!1669 wide&deep data_pre_process

Merge pull request !1669 from wukesong/data_pre_process

model_zoo/wide_and_deep/src/preprocess_data.py

@@ -0,0 +1,278 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Download raw data and preprocessed data."""
+import os
+import pickle
+import collections
+import argparse
+import numpy as np
+from mindspore.mindrecord import FileWriter
+
+TRAIN_LINE_COUNT = 45840617
+TEST_LINE_COUNT = 6042135
+
+
+class CriteoStatsDict():
+    """preprocessed data"""
+
+    def __init__(self):
+        self.field_size = 39
+        self.val_cols = ["val_{}".format(i + 1) for i in range(13)]
+        self.cat_cols = ["cat_{}".format(i + 1) for i in range(26)]
+
+        self.val_min_dict = {col: 0 for col in self.val_cols}
+        self.val_max_dict = {col: 0 for col in self.val_cols}
+
+        self.cat_count_dict = {col: collections.defaultdict(int) for col in self.cat_cols}
+
+        self.oov_prefix = "OOV"
+
+        self.cat2id_dict = {}
+        self.cat2id_dict.update({col: i for i, col in enumerate(self.val_cols)})
+        self.cat2id_dict.update(
+            {self.oov_prefix + col: i + len(self.val_cols) for i, col in enumerate(self.cat_cols)})
+
+    def stats_vals(self, val_list):
+        """Handling weights column"""
+        assert len(val_list) == len(self.val_cols)
+
+        def map_max_min(i, val):
+            key = self.val_cols[i]
+            if val != "":
+                if float(val) > self.val_max_dict[key]:
+                    self.val_max_dict[key] = float(val)
+                if float(val) < self.val_min_dict[key]:
+                    self.val_min_dict[key] = float(val)
+
+        for i, val in enumerate(val_list):
+            map_max_min(i, val)
+
+    def stats_cats(self, cat_list):
+        """Handling cats column"""
+
+        assert len(cat_list) == len(self.cat_cols)
+
+        def map_cat_count(i, cat):
+            key = self.cat_cols[i]
+            self.cat_count_dict[key][cat] += 1
+
+        for i, cat in enumerate(cat_list):
+            map_cat_count(i, cat)
+
+    def save_dict(self, dict_path, prefix=""):
+        with open(os.path.join(dict_path, "{}val_max_dict.pkl".format(prefix)), "wb") as file_wrt:
+            pickle.dump(self.val_max_dict, file_wrt)
+        with open(os.path.join(dict_path, "{}val_min_dict.pkl".format(prefix)), "wb") as file_wrt:
+            pickle.dump(self.val_min_dict, file_wrt)
+        with open(os.path.join(dict_path, "{}cat_count_dict.pkl".format(prefix)), "wb") as file_wrt:
+            pickle.dump(self.cat_count_dict, file_wrt)
+
+    def load_dict(self, dict_path, prefix=""):
+        with open(os.path.join(dict_path, "{}val_max_dict.pkl".format(prefix)), "rb") as file_wrt:
+            self.val_max_dict = pickle.load(file_wrt)
+        with open(os.path.join(dict_path, "{}val_min_dict.pkl".format(prefix)), "rb") as file_wrt:
+            self.val_min_dict = pickle.load(file_wrt)
+        with open(os.path.join(dict_path, "{}cat_count_dict.pkl".format(prefix)), "rb") as file_wrt:
+            self.cat_count_dict = pickle.load(file_wrt)
+        print("val_max_dict.items()[:50]:{}".format(list(self.val_max_dict.items())))
+        print("val_min_dict.items()[:50]:{}".format(list(self.val_min_dict.items())))
+
+    def get_cat2id(self, threshold=100):
+        for key, cat_count_d in self.cat_count_dict.items():
+            new_cat_count_d = dict(filter(lambda x: x[1] > threshold, cat_count_d.items()))
+            for cat_str, _ in new_cat_count_d.items():
+                self.cat2id_dict[key + "_" + cat_str] = len(self.cat2id_dict)
+        print("cat2id_dict.size:{}".format(len(self.cat2id_dict)))
+        print("cat2id.dict.items()[:50]:{}".format(list(self.cat2id_dict.items())[:50]))
+
+    def map_cat2id(self, values, cats):
+        """Cat to id"""
+
+        def minmax_scale_value(i, val):
+            max_v = float(self.val_max_dict["val_{}".format(i + 1)])
+            return float(val) * 1.0 / max_v
+
+        id_list = []
+        weight_list = []
+        for i, val in enumerate(values):
+            if val == "":
+                id_list.append(i)
+                weight_list.append(0)
+            else:
+                key = "val_{}".format(i + 1)
+                id_list.append(self.cat2id_dict[key])
+                weight_list.append(minmax_scale_value(i, float(val)))
+
+        for i, cat_str in enumerate(cats):
+            key = "cat_{}".format(i + 1) + "_" + cat_str
+            if key in self.cat2id_dict:
+                id_list.append(self.cat2id_dict[key])
+            else:
+                id_list.append(self.cat2id_dict[self.oov_prefix + "cat_{}".format(i + 1)])
+            weight_list.append(1.0)
+        return id_list, weight_list
+
+
+def mkdir_path(file_path):
+    if not os.path.exists(file_path):
+        os.makedirs(file_path)
+
+
+def statsdata(file_path, dict_output_path, criteo_stats_dict):
+    """Preprocess data and save data"""
+    with open(file_path, encoding="utf-8") as file_in:
+        errorline_list = []
+        count = 0
+        for line in file_in:
+            count += 1
+            line = line.strip("\n")
+            items = line.split("\t")
+            if len(items) != 40:
+                errorline_list.append(count)
+                print("line: {}".format(line))
+                continue
+            if count % 1000000 == 0:
+                print("Have handled {}w lines.".format(count // 10000))
+            values = items[1:14]
+            cats = items[14:]
+
+            assert len(values) == 13, "values.size: {}".format(len(values))
+            assert len(cats) == 26, "cats.size: {}".format(len(cats))
+            criteo_stats_dict.stats_vals(values)
+            criteo_stats_dict.stats_cats(cats)
+    criteo_stats_dict.save_dict(dict_output_path)
+
+
+def random_split_trans2mindrecord(input_file_path, output_file_path, criteo_stats_dict, part_rows=2000000,
+                                  line_per_sample=1000,
+                                  test_size=0.1, seed=2020):
+    """Random split data and save mindrecord"""
+    test_size = int(TRAIN_LINE_COUNT * test_size)
+    all_indices = [i for i in range(TRAIN_LINE_COUNT)]
+    np.random.seed(seed)
+    np.random.shuffle(all_indices)
+    print("all_indices.size:{}".format(len(all_indices)))
+    test_indices_set = set(all_indices[:test_size])
+    print("test_indices_set.size:{}".format(len(test_indices_set)))
+    print("-----------------------" * 10 + "\n" * 2)
+
+    train_data_list = []
+    test_data_list = []
+    ids_list = []
+    wts_list = []
+    label_list = []
+
+    writer_train = FileWriter(os.path.join(output_file_path, "train_input_part.mindrecord"), 21)
+    writer_test = FileWriter(os.path.join(output_file_path, "test_input_part.mindrecord"), 3)
+
+    schema = {"label": {"type": "float32", "shape": [-1]}, "feat_vals": {"type": "float32", "shape": [-1]},
+              "feat_ids": {"type": "int32", "shape": [-1]}}
+    writer_train.add_schema(schema, "CRITEO_TRAIN")
+    writer_test.add_schema(schema, "CRITEO_TEST")
+
+    with open(input_file_path, encoding="utf-8") as file_in:
+        items_error_size_lineCount = []
+        count = 0
+        train_part_number = 0
+        test_part_number = 0
+        for i, line in enumerate(file_in):
+            count += 1
+            if count % 1000000 == 0:
+                print("Have handle {}w lines.".format(count // 10000))
+            line = line.strip("\n")
+            items = line.split("\t")
+            if len(items) != 40:
+                items_error_size_lineCount.append(i)
+                continue
+            label = float(items[0])
+            values = items[1:14]
+            cats = items[14:]
+
+            assert len(values) == 13, "values.size: {}".format(len(values))
+            assert len(cats) == 26, "cats.size: {}".format(len(cats))
+
+            ids, wts = criteo_stats_dict.map_cat2id(values, cats)
+
+            ids_list.extend(ids)
+            wts_list.extend(wts)
+            label_list.append(label)
+
+            if count % line_per_sample == 0:
+                if i not in test_indices_set:
+                    train_data_list.append({"feat_ids": np.array(ids_list, dtype=np.int32),
+                                            "feat_vals": np.array(wts_list, dtype=np.float32),
+                                            "label": np.array(label_list, dtype=np.float32)
+                                            })
+                else:
+                    test_data_list.append({"feat_ids": np.array(ids_list, dtype=np.int32),
+                                           "feat_vals": np.array(wts_list, dtype=np.float32),
+                                           "label": np.array(label_list, dtype=np.float32)
+                                           })
+                if train_data_list and len(train_data_list) % part_rows == 0:
+                    writer_train.write_raw_data(train_data_list)
+                    train_data_list.clear()
+                    train_part_number += 1
+
+                if test_data_list and len(test_data_list) % part_rows == 0:
+                    writer_test.write_raw_data(test_data_list)
+                    test_data_list.clear()
+                    test_part_number += 1
+
+                ids_list.clear()
+                wts_list.clear()
+                label_list.clear()
+
+        if train_data_list:
+            writer_train.write_raw_data(train_data_list)
+        if test_data_list:
+            writer_test.write_raw_data(test_data_list)
+    writer_train.commit()
+    writer_test.commit()
+
+    print("-------------" * 10)
+    print("items_error_size_lineCount.size(): {}.".format(len(items_error_size_lineCount)))
+    print("-------------" * 10)
+    np.save("items_error_size_lineCount.npy", items_error_size_lineCount)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="criteo data")
+    parser.add_argument("--data_path", type=str, default="./criteo_data/")
+
+    args, _ = parser.parse_known_args()
+    data_path = args.data_path
+
+    download_data_path = data_path + "origin_data/"
+    mkdir_path(download_data_path)
+
+    os.system(
+        "wget -P {} -c https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz --no-check-certificate".format(
+            download_data_path))
+    os.system("tar -zxvf {}dac.tar.gz".format(download_data_path))
+
+    criteo_stats = CriteoStatsDict()
+    data_file_path = data_path + "origin_data/train.txt"
+    stats_output_path = data_path + "stats_dict/"
+    mkdir_path(stats_output_path)
+    statsdata(data_file_path, stats_output_path, criteo_stats)
+
+    criteo_stats.load_dict(dict_path=stats_output_path, prefix="")
+    criteo_stats.get_cat2id(threshold=100)
+
+    in_file_path = data_path + "origin_data/train.txt"
+    output_path = data_path + "mindrecord/"
+    mkdir_path(output_path)
+    random_split_trans2mindrecord(in_file_path, output_path, criteo_stats, part_rows=2000000, line_per_sample=1000,
+                                  test_size=0.1, seed=2020)