Merge branch 'metrics' of https://github.com/123malin/PaddleRec into metrics

models/rank/dien/README.md

+
+# DIEN
+
+## 注意: config.yaml中指定了训练阶段的dataset名称为sample_1，预测阶段的dataset名称为infer_sample。同时在reader.py 和 infer_reader.py中，通个这两个dataset的名字读取的dataset的相关配置，如果修改了config.yaml中的dataset名字，需要在对应的reader.py 或者 infer_reader.py中同步修改下。
+ 

models/rank/dien/config.yaml

@@ -19,14 +19,14 @@ workspace: "paddlerec.models.rank.dien"
 dataset:
   - name: sample_1
     type: DataLoader
-    batch_size: 5
+    batch_size: 32
     data_path: "{workspace}/data/train_data"
     data_converter: "{workspace}/reader.py"
   - name: infer_sample
     type: DataLoader
-    batch_size: 5
+    batch_size: 32
     data_path: "{workspace}/data/train_data"
-    data_converter: "{workspace}/reader.py"
+    data_converter: "{workspace}/infer_reader.py"
 
 hyper_parameters:
     optimizer:

models/rank/dien/infer_reader.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.
+from __future__ import print_function
+
+import os
+import random
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+import numpy as np
+
+from paddlerec.core.reader import ReaderBase
+from paddlerec.core.utils import envs
+
+
+class Reader(ReaderBase):
+    def init(self):
+        self.train_data_path = envs.get_global_env(
+            "dataset.infer_sample.data_path", None)
+        self.res = []
+        self.max_len = 0
+        self.neg_candidate_item = []
+        self.neg_candidate_cat = []
+        self.max_neg_item = 10000
+        self.max_neg_cat = 1000
+
+        data_file_list = os.listdir(self.train_data_path)
+        for i in range(0, len(data_file_list)):
+            train_data_file = os.path.join(self.train_data_path,
+                                           data_file_list[i])
+            with open(train_data_file, "r") as fin:
+                for line in fin:
+                    line = line.strip().split(';')
+                    hist = line[0].split()
+                    self.max_len = max(self.max_len, len(hist))
+        fo = open("tmp.txt", "w")
+        fo.write(str(self.max_len))
+        fo.close()
+        self.batch_size = envs.get_global_env(
+            "dataset.infer_sample.batch_size", 32, None)
+        self.group_size = self.batch_size * 20
+
+    def _process_line(self, line):
+        line = line.strip().split(';')
+        hist = line[0].split()
+        hist = [int(i) for i in hist]
+        cate = line[1].split()
+        cate = [int(i) for i in cate]
+        return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
+
+    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 self._process_line(line)
+
+        return data_iter
+
+    def pad_batch_data(self, input, max_len):
+        res = np.array([x + [0] * (max_len - len(x)) for x in input])
+        res = res.astype("int64").reshape([-1, max_len])
+        return res
+
+    def make_data(self, b):
+        max_len = max(len(x[0]) for x in b)
+        # item = self.pad_batch_data([x[0] for x in b], max_len)
+        # cat = self.pad_batch_data([x[1] for x in b], max_len)
+        item = [x[0] for x in b]
+        cat = [x[1] for x in b]
+        neg_item = [None] * len(item)
+        neg_cat = [None] * len(cat)
+
+        for i in range(len(b)):
+            neg_item[i] = []
+            neg_cat[i] = []
+            if len(self.neg_candidate_item) < self.max_neg_item:
+                self.neg_candidate_item.extend(b[i][0])
+                if len(self.neg_candidate_item) > self.max_neg_item:
+                    self.neg_candidate_item = self.neg_candidate_item[
+                        0:self.max_neg_item]
+            else:
+                len_seq = len(b[i][0])
+                start_idx = random.randint(0, self.max_neg_item - len_seq - 1)
+                self.neg_candidate_item[start_idx:start_idx + len_seq + 1] = b[
+                    i][0]
+
+            if len(self.neg_candidate_cat) < self.max_neg_cat:
+                self.neg_candidate_cat.extend(b[i][1])
+                if len(self.neg_candidate_cat) > self.max_neg_cat:
+                    self.neg_candidate_cat = self.neg_candidate_cat[
+                        0:self.max_neg_cat]
+            else:
+                len_seq = len(b[i][1])
+                start_idx = random.randint(0, self.max_neg_cat - len_seq - 1)
+                self.neg_candidate_item[start_idx:start_idx + len_seq + 1] = b[
+                    i][1]
+            for _ in range(len(b[i][0])):
+                neg_item[i].append(self.neg_candidate_item[random.randint(
+                    0, len(self.neg_candidate_item) - 1)])
+            for _ in range(len(b[i][1])):
+                neg_cat[i].append(self.neg_candidate_cat[random.randint(
+                    0, len(self.neg_candidate_cat) - 1)])
+
+        len_array = [len(x[0]) for x in b]
+        mask = np.array(
+            [[0] * x + [-1e9] * (max_len - x) for x in len_array]).reshape(
+                [-1, max_len, 1])
+        target_item_seq = np.array(
+            [[x[2]] * max_len for x in b]).astype("int64").reshape(
+                [-1, max_len])
+        target_cat_seq = np.array(
+            [[x[3]] * max_len for x in b]).astype("int64").reshape(
+                [-1, max_len])
+        res = []
+        for i in range(len(b)):
+            res.append([
+                item[i], cat[i], b[i][2], b[i][3], b[i][4], mask[i],
+                target_item_seq[i], target_cat_seq[i], neg_item[i], neg_cat[i]
+            ])
+        return res
+
+    def batch_reader(self, reader, batch_size, group_size):
+        def batch_reader():
+            bg = []
+            for line in reader:
+                bg.append(line)
+                if len(bg) == group_size:
+                    sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+                    bg = []
+                    for i in range(0, group_size, batch_size):
+                        b = sortb[i:i + batch_size]
+                        yield self.make_data(b)
+            len_bg = len(bg)
+            if len_bg != 0:
+                sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+                bg = []
+                remain = len_bg % batch_size
+                for i in range(0, len_bg - remain, batch_size):
+                    b = sortb[i:i + batch_size]
+                    yield self.make_data(b)
+
+        return batch_reader
+
+    def base_read(self, file_dir):
+        res = []
+        for train_file in file_dir:
+            with open(train_file, "r") as fin:
+                for line in fin:
+                    line = line.strip().split(';')
+                    hist = line[0].split()
+                    cate = line[1].split()
+                    res.append([hist, cate, line[2], line[3], float(line[4])])
+        return res
+
+    def generate_batch_from_trainfiles(self, files):
+        data_set = self.base_read(files)
+        random.shuffle(data_set)
+        return self.batch_reader(data_set, self.batch_size,
+                                 self.batch_size * 20)

models/rank/dien/reader.py

@@ -51,7 +51,7 @@ class Reader(ReaderBase):
         fo.write(str(self.max_len))
         fo.close()
         self.batch_size = envs.get_global_env("dataset.sample_1.batch_size",
-                                              32, "train.reader")
+                                              32, None)
         self.group_size = self.batch_size * 20
 
     def _process_line(self, line):

models/rank/din/README.md

+
+# DIN
+
+## 注意: config.yaml中指定了训练阶段的dataset名称为sample_1，预测阶段的dataset名称为infer_sample。同时在reader.py 和 infer_reader.py中，通个这两个dataset的名字读取的dataset的相关配置，如果修改了config.yaml中的dataset名字，需要在对应的reader.py 或者 infer_reader.py中同步修改下。
+ 

models/rank/din/config.yaml

@@ -26,7 +26,7 @@ dataset:
     type: DataLoader
     batch_size: 5
     data_path: "{workspace}/data/train_data"
-    data_converter: "{workspace}/reader.py"
+    data_converter: "{workspace}/infer_reader.py"
 
 hyper_parameters:
     optimizer:

models/rank/din/infer_reader.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.
+from __future__ import print_function
+
+import os
+import random
+
+try:
+    import cPickle as pickle
+except ImportError:
+    import pickle
+
+import numpy as np
+
+from paddlerec.core.reader import ReaderBase
+from paddlerec.core.utils import envs
+
+
+class Reader(ReaderBase):
+    def init(self):
+        self.train_data_path = envs.get_global_env(
+            "dataset.infer_sample.data_path", None)
+        self.res = []
+        self.max_len = 0
+
+        data_file_list = os.listdir(self.train_data_path)
+        for i in range(0, len(data_file_list)):
+            train_data_file = os.path.join(self.train_data_path,
+                                           data_file_list[i])
+            with open(train_data_file, "r") as fin:
+                for line in fin:
+                    line = line.strip().split(';')
+                    hist = line[0].split()
+                    self.max_len = max(self.max_len, len(hist))
+        fo = open("tmp.txt", "w")
+        fo.write(str(self.max_len))
+        fo.close()
+        self.batch_size = envs.get_global_env(
+            "dataset.infer_sample.batch_size", 32, None)
+        self.group_size = self.batch_size * 20
+
+    def _process_line(self, line):
+        line = line.strip().split(';')
+        hist = line[0].split()
+        hist = [int(i) for i in hist]
+        cate = line[1].split()
+        cate = [int(i) for i in cate]
+        return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
+
+    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 self._process_line(line)
+
+        return data_iter
+
+    def pad_batch_data(self, input, max_len):
+        res = np.array([x + [0] * (max_len - len(x)) for x in input])
+        res = res.astype("int64").reshape([-1, max_len])
+        return res
+
+    def make_data(self, b):
+        max_len = max(len(x[0]) for x in b)
+        item = self.pad_batch_data([x[0] for x in b], max_len)
+        cat = self.pad_batch_data([x[1] for x in b], max_len)
+        len_array = [len(x[0]) for x in b]
+        mask = np.array(
+            [[0] * x + [-1e9] * (max_len - x) for x in len_array]).reshape(
+                [-1, max_len, 1])
+        target_item_seq = np.array(
+            [[x[2]] * max_len for x in b]).astype("int64").reshape(
+                [-1, max_len])
+        target_cat_seq = np.array(
+            [[x[3]] * max_len for x in b]).astype("int64").reshape(
+                [-1, max_len])
+        res = []
+        for i in range(len(b)):
+            res.append([
+                item[i], cat[i], b[i][2], b[i][3], b[i][4], mask[i],
+                target_item_seq[i], target_cat_seq[i]
+            ])
+        return res
+
+    def batch_reader(self, reader, batch_size, group_size):
+        def batch_reader():
+            bg = []
+            for line in reader:
+                bg.append(line)
+                if len(bg) == group_size:
+                    sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+                    bg = []
+                    for i in range(0, group_size, batch_size):
+                        b = sortb[i:i + batch_size]
+                        yield self.make_data(b)
+            len_bg = len(bg)
+            if len_bg != 0:
+                sortb = sorted(bg, key=lambda x: len(x[0]), reverse=False)
+                bg = []
+                remain = len_bg % batch_size
+                for i in range(0, len_bg - remain, batch_size):
+                    b = sortb[i:i + batch_size]
+                    yield self.make_data(b)
+
+        return batch_reader
+
+    def base_read(self, file_dir):
+        res = []
+        for train_file in file_dir:
+            with open(train_file, "r") as fin:
+                for line in fin:
+                    line = line.strip().split(';')
+                    hist = line[0].split()
+                    cate = line[1].split()
+                    res.append([hist, cate, line[2], line[3], float(line[4])])
+        return res
+
+    def generate_batch_from_trainfiles(self, files):
+        data_set = self.base_read(files)
+        random.shuffle(data_set)
+        return self.batch_reader(data_set, self.batch_size,
+                                 self.batch_size * 20)

models/rank/din/reader.py

@@ -47,7 +47,7 @@ class Reader(ReaderBase):
         fo.write(str(self.max_len))
         fo.close()
         self.batch_size = envs.get_global_env("dataset.sample_1.batch_size",
-                                              32, "train.reader")
+                                              32, None)
         self.group_size = self.batch_size * 20
 
     def _process_line(self, line):