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import tensorflow as tf
import json
import pandas as pd
import time
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import utils.connUtils as connUtils
import utils.configUtils as configUtils
ITEM_NUMBER_COLUMNS = ["item_"+c for c in ["smart_rank2"]]
embedding_columns = ["itemid","userid"] + ["item_"+c for c in ["doctor_id","hospital_id"]]
multi_columns = ["tags_v3","first_demands","second_demands","first_solutions","second_solutions","first_positions","second_positions"]
one_hot_columns = ["item_"+c for c in ["service_type","doctor_type","doctor_famous","hospital_city_tag_id","hospital_type","hospital_is_high_quality"]]
# history_columns = ["userRatedHistory"]
# 数据加载
# data_path_train = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
# data_path_test = "/Users/zhigangzheng/Desktop/work/guoyu/service_sort/train/part-00000-a61205d1-ad4e-4fa7-895d-ad8db41189e6-c000.csv"
VERSION = configUtils.SERVICE_VERSION
trainDay = time.strftime("%Y%m%d", time.localtime())
data_path_test = "/data/files/service_feature_{}_test.csv".format(VERSION)
model_file = configUtils.SERVICE_MODEL_PATH + "/" + trainDay
def is_float(s):
try:
float(s)
return True
except ValueError:
return False
#数据字典
def getDataVocabFromRedis(version):
conn = connUtils.getRedisConn()
key = "Strategy:rec:vocab:service:"+version
dataVocabStr = conn.get(key)
if dataVocabStr:
dataVocab = json.loads(str(dataVocabStr, encoding="utf-8"),encoding='utf-8')
print("-----data_vocab-----")
for k, v in dataVocab.items():
print(k, len(v))
else:
dataVocab = None
return dataVocab
# 数据类型转换
def csvTypeConvert(columns,df,data_vocab):
df["label"] = df["label"].astype("int")
for k in columns:
# 离散na值填充
if data_vocab.get(k):
df[k] = df[k].fillna("-1")
df[k] = df[k].astype("string")
elif k != "label":
# df[k] = df[k].map(lambda x:x if is_float(x) else 0)
df[k] = df[k].fillna(0)
df[k] = df[k].astype("float")
# print(df.dtypes)
return df
def loadData(data_path):
print("读取数据...")
timestmp1 = int(round(time.time() * 1000))
df = pd.read_csv(data_path, sep="|")
timestmp2 = int(round(time.time() * 1000))
print("读取数据耗时ms:{}".format(timestmp2 - timestmp1))
return df
def getTrainColumns(train_columns,data_vocab):
emb_columns = []
number_columns = []
oneHot_columns = []
dataColumns = []
inputs = {}
# 离散特征
for feature in train_columns:
if data_vocab.get(feature):
if feature.count("__")>0:
cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
col = tf.feature_column.embedding_column(cat_col, 5)
emb_columns.append(col)
dataColumns.append(feature)
inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
elif feature in one_hot_columns or feature.count("Bucket") > 0:
cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature, vocabulary_list=data_vocab[feature])
# col = tf.feature_column.indicator_column(cat_col)
col = tf.feature_column.embedding_column(cat_col, 3)
oneHot_columns.append(col)
dataColumns.append(feature)
inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
else:
cat_col = tf.feature_column.categorical_column_with_vocabulary_list(key=feature,vocabulary_list=data_vocab[feature])
col = tf.feature_column.embedding_column(cat_col, 10)
emb_columns.append(col)
dataColumns.append(feature)
inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='string')
elif feature in ITEM_NUMBER_COLUMNS:
col = tf.feature_column.numeric_column(feature)
number_columns.append(col)
dataColumns.append(feature)
inputs[feature] = tf.keras.layers.Input(name=feature, shape=(), dtype='float32')
return emb_columns,number_columns,oneHot_columns,dataColumns,inputs
def test(df_train,n=100):
import requests
# ddd = {}
# datasColumnss = df_train.columns.to_list()
# dd = df_train.sample(n=n)
# for c in datasColumnss:
# vvv = dd[c].tolist()
# ddd[c] = vvv
# # print(ddd)
# pre_data = {"inputs":ddd}
# pre_data = json.dumps(pre_data)
# pre_data = pre_data.replace("'",'"')
# print(pre_data)
# print("测试样本数：{},测试耗时ms:{}".format(n,int(round(time.time()*1000))- timestmp1))
# print(r)
for i in range(100):
ddd = {}
datasColumnss = df_train.columns.to_list()
dd = df_train.sample(n=n)
for c in datasColumnss:
vvv = dd[c].tolist()
ddd[c] = vvv
# print(ddd)
pre_data = {"inputs": ddd}
pre_data = json.dumps(pre_data)
# pre_data = pre_data.replace("'",'"')
# print(pre_data)
timestmp1 = int(round(time.time() * 1000))
r = requests.post('http://tensorserving.paas-develop.env/v1/models/service:predict', data=pre_data)
print("测试样本数：{},测试耗时ms:{}".format(n, int(round(time.time() * 1000)) - timestmp1))
print(r)
if __name__ == '__main__':
n = int(sys.argv[1])
# redis中加载数据字典
print("redis 中加载模型字典...")
data_vocab = getDataVocabFromRedis(VERSION)
assert data_vocab
print("读取数据...")
timestmp1 = int(round(time.time()))
df_test = loadData(data_path_test)
timestmp2 = int(round(time.time()))
print("读取数据耗时s:{}".format(timestmp2 - timestmp1))
# 获取训练列
columns = df_test.columns.tolist()
print(columns)
emb_columns,number_columns,oneHot_columns, datasColumns,inputs = getTrainColumns(columns, data_vocab)
df_test = df_test[datasColumns + ["label"]]
# 数据类型转换
df_test = csvTypeConvert(datasColumns,df_test,data_vocab)
test(df_test,n)