Step03_SageMaker_XGBoost_predict_multimodel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用XGBoost的Amazon SageMaker多模型端点\n",
    "借助[Amazon SageMaker多模型终端节点]（https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html），客户可以创建无缝托管多达数千个模型的终端节点。这些端点非常适合以下情况：大量模型中的任何一个（可以从公共推理容器中提供以节省推理成本）需要按需调用，并且不经常调用的模型可以接受这些情况一些额外的延迟。对于要求持续低推理延迟的应用程序，部署单一模型的端点仍然是最佳选择。\n",
    "\n",
    "在较高级别，Amazon SageMaker可根据需要管理多模型终端节点的模型加载和卸载。当针对特定模型发出调用请求时，Amazon SageMaker将请求路由到分配给该模型的实例，将模型工件从S3下载到该实例，然后启动将模型加载到容器的内存中。加载完成后，Amazon SageMaker会执行请求的调用并返回结果。如果模型已经在选定实例的内存中加载，则跳过下载和加载步骤，并立即执行调用。\n",
    "\n",
    "为了演示如何创建和使用多模型端点，此笔记本提供了一个示例，其中使用了一组XGBoost模型，每个模型都可以预测单个位置的房价。该域用作简单示例，可以轻松地尝试使用多模型端点。\n",
    "\n",
    "Amazon SageMaker多模型终端功能旨在与Mxnet，PyTorch和Scikit-Learn机器学习框架（即将推出TensorFlow），SageMaker XGBoost，KNN和Linear Learner算法一起使用。\n",
    "\n",
    "此外，Amazon SageMaker多模型终端节点还设计用于在您自带与多模型服务器库集成的容器的情况下使用。可以在[here]（https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/multi_model_bring_your_own）和文档[here。]（https://docs.aws。 amazon.com/sagemaker/latest/dg/build-multi-model-build-container.html）\n",
    "\n",
    "\n",
    "### Contents\n",
    "\n",
    "1. [Generate synthetic data for housing models](#Generate-synthetic-data-for-housing-models)\n",
    "1. [Train multiple house value prediction models](#Train-multiple-house-value-prediction-models)\n",
    "1. [Create the Amazon SageMaker MultiDataModel entity](#Create-the-Amazon-SageMaker-MultiDataModel-entity)\n",
    "  1. [Create the Multi-Model Endpoint](#Create-the-multi-model-endpoint)\n",
    "  1. [Deploy the Multi-Model Endpoint](#deploy-the-multi-model-endpoint)\n",
    "1. [Get Predictions from the endpoint](#Get-predictions-from-the-endpoint)\n",
    "1. [Additional Information](#Additional-information)\n",
    "1. [Clean up](#Clean-up)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb-10000871.csv-TC58-SW100-WC60-20201202011847\n",
      "mme-xgb-TC58-SW100-WC99to10-v2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np                                # For matrix operations and numerical processing\n",
    "import pandas as pd                               # For munging tabular data\n",
    "import os \n",
    "from datetime import datetime\n",
    "from time import gmtime, strftime, sleep\n",
    "import boto3\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "#from sagemaker.tuner import IntegerParameter, CategoricalParameter, ContinuousParameter, HyperparameterTuner\n",
    "from time import sleep\n",
    "import time\n",
    "\n",
    "region = boto3.Session().region_name    \n",
    "smclient = boto3.Session().client('sagemaker')\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "bucket = sagemaker.Session().default_bucket()\n",
    "\n",
    "prefix = 'xgb-10000871.csv-TC58-SW100-WC60-20201202011847'\n",
    "prefix_target_model = 'TC58-SW100-WC60.tar.gz'\n",
    "s3_wholedata_path = s3_data='s3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix)\n",
    "endpoint_name = 'mme-xgb-TC58-SW100-WC99to10-v2' #'xgb-TC58-SW100-WC60-temp01'\n",
    "\n",
    "\n",
    "#!pip install s3fs -U\n",
    "role = get_execution_role()\n",
    "model_data = pd.read_csv(s3_wholedata_path)\n",
    "#model_data.head(3) #for testing\n",
    "#model_data.info() #for testing\n",
    "\n",
    "#print(bucket)\n",
    "#print(prefix)\n",
    "#print(prefix_target_model)\n",
    "print(prefix)\n",
    "print(endpoint_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测 1：准备sagemaker endpoint\n",
    "xgb_predictor = sagemaker.predictor.RealTimePredictor(endpoint=endpoint_name)\n",
    "\n",
    "#from sagemaker.predictor import csv_serializer\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "xgb_predictor.content_type = 'text/csv'\n",
    "xgb_predictor.serializer = csv_serializer\n",
    "xgb_predictor.deserializer = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inference预测处理：传入modeldata数据和 sagemaker inference handle，获得预测结果\n",
    "def sagemaker_predict(data, xgb_predictor, target_model, rows=100):\n",
    "    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))    \n",
    "    predictions = ''\n",
    "    for array in split_array:\n",
    "        split_result = xgb_predictor.predict(data=array, target_model=target_model).decode('utf-8')\n",
    "        #print(split_result)\n",
    "        split_result = str.strip(split_result, '[]') # 去掉多余的前后[]符号\n",
    "        #split_result=split_result.replace(' ', '')\n",
    "        #print('res='+split_result)\n",
    "             \n",
    "        predictions = ','.join([predictions, split_result])\n",
    "            \n",
    "    #print(predictions)\n",
    "    #return np.fromstring(predictions[1:], sep=',', dtype=np.float64) \n",
    "    return np.fromstring(predictions[1:], sep=',') \n",
    "\n",
    "#预测 2：预测并获得预测结果\n",
    "model_data_to_numpy = model_data.to_numpy()[:, 1:] #转换所有的行，从第一列开始（忽略0列）\n",
    "\n",
    "#调用函数\n",
    "predictions = sagemaker_predict(model_data_to_numpy,xgb_predictor, prefix_target_model, rows=100)\n",
    "#print('predictions=')\n",
    "#print(predictions)\n",
    "#print('predictions round=')\n",
    "#print(np.round(predictions))\n",
    "#print('\\n')\n",
    "#print('expect round=')\n",
    "#print(model_data.to_numpy()[:, 0:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_data: 1=19, 0=2000 / \n",
      "predictions Result: 1=15, 0=2004\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn AUC: 0.9391052631578947\n",
      "\n",
      "xgb_predictor.endpoint = mme-xgb-TC58-SW100-WC99to10-v2\n",
      "\n",
      "s3_wholedata_path = s3://sagemaker-us-west-2-153705321444/xgb-10000871.csv-TC58-SW100-WC60-20201202011847/wholedata/whole_data.csv\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from numpy import asarray\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "#from xgboost import XGBRegressor\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "y_labels = model_data.iloc[:, 0]\n",
    "y_scores = predictions\n",
    "\n",
    "#结果 1：测试数据集结果 与 预测结果 的数量对比\n",
    "print('model_data: 1='+ str((y_labels == 1).sum()) + ', 0='+str((y_labels == 0).sum()) +' / '),\n",
    "print('predictions Result: 1='+ str((np.round(y_scores) == 1).sum()) + ', 0='+str((np.round(y_scores) == 0).sum()))\n",
    "\n",
    "#结果 2：交叉表，说明 测试数据集结果 与 预测结果 的正确情况对比\n",
    "display(pd.crosstab(index=model_data.iloc[:, 0], columns=np.round(predictions), \n",
    "            rownames=['model_data'], colnames=['predictions_data'],margins=False))\n",
    "#print('\\n')\n",
    "\n",
    "#结果 4：ROC图，越大越好，无限接近与1\n",
    "from sklearn.metrics import roc_curve, auc                     #导入库\n",
    "fpr, tpr, thresholds  =  roc_curve(y_labels, y_scores)  #计算真正率和假正率\n",
    "roc_auc = auc(fpr,tpr)                                         #计算auc的值\n",
    "plt.figure()\n",
    "lw = 3\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(fpr, tpr, color='darkorange',lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) #假正率为横坐标，真正率为纵坐标做曲线\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "#结果 3：AUC值，越大越好，无限接近与1\n",
    "import numpy as np\n",
    "from sklearn.metrics import roc_auc_score\n",
    "##y_labels=[0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 1.\n",
    "##y_scores=[4.92438182e-01 1.74370125e-01 8.58997216e-01 1.30175965e-01 \n",
    "# 调用sklearn中的方法计算AUC，与后面自己写的方法作对比\n",
    "print('sklearn AUC:', roc_auc_score(y_labels, y_scores))\n",
    "#打印全局变量值\n",
    "print('\\nxgb_predictor.endpoint = ' + str(xgb_predictor.endpoint)),\n",
    "print('\\ns3_wholedata_path = '+ str(s3_wholedata_path)),\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>predictions_data</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_data</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "predictions_data     0   1\n",
       "model_data                \n",
       "0.0               2000   0\n",
       "1.0                  4  15"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#结果 5：分布柱状图；用于分析和优化结果（round函数）；\n",
    "plt.hist(predictions, bins=10, range=(0, 1))\n",
    "plt.show()\n",
    "\n",
    "#用于分析和优化结果（round函数）；\n",
    "round_predictions=0.45\n",
    "pd.crosstab(index=model_data.iloc[:, 0], columns=np.where(predictions > round_predictions, 1, 0), \n",
    "            rownames=['model_data'], colnames=['predictions_data'],margins=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用XGBoost的Amazon SageMaker多模型端点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix_WC10 = 'xgb-10000871.csv-TC58-SW100-WC10-20201202080714'\n",
    "prefix_model_WC10 = 'TC58-SW100-WC10.tar.gz'\n",
    "s3_wholedata_path_WC10 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC10)\n",
    "\n",
    "prefix_WC20 = 'xgb-10000871.csv-TC58-SW100-WC20-20201202051756'\n",
    "prefix_model_WC20 = 'TC58-SW100-WC20.tar.gz'\n",
    "s3_wholedata_path_WC20 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC20)\n",
    "\n",
    "prefix_WC40 = 'xgb-10000871.csv-TC58-SW100-WC40-20201202032834'\n",
    "prefix_model_WC40 = 'TC58-SW100-WC40.tar.gz'\n",
    "s3_wholedata_path_WC40 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC40)\n",
    "\n",
    "prefix_WC60 = 'xgb-10000871.csv-TC58-SW100-WC60-20201202011847'\n",
    "prefix_model_WC60 = 'TC58-SW100-WC60.tar.gz'\n",
    "s3_wholedata_path_WC60 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC60)\n",
    "\n",
    "prefix_WC80 = 'xgb-10000871.csv-TC58-SW100-WC80-20201202023456'\n",
    "prefix_model_WC80 = 'TC58-SW100-WC80.tar.gz'\n",
    "s3_wholedata_path_WC80 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC80)\n",
    "\n",
    "prefix_WC100 = 'xgb-10000871.csv-TC58-SW100-WC99-20201202064356'\n",
    "prefix_model_WC100 = 'TC58-SW100-WC99.tar.gz'\n",
    "s3_wholedata_path_WC100 = 's3://{}/{}/wholedata/whole_data.csv'.format(bucket, prefix_WC100)\n",
    "\n",
    "endpoint_name = 'mme-xgb-TC58-SW100-WC99to10-v2' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data_WC10 = pd.read_csv(s3_wholedata_path_WC10)\n",
    "model_data_WC20 = pd.read_csv(s3_wholedata_path_WC20)\n",
    "model_data_WC40 = pd.read_csv(s3_wholedata_path_WC40)\n",
    "model_data_WC60 = pd.read_csv(s3_wholedata_path_WC60)\n",
    "model_data_WC80 = pd.read_csv(s3_wholedata_path_WC80)\n",
    "model_data_WC100 = pd.read_csv(s3_wholedata_path_WC100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测 1：准备sagemaker endpoint\n",
    "xgb_predictor = sagemaker.predictor.RealTimePredictor(endpoint=endpoint_name)\n",
    "\n",
    "#from sagemaker.predictor import csv_serializer\n",
    "from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "xgb_predictor.content_type = 'text/csv'\n",
    "xgb_predictor.serializer = csv_serializer\n",
    "#xgb_predictor.deserializer = json_deserializer\n",
    "#xgb_predictor.deserializer = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "import time\n",
    "\n",
    "# inference预测处理 - string deserializer 字符串处理后取回\n",
    "def predict_multimodel_matchrow_v1(multimodel, modelmatchrow, xgb_predictor):    \n",
    "    xgb_predictor.deserializer = None\n",
    "    modelmatch_predictions=''\n",
    "    #t_res = ''\n",
    "    for i in range(0,len(multimodel)):\n",
    "        #print(modelmatchrow[i])\n",
    "        #modelmatch_predictions.append(xgb_predictor.predict(data=modelmatchrow[i], target_model=multimodel[i]).decode('utf-8'))  \n",
    "        t_res = xgb_predictor.predict(data=modelmatchrow[i], target_model=multimodel[i]).decode('utf-8')        \n",
    "        #print(t_res)\n",
    "        t_res = str.strip(t_res, '[]') # 去掉多余的前后[]符号        \n",
    "        #print(t_res)\n",
    "        modelmatch_predictions = ','.join([modelmatch_predictions, t_res])\n",
    "\n",
    "    #print(modelmatch_predictions)        \n",
    "    #return np.fromstring(predictions[1:], sep=',', dtype=np.float64) \n",
    "    return np.fromstring(modelmatch_predictions[1:], sep=',', dtype=np.float) \n",
    "\n",
    "\n",
    "# inference预测处理 - json_deserializer 数组原生取回\n",
    "def predict_multimodel_matchrow_v2(multimodel, modelmatchrow, xgb_predictor):    \n",
    "    xgb_predictor.deserializer = json_deserializer\n",
    "    modelmatch_predictions=[]   \n",
    "    for i in range(0,len(multimodel)):\n",
    "        modelmatch_predictions.append(xgb_predictor.predict(data=modelmatchrow[i], target_model=multimodel[i]))  \n",
    "\n",
    "    return modelmatch_predictions \n",
    "\n",
    "\n",
    "def predict_result_factory(warningcycle, multimodel, modelmatchrow, predictions, predictionsround, expectround):    \n",
    "    from datetime import datetime\n",
    "    from pytz import timezone\n",
    "\n",
    "    fmt = \"%Y-%m-%d %H:%M:%S\"\n",
    "    now_time = datetime.now(timezone('Asia/Shanghai'))\n",
    "    now_time = (now_time.strftime(fmt))\n",
    "\n",
    "    res = {'datetime_now':now_time, \n",
    "           '01_warningcycle':warningcycle[0],'01_modelname':multimodel[0], '01_data':str(modelmatchrow[0]).replace('\\n', '').replace('\\r', ''), '01_predictscore':predictions[0], '01_predictround':predictionsround[0], '01_expectround':expectround[0],\n",
    "           '02_warningcycle':warningcycle[1],'02_modelname':multimodel[1], '02_data':str(modelmatchrow[1]).replace('\\n', '').replace('\\r', ''), '02_predictscore':predictions[1], '02_predictround':predictionsround[1], '02_expectround':expectround[1],\n",
    "           '03_warningcycle':warningcycle[2],'03_modelname':multimodel[2], '03_data':str(modelmatchrow[2]).replace('\\n', '').replace('\\r', ''), '03_predictscore':predictions[2], '03_predictround':predictionsround[2], '03_expectround':expectround[2],\n",
    "           '04_warningcycle':warningcycle[3],'04_modelname':multimodel[3], '04_data':str(modelmatchrow[3]).replace('\\n', '').replace('\\r', ''), '04_predictscore':predictions[3], '04_predictround':predictionsround[3], '04_expectround':expectround[3],\n",
    "           '05_warningcycle':warningcycle[4],'05_modelname':multimodel[4], '05_data':str(modelmatchrow[4]).replace('\\n', '').replace('\\r', ''), '05_predictscore':predictions[4], '05_predictround':predictionsround[4], '05_expectround':expectround[4],\n",
    "           '06_warningcycle':warningcycle[5],'06_modelname':multimodel[5], '06_data':str(modelmatchrow[5]).replace('\\n', '').replace('\\r', ''), '06_predictscore':predictions[5], '06_predictround':predictionsround[5], '06_expectround':expectround[5],\n",
    "          }    \n",
    "    return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://sagemaker-us-west-2-153705321444/xgb-10000871.csv-TC58-SW100-WC99to10-MultiModel/predictionsresult_data02.csv\n",
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     ]
    }
   ],
   "source": [
    "prefix = 'xgb-10000871.csv-TC58-SW100-WC99to10-MultiModel'\n",
    "s3_predictresult_path = 's3://{}/{}/predictionsresult_data02.csv'.format(bucket, prefix)\n",
    "print(s3_predictresult_path)\n",
    "\n",
    "p_multimodel = [prefix_model_WC10,prefix_model_WC20,prefix_model_WC40,prefix_model_WC60,prefix_model_WC80,prefix_model_WC100]\n",
    "p_warningcycle = [10,20,40,60,80,100]\n",
    "p_matchrow = []\n",
    "\n",
    "predict_result = pd.DataFrame(columns=('datetime_now',\n",
    "                            '01_warningcycle','01_modelname', '01_data', '01_predictscore', '01_predictround', '01_expectround',\n",
    "                            '02_warningcycle','02_modelname', '02_data', '02_predictscore', '02_predictround', '02_expectround',\n",
    "                            '03_warningcycle','03_modelname', '03_data', '03_predictscore', '03_predictround', '03_expectround',\n",
    "                            '04_warningcycle','04_modelname', '04_data', '04_predictscore', '04_predictround', '04_expectround',\n",
    "                            '05_warningcycle','05_modelname', '05_data', '05_predictscore', '05_predictround', '05_expectround',\n",
    "                            '06_warningcycle','06_modelname', '06_data', '06_predictscore', '06_predictround', '06_expectround'\n",
    "                           ))\n",
    "\n",
    "i=0\n",
    "model_data_len = len(model_data_WC10)\n",
    "\n",
    "while i < model_data_len:\n",
    "    \n",
    "    p_matchrow = [model_data_WC10.to_numpy()[:, 1:][i], model_data_WC20.to_numpy()[:, 1:][i], \n",
    "                  model_data_WC40.to_numpy()[:, 1:][i], model_data_WC60.to_numpy()[:, 1:][i],\n",
    "                  model_data_WC80.to_numpy()[:, 1:][i], model_data_WC100.to_numpy()[:, 1:][i]]\n",
    "    \n",
    "    p_matchrow_expectscore = [model_data_WC10.to_numpy()[:, 0:1][i], model_data_WC20.to_numpy()[:, 0:1][i], \n",
    "                  model_data_WC40.to_numpy()[:, 0:1][i], model_data_WC60.to_numpy()[:, 0:1][i],\n",
    "                  model_data_WC80.to_numpy()[:, 0:1][i], model_data_WC100.to_numpy()[:, 0:1][i]]\n",
    "        \n",
    "    #print(p_multimodel)\n",
    "    predictions = predict_multimodel_matchrow_v2(p_multimodel,p_matchrow,xgb_predictor)\n",
    "    #print(predictions)\n",
    "    predictionsround=np.round(predictions)\n",
    "    #print(p_matchrow_expectscore)    \n",
    "    t_predict_result = predict_result_factory(p_warningcycle, p_multimodel, p_matchrow, predictions, predictionsround, p_matchrow_expectscore)\n",
    "    #print(t_predict_result)    \n",
    "    predict_result = predict_result.append(t_predict_result, ignore_index=True)\n",
    "    predict_result.to_csv(s3_predictresult_path)    \n",
    "    print('cycle number={0}, resutl size={1}'.format(i, predict_result.size))\n",
    "\n",
    "    i+=1    \n",
    "    if (i == model_data_len): \n",
    "        i=0\n",
    "    time.sleep(10)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for row in model_data.iteritems():\n",
    "    print(row) # 输出各列\n",
    "    predictions = sagemaker_predict(model_data_to_numpy,xgb_predictor, prefix_target_model, rows=100)    \n",
    "    break\n",
    "    \n",
    "\n",
    "    s3_predictions=[]   \n",
    "    for i in range(0,len(multimodel)):\n",
    "        modelmatch_predictions.append(xgb_predictor.predict(data=modelmatchrow[i], target_model=multimodel[i]))  \n",
    "\n",
    "    return modelmatch_predictions \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from datetime import datetime\n",
    "from pytz import timezone\n",
    "\n",
    "fmt = \"%Y-%m-%d %H:%M:%S\"\n",
    "now_time = datetime.now(timezone('Asia/Shanghai'))\n",
    "now_time = (now_time.strftime(fmt))\n",
    "\n",
    "\n",
    "res = pd.DataFrame(columns=('datetime_now',\n",
    "                            '01_modelname', '01_data', '01_predictscore', '01_predictround', '01_expectround',\n",
    "                            '02_modelname', '02_data', '02_predictscore', '02_predictround', '02_expectround',\n",
    "                            '03_modelname', '03_data', '03_predictscore', '03_predictround', '03_expectround',\n",
    "                            '04_modelname', '04_data', '04_predictscore', '04_predictround', '04_expectround',\n",
    "                            '05_modelname', '05_data', '05_predictscore', '05_predictround', '05_expectround',\n",
    "                            '06_modelname', '06_data', '06_predictscore', '06_predictround', '06_expectround'\n",
    "                           ))\n",
    "res = res.append([\n",
    "    {'datetime_now':now_time, '01_modelname':now_time, '01_data':now_time, '01_predictscore':now_time, '01_predictround':now_time, '01_expectround':now_time}\n",
    "                 ], ignore_index=True)\n",
    "print(res.head(1))\n",
    "\n",
    "\n",
    "s3_predictresult_path = 's3://{}/{}/wholedata/s3_predictions.csv'.format(bucket, prefix)\n",
    "print(s3_predictresult_path)\n",
    "\n",
    "res.to_csv(s3_predictresult_path)"
   ]
  }
 ],
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