wide&deep data process

model_zoo/wide_and_deep/src/process_data.py

+# 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.
+# ============================================================================
+"""
+Criteo data process
+"""
+
+import os
+import pickle
+import collections
+import argparse
+
+import numpy as np
+import pandas as pd
+
+TRAIN_LINE_COUNT = 45840617
+TEST_LINE_COUNT = 6042135
+
+class CriteoStatsDict():
+    """create data dict"""
+    def __init__(self):
+        self.field_size = 39 # value_1-13; cat_1-26;
+        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):
+        """vals status"""
+        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):
+        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, output_path, prefix=""):
+        with open(os.path.join(output_path, "{}val_max_dict.pkl".format(prefix)), "wb") as file_wrt:
+            pickle.dump(self.val_max_dict, file_wrt)
+        with open(os.path.join(output_path, "{}val_min_dict.pkl".format(prefix)), "wb") as file_wrt:
+            pickle.dump(self.val_min_dict, file_wrt)
+        with open(os.path.join(output_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):
+        """get cat to id"""
+        # before_all_count = 0
+        # after_all_count = 0
+        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("before_all_count: {}".format(before_all_count)) # before_all_count: 33762577
+        # print("after_all_count: {}".format(after_all_count)) # after_all_count: 184926
+        print("cat2id_dict.size: {}".format(len(self.cat2id_dict)))
+        print("cat2id_dict.items()[:50]: {}".format(self.cat2id_dict.items()[:50]))
+    #
+    def map_cat2id(self, values, cats):
+        """map cat to id"""
+        def minmax_sclae_value(i, val):
+            # min_v = float(self.val_min_dict["val_{}".format(i+1)])
+            max_v = float(self.val_max_dict["val_{}".format(i + 1)])
+            # return (float(val) - min_v) * 1.0 / (max_v - min_v)
+            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_sclae_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(data_file_path, output_path, criteo_stats):
+    """data status"""
+    with open(data_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.strip("\t")
+            if len(items) != 40:
+                errorline_list.append(count)
+                print("line: {}".format(line))
+                continue
+            if count % 1000000 == 0:
+                print("Have handle {}w lines.".format(count//10000))
+            # if count % 5000000 == 0:
+            #    print("Have handle {}w lines.".format(count//10000))
+            # label = items[0]
+            values = items[1:14]
+            cats = items[14:]
+            assert len(values) == 13, "value.size: {}".format(len(values))
+            assert len(cats) == 26, "cat.size: {}".format(len(cats))
+            criteo_stats.stats_vals(values)
+            criteo_stats.stats_cats(cats)
+    criteo_stats.save_dict(output_path)
+    #
+
+
+def add_write(file_path, wr_str):
+    with open(file_path, "a", encoding="utf-8") as file_out:
+        file_out.write(wr_str + "\n")
+#
+
+
+def random_split_trans2h5(in_file_path, output_path, criteo_stats, part_rows=2000000, test_size=0.1, seed=2020):
+    """random split trans2h5"""
+    test_size = int(TRAIN_LINE_COUNT * test_size)
+    # train_size = 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)))
+    # lines_count_dict = collections.defaultdict(int)
+    test_indices_set = set(all_indices[:test_size])
+    print("test_indices_set.size: {}".format(len(test_indices_set)))
+    print("------" * 10 + "\n" * 2)
+
+    train_feature_file_name = os.path.join(output_path, "train_input_part_{}.h5")
+    train_label_file_name = os.path.join(output_path, "train_output_part_{}.h5")
+    test_feature_file_name = os.path.join(output_path, "test_input_part_{}.h5")
+    test_label_file_name = os.path.join(output_path, "test_input_part_{}.h5")
+    train_feature_list = []
+    train_label_list = []
+    test_feature_list = []
+    test_label_list = []
+    with open(in_file_path, encoding="utf-8") as file_in:
+        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:
+                continue
+            label = float(items[0])
+            values = items[1:14]
+            cats = items[14:]
+            assert len(values) == 13, "value.size: {}".format(len(values))
+            assert len(cats) == 26, "cat.size: {}".format(len(cats))
+            ids, wts = criteo_stats.map_cat2id(values, cats)
+            if i not in test_indices_set:
+                train_feature_list.append(ids + wts)
+                train_label_list.append(label)
+            else:
+                test_feature_list.append(ids + wts)
+                test_label_list.append(label)
+            if train_label_list and (len(train_label_list) % part_rows == 0):
+                pd.DataFrame(np.asarray(train_feature_list)).to_hdf(train_feature_file_name.format(train_part_number),
+                                                                    key="fixed")
+                pd.DataFrame(np.asarray(train_label_list)).to_hdf(train_label_file_name.format(train_part_number),
+                                                                  key="fixed")
+                train_feature_list = []
+                train_label_list = []
+                train_part_number += 1
+            if test_label_list and (len(test_label_list) % part_rows == 0):
+                pd.DataFrame(np.asarray(test_feature_list)).to_hdf(test_feature_file_name.format(test_part_number),
+                                                                   key="fixed")
+                pd.DataFrame(np.asarray(test_label_list)).to_hdf(test_label_file_name.format(test_part_number),
+                                                                 key="fixed")
+                test_feature_list = []
+                test_label_list = []
+                test_part_number += 1
+        #
+        if train_label_list:
+            pd.DataFrame(np.asarray(train_feature_list)).to_hdf(train_feature_file_name.format(train_part_number),
+                                                                key="fixed")
+            pd.DataFrame(np.asarray(train_label_list)).to_hdf(train_label_file_name.format(train_part_number),
+                                                              key="fixed")
+        if test_label_list:
+            pd.DataFrame(np.asarray(test_feature_list)).to_hdf(test_feature_file_name.format(test_part_number),
+                                                               key="fixed")
+            pd.DataFrame(np.asarray(test_label_list)).to_hdf(test_label_file_name.format(test_part_number),
+                                                             key="fixed")
+#
+
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description="Get and Process datasets")
+    parser.add_argument("--raw_data_path", default="/opt/npu/data/origin_criteo_data/", help="The path to save dataset")
+    parser.add_argument("--output_path", default="/opt/npu/data/origin_criteo_data/h5_data/",
+                        help="The path to save dataset")
+    args, _ = parser.parse_known_args()
+    base_path = args.raw_data_path
+    criteo_stat = CriteoStatsDict()
+    # step 1, stats the vocab and normalize value
+    datafile_path = base_path + "train_small.txt"
+    stats_out_path = base_path + "stats_dict/"
+    mkdir_path(stats_out_path)
+    statsdata(datafile_path, stats_out_path, criteo_stat)
+    print("------" * 10)
+    criteo_stat.load_dict(dict_path=stats_out_path, prefix="")
+    criteo_stat.get_cat2id(threshold=100)
+    # step 2, transform data trans2h5; version 2: np.random.shuffle
+    infile_path = base_path + "train_small.txt"
+    mkdir_path(args.output_path)
+    random_split_trans2h5(infile_path, args.output_path, criteo_stat, part_rows=2000000, test_size=0.1, seed=2020)