notebooks/cmapss_notebooks/CMAPSS_FD004_random_forest_piecewise_linear_degradation_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RUL prediction using random forest\n",
    "\n",
    "In this notebook, we will apply random forest to predict RUL of NASA's turbofan engine dataset FD004. We will use scikit-learn to implement random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(324)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version:  1.18.5\n",
      "Pandas version:  1.0.5\n",
      "Scikit-learn version:  0.23.1\n"
     ]
    }
   ],
   "source": [
    "print(\"Numpy version: \", np.__version__)\n",
    "print(\"Pandas version: \", pd.__version__)\n",
    "print(\"Scikit-learn version: \", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n",
    "We strongly encourage readers to go through the [dataset description and prreprocessing notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_data_description_and_preprocessing.ipynb). In that notebook we have explained how data preprocessing functions work with simple examples. In this notebook we will only use those functions. So prior familiarity with these functions is an advantage. Below are the parameters that we will use for data preprocessing:\n",
    "\n",
    "* Degradation model: Piecewise linear\n",
    "* Early RUL: 125\n",
    "* Window length: 1\n",
    "* Shift: 1\n",
    "* Data scaling: No (Tree based methods don't require data scaling)\n",
    "\n",
    "We will calculate two prediction scores on test data. In one case, we will take last 5 examples of test data for engine, calculate their predictions, and finally average those for each engine. In the second case, we will take only the last example of each engine and make predictions. The logic behind taking last 5 examples and averaging their predictions is to make the prediction robust against outliers. Due to some external factor, if our last example happens to be corrupted, its prediction outcome might be far off from the actual one. But if we average predictions from last 5 examples, we will get a more conservative estimate. \n",
    "\n",
    "In the following cell we will show boxplots of each column of training data. That will give us an idea about the values in different columns. If all the values in a column are constant, we drop those columns from our analysis.\n",
    "\n",
    "Readers can download the data from [here](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan). In the following cells, wherever data are read from a folder, readers should change the string to point to the respective folder from their system to run this notebook seamlessly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x1512 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/train_FD004.txt\", sep= \"\\s+\", header = None)\n",
    "plt.figure(figsize = (16, 21))\n",
    "for i in range(21):\n",
    "    temp_data = train_data.iloc[:,i+5]\n",
    "    plt.subplot(7,3,i+1)\n",
    "    plt.boxplot(temp_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_targets(data_length, early_rul = None):\n",
    "    \"\"\" \n",
    "    Takes datalength and earlyrul as input and \n",
    "    creates target rul.\n",
    "    \"\"\"\n",
    "    if early_rul == None:\n",
    "        return np.arange(data_length-1, -1, -1)\n",
    "    else:\n",
    "        early_rul_duration = data_length - early_rul\n",
    "        if early_rul_duration <= 0:\n",
    "            return np.arange(data_length-1, -1, -1)\n",
    "        else:\n",
    "            return np.append(early_rul*np.ones(shape = (early_rul_duration,)), np.arange(early_rul-1, -1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_input_data_with_targets(input_data, target_data = None, window_length = 1, shift = 1):\n",
    "    \"\"\"Depending on values of window_length and shift, this function generates batchs of data and targets \n",
    "    from input_data and target_data.\n",
    "    \n",
    "    Number of batches = np.floor((len(input_data) - window_length)/shift) + 1\n",
    "    \n",
    "    **We don't check input dimensions uisng exception handling. So readers should be careful while using these\n",
    "    functions. If input data are not of desired dimension, either error occurs or something undesirable is \n",
    "    produced as output.**\n",
    "    \n",
    "    Arguments:\n",
    "        input_data: input data to function (Must be 2 dimensional)\n",
    "        target_data: input rul values (Must be 1D array)s\n",
    "        window_length: window length of data\n",
    "        shift: Distance by which the window moves for next batch. This is closely related to overlap\n",
    "               between data. For example, if window length is 30 and shift is 1, there is an overlap of \n",
    "               29 data points between two consecutive batches.\n",
    "        \n",
    "    \"\"\"\n",
    "    num_batches = np.int(np.floor((len(input_data) - window_length)/shift)) + 1\n",
    "    num_features = input_data.shape[1]\n",
    "    output_data = np.repeat(np.nan, repeats = num_batches * window_length * num_features).reshape(num_batches, window_length,\n",
    "                                                                                                  num_features)\n",
    "    if target_data is None:\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "        return output_data\n",
    "    else:\n",
    "        output_targets = np.repeat(np.nan, repeats = num_batches)\n",
    "        for batch in range(num_batches):\n",
    "            output_data[batch,:,:] = input_data[(0+shift*batch):(0+shift*batch+window_length),:]\n",
    "            output_targets[batch] = target_data[(shift*batch + (window_length-1))]\n",
    "        return output_data, output_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_test_data(test_data_for_an_engine, window_length, shift, num_test_windows = 1):\n",
    "    \"\"\" This function takes test data for an engine as first input. The next two inputs\n",
    "    window_length and shift are same as other functins. \n",
    "    \n",
    "    Finally it takes num_test_windows as the last input. num_test_windows sets how many examplles we\n",
    "    want from test data (from last). By default it extracts only the last example.\n",
    "    \n",
    "    The function return last examples and number of last examples (a scaler) as output. \n",
    "    We need the second output later. If we are extracting more than 1 last examples, we have to \n",
    "    average their prediction results. The second scaler halps us do just that.\n",
    "    \"\"\"\n",
    "    max_num_test_batches = np.int(np.floor((len(test_data_for_an_engine) - window_length)/shift)) + 1\n",
    "    if max_num_test_batches < num_test_windows:\n",
    "        required_len = (max_num_test_batches -1)* shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, max_num_test_batches\n",
    "    else:\n",
    "        required_len = (num_test_windows - 1) * shift + window_length\n",
    "        batched_test_data_for_an_engine = process_input_data_with_targets(test_data_for_an_engine[-required_len:, :],\n",
    "                                                                          target_data = None,\n",
    "                                                                          window_length = window_length, shift = shift)\n",
    "        return batched_test_data_for_an_engine, num_test_windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed trianing data shape:  (60994, 1, 21)\n",
      "Processed training ruls shape:  (60994,)\n",
      "Processed test data shape:  (1240, 1, 21)\n",
      "True RUL shape:  (248,)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"/home/biswajit/data/cmapss_data/test_FD004.txt\", sep = \"\\s+\", header = None)\n",
    "true_rul = pd.read_csv('/home/biswajit/data/cmapss_data/RUL_FD004.txt', sep = '\\s+', header = None)\n",
    "\n",
    "window_length = 1\n",
    "shift = 1\n",
    "early_rul = 125            \n",
    "processed_train_data = []\n",
    "processed_train_targets = []\n",
    "\n",
    "# How many test windows to take for each engine. If set to 1 (this is the default), only last window of test data for \n",
    "# each engine are taken. If set to a different number, that many windows from last are taken. \n",
    "# Final output is the average of output of all windows.\n",
    "num_test_windows = 5     \n",
    "processed_test_data = []\n",
    "num_test_windows_list = []\n",
    "\n",
    "columns_to_be_dropped = [0,1,2,3,4]\n",
    "\n",
    "num_machines = np.min([len(train_data[0].unique()), len(test_data[0].unique())])\n",
    "\n",
    "for i in np.arange(1, num_machines + 1):\n",
    "    \n",
    "    temp_train_data = train_data[train_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    temp_test_data = test_data[test_data[0] == i].drop(columns=columns_to_be_dropped).values\n",
    "    \n",
    "    # Verify if data of given window length can be extracted from both training and test data\n",
    "    if (len(temp_test_data) < window_length):\n",
    "        print(\"Test engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    elif (len(temp_train_data) < window_length):\n",
    "        print(\"Train engine {} doesn't have enough data for window_length of {}\".format(i, window_length))\n",
    "        raise AssertionError(\"Window length is larger than number of data points for some engines. \"\n",
    "                             \"Try decreasing window length.\")\n",
    "    \n",
    "    temp_train_targets = process_targets(data_length = temp_train_data.shape[0], early_rul = early_rul)\n",
    "    data_for_a_machine, targets_for_a_machine = process_input_data_with_targets(temp_train_data, temp_train_targets, \n",
    "                                                                                window_length = window_length, shift = shift)\n",
    "    \n",
    "    # Prepare test data\n",
    "    test_data_for_an_engine, num_windows = process_test_data(temp_test_data, window_length = window_length, shift = shift,\n",
    "                                                             num_test_windows = num_test_windows)\n",
    "    \n",
    "    processed_train_data.append(data_for_a_machine)\n",
    "    processed_train_targets.append(targets_for_a_machine)\n",
    "    \n",
    "    processed_test_data.append(test_data_for_an_engine)\n",
    "    num_test_windows_list.append(num_windows)\n",
    "\n",
    "processed_train_data = np.concatenate(processed_train_data)\n",
    "processed_train_targets = np.concatenate(processed_train_targets)\n",
    "processed_test_data = np.concatenate(processed_test_data)\n",
    "true_rul = true_rul[0].values\n",
    "\n",
    "# Shuffle data\n",
    "index = np.random.permutation(len(processed_train_targets))\n",
    "processed_train_data, processed_train_targets = processed_train_data[index], processed_train_targets[index]\n",
    "\n",
    "print(\"Processed trianing data shape: \", processed_train_data.shape)\n",
    "print(\"Processed training ruls shape: \", processed_train_targets.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)\n",
    "print(\"True RUL shape: \", true_rul.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed train data shape:  (60994, 21)\n",
      "Processed test data shape:  (1240, 21)\n"
     ]
    }
   ],
   "source": [
    "processed_train_data = processed_train_data.reshape(-1, processed_train_data.shape[2])\n",
    "processed_test_data = processed_test_data.reshape(-1, processed_test_data.shape[2])\n",
    "print(\"Processed train data shape: \", processed_train_data.shape)\n",
    "print(\"Processed test data shape: \", processed_test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  29.778145192080803\n"
     ]
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(n_estimators= 100, max_features = \"sqrt\",\n",
    "                                 n_jobs = -1, random_state = 33)\n",
    "rf_model.fit(processed_train_data, processed_train_targets)\n",
    "rul_pred = rf_model.predict(processed_test_data)\n",
    "\n",
    "# First split predictions according to number of windows of each engine\n",
    "preds_for_each_engine = np.split(rul_pred, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                             for ruls_for_each_engine, num_windows in zip(preds_for_each_engine, num_test_windows_list)]\n",
    "RMSE = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine))\n",
    "print(\"RMSE: \", RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With somewhat arbitrary choice of hyperparameters, we obtained an RMSE value of 29.77. But can we do better? To find out better values of hyperparameters, we can do a principled hyperparameter tuning. There are different ways to do hyperparameter search. Here, we will use simple grid search. \n",
    "\n",
    "In grid search, we first define a grid of parameters. Then for each set of parameters, we will fit a 10 xgboost models (as it is 10 fold cross validation). Finally we will average the result of all folds for a particular parameter choice. The parameter choice that gives best score for cross validation is chosen as the best hyperparameter.\n",
    "\n",
    "Grid search of hyperparameters (with cross validation) is computationally intensive. It might take a long time on a personal computer. If that is the case, readers are advised to comment the next cell and directly use the best hyperparameter values as done in subsequent cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'n_estimators': [100, 200, 250, 300, 350, 400]},\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {\"n_estimators\": [100, 200, 250, 300, 350, 400],\n",
    "              \"max_features\": [\"auto\", \"sqrt\", \"log2\"]}\n",
    "grid = GridSearchCV(RandomForestRegressor(), param_grid = param_grid,scoring = \"neg_root_mean_squared_error\",\n",
    "                    n_jobs = -1, cv = 10)\n",
    "grid.fit(processed_train_data, processed_train_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 'sqrt', 'n_estimators': 350}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the best model to predict on test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE after hyperparameter tuning:  29.75594534887109\n"
     ]
    }
   ],
   "source": [
    "best_rf_model = grid.best_estimator_\n",
    "rul_pred_tuned = best_rf_model.predict(processed_test_data)\n",
    "\n",
    "preds_for_each_engine_tuned = np.split(rul_pred_tuned, np.cumsum(num_test_windows_list)[:-1])\n",
    "mean_pred_for_each_engine_tuned = [np.average(ruls_for_each_engine, weights = np.repeat(1/num_windows, num_windows)) \n",
    "                                   for ruls_for_each_engine, num_windows in zip(preds_for_each_engine_tuned,\n",
    "                                                                                num_test_windows_list)]\n",
    "RMSE_tuned = np.sqrt(mean_squared_error(true_rul, mean_pred_for_each_engine_tuned))\n",
    "print(\"RMSE after hyperparameter tuning: \", RMSE_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that while prediction, we are predicting RUL values for last 5 examples of every engine. Then we take mean of all 5 predictions for each engine and calculate final RMSE.\n",
    "\n",
    "If instead we wish to take only the last example of every engine to make predictions and calculate RUL, we can do so by taking the last prediction of every engine as calculated before and calculate RMSE as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Taking only last examples):  30.145783450459557\n"
     ]
    }
   ],
   "source": [
    "indices_of_last_examples = np.cumsum(num_test_windows_list) - 1\n",
    "preds_for_last_example = np.concatenate(preds_for_each_engine_tuned)[indices_of_last_examples]\n",
    "\n",
    "RMSE_new = np.sqrt(mean_squared_error(true_rul, preds_for_last_example))\n",
    "print(\"RMSE (Taking only last examples): \", RMSE_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not convinced by above calculations, take a look at the last section of [this notebook](https://github.com/biswajitsahoo1111/rul_codes_open/blob/master/notebooks/cmapss_notebooks/CMAPSS_FD001_xgboost_piecewise_linear_degradation_model.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For CMAPSS data, along with RMSE another metric (S-score) is usually reported in literature. S-score is defined as:\n",
    "\n",
    "$$S= \\sum_{i=1}^N{s_i}$$\n",
    "\n",
    "where, \n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    s_i=\n",
    "    \\begin{cases}\n",
    "      (e^{-\\frac{d_i}{13}})-1, & \\text{for}\\ d_i < 1 \\\\\n",
    "      (e^{\\frac{d_i}{10}})-1, & \\text{for}\\ d_i \\geq 1\\\\\n",
    "    \\end{cases}\n",
    "  \\end{equation}\n",
    "  $$\n",
    "  \n",
    "We can compute the S-metric as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_s_score(rul_true, rul_pred):\n",
    "    \"\"\"\n",
    "    Both rul_true and rul_pred should be 1D numpy arrays.\n",
    "    \"\"\"\n",
    "    diff = rul_pred - rul_true\n",
    "    return np.sum(np.where(diff < 0, np.exp(-diff/13)-1, np.exp(diff/10)-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S-score:  8219.160496057715\n"
     ]
    }
   ],
   "source": [
    "s_score = compute_s_score(true_rul, preds_for_last_example)\n",
    "print(\"S-score: \", s_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot true and predicted RUL values\n",
    "plt.plot(true_rul, label = \"True RUL\", color = \"red\")\n",
    "plt.plot(preds_for_last_example, label = \"Pred RUL\", color = \"blue\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot variable importance score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = best_rf_model.feature_importances_\n",
    "indices = np.argsort(feature_importance)[::-1]\n",
    "num_features = processed_train_data.shape[1]\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(num_features), feature_importance[indices],\n",
    "        color=\"blue\", align=\"center\")\n",
    "plt.xticks(range(num_features), indices)\n",
    "plt.xlim([-1, num_features])\n",
    "plt.xlabel(\"Feature Columns\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final note remember that hyperparameter tuning is more of an art than science. It is possible to obtain better results than what has been obtained here by choosing better set of hyperparameters.\n",
    "\n",
    "For other reproducible results on RUL, interested readers can visit my [project page](https://biswajitsahoo1111.github.io/rul_codes_open). "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf_cpu_23",
   "language": "python",
   "name": "tf_cpu_23"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}