') # None * num_field(defalut:39)
raw_feat_value = fluid.data(name='feat_value', shape=[None, num_field], dtype='float32') # None * num_field
self.label = fluid.data(name='label', shape=[None, 1], dtype='float32') # None * 1
feat_idx = fluid.layers.reshape(raw_feat_idx,[-1, 1]) # (None * num_field) * 1
feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
# ------------------------- set _data_var --------------------------
raw_feat_idx = self._sparse_data_var[1]
raw_feat_value = self._dense_data_var[0]
self.label = self._sparse_data_var[0]
self._data_var.append(raw_feat_idx)
self._data_var.append(raw_feat_value)
self._data_var.append(self.label)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
#------------------------- first order term --------------------------
......@@ -123,7 +116,7 @@ class Model(ModelBase):
#------------------------- Cost(logloss) --------------------------
cost = fluid.layers.log_loss(input=self.predict, label=self.label)
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
......
models/rank/deepfm/slot_data/train_data/sample_test.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/deepfm/slot_data/train_data/sample_train.txt 0 → 100644
浏览文件 @ 522d4d4d
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models/rank/dnn/config.yaml
浏览文件 @ 522d4d4d
......@@ -22,9 +22,10 @@ train:
reader:
batch_size: 2
class: "{workspace}/../criteo_reader.py"
train_data_path: "{workspace}/data/train"
train_data_path: "{workspace}/slot_data/train"
reader_debug_mode: False
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
model:
models: "{workspace}/model.py"
......
models/rank/dnn/model.py
浏览文件 @ 522d4d4d
......@@ -24,43 +24,9 @@ class Model(ModelBase):
ModelBase.__init__(self, config)
def input(self):
def sparse_inputs():
ids = envs.get_global_env("hyper_parameters.sparse_inputs_slots", None, self._namespace)
sparse_input_ids = [
fluid.layers.data(name="S" + str(i),
shape=[1],
lod_level=1,
dtype="int64") for i in range(1, ids)
]
return sparse_input_ids
def dense_input():
dim = envs.get_global_env("hyper_parameters.dense_input_dim", None, self._namespace)
dense_input_var = fluid.layers.data(name="D",
shape=[dim],
dtype="float32")
return dense_input_var
def label_input():
label = fluid.layers.data(name="click", shape=[1], dtype="int64")
return label
self.sparse_inputs = sparse_inputs()
self.dense_input = dense_input()
self.label_input = label_input()
self._data_var.append(self.dense_input)
for input in self.sparse_inputs:
self._data_var.append(input)
self._data_var.append(self.label_input)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
self.sparse_inputs = self._sparse_data_var[1:]
self.dense_input = self._dense_data_var[0]
self.label_input = self._sparse_data_var[0]
def net(self):
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
......
models/rank/dnn/slot_data/test/sample_test.txt 0 → 100644
浏览文件 @ 522d4d4d
click:0 dense_feature:0.0 dense_feature:0.0878938640133 dense_feature:0.0 dense_feature:0.3 dense_feature:0.104078125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.8 dense_feature:0.098 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.42 1:371155 2:227084 3:958027 4:942782 5:286835 6:948614 7:189853 8:180477 9:27346 10:708281 11:943097 12:808275 13:389162 14:873363 15:460308 16:261673 17:924903 18:875670 19:26230 20:26229 21:327495 22:179327 23:410878 24:169709 25:26224 26:26223
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models/rank/dnn/slot_data/train/sample_train.txt 0 → 100644
浏览文件 @ 522d4d4d
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click:1 dense_feature:0.05 dense_feature:0.00497512437811 dense_feature:0.04 dense_feature:0.22 dense_feature:0.015921875 dense_feature:0.022 dense_feature:0.04 dense_feature:0.4 dense_feature:0.182 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.22 1:737395 2:64837 3:751736 4:291977 5:286835 6:25207 7:377931 8:718499 9:27346 10:724396 11:433484 12:517940 13:439712 14:201966 15:628624 16:780717 17:342789 18:895034 19:880474 20:31181 21:463725 22:26235 23:410878 24:787115 25:884722 26:164940
click:1 dense_feature:0.0 dense_feature:0.00995024875622 dense_feature:0.15 dense_feature:0.48 dense_feature:0.051375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.06 dense_feature:0.556 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.5 1:737395 2:532829 3:158777 4:112926 5:286835 6:948614 7:764249 8:67180 9:27346 10:795273 11:330644 12:524443 13:78129 14:873363 15:127209 16:146094 17:342789 18:976129 19:26230 20:26229 21:901094 22:26235 23:410878 24:259263 25:26224 26:26223
click:1 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:1.75 dense_feature:0.0 dense_feature:0.922828125 dense_feature:1.078 dense_feature:0.0 dense_feature:0.0 dense_feature:0.112 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:26973 2:62956 3:428206 4:935291 5:286835 6:446063 7:360307 8:437731 9:502607 10:957425 11:626052 12:641189 13:988058 14:217110 15:637914 16:293992 17:342789 18:832710 19:774721 20:537425 21:516798 22:191474 23:700326 24:204648 25:884722 26:776972
click:1 dense_feature:1.95 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.1 dense_feature:0.01878125 dense_feature:0.044 dense_feature:0.42 dense_feature:0.24 dense_feature:0.358 dense_feature:0.1 dense_feature:0.2 dense_feature:0.1 dense_feature:0.26 1:737395 2:638265 3:526671 4:362576 5:938478 6:948614 7:999918 8:67180 9:27346 10:806276 11:181589 12:688684 13:367155 14:255651 15:709602 16:386859 17:228085 18:204112 19:668832 20:537425 21:541553 22:191474 23:410878 24:606704 25:49230 26:68113
click:0 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.38159375 dense_feature:0.022 dense_feature:0.18 dense_feature:0.0 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:841163 3:284187 4:385559 5:286835 6:446063 7:311604 8:67180 9:27346 10:38910 11:76230 12:520869 13:429321 14:255651 15:296507 16:542357 17:342789 18:377250 19:880474 20:31181 21:325494 22:26235 23:410878 24:26284 25:499868 26:467348
click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.08 dense_feature:0.0 dense_feature:0.077125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.03 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:238813 3:821667 4:209184 5:286835 6:906818 7:261420 8:67180 9:27346 10:748867 11:277196 12:790086 13:495408 14:873363 15:572266 16:281532 17:342789 18:99340 19:880474 20:537425 21:815896 22:26235 23:669531 24:17430 25:734238 26:251811
click:0 dense_feature:0.0 dense_feature:0.210613598673 dense_feature:0.01 dense_feature:0.0 dense_feature:0.041375 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.026 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:532829 3:559456 4:565823 5:286835 6:948614 7:48897 8:67180 9:27346 10:708281 11:214000 12:431427 13:477774 14:873363 15:637383 16:678446 17:276641 18:849284 19:26230 20:26229 21:758879 22:26235 23:410878 24:399458 25:26224 26:26223
click:1 dense_feature:0.2 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.00440625 dense_feature:0.036 dense_feature:0.04 dense_feature:0.3 dense_feature:0.03 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:715353 2:532829 3:967094 4:707735 5:286835 6:948614 7:555710 8:154004 9:27346 10:708281 11:514992 12:158604 13:780149 14:255651 15:285282 16:149708 17:342789 18:553067 19:26230 20:26229 21:229985 22:26235 23:700326 24:777746 25:26224 26:26223
click:1 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.23178125 dense_feature:0.222 dense_feature:0.06 dense_feature:0.0 dense_feature:0.408 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.0 1:715353 2:227084 3:456811 4:828682 5:286835 6:948614 7:406567 8:67180 9:27346 10:66123 11:598531 12:527138 13:731439 14:813514 15:35257 16:43339 17:342789 18:918487 19:26230 20:26229 21:580653 22:26235 23:410878 24:495283 25:26224 26:26223
click:0 dense_feature:0.15 dense_feature:0.462686567164 dense_feature:0.08 dense_feature:0.22 dense_feature:0.00015625 dense_feature:0.022 dense_feature:0.03 dense_feature:0.52 dense_feature:0.022 dense_feature:0.1 dense_feature:0.1 dense_feature:0.0 dense_feature:0.22 1:576931 2:99294 3:263211 4:501662 5:938478 6:154084 7:128918 8:67180 9:27346 10:912799 11:801006 12:506258 13:378182 14:201966 15:150934 16:240427 17:681378 18:393279 19:26230 20:26229 21:152038 22:26235 23:700326 24:551443 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00331674958541 dense_feature:0.0 dense_feature:0.0 dense_feature:0.181484375 dense_feature:0.06 dense_feature:0.01 dense_feature:0.0 dense_feature:0.056 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:230803 2:283434 3:367596 4:197992 5:938478 6:948614 7:268098 8:67180 9:27346 10:870993 11:632267 12:139817 13:718764 14:255651 15:884839 16:80117 17:276641 18:556463 19:880474 20:537425 21:271358 22:26235 23:410878 24:488077 25:253255 26:584828
click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.0 dense_feature:0.16 dense_feature:4.790078125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.28 dense_feature:0.016 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.2 1:737395 2:532829 3:158777 4:112926 5:286835 6:948614 7:277312 8:67180 9:502607 10:708281 11:755513 12:524443 13:4029 14:873363 15:503814 16:146094 17:644532 18:121590 19:26230 20:26229 21:901094 22:191474 23:744389 24:259263 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:3.30845771144 dense_feature:0.0 dense_feature:0.04 dense_feature:0.022671875 dense_feature:0.062 dense_feature:0.01 dense_feature:0.4 dense_feature:0.062 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:663372 2:529436 3:511823 4:942782 5:286835 6:906818 7:190054 8:67180 9:27346 10:708281 11:32527 12:494263 13:652478 14:873363 15:616057 16:17325 17:342789 18:325238 19:26230 20:26229 21:256747 22:179327 23:410878 24:169709 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.01 dense_feature:0.16 dense_feature:0.206765625 dense_feature:0.328 dense_feature:0.13 dense_feature:0.16 dense_feature:0.176 dense_feature:0.0 dense_feature:0.7 dense_feature:0.0 dense_feature:0.16 1:737395 2:552854 3:606082 4:267619 5:286835 6:948614 7:918889 8:67180 9:27346 10:708281 11:400024 12:972010 13:66330 14:255651 15:432931 16:650209 17:506108 18:212910 19:26230 20:26229 21:107726 22:26235 23:410878 24:718419 25:26224 26:26223
models/rank/wide_deep/config.yaml
浏览文件 @ 522d4d4d
......@@ -22,8 +22,9 @@ train:
reader:
batch_size: 2
class: "{workspace}/reader.py"
train_data_path: "{workspace}/data/train_data"
train_data_path: "{workspace}/slot_data/train_data"
sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58"
model:
models: "{workspace}/model.py"
......
models/rank/wide_deep/model.py
浏览文件 @ 522d4d4d
......@@ -35,12 +35,9 @@ class Model(ModelBase):
return l3
def train_net(self):
wide_input = fluid.data(name='wide_input', shape=[None, 8], dtype='float32')
deep_input = fluid.data(name='deep_input', shape=[None, 58], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='float32')
self._data_var.append(wide_input)
self._data_var.append(deep_input)
self._data_var.append(label)
wide_input = self._dense_data_var[0]
deep_input = self._dense_data_var[1]
label = self._sparse_data_var[0]
hidden1_units = envs.get_global_env("hyper_parameters.hidden1_units", 75, self._namespace)
hidden2_units = envs.get_global_env("hyper_parameters.hidden2_units", 50, self._namespace)
......@@ -71,7 +68,7 @@ class Model(ModelBase):
self._metrics["BATCH_AUC"] = batch_auc
self._metrics["ACC"] = acc
cost = fluid.layers.sigmoid_cross_entropy_with_logits(x=prediction, label=label)
cost = fluid.layers.sigmoid_cross_entropy_with_logits(x=prediction, label=fluid.layers.cast(label, dtype='float32'))
avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost
......
models/rank/wide_deep/reader.py 已删除 100755 → 0
浏览文件 @ 91dad673
# 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
try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(Reader):
def init(self):
pass
def _process_line(self, line):
line = line.strip().split(',')
features = list(map(float, line))
wide_feat = features[0:8]
deep_feat = features[8:58+8]
label = features[-1]
return wide_feat, deep_feat, [label]
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
wide_feat, deep_deat, label = self._process_line(line)
yield [('wide_input', wide_feat), ('deep_input', deep_deat), ('label', label)]
return data_iter
\ No newline at end of file
models/rank/wide_deep/slot_data/train_data/train_data.txt 0 → 100644
浏览文件 @ 522d4d4d
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
models/rank/xdeepfm/config.yaml
浏览文件 @ 522d4d4d
......@@ -22,8 +22,9 @@ train:
reader:
batch_size: 2
class: "{workspace}/criteo_reader.py"
train_data_path: "{workspace}/data/train_data"
train_data_path: "{workspace}/slot_data/train_data"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
model:
models: "{workspace}/model.py"
......
models/rank/xdeepfm/criteo_reader.py 已删除 100755 → 0
浏览文件 @ 91dad673
# 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
try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(Reader):
def init(self):
pass
def _process_line(self, line):
features = line.strip('\n').split('\t')
feat_idx = []
feat_value = []
for idx in range(1, 40):
feat_idx.append(int(features[idx]))
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
def generate_sample(self, line):
def data_iter():
feat_idx, feat_value, label = self._process_line(line)
yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label',
label)]
return data_iter
\ No newline at end of file
models/rank/xdeepfm/model.py
浏览文件 @ 522d4d4d
......@@ -21,10 +21,11 @@ class Model(ModelBase):
# ------------------------- network input --------------------------
num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field], dtype='int64')
raw_feat_value = fluid.data(name='feat_value', shape=[None, num_field], dtype='float32')
self.label = fluid.data(name='label', shape=[None, 1], dtype='float32') # None * 1
feat_idx = fluid.layers.reshape(raw_feat_idx, [-1, 1]) # (None * num_field) * 1
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, num_field, 1]) # None * num_field * 1
feat_embeddings = fluid.embedding(
......@@ -39,15 +40,6 @@ class Model(ModelBase):
[-1, num_field, sparse_feature_dim]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
# ------------------------- set _data_var --------------------------
self._data_var.append(raw_feat_idx)
self._data_var.append(raw_feat_value)
self._data_var.append(self.label)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
# -------------------- linear --------------------
weights_linear = fluid.embedding(
......@@ -142,7 +134,7 @@ class Model(ModelBase):
def train_net(self):
self.xdeepfm_net()
cost = fluid.layers.log_loss(input=self.predict, label=self.label, epsilon=0.0000001)
cost = fluid.layers.log_loss(input=self.predict, label=fluid.layers.cast(self.label, "float32"), epsilon=0.0000001)
batch_cost = fluid.layers.reduce_mean(cost)
self._cost = batch_cost
......
models/rank/xdeepfm/slot_data/test_data/sample_test.txt 0 → 100644
浏览文件 @ 522d4d4d
feat_idx:0 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:268086 feat_idx:709770 feat_idx:884742 feat_idx:1063022 feat_idx:360051 feat_idx:404876 feat_idx:969844 feat_idx:144963 feat_idx:148475 feat_idx:697060 feat_idx:581680 feat_idx:458845 feat_idx:542184 feat_idx:295794 feat_idx:532191 feat_idx:787367 feat_idx:854924 feat_idx:146463 feat_idx:734534 feat_idx:1047606 feat_idx:845441 feat_idx:0 feat_idx:122096 feat_idx:115458 feat_idx:108823 feat_idx:100509 feat_value:0.0 feat_value:4.65697498428e-05 feat_value:0.000335698481727 feat_value:0.0061919504644 feat_value:0.000549235698801 feat_value:0.000155439092236 feat_value:1.77585196498e-05 feat_value:0.0013229700678 feat_value:0.00175746924429 feat_value:0.0 feat_value:0.004329004329 feat_value:0.0 feat_value:0.000811578520222 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:1
feat_idx:0 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:268086 feat_idx:439682 feat_idx:270243 feat_idx:1018060 feat_idx:314332 feat_idx:0 feat_idx:463705 feat_idx:144963 feat_idx:148475 feat_idx:754940 feat_idx:983169 feat_idx:799952 feat_idx:302064 feat_idx:59528 feat_idx:714113 feat_idx:597666 feat_idx:13161 feat_idx:660508 feat_idx:0 feat_idx:0 feat_idx:257521 feat_idx:0 feat_idx:122096 feat_idx:457076 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:6.59738122773e-05 feat_value:0.000213626306554 feat_value:0.00722394220846 feat_value:0.000296682270948 feat_value:8.11995257948e-05 feat_value:8.8792598249e-05 feat_value:0.00214982636018 feat_value:0.00234329232572 feat_value:0.0 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000946841606925 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:1
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models/rank/xdeepfm/slot_data/train_data/sample_train.txt 0 → 100644
浏览文件 @ 522d4d4d
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setup.py
浏览文件 @ 522d4d4d
......@@ -43,6 +43,7 @@ def build(dirname):
package_data = {}
models_copy = ['data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy', 'tree/*.txt']
models_copy += ['slot_data/*.txt', 'slot_data/*/*.txt', 'slot_data/*.pkl2', 'slot_data/*/*.csv', 'slot_data/*.csv']
engine_copy = ['*/*.sh']
for package in packages:
if package.startswith("paddlerec.models."):
......
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