add linear regression

models/rank/linear_regression/__init__.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.

models/rank/linear_regression/config.yaml

+# 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.
+
+# global settings 
+debug: false
+workspace: "/home/aistudio/PaddleRec-master/models/rank/linear_regression"
+
+
+dataset:
+  - name: dataset_train
+    type: QueueDataset
+    batch_size: 1
+    data_path: "{workspace}/data/train_data/"
+    sparse_slots: "userid gender age occupation movieid title genres"
+    dense_slots: "label:1"
+  - name: dataset_infer
+    type: QueueDataset
+    batch_size: 1
+    data_path: "{workspace}/data/test_data/"
+    sparse_slots: "userid gender age occupation movieid title genres"
+    dense_slots: "label:1"
+
+hyper_parameters:
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    sparse_feature_number: 1000000
+    sparse_feature_dim: 1
+    reg: 0.001
+
+
+mode: train_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_runner
+    class: train
+    epochs: 1
+    device: cpu
+    init_model_path: ""
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+    print_interval: 100
+  - name: infer_runner
+    class: infer
+    device: cpu
+    init_model_path: "increment/0"
+    print_interval: 1
+    
+
+phase:
+- name: phase1
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 12
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/linear_regression/data/download_preprocess.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 os
+import shutil
+import sys
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from paddlerec.tools.tools import download_file_and_uncompress, download_file
+
+if __name__ == '__main__':
+    url = "http://files.grouplens.org/datasets/movielens/ml-1m.zip"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url)
+    print("download and extract finished")
+
+    # print("preprocessing...")
+    # os.system("python preprocess.py")
+    # print("preprocess done")
+
+    # shutil.rmtree("raw_data")
+    print("done")

models/rank/linear_regression/data/preprocess.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.
+
+#coding=utf8
+import os
+import sys
+reload(sys)
+sys.setdefaultencoding('utf-8')
+import random
+import json
+
+user_fea = ["userid", "gender", "age", "occupation"]
+movie_fea = ["movieid", "title", "genres"]
+rating_fea = ["userid", "movieid", "rating", "time"]
+dict_size = 1000000
+hash_dict = dict()
+
+data_path = "ml-1m"
+test_user_path = "online_user"
+
+
+def process(path, output_path):
+    user_dict = parse_data(data_path + "/users.dat", user_fea)
+    movie_dict = parse_movie_data(data_path + "/movies.dat", movie_fea)
+
+    res = []
+    for line in open(path):
+        line = line.strip()
+        arr = line.split("::")
+        userid = arr[0]
+        movieid = arr[1]
+        out_str = "time:%s\t%s\t%s\tlabel:%s" % (arr[3], user_dict[userid],
+                                                 movie_dict[movieid], arr[2])
+        log_id = hash(out_str) % 1000000000
+        res.append("%s\t%s" % (log_id, out_str))
+    with open(output_path, 'w') as fout:
+        for line in res:
+            fout.write(line)
+            fout.write("\n")
+
+
+def parse_data(file_name, feas):
+    dict = {}
+    for line in open(file_name):
+        line = line.strip()
+        arr = line.split("::")
+        out_str = ""
+        for i in range(0, len(feas)):
+            out_str += "%s:%s\t" % (feas[i], arr[i])
+
+        dict[arr[0]] = out_str.strip()
+    return dict
+
+
+def parse_movie_data(file_name, feas):
+    dict = {}
+    for line in open(file_name):
+        line = line.strip()
+        arr = line.split("::")
+        title_str = ""
+        genres_str = ""
+
+        for term in arr[1].split(" "):
+            term = term.strip()
+            if term != "":
+                title_str += "%s " % (term)
+        for term in arr[2].split("|"):
+            term = term.strip()
+            if term != "":
+                genres_str += "%s " % (term)
+        out_str = "movieid:%s\ttitle:%s\tgenres:%s" % (
+            arr[0], title_str.strip(), genres_str.strip())
+        dict[arr[0]] = out_str.strip()
+    return dict
+
+
+def to_hash(in_str):
+    feas = in_str.split(":")[0]
+    arr = in_str.split(":")[1]
+    out_str = "%s:%s" % (feas, (arr + arr[::-1] + arr[::-2] + arr[::-3]))
+    hash_id = hash(out_str) % dict_size
+    #  if hash_id in hash_dict and hash_dict[hash_id] != out_str:
+    #      print(hash_id, out_str, hash(out_str))
+    #      print("conflict")
+    #  exit(-1)
+
+    return "%s:%s" % (feas, hash_id)
+
+
+def to_hash_list(in_str):
+    arr = in_str.split(":")
+    tmp_arr = arr[1].split(" ")
+    out_str = ""
+    for item in tmp_arr:
+        item = item.strip()
+        if item != "":
+            key = "%s:%s" % (arr[0], item)
+            out_str += "%s " % (to_hash(key))
+    return out_str.strip()
+
+
+def get_hash(path):
+    #0-34831 1-time:974673057 2-userid:2021 3-gender:M 4-age:25 5-occupation:0 6-movieid:1345  7-title:Carrie (1976)  8-genres:Horror  9-label:2
+    for line in open(path):
+        arr = line.strip().split("\t")
+        out_str = "logid:%s %s %s %s %s %s %s %s %s %s" % \
+                 (arr[0], arr[1], to_hash(arr[2]), to_hash(arr[3]), to_hash(arr[4]), to_hash(arr[5]), \
+                 to_hash(arr[6]), to_hash_list(arr[7]), to_hash_list(arr[8]), arr[9])
+        print out_str
+
+
+def split(path, output_dir, num=24):
+    contents = []
+    with open(path) as f:
+        contents = f.readlines()
+    lines_per_file = len(contents) / num
+    print("contents: ", str(len(contents)))
+    print("lines_per_file: ", str(lines_per_file))
+
+    for i in range(1, num + 1):
+        with open(os.path.join(output_dir, "part_" + str(i)), 'w') as fout:
+            data = contents[(i - 1) * lines_per_file:min(i * lines_per_file,
+                                                         len(contents))]
+            for line in data:
+                fout.write(line)
+
+
+if __name__ == "__main__":
+    random.seed(1111111)
+    if sys.argv[1] == "process_raw":
+        process(sys.argv[2], sys.argv[3])
+    elif sys.argv[1] == "hash":
+        get_hash(sys.argv[2])
+    elif sys.argv[1] == "split":
+        split(sys.argv[2], sys.argv[3])

models/rank/linear_regression/data/split.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 random
+
+train = dict()
+test = dict()
+data_path = "ml-1m"
+
+for line in open(data_path + "/ratings.dat"):
+    fea = line.rstrip().split("::")
+    if fea[0] not in train:
+        train[fea[0]] = [line]
+    elif fea[0] not in test:
+        test[fea[0]] = dict()
+        test[fea[0]]['time'] = int(fea[3])
+        test[fea[0]]['content'] = line
+    else:
+        time = int(fea[3])
+        if time <= test[fea[0]]['time']:
+            train[fea[0]].append(line)
+        else:
+            train[fea[0]].append(test[fea[0]]['content'])
+            test[fea[0]]['time'] = time
+            test[fea[0]]['content'] = line
+
+train_data = []
+for key in train:
+    for line in train[key]:
+        train_data.append(line)
+
+random.shuffle(train_data)
+train_num = 10000
+idx = 0
+
+with open(data_path + "/train.dat", 'w') as f:
+    for line in train_data:
+        idx += 1
+        if idx > train_num:
+            break
+        f.write(line)
+
+with open(data_path + "/test.dat", 'w') as f:
+    for key in test:
+        f.write(test[key]['content'])

models/rank/linear_regression/data_prepare.sh

+cd data
+# 1. download data
+python download_preprocess.py 
+
+# 2. split data
+python split.py
+
+# 3. 数据拼接
+python preprocess.py process_raw ml-1m/train.dat raw_train
+python preprocess.py process_raw ml-1m/test.dat raw_test
+
+# 4. hash
+python preprocess.py hash raw_train > train_data/data
+python preprocess.py hash raw_test > test_data/data
+cd ..

models/rank/linear_regression/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.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 --------------------------
+
+        sparse_var = self._sparse_data_var
+        self.label = self._dense_data_var[0]
+
+        def embedding_layer(input):
+            emb = fluid.embedding(
+                input=input,
+                is_sparse=True,
+                is_distributed=is_distributed,
+                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)))
+            reshape_emb = fluid.layers.reshape(emb, shape=[-1, 1])
+            return reshape_emb
+
+        sparse_embed_seq = list(map(embedding_layer, sparse_var))
+        weight = fluid.layers.concat(sparse_embed_seq, axis=0)
+        weight_sum = fluid.layers.reduce_sum(weight)
+        b_linear = fluid.layers.create_parameter(
+            shape=[1],
+            dtype='float32',
+            default_initializer=fluid.initializer.ConstantInitializer(value=0))
+
+        self.predict = fluid.layers.relu(weight_sum + b_linear)
+        cost = fluid.layers.square_error_cost(
+            input=self.predict, label=self.label)
+        avg_cost = fluid.layers.reduce_sum(cost)
+
+        self._cost = avg_cost
+
+        self._metrics["COST"] = self._cost
+        self._metrics["Predict"] = self.predict
+        if is_infer:
+            self._infer_results["Predict"] = self.predict
+            self._infer_results["COST"] = self._cost

models/rank/linear_regression/parse_param.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 numpy as np
+import sys
+
+params = []
+with open(sys.argv[1]) as f:
+    for line in f:
+        line = line.strip().strip('data: ').strip(',').split(',')
+        line = map(float, line)
+        params.append(line)
+
+feas = []
+with open(sys.argv[2]) as f:
+    for line in f:
+        line = line.strip().split('\t')
+        feas.append(line)
+
+score = []
+with open(sys.argv[3]) as f:
+    for line in f:
+        line = float(line.strip().strip('data: ').strip()[1:-1])
+        score.append(line)
+
+assert (len(params) == len(feas))
+length = len(params)
+
+bias = None
+for i in range(length):
+    label = feas[i][-1]
+    tmp = feas[i][2:-3]
+    tmp_fea = feas[i][-3].split(":")
+    _ = tmp_fea[1].split(" ")
+    for j in range(len(_)):
+        if _[j] != "":
+            tmp.append(tmp_fea[0] + ":" + _[j])
+    tmp_fea = feas[i][-2].split(":")
+    _ = tmp_fea[1].split(" ")
+    for j in range(len(_)):
+        if _[j] != "":
+            tmp.append(tmp_fea[0] + ":" + _[j])
+    sort_p = np.argsort(np.array(params[i]))[::-1]
+
+    res = []
+    for j in range(len(sort_p)):
+        res.append(tmp[sort_p[j]] + "_" + str(params[i][sort_p[j]]))
+
+    res.append(label)
+    res.append(str(score[i]))
+    bias = score[i] - sum(params[i])
+    print("; ".join(res))
+    assert (len(params[i]) == len(tmp))