Main.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Main.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPwE1mqnawV4lE7l3N0OjBP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luqmanbuang/Aircraft-Engine-Lifetime-Prediction-with-Machine-Learning/blob/master/Main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0g8odVvjNyy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95b4f396-0368-4170-e497-f7667838ec8a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQdN0HGZ33oH"
      },
      "source": [
        "#The dataset\n",
        "\n",
        "Dataset is divided into training and test subsets consisting of multiple multivariate time series. Each time series is from a different engine unit – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine unit starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. However in the dataset used in this project, df_FD001, the engines are only sujected to a single operational setting and hence does not contribute to much degradation learning. Additionaly, the data is contaminated with sensor noise.\n",
        "<br>  \n",
        "The engine is operating normally at the start of each time series and is assumed to be at perfect health. It develops a fault at some point during the series. In the <ins>training set</ins>, the fault grows in magnitude until system failure. **In the test set, the time series ends some time prior to system failure.**\n",
        "<br>  \n",
        "The **objective of this project is to predict the number of remaining operational cycles before failure in the test set**, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. A vector of the actual Remaining Useful Life (RUL) values for the test data is available and will be used to compare the accuracy of the prediction model.  \n",
        "<br> \n",
        "The screenshot below shows the first five rows of engine unit 1. Each row represents a screenshot of the same engine unit (unit 1) at different times — called cycles. In the test and train dataset, there are 100 of such engine units with varying numbers of cycles each. 21 columns representing data from 21 different engine sensors are included along each row. For simplicity, these sensors are called sensors S1 to S21. For example, Sensor 1 is the sensor that detects the total temperature at the fan inlet while Sensor 8 tracks the fan speed of the engine in RPM.\n",
        "<br>  \n",
        "![Dataset example](https://drive.google.com/uc?id=1hEt0xMQv-6IOwO1A5kMkbL8wkPIBlkcX) \n",
        "<br>  \n",
        "Degradation of the engine health can be observed by plotting the data from these sensors. In a sample plot below, we can see that as the engine's health deteriorates over time the engine's core speed increases. A possible explanation why this might occur is that the engine needs to generate a higher RPM in order to produce or maintain the same power output. Similar degradation patterns can be observed in the other sensors. The goal is to <ins>let the machine learning algorithms learn the degradation patterns and its corelation to the engine's health</ins> in order to ultimately predict the engine's Remaining Useful Life in operation. The training dataset will be used to train the machine learning algorithm, while the test dataset is used to validate its accuracy.  \n",
        "<br>  \n",
        "![sensor degradation](https://drive.google.com/uc?id=1n5YQFTOmvdPyro86zeCB3VDMXhLTD54G)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8yHCkKor8Bl"
      },
      "source": [
        "#1. Importing and preprocessing the dataset\n",
        "\n",
        "##1.1 Data feature selection \n",
        "Firstly, both the train and test datasets are uploaded into the notebook and preprocessed to prepare it for the machine learning process. Plotting the different sensors for every engine units uncovers that some sensors do not contain characteristics that show engine degradation compared to those seen in the example in the previous section. Instead they exhibit constant and flatline data output. Sensor 1 of Unit 29 is an example of flatline data: \n",
        "<br>  \n",
        "\n",
        "![Dataset example](https://drive.google.com/uc?id=10nQjcDX1uF8JvT2PPKtmmTGc9yLtoos3)\n",
        "\n",
        "Sensors such as S2 and S3 are useful for the machine learning process as they show regressive characteristics. Howvever in contrary, sensors such as S5 and S6 are removed as they are flat and will not contribute to the machine learning process. In total, sensors Op3, S1, S5, S6, S9, S16, S10, S18 and S19 were removed.\n",
        "<br>\n",
        "\n",
        "![Dataset example](https://drive.google.com/uc?id=1JMP_FgtpRi8dx3MO441snXgc18phsOt_)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZe9e2xwonSi"
      },
      "source": [
        "\n",
        "##1.2 Feature scaling\n",
        "After they are removed, the train and test datasets are combined together for feature scaling using `sklearn.preprocessing.MinMaxScaler()`. Feature scaling of the data is done so that each column (the sensor data) of the dataset share a common scale. Their features are normalized between between zero and one without distorting the differences in the range of their values. After their values are normalized, the train and test datasets are split again for the model training and testing process."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLmKeK4fjGas"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "#Importing both test and train datasets\n",
        "df_test_FD001 = pd.read_csv('/content/gdrive/My Drive/Capstone/test_FD001.txt', sep=' ', header=None)\n",
        "df_train_FD001 = pd.read_csv('/content/gdrive/My Drive/Capstone/train_FD001.txt', sep=' ', header=None)\n",
        "\n",
        "\n",
        "# dropping NAN values\n",
        "df_test_FD001 = df_test_FD001.dropna(axis=1, how='all')\n",
        "df_train_FD001 = df_train_FD001.dropna(axis=1, how='all')\n",
        "\n",
        "# Naming the columns\n",
        "df_test_FD001.columns = [\"unit\", \"cycles\", \"Op1\",\n",
        "                      \"Op2\", \"Op3\", \"S1\", \"S2\",\n",
        "                      \"S3\", \"S4\", \"S5\", \"S6\", \"S7\", \"S8\", \"S9\", \"S10\", \"S11\",\n",
        "                      \"S12\", \"S13\", \"S14\", \"S15\", \"S16\", \"S17\", \"S18\", \"S19\", \"S20\", \"S21\"]\n",
        "\n",
        "df_train_FD001.columns = [\"unit\", \"cycles\", \"Op1\",\n",
        "                      \"Op2\", \"Op3\", \"S1\", \"S2\",\n",
        "                      \"S3\", \"S4\", \"S5\", \"S6\", \"S7\", \"S8\", \"S9\", \"S10\", \"S11\",\n",
        "                      \"S12\", \"S13\", \"S14\", \"S15\", \"S16\", \"S17\", \"S18\", \"S19\", \"S20\", \"S21\"]\n",
        "\n",
        "# data preprocessing; removing unnecessary data\n",
        "df_test_FD001.drop(['Op3','S1', 'S5', 'S6', 'S9', 'S16', 'S10', 'S18', 'S19'], axis=1, inplace=True)\n",
        "df_train_FD001.drop(['Op3','S1', 'S5', 'S6', 'S9', 'S16', 'S10', 'S18', 'S19'], axis=1, inplace=True)\n",
        "\n",
        "# test dataset begins with unit 101 to differentiate from the train dataset\n",
        "df_test_FD001['unit'] += 100\n",
        "\n",
        "# Combine the two datasets into a single dataframe\n",
        "df_combined = pd.concat([df_train_FD001, df_test_FD001]) \n",
        "\n",
        "# Perform scalling on the the combined dataset so that their scale is the same\n",
        "scaler = MinMaxScaler()\n",
        "df_combined.iloc[:,2:18] = scaler.fit_transform(df_combined.iloc[:,2:18])\n",
        "\n",
        "# Split them again after performing scalling\n",
        "df_train_FD001 = df_combined[(df_combined.unit <= 100)]\n",
        "df_test_FD001 = df_combined[(df_combined.unit >= 101)]\n",
        "\n",
        "# After separation, the first unit of the test dataset is reset to 0 again\n",
        "df_test_FD001['unit'] -= 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "diT_KsXE3e4H"
      },
      "source": [
        "#2. Calculation\n",
        "\n",
        "##2.1 Remaining Useful Life (RUL)\n",
        "The Remaining Useful Life of an engine unit at a cycle is calculated by subtracting its final cycle *(after which the engine stops operating or also the maximum/total number of cycles)*, to the current cycle that we want to calculate its RUL. This value represents the amount of cycles remaining before engine failure. *For an engine that lasts 200 cycles, the RUL of that engine will be 200 at its very first cycle, and 0 at its final or maximum cycle.*  \n",
        "![RUL calculation](https://drive.google.com/uc?id=1plHNZOQJv5CNyfkJ70AaHsya_7VBoMhj)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skiWkIBN3PHQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "143597d5-1b51-4f01-e51d-7340b933bc7a"
      },
      "source": [
        "# Finding the maximum cycle of an engine unit which is used to find its Remaining Useful Life (RUL)\n",
        "df_train_FD001 = pd.merge(df_train_FD001, df_train_FD001.groupby('unit', as_index=False)['cycles'].max(), how='left', on='unit')\n",
        "df_train_FD001.rename(columns={\"cycles_x\": \"cycles\", \"cycles_y\": \"Final Cycle\"}, inplace=True)\n",
        "\n",
        "df_train_FD001['RUL'] = df_train_FD001['Final Cycle'] - df_train_FD001['cycles']\n",
        "\n",
        "# Defining the Health Index, where value of 1 denotes healthy engine and 0 denotes failure\n",
        "def HealthIndex(dataFrame,q):\n",
        "    return(dataFrame.RUL[q]-dataFrame.RUL.min()) / (dataFrame.RUL.max()-dataFrame.RUL.min())\n",
        "\n",
        "healthIndex_q = []\n",
        "healthIndex = []\n",
        "\n",
        "for i in range(df_train_FD001['unit'].min(), df_train_FD001['unit'].max() + 1):\n",
        "    dataFrame = df_train_FD001[df_train_FD001.unit == i]\n",
        "    dataFrame = dataFrame.reset_index(drop = True)\n",
        "    for q in range(len(dataFrame)):\n",
        "        healthIndex_q = HealthIndex(dataFrame, q)\n",
        "        healthIndex.append(healthIndex_q)\n",
        "\n",
        "df_train_FD001['Health Index'] = healthIndex\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set(font_scale=1.5)\n",
        "plt.figure(figsize = (20, 5))\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "y_axis =  df_train_FD001['RUL'][df_train_FD001['unit']==3]\n",
        "x_axis = df_train_FD001['cycles'][df_train_FD001['unit'] == 3]\n",
        "\n",
        "plt.plot(x_axis, y_axis, linewidth=4.5, label='Remaning Useful Life')\n",
        "plt.axhline(0, color=\"red\", linestyle='dashed', linewidth=1.6, label='RUL at final Cycle')\n",
        "plt.xlabel('Cycles')\n",
        "plt.ylabel('Remaining Useful Life')\n",
        "plt.title('Actual Remaining Useful Life; Engine Unit 3', x=0.5, y=1.05, ha='center', fontsize='large');\n",
        "plt.legend(loc='best');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLMrhf5wZctg"
      },
      "source": [
        "##2.2 Health Index (H.I.)\n",
        "The RUL can be used to calculate the health index of the engine. The health index of the engine represents the state of the engine's health where values closer to one represents a healthier engine and values closer to 0 signifies that the engine reaching the end of its operational life. The health index is caculated by dividing the the current cycle of the time series by the final cycle of the unit. *For example, calculating the H.I of an engine at its very first cycle where it is at best health returns a value of one as the RUL of the engine at the first cycle is also equals to the final cycle of the engine.*\n",
        "\n",
        "![HI calculation](https://drive.google.com/uc?id=1Zbk_7BXxpUSJWeA2JppKUnvlXF9-E8vV)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU8VY9jtYwrn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "4c1b8b40-43be-49a3-acbf-cc743d04afb0"
      },
      "source": [
        "sns.set(font_scale=1.5)\n",
        "plt.figure(figsize = (20, 5))\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "y_axis =  df_train_FD001['Health Index'][df_train_FD001['unit']==3]\n",
        "x_axis = df_train_FD001['cycles'][df_train_FD001['unit'] == 3]\n",
        "\n",
        "plt.plot(x_axis, y_axis, linewidth=4.5, label='Health Index')\n",
        "plt.axhline(0, color=\"red\", linestyle='dashed', linewidth=1.6, label='H.I at final cycle')\n",
        "plt.xlabel('Cycles')\n",
        "plt.ylabel('Health Index')\n",
        "plt.title('Actual Health Index; Engine Unit 3', x=0.5, y=1.05, ha='center', fontsize='large')\n",
        "plt.legend(loc='best');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wh0BHDHSqyPE"
      },
      "source": [
        "#3. Defining test and train data\n",
        "##Train data\n",
        "Sensor data values from the **train dataset** are defined as X_train and the Health Index calculated in the earlier section defined as y_train. The X_train and y_train data will be used to fit and train the machine learning models.  \n",
        "##Test data\n",
        "Sensor data values from the **test dataset** is defined as X_test. What is being predicted is the Health Index of the engine. <ins>When X_test is passed into the trained machine learning model, it is able to return a prediction of engines' Health Index.</ins>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeYJ9C9I_YVC"
      },
      "source": [
        "# Defining train values that will be used to train the machine learning model\n",
        "X_train = df_train_FD001[['cycles', 'Op1', 'Op2', 'S2', 'S3', 'S4', 'S7', 'S8', 'S11', 'S12',\n",
        "          'S13', 'S14', 'S15', 'S17', 'S20', 'S21']].values\n",
        "y_train = df_train_FD001[['Health Index']].values.ravel()\n",
        "\n",
        "# Defining test values that will be used to perform prediction based on the trained model\n",
        "X_test = df_test_FD001[['cycles', 'Op1', 'Op2', 'S2', 'S3', 'S4', 'S7', 'S8', 'S11', 'S12',\n",
        "          'S13', 'S14', 'S15', 'S17', 'S20', 'S21']].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqXcbA573cVk"
      },
      "source": [
        "#4. Machine Learning Models\n",
        "Three machine learning models are used to predict the RUL of an engine.  \n",
        "The models used are:\n",
        "\n",
        "1.   Linear Regression (Lreg)\n",
        "2.   K Nearest Neighbour Regressor (KNR)\n",
        "3.   Artificial Neural Networks (NN)\n",
        "\n",
        "The SciKit machine learning library was used for both the multiple linear regression and the K-Nearest\n",
        "Neighbour Regression models. Whereas, the ANN model was made using the open-source TensorFlow library\n",
        "made by Google.\n",
        "Sixteen independent variables in the form of thirteen engine sensors, two operational cycles and the engine\n",
        "cycles were used as features to train the machine learning model. While the Health Index is used as the\n",
        "dependent variable; the variable that is being predicted by the machine learning model in the test dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWnewBJrrSqX"
      },
      "source": [
        "## 4.1. Linear Regression model\n",
        "The default linear regression model from the SciKit-learn library is used in this project.\n",
        "  \n",
        "Multiple linear regression is an extended version of the linear equation . ŷ = βX + c In predictive analysis such as this, the relationship between two or more independent variables X with a dependent variable ŷ are found calculated with multiple linear regression by fitting the linear equation to observed data. The multiple linear regression equation is similar to the linear equation, where instead ŷ = β0 + β1X1 + β2X2 + .... + βpXp is used. Each independent variable Xp is correlated to the dependent or ‘predicted’ variable ŷ . In X1, X2, ... Xp , p is defined to be the number of independent or ‘predictor’ variables. β0 is the y-intercept or the value of the dependent variable ŷ when the rest of the independent variables are zero, while β1 to βp are the slope coefficients of each independent variable (Yale University, 1998)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQtvppGFFnAZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0099b35-4912-4db8-d43a-107e9d5a7275"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lreg_model = LinearRegression()\n",
        "lreg_model.fit(X_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lOS1RHTIrdHO"
      },
      "source": [
        "## 4.2. KNN Regressor Model\n",
        "The KNN algorithm is used for both regression and classification problems. For this project however, the\n",
        "KNN regression algorithm is implemented. KNN regression uses the similarity in features between the test\n",
        "point and the training points to predict a value. The similarity of the training points is determined by the\n",
        "distance between the train and test data points. For this project, the distance is calculated based on the\n",
        "Euclidean Distance. It is the square root of the sum of the squared differences between test point x and the\n",
        "train point y (A. Singh, 2018).\n",
        "\n",
        "As there are no true statistical methods to find the ‘K’ factor, a random ‘K’ number was first initialized and\n",
        "then iterated to find the most favourable value (A. Band, 2020) — where the ‘K’ returns the lowest root mean\n",
        "square error. Root mean square error is an analysis method explained later in section 6.4 . Doing this method,\n",
        "this project determined a suitable ‘K’ factor of **12**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntfsVqLYLN7g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f6ca80c-cd66-4cd6-f00a-329504b3df4d"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "knr_model = KNeighborsRegressor(n_neighbors=12)\n",
        "knr_model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=12, p=2,\n",
              "                    weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGb3P3Pmr0A-"
      },
      "source": [
        "## 4.3. Neural Network Model\n",
        "Neural Network is a technique relating to the building of a computer program that learns from the data that it\n",
        "acquires. The term ‘Artificial Neural Network’ is coined based on the similarity to human brain structure and\n",
        "its workings. In ANN, several software “neurons” called “nodes” are interconnected together.\n",
        "  \n",
        "The neural network model is defined with 16 inputs nodes that comes from the engine sensor datas. They are then passed through a single hidden layer with 50 nodes, with a single output. Where is then trained over 55 epochs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3fyiFRb-4ik"
      },
      "source": [
        "# Importing keras deep learning API into colab notebook\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Activation\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "\n",
        "# Defining Neural Network model\n",
        "nn_model = Sequential()\n",
        "nn_model.add(Dense(50, input_dim=16, kernel_initializer='normal', activation='relu'))\n",
        "nn_model.add(Dense(1, activation='relu'))\n",
        "\n",
        "# Compiling model\n",
        "nn_model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "\n",
        "# Training the Neural Network model\n",
        "nn_model.fit(X_train, y_train, epochs = 55, shuffle=True, verbose = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eRNluPBHOii"
      },
      "source": [
        "## 4.4. Saved Neural Network Model\n",
        "Alternatively, the best Neural Network model as a result of multiple runs was saved and can be uploaded again so that the most accurate model can be retrieved again for an accurate prediction. This method is used for the subsequent sections."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jzahZhS-FnG3"
      },
      "source": [
        "import keras\n",
        "\n",
        "# from tensorflow import keras\n",
        "nn_model = keras.models.load_model('/content/gdrive/My Drive/Capstone/BestModel')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLk8CODSSkHM"
      },
      "source": [
        "#5. `MachineLearningModel()` class and its methods\n",
        "The `MachineLearningModel()` class is created to reduce code duplication when calling multiple machine learning models to predict the engine degradation. The details of its methods are further explained below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UgD3Oxr9B9MW"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import seaborn as sns\n",
        "\n",
        "# Apply the default theme\n",
        "sns.set_theme()\n",
        "sns.set(font_scale=1.5)\n",
        "plt.style.context(\"seaborn-whitegrid\")\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "class MachineLearningModel:\n",
        "  def __init__(self,model,name,testDataFrame, X_test, OutputDataFrame = None):\n",
        "    self.model = model\n",
        "    self.name = name\n",
        "    self.testDataFrame = testDataFrame\n",
        "    self.X_test = X_test\n",
        "\n",
        "    # Values of the predictions and calculations are stored in the test dataframe by default unless an alternate dataframe is specified \n",
        "    if OutputDataFrame is not None:\n",
        "      self.OutputDataFrame = OutputDataFrame\n",
        "    else:\n",
        "      self.OutputDataFrame = self.testDataFrame\n",
        "\n",
        "  def predict(self):    \n",
        "    # Function predicts the Health Index values based on the trained machine learning model.\n",
        "    Prediction = self.model.predict(self.X_test)\n",
        "\n",
        "    # The predicted H.I. values are appended into the dataframe \n",
        "    self.OutputDataFrame[f'Pred_H.I._{self.name}'] = Prediction\n",
        "\n",
        "    # Calculating the predicted final cycle of the engine units      \n",
        "    self.OutputDataFrame[f'PredFinalCycle_{self.name}'] = self.OutputDataFrame['cycles'] / (1 - self.OutputDataFrame[f'Pred_H.I._{self.name}'])\n",
        "\n",
        "  def PlotPredFinalCycle(self,displayUnit=1, display_mean = True):\n",
        "    # Returning the average final cycle value of a particular engine unit\n",
        "    df = self.OutputDataFrame[f'PredFinalCycle_{self.name}']\n",
        "    meanValue = df[(self.OutputDataFrame['unit'] == displayUnit)][-10:].mean()\n",
        "    print(f'Mean value of last 10 cycles of engine unit {displayUnit}: {meanValue}') \n",
        "\n",
        "    # plotting the predicted final cycles\n",
        "    plt.figure(figsize = (18, 10))\n",
        "    y_axis = df[(self.OutputDataFrame['unit'] == displayUnit)]\n",
        "    x_axis = self.OutputDataFrame['cycles'][self.OutputDataFrame['unit'] == displayUnit]\n",
        "\n",
        "    plt.plot(x_axis, y_axis, linewidth=2.5, label='Predicted Final Cycle')\n",
        "    plt.xlabel('Cycles')\n",
        "    plt.ylabel('Predicted Final Cycle')\n",
        "    if display_mean == True:\n",
        "      plt.axhline(meanValue, color=\"red\", linestyle='dashed', linewidth=1.6, label='Calculated mean')\n",
        "\n",
        "    # set title and axis labels\n",
        "    errorDist = plt.title(f'Predicted Final Cycle of engine unit {displayUnit}', x=0.5, y=1.05, ha='center', fontsize='xx-large')\n",
        "    plt.setp(errorDist, color='black')\n",
        "    plt.legend(loc='best')\n",
        "\n",
        "  def DataFrame(self, displayUnit=None, remove_sensor_data=False):\n",
        "    df = self.OutputDataFrame\n",
        "    if displayUnit:\n",
        "      df = df[df['unit'] == displayUnit]\n",
        "\n",
        "    # function returns dataframe\n",
        "    droppedColumns = ['Op1', 'Op2', 'S2', 'S3', 'S4', 'S7', 'S8', 'S11', 'S12',\n",
        "          'S13', 'S14', 'S15', 'S17', 'S20', 'S21']\n",
        "    if remove_sensor_data == True:\n",
        "      df.drop(droppedColumns, axis=1, errors='ignore', inplace=True)\n",
        "    return df\n",
        "\n",
        "  def CalcPredFinalCycle(self):\n",
        "    df = self.OutputDataFrame[f'PredFinalCycle_{self.name}']\n",
        "\n",
        "    # Returns a uniform PredFinalCycle based on the calculated average of the last 10 cycles \n",
        "    for i in range(self.OutputDataFrame['unit'].min(), self.OutputDataFrame['unit'].max() + 1):\n",
        "        df[(self.OutputDataFrame['unit'] == i)] = df[(self.OutputDataFrame['unit'] == i)][-10:].mean()\n",
        "  \n",
        "  def CalcPredRUL(self):\n",
        "    self.OutputDataFrame[[f'Pred_RUL_{self.name}']] = self.OutputDataFrame[f'PredFinalCycle_{self.name}'] * self.OutputDataFrame[f'Pred_H.I._{self.name}']\n",
        "\n",
        "    # Appending the RUL values of each engine unit into an array\n",
        "    df =  self.OutputDataFrame[f'Pred_RUL_{self.name}']\n",
        "    pred_final = []\n",
        "    for i in range(1,101):\n",
        "      pred_final.append(df[(self.OutputDataFrame['unit'] == i)].min())\n",
        "\n",
        "    self.predicted = pred_final\n",
        "    return pred_final  \n",
        "\n",
        "  def PlotRUL(self, displayUnit = None, line_width=2.5, show_final_RUL=False, show_best_fit=False):\n",
        "    # Visualizing the predicted cycles of the engine units in the test dataset.\n",
        "    # x-axis represents the cycles\n",
        "    engine_unit = ''\n",
        "    if displayUnit is None:\n",
        "      y_axis =  self.OutputDataFrame[f'Pred_RUL_{self.name}']\n",
        "      x_axis = range(len(self.OutputDataFrame['cycles']))\n",
        "      plt.figure(figsize = (18, 10)) \n",
        "    else:\n",
        "      y_axis =  self.OutputDataFrame[f'Pred_RUL_{self.name}'][self.OutputDataFrame['unit'] == displayUnit]\n",
        "      x_axis = self.OutputDataFrame['cycles'][self.OutputDataFrame['unit'] == displayUnit]\n",
        "      engine_unit = f'Engine {displayUnit}, '\n",
        "      plt.figure(figsize = (20, 5)) \n",
        "   \n",
        "    if show_final_RUL:\n",
        "      min_RUL = y_axis[(self.OutputDataFrame['unit'] == displayUnit)].min()\n",
        "      plt.axhline(min_RUL, color=\"red\", linestyle='dashed', linewidth=1.6, label='Final Cycle')\n",
        "      print(f\"RUL of engine unit {displayUnit} is {min_RUL}\")\n",
        "      \n",
        "\n",
        "    plt.plot(x_axis, y_axis, linewidth=line_width, label='Predicted RUL')\n",
        "    plt.xlabel('Cycles')\n",
        "    plt.ylabel('Predicted RUL')\n",
        "    plt.title(f'Predicted Remaining Useful Life; {engine_unit}{self.name}', x=0.5, y=1.05, ha='center', fontsize='x-large')\n",
        "    \n",
        "    if show_best_fit:\n",
        "      bf_x_axis = x_axis.values.reshape(-1,1)\n",
        "      RUL_lreg = LinearRegression()\n",
        "      RUL_lreg.fit(bf_x_axis,y_axis)\n",
        "      best_fit = RUL_lreg.predict(bf_x_axis)\n",
        "      plt.plot(x_axis, best_fit, linewidth=.75*line_width, label='Best fit', linestyle='dashed')\n",
        "    \n",
        "    plt.legend(loc='best')\n",
        "\n",
        "  def RUL_bestFit(self):\n",
        " \n",
        "    for i in range(self.OutputDataFrame['unit'].min(), self.OutputDataFrame['unit'].max() + 1):\n",
        "      x_axis = self.OutputDataFrame['cycles'][self.OutputDataFrame['unit'] == i]\n",
        "      y_axis =  self.OutputDataFrame[f'Pred_RUL_{self.name}'][self.OutputDataFrame['unit'] == i]\n",
        "\n",
        "      bf_x_axis = x_axis.values.reshape(-1,1)\n",
        "      RUL_lreg = LinearRegression()\n",
        "      RUL_lreg.fit(bf_x_axis,y_axis)\n",
        "      self.OutputDataFrame[f'Pred_RUL_{self.name}'][self.OutputDataFrame['unit'] == i] = RUL_lreg.predict(bf_x_axis)\n",
        "\n",
        "    df =  self.OutputDataFrame[f'Pred_RUL_{self.name}']\n",
        "    pred_final = []\n",
        "    for i in range(1,101):\n",
        "      pred_final.append(df[(self.OutputDataFrame['unit'] == i)].min())\n",
        "\n",
        "    self.predicted = pred_final\n",
        "    return pred_final  \n",
        "\n",
        "  def DisplayEngineRUL(self, displayUnit=1):\n",
        "    # Returning the Remaining useful life of an engine unit of the test dataset.\n",
        "    # It is found by returning the minumum cycle to failure value of each engine unit\n",
        "    df =  self.OutputDataFrame[f'Pred_RUL_{self.name}'] \n",
        "    value = df[(self.OutputDataFrame['unit'] == displayUnit)].min()\n",
        "    print(f'Predicted RUL of unit {displayUnit}: {value}')\n",
        "\n",
        "  def GetRMSE(self,actual,predicted=None):\n",
        "    if predicted is None:\n",
        "      predicted = self.predicted\n",
        "    \n",
        "    # Calculating the Root Mean Square Error of the actual and predicted values\n",
        "    RMSE = mean_squared_error(actual, predicted, squared=False)\n",
        "    return RMSE\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PWeqCVoha46"
      },
      "source": [
        "The Results fataframe is defined where the results of the predictions and calculation for each ML models are stored. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QotaKxff_MC"
      },
      "source": [
        "Results_nn = df_test_FD001[['unit', 'cycles']]\n",
        "Results_knr = df_test_FD001[['unit', 'cycles']]\n",
        "Results_lreg = df_test_FD001[['unit', 'cycles']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVY9Hjd5FVue"
      },
      "source": [
        "#6. Prediction\n",
        "Using the `MachineLearningModel()` class, the prediction of engine RUL using multiple machine learning models can be made without code duplication. In this section, the process is explained using the best performing model: Neural Networks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NA7zloFeHtr2"
      },
      "source": [
        "##6.1 Neural Network model prediction\n",
        "The `MachineLearningModel()` class accepts six attributes such as:\n",
        "\n",
        "1.   Trained machine learning model\n",
        "2.   The name of the model in a string data type used when storing their results — *eg.; 'nn', 'lreg', 'knr'*\n",
        "3.   The test dataframe where the results are stored by default\n",
        "4.   The values of the test set in `numpy.ndarray` type that is used to predict the model\n",
        "5.   If specified, an alternative dataframe where the results are stored\n",
        "\n",
        "The `MachineLearningClass.predict()` method inputs the X_test data into the prediction model and saves the output into the dataframe. This output is named \"Pred_H.I._nn\" in this example or the Predicted Health Index of the engine. Using the predicted H.I, the final cycle of the engine can be calculated by dividing the current cycle by one minus the Health Index. \n",
        "\n",
        "![predicted final cycle](https://drive.google.com/uc?id=1250YEDm1IfsoAWqYbXZASC6d5i8qjpfb)\n",
        "\n",
        "```\n",
        "  def predict(self):    \n",
        "    # Function predicts the Health Index values based on the trained machine learning model.\n",
        "    Prediction = self.model.predict(self.X_test)\n",
        "\n",
        "    # The predicted H.I. values are appended into the dataframe \n",
        "    self.OutputDataFrame[f'Pred_H.I._{self.name}'] = Prediction\n",
        "\n",
        "    # Calculating the predicted final cycle of the engine units      \n",
        "    self.OutputDataFrame[f'PredFinalCycle_{self.name}'] = self.OutputDataFrame['cycles'] / (1 - self.OutputDataFrame[f'Pred_H.I._{self.name}'])\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNE3kjHBSX62"
      },
      "source": [
        "NeuralNetwork = MachineLearningModel(nn_model, 'nn', df_test_FD001, X_test, Results_nn)\n",
        "NeuralNetwork.predict()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puGa0DE4IEPE"
      },
      "source": [
        "By displaying the last five rows of the dataframe, the predicted RUL of unit 100 based can be calculated by subtracting the final cycle with the predicted final cycle (222 - 198) which returns a value of 19. In other words, this means that it is predicted that the engine's operational life will end in approximately 24 cycles.\n",
        "\n",
        "![Predicted RUL calculation](https://drive.google.com/uc?id=1U37h-6AQnS6VbtkgDK65uIVv7GdXGksO)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSZPe7RUw4zk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "24c3f39a-2fd3-4ffe-839e-d31f6553a02a"
      },
      "source": [
        "# displaying the last 5 rows of the dataframe\n",
        "NeuralNetwork.DataFrame(100).tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit</th>\n",
              "      <th>cycles</th>\n",
              "      <th>Pred_H.I._nn</th>\n",
              "      <th>PredFinalCycle_nn</th>\n",
              "      <th>Pred_RUL_nn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13091</th>\n",
              "      <td>100</td>\n",
              "      <td>194</td>\n",
              "      <td>0.137112</td>\n",
              "      <td>224.826355</td>\n",
              "      <td>31.226948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13092</th>\n",
              "      <td>100</td>\n",
              "      <td>195</td>\n",
              "      <td>0.144416</td>\n",
              "      <td>227.914627</td>\n",
              "      <td>32.890553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13093</th>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "      <td>0.146855</td>\n",
              "      <td>229.738129</td>\n",
              "      <td>33.445866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13094</th>\n",
              "      <td>100</td>\n",
              "      <td>197</td>\n",
              "      <td>0.132384</td>\n",
              "      <td>227.058945</td>\n",
              "      <td>30.150166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13095</th>\n",
              "      <td>100</td>\n",
              "      <td>198</td>\n",
              "      <td>0.111879</td>\n",
              "      <td>222.942581</td>\n",
              "      <td>25.480204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       unit  cycles  Pred_H.I._nn  PredFinalCycle_nn  Pred_RUL_nn\n",
              "13091   100     194      0.137112         224.826355    31.226948\n",
              "13092   100     195      0.144416         227.914627    32.890553\n",
              "13093   100     196      0.146855         229.738129    33.445866\n",
              "13094   100     197      0.132384         227.058945    30.150166\n",
              "13095   100     198      0.111879         222.942581    25.480204"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_49S9x0K1lKS"
      },
      "source": [
        "\n",
        "Taking unit 20 for example, it is observed that the value of the predicted final cycle throughout the timeseries is noisy and unstable. In a perfect world this should not happen as the final cycle should be same throughout. As a result, in this project the **<ins>average value of the last ten cycles is used to represent the predicted final cycle of the entire engine unit</ins>**. For the purpose of this project, these average values are refered as the adjusted predicted final cycle. For the example plotted below, the dotted line represent the averaged value used.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRsgHnByzqTc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "9439a208-4501-4e62-e41b-1d6b0a11821a"
      },
      "source": [
        "NeuralNetwork.PlotPredFinalCycle(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean value of last 10 cycles of engine unit 20: 209.6955108642578\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi1kaLstYeBm"
      },
      "source": [
        "After averaging the predicted final cycle' values, we can see that it's values are now stable throughout the timeseries. Now we can proceed to calculate the RUL of the engine."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWVzHNSFY5Ft",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "7dc4dc10-a016-4164-cc22-9f0ebc72cace"
      },
      "source": [
        "NeuralNetwork.CalcPredFinalCycle()\n",
        "NeuralNetwork.PlotPredFinalCycle(20, display_mean=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean value of last 10 cycles of engine unit 20: 209.69552612304688\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eFi1aJFv2ww8/NMtOmTIl0bqcnZ0NKaY10KRJk5JsHRIREWGsWbMmwXJLrrU17wdLde7c2dxnvXr1jNu3bycoc/78+Xj3zahRoxKt6+LFi6l6Um8Ja56TuJ+D+l9LjTt37iQo98cff5h1OTk5GTdv3kxQJioqynjuuefMugIDA40nT54kKBcUFJSgtYU1WnJIMipXrmwEBwcnKHft2jWjVKlSZrmkWmTZ4v2WmvdHalpyxP58+umniZabMGGCWeaVV15JtExqP49TaimaEeK2KE3qPhwwYIBZZty4cSnW+fPPP5vlK1SoYO2QgWyNJAcAq7EkyfH3338br7zyilkuT548Cf65j5vkqFGjRrL/2IWHhxseHh6GJKNUqVLJJggMI35z0qf/KYuIiDDrkmRs3bo1yXqCg4PjJWrSmuSYOnWqub5+/frJJnOSk5okx61bt8zYfXx8Utxn3C/Dy5cvj7cubvcEJyenRJNCsfbt22d+gbd2ksOSn7hfgFP7T3XFihWNsLCwJGPp3r27WXbXrl3pOq7Yrlxubm5JXhtrJDnef/99M+ZWrVqluZ7ENGzY0ExMnD17NtEykZGRRunSpQ1JhrOzs3Hr1q0EZdq2bWvG+M4776QplpSutTXvB0udPHnSvBfy5cuXbNeGAwcOmF0a3NzcEk0OWDvJYe1zEvdz0N3dPdFrHWv06NFm2SVLliRYv379+njJhsQSHLFmzJhh9SSHo6OjcerUqST3OWvWLLPsxIkTE6y3xfvNMDI2ydG7d+8k64qMjDT/rrq6uiZ6vFk9yRH3QUqePHmSfJASt+vd9OnTU6z3yJEjZvnChQtbO2wgW2PgUQAZYsuWLVq7dq35s3TpUo0bN05Vq1Y1m69K0oQJE1SoUKEk6xk2bFi8bilP27p1q65evSpJGjp0qPLly5dsXK1atVLx4sUlST/99FO8db/99ptZl4+Pj/z9/ZOsx9PTUz179kx2X5ZYtGiR+Xry5MlydMz4XoTLly/X33//LSmmCXFK++zVq5dZ5ulz9sMPP5jddtq1a5fsTBt169ZV8+bN0xO6zQwdOjTZWStatGhhvj5+/Hi69tW4cWNJMd2AMrIJ8507d8zXBQoUsGrdr7/+uiTJMAzNmTMn0TI//vijLl++LClmEL4iRYokiC92qum8efPqgw8+sGqMsax5P1hq5cqVZveb/v37q0SJEkmWrVOnjl566SVJMe+JtO4zNTLynPTt2zfBtY4rpXtp9erV5us333xTTk5OSdY1cOBAq7+327RpI29v7yTXpxS/Ld5vGS25ATZz5swpX19fSTEDc1pjcODMtG/fPvPzTJKmTp1q/g/xtLiDxLq6uqZYd9wysTNcAbAOxuQAkCFi+4cnxdnZWRMmTNDw4cOTLRf7hS8pcUdcj4iI0Nq1a1OMLV++fLpx44b++OOPeMv37dtnvm7ZsmWK9bRs2TLZqeRS8tdff5n/BLu7u6tp06Zpris14p6z+/fvW3TO8uTJo3v37lnlnCU3W0RaDRs2TH5+fsmWqVevXprrT6kfealSpczXf/31V5LlwsPDtXLlSm3YsEFHjhzR9evX9ejRI0VFRSVa/sqVK2kaT8HWunTpohEjRujWrVtasGCBJk6cGG8sCUmaNWuW+fq1115LUMevv/5qJgKaN2+eYgIzrax5P1hq79695mtLxox46aWXzITP3r171atXrzTt11IZeU7Sey/F/cxJLhEtSS4uLmrUqFGqZsRKSXrjt8X7LSO5ubmpRo0ayZax9PMxqzl79qxeeeUVc+yXvn37pvi/DYCsgSQHgEzh4uKiAgUKqEqVKmrWrJkCAwPl6emZ4nalS5dOdn3caVnHjx+fqpju3r0b7/fYVhySVLFixRS3r1SpUqr297QrV66YX+KqVKkSbyrYjBT3nCX25TI5tj5nSfHx8VGHDh0ypG4pZlDI5MT9Ah/7D/HT9u/frx49eujChQsW7zd2MNqMELcFVWLTMaeHs7OzBg4cqI8//lh37tzRihUr1Lt3b3P9xYsXtWXLFknS888/r0aNGiWoI7aVh6RkWwillzXvB0vFvW+SaxWQWJlr166laZ+pkZHnJL33UtzjL1euXIrxVKhQIcUyqZHe+G3xfstIBQsWTPFvlyWfj1nNhQsX5OfnZw6S3rlzZ3Nw86TkzZvXfG3JwORxy2RUEhfIrkhyAMgQFy9eTNO89U9Lqclner6cRURExPs9blNTNze3FLe3pExy4jZPjfvPUUZLzzl7evaKzD5ntpIjR/p6d166dEn+/v7m+fLy8lLr1q3l7e2tokWLKleuXOY+tm/frhkzZkhSki08rCHu09WzZ89avf4hQ4ZoypQpioqK0qxZs+IlOb7++mtFR0dLSvqLXmbdH9a8HyyV2vsmT5485uvMaNaekeckvffSo0ePJMXMfJVcV8ZY1v7MSW/8tni/ZaT0no+s6MKFC2rWrJmuXLkiKaZl2tKlS1PsWhS3a9Tt27dT3E/cMtbuVgVkdyQ5ADzT4v7z/8svv6TYvSU5cb9IhYaGpljekjLJifvkJu6XnowW95z9+eefKbaWSU5mn7Nn1ccff2xe47fffluffvppkl8O4j7lz0hNmjQxX1+4cEHXr19PdmyI1CpVqpTat2+v1atXa8+ePTp69KiqV6+uJ0+eaP78+ZJi3otxkx9xZdb9Yc37wVKpvW9iv9hLmfPE1xbnxFJ58uTR/fv39fjxY0VFRaWY6MhqnzlZ+dzGysjkalZ3/vx5+fr6mi3Junbtqu+++86i8bIqV65svr548WKK5eOWibstgPSzv/QrgGwl7tPoP//8M111eXh4mK8tebJ95syZdO2vVKlSZjPfkydPml1XMtqzfM6eVZs3b5YkFSlSRJ988kmyTz8t+efYGqpUqRKvy9jChQutvo+4A/bNnj1bUszAkbFNwHv37p1kK40yZcqYr0+cOGH12GJZ836wVNz75vTp0ymWj1umZMmSGRJTXLY4J5aKe/yWdP06d+5cRoaTarY6t87OzpIsaw0SEhKS0eFkSWfPnlXTpk3NBEdAQIBFLThiVa9e3Xy9f//+FMvHLRN3WwDpR5IDwDOtWbNm5uuNGzemq6769eubr7du3ZpieUvKJMfd3V3PP/+8pJjB2Hbs2JHmumKTJZYkSp7lc/asunHjhqSYMQSSe/JsGEa6r0lqxJ0V4YsvvjCTD9bi5+dnjqexZMkSPXr0KMUBR2M1atTIfF9v3749w7ppWPN+sFTc+yY2AZacTZs2JbptRrHFObFU3AGEUxrEODw8XLt27crokFLFVue2YMGCkuKPdZOUPXv2ZHQ4qRY3MZwRDwROnz6tZs2amS3pevbsqSVLlljUJSpWkyZNzO5Rp0+fTnYmmejo6Hj3/ssvv5zGyAEkhiQHgGda69atzencli1bpoMHD6a5rvr165tP2Q4ePKjt27cnWfby5cv67rvv0ryvWP369TNfv/vuu4qMjExTPbFNoOM2a09KQECAOR3ql19+ma6niW3atDHHTVm/fr1OnTqVZNn//ve/+vnnn9O8r2dZ7D++586dS/Yf9G+//VYnT57MrLA0cOBAc8DYW7duqUePHqlq3n/8+HG99957yZYZOnSopJguJ++9955++eUXSTGzVCT39LJQoUJq27atue2ECRMsjis1rHk/WKpLly5mAmfhwoW6fv16kmV///13c+pQNze3TPkyZItzYqlOnTqZr2fMmJFgbKW45s6da/VBddPLVuf2ueeekxQz4HVynzGbNm3SsWPHMiWm1IjbzceSv3OpcerUKTVr1swc1LZv375avHhxqhIcUswYYh07djR//+yzz5Isu3LlSl26dElSzBhNKc3aAyB1SHIAeKblzp1bEydOlBTTj7hNmzYKCgpKdps7d+7os88+S/AU0NHRUe+88475e58+fRJtSn7nzh117tzZKn29Bw8ebHYZ2Lt3rwICApJ9Yr1nzx6zVUBcsbMM3L17N97o/YkpWbKkeZz379+Xv7+/jhw5kuw2V69e1fjx43X06NF4y93d3c0p9SIiItSlS5dE47t06ZK6d+9uDjaZ3cQ+fb5z544mT56caJmNGzdqyJAhmRmWcuXKpbVr15pdRrZv364XX3xRu3fvTna7kJAQjR49WnXr1k2xK0nfvn3N+qdPn24uj01+JOfDDz+Uk5OTpJgvDJMnT07yPRQVFaX169enWOfTrHk/WKpy5crq3Lmzuc9OnTolOnNGcHCwunTpYo6RMHToULm7u6dpn6lhi3NiqTZt2pitg06ePKlBgwYlmujYsWOHxowZk6GxpIWtzm2bNm3M1yNGjEj0nB06dEiBgYFp3kdGijuTTnoeZjzt5MmT8vX1Nf9u9e/fXwsWLEjzgKrjxo0zP7Nmz56tlStXJihz6tQpDRs2zPx94sSJmTa7GpBdMPAogGfegAEDdOTIEc2YMUM3b96Un5+fGjdurFatWsnLy0vOzs66f/++zp49q/3792vXrl2KjIzU4sWLE9Q1bNgwrV27Vjt27NC1a9dUq1Yt9e3bVw0bNpSjo6OOHj2q+fPnKyQkRF27dtWKFSvSFbubm5tWr16tZs2a6eHDh1q1apV27Nih7t27q3bt2sqXL5/u37+vP/74Q5s2bdIff/yh33//3Wy9Eqtly5bmP8odO3bUa6+9Jg8PD/NJlIeHh9k1Ror58njs2DGtW7dOZ8+elY+Pj1q2bCk/Pz+VKlVKOXPm1F9//aVTp07pt99+0/79+2UYhlq0aJHgGCZOnKhNmzbp1KlTOnHihKpUqaJXX31VPj4+MgxD+/fv14IFC/To0SOrnLNn0YgRI8wuB++99562b9+utm3bqmTJkrp586Y2btyoTZs2ydHRUX369En0vZlRqlatqm3btqlTp066evWqjhw5okaNGsnHx0f+/v6qUKGCChQooIcPH+rq1avasWOHdu3aZfFMD3nz5lWfPn301VdfmcsKFy6sLl26pLhtrVq19OWXX2rw4MEyDEPvvvuuFi1apC5dusjb21u5cuXSrVu39Pvvv+vHH39UWFhYmp7cW/N+sNSsWbN04MABXbp0SXv37lXlypXVv39/1ahRQ1FRUdq/f78WLlxoPrWuXbt2hrVmSYwtzoklcuTIoYULF6pJkyZ6/PixFi5cqL1796pPnz6qUKGCHj58qG3btmn58uVycXFRhw4dtHbtWnPbrMAW57Z///769NNPdePGDW3evFl16tRRYGCgSpcurbt37yooKEgrV65Urly54p2zrKJly5aaNm2aJOmf//ynbt26pcqVK5tjjbi6uqpp06apqvPKlSvy8/MzExxVq1ZVu3btLEqWtmzZUrlz506wvGLFivrkk080cuRIGYahbt26qWvXrmrVqpVcXFx04MABzZ8/3xxMuXPnzurZs2eq4gZgAQMArESS+XPx4sU01+Pp6WnWkxpffvmlkS9fvnhxJPXj4uJi/PTTT4nW8+DBA8Pf3z/Z7fv27WucO3fO/L1fv36J1tWvXz+LzsmJEyeM5557zqLYjxw5kmD7GzduGCVLlkxym8Tii4qKMsaNG2e4uLhYtN+8efMaR48eTTT+a9euGTVr1kxyWwcHB2Ps2LFGUFCQuWz8+PFJng9LNG3a1KxrwYIFqdrWkjji1p/S+9mS+iZPnmw4ODgkeY7y5MljfPfdd8aCBQtSPK7Ye8TT09PiY07JzZs3jQEDBhhOTk4WvR8cHR2Nfv36GcHBwSnWfeLEiXjb/vOf/0xVbKtWrTIKFSqUYkzu7u4JtrX0PWfN+8FSV65cMerWrZvivtq0aWPcv38/yXouXrxolm3atGm6YorLWuck7udgUFBQsvuMeyxJfa4ahmFs2bLFcHd3TzaWNWvWGO+995657NChQ4nWldK5Gz9+fKo+ayy5FrZ4v/3yyy/J/o0sUqSIsW3btnjHm9T1il1vyWdQSvVZco9GRUUZLVq0SDL2tHwWxt1van9S+pvwr3/9y3B2dk62jt69exuPHz9OddwAUpY1UtoAYAVDhw7Vn3/+qS+++EJt2rRR6dKllTt3bjk5OalQoUKqU6eOBgwYoCVLlujGjRtq3bp1ovXkzZtXW7du1dKlS9WqVSsVKdfHNjcAACAASURBVFJEzs7O5pSY69ev16JFi1LdXzc5VatW1dGjR7V8+XIFBASobNmycnNzk5OTkwoXLqyGDRvqn//8p/bv35/oOAbFihXT4cOHNXbsWNWuXVsFChRIMb4cOXLoww8/VHBwsD7++GP5+/urZMmSypUrl5ydnVW0aFE1aNBAQ4cO1apVq3Tz5s14rUHiKlGihA4cOKDZs2erSZMmKliwoFxcXOTp6amePXtq586dmjRpklXO1bNqzJgx2rVrl7p16yYPDw85OTnJ3d1d1atX1+jRo3X06FH16NHDZvEVLVpU8+bN04ULFzRt2jR16NBBFStWVMGCBeXo6KgCBQrI29tb3bt311dffaXr169r4cKF8WZoSUrVqlXNqTIdHBzMLk6W6tSpk4KDgzVt2jS1atVKJUuWlLOzs1xcXFSqVCm1bNlSU6ZM0eHDh9N07JJ17wdLeXh4aO/evVqxYoW6du2qMmXKKFeuXHJzc1P58uXVt29fbdmyRT/88EOmTB37NFucE0u1aNFCp0+f1tixY1WtWjW5ubkpT548qlKlikaOHKkjR46oQ4cOun37trlNoUKFMiU2S9ji3DZu3FgnTpzQW2+9JW9vb7m6uipv3ryqVq2a3n//fR07dkzNmze30hFaV44cOfTjjz9qxowZ8vX1VdGiRc1uIVnRO++8o6NHj2rkyJF67rnnlD9/frm6uqpcuXLq06ePtm/frsWLF8vFxcXWoQJ2ycEwMmnOQgAAkC3t3btXDRo0kBQzWHDsQJpARqtVq5YOHz4sd3f3RMc9AQDYH1pyAACADPXll1+ar19//XUbRoLsZOfOnWbrHj8/PxtHAwDILCQ5AABAhjl+/Li+//57SVKlSpUyZQpU2L8dO3YkO1vToUOH4g3oSHINALIPZlcBAABWExYWpp07dyoqKkqnT5/W1KlTFRkZKUn66KOPsswMF3i2BQQEKGfOnGrdurVq1qypokWLyjAMXbt2TUFBQdq4caOZBHn11Vfl6+tr44gBAJmFMTkAAIDVBAcHq2zZsgmWd+vWTcuWLbNBRLBHxYsX182bN1MsN2TIEE2fPj1LD1IJALAuWnIAAIAMkTdvXlWoUEEDBgzQoEGDbB0O7MiqVau0du1a7du3T1evXtXdu3f16NEj5cuXT2XKlFHjxo01YMAA1ahRw9ahAgAyGS05AAAAAACAXaBjLAAAAAAAsAskOQAAAAAAgF0gyQEAAAAAAOwCSQ4AAAAAAGAXSHIAAAAAAAC7QJIDAAAAAADYBZIcAAAAAADALpDkAAAAAAAAdoEkBwAAAAAAsAskOQAAAAAAgF0gyQEAAAAAAOwCSQ4AAAAAAGAXSHIAAAAAAAC7QJIDAAAAAADYBZIcAAAAAADALpDkAAAAAAAAdoEkBwAAAAAAsAskOQAAAAAAgF0gyQEAAAAAAOwCSQ4AAAAAAGAXSHIAAAAAAAC7QJIDAAAAAADYBZIcAAAAAADALpDkAAAAAAAAdoEkBwAAAAAAsAuOtg4gK4qOjlZoaKicnJzk4OBg63AAAAAAAMD/GIahiIgIubm5KUeO+G03SHIkIjQ0VGfOnLF1GAAAAAAAIAmVKlVS3rx54y0jyZEIJycnSTEnzNnZ2cbRJO348eOqVq2arcNAJuKaZy9c7+yHa579cM2zH6559sL1zn645pnjyZMnOnPmjPndPS6SHImI7aLi7OwsFxcXG0eTvKweH6yPa569cL2zH6559sM1z3645tkL1zv74ZpnnsSGl2DgUQAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gdlVAAAAAGRrjx8/VkhIiB4/fqzIyEir1evo6KiTJ09arT5kfVzz9HFyclLRokWVL1++NNdBkgMAAABAtnX//n3dvHlTRYoUUfHixeXo6JjotJRpERoaKjc3N6vUhWcD1zztDMNQWFiYrl69KklpTnTQXQUAAABAtnX79m2VKlVK7u7ucnJyslqCA0DqODg4KHfu3PLw8NCtW7fSXA9JDgAAAADZ1pMnT+Tq6mrrMAD8j6urqyIiItK8PUkOAAAAANkarTeArCO99yNJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAYFVXrlyRt7e3Vq9ebS4bM2aM/Pz8bBhVfDNmzJC3t3e668mM47JWrBklK8XHFLIAAAAAYGdWr16tsWPHmr+7uLjIw8NDzZs31+DBg5U3b14bRme58PBwff3116pbt67q1atnkxjGjBmjNWvWJLpu2bJlmRyNZX777TctXrxYhw8f1oMHD5Q/f37VrFlTXbt2VbNmzWwdXoYiyQEAAAAAdmrEiBEqUaKEwsLCtGfPHs2dO1f79+/XsmXLMn3A1QkTJsgwjFRtEx4erpkzZ+qNN96wWZJDipnx48MPP0ywvEyZMmk6roz0+eefa/bs2Spbtqx69uyp4sWL6+7duwoKCtLgwYP19ddfq2nTprYOM8OQ5AAAAAAAO9W0aVNVqVJFkhQQEKA333xTmzdv1u+//y4fH59EtwkLC8uQaXWdnJysXmdmcXR0VPv27W0dRoo2btyo2bNnq02bNvr000/l6Pj/v/IPGjRIW7dulbOzsw0jzHiMyQEAAAAA2URsa4irV69Kkvr06aP27dvr6NGj6tGjh6pXr6558+ZJkh4/fqzPP/9czZs3V7Vq1eTr66tp06YpMjIyXp0PHjzQmDFjVLt2bdWpU0ejR4/Ww4cPE+w7sbEroqOjtWDBArVt21bPP/+8GjRooCFDhujs2bO6cuWKXnjhBUnSzJkz5e3tLW9vb82YMcPc/uzZs3rjjTdUt25dVa9eXd26ddPu3bsT7Pu///2vOnfurOeff17+/v76/vvv03EWkz+u2PFIFi5cqKVLl8rf31/VqlVT586ddfTo0Xjbnjp1SmPGjFHz5s31/PPP68UXX9TYsWP1119/pSmW6dOnq0CBApowYUK8BEesFi1aqEGDBrp8+bIZ49N2794tb29v7dy501x2/fp1jR07Vo0aNdLzzz+vFi1aaOLEiSnGs2rVKnXs2FHVq1dXvXr1NHr0aN2+fTtNx2YpWnIAAAAAQDZx+fJlSVKBAgXMZXfv3tXgwYPVrl07dejQQSVKlFB0dLSGDBmiI0eOKCAgQF5eXjpx4oRmz56tGzduaPLkyZIkwzA0dOhQHTx4UD169FC5cuW0detWjR492qJ4xowZo3Xr1snX11fdu3dXeHi49u3bpxMnTqhly5b66KOPNG7cOLVo0UItWrSQJHOAy9OnT6tnz54qWbKkBg0aJBcXF23YsEEDBw7UN998owYNGpjlBgwYoEKFCmnYsGGKjIzUjBkzVKhQIYvPm2EYunv3brxlzs7OypMnT5LbrFu3Tn///be6d+8uBwcHzZs3T8OGDdO2bdvMVi179uzR5cuX1alTJxUpUkRnz57V8uXLde7cOS1fvjxVXYqCg4N18eJFdenSRW5ubsmWLV26tHx8fLRhwwYFBgbGW7dhwwYVKlRIL774oiTp5s2b6tq1q0JDQ9W9e3eVLVtW165d08aNG/X+++8nuY+ZM2fqyy+/VJs2bdStWzeFhIToP//5j44dO6bVq1crV65cFh9bapDkAAAAAAA79eDBA929e1ePHz/W7t279d1336lQoUKqU6eOWebWrVv6+OOP1aVLF3PZ2rVrtX//fi1dulQ1atQwl5cqVUqfffaZ/vGPf6h8+fL6+eefdeDAAY0dO9b8styjRw/17ds3xdh+++03rVu3Tv3799eYMWPM5f/4xz9kGIYcHBz00ksvady4cfL29k7QXWTSpEny9PTUsmXLzKRBjx491LFjR33++edmkmP69OlycHDQ0qVLVaxYMUlSq1at1K5dO4vP46NHj8z6YjVr1kxz5sxJcpsbN25oy5Yt5iCvZcuW1dChQ7Vr1y75+vpKknr27KlXX3013nY1a9bUyJEjdfDgwXjXKSXnz5+XJFWsWNGi8u3bt9f48eMVHBwsLy8vSTGtd7Zs2aJOnTqZLUGmTp2qu3fvatWqVWbXJ0kaPnx4knVfuXJFX331lUaNGhXv+Jo0aaKAgACtWbNGPXr0sPjYUoMkBwAAAAA85cyff+n7racVFh6ZcuEkREVFKWfOnOmKw9XFUQEtvFWpjHuatn862VCuXDl98skn8cbccHV1TZBA2Lx5sypWrKjSpUvHa8EQ+0V///79Kl++vH755Rc5OTmpe/fuZpmcOXOqd+/e+u9//5tsbFu2bFHOnDn1+uuvJ1iXUguGe/fuad++fRo5cmSCrjGNGjXSwoULFRYWJmdnZ+3atUstW7Y0ExySVL58eTVq1Chel4zk5M6dW19++WW8ZQULFkx2mzZt2sSbxSY2YRHbmkZSvNYM4eHhCg0NNZNKJ06cSFWS49GjR5KUYiuOWC+//LI+/vhjbdiwQcOGDZMkbd++XaGhoXrllVckxXQn+vnnn+Xv7x8vwSElf422bdsmwzDUokWLeO+fMmXKqEiRItq/f7/9JTmOHj2qNWvWaN++fbp27ZoKFCigWrVqafjw4fL09JQUM+DN6tWrtW3bNp09e1ahoaHy8vJSt27d1K1btwQfGE+ePNG0adO0bt06PXjwQJUrV9aIESMSZNwAAAAAIDnrfjmvA3/ctHUYkqTcLk56p3ftNG374YcfqkyZMsqZM6eKFi2qsmXLJihTrFixBIOCXrp0SefPn0/yu1TsF9erV6+qWLFiCQYqTWw/T7t8+bKKFy+epuls//zzTxmGoc8++0yfffZZomXu3bsnR0dHPX782PyO+XSMliY5cubMqYYNG6YqxpIlS8b7PX/+/JJiWtfEjXHmzJnauHGj7ty5E698YuOaJCe260xoaKhF5fPlyydfX1/98MMPZpJjw4YN8vLyUvXq1SXFXOfQ0FCLW4fECg4OVnR0tPz9/RNd/3TXH2uyWZJj3rx5OnTokFq3bi1vb2+FhITo22+/VYcOHbRy5UqVL19ely9f1oQJE9SgQQMFBgYqT5482rVrl/7v//5Px44d06RJk+LVOWbMGG3ZskV9+/aVp6en1qxZo4EDB2rx4sWqVauWjY4UAAAAwLOmfZPyCguPzBItOV5pUi7N29eoUSPBE/inJTY2QnR0tKpWrapRo0Yluk3p0qXTHJM1REdHS5IGDhyYZPKhYMGC8RIKmS1HjsTn+Yg73ezw4cP1+++/a8CAAapSpYpy586t6Ohos8tOapQrF/M+OXv2rMXbtG/fXkOHDtWxY8dUunRp/frrrxoyZEiq9puY6Oho5cyZU3Pnzk20xUe+fPnSvY+k2CzJERgYqKlTp8abvubll19Wu3btNHfuXE2ZMkWFCxfWhg0b4mWNAgICNHbsWK1atUqvvfaaeXMdPXpUP/74Y7y+YB06dFDbtm01depUffvtt5l6fAAAAACeXZXKuGvcgPrpqiM0NNTirgNZTZkyZXTu3LkUWy94eHho3759CaadvXjxokX72LNnjx48eJDkl96kukTEfg90cXFJNsaCBQsqV65cunTpUoJ1lsSYke7fv6/ffvtNw4YN0xtvvGEuDw4OTlN9ZcuWlZeXl37++We99957yp07d4rbNGnSRO7u7tqwYYPKlSuniIiIeGOVFCxYUG5ubqlKnEgx1zYqKkqenp4qVapUqo8lPWw2hayPj0+C+Xm9vLxUsWJFc8CUggULJtosJnZU3QsXLpjLNm3aJCcnJ3Xt2tVc5uLioi5duujgwYO6detWRhwGAAAAANidVq1a6erVq1qzZk2CdaGhoQoPD5cU8yU5IiJCy5YtM9dHRUVpyZIlKe6jRYsWioqK0ldffZVgXWwrBhcXFzk4OCRokVGoUCG98MILWrp0aaJdH2KX5cyZU40aNdLWrVt18+b/7350/vx57dq1K8UYM1JSrXwWLVqU5jrfeOMN/fXXXxo3blyCqX6lmLEy9u7da/7u5OSkNm3aaOPGjVq3bp1q1KgRr2tPjhw51Lx5c23btk1//PFHvLqSa2nSokUL5ciRI8E4JlJMK4979+6l5fAskqUGHjUMQ7dv31blypWTLRc7r667+/8ffOfkyZMqW7Zsgkxp9erVZRiGTp48qaJFi1o/aAAAAACwMx06dNDGjRs1duxY7d69W7Vq1VJERITOnTunn376SatXr5anp6f8/Pzk4+OjTz75RH/++afKly+vLVu2WDSeRIMGDdS2bVstWLBAwcHBevHFFxUZGal9+/apdevW6tChg5ydnVWpUiX99NNP8vLyUoECBVSxYkVVqlRJ48aNU69evdS2bVt17dpVpUqV0q1bt3Tw4EGFh4ebrfmHDRumX3/9VT169FBAQICZhKlQoYJOnz6d0acySXny5NELL7ygefPmKSIiQsWKFdPu3bt15cqVNNfZrl07nTp1SvPmzdOJEyfUpk0blShRQn/99Zd27typ/fv36+uvv463Tfv27bVkyRKFhITogw8+SFDnyJEjtXv3bvXq1UsBAQEqW7asrl+/ro0bN2rz5s2JxuHp6ak333xTX3zxhS5fvixfX1+5urrq8uXL2rx5s1577bV4DRSsKUslOdavX6+bN29qxIgRSZZ58uSJFi1apDJlyqhatWrm8pCQkHij5cYqUqSIJNGSAwAAAAAslDNnTs2aNUvz58/X+vXrtWnTJrm5ualMmTIaNGiQ+d0rR44cmjVrliZNmqR169bJwcFBfn5+GjNmjDp06JDifj799FN5e3tr1apV2rVrl/Lly6fq1avH+643YcIEffTRR5oyZYqePHmiN954Q5UqVVKlSpW0cuVKzZgxQytWrNCDBw9UuHBhPffcc/FmlalcubK++eYbTZ48WdOnT1fx4sU1bNgwhYSE2DTJIUmfffaZJkyYoO+++06GYejFF1/U3Llz1bhx4zTXOWrUKDVs2FBLlizRt99+qwcPHih//vyqVauW5s6dqyZNmsQrX716dZUtW1aXL1/Wyy+/nKC+EiVKaPny5friiy+0Zs0ahYaGqkSJEmrWrFmycbz22mvy9PTUf/7zH82YMUMODg4qWbKk/P39Uz2Ia2o4GKkdzSSDnD9/Xt26dZO3t7eWLFmS5CAtH3zwgVasWJHgwvv7+6tChQqaPXt2vPKXL1+Wv7+/PvjgA/Xu3duiWMLDw3X8+PG0HwwAAACAZ4Kjo6MqVKhg6zAAm+rWrZuKFy+u6dOn2zoUSdK5c+cS7W7ztGrVqsnFxSXesizRkiMkJESDBw9W/vz5NW3atCQTHPPmzdPy5cv19ttvJ8hs5cqVSxEREQm2ie0r9vSBWyKxE5aVHDx4ULVrp20qKTybuObZC9c7++GaZz9c8+yHa571nDx5MsMGB32WBx5F2jyL1/zw4cM6d+6cXn/99SwTu7Ozs2rUqJHk+uQaJtg8yfHw4UMNHDhQDx8+1NKlS83uJU9bvXq1pk6dql69emnQoEEJ1hcpUiTRLikhISGSxHgcAAAAAAD8z5kzZ3T8+HHNnz9fJUuWNCf4eNbZbHYVKSb7MmTIEAUHB2vOnDnmvL5P27Ztm95//321bNlS77//fqJlKleurIsXLyo0NDTe8iNHjpjrAQAAAACAtHnzZr377ruKjo7Wv/71Lzk5Odk6JKuwWZIjKipKw4cP1+HDhzVt2jTVrFkz0XIHDhzQyJEjVadOHU2dOjXJriytW7dWRESEVqxYYS578uSJVq9eLR8fn0QHJQUAAAAAIDsaNmyYTp06pY0bN6pOnTq2DsdqbNZdZcqUKdq+fbt8fX117949rVu3zlzn5uYmf39/Xb16Va+99pocHBzUqlUr/fTTT/Hq8PHxUenSpSVJNWrUUOvWrTV16lSFhISoTJkyWrNmja5du6bJkydn6rEBAAAAAIDMZ7Mkx6lTpyRJQUFBCgoKirfOw8ND/v7+unLlijm/8kcffZSgjsmTJ5tJDilm+qEvvvhC69at0/379+Xt7a2vv/6awZ0AAAAAAMgGbJbkWLx4cYpl6tWrl6p5i11cXDR69GiNHj06PaEBAAAAyEYMw5CDg4OtwwCgmPsxPWw68CgAAAAA2JKzs7PCwsJsHQaA/wkLC0vXIKgkOQAAAABkW4ULF9aVK1d09+5dRUREpPspMoC0MQxDf//9t65evaqiRYumuR6bdVcBAAAAAFvLnz+/XFxcFBISojt37igyMtJqdT958kTOzs5Wqw9ZH9c8fZycnFSsWDHly5cvzXWQ5AAAAACQreXKlSvehAbWcvDgQdWoUcPq9SLr4prbHt1VAAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ5AAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ5AAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ5AAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ5AAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ5AAAAAACAXSDJAQAAAAAA7AJJDgAAAAAAYBdIcgAAAAAAALtAkgMAAAAAANgFkhwAAAAAAMAukOQAAAAAAAB2gSQHAAAAAACwCyQ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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6SsehVKZCWN"
      },
      "source": [
        "With the values of the predicted final cycle averaged and stable throughout the timeseries, the Predicted RUL can now be calculated by multiplying the predicted final cycle with the Predicted Health Index.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58qAUexCW9ue",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "4e74b653-baea-4206-89d6-501a495ec07f"
      },
      "source": [
        "RUL_nn = NeuralNetwork.CalcPredRUL()\n",
        "NeuralNetwork.DataFrame().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit</th>\n",
              "      <th>cycles</th>\n",
              "      <th>Pred_H.I._nn</th>\n",
              "      <th>PredFinalCycle_nn</th>\n",
              "      <th>Pred_RUL_nn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.982264</td>\n",
              "      <td>186.023041</td>\n",
              "      <td>182.723694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.982387</td>\n",
              "      <td>186.023041</td>\n",
              "      <td>182.746613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.984519</td>\n",
              "      <td>186.023041</td>\n",
              "      <td>183.143143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.964011</td>\n",
              "      <td>186.023041</td>\n",
              "      <td>179.328339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.967212</td>\n",
              "      <td>186.023041</td>\n",
              "      <td>179.923782</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   unit  cycles  Pred_H.I._nn  PredFinalCycle_nn  Pred_RUL_nn\n",
              "0     1       1      0.982264         186.023041   182.723694\n",
              "1     1       2      0.982387         186.023041   182.746613\n",
              "2     1       3      0.984519         186.023041   183.143143\n",
              "3     1       4      0.964011         186.023041   179.328339\n",
              "4     1       5      0.967212         186.023041   179.923782"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqC5nuo4dru0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "d91c792b-cae2-4279-f52f-dfa32def79c5"
      },
      "source": [
        "NeuralNetwork.PlotRUL(line_width=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXqeKdifFcE7"
      },
      "source": [
        "We display engine unit 50 to have a better observation of the RUL results. In this example, can see that the RUL value decreases as the cycle as the cycle number increases. It shows that the engine's health is degrading as the cycle increases. At this point we can safely say that our method to predict the RUL is working. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHRYgjYeTT4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "befeed57-318e-4b0a-dcdc-64dcfcf565b4"
      },
      "source": [
        "NeuralNetwork.PlotRUL(20, line_width=4.5, show_final_RUL=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RUL of engine unit 20 is 10.720968246459961\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oN0m3MhKIiIP"
      },
      "source": [
        "To determine the accuracy of the model however, we will compare the RUL of the engines at their final cycle with the actual/true values. This will be done at the end when prediction is done with all three models. The other two models must first go through the same steps to predict the RUL of the test dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7qv8uNeCkx9"
      },
      "source": [
        "## 6.2 K Nearest Neighbour Regressor prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD83yAOwT5r9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "cc9b132a-a468-4032-e6f2-b98b4a5f8684"
      },
      "source": [
        "KNRegressor = MachineLearningModel(knr_model, 'knr', df_test_FD001, X_test, Results_knr)\n",
        "KNRegressor.predict()\n",
        "KNRegressor.CalcPredFinalCycle()\n",
        "RUL_knr = KNRegressor.CalcPredRUL()\n",
        "KNRegressor.DataFrame()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit</th>\n",
              "      <th>cycles</th>\n",
              "      <th>Pred_H.I._knr</th>\n",
              "      <th>PredFinalCycle_knr</th>\n",
              "      <th>Pred_RUL_knr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>200.993359</td>\n",
              "      <td>200.993359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.995298</td>\n",
              "      <td>200.993359</td>\n",
              "      <td>200.048301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.989815</td>\n",
              "      <td>200.993359</td>\n",
              "      <td>198.946302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.986484</td>\n",
              "      <td>200.993359</td>\n",
              "      <td>198.276766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.979469</td>\n",
              "      <td>200.993359</td>\n",
              "      <td>196.866760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13091</th>\n",
              "      <td>100</td>\n",
              "      <td>194</td>\n",
              "      <td>0.146055</td>\n",
              "      <td>235.520245</td>\n",
              "      <td>34.398851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13092</th>\n",
              "      <td>100</td>\n",
              "      <td>195</td>\n",
              "      <td>0.211693</td>\n",
              "      <td>235.520245</td>\n",
              "      <td>49.857898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13093</th>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "      <td>0.141655</td>\n",
              "      <td>235.520245</td>\n",
              "      <td>33.362532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13094</th>\n",
              "      <td>100</td>\n",
              "      <td>197</td>\n",
              "      <td>0.173862</td>\n",
              "      <td>235.520245</td>\n",
              "      <td>40.947919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13095</th>\n",
              "      <td>100</td>\n",
              "      <td>198</td>\n",
              "      <td>0.119559</td>\n",
              "      <td>235.520245</td>\n",
              "      <td>28.158609</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13096 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       unit  cycles  Pred_H.I._knr  PredFinalCycle_knr  Pred_RUL_knr\n",
              "0         1       1       1.000000          200.993359    200.993359\n",
              "1         1       2       0.995298          200.993359    200.048301\n",
              "2         1       3       0.989815          200.993359    198.946302\n",
              "3         1       4       0.986484          200.993359    198.276766\n",
              "4         1       5       0.979469          200.993359    196.866760\n",
              "...     ...     ...            ...                 ...           ...\n",
              "13091   100     194       0.146055          235.520245     34.398851\n",
              "13092   100     195       0.211693          235.520245     49.857898\n",
              "13093   100     196       0.141655          235.520245     33.362532\n",
              "13094   100     197       0.173862          235.520245     40.947919\n",
              "13095   100     198       0.119559          235.520245     28.158609\n",
              "\n",
              "[13096 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RsZsXIx_dCW5"
      },
      "source": [
        "##6.3 Linear regression model prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxZ5rHMa1waq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "1c72effc-5c59-43b1-b8ba-b0979ac77eeb"
      },
      "source": [
        "LinearRegression = MachineLearningModel(lreg_model, 'lreg', df_test_FD001, X_test, Results_lreg)\n",
        "LinearRegression.predict()\n",
        "LinearRegression.CalcPredFinalCycle()\n",
        "RUL_lreg = LinearRegression.CalcPredRUL()\n",
        "LinearRegression.DataFrame()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unit</th>\n",
              "      <th>cycles</th>\n",
              "      <th>Pred_H.I._lreg</th>\n",
              "      <th>PredFinalCycle_lreg</th>\n",
              "      <th>Pred_RUL_lreg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.903406</td>\n",
              "      <td>149.373038</td>\n",
              "      <td>134.944430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.910623</td>\n",
              "      <td>149.373038</td>\n",
              "      <td>136.022475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0.880623</td>\n",
              "      <td>149.373038</td>\n",
              "      <td>131.541287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.885530</td>\n",
              "      <td>149.373038</td>\n",
              "      <td>132.274341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0.905769</td>\n",
              "      <td>149.373038</td>\n",
              "      <td>135.297434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13091</th>\n",
              "      <td>100</td>\n",
              "      <td>194</td>\n",
              "      <td>0.135680</td>\n",
              "      <td>227.602371</td>\n",
              "      <td>30.881130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13092</th>\n",
              "      <td>100</td>\n",
              "      <td>195</td>\n",
              "      <td>0.135120</td>\n",
              "      <td>227.602371</td>\n",
              "      <td>30.753589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13093</th>\n",
              "      <td>100</td>\n",
              "      <td>196</td>\n",
              "      <td>0.138762</td>\n",
              "      <td>227.602371</td>\n",
              "      <td>31.582520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13094</th>\n",
              "      <td>100</td>\n",
              "      <td>197</td>\n",
              "      <td>0.127759</td>\n",
              "      <td>227.602371</td>\n",
              "      <td>29.078230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13095</th>\n",
              "      <td>100</td>\n",
              "      <td>198</td>\n",
              "      <td>0.089408</td>\n",
              "      <td>227.602371</td>\n",
              "      <td>20.349435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13096 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       unit  cycles  Pred_H.I._lreg  PredFinalCycle_lreg  Pred_RUL_lreg\n",
              "0         1       1        0.903406           149.373038     134.944430\n",
              "1         1       2        0.910623           149.373038     136.022475\n",
              "2         1       3        0.880623           149.373038     131.541287\n",
              "3         1       4        0.885530           149.373038     132.274341\n",
              "4         1       5        0.905769           149.373038     135.297434\n",
              "...     ...     ...             ...                  ...            ...\n",
              "13091   100     194        0.135680           227.602371      30.881130\n",
              "13092   100     195        0.135120           227.602371      30.753589\n",
              "13093   100     196        0.138762           227.602371      31.582520\n",
              "13094   100     197        0.127759           227.602371      29.078230\n",
              "13095   100     198        0.089408           227.602371      20.349435\n",
              "\n",
              "[13096 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-gvGr7gdRHt"
      },
      "source": [
        "#7. Results and analysis\n",
        "At the end, we observe the prediction results of the Neural Network, KNN model and the Linear Regression model. Several statistical methods are used to validate the accuracy of the predictive models. They revolve around comparing the values of the predicted results and the actual RUL values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLro7nyZJZvm"
      },
      "source": [
        "##7.1 Residual (e)\n",
        "Residuals in a statistical or machine learning model are the differences between observed and predicted values of data. They are a diagnostic measure used when assessing the quality of a model. They are also known as errors.\n",
        "\n",
        "We upload the actual/true RUL values of each engine units and compare their predicted RUL values. The difference between them - called residuals - are stored to validate the accuracy of our machine learning models. \n",
        "![difference calculation](https://drive.google.com/uc?id=1Un5nvlf3f5i63EdRZs5s2SUqPIrgGN-P)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-b4ZX0aAAcE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "outputId": "50c1ec55-a4e5-44ec-c663-4e6b2d6e93c6"
      },
      "source": [
        "FinalResult = pd.read_csv('/content/gdrive/My Drive/Capstone/RUL_FD001.txt', sep=' ', header=None)\n",
        "FinalResult = FinalResult.dropna(axis=1, how='all')\n",
        "FinalResult.columns = [\"Actual RUL\"]\n",
        "\n",
        "unit = []\n",
        "for i in range(1,101):\n",
        "  unit.append(i)\n",
        "FinalResult['Unit'] = unit\n",
        "\n",
        "names = ['lreg', 'knr', 'nn']\n",
        "models = [RUL_lreg, RUL_knr, RUL_nn]\n",
        "\n",
        "for i in range(len(names)):\n",
        "  predRUL_column_name = f'Pred_RUL_{names[i]}'\n",
        "  diff_column_name = f'diff_{names[i]}'\n",
        "  FinalResult[predRUL_column_name] = models[i]\n",
        "  FinalResult[diff_column_name] = FinalResult['Actual RUL'] - FinalResult[predRUL_column_name]\n",
        "\n",
        "FinalResult"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual RUL</th>\n",
              "      <th>Unit</th>\n",
              "      <th>Pred_RUL_lreg</th>\n",
              "      <th>diff_lreg</th>\n",
              "      <th>Pred_RUL_knr</th>\n",
              "      <th>diff_knr</th>\n",
              "      <th>Pred_RUL_nn</th>\n",
              "      <th>diff_nn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "      <td>119.995048</td>\n",
              "      <td>-7.995048</td>\n",
              "      <td>169.072196</td>\n",
              "      <td>-57.072196</td>\n",
              "      <td>155.577972</td>\n",
              "      <td>-43.577972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>98</td>\n",
              "      <td>2</td>\n",
              "      <td>105.525296</td>\n",
              "      <td>-7.525296</td>\n",
              "      <td>147.369550</td>\n",
              "      <td>-49.369550</td>\n",
              "      <td>133.558121</td>\n",
              "      <td>-35.558121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69</td>\n",
              "      <td>3</td>\n",
              "      <td>81.817143</td>\n",
              "      <td>-12.817143</td>\n",
              "      <td>49.362621</td>\n",
              "      <td>19.637379</td>\n",
              "      <td>53.309608</td>\n",
              "      <td>15.690392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>82</td>\n",
              "      <td>4</td>\n",
              "      <td>99.963074</td>\n",
              "      <td>-17.963074</td>\n",
              "      <td>74.435711</td>\n",
              "      <td>7.564289</td>\n",
              "      <td>73.373322</td>\n",
              "      <td>8.626678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>91</td>\n",
              "      <td>5</td>\n",
              "      <td>106.407354</td>\n",
              "      <td>-15.407354</td>\n",
              "      <td>92.050954</td>\n",
              "      <td>-1.050954</td>\n",
              "      <td>85.590111</td>\n",
              "      <td>5.409889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>137</td>\n",
              "      <td>96</td>\n",
              "      <td>152.315784</td>\n",
              "      <td>-15.315784</td>\n",
              "      <td>108.940084</td>\n",
              "      <td>28.059916</td>\n",
              "      <td>118.467880</td>\n",
              "      <td>18.532120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>82</td>\n",
              "      <td>97</td>\n",
              "      <td>108.914928</td>\n",
              "      <td>-26.914928</td>\n",
              "      <td>77.499891</td>\n",
              "      <td>4.500109</td>\n",
              "      <td>83.692139</td>\n",
              "      <td>-1.692139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>59</td>\n",
              "      <td>98</td>\n",
              "      <td>93.455681</td>\n",
              "      <td>-34.455681</td>\n",
              "      <td>71.053977</td>\n",
              "      <td>-12.053977</td>\n",
              "      <td>69.172104</td>\n",
              "      <td>-10.172104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>117</td>\n",
              "      <td>99</td>\n",
              "      <td>165.614785</td>\n",
              "      <td>-48.614785</td>\n",
              "      <td>120.392189</td>\n",
              "      <td>-3.392189</td>\n",
              "      <td>124.278015</td>\n",
              "      <td>-7.278015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>20</td>\n",
              "      <td>100</td>\n",
              "      <td>20.349435</td>\n",
              "      <td>-0.349435</td>\n",
              "      <td>28.158609</td>\n",
              "      <td>-8.158609</td>\n",
              "      <td>25.480204</td>\n",
              "      <td>-5.480204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Actual RUL  Unit  Pred_RUL_lreg  ...   diff_knr  Pred_RUL_nn    diff_nn\n",
              "0          112     1     119.995048  ... -57.072196   155.577972 -43.577972\n",
              "1           98     2     105.525296  ... -49.369550   133.558121 -35.558121\n",
              "2           69     3      81.817143  ...  19.637379    53.309608  15.690392\n",
              "3           82     4      99.963074  ...   7.564289    73.373322   8.626678\n",
              "4           91     5     106.407354  ...  -1.050954    85.590111   5.409889\n",
              "..         ...   ...            ...  ...        ...          ...        ...\n",
              "95         137    96     152.315784  ...  28.059916   118.467880  18.532120\n",
              "96          82    97     108.914928  ...   4.500109    83.692139  -1.692139\n",
              "97          59    98      93.455681  ... -12.053977    69.172104 -10.172104\n",
              "98         117    99     165.614785  ...  -3.392189   124.278015  -7.278015\n",
              "99          20   100      20.349435  ...  -8.158609    25.480204  -5.480204\n",
              "\n",
              "[100 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ppjo86lfr8V"
      },
      "source": [
        "##7.2 Kernel Density Estimation (KDE)\n",
        "Plotting the Kernel Density Estimation (KDE) of the models below, we can have a better grasp of the performance on our ML models. It gives a quick sneak peak of the error distribution; can be used as a quick eye test to compare accuracy between the machine learning models. The mode of the error distribution are generally centered near zero. However, it can be observed that the mode for all three errors are generally slightly shifted towards the left. This can be interpreted that there is a slightly more probability for the difference between the actual and predicted RUL values of our predictive models to be negative *— or in other words the prediction has slightly more chance to overshoot the actual RUL than not.* "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dl4oBRaHIoqI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "241ac7f4-65db-4c49-85f2-70712adbe604"
      },
      "source": [
        "import matplotlib\n",
        "names = ['lreg', 'knr', 'nn']\n",
        "lables = ['Linear Regression','K Nearest Neighbour','Neural Network']\n",
        "\n",
        "plt.figure(figsize=(18,10))\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "for i in range(len(names)):\n",
        "  FinalResult[f'diff_{names[i]}'].plot(label = lables[i], kind = 'kde', linewidth=4.5, legend = True)\n",
        "  sns.kdeplot(FinalResult[f'diff_{names[i]}'], shade=True)\n",
        "plt.axvline(0, color=\"black\", linestyle='dashed', linewidth=1)\n",
        "\n",
        "# set title and axis labels\n",
        "errorDist = plt.title('Error Distribution', x=0.5, y=1.05, ha='center', fontsize='25')\n",
        "plt.setp(errorDist, color = 'black')\n",
        "plt.xlabel('Error')\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cf77UdfIulmq"
      },
      "source": [
        "##7.3 Standard Deviation (σ)\n",
        "Calling the `describe()` function it displays the a summary of statistics pertaining to the prediction errors. <ins>Standard deviation</ins> is the measure of dispersion, or how spread out values are, in a data. It’s represented by the sigma (σ) symbol and found by taking the square root of the variance. The variance is just the average of the squared differences from the mean. \n",
        "  \n",
        "    \n",
        "![Standard deviation](https://www.gstatic.com/education/formulas/images_long_sheet/en/population_standard_deviation.svg)\n",
        "<br>\n",
        "\n",
        "We observe the **standard deviation** of the three predictive models:\n",
        "\n",
        "1.   Artificial Neural Networks: 20.772\n",
        "2.   K Nearest Regressor: 27.157\n",
        "3.   Linear Regression: 31.562\n",
        "\n",
        "Using this metric is a crucial test of the machine learning models' accuracy against the actual data. In addition, standard deviation can be used to measure confidence in a model’s statistical conclusions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zl6iYK2Puark",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "4c454476-c537-43fe-8948-e31749ff67fd"
      },
      "source": [
        "Error = FinalResult[['diff_nn','diff_knr','diff_lreg']]\n",
        "Error.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diff_nn</th>\n",
              "      <th>diff_knr</th>\n",
              "      <th>diff_lreg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-4.305059</td>\n",
              "      <td>-1.013148</td>\n",
              "      <td>-11.657916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20.771619</td>\n",
              "      <td>27.157247</td>\n",
              "      <td>31.561666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-49.607895</td>\n",
              "      <td>-57.072196</td>\n",
              "      <td>-90.599290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-15.518355</td>\n",
              "      <td>-17.650478</td>\n",
              "      <td>-30.932689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-4.750236</td>\n",
              "      <td>-2.532193</td>\n",
              "      <td>-13.343738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.493423</td>\n",
              "      <td>12.150219</td>\n",
              "      <td>7.466374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.266850</td>\n",
              "      <td>84.880618</td>\n",
              "      <td>81.888109</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          diff_nn    diff_knr   diff_lreg\n",
              "count  100.000000  100.000000  100.000000\n",
              "mean    -4.305059   -1.013148  -11.657916\n",
              "std     20.771619   27.157247   31.561666\n",
              "min    -49.607895  -57.072196  -90.599290\n",
              "25%    -15.518355  -17.650478  -30.932689\n",
              "50%     -4.750236   -2.532193  -13.343738\n",
              "75%      7.493423   12.150219    7.466374\n",
              "max     50.266850   84.880618   81.888109"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ModSQfw7PTyF"
      },
      "source": [
        "##7.4 Boxplot and its outliers\n",
        "A boxplot visualizes the Interquartile Range (IQR), the Q1 and Q2 values, maximum and minimum values, and also the outliers in the data. Outliers can be seen as the values outside of the \"whiskers\" of the boxplot. They are defined as values lesser than **Q1 - (1.5 x IQR)** and values more than **Q3 + (1.5 x IQR)** . In the box and whisker plot of the error distribution, it is observed that there aren't many outliers in the data. Ignoring the outliers, the Neural Networks has the smallest range and also the smallest interquartile range (IQR). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CL98I8mNx155",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "cb4649cc-2f92-49a8-d3ac-6500d339f0c4"
      },
      "source": [
        "Error = FinalResult[['diff_nn','diff_knr','diff_lreg']]\n",
        "\n",
        "plt.figure(figsize = (18, 9))\n",
        "boxplot = sns.boxplot(data=Error, orient=\"h\", palette=\"Set2\", showfliers = True)\n",
        "plt.title('Error Distribution', x=0.5, y=1.05, ha='center', fontsize = '25')\n",
        "plt.xlabel('Error');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bCfXk-HzyEJe"
      },
      "source": [
        "##7.5 Root Mean Square Error (RMSE)\n",
        "RMSE is calculated by taking the square root of the average of the squared differences between the actual and predicted values. This value is able to represent the estimation of the standard deviation σ of a typical observed value from the model’s prediction. In other words, a lower RMSE value means a more accurate prediction. The values obtained in this project is compared with the RMSE of existing works to ascertain the accuracy standing of our model.\n",
        "<br>  \n",
        "![RMSE calculation](https://miro.medium.com/max/725/1*lqDsPkfXPGen32Uem1PTNg.png) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_87h2Jw0yDy5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e13715f-1903-415c-8a7c-cfb1895afad1"
      },
      "source": [
        "RMSE_nn = NeuralNetwork.GetRMSE(FinalResult['Actual RUL'])\n",
        "RMSE_knr = KNRegressor.GetRMSE(FinalResult['Actual RUL'])\n",
        "RMSE_lreg = LinearRegression.GetRMSE(FinalResult['Actual RUL'])\n",
        "\n",
        "print('Neural Network RMSE:', RMSE_nn)\n",
        "print('K Nearest Neighbour RMSE:', RMSE_knr)\n",
        "print('Linear Regression RMSE:', RMSE_lreg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Neural Network RMSE: 21.111112570983536\n",
            "K Nearest Neighbour RMSE: 27.040106378523973\n",
            "Linear Regression RMSE: 33.49752785361099\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0of7vpYBNiZI"
      },
      "source": [
        "#8. Summary\n",
        "In this work, the performance of three Machine Learning algorithms are studied. Machine Learning is used to predict the Health Index of engines in this study. The engine’s Remaining Useful Life is then calculated using the predicted H.I.\n",
        "\n",
        "<br>\n",
        "\n",
        "Three machine learning models were used to predict the RUL of an engine:\n",
        "1.   Linear Regression (Lreg)\n",
        "2.   K Nearest Neighbour Regressor (KNR)\n",
        "3.   Artificial Neural Networks (NN)\n",
        "\n",
        "The Health Index and the Remaining Useful Life of a turbofan engine is successfully predicted using this approach. The model accuracy is then assesed.\n",
        "\n",
        "Accuracy of the prediction analyzed by:\n",
        "1. Residual\n",
        "2. Kernel Distribution Estimation\n",
        "3. Standard Deviation\n",
        "4. Root Mean Square Error; RMSE\n",
        "5. Cross-validation of RMSE\n",
        "\n",
        "Cross validation of RMSE with works attempting similar predictive algorithm revealed that the predictive models yielded similar or better results. Using this model can be used to help airlines save maintenance costs by reducing the frequency of pre-scheduled engine checks that takes away the aircraft from active service."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cv3zYXkZ__wg"
      },
      "source": [
        "#Appendices\n",
        "##a. Sensor names\n",
        "![Sensors](https://drive.google.com/uc?id=1YEqSg4rKSAnme66ZAl2sETCWLtbNv7oW) "
      ]
    }
  ]
}