3-biketrain-xgboost_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "585d42a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c9f1bb",
   "metadata": {},
   "source": [
    "## XGBoost Cloud Prediction Invocation Template"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb20b09b",
   "metadata": {},
   "source": [
    "Invoke SageMaker Prediction Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "878d91fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "import re\n",
    "import sagemaker\n",
    "from sagemaker import get_execution_role"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8c59d50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "source": [
    "# Acquire realtime endpoint\n",
    "endpoint_name = 'xgboost-biketrain-vl'\n",
    "predictor = sagemaker.predictor.Predictor(endpoint_name = endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9caba103",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sagemaker.predictor import csv_serializer, json_deserializer\n",
    "from sagemaker.serializers import CSVSerializer\n",
    "from sagemaker.deserializers import JSONDeserializer, CSVDeserializer\n",
    "from sagemaker.content_types import CONTENT_TYPE_CSV, CONTENT_TYPE_JSON\n",
    "\n",
    "predictor.content_type = \"text/csv\"\n",
    "predictor.serializer = CSVSerializer()\n",
    "predictor.deserializer = JSONDeserializer()\n",
    "predictor.accept = CONTENT_TYPE_JSON\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5bd4d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  day  dayofweek  hour  \n",
       "0        56    26.0027  2011      1   20          3     0  \n",
       "1        56     0.0000  2011      1   20          3     1  \n",
       "2        56     0.0000  2011      1   20          3     2  \n",
       "3        56    11.0014  2011      1   20          3     3  \n",
       "4        56    11.0014  2011      1   20          3     4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.read_csv('bike_test.csv')\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c53c029e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp',\n",
       "       'humidity', 'windspeed', 'year', 'month', 'day', 'dayofweek', 'hour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.columns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be103423",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>11.365</td>\n",
       "      <td>56</td>\n",
       "      <td>26.0027</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  holiday  workingday  weather   temp   atemp  humidity  windspeed  \\\n",
       "0       1        0           1        1  10.66  11.365        56    26.0027   \n",
       "1       1        0           1        1  10.66  13.635        56     0.0000   \n",
       "2       1        0           1        1  10.66  13.635        56     0.0000   \n",
       "3       1        0           1        1  10.66  12.880        56    11.0014   \n",
       "\n",
       "   year  month  day  dayofweek  hour  \n",
       "0  2011      1   20          3     0  \n",
       "1  2011      1   20          3     1  \n",
       "2  2011      1   20          3     2  \n",
       "3  2011      1   20          3     3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.drop(columns = ['datetime'], inplace=True)\n",
    "df_all.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fac43f50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Need to pass an array to the prediction\n",
    "# can pass a numpy array or a list of values [[19,1], [20,1]]\n",
    "#arr_test = df_all.to_numpy()\n",
    "\n",
    "arr_test = df_all[df_all.columns[1:]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "08f60cad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  1., ...,  3.,  0., 14.],\n",
       "       [ 1.,  0.,  1., ...,  3.,  1., 11.],\n",
       "       [ 1.,  0.,  1., ...,  3.,  2., 10.],\n",
       "       ...,\n",
       "       [ 1.,  0.,  1., ...,  0., 21., 33.],\n",
       "       [ 1.,  0.,  1., ...,  0., 22., 37.],\n",
       "       [ 1.,  0.,  1., ...,  0., 23., 37.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all[df_all.columns[1:]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb0179cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  1., ..., 20.,  3.,  0.],\n",
       "       [ 0.,  1.,  1., ..., 20.,  3.,  1.],\n",
       "       [ 0.,  1.,  1., ..., 20.,  3.,  2.],\n",
       "       ...,\n",
       "       [ 0.,  1.,  1., ..., 31.,  0., 21.],\n",
       "       [ 0.,  1.,  1., ..., 31.,  0., 22.],\n",
       "       [ 0.,  1.,  1., ..., 31.,  0., 23.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ede81c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb8edb68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 12)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a1c9b5e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000e+00, 1.00000e+00, 1.00000e+00, 1.06600e+01, 1.13650e+01,\n",
       "        5.60000e+01, 2.60027e+01, 2.01100e+03, 1.00000e+00, 2.00000e+01,\n",
       "        3.00000e+00, 0.00000e+00],\n",
       "       [0.00000e+00, 1.00000e+00, 1.00000e+00, 1.06600e+01, 1.36350e+01,\n",
       "        5.60000e+01, 0.00000e+00, 2.01100e+03, 1.00000e+00, 2.00000e+01,\n",
       "        3.00000e+00, 1.00000e+00],\n",
       "       [0.00000e+00, 1.00000e+00, 1.00000e+00, 1.06600e+01, 1.36350e+01,\n",
       "        5.60000e+01, 0.00000e+00, 2.01100e+03, 1.00000e+00, 2.00000e+01,\n",
       "        3.00000e+00, 2.00000e+00],\n",
       "       [0.00000e+00, 1.00000e+00, 1.00000e+00, 1.06600e+01, 1.28800e+01,\n",
       "        5.60000e+01, 1.10014e+01, 2.01100e+03, 1.00000e+00, 2.00000e+01,\n",
       "        3.00000e+00, 3.00000e+00],\n",
       "       [0.00000e+00, 1.00000e+00, 1.00000e+00, 1.06600e+01, 1.28800e+01,\n",
       "        5.60000e+01, 1.10014e+01, 2.01100e+03, 1.00000e+00, 2.00000e+01,\n",
       "        3.00000e+00, 4.00000e+00]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4bdeb88",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = predictor.predict(arr_test[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35f83b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'predictions': [{'score': 2.770601511001587}, {'score': 2.5148653984069824}]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f00cf89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result['predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "28d1eb93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.770601511001587\n",
      "2.5148653984069824\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(result['predictions'])):\n",
    " print(result['predictions'][i]['score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4b73ff",
   "metadata": {},
   "source": [
    "## Split the input data into chunks\n",
    "\n",
    "There are thousands of rows in this data set for which need inference.\n",
    "When communicating over internet, it is a good idea to split the data into chunks to prevent payload and timeout error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cad69791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For large number of predictions we can split the input data and\n",
    "# query the prediction service\n",
    "# array_split is convenient to specify how many splits are needed\n",
    "predictions = []\n",
    "for arr in np.array_split(arr_test,10):\n",
    "    result = predictor.predict(arr)\n",
    "    for i in range(len(result['predictions'])):\n",
    "        predictions.append(result['predictions'][i]['score'])\n",
    "#     result = result.decode(\"utf-8\")\n",
    "#     result = result.split(',')\n",
    "#     print(arr.shape)\n",
    "    #print(result)\n",
    "     #predictions += [float(r) for r in result]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "64c26472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6493"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27c9869e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6493, 12)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a9d859b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "870be564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c906e02b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.5148653984069824,\n",
       " 2.46087384223938,\n",
       " 2.824162721633911,\n",
       " 3.434159517288208,\n",
       " 3.683140993118286,\n",
       " 3.5565006732940674,\n",
       " 3.5565006732940674,\n",
       " 3.652454137802124,\n",
       " 3.557997465133667]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4a838506",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = np.expm1(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "62a68c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d038aada",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reuploading the file since we dropped th edatetime column\n",
    "df_all = pd.read_csv('bike_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "063dc5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all['count'] = res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9d3e9a6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
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       "      <th>day</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-20 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11.364944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10.715044</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
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       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15.846834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>30.005342</td>\n",
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      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  day  dayofweek  hour      count  \n",
       "0        56    26.0027  2011      1   20          3     0  14.968236  \n",
       "1        56     0.0000  2011      1   20          3     1  11.364944  \n",
       "2        56     0.0000  2011      1   20          3     2  10.715044  \n",
       "3        56    11.0014  2011      1   20          3     3  15.846834  \n",
       "4        56    11.0014  2011      1   20          3     4  30.005342  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7a0bb4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_all['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cd246c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all['count']= df_all['count'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b94cf53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-20 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>11</td>\n",
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       "      <th>2</th>\n",
       "      <td>2011-01-20 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>13.635</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-20 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-20 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.66</td>\n",
       "      <td>12.880</td>\n",
       "      <td>56</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  season  holiday  workingday  weather   temp   atemp  \\\n",
       "0  2011-01-20 00:00:00       1        0           1        1  10.66  11.365   \n",
       "1  2011-01-20 01:00:00       1        0           1        1  10.66  13.635   \n",
       "2  2011-01-20 02:00:00       1        0           1        1  10.66  13.635   \n",
       "3  2011-01-20 03:00:00       1        0           1        1  10.66  12.880   \n",
       "4  2011-01-20 04:00:00       1        0           1        1  10.66  12.880   \n",
       "\n",
       "   humidity  windspeed  year  month  day  dayofweek  hour  count  \n",
       "0        56    26.0027  2011      1   20          3     0     14  \n",
       "1        56     0.0000  2011      1   20          3     1     11  \n",
       "2        56     0.0000  2011      1   20          3     2     10  \n",
       "3        56    11.0014  2011      1   20          3     3     15  \n",
       "4        56    11.0014  2011      1   20          3     4     30  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['count'] = df_all['count'].round()\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ecfd8d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all[['datetime', 'count']].to_csv('predicted_count_cloud.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f004b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete Endpoint to prevent unnecessary charges\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65fe6b3",
   "metadata": {},
   "source": [
    "### Clean Up\n",
    "\n",
    "To avoid incurring unnecessary charges, use the AWS Management Console to delete the endpoints and resources that you created while running the exercises.\n",
    "\n",
    "    1.Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/ and delete the following resources:\n",
    "\n",
    "        The endpoint. \n",
    "        Deleting the endpoint also deletes the ML compute instance or instances that support it.\n",
    "\n",
    "        Under Inference, choose Endpoints.\n",
    "\n",
    "        Choose the endpoint that you created in the example, choose Actions, and then choose Delete.\n",
    "\n",
    "        The endpoint configuration.\n",
    "\n",
    "        Under Inference, choose Endpoint configurations.\n",
    "\n",
    "        Choose the endpoint configuration that you created in the example, choose Actions, and then choose Delete.\n",
    "\n",
    "        The model.\n",
    "\n",
    "        Under Inference, choose Models.\n",
    "\n",
    "        Choose the model that you created in the example, choose Actions, and then choose Delete.\n",
    "\n",
    "        The notebook instance. \n",
    "        Before deleting the notebook instance, stop it.\n",
    "\n",
    "        Under Notebook, choose Notebook instances.\n",
    "\n",
    "        Choose the notebook instance that you created in the example, choose Actions, and then choose Stop. The notebook instance takes several minutes to stop. When the Status changes to Stopped, move on to the next step.\n",
    "\n",
    "        Choose Actions, and then choose Delete.\n",
    "\n",
    "    2.Open the Amazon S3 console at https://console.aws.amazon.com/s3/, and then delete the bucket that you created for storing model artifacts and the training dataset.\n",
    "\n",
    "    3.Open the Amazon CloudWatch console at https://console.aws.amazon.com/cloudwatch/, and then delete all of the log groups that have names starting with /aws/sagemaker/."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05321369",
   "metadata": {},
   "source": [
    "Note:\n",
    "Training jobs and logs cannot be deleted and are retained indefinitely.\n",
    "If you plan to explore other exercises in this guide, you might want to keep some of these resources, such as your notebook instance, S3 bucket, and IAM role."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9762da44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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