docking_prediction/regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.11.2-py3-none-any.whl (292 kB)\n",
      "\u001b[K     |████████████████████████████████| 292 kB 3.3 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: matplotlib>=2.2 in /home/szymon/.local/lib/python3.9/site-packages (from seaborn) (3.5.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/szymon/.local/lib/python3.9/site-packages (from seaborn) (1.3.5)\n",
      "Requirement already satisfied: numpy>=1.15 in /home/szymon/.local/lib/python3.9/site-packages (from seaborn) (1.22.2)\n",
      "Collecting scipy>=1.0\n",
      "  Downloading scipy-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (42.1 MB)\n",
      "\u001b[K     |████████████████████████████████| 42.1 MB 40.4 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /home/szymon/.local/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/szymon/.local/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/szymon/.local/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (4.29.1)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/szymon/.local/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (3.0.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/szymon/.local/lib/python3.9/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/lib/python3/dist-packages (from matplotlib>=2.2->seaborn) (2.8.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /usr/lib/python3/dist-packages (from matplotlib>=2.2->seaborn) (8.1.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/lib/python3/dist-packages (from pandas>=0.23->seaborn) (2021.1)\n",
      "Installing collected packages: scipy, seaborn\n",
      "Successfully installed scipy-1.8.0 seaborn-0.11.2\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 16:43:41.488780: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-02-09 16:43:41.488804: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('extended_training_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>base_speed</th>\n",
       "      <th>distance_from_wall</th>\n",
       "      <th>rotation_angle</th>\n",
       "      <th>distance_setpoint</th>\n",
       "      <th>docking_time</th>\n",
       "      <th>docking_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4495</th>\n",
       "      <td>2.5</td>\n",
       "      <td>531.848167</td>\n",
       "      <td>0.391318</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.515198</td>\n",
       "      <td>1674.391470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4496</th>\n",
       "      <td>2.5</td>\n",
       "      <td>523.195455</td>\n",
       "      <td>0.390732</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.465354</td>\n",
       "      <td>1703.937812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4497</th>\n",
       "      <td>2.5</td>\n",
       "      <td>538.721214</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.691364</td>\n",
       "      <td>1713.239634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>2.5</td>\n",
       "      <td>535.153784</td>\n",
       "      <td>0.390795</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.630805</td>\n",
       "      <td>1711.575030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>2.5</td>\n",
       "      <td>530.107834</td>\n",
       "      <td>0.387953</td>\n",
       "      <td>500.0</td>\n",
       "      <td>11.744208</td>\n",
       "      <td>1698.880013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      base_speed  distance_from_wall  rotation_angle  distance_setpoint  \\\n",
       "4495         2.5          531.848167        0.391318              500.0   \n",
       "4496         2.5          523.195455        0.390732              500.0   \n",
       "4497         2.5          538.721214        0.390282              500.0   \n",
       "4498         2.5          535.153784        0.390795              500.0   \n",
       "4499         2.5          530.107834        0.387953              500.0   \n",
       "\n",
       "      docking_time  docking_distance  \n",
       "4495     11.515198       1674.391470  \n",
       "4496     11.465354       1703.937812  \n",
       "4497     11.691364       1713.239634  \n",
       "4498     11.630805       1711.575030  \n",
       "4499     11.744208       1698.880013  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = df.sample(frac=0.8, random_state=0)\n",
    "test_dataset = df.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot=sns.pairplot(train_dataset[[ \n",
    "                            # 'base_speed',\n",
    "                            'distance_from_wall', \n",
    "                            'rotation_angle',\n",
    "                            # 'distance_setpoint', \n",
    "                            'docking_time', \n",
    "                            'docking_distance']], \n",
    "                            diag_kind='kde'\n",
    "                            )\n",
    "plt.savefig('output.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base_speed</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_wall</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>512.445866</td>\n",
       "      <td>164.546498</td>\n",
       "      <td>286.976513</td>\n",
       "      <td>344.321811</td>\n",
       "      <td>491.597934</td>\n",
       "      <td>671.739832</td>\n",
       "      <td>820.912826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rotation_angle</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>-0.053640</td>\n",
       "      <td>0.355592</td>\n",
       "      <td>-1.304903</td>\n",
       "      <td>-0.189153</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153388</td>\n",
       "      <td>0.691801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_setpoint</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docking_time</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>10.414083</td>\n",
       "      <td>6.193692</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>6.026043</td>\n",
       "      <td>9.547753</td>\n",
       "      <td>14.893725</td>\n",
       "      <td>25.728777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docking_distance</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>1570.180704</td>\n",
       "      <td>830.120669</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1025.795334</td>\n",
       "      <td>1462.633703</td>\n",
       "      <td>2243.551784</td>\n",
       "      <td>3454.872171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count         mean         std         min          25%  \\\n",
       "base_speed          3600.0     2.500000    0.000000    2.500000     2.500000   \n",
       "distance_from_wall  3600.0   512.445866  164.546498  286.976513   344.321811   \n",
       "rotation_angle      3600.0    -0.053640    0.355592   -1.304903    -0.189153   \n",
       "distance_setpoint   3600.0   500.000000    0.000000  500.000000   500.000000   \n",
       "docking_time        3600.0    10.414083    6.193692    0.000585     6.026043   \n",
       "docking_distance    3600.0  1570.180704  830.120669    0.000000  1025.795334   \n",
       "\n",
       "                            50%          75%          max  \n",
       "base_speed             2.500000     2.500000     2.500000  \n",
       "distance_from_wall   491.597934   671.739832   820.912826  \n",
       "rotation_angle         0.000000     0.153388     0.691801  \n",
       "distance_setpoint    500.000000   500.000000   500.000000  \n",
       "docking_time           9.547753    14.893725    25.728777  \n",
       "docking_distance    1462.633703  2243.551784  3454.872171  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = train_dataset.copy()\n",
    "test_features = test_dataset.copy()\n",
    "\n",
    "train_labels = train_features.pop('docking_distance')\n",
    "t=train_features.pop('docking_time')\n",
    "\n",
    "test_labels = test_features.pop('docking_distance')\n",
    "t=test_features.pop('docking_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>base_speed</th>\n",
       "      <th>distance_from_wall</th>\n",
       "      <th>rotation_angle</th>\n",
       "      <th>distance_setpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>2.5</td>\n",
       "      <td>712.493222</td>\n",
       "      <td>0.669720</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2560</th>\n",
       "      <td>2.5</td>\n",
       "      <td>313.364074</td>\n",
       "      <td>-0.228212</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>2.5</td>\n",
       "      <td>478.538854</td>\n",
       "      <td>-0.261845</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>2.5</td>\n",
       "      <td>484.815979</td>\n",
       "      <td>0.201843</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>2.5</td>\n",
       "      <td>344.761930</td>\n",
       "      <td>-0.334078</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>2.5</td>\n",
       "      <td>687.955017</td>\n",
       "      <td>-0.142119</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>2.5</td>\n",
       "      <td>366.831078</td>\n",
       "      <td>-0.153615</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>2.5</td>\n",
       "      <td>316.465797</td>\n",
       "      <td>-0.224896</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2.5</td>\n",
       "      <td>673.384589</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>2.5</td>\n",
       "      <td>334.352557</td>\n",
       "      <td>-0.368662</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      base_speed  distance_from_wall  rotation_angle  distance_setpoint\n",
       "854          2.5          712.493222        0.669720              500.0\n",
       "2560         2.5          313.364074       -0.228212              500.0\n",
       "2212         2.5          478.538854       -0.261845              500.0\n",
       "2347         2.5          484.815979        0.201843              500.0\n",
       "1529         2.5          344.761930       -0.334078              500.0\n",
       "...          ...                 ...             ...                ...\n",
       "1178         2.5          687.955017       -0.142119              500.0\n",
       "2129         2.5          366.831078       -0.153615              500.0\n",
       "2532         2.5          316.465797       -0.224896              500.0\n",
       "397          2.5          673.384589        0.000000              500.0\n",
       "1291         2.5          334.352557       -0.368662              500.0\n",
       "\n",
       "[3600 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "854     2749.664581\n",
       "2560     854.690988\n",
       "2212    2245.719676\n",
       "2347     396.830808\n",
       "1529    3183.864462\n",
       "           ...     \n",
       "1178    2409.393773\n",
       "2129    1088.111901\n",
       "2532     822.922639\n",
       "397     1008.197378\n",
       "1291    1708.562482\n",
       "Name: docking_distance, Length: 3600, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>base_speed</th>\n",
       "      <th>distance_from_wall</th>\n",
       "      <th>rotation_angle</th>\n",
       "      <th>distance_setpoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>317.405327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.5</td>\n",
       "      <td>311.945307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.5</td>\n",
       "      <td>310.614766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.5</td>\n",
       "      <td>309.597484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.5</td>\n",
       "      <td>308.845273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4480</th>\n",
       "      <td>2.5</td>\n",
       "      <td>536.588567</td>\n",
       "      <td>0.391941</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4481</th>\n",
       "      <td>2.5</td>\n",
       "      <td>536.474226</td>\n",
       "      <td>0.386694</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4487</th>\n",
       "      <td>2.5</td>\n",
       "      <td>526.448464</td>\n",
       "      <td>0.390552</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4497</th>\n",
       "      <td>2.5</td>\n",
       "      <td>538.721214</td>\n",
       "      <td>0.390282</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>2.5</td>\n",
       "      <td>535.153784</td>\n",
       "      <td>0.390795</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>900 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      base_speed  distance_from_wall  rotation_angle  distance_setpoint\n",
       "0            2.5          317.405327        0.000000              500.0\n",
       "7            2.5          311.945307        0.000000              500.0\n",
       "12           2.5          310.614766        0.000000              500.0\n",
       "21           2.5          309.597484        0.000000              500.0\n",
       "24           2.5          308.845273        0.000000              500.0\n",
       "...          ...                 ...             ...                ...\n",
       "4480         2.5          536.588567        0.391941              500.0\n",
       "4481         2.5          536.474226        0.386694              500.0\n",
       "4487         2.5          526.448464        0.390552              500.0\n",
       "4497         2.5          538.721214        0.390282              500.0\n",
       "4498         2.5          535.153784        0.390795              500.0\n",
       "\n",
       "[900 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>base_speed</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_from_wall</th>\n",
       "      <td>512.445866</td>\n",
       "      <td>164.546498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rotation_angle</th>\n",
       "      <td>-0.053640</td>\n",
       "      <td>0.355592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_setpoint</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docking_time</th>\n",
       "      <td>10.414083</td>\n",
       "      <td>6.193692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docking_distance</th>\n",
       "      <td>1570.180704</td>\n",
       "      <td>830.120669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean         std\n",
       "base_speed             2.500000    0.000000\n",
       "distance_from_wall   512.445866  164.546498\n",
       "rotation_angle        -0.053640    0.355592\n",
       "distance_setpoint    500.000000    0.000000\n",
       "docking_time          10.414083    6.193692\n",
       "docking_distance    1570.180704  830.120669"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.describe().transpose()[['mean', 'std']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalizer = tf.keras.layers.Normalization(axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-06 21:29:12.836054: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-02-06 21:29:12.836121: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-02-06 21:29:12.836153: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (szymon): /proc/driver/nvidia/version does not exist\n",
      "2022-02-06 21:29:12.836488: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "normalizer.adapt(np.array(train_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.5   512.446  -0.054 500.   ]]\n"
     ]
    }
   ],
   "source": [
    "print(normalizer.mean.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression with a deep neural network (DNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss(history):\n",
    "  plt.plot(history.history['loss'], label='loss')\n",
    "  plt.plot(history.history['val_loss'], label='val_loss')\n",
    "  plt.ylim([0, 2500])\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Error')\n",
    "  plt.legend()\n",
    "  plt.grid(True)\n",
    "  plt.savefig('loss.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_and_compile(norm):\n",
    "    model = keras.Sequential([\n",
    "        norm,\n",
    "        layers.Dense(256, activation='relu'),\n",
    "        layers.Dense(128, activation='relu'),\n",
    "        layers.Dense(64, activation='relu'), \n",
    "        layers.Dense(32, activation='relu'), \n",
    "        layers.Dense(1)\n",
    "    ])\n",
    "\n",
    "    model.compile(loss='mean_absolute_error', optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " normalization (Normalizatio  (None, 4)                9         \n",
      " n)                                                              \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               1280      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44,554\n",
      "Trainable params: 44,545\n",
      "Non-trainable params: 9\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dnn_model = build_and_compile(normalizer)\n",
    "dnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 56s, sys: 37.2 s, total: 6min 33s\n",
      "Wall time: 4min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n",
    "\n",
    "history = dnn_model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    callbacks=early_stop,\n",
    "    validation_split=0.2,\n",
    "    verbose=0, epochs=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37.88454055786133\n"
     ]
    }
   ],
   "source": [
    "test_results = {}\n",
    "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)\n",
    "print(test_results['dnn_model'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "base_speed              2.500000\n",
      "distance_from_wall    317.405327\n",
      "rotation_angle          0.000000\n",
      "distance_setpoint     500.000000\n",
      "Name: 0, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   0.,   2.,   0.,   2.,   2.,   7.,  10.,   1.,   2.,\n",
       "          1.,   3.,   6.,  10.,  90., 595., 126.,  28.,   7.,   2.,   2.,\n",
       "          0.,   0.,   0.,   2.,   0.,   0.,   0.,   1.]),\n",
       " array([-71.349, -66.947, -62.544, -58.141, -53.739, -49.336, -44.934,\n",
       "        -40.531, -36.128, -31.726, -27.323, -22.92 , -18.518, -14.115,\n",
       "         -9.713,  -5.31 ,  -0.907,   3.495,   7.898,  12.3  ,  16.703,\n",
       "         21.106,  25.508,  29.911,  34.314,  38.716,  43.119,  47.521,\n",
       "         51.924,  56.327,  60.729]),\n",
       " <BarContainer object of 30 artists>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = dnn_model.predict(test_features).flatten()\n",
    "\n",
    "test_predictions = np.array(test_predictions)\n",
    "test_labels = np.array(list(test_labels))\n",
    "\n",
    "print(test_features.iloc[0,:])\n",
    "\n",
    "errors = (test_predictions - test_labels)/10\n",
    "# print(np.sort(np.absolute(errors)))\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.hist(errors, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, None) for input KerasTensor(type_spec=TensorSpec(shape=(None, None), dtype=tf.float32, name='normalization_input'), name='normalization_input', description=\"created by layer 'normalization_input'\"), but it was called on an input with incompatible shape (None,).\n",
      "[1.97e+10]\n"
     ]
    }
   ],
   "source": [
    "test_predictions = dnn_model.predict([100]).flatten()\n",
    "print(test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-06 21:35:54.675373: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: docking_distance_prediction/assets\n"
     ]
    }
   ],
   "source": [
    "dnn_model.save('docking_distance_prediction/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-02-09 16:43:51.133646: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-02-09 16:43:51.133677: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-02-09 16:43:51.133691: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (szymon): /proc/driver/nvidia/version does not exist\n",
      "2022-02-09 16:43:51.134436: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "loaded_model = keras.models.load_model('docking_distance_prediction/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1436.595]\n",
      "   base_speed  distance_from_wall  rotation_angle  distance_setpoint\n",
      "7         2.5          311.945307             0.0              500.0\n"
     ]
    }
   ],
   "source": [
    "test_predictions = loaded_model.predict(test_features.iloc[1:2,:]).flatten()\n",
    "print(test_predictions)\n",
    "print(test_features.iloc[1:2,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   base_speed  distance_from_wall  rotation_angle  distance_setpoint\n",
      "0         2.5                 350             0.2              500.0\n"
     ]
    }
   ],
   "source": [
    "data = {'base_speed':[2.5], \n",
    "        'distance_from_wall':[350], \n",
    "        'rotation_angle':[0.2], \n",
    "        'distance_setpoint':[500.0]}\n",
    "\n",
    "current_df = pd.DataFrame(data)\n",
    "\n",
    "print(current_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2266.4192\n"
     ]
    }
   ],
   "source": [
    "print(loaded_model.predict(current_df)[0][0])"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}