{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ann-churn.h5\n",
      "Bayesian-OPT-Copy1.ipynb\n",
      "Bayesian-OPT.ipynb\n",
      "Build Container.ipynb\n",
      "container/\n",
      "container/scripts/\n",
      "container/scripts/predictor.py\n",
      "container/scripts/train\n",
      "container/scripts/wsgi.py\n",
      "container/scripts/.ipynb_checkpoints/\n",
      "container/scripts/nginx.conf\n",
      "container/scripts/serve\n",
      "container/requirements.txt\n",
      "container/local_test/\n",
      "container/local_test/serve_local.sh\n",
      "container/local_test/test_dir/\n",
      "container/local_test/test_dir/input/\n",
      "container/local_test/test_dir/input/config/\n",
      "container/local_test/test_dir/input/config/resourceConfig.json\n",
      "container/local_test/test_dir/input/config/hyperparameters.json\n",
      "container/local_test/test_dir/output/\n",
      "container/local_test/test_dir/output/success\n",
      "container/local_test/test_dir/model/\n",
      "container/local_test/test_dir/model/decision-tree-model.pkl\n",
      "container/local_test/test_dir/model/ann-churn.pkl\n",
      "container/local_test/train_local.sh\n",
      "container/local_test/predict.sh\n",
      "container/Dockerfile\n",
      "container/.ipynb_checkpoints/\n",
      "container/build_and_push.sh\n",
      "container_hpo.tar.gz\n",
      "tar: container_hpo.tar.gz: file changed as we read it\n",
      "data/\n",
      "data/SL_train_DG_mod.csv\n",
      "data/.ipynb_checkpoints/\n",
      "optml/\n",
      "optml/output/\n",
      "optml/output/failure\n",
      "optml/data/\n",
      "optml/data/SL_train_DG.csv\n",
      "optml/data/SL_test_DG.csv\n",
      "optml/data/SL_test_DG_Nooutput.csv\n",
      "optml/model/\n",
      "optml/model/ann-churn.h5\n",
      "path_best_model.h5\n",
      "pima-indians-diabetes.data.csv\n",
      "printing.py\n",
      "__pycache__/\n",
      "__pycache__/temp_model.cpython-36.pyc\n",
      "ReadMe.md\n",
      "Reconstruct-HPO.ipynb\n",
      "Reconstruct.ipynb\n",
      "requirements.txt\n",
      "RF_trials.csv\n",
      "saved/\n",
      "saved/model.h5\n",
      "saved/model.json\n",
      "scaler.pkl\n",
      "SL_test_DG_mod.csv\n",
      "test_dir/\n",
      "test_dir/output/\n",
      "test_dir/output/failure\n",
      "test_dir/model/\n",
      "Untitled.ipynb\n"
     ]
    }
   ],
   "source": [
    "!tar cvfz container_hpo.tar.gz *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadmodel(weightFile):\n",
    "    ###############\n",
    "    reg = Sequential()\n",
    "    reg.add(Dense(1000, kernel_initializer='normal',input_dim = 28, activation='relu'))\n",
    "    reg.add(Dense(512, kernel_initializer='normal',activation='relu'))\n",
    "    reg.add(Dense(256, kernel_initializer='normal',activation='relu'))\n",
    "    reg.add(Dense(128, kernel_initializer='normal',activation='relu'))\n",
    "    reg.add(Dense(64, kernel_initializer='normal',activation='relu'))\n",
    "    reg.add(Dense(1, kernel_initializer='normal',activation='tanh'))\n",
    "    reg.load_weights(weightFile)\n",
    "    return reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = loadmodel(os.path.join(model_path, 'ann-churn.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "# later...\n",
    "from keras.models import model_from_json\n",
    "\n",
    "# load json and create model\n",
    "json_file = open('saved/model.json', 'r')\n",
    "loaded_model_json = json_file.read()\n",
    "json_file.close()\n",
    "loaded_model = model_from_json(loaded_model_json)\n",
    "# load weights into new model\n",
    "loaded_model.load_weights(\"saved/model.h5\")\n",
    "print(\"Loaded model from disk\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import load\n",
    "\n",
    "scaler = load(open('scaler.pkl', 'rb'))\n",
    "\n",
    "train = pd.read_csv('SL_test_DG_mod.csv')\n",
    "\n",
    "train = train.dropna()\n",
    "\n",
    "train_x = train.drop(['PE_'], axis=1)\n",
    "train_y = train['PE_']\n",
    "\n",
    "train_x = train_x.astype('float32')\n",
    "train_y = train_y.astype('float32')\n",
    "\n",
    "#######\n",
    "train_x = scaler.transform(train_x)\n",
    "\n",
    "# Feature Scaling\n",
    "# sc = StandardScaler()\n",
    "pred = model.predict(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.47818068],\n",
       "       [0.4640664 ],\n",
       "       [0.5198002 ],\n",
       "       ...,\n",
       "       [0.5362443 ],\n",
       "       [0.42633906],\n",
       "       [0.55470717]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.8060817\n",
      "\n"
     ]
    }
   ],
   "source": [
    "endpointName = 'garrett-2020-06-16-15-27-13-763'\n",
    "import boto3\n",
    "from pickle import load\n",
    "\n",
    "# Process and prepare the data\n",
    "def transform_data(dataset):\n",
    "    #######\n",
    "    train = dataset.dropna()\n",
    "\n",
    "    train_x = train.drop(['PE_'], axis=1)\n",
    "    train_y = train['PE_']\n",
    "\n",
    "    train_x = train_x.astype('float32')\n",
    "    train_y = train_y.astype('float32')\n",
    "    \n",
    "    #######\n",
    "    \n",
    "    # Feature Scaling\n",
    "    \n",
    "\n",
    "    scaler = load(open('scaler.pkl', 'rb'))\n",
    "    train_x = scaler.fit_transform(train_x)\n",
    "    return pd.DataFrame(train_x)\n",
    "\n",
    "test_X = pd.read_csv('SL_test_DG_mod.csv')\n",
    "\n",
    "test_X = transform_data(test_X)\n",
    "\n",
    "\n",
    "test_file = io.StringIO()\n",
    "test_X.to_csv(test_file, header=None, index=None)\n",
    "\n",
    "# Talk to SageMaker\n",
    "client = boto3.client('sagemaker-runtime')\n",
    "response = client.invoke_endpoint(\n",
    "    EndpointName=endpointName,\n",
    "    Body=test_file.getvalue(),\n",
    "    ContentType='text/csv',\n",
    "    Accept='Accept'\n",
    ")\n",
    "\n",
    "print(response['Body'].read().decode('ascii'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `a` not found.\n"
     ]
    }
   ],
   "source": [
    "a?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import boto3\n",
    "import os\n",
    "import logging\n",
    "import urllib\n",
    "import io\n",
    "import numpy as np\n",
    "# import pickle\n",
    "import pandas as pd\n",
    "from contextlib import closing\n",
    "from pickle import load\n",
    "\n",
    "# scaler = load(open('scaler.pkl', 'rb'))\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "\n",
    "AREA_RATIO = [0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0]\n",
    "SPEED = [93700, 113900, 134100, 154300, 174500, 194700, 214900]\n",
    "\n",
    "def lambda_handler(event):    \n",
    "    print('successfull call lambda_handler now',event)\n",
    "\n",
    "    params = eval(event[\"params\"])\n",
    "    logger.info('params: {}'.format(params))\n",
    "    params = [float(i) for i in params]\n",
    "    # endpoint_name = 'sagemaker-tensorflow-2020-05-29-16-08-40-101'\n",
    "    endpointName = 'garrett-2020-06-23-00-24-43-607'\n",
    "\n",
    "    client = boto3.client('runtime.sagemaker')\n",
    "    ############################\n",
    "    k =1\n",
    "    all_data = []\n",
    "    xaxis = []\n",
    "\n",
    "    for i in AREA_RATIO:\n",
    "        for j in SPEED:\n",
    "            a = params + [i,j]\n",
    "            all_data.append(a)\n",
    "            xaxis.append([str(i)+', '+str(j)])\n",
    "    # all_data = scaler.transform(all_data)\n",
    "    # all_data = all_data.tolist()\n",
    "\n",
    "    logger.info('xaxis: {}'.format(xaxis))\n",
    "    logger.info('all_data[0]: {}'.format(all_data[0]))\n",
    "\n",
    "    all_data = pd.DataFrame(np.array(all_data))\n",
    "    test_file = io.StringIO()\n",
    "    all_data.to_csv(test_file, header=None, index=None)\n",
    "\n",
    "    # Talk to SageMaker\n",
    "    client = boto3.client('sagemaker-runtime')\n",
    "    response = client.invoke_endpoint(\n",
    "        EndpointName=endpointName,\n",
    "        Body=test_file.getvalue(),\n",
    "        ContentType='text/csv',\n",
    "        Accept='Accept'\n",
    "    )\n",
    "\n",
    "    pred = response['Body'].read().decode('ascii')\n",
    "    print(pred)\n",
    "\n",
    "    # pred = client.invoke_endpoint(EndpointName=endpoint_name, Body=json.dumps(all_data))\n",
    "    # pred = json.loads(pred['Body'].read().decode(\"utf-8\"))\n",
    "    # pred = pred['outputs']['score']['floatVal']\n",
    "    \n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'xaxis' : json.dumps(xaxis),\n",
    "        'AREA_RATIO' : json.dumps(AREA_RATIO),\n",
    "        'SPEED' : json.dumps(SPEED),\n",
    "        'preds': json.dumps(pred)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "event = {\n",
    "  \"params\": \"[100.5, 117.4000015258789, 7.300000190734863, 2.9000000953674316, 3.299999952316284, 2.4000000953674316, 1.2999999523162842, 1.7999999523162842, 50.0, 0.14499999582767487, 0.23399999737739563, 0.3659999966621399, 1.0299999713897705, 3.0999999046325684, 17.5, 7.0, -18.5, -27.5, -28.5, 0.10000000149011612, -29.5, -44.0, -52.0, 10.5, 0.6000000238418579, -73.0]\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(eval(event['params']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "successfull call lambda_handler now {'params': '[100.5, 117.4000015258789, 7.300000190734863, 2.9000000953674316, 3.299999952316284, 2.4000000953674316, 1.2999999523162842, 1.7999999523162842, 50.0, 0.14499999582767487, 0.23399999737739563, 0.3659999966621399, 1.0299999713897705, 3.0999999046325684, 17.5, 7.0, -18.5, -27.5, -28.5, 0.10000000149011612, -29.5, -44.0, -52.0, 10.5, 0.6000000238418579, -73.0]'}\n",
      "0.7513404\n",
      "0.73758537\n",
      "0.72576225\n",
      "0.7100778\n",
      "0.69597936\n",
      "0.6786843\n",
      "0.6518586\n",
      "0.7797066\n",
      "0.76902044\n",
      "0.75591147\n",
      "0.74313563\n",
      "0.7331241\n",
      "0.7195874\n",
      "0.6970369\n",
      "0.79954535\n",
      "0.7901364\n",
      "0.7816141\n",
      "0.7712438\n",
      "0.7619729\n",
      "0.7454516\n",
      "0.7194436\n",
      "0.81880075\n",
      "0.8124648\n",
      "0.8038909\n",
      "0.7960073\n",
      "0.78444916\n",
      "0.7605214\n",
      "0.71954405\n",
      "0.8365419\n",
      "0.8301322\n",
      "0.8231977\n",
      "0.8144091\n",
      "0.7993242\n",
      "0.76195836\n",
      "0.7028263\n",
      "0.846545\n",
      "0.8386305\n",
      "0.82863885\n",
      "0.8175032\n",
      "0.791481\n",
      "0.7276696\n",
      "0.65867585\n",
      "0.84084535\n",
      "0.83346456\n",
      "0.8249475\n",
      "0.8091433\n",
      "0.7551916\n",
      "0.68137133\n",
      "0.6046959\n",
      "0.8315685\n",
      "0.82342297\n",
      "0.8089565\n",
      "0.77260387\n",
      "0.70146453\n",
      "0.6269061\n",
      "0.5565283\n",
      "0.8122493\n",
      "0.79937613\n",
      "0.776701\n",
      "0.7162716\n",
      "0.6446864\n",
      "0.57791007\n",
      "0.5169421\n",
      "0.76670486\n",
      "0.7363153\n",
      "0.65662277\n",
      "0.5959822\n",
      "0.5451252\n",
      "0.4913135\n",
      "0.45074877\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'statusCode': 200,\n",
       " 'xaxis': '[[\"0.2, 93700\"], [\"0.2, 113900\"], [\"0.2, 134100\"], [\"0.2, 154300\"], [\"0.2, 174500\"], [\"0.2, 194700\"], [\"0.2, 214900\"], [\"0.25, 93700\"], [\"0.25, 113900\"], [\"0.25, 134100\"], [\"0.25, 154300\"], [\"0.25, 174500\"], [\"0.25, 194700\"], [\"0.25, 214900\"], [\"0.3, 93700\"], [\"0.3, 113900\"], [\"0.3, 134100\"], [\"0.3, 154300\"], [\"0.3, 174500\"], [\"0.3, 194700\"], [\"0.3, 214900\"], [\"0.35, 93700\"], [\"0.35, 113900\"], [\"0.35, 134100\"], [\"0.35, 154300\"], [\"0.35, 174500\"], [\"0.35, 194700\"], [\"0.35, 214900\"], [\"0.4, 93700\"], [\"0.4, 113900\"], [\"0.4, 134100\"], [\"0.4, 154300\"], [\"0.4, 174500\"], [\"0.4, 194700\"], [\"0.4, 214900\"], [\"0.5, 93700\"], [\"0.5, 113900\"], [\"0.5, 134100\"], [\"0.5, 154300\"], [\"0.5, 174500\"], [\"0.5, 194700\"], [\"0.5, 214900\"], [\"0.6, 93700\"], [\"0.6, 113900\"], [\"0.6, 134100\"], [\"0.6, 154300\"], [\"0.6, 174500\"], [\"0.6, 194700\"], [\"0.6, 214900\"], [\"0.7, 93700\"], [\"0.7, 113900\"], [\"0.7, 134100\"], [\"0.7, 154300\"], [\"0.7, 174500\"], [\"0.7, 194700\"], [\"0.7, 214900\"], [\"0.8, 93700\"], [\"0.8, 113900\"], [\"0.8, 134100\"], [\"0.8, 154300\"], [\"0.8, 174500\"], [\"0.8, 194700\"], [\"0.8, 214900\"], [\"1.0, 93700\"], [\"1.0, 113900\"], [\"1.0, 134100\"], [\"1.0, 154300\"], [\"1.0, 174500\"], [\"1.0, 194700\"], [\"1.0, 214900\"]]',\n",
       " 'AREA_RATIO': '[0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0]',\n",
       " 'SPEED': '[93700, 113900, 134100, 154300, 174500, 194700, 214900]',\n",
       " 'preds': '\"0.7513404\\\\n0.73758537\\\\n0.72576225\\\\n0.7100778\\\\n0.69597936\\\\n0.6786843\\\\n0.6518586\\\\n0.7797066\\\\n0.76902044\\\\n0.75591147\\\\n0.74313563\\\\n0.7331241\\\\n0.7195874\\\\n0.6970369\\\\n0.79954535\\\\n0.7901364\\\\n0.7816141\\\\n0.7712438\\\\n0.7619729\\\\n0.7454516\\\\n0.7194436\\\\n0.81880075\\\\n0.8124648\\\\n0.8038909\\\\n0.7960073\\\\n0.78444916\\\\n0.7605214\\\\n0.71954405\\\\n0.8365419\\\\n0.8301322\\\\n0.8231977\\\\n0.8144091\\\\n0.7993242\\\\n0.76195836\\\\n0.7028263\\\\n0.846545\\\\n0.8386305\\\\n0.82863885\\\\n0.8175032\\\\n0.791481\\\\n0.7276696\\\\n0.65867585\\\\n0.84084535\\\\n0.83346456\\\\n0.8249475\\\\n0.8091433\\\\n0.7551916\\\\n0.68137133\\\\n0.6046959\\\\n0.8315685\\\\n0.82342297\\\\n0.8089565\\\\n0.77260387\\\\n0.70146453\\\\n0.6269061\\\\n0.5565283\\\\n0.8122493\\\\n0.79937613\\\\n0.776701\\\\n0.7162716\\\\n0.6446864\\\\n0.57791007\\\\n0.5169421\\\\n0.76670486\\\\n0.7363153\\\\n0.65662277\\\\n0.5959822\\\\n0.5451252\\\\n0.4913135\\\\n0.45074877\\\\n\"'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambda_handler(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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