Predictive Maintenance Modeling on Amazon SageMaker - Advanced.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Maintenance Modeling on Amazon SageMaker - Advanced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictive Maintenance using ML enables you to perform automated data processing on any data. \n",
    "In this notebook, we are going leverage **Apache Airflow** with **Amazaon SageMaker** to build a model that detects potential equipment failures and provides recommended actions to take. The diagram below is the genearl architecture of SageMaker and Airflow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AWS Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "file_name1 = './img/predictive-maintenance.png'\n",
    "Image(file_name1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictive Maintenance Using Machine Learning architecture\n",
    "This solution includes an AWS CloudFormation template that deploys an example dataset of a turbofan degradation simulation contained in an Amazon Simple Storage Service (Amazon S3) bucket and an Amazon SageMaker endpoint with an ML model that will be trained on the dataset to predict remaining useful life (RUL).\n",
    "\n",
    "The solution uses a SageMaker notebook instance to orchestrate the model, and a SageMaker training instance to perform the training. The training code and trained model are stored in the solution's Amazon S3 bucket.\n",
    "\n",
    "The solution also deploys an Amazon CloudWatch Events rule that is configured to run once per day. The rule is configured to trigger an AWS Lambda function that creates an Amazon SageMaker batch transform job that uses the trained model to predict RUL from the example dataset.\n",
    "\n",
    "By default, the solution is configured to predict RUL from the example dataset.\n",
    "\n",
    "For more information on how to deploy your solution, see the [deployment guide](https://docs.aws.amazon.com/solutions/latest/predictive-maintenance-using-machine-learning/welcome.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you want deploy solutions similar to this, you must modify the solution and convert your dataset to an Apache MXNet Gluon dataset. \n",
    "#### For more information on how to setup a training script for SageMaker using the MXNet estimator, Please go to this [Link](https://sagemaker.readthedocs.io/en/stable/using_mxnet.html#preparing-the-mxnet-training-script)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this dataset, Four different sets were simulated under different combinations of operational conditions and fault modes. Several sensor channels were recorded for the dataset to characterize fault evolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                # For matrix operations and numerical processing\n",
    "import pandas as pd                               # For munging tabular data\n",
    "import matplotlib.pyplot as plt                   # For charts and visualizations\n",
    "%matplotlib inline\n",
    "from IPython.display import Image                 # For displaying images in the notebook\n",
    "from IPython.display import display               # For displaying outputs in the notebook\n",
    "from time import gmtime, strftime                 # For labeling SageMaker models, endpoints, etc.\n",
    "import sys                                        # For writing outputs to notebook\n",
    "import math                                       # For ceiling function\n",
    "import json                                       # For parsing hosting outputs\n",
    "import os                                         # For manipulating filepath names\n",
    "import jsonlines\n",
    "import boto3 \n",
    "import csv\n",
    "import io\n",
    "import re\n",
    "import s3fs\n",
    "import copy\n",
    "import random\n",
    "import time\n",
    "from time import strftime, gmtime\n",
    "from scipy.sparse import lil_matrix\n",
    "\n",
    "# plot aesthetics\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.set_context('paper')\n",
    "five_thirty_eight = [\"#30a2da\", \"#fc4f30\", \"#e5ae38\", \"#6d904f\", \"#8b8b8b\",]\n",
    "sns.set_palette(five_thirty_eight)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from urllib.request import urlretrieve\n",
    "import zipfile\n",
    "from dateutil.parser import parse\n",
    "from random import shuffle\n",
    "import random\n",
    "import datetime\n",
    "import urllib\n",
    "\n",
    "import sagemaker                                 \n",
    "from sagemaker.predictor import csv_serializer \n",
    "from sagemaker.predictor import json_deserializer\n",
    "import sagemaker.amazon.common as smac\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.tuner import HyperparameterTuner, ContinuousParameter\n",
    "from sagemaker.analytics import HyperparameterTuningJobAnalytics, TrainingJobAnalytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sagemaker session : <sagemaker.session.Session object at 0x000001C416EBB7C8>\n",
      "S3 bucket : sagemaker-us-west-2-570447867175\n",
      "Prefix : pred-maintenance\n",
      "Region selected : us-west-2\n",
      "IAM role : arn:aws:iam::570447867175:role/SageMakerNotebookRole\n"
     ]
    }
   ],
   "source": [
    "sess   = sagemaker.Session()\n",
    "bucket = sess.default_bucket()                     \n",
    "prefix = 'pred-maintenance'\n",
    "region = boto3.Session().region_name\n",
    "role = 'arn:aws:iam::570447867175:role/SageMakerNotebookRole' # pass your IAM role name\n",
    "\n",
    "print('Sagemaker session :', sess)\n",
    "print('S3 bucket :', bucket)\n",
    "print('Prefix :', prefix)\n",
    "print('Region selected :', region)\n",
    "print('IAM role :', role)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = 'data'\n",
    "if not os.path.exists(data_folder):\n",
    "    os.makedirs(data_folder)\n",
    "urllib.request.urlretrieve('https://ti.arc.nasa.gov/m/project/prognostic-repository/CMAPSSData.zip', os.path.join(data_folder, 'CMAPSSData.zip'))\n",
    "\n",
    "with zipfile.ZipFile(os.path.join(data_folder, 'CMAPSSData.zip'), \"r\") as zip_ref:\n",
    "    zip_ref.extractall(data_folder)\n",
    "    \n",
    "columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3','s4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14','s15', 's16', 's17', 's18', 's19', 's20', 's21']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (1) Training Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 different dataset and we would need to train our models on each individually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalize sensor readings\n",
    "train_df = []\n",
    "eps = 0.000001 # for floating point issues during normalization \n",
    "for i in range(1,5):\n",
    "    df = pd.read_csv('data/train_FD{:03d}.txt'.format(i), delimiter=' ', header=None)\n",
    "    df.drop(df.columns[[26, 27]], axis=1, inplace=True)\n",
    "    df.columns = columns\n",
    "    df[columns[2:]]=(df[columns[2:]]-df[columns[2:]].min()+eps)/(df[columns[2:]].max()-df[columns[2:]].min()+eps)\n",
    "    train_df.append(df)\n",
    "\n",
    "# compute RUL (remaining useful life)\n",
    "for i, df in enumerate(train_df):\n",
    "    rul = pd.DataFrame(df.groupby('id')['cycle'].max()).reset_index()\n",
    "    rul.columns = ['id', 'max']\n",
    "    df = df.merge(rul, on=['id'], how='left')\n",
    "    df['RUL'] = df['max'] - df['cycle']\n",
    "    df.drop('max', axis=1, inplace=True)\n",
    "    train_df[i]=df\n",
    "\n",
    "train_df[0].head()\n",
    "o = train_df[0][columns[2:10]][train_df[0]['id'] == 3].plot(subplots=True, sharex=True, figsize=(20,10), title=\"Train: 8 sensors of Engine 1 before failure\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### (2) Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = []\n",
    "for i in range(1,5):\n",
    "    # Load time series\n",
    "    df = pd.read_csv('data/test_FD{:03d}.txt'.format(i), delimiter=' ', header=None)\n",
    "    df.drop(df.columns[[26, 27]], axis=1, inplace=True)\n",
    "    \n",
    "    # Load the RUL values\n",
    "    df_rul = pd.read_csv('data/RUL_FD{:03d}.txt'.format(i), delimiter=' ', header=None)    \n",
    "    df_rul.drop(df_rul.columns[1], axis=1, inplace=True)\n",
    "    df_rul.index += 1\n",
    "    \n",
    "    # Merge RUL and timeseries and compute RUL per timestamp\n",
    "    df = df.merge(df_rul, left_on=df.columns[0], right_index=True, how='left')\n",
    "    df.columns = columns + ['RUL_end']\n",
    "    rul = pd.DataFrame(df.groupby('id')['cycle'].max()).reset_index()\n",
    "    rul.columns = ['id', 'max']\n",
    "    df = df.merge(rul, on=['id'], how='left') # We get the number of cycles per series\n",
    "    df['RUL'] = df['max'] + df['RUL_end'] - df['cycle'] # The RUL is the number of cycles per series + RUL - how many cycles have already ran\n",
    "    df.drop(['max','RUL_end'], axis=1, inplace=True)\n",
    "    \n",
    "    # Normalize\n",
    "    df[columns[2:]]=(df[columns[2:]]-df[columns[2:]].min()+eps)/(df[columns[2:]].max()-df[columns[2:]].min()+eps)\n",
    "    test_df.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[        id  cycle  setting1  setting2  setting3   s1        s2        s3  \\\n",
       " 0        1      1  0.656271  0.692544       1.0  1.0  0.596215  0.421968   \n",
       " 1        1      2  0.343791  0.231360       1.0  1.0  0.182966  0.504025   \n",
       " 2        1      3  0.531279  0.538816       1.0  1.0  0.419559  0.464814   \n",
       " 3        1      4  0.775014  0.461952       1.0  1.0  0.413249  0.391587   \n",
       " 4        1      5  0.600025  0.461952       1.0  1.0  0.435331  0.471306   \n",
       " ...    ...    ...       ...       ...       ...  ...       ...       ...   \n",
       " 13091  100    194  0.818761  0.461952       1.0  1.0  0.665615  0.789665   \n",
       " 13092  100    195  0.443785  0.385088       1.0  1.0  0.659306  0.692028   \n",
       " 13093  100    196  0.475033  0.231360       1.0  1.0  0.728707  0.626071   \n",
       " 13094  100    197  0.275045  0.538816       1.0  1.0  0.671924  0.673851   \n",
       " 13095  100    198  0.593775  0.692544       1.0  1.0  0.574133  0.846014   \n",
       " \n",
       "              s4   s5  ...       s13       s14       s15  s16    s17  s18  s19  \\\n",
       " 0      0.282214  1.0  ...  0.325583  0.152259  0.347079  1.0  0.375  1.0  1.0   \n",
       " 1      0.225240  1.0  ...  0.395350  0.277907  0.227712  1.0  0.500  1.0  1.0   \n",
       " 2      0.346130  1.0  ...  0.325583  0.192892  0.533559  1.0  0.500  1.0  1.0   \n",
       " 3      0.449867  1.0  ...  0.372094  0.217896  0.282362  1.0  0.250  1.0  1.0   \n",
       " 4      0.357974  1.0  ...  0.325583  0.187891  0.337012  1.0  0.125  1.0  1.0   \n",
       " ...         ...  ...  ...       ...       ...       ...  ...    ...  ...  ...   \n",
       " 13091  0.641209  1.0  ...  0.255816  0.935703  0.664911  1.0  0.625  1.0  1.0   \n",
       " 13092  0.769042  1.0  ...  0.465118  0.914003  0.567596  1.0  0.750  1.0  1.0   \n",
       " 13093  0.458036  1.0  ...  0.348839  0.971066  0.594920  1.0  0.750  1.0  1.0   \n",
       " 13094  0.714111  1.0  ...  0.441862  1.000000  0.662993  1.0  0.750  1.0  1.0   \n",
       " 13095  0.829079  1.0  ...  0.372094  0.947848  0.755035  1.0  0.875  1.0  1.0   \n",
       " \n",
       "             s20       s21  RUL  \n",
       " 0      0.500000  0.620100  142  \n",
       " 1      0.645455  0.645719  141  \n",
       " 2      0.700000  0.681104  140  \n",
       " 3      0.627273  0.620383  139  \n",
       " 4      0.618182  0.676009  138  \n",
       " ...         ...       ...  ...  \n",
       " 13091  0.309092  0.370843   24  \n",
       " 13092  0.236364  0.483653   23  \n",
       " 13093  0.281819  0.381742   22  \n",
       " 13094  0.318182  0.473461   21  \n",
       " 13095  0.354546  0.353999   20  \n",
       " \n",
       " [13096 rows x 27 columns],\n",
       "         id  cycle  setting1  setting2      setting3            s1        s2  \\\n",
       " 0        1      1  0.238019  0.297150  1.000000e+00  5.979368e-01  0.639243   \n",
       " 1        1      2  0.476162  0.831354  1.000000e+00  6.269852e-01  0.664861   \n",
       " 2        1      3  0.833282  0.997625  1.000000e+00  6.026878e-02  0.189055   \n",
       " 3        1      4  0.999967  0.998812  1.000000e+00  1.357405e-08  0.135525   \n",
       " 4        1      5  0.595089  0.737886  2.500000e-08  2.380888e-01  0.012028   \n",
       " ...    ...    ...       ...       ...           ...           ...       ...   \n",
       " 33986  259    119  0.833210  0.997981  1.000000e+00  6.026878e-02  0.185015   \n",
       " 33987  259    120  0.999967  0.998219  1.000000e+00  1.357405e-08  0.128638   \n",
       " 33988  259    121  0.999955  0.997625  1.000000e+00  1.357405e-08  0.130750   \n",
       " 33989  259    122  0.000057  0.000357  1.000000e+00  1.000000e+00  0.984024   \n",
       " 33990  259    123  0.999888  0.997625  1.000000e+00  1.357405e-08  0.131852   \n",
       " \n",
       "              s3        s4            s5  ...       s13       s14       s15  \\\n",
       " 0      0.691329  0.680395  6.171802e-01  ...  0.995085  0.670742  0.117565   \n",
       " 1      0.647519  0.534663  5.079366e-01  ...  0.994920  0.514944  0.337173   \n",
       " 2      0.312512  0.243225  1.465920e-01  ...  0.993898  0.514893  0.376695   \n",
       " 3      0.286753  0.242061  9.337067e-08  ...  0.993815  0.550269  0.394807   \n",
       " 4      0.022001  0.050986  2.931840e-01  ...  0.000718  0.036627  0.940070   \n",
       " ...         ...       ...           ...  ...       ...       ...       ...   \n",
       " 33986  0.331523  0.245972  1.465920e-01  ...  0.995665  0.605044  0.367176   \n",
       " 33987  0.290703  0.232087  9.337067e-08  ...  0.995444  0.656398  0.379288   \n",
       " 33988  0.285244  0.219835  9.337067e-08  ...  0.995527  0.631614  0.390177   \n",
       " 33989  0.944915  0.935747  1.000000e+00  ...  0.994588  0.792618  0.036188   \n",
       " 33990  0.267029  0.239759  9.337067e-08  ...  0.995693  0.648230  0.376176   \n",
       " \n",
       "           s16       s17           s18           s19       s20       s21  RUL  \n",
       " 0      1.0000  0.694737  8.541226e-01  1.000000e+00  0.625944  0.630724  275  \n",
       " 1      0.0001  0.642105  8.646934e-01  1.000000e+00  0.484214  0.496301  274  \n",
       " 2      0.0001  0.315789  6.511628e-01  1.000000e+00  0.164722  0.159476  273  \n",
       " 3      0.0001  0.305263  6.279070e-01  1.000000e+00  0.005834  0.016866  272  \n",
       " 4      0.0001  0.021053  2.114165e-09  6.635700e-08  0.141730  0.140124  271  \n",
       " ...       ...       ...           ...           ...       ...       ...  ...  \n",
       " 33986  0.0001  0.326316  6.511628e-01  1.000000e+00  0.163349  0.159133   55  \n",
       " 33987  0.0001  0.305263  6.279070e-01  1.000000e+00  0.013384  0.016260   54  \n",
       " 33988  0.0001  0.294737  6.279070e-01  1.000000e+00  0.013384  0.020433   53  \n",
       " 33989  1.0000  0.947368  1.000000e+00  1.000000e+00  0.991764  0.985410   52  \n",
       " 33990  0.0001  0.294737  6.279070e-01  1.000000e+00  0.015443  0.012858   51  \n",
       " \n",
       " [33991 rows x 27 columns],\n",
       "         id  cycle  setting1  setting2  setting3   s1        s2        s3  \\\n",
       " 0        1      1  0.411799  0.167361       1.0  1.0  0.330128  0.400713   \n",
       " 1        1      2  0.547085  0.333888       1.0  1.0  0.355769  0.470392   \n",
       " 2        1      3  0.594142  0.250624       1.0  1.0  0.246795  0.397146   \n",
       " 3        1      4  0.670608  0.583680       1.0  1.0  0.413462  0.462545   \n",
       " 4        1      5  0.505911  0.583680       1.0  1.0  0.496795  0.569560   \n",
       " ...    ...    ...       ...       ...       ...  ...       ...       ...   \n",
       " 16591  100    243  0.576496  0.250624       1.0  1.0  0.682692  0.711296   \n",
       " 16592  100    244  0.370625  0.666944       1.0  1.0  0.500000  0.622830   \n",
       " 16593  100    245  0.200047  0.666944       1.0  1.0  0.573718  0.683234   \n",
       " 16594  100    246  0.476501  0.583680       1.0  1.0  0.410257  0.726516   \n",
       " 16595  100    247  0.647080  0.833472       1.0  1.0  0.730769  0.653032   \n",
       " \n",
       "              s4   s5  ...       s13       s14       s15  s16       s17  s18  \\\n",
       " 0      0.345269  1.0  ...  0.628931  0.326196  0.531662  1.0  0.363636  1.0   \n",
       " 1      0.384025  1.0  ...  0.672956  0.372280  0.621865  1.0  0.363636  1.0   \n",
       " 2      0.246705  1.0  ...  0.628931  0.381706  0.469834  1.0  0.363636  1.0   \n",
       " 3      0.313988  1.0  ...  0.641510  0.367974  0.516429  1.0  0.454546  1.0   \n",
       " 4      0.324218  1.0  ...  0.647799  0.334575  0.646954  1.0  0.363636  1.0   \n",
       " ...         ...  ...  ...       ...       ...       ...  ...       ...  ...   \n",
       " 16591  0.627582  1.0  ...  0.817610  0.660887  0.286442  1.0  0.727273  1.0   \n",
       " 16592  0.752705  1.0  ...  0.830189  0.672524  0.378437  1.0  0.636364  1.0   \n",
       " 16593  0.655518  1.0  ...  0.842767  0.659490  0.383514  1.0  0.636364  1.0   \n",
       " 16594  0.535117  1.0  ...  0.874214  0.683696  0.277183  1.0  0.727273  1.0   \n",
       " 16595  0.547315  1.0  ...  0.849057  0.742348  0.361710  1.0  0.454546  1.0   \n",
       " \n",
       "        s19       s20       s21  RUL  \n",
       " 0      1.0  0.524476  0.503085  276  \n",
       " 1      1.0  0.503497  0.544906  275  \n",
       " 2      1.0  0.545455  0.475451  274  \n",
       " 3      1.0  0.454546  0.541821  273  \n",
       " 4      1.0  0.538462  0.439182  272  \n",
       " ...    ...       ...       ...  ...  \n",
       " 16591  1.0  0.755245  0.562423   32  \n",
       " 16592  1.0  0.769231  0.693437   31  \n",
       " 16593  1.0  0.776224  0.628177   30  \n",
       " 16594  1.0  0.755245  0.776832   29  \n",
       " 16595  1.0  0.762238  0.758204   28  \n",
       " \n",
       " [16596 rows x 27 columns],\n",
       "         id  cycle  setting1  setting2      setting3            s1        s2  \\\n",
       " 0        1      1  0.476271  0.831354  1.000000e+00  6.269852e-01  0.656561   \n",
       " 1        1      2  0.595087  0.736342  2.500000e-08  2.380888e-01  0.007372   \n",
       " 2        1      3  0.999810  1.000000  1.000000e+00  1.357405e-08  0.127258   \n",
       " 3        1      4  0.999893  0.997862  1.000000e+00  1.357405e-08  0.126889   \n",
       " 4        1      5  0.833363  0.997625  1.000000e+00  6.026878e-02  0.181349   \n",
       " ...    ...    ...       ...       ...           ...           ...       ...   \n",
       " 41209  248    277  0.999788  0.997743  1.000000e+00  1.357405e-08  0.137118   \n",
       " 41210  248    278  0.476162  0.831948  1.000000e+00  6.269852e-01  0.668817   \n",
       " 41211  248    279  0.833146  0.999169  1.000000e+00  6.026878e-02  0.188905   \n",
       " 41212  248    280  0.476164  0.831354  1.000000e+00  6.269852e-01  0.670568   \n",
       " 41213  248    281  0.833353  0.997862  1.000000e+00  6.026878e-02  0.193328   \n",
       " \n",
       "              s3        s4            s5  ...       s13       s14       s15  \\\n",
       " 0      0.654027  0.501557  5.079366e-01  ...  0.996016  0.574006  0.362211   \n",
       " 1      0.041486  0.016265  2.931840e-01  ...  0.000968  0.039366  0.945325   \n",
       " 2      0.270191  0.200168  9.337067e-08  ...  0.996487  0.637839  0.423747   \n",
       " 3      0.269020  0.203579  9.337067e-08  ...  0.996348  0.660202  0.410736   \n",
       " 4      0.305358  0.229632  1.465920e-01  ...  0.996265  0.599510  0.390686   \n",
       " ...         ...       ...           ...  ...       ...       ...       ...   \n",
       " 41209  0.336302  0.257737  9.337067e-08  ...  0.998008  0.757378  0.440349   \n",
       " 41210  0.691373  0.583300  5.079366e-01  ...  0.997538  0.683084  0.381515   \n",
       " 41211  0.353327  0.262186  1.465920e-01  ...  0.998395  0.723458  0.424529   \n",
       " 41212  0.676010  0.571856  5.079366e-01  ...  0.997649  0.678501  0.379915   \n",
       " 41213  0.374928  0.286237  1.465920e-01  ...  0.998257  0.715764  0.423889   \n",
       " \n",
       "           s16       s17           s18           s19       s20       s21  RUL  \n",
       " 0      0.0001  0.631579  8.646934e-01  1.000000e+00  0.478320  0.483780  251  \n",
       " 1      0.0001  0.042105  2.114165e-09  6.635700e-08  0.141260  0.137380  250  \n",
       " 2      0.0001  0.273684  6.279070e-01  1.000000e+00  0.006775  0.016466  249  \n",
       " 3      0.0001  0.273684  6.279070e-01  1.000000e+00  0.012534  0.005168  248  \n",
       " 4      0.0001  0.326316  6.511628e-01  1.000000e+00  0.157859  0.157613  247  \n",
       " ...       ...       ...           ...           ...       ...       ...  ...  \n",
       " 41209  0.0001  0.305263  6.279070e-01  1.000000e+00  0.011518  0.006599   30  \n",
       " 41210  0.0001  0.673684  8.646934e-01  1.000000e+00  0.478997  0.480834   29  \n",
       " 41211  0.0001  0.357895  6.511628e-01  1.000000e+00  0.152439  0.152314   28  \n",
       " 41212  1.0000  0.673684  8.646934e-01  1.000000e+00  0.482724  0.483101   27  \n",
       " 41213  0.0001  0.347368  6.511628e-01  1.000000e+00  0.153455  0.152953   26  \n",
       " \n",
       " [41214 rows x 27 columns]]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Convert to Protobuf format and Upload to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_bucket_resource = boto3.resource('s3').Bucket(bucket)\n",
    "\n",
    "# Upload raw data files to S3\n",
    "for subdir, dirs, files in os.walk(data_folder):\n",
    "    for file in files:\n",
    "        full_path = os.path.join(subdir, file)\n",
    "        s3_path = os.path.join(prefix, full_path)\n",
    "        s3_bucket_resource.Object(s3_path).upload_file(full_path)\n",
    "\n",
    "# Upload processed test data for inference\n",
    "for i in range(len(test_df)):\n",
    "    local_test_file = 'data/test-{}.csv'.format(i)\n",
    "    test_df[i].to_csv(local_test_file)\n",
    "    s3_test_file = os.path.join(prefix, 'data', 'test-{}.csv'.format(i))\n",
    "    s3_bucket_resource.Object(s3_test_file).upload_file(local_test_file)\n",
    "\n",
    "# Upload processed data for training\n",
    "for i in range(len(train_df)):\n",
    "    local_train_file = 'data/train-{}.csv'.format(i)\n",
    "    train_df[i].to_csv(local_train_file)\n",
    "    s3_train_file = os.path.join(prefix, 'train', 'train-{}.csv'.format(i))\n",
    "    s3_bucket_resource.Object(s3_train_file).upload_file(local_train_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uploaded training data location: s3://sagemaker-us-west-2-570447867175/pred-maintenance/train\n",
      "uploaded test data location: s3://sagemaker-us-west-2-570447867175/pred-maintenance/test\n"
     ]
    }
   ],
   "source": [
    "s3_train_data = 's3://{}/{}/{}'.format(bucket, prefix, 'train')\n",
    "s3_test_data = 's3://{}/{}/{}'.format(bucket, prefix, 'test')\n",
    "\n",
    "print('uploaded training data location: {}'.format(s3_train_data))\n",
    "print('uploaded test data location: {}'.format(s3_test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output will be uploaded to: s3://sagemaker-us-west-2-570447867175/pred-maintenance/output\n"
     ]
    }
   ],
   "source": [
    "# output path\n",
    "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
    "print('Output will be uploaded to: {}'.format(output_location))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Model Training with MXNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training MXNet models using MXNet Estimators is a two-step process. First, you prepare your training script, then second, you run this on SageMaker via an MXNet Estimator. The training script we have prepared for the model is located in the sagemaker_predictive_maintenance_entry_point folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import os\n",
      "import json\n",
      "import time\n",
      "import argparse\n",
      "import logging\n",
      "import random\n",
      "\n",
      "import mxnet as mx\n",
      "from mxnet import gluon, autograd, nd\n",
      "import gluonnlp\n",
      "from gluonnlp.data.batchify import Pad, Stack, Tuple\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "\n",
      "def get_logger(name):\n",
      "    logger = logging.getLogger(name)\n",
      "    log_format = '%(asctime)s %(levelname)s %(name)s: %(message)s'\n",
      "    logging.basicConfig(format=log_format, level=logging.INFO)\n",
      "    return logger\n",
      "\n",
      "\n",
      "def parse_args():\n",
      "    parser = argparse.ArgumentParser()\n",
      "\n",
      "    parser.add_argument('--num-gpus', type=int, default=1)\n",
      "    parser.add_argument('--training-dir', type=str, default=os.environ['SM_CHANNEL_TRAIN'])\n",
      "    parser.add_argument('--num-datasets', type=int, default=1)\n",
      "    parser.add_argument('--batch-size', type=int, default=1)\n",
      "    parser.add_argument('--epochs', type=int, default=2)\n",
      "    parser.add_argument('--learning-rate', type=float, default=0.001)\n",
      "    parser.add_argument('--log-interval', type=int, default=1000)\n",
      "    parser.add_argument('--is-many-to-one', type=bool, default=False)\n",
      "    parser.add_argument('--num-layers', type=int, default=1)\n",
      "    parser.add_argument('--num-units', type=int, default=1)\n",
      "    parser.add_argument('--dropout', type=float, default=0.3)\n",
      "    parser.add_argument('--optimizer', type=str, default='adam')\n",
      "    parser.add_argument('--momentum', type=float, default=0.9)\n",
      "    parser.add_argument('--wd', type=float, default=0.00001)\n",
      "    parser.add_argument('--clip-gradient', type=float, default=20)\n",
      "    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])\n",
      "\n",
      "    return parser.parse_args()\n",
      "\n",
      "\n",
      "def read_data(training_dir, num_datasets):\n",
      "    train_df = [pd.read_csv(os.path.join(training_dir, 'train-{}.csv'.format(i))) for i in range(num_datasets)]\n",
      "    return train_df\n",
      "\n",
      "\n",
      "class CombinedDataset(gluon.data.Dataset):\n",
      "    \"\"\"\n",
      "    A dataset that accepts several dataset and serves\n",
      "    them as one\n",
      "    \"\"\"\n",
      "\n",
      "    def __init__(self, datasets):\n",
      "\n",
      "        self.datasets = datasets\n",
      "\n",
      "        self.lengths = []\n",
      "        start = 0\n",
      "        for d in datasets:\n",
      "            end = start + len(d)\n",
      "            self.lengths.append((start, end))\n",
      "            start = end\n",
      "\n",
      "        self.length = sum([len(d) for d in datasets])\n",
      "\n",
      "    def __getitem__(self, idx):\n",
      "        current_running = 0\n",
      "        for i, (start, end) in enumerate(self.lengths):\n",
      "            print(start, end, idx)\n",
      "            if idx >= end:\n",
      "                current_running += end\n",
      "            else:\n",
      "                return self.datasets[i][idx - current_running]\n",
      "\n",
      "    def __len__(self):\n",
      "        return self.length\n",
      "\n",
      "\n",
      "class PredictiveMaintenanceDataset(gluon.data.Dataset):\n",
      "\n",
      "    def __init__(self, dataframe, is_train=True, is_many_to_one=True):\n",
      "        self.dataframe = dataframe\n",
      "        self.is_train = is_train\n",
      "        self.is_many_to_one = is_many_to_one\n",
      "\n",
      "    def __getitem__(self, idx):\n",
      "        data = np.array(self.dataframe[self.dataframe['id'] == idx + 1][self.dataframe.columns[2:-1]])\n",
      "        label = np.array(self.dataframe[self.dataframe['id'] == idx + 1]['RUL'])\n",
      "        if self.is_train:\n",
      "            start = random.randint(0, data.shape[0] // 2 - 2)\n",
      "            end = random.randint(data.shape[0] // 2 + 2, data.shape[0] - 1)\n",
      "        else:\n",
      "            start = 0\n",
      "            end = data.shape[0] - 1\n",
      "\n",
      "        data = data[start:end, :].astype('float32')\n",
      "        label = label[start].astype('float32') if self.is_many_to_one else label[start:end].astype('float32')\n",
      "\n",
      "        # Max RUL\n",
      "        max_rul = 130.0\n",
      "        if not self.is_many_to_one:\n",
      "            label[label > max_rul] = max_rul\n",
      "        else:\n",
      "            label = min(label, max_rul)\n",
      "            \n",
      "        label = label.astype('float32')\n",
      "\n",
      "        # Duplicate first element to avoid cliff-edge\n",
      "        # data = np.concatenate((np.expand_dims(data[0], axis=0), data), axis=0)\n",
      "        # label = np.concatenate((np.expand_dims(label[0], axis=0), label), axis=0)\n",
      "        return data, label\n",
      "\n",
      "    def __len__(self):\n",
      "        return len(self.dataframe['id'].unique())\n",
      "\n",
      "\n",
      "def RMSE_many_to_one(predictions, labels, data_lengths):\n",
      "    # predictions = predictions.sum(axis=1).squeeze() / data_lengths.astype('float32')\n",
      "    loss = (predictions - labels).square()\n",
      "    return loss\n",
      "\n",
      "\n",
      "def RMSE_many_to_many(predictions, labels, data_lengths):\n",
      "    loss = (labels.expand_dims(axis=2) - predictions).square()\n",
      "    loss = nd.SequenceMask(loss, data_lengths, use_sequence_length=True, axis=1)\n",
      "    weight = 1 / (labels + 1)\n",
      "    loss_no_weight = loss.sum(axis=1).squeeze() / data_lengths.astype('float32')\n",
      "    loss_weighted = ((loss.squeeze() * weight).sum(axis=1).squeeze() / data_lengths.astype('float32'))\n",
      "    return loss_weighted, loss_no_weight\n",
      "\n",
      "\n",
      "class TimeSeriesNet(gluon.nn.HybridBlock):\n",
      "\n",
      "    def __init__(self, num_layers, num_units, dropout):\n",
      "        super(TimeSeriesNet, self).__init__()\n",
      "        \n",
      "        self.num_layers = num_layers\n",
      "        self.num_units = num_units\n",
      "        self.dropout = dropout\n",
      "\n",
      "        with self.name_scope():\n",
      "            self.net = gluon.nn.HybridSequential(prefix='predictive_maintenance_')\n",
      "            with self.net.name_scope():\n",
      "                self.net.add(\n",
      "                    gluon.nn.HybridLambda(lambda F, x: x.transpose((0, 2, 1))),\n",
      "                    gluon.nn.Conv1D(channels=32, kernel_size=3, padding=1),\n",
      "                    gluon.nn.Conv1D(channels=32, kernel_size=3, padding=1),\n",
      "                    gluon.nn.HybridLambda(lambda F, x: x.transpose((0, 2, 1))),\n",
      "                    gluon.rnn.LSTM(num_units, num_layers=num_layers, bidirectional=True, layout='NTC', dropout=dropout,\n",
      "                                   state_clip_min=-10, state_clip_max=10, state_clip_nan=True),\n",
      "                    gluon.nn.Activation('softrelu'),\n",
      "                )\n",
      "            self.proj = gluon.nn.Dense(1, flatten=False)\n",
      "\n",
      "    def hybrid_forward(self, F, x):\n",
      "        return self.proj(self.net(x))\n",
      "\n",
      "\n",
      "def train(net, train_dataloader, epochs, batch_size, is_many_to_one, model_dir):\n",
      "    loss_fn = RMSE_many_to_one if is_many_to_one else RMSE_many_to_many\n",
      "    INPUT_SCALER = 300\n",
      "\n",
      "    for e in range(epochs):\n",
      "        loss_avg = 0\n",
      "        for i, ((data, data_lengths), (label)) in enumerate(train_data):\n",
      "            data = data.as_in_context(ctx).astype('float32')\n",
      "            label = label.as_in_context(ctx).astype('float32')\n",
      "            data_lengths = data_lengths.as_in_context(ctx).astype('float32')\n",
      "            with autograd.record():\n",
      "                pred = net(data)\n",
      "                loss, loss_no_weight = loss_fn(pred, label / INPUT_SCALER, data_lengths)\n",
      "                loss = loss.mean()\n",
      "            loss.backward()\n",
      "            trainer.step(data.shape[0])\n",
      "            loss_avg += loss_no_weight.mean().sqrt().asnumpy()\n",
      "        logging.info(\"Epoch {}: Average RMSE {}\".format(e, INPUT_SCALER * loss_avg / (i + 1)))\n",
      "    \n",
      "    save_model(net, model_dir)\n",
      "    logging.info(\"Saved model params\")\n",
      "    logging.info(\"End of training\")\n",
      "\n",
      "def save_model(net, model_dir):\n",
      "    net.save_parameters(os.path.join(model_dir, \"net.params\"))\n",
      "    f = open(os.path.join(model_dir, \"model.params\"), 'w')\n",
      "    json.dump({'num_layers': net.num_layers,\n",
      "               'num_units': net.num_units,\n",
      "               'dropout': net.dropout},\n",
      "              f)\n",
      "    f.close()\n",
      "\n",
      "if __name__ == '__main__':\n",
      "    logging = get_logger(__name__)\n",
      "    logging.info('numpy version:{} MXNet version::{}'.format(np.__version__, mx.__version__))\n",
      "    options = parse_args()\n",
      "\n",
      "    ctx = mx.gpu() if options.num_gpus > 0 else mx.cpu()\n",
      "\n",
      "    train_df = read_data(options.training_dir, options.num_datasets)\n",
      "\n",
      "    train_datasets = [PredictiveMaintenanceDataset(df, is_train=True, is_many_to_one=options.is_many_to_one) for df in\n",
      "                      train_df]\n",
      "\n",
      "    batchify = Tuple(Pad(ret_length=True), Stack() if options.is_many_to_one else Pad())\n",
      "\n",
      "    dataset_index = 0\n",
      "    train_data = gluon.data.DataLoader(train_datasets[dataset_index], shuffle=True, batch_size=options.batch_size,\n",
      "                                       num_workers=8,\n",
      "                                       batchify_fn=batchify)\n",
      "\n",
      "    logging.info(\"We have {} training timeseries\".format(len(train_datasets[dataset_index])))\n",
      "\n",
      "    net = TimeSeriesNet(options.num_layers, options.num_units, options.dropout)\n",
      "    net.hybridize(static_alloc=True)\n",
      "    net.initialize(mx.init.Normal(), ctx=ctx)\n",
      "    logging.info('Model created and initialized')\n",
      "\n",
      "    optimizer_params = {'learning_rate': options.learning_rate, 'wd': options.wd,\n",
      "                        'clip_gradient': options.clip_gradient}\n",
      "\n",
      "    if options.optimizer == 'sgd':\n",
      "        optimizer_params['momentum'] = options.momentum\n",
      "\n",
      "    trainer = gluon.Trainer(net.collect_params(), options.optimizer, optimizer_params)\n",
      "\n",
      "    train(net, train_data, options.epochs, options.batch_size, options.is_many_to_one, options.model_dir)\n",
      "    \n",
      "\n",
      "class TimeSeriesNetInfer(gluon.nn.HybridBlock):\n",
      "\n",
      "    def __init__(self, num_layers, num_units, dropout):\n",
      "        super(TimeSeriesNetInfer, self).__init__()\n",
      "        \n",
      "        self.num_layers = num_layers\n",
      "        self.num_units = num_units\n",
      "        self.dropout = dropout\n",
      "\n",
      "        with self.name_scope():\n",
      "            self.net = gluon.nn.HybridSequential(prefix='predictive_maintenance_')\n",
      "            with self.net.name_scope():\n",
      "                self.net.add(\n",
      "                    gluon.nn.HybridLambda(lambda F, x: x.transpose((0, 2, 1))),\n",
      "                    gluon.nn.Conv1D(channels=32, kernel_size=3, padding=1),\n",
      "                    gluon.nn.Conv1D(channels=32, kernel_size=3, padding=1),\n",
      "                    gluon.nn.HybridLambda(lambda F, x: x.transpose((0, 2, 1))),\n",
      "                    gluon.rnn.LSTM(num_units, num_layers=num_layers, bidirectional=True, layout='NTC', dropout=dropout),\n",
      "                    gluon.nn.Activation('softrelu'),\n",
      "                )\n",
      "            self.proj = gluon.nn.Dense(1, flatten=False)\n",
      "\n",
      "    def hybrid_forward(self, F, x):\n",
      "        return self.proj(self.net(x))\n",
      "    \n",
      "def model_fn(model_dir):\n",
      "    ctx = mx.gpu() if mx.context.num_gpus() > 0 else mx.cpu()\n",
      "    with open(os.path.join(model_dir, \"model.params\"), 'r') as f:\n",
      "        net_params = json.load(f)\n",
      "     \n",
      "    net = TimeSeriesNetInfer(net_params['num_layers'], net_params['num_units'], net_params['dropout'])\n",
      "    net.load_parameters(os.path.join(model_dir, \"net.params\"), ctx)\n",
      "    return net\n",
      "    \n",
      "def transform_fn(net, data, input_content_type, output_content_type):\n",
      "    data_dict = json.loads(data.decode())\n",
      "    input_data = nd.array(data_dict['input'])\n",
      "    \n",
      "    ctx = mx.gpu() if mx.context.num_gpus() > 0 else mx.cpu()\n",
      "    input_data = input_data.as_in_context(ctx)\n",
      "    pred = net(input_data)\n",
      "    \n",
      "    response = json.dumps(pred.asnumpy().tolist())\n",
      "    return response, output_content_type\n"
     ]
    }
   ],
   "source": [
    "!pygmentize sagemaker_predictive_maintenance_entry_point.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.mxnet import MXNet\n",
    "\n",
    "training_job_name = 'pred-maintenance-mxnet'\n",
    "train_instance_type = 'ml.p2.xlarge'\n",
    "\n",
    "m = MXNet(entry_point='sagemaker_predictive_maintenance_entry_point.py',\n",
    "          #source_dir='sagemaker_predictive_maintenance_entry_point',\n",
    "          py_version='py3',\n",
    "          role=role, \n",
    "          train_instance_count=1, \n",
    "          train_instance_type=train_instance_type,\n",
    "          output_path=output_location,\n",
    "          hyperparameters={'num-datasets' : len(train_df),\n",
    "                           'num-gpus': 1,\n",
    "                           'epochs': 500,\n",
    "                           'optimizer': 'adam',\n",
    "                           'batch-size':1,\n",
    "                           'log-interval': 100},\n",
    "         input_mode='File',\n",
    "         train_max_run=3600,\n",
    "         framework_version='1.6.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start training\n",
    "m.fit({'train': s3_train_data}, job_name=training_job_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Create Transformer Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now call the transformer function to create a SageMaker Model with the trained model. The SageMaker Model will have the same name as the training job that just completed. This will ensure that SageMaker stores a reference to the trained model which can be used for predictions later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_output = 's3://{}/{}/{}'.format(bucket, prefix, 'batch-inference')\n",
    "transformer = m.transformer(instance_count=1, instance_type='ml.t2.medium', output_path=batch_output) # or instance_type='ml.m4.xlarge'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Transform test data using the transformer model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the transformer SageMaker Model, we can run a SageMaker Batch Transform job to get some predictions on test dataset for the model. Here we have a function that takes some test data in S3 and copies it to a new location where it's used as the input to the transform function of the Batch Transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_test_key = \"pred-maintenance-artifacts/data/test-0.csv\"\n",
    "s3_transform_input = os.path.join(prefix, \"batch-transform-input\")\n",
    "\n",
    "def get_transform_input():\n",
    "    s3_client = boto3.client('s3')\n",
    "    s3_response = s3_client.get_object(Bucket=bucket, Key=s3_test_key)\n",
    "    test_file = s3_response[\"Body\"].read()\n",
    "\n",
    "    test_df_entry = pd.read_csv(io.BytesIO(test_file))\n",
    "    test_data = test_df_entry[test_df_entry['id']==0+1][test_df_entry.columns[2:-1]].values\n",
    "    test_data = test_data[0:test_data.shape[0]-1,:].astype('float32')\n",
    "    data_payload = {'input':np.expand_dims(test_data, axis=0).tolist()}\n",
    "    \n",
    "    job_name = 'predictive-maintenance-batch-transform-job-{}'.format(strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime()))\n",
    "    s3_batch_transform_input_key = os.path.join(s3_transform_input, job_name)\n",
    "    \n",
    "    s3_client.put_object(Body=json.dumps(data_payload),\n",
    "                         Bucket=bucket, \n",
    "                         Key=s3_batch_transform_input_key)\n",
    "    return job_name, 's3://{}/{}'.format(bucket, s3_batch_transform_input_key)\n",
    "\n",
    "job_name, input_key = get_transform_input()\n",
    "transformer.transform(input_key, wait=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Evaluate model prediction on test-set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_transform_output():\n",
    "    s3_client = boto3.client('s3')\n",
    "    s3_response = s3_client.get_object(Bucket=bucket, Key=os.path.join(prefix, \n",
    "                                                                       'batch-inference', \n",
    "                                                                       job_name+'.out'))\n",
    "    transform_out = np.array(eval(s3_response[\"Body\"].read()))\n",
    "    return transform_out\n",
    "    \n",
    "get_transform_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference\n",
    ">https://github.com/awslabs/predictive-maintenance-using-machine-learning\n",
    ">https://docs.aws.amazon.com/solutions/latest/predictive-maintenance-using-machine-learning/overview.html"
   ]
  }
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