4_FD001_Survival_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro\n",
    "In this notebook we'll develop a Survival Regression model on the FD001 dataset for predictive maintenance. As the results of the chosen Survival model does not directly inform us of the Remaining Useful Life (RUL) of the engines, we fit a second model to get an indication of RUL. You can find a full explanation of the code in my accompanying blog post [here](https://towardsdatascience.com/survival-analysis-for-predictive-maintenance-of-turbofan-engines-7e2e9b82dc0e?source=friends_link&sk=d290f4c4a5c112360e679f12234043bf)  \n",
    "\n",
    "<disclaimer: I aim to showcase the effect of different methods and choices made during model development. These effects are often shown using the test set, something which is considered (very) bad practice but helps for educational purposes.>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from lifelines import KaplanMeierFitter, CoxTimeVaryingFitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data and compute RUL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define filepaths to read data\n",
    "dir_path = './CMAPSSData/'\n",
    "train_file = 'train_FD001.txt'\n",
    "test_file = 'test_FD001.txt'\n",
    "\n",
    "# define column names for easy indexing\n",
    "index_names = ['unit_nr', 'time_cycles']\n",
    "setting_names = ['setting_1', 'setting_2', 'setting_3']\n",
    "sensor_names = ['s_{}'.format(i) for i in range(1,22)] \n",
    "col_names = index_names + setting_names + sensor_names\n",
    "\n",
    "# read data\n",
    "train = pd.read_csv((dir_path+train_file), sep='\\s+', header=None, \n",
    "                 names=col_names)\n",
    "X_test = pd.read_csv((dir_path+test_file), sep='\\s+', header=None, \n",
    "                 names=col_names)\n",
    "y_test = pd.read_csv((dir_path+'RUL_FD001.txt'), sep='\\s+', header=None, \n",
    "                 names=['RemainingUsefulLife'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   unit_nr  time_cycles  setting_1  setting_2  setting_3     s_1     s_2  \\\n",
       "0        1            1    -0.0007    -0.0004      100.0  518.67  641.82   \n",
       "1        1            2     0.0019    -0.0003      100.0  518.67  642.15   \n",
       "2        1            3    -0.0043     0.0003      100.0  518.67  642.35   \n",
       "3        1            4     0.0007     0.0000      100.0  518.67  642.35   \n",
       "4        1            5    -0.0019    -0.0002      100.0  518.67  642.37   \n",
       "\n",
       "       s_3      s_4    s_5  ...    s_12     s_13     s_14    s_15  s_16  s_17  \\\n",
       "0  1589.70  1400.60  14.62  ...  521.66  2388.02  8138.62  8.4195  0.03   392   \n",
       "1  1591.82  1403.14  14.62  ...  522.28  2388.07  8131.49  8.4318  0.03   392   \n",
       "2  1587.99  1404.20  14.62  ...  522.42  2388.03  8133.23  8.4178  0.03   390   \n",
       "3  1582.79  1401.87  14.62  ...  522.86  2388.08  8133.83  8.3682  0.03   392   \n",
       "4  1582.85  1406.22  14.62  ...  522.19  2388.04  8133.80  8.4294  0.03   393   \n",
       "\n",
       "   s_18   s_19   s_20     s_21  \n",
       "0  2388  100.0  39.06  23.4190  \n",
       "1  2388  100.0  39.00  23.4236  \n",
       "2  2388  100.0  38.95  23.3442  \n",
       "3  2388  100.0  38.88  23.3739  \n",
       "4  2388  100.0  38.90  23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "train.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   unit_nr  time_cycles  RUL\n",
       "0        1            1  191\n",
       "1        1            2  190\n",
       "2        1            3  189\n",
       "3        1            4  188\n",
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      ]
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   ],
   "source": [
    "def add_remaining_useful_life(df):\n",
    "    # Get the total number of cycles for each unit\n",
    "    grouped_by_unit = df.groupby(by=\"unit_nr\")\n",
    "    max_cycle = grouped_by_unit[\"time_cycles\"].max()\n",
    "    \n",
    "    # Merge the max cycle back into the original frame\n",
    "    result_frame = df.merge(max_cycle.to_frame(name='max_cycle'), left_on='unit_nr', right_index=True)\n",
    "    \n",
    "    # Calculate remaining useful life for each row\n",
    "    remaining_useful_life = result_frame[\"max_cycle\"] - result_frame[\"time_cycles\"]\n",
    "    result_frame[\"RUL\"] = remaining_useful_life\n",
    "    \n",
    "    # drop max_cycle as it's no longer needed\n",
    "    result_frame = result_frame.drop(\"max_cycle\", axis=1)\n",
    "    return result_frame\n",
    "\n",
    "train = add_remaining_useful_life(train)  # add computed RUL for later use\n",
    "display(train[index_names+['RUL']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['RUL'].clip(upper=125, inplace=True)  # clip RUL as discussed in SVR and problem framing analysis\n",
    "\n",
    "drop_sensors = ['s_1','s_5','s_6','s_10','s_16','s_18','s_19']  # non-informative features, derived from EDA\n",
    "drop_labels = setting_names + drop_sensors\n",
    "train.drop(labels=drop_labels, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['unit_nr', 'time_cycles', 's_2', 's_3', 's_4', 's_7', 's_8', 's_9',\n",
      "       's_11', 's_12', 's_13', 's_14', 's_15', 's_17', 's_20', 's_21', 'RUL'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)\n",
    "remaining_sensors = ['s_2', 's_3', 's_4', 's_7', 's_8', 's_9',\n",
    "       's_11', 's_12', 's_13', 's_14', 's_15', 's_17', 's_20', 's_21']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation\n",
    "The CoxTimeVaryingFitter we'll use later requires a breakdown column.  \n",
    "let's add that column indicating if the engines broke down (1) or are still functioning (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['breakdown'] = 0\n",
    "idx_last_record = train.reset_index().groupby(by='unit_nr')['index'].last()  # engines breakdown at the last cycle\n",
    "train.at[idx_last_record, 'breakdown'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, CoxTimeVarying fitter requires an observation start and stop column.  \n",
    "The time_cycles column will indicate the end of an observation and time_cycles -1 the start of an observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20630</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>643.85</td>\n",
       "      <td>1600.38</td>\n",
       "      <td>1432.14</td>\n",
       "      <td>550.79</td>\n",
       "      <td>2388.26</td>\n",
       "      <td>9061.48</td>\n",
       "      <td>48.20</td>\n",
       "      <td>519.30</td>\n",
       "      <td>2388.26</td>\n",
       "      <td>8137.33</td>\n",
       "      <td>8.5036</td>\n",
       "      <td>396</td>\n",
       "      <td>38.37</td>\n",
       "      <td>23.0522</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       unit_nr  time_cycles     s_2      s_3      s_4     s_7      s_8  \\\n",
       "20626      100          196  643.49  1597.98  1428.63  551.43  2388.19   \n",
       "20627      100          197  643.54  1604.50  1433.58  550.86  2388.23   \n",
       "20628      100          198  643.42  1602.46  1428.18  550.94  2388.24   \n",
       "20629      100          199  643.23  1605.26  1426.53  550.68  2388.25   \n",
       "20630      100          200  643.85  1600.38  1432.14  550.79  2388.26   \n",
       "\n",
       "           s_9   s_11    s_12     s_13     s_14    s_15  s_17   s_20     s_21  \\\n",
       "20626  9065.52  48.07  519.49  2388.26  8137.60  8.4956   397  38.49  22.9735   \n",
       "20627  9065.11  48.04  519.68  2388.22  8136.50  8.5139   395  38.30  23.1594   \n",
       "20628  9065.90  48.09  520.01  2388.24  8141.05  8.5646   398  38.44  22.9333   \n",
       "20629  9073.72  48.39  519.67  2388.23  8139.29  8.5389   395  38.29  23.0640   \n",
       "20630  9061.48  48.20  519.30  2388.26  8137.33  8.5036   396  38.37  23.0522   \n",
       "\n",
       "       RUL  breakdown  start  \n",
       "20626    4          0    195  \n",
       "20627    3          0    196  \n",
       "20628    2          0    197  \n",
       "20629    1          0    198  \n",
       "20630    0          1    199  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['start'] = train['time_cycles'] - 1 \n",
    "train.tail() # check results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## introduce right sensoring in training set\n",
    "Since the engines in the training set are all run to failure, we artificially introduce right censoring to allow playing with the data in a bit more realistic setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut_off = 200\n",
    "\n",
    "train_censored = train[train['time_cycles'] <= cut_off].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KaplanMeier curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create kaplan meier curve\n",
    "data = train_censored[index_names+['breakdown']].groupby('unit_nr').last()\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "survival = KaplanMeierFitter()\n",
    "survival.fit(data['time_cycles'], data['breakdown'])\n",
    "survival.plot()\n",
    "plt.ylabel(\"Probability of survival\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KaplanMeier curve provides us with some initial information of survival probabilities. For example, engines have a 100% probability of surviving the first 128 time_cycles. After that point the first engines start to break down, but there is still a 46% probability of the engine surviving past 200 time_cycles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CoxTimeVaryingFitter\n",
    "I have tried several variations with the regular CoxPHFitter, but it did not yield good results for this particular case. The reason I wanted to use the regular CoxPHFitter is because it comes with a predict_expectation right out of the box, providing time till event (or in our case RUL) predictions.  \n",
    "\n",
    "Finally, I've setteled on the CoxTimeVaryingFitter. Which can leverage the timeseries nature of the data, as it is able to take multiple observations for each engine into account. The downside to this model is, its results are less intuitive to interpret. In general, higher partial hazards indicate a higher risk of failure, but this is no direct indication of time till event. For a more detailed explanation you can refer to the blog post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cols = index_names + remaining_sensors + ['start', 'breakdown']\n",
    "predict_cols = ['time_cycles'] + remaining_sensors + ['start', 'breakdown']  # breakdown value will be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Iteration 1: norm_delta = 0.95065, step_size = 1.00000, ll = -230.78680, newton_decrement = 107.16417, seconds_since_start = 0.1\r",
      "Iteration 2: norm_delta = 0.76782, step_size = 1.00000, ll = -106.85153, newton_decrement = 21.89202, seconds_since_start = 0.1\r",
      "Iteration 3: norm_delta = 0.71813, step_size = 1.00000, ll = -78.95941, newton_decrement = 9.26325, seconds_since_start = 0.1\r",
      "Iteration 4: norm_delta = 0.58832, step_size = 1.00000, ll = -67.33395, newton_decrement = 2.71146, seconds_since_start = 0.1\r",
      "Iteration 5: norm_delta = 0.24656, step_size = 1.00000, ll = -64.16014, newton_decrement = 0.27774, seconds_since_start = 0.1\r",
      "Iteration 6: norm_delta = 0.02991, step_size = 1.00000, ll = -63.86355, newton_decrement = 0.00342, seconds_since_start = 0.1\r",
      "Iteration 7: norm_delta = 0.00038, step_size = 1.00000, ll = -63.86010, newton_decrement = 0.00000, seconds_since_start = 0.1\r",
      "Iteration 8: norm_delta = 0.00000, step_size = 1.00000, ll = -63.86010, newton_decrement = 0.00000, seconds_since_start = 0.1Convergence completed after 8 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 18627 periods, 100 subjects, 54 events>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctv = CoxTimeVaryingFitter()\n",
    "ctv.fit(train_censored[train_cols], id_col=\"unit_nr\", event_col='breakdown', \n",
    "        start_col='start', stop_col='time_cycles', show_progress=True, step_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<lifelines.CoxTimeVaryingFitter: fitted with 18627 periods, 100 subjects, 54 events>\n",
      "         event col = 'breakdown'\n",
      "number of subjects = 100\n",
      " number of periods = 18627\n",
      "  number of events = 54\n",
      "    log-likelihood = -63.86\n",
      "  time fit was run = 2020-09-02 15:26:11 UTC\n",
      "\n",
      "---\n",
      "       coef  exp(coef)   se(coef)   coef lower 95%   coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%\n",
      "s_2    2.05       7.75       0.72             0.64             3.45                 1.90                31.64\n",
      "s_3    0.07       1.07       0.04            -0.01             0.15                 0.99                 1.16\n",
      "s_4    0.16       1.18       0.05             0.07             0.26                 1.07                 1.29\n",
      "s_7   -1.09       0.34       0.50            -2.07            -0.11                 0.13                 0.90\n",
      "s_8   -5.03       0.01       4.84           -14.51             4.45                 0.00                85.79\n",
      "s_9   -0.02       0.98       0.04            -0.10             0.05                 0.91                 1.05\n",
      "s_11   5.12     167.43       1.70             1.78             8.46                 5.95              4714.95\n",
      "s_12  -1.14       0.32       0.53            -2.17            -0.10                 0.11                 0.90\n",
      "s_13  12.90   3.99e+05       5.51             2.09            23.70                 8.09             1.97e+10\n",
      "s_14   0.04       1.04       0.04            -0.04             0.12                 0.96                 1.13\n",
      "s_15   5.07     159.96       9.53           -13.61            23.76                 0.00             2.09e+10\n",
      "s_17   0.41       1.51       0.20             0.01             0.81                 1.01                 2.25\n",
      "s_20  -5.13       0.01       2.02            -9.09            -1.17                 0.00                 0.31\n",
      "s_21  -4.23       0.01       2.67            -9.46             1.01                 0.00                 2.75\n",
      "\n",
      "         z      p   -log2(p)\n",
      "s_2   2.85 <0.005       7.84\n",
      "s_3   1.73   0.08       3.57\n",
      "s_4   3.49 <0.005      11.03\n",
      "s_7  -2.17   0.03       5.06\n",
      "s_8  -1.04   0.30       1.74\n",
      "s_9  -0.58   0.56       0.82\n",
      "s_11  3.01 <0.005       8.56\n",
      "s_12 -2.15   0.03       4.99\n",
      "s_13  2.34   0.02       5.69\n",
      "s_14  0.95   0.34       1.54\n",
      "s_15  0.53   0.59       0.75\n",
      "s_17  2.03   0.04       4.56\n",
      "s_20 -2.54   0.01       6.49\n",
      "s_21 -1.58   0.11       3.14\n",
      "---\n",
      "Log-likelihood ratio test = 333.85 on 14 df, -log2(p)=205.97\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctv.print_summary()\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "ctv.plot()\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>coef</th>\n",
       "      <th>exp(coef)</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>coef lower 95%</th>\n",
       "      <th>coef upper 95%</th>\n",
       "      <th>exp(coef) lower 95%</th>\n",
       "      <th>exp(coef) upper 95%</th>\n",
       "      <th>z</th>\n",
       "      <th>p</th>\n",
       "      <th>-log2(p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>s_2</td>\n",
       "      <td>2.047076</td>\n",
       "      <td>7.745221</td>\n",
       "      <td>0.718074</td>\n",
       "      <td>0.639677</td>\n",
       "      <td>3.454475</td>\n",
       "      <td>1.895868e+00</td>\n",
       "      <td>3.164168e+01</td>\n",
       "      <td>2.850787</td>\n",
       "      <td>0.004361</td>\n",
       "      <td>7.841085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_3</td>\n",
       "      <td>0.070081</td>\n",
       "      <td>1.072595</td>\n",
       "      <td>0.040596</td>\n",
       "      <td>-0.009485</td>\n",
       "      <td>0.149647</td>\n",
       "      <td>9.905601e-01</td>\n",
       "      <td>1.161425e+00</td>\n",
       "      <td>1.726324</td>\n",
       "      <td>0.084289</td>\n",
       "      <td>3.568509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_4</td>\n",
       "      <td>0.164506</td>\n",
       "      <td>1.178811</td>\n",
       "      <td>0.047103</td>\n",
       "      <td>0.072187</td>\n",
       "      <td>0.256825</td>\n",
       "      <td>1.074856e+00</td>\n",
       "      <td>1.292819e+00</td>\n",
       "      <td>3.492512</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>11.029195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_7</td>\n",
       "      <td>-1.086248</td>\n",
       "      <td>0.337480</td>\n",
       "      <td>0.500341</td>\n",
       "      <td>-2.066899</td>\n",
       "      <td>-0.105597</td>\n",
       "      <td>1.265777e-01</td>\n",
       "      <td>8.997872e-01</td>\n",
       "      <td>-2.171014</td>\n",
       "      <td>0.029930</td>\n",
       "      <td>5.062258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_8</td>\n",
       "      <td>-5.030594</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>4.838076</td>\n",
       "      <td>-14.513049</td>\n",
       "      <td>4.451861</td>\n",
       "      <td>4.978091e-07</td>\n",
       "      <td>8.578647e+01</td>\n",
       "      <td>-1.039792</td>\n",
       "      <td>0.298436</td>\n",
       "      <td>1.744504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_9</td>\n",
       "      <td>-0.022045</td>\n",
       "      <td>0.978196</td>\n",
       "      <td>0.038271</td>\n",
       "      <td>-0.097054</td>\n",
       "      <td>0.052964</td>\n",
       "      <td>9.075067e-01</td>\n",
       "      <td>1.054392e+00</td>\n",
       "      <td>-0.576025</td>\n",
       "      <td>0.564598</td>\n",
       "      <td>0.824703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_11</td>\n",
       "      <td>5.120580</td>\n",
       "      <td>167.432485</td>\n",
       "      <td>1.703049</td>\n",
       "      <td>1.782666</td>\n",
       "      <td>8.458494</td>\n",
       "      <td>5.945688e+00</td>\n",
       "      <td>4.714953e+03</td>\n",
       "      <td>3.006714</td>\n",
       "      <td>0.002641</td>\n",
       "      <td>8.564765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_12</td>\n",
       "      <td>-1.136686</td>\n",
       "      <td>0.320881</td>\n",
       "      <td>0.528755</td>\n",
       "      <td>-2.173028</td>\n",
       "      <td>-0.100345</td>\n",
       "      <td>1.138325e-01</td>\n",
       "      <td>9.045256e-01</td>\n",
       "      <td>-2.149739</td>\n",
       "      <td>0.031576</td>\n",
       "      <td>4.985035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_13</td>\n",
       "      <td>12.897450</td>\n",
       "      <td>399292.512067</td>\n",
       "      <td>5.513919</td>\n",
       "      <td>2.090367</td>\n",
       "      <td>23.704532</td>\n",
       "      <td>8.087885e+00</td>\n",
       "      <td>1.971276e+10</td>\n",
       "      <td>2.339071</td>\n",
       "      <td>0.019332</td>\n",
       "      <td>5.692885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_14</td>\n",
       "      <td>0.040657</td>\n",
       "      <td>1.041495</td>\n",
       "      <td>0.042873</td>\n",
       "      <td>-0.043373</td>\n",
       "      <td>0.124687</td>\n",
       "      <td>9.575541e-01</td>\n",
       "      <td>1.132794e+00</td>\n",
       "      <td>0.948305</td>\n",
       "      <td>0.342974</td>\n",
       "      <td>1.543827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_15</td>\n",
       "      <td>5.074931</td>\n",
       "      <td>159.961077</td>\n",
       "      <td>9.534276</td>\n",
       "      <td>-13.611908</td>\n",
       "      <td>23.761769</td>\n",
       "      <td>1.225811e-06</td>\n",
       "      <td>2.087397e+10</td>\n",
       "      <td>0.532283</td>\n",
       "      <td>0.594530</td>\n",
       "      <td>0.750178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_17</td>\n",
       "      <td>0.412328</td>\n",
       "      <td>1.510329</td>\n",
       "      <td>0.203065</td>\n",
       "      <td>0.014327</td>\n",
       "      <td>0.810328</td>\n",
       "      <td>1.014430e+00</td>\n",
       "      <td>2.248646e+00</td>\n",
       "      <td>2.030518</td>\n",
       "      <td>0.042304</td>\n",
       "      <td>4.563066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_20</td>\n",
       "      <td>-5.130391</td>\n",
       "      <td>0.005914</td>\n",
       "      <td>2.021437</td>\n",
       "      <td>-9.092334</td>\n",
       "      <td>-1.168447</td>\n",
       "      <td>1.125251e-04</td>\n",
       "      <td>3.108492e-01</td>\n",
       "      <td>-2.537992</td>\n",
       "      <td>0.011149</td>\n",
       "      <td>6.486935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>s_21</td>\n",
       "      <td>-4.226302</td>\n",
       "      <td>0.014606</td>\n",
       "      <td>2.672637</td>\n",
       "      <td>-9.464573</td>\n",
       "      <td>1.011970</td>\n",
       "      <td>7.755113e-05</td>\n",
       "      <td>2.751015e+00</td>\n",
       "      <td>-1.581323</td>\n",
       "      <td>0.113804</td>\n",
       "      <td>3.135374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           coef      exp(coef)  se(coef)  coef lower 95%  coef upper 95%  \\\n",
       "s_2    2.047076       7.745221  0.718074        0.639677        3.454475   \n",
       "s_3    0.070081       1.072595  0.040596       -0.009485        0.149647   \n",
       "s_4    0.164506       1.178811  0.047103        0.072187        0.256825   \n",
       "s_7   -1.086248       0.337480  0.500341       -2.066899       -0.105597   \n",
       "s_8   -5.030594       0.006535  4.838076      -14.513049        4.451861   \n",
       "s_9   -0.022045       0.978196  0.038271       -0.097054        0.052964   \n",
       "s_11   5.120580     167.432485  1.703049        1.782666        8.458494   \n",
       "s_12  -1.136686       0.320881  0.528755       -2.173028       -0.100345   \n",
       "s_13  12.897450  399292.512067  5.513919        2.090367       23.704532   \n",
       "s_14   0.040657       1.041495  0.042873       -0.043373        0.124687   \n",
       "s_15   5.074931     159.961077  9.534276      -13.611908       23.761769   \n",
       "s_17   0.412328       1.510329  0.203065        0.014327        0.810328   \n",
       "s_20  -5.130391       0.005914  2.021437       -9.092334       -1.168447   \n",
       "s_21  -4.226302       0.014606  2.672637       -9.464573        1.011970   \n",
       "\n",
       "      exp(coef) lower 95%  exp(coef) upper 95%         z         p   -log2(p)  \n",
       "s_2          1.895868e+00         3.164168e+01  2.850787  0.004361   7.841085  \n",
       "s_3          9.905601e-01         1.161425e+00  1.726324  0.084289   3.568509  \n",
       "s_4          1.074856e+00         1.292819e+00  3.492512  0.000478  11.029195  \n",
       "s_7          1.265777e-01         8.997872e-01 -2.171014  0.029930   5.062258  \n",
       "s_8          4.978091e-07         8.578647e+01 -1.039792  0.298436   1.744504  \n",
       "s_9          9.075067e-01         1.054392e+00 -0.576025  0.564598   0.824703  \n",
       "s_11         5.945688e+00         4.714953e+03  3.006714  0.002641   8.564765  \n",
       "s_12         1.138325e-01         9.045256e-01 -2.149739  0.031576   4.985035  \n",
       "s_13         8.087885e+00         1.971276e+10  2.339071  0.019332   5.692885  \n",
       "s_14         9.575541e-01         1.132794e+00  0.948305  0.342974   1.543827  \n",
       "s_15         1.225811e-06         2.087397e+10  0.532283  0.594530   0.750178  \n",
       "s_17         1.014430e+00         2.248646e+00  2.030518  0.042304   4.563066  \n",
       "s_20         1.125251e-04         3.108492e-01 -2.537992  0.011149   6.486935  \n",
       "s_21         7.755113e-05         2.751015e+00 -1.581323  0.113804   3.135374  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ctv.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log-likelihood is an indication of goodness of fit, values closer to zero indicate a better fit.  \n",
    "Looking at the p-values of the coefficients, sensors 9 and 15 have p-values > 0.50. However, removing the sensors returned a log-likelihood of -64.20, indicating the model becomes worse when removing these sensors.\n",
    "Last but not least the exp(coef) show the scaling hazard risk. E.g. an increment of 1 unit for the sensor values of sensor 11 increases the risk of breakdown by 167.43 [1-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# predict and evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_censored.groupby(\"unit_nr\").last()\n",
    "df = df[df['breakdown'] == 0]  # get engines from dataset which are still functioning so we can predict their RUL\n",
    "df_to_predict = df[df['breakdown'] == 0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>predictions</th>\n",
       "      <th>RUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.128757</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-1.607369</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.073379</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>21.145492</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11.751951</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>4.391817</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>14.439742</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>20.540832</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>-3.512471</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>10.624723</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    predictions  RUL\n",
       "2     -1.128757   87\n",
       "5     -1.607369   69\n",
       "7      3.073379   59\n",
       "9     21.145492    1\n",
       "10    11.751951   22\n",
       "11     4.391817   40\n",
       "15    14.439742    7\n",
       "16    20.540832    9\n",
       "17    -3.512471   76\n",
       "20    10.624723   34"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = ctv.predict_log_partial_hazard(df_to_predict[predict_cols])\n",
    "predictions.rename(columns={0: \"predictions\"}, inplace=True)\n",
    "\n",
    "df_last = train.groupby('unit_nr').last()\n",
    "predictions['RUL'] = df_to_predict['RUL']\n",
    "predictions.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When comparing the log_partial_hazard with computed RUL you can see it generally informs quite well about imminence of breakdown. Higher log_partial_hazards are returned for engines more at risk of breaking down most of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.plot(predictions['RUL'], predictions['predictions'], '.b')\n",
    "xlim = plt.gca().get_xlim()\n",
    "plt.xlim(xlim[1], xlim[0])\n",
    "plt.xlabel('RUL')\n",
    "plt.ylabel('log_partial_hazard')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph reconfirms higher log_partial_hazard generally informs on the imminence of breakdown.  \n",
    "Let's see how the hazard of the engines behave over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now lets look at some hazard trajectories\n",
    "X = train.loc[train['unit_nr'].isin(df_to_predict.index)]\n",
    "X_unique = len(X['unit_nr'].unique())\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "for i in range(1, X_unique, 2):\n",
    "    X_sub = X.loc[X['unit_nr'] == i]\n",
    "    predictions = ctv.predict_partial_hazard(X_sub)[0].values\n",
    "    plt.plot(X_sub['time_cycles'].values, np.log(predictions))\n",
    "\n",
    "plt.xlabel('time_cycles')\n",
    "plt.ylabel('log_partial_hazard')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: The practical thing to do here would be to set a threshold after which maintenance should be performed.  \n",
    "However that doesn't really inform you of the remaining useful life.  \n",
    "You could in theory build another model to predict when this threshold is reached to get more of an indication of time till event.  \n",
    "I'm going to try and relate the hazard values directly to computed RUL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressing the log_partial_hazard to RUL\n",
    "First predict the hazard for all engines in the set and check their relation to RUL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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_nr</th>\n",
       "      <th>time_cycles</th>\n",
       "      <th>s_2</th>\n",
       "      <th>s_3</th>\n",
       "      <th>s_4</th>\n",
       "      <th>s_7</th>\n",
       "      <th>s_8</th>\n",
       "      <th>s_9</th>\n",
       "      <th>s_11</th>\n",
       "      <th>s_12</th>\n",
       "      <th>s_13</th>\n",
       "      <th>s_14</th>\n",
       "      <th>s_15</th>\n",
       "      <th>s_17</th>\n",
       "      <th>s_20</th>\n",
       "      <th>s_21</th>\n",
       "      <th>RUL</th>\n",
       "      <th>breakdown</th>\n",
       "      <th>start</th>\n",
       "      <th>hazard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>554.36</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9046.19</td>\n",
       "      <td>47.47</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>392</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-6.856341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>553.75</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>9044.07</td>\n",
       "      <td>47.49</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>392</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.700971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>554.26</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9052.94</td>\n",
       "      <td>47.27</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>390</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-7.370774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>554.45</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>9049.48</td>\n",
       "      <td>47.13</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>392</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-8.140738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>554.00</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9055.15</td>\n",
       "      <td>47.28</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>393</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-5.260691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unit_nr  time_cycles     s_2      s_3      s_4     s_7      s_8      s_9  \\\n",
       "0        1            1  641.82  1589.70  1400.60  554.36  2388.06  9046.19   \n",
       "1        1            2  642.15  1591.82  1403.14  553.75  2388.04  9044.07   \n",
       "2        1            3  642.35  1587.99  1404.20  554.26  2388.08  9052.94   \n",
       "3        1            4  642.35  1582.79  1401.87  554.45  2388.11  9049.48   \n",
       "4        1            5  642.37  1582.85  1406.22  554.00  2388.06  9055.15   \n",
       "\n",
       "    s_11    s_12     s_13     s_14    s_15  s_17   s_20     s_21  RUL  \\\n",
       "0  47.47  521.66  2388.02  8138.62  8.4195   392  39.06  23.4190  125   \n",
       "1  47.49  522.28  2388.07  8131.49  8.4318   392  39.00  23.4236  125   \n",
       "2  47.27  522.42  2388.03  8133.23  8.4178   390  38.95  23.3442  125   \n",
       "3  47.13  522.86  2388.08  8133.83  8.3682   392  38.88  23.3739  125   \n",
       "4  47.28  522.19  2388.04  8133.80  8.4294   393  38.90  23.4044  125   \n",
       "\n",
       "   breakdown  start    hazard  \n",
       "0          0      0 -6.856341  \n",
       "1          0      1 -4.700971  \n",
       "2          0      2 -7.370774  \n",
       "3          0      3 -8.140738  \n",
       "4          0      4 -5.260691  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hazard = train_censored.copy()\n",
    "df_hazard['hazard'] = ctv.predict_log_partial_hazard(df_hazard)\n",
    "df_hazard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_hazard.plot('hazard', 'RUL', 'scatter', figsize=(15,5))\n",
    "plt.xlabel('hazard')\n",
    "plt.ylabel('RUL')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The relationship is non-linear and there is quite a lot of spread, making it difficult to pinpoint RUL based on the log_partial_hazard.  \n",
    "We can try fitting an exponential model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_hat, label='test'):\n",
    "    mse = mean_squared_error(y_true, y_hat)\n",
    "    rmse = np.sqrt(mse)\n",
    "    variance = r2_score(y_true, y_hat)\n",
    "    print('{} set RMSE:{}, R2:{}'.format(label, rmse, variance))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/52930401/how-to-get-a-robust-nonlinear-regression-fit-using-scipy-optimize-least-squares\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def exponential_model(z, a, b):\n",
    "    return a * np.exp(-b * z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Idea of fit\n",
    "Note: Below graph is to showcase the idea. The line that was actually fitted is less accurate as it takes data points of all engines into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check specific unit_nr\n",
    "y_hat = exponential_model(df_hazard.loc[df_hazard['unit_nr']==1, 'hazard'], 70, 0.1)\n",
    "\n",
    "plt.plot(df_hazard.loc[df_hazard['unit_nr']==1, 'hazard'], df_hazard.loc[df_hazard['unit_nr']==1, 'RUL'], 'o',\n",
    "        df_hazard.loc[df_hazard['unit_nr']==1, 'hazard'], y_hat)\n",
    "plt.xlabel(\"log_partial_hazard\")\n",
    "plt.ylabel(\"RUL\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.85954699e+01, 4.35302167e-02])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt, pcov = curve_fit(exponential_model, df_hazard['hazard'], df_hazard['RUL'])\n",
    "popt  # the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep test set\n",
    "test = X_test.drop(labels=drop_labels, axis=1)\n",
    "test['breakdown'] = 0\n",
    "test['start'] = test['time_cycles'] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set RMSE:26.302858422243872, R2:0.5487597218187095\n",
      "test set RMSE:27.135244169256758, R2:0.5736091039386049\n"
     ]
    }
   ],
   "source": [
    "# predict and evaluate\n",
    "y_hat = exponential_model(df_hazard['hazard'], *popt)\n",
    "evaluate(df_hazard['RUL'], y_hat, 'train')\n",
    "\n",
    "y_pred = ctv.predict_log_partial_hazard(test.groupby('unit_nr').last())\n",
    "y_hat = exponential_model(y_pred, *popt)\n",
    "evaluate(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# repeat but train on complete dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 8: norm_delta = 0.00000, step_size = 1.00000, ll = -114.77106, newton_decrement = 0.00000, seconds_since_start = 0.2Convergence completed after 8 iterations.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxTimeVaryingFitter: fitted with 20631 periods, 100 subjects, 100 events>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctv2 = CoxTimeVaryingFitter()\n",
    "ctv2.fit(train[train_cols], id_col=\"unit_nr\", event_col='breakdown', \n",
    "        start_col='start', stop_col='time_cycles', show_progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set RMSE:26.226364780597272, R2:0.6039289060308352\n",
      "test set RMSE:26.580988808221285, R2:0.5908498441209542\n"
     ]
    }
   ],
   "source": [
    "train['hazard'] = ctv2.predict_log_partial_hazard(train)\n",
    "popt2, pcov2 = curve_fit(exponential_model, train['hazard'], train['RUL'])\n",
    "\n",
    "y_hat = exponential_model(train['hazard'], *popt2)\n",
    "evaluate(train['RUL'], y_hat, 'train')\n",
    "\n",
    "y_pred = ctv2.predict_log_partial_hazard(test.groupby('unit_nr').last())\n",
    "y_hat = exponential_model(y_pred, *popt2)\n",
    "evaluate(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although it doesn't come as close to the SVR or time-series analysis solutions, part of the innaccuracy can be explained by fitting another model ontop of the predicted log_partial_hazard, which results in errors ontop of errors (as no model is perfect). I believe when you step away from the RUL paradigm we've been using and set a threshold for the log_partial_hazard this method could be very appropriate to define when maintenance is required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I would like to give a special thanks to lifelines author Cameron Davidson-Pilon for taking the time to provide me with some pointers on how to best utilize the lifelines package for the dataset at hand!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## references and reading materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# references\n",
    "# [1] https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqhow-are-the-likelihood-ratio-wald-and-lagrange-multiplier-score-tests-different-andor-similar/\n",
    "# [2] https://www.reddit.com/r/statistics/comments/23sk6h/what_does_a_loglikelihood_value_indicate_and_how/\n",
    "# [3] https://medium.com/@zachary.james.angell/applying-survival-analysis-to-customer-churn-40b5a809b05a\n",
    "\n",
    "\n",
    "# reading materials\n",
    "# Kaplan Meier Curve\n",
    "# https://lifelines.readthedocs.io/en/latest/Survival%20analysis%20with%20lifelines.html\n",
    "# https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator\n",
    "\n",
    "# model interpretation & loglikelihood\n",
    "# https://medium.com/@zachary.james.angell/applying-survival-analysis-to-customer-churn-40b5a809b05a\n",
    "# https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqhow-are-the-likelihood-ratio-wald-and-lagrange-multiplier-score-tests-different-andor-similar/\n",
    "# https://www.reddit.com/r/statistics/comments/23sk6h/what_does_a_loglikelihood_value_indicate_and_how/\n",
    "\n",
    "\n",
    "# Generic stuff\n",
    "# https://towardsdatascience.com/churn-prediction-and-prevention-in-python-2d454e5fd9a5\n",
    "# https://github.com/CamDavidsonPilon/lifelines/issues/619  # use CPH model with continuous variables\n",
    "# https://towardsdatascience.com/survival-analysis-part-a-70213df21c2e\n",
    "# https://en.wikipedia.org/wiki/Survival_analysis\n",
    "\n",
    "\n",
    "# https://datascience.stackexchange.com/questions/14252/how-to-use-survival-analysis-for-predictive-maintenance-for-time-series-data\n",
    "# https://square.github.io/pysurvival/tutorials/maintenance.html\n",
    "# https://stats.stackexchange.com/questions/177175/survival-analysis-account-for-repeated-measures?noredirect=1&lq=1\n",
    "# https://stats.stackexchange.com/questions/199744/using-survival-analysis-with-multiple-events\n",
    "\n",
    "# belowlink did not work out the way I imagined, hence I switched to the timevaryingfitter\n",
    "# https://lifelines.readthedocs.io/en/latest/Survival%20Regression.html#clusters-correlations  \n",
    "# https://lifelines.readthedocs.io/en/latest/Examples.html#correlations-between-subjects-in-a-cox-model"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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