8. Traffic Prediction.ipynb

{
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     "text": [
      "Original NE: POINT (31.5 66.8)\n",
      "Original SW: POINT (8.5 53)\n",
      "Transformed NE: (697681.2863658136, 7416224.9393330095)\n",
      "Transformed SW: (-735260.4063631196, 6033964.466017238)\n",
      "col_count 573\n",
      "row_count 552\n",
      "grid_size 316296\n",
      "Original NE: POINT (31.5 66.8)\n",
      "Original SW: POINT (8.5 53)\n",
      "Transformed NE: (697681.2863658136, 7416224.9393330095)\n",
      "Transformed SW: (-735260.4063631196, 6033964.466017238)\n",
      "col_count 286\n",
      "row_count 276\n",
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       "   original  connected  cost\n",
       "0       350        351   1.0\n",
       "1       350        625   1.0\n",
       "2       350        626   1.0\n",
       "3       350        627   1.0\n",
       "4       351        350   1.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pygradu import shortest_path\n",
    "from pygradu import gridify\n",
    "\n",
    "import shapely.geometry\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import importlib\n",
    "\n",
    "importlib.reload(gridify)\n",
    "\n",
    "grid_2500m = gridify.area_to_grid(side_length=2500)\n",
    "grid_5km = gridify.area_to_grid(side_length=5000)\n",
    "\n",
    "MODELS_DIR = 'data/models/'\n",
    "DATASET_DIR = 'data/datasets/'\n",
    "\n",
    "graph_df_adj_2500m = pd.read_csv(MODELS_DIR + 'complete_graph_adjacent_2500m.csv')[['original', 'connected', 'cost']]\n",
    "graph_df_ship_2500m = pd.read_csv(MODELS_DIR + 'complete_graph_ship_model_2500m.csv')[['original', 'connected', 'cost']]\n",
    "\n",
    "graph_df_adj_5km = pd.read_csv(MODELS_DIR + 'complete_graph_adjacent_5km.csv')[['original', 'connected', 'cost']]\n",
    "graph_df_ship_5km = pd.read_csv(MODELS_DIR + 'complete_graph_ship_model_5km.csv')[['original', 'connected', 'cost']]\n",
    "\n",
    "graph_df_adj_5km.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.61 s, sys: 105 ms, total: 1.72 s\n",
      "Wall time: 1.42 s\n",
      "CPU times: user 10.1 s, sys: 524 ms, total: 10.6 s\n",
      "Wall time: 10.4 s\n",
      "CPU times: user 270 ms, sys: 15.6 ms, total: 285 ms\n",
      "Wall time: 290 ms\n",
      "CPU times: user 1.71 s, sys: 81 ms, total: 1.79 s\n",
      "Wall time: 1.45 s\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(shortest_path)\n",
    "\n",
    "%time graph_adj_2500m = shortest_path.df_to_graph(graph_df_adj_2500m)\n",
    "%time graph_ship_2500m = shortest_path.df_to_graph(graph_df_ship_2500m)\n",
    "\n",
    "%time graph_adj_5km = shortest_path.df_to_graph(graph_df_adj_5km)\n",
    "%time graph_ship_5km = shortest_path.df_to_graph(graph_df_ship_5km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CONT': nan,\n",
       " 'GC': 4.114166666666667,\n",
       " 'PAS': 4.644285714285715,\n",
       " 'RORO': nan,\n",
       " 'T': nan}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert speed model to dict\n",
    "\n",
    "speed_model_2500m = pd.read_csv(MODELS_DIR + 'speed_model_2500m.csv', index_col=0).to_dict()\n",
    "speed_model_2500m = {int(k):v for k,v in speed_model_2500m.items()}\n",
    "\n",
    "speed_model_5km = pd.read_csv(MODELS_DIR + 'speed_model_5km.csv', index_col=0).to_dict()\n",
    "speed_model_5km = {int(k):v for k,v in speed_model_5km.items()}\n",
    "speed_model_5km[354]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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       "               timestamp       mmsi        lat        lon  speed  node  \\\n",
       "2741 2019-06-07 11:27:49  209014000  53.974982  11.340805   4.63   849   \n",
       "2742 2019-06-07 11:35:43  209014000  53.992868  11.331420   4.63   849   \n",
       "2743 2019-06-07 11:41:43  209014000  54.007890  11.331562   4.63   849   \n",
       "2744 2019-06-07 11:47:53  209014000  54.018665  11.345753   4.42   849   \n",
       "2745 2019-06-07 11:52:59  209014000  54.020092  11.367012   4.53   849   \n",
       "\n",
       "     vessel_type  start_port  end_port    end_lat    end_lon  \\\n",
       "2741          GC        2803     19942  56.889288  12.479425   \n",
       "2742          GC        2803     19942  56.889288  12.479425   \n",
       "2743          GC        2803     19942  56.889288  12.479425   \n",
       "2744          GC        2803     19942  56.889288  12.479425   \n",
       "2745          GC        2803     19942  56.889288  12.479425   \n",
       "\n",
       "                     ata                 atd  in_port  voyage  \\\n",
       "2741 2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "2742 2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "2743 2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "2744 2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "2745 2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "\n",
       "      end_port_sea_area  \n",
       "2741                 -1  \n",
       "2742                 -1  \n",
       "2743                 -1  \n",
       "2744                 -1  \n",
       "2745                 -1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load test set\n",
    "\n",
    "test_voyages = pd.read_csv(DATASET_DIR + 'validation_set_summer.csv', index_col=0, parse_dates = ['timestamp', 'ata', 'atd'])\n",
    "test_voyages.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load shallow water\n",
    "\n",
    "shallow_graph_2500m = set(pd.read_csv(MODELS_DIR + 'shallow_water_model_2500m.csv', index_col=0).original.values)\n",
    "shallow_graph_5km = set(pd.read_csv(MODELS_DIR + 'shallow_water_model_5km.csv', index_col=0).original.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
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       "      <th>2742</th>\n",
       "      <td>2019-06-07 11:35:43</td>\n",
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       "      <td>2019-05-29 12:23:39</td>\n",
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       "      <td>2019-05-19 06:17:29</td>\n",
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       "      <td>2019-05-28 20:40:46</td>\n",
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       "      <td>2019-05-30 11:44:19</td>\n",
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      "text/plain": [
       "                timestamp       mmsi        lat        lon  speed   node  \\\n",
       "2742  2019-06-07 11:35:43  209014000  53.992868  11.331420   4.63    849   \n",
       "11820 2019-05-28 12:26:49  209185000  61.622367  17.239705   6.02  11387   \n",
       "15524 2019-06-04 00:07:23  209297000  54.464360  18.705115   5.97    483   \n",
       "16933 2019-05-16 14:07:09  209314000  63.262708  18.750272   3.34  13743   \n",
       "17582 2019-05-28 20:40:46  209314000  63.288260  19.256657   5.81  13745   \n",
       "\n",
       "      vessel_type  start_port  end_port    end_lat    end_lon  \\\n",
       "2742           GC        2803     19942  56.889288  12.479425   \n",
       "11820          GC     3012222       935  65.234398  21.628273   \n",
       "15524          GC        1242     15942  56.968345  24.077253   \n",
       "16933          GC        1109      3347  53.906010  10.766170   \n",
       "17582          GC         947     24228  58.381102  24.477098   \n",
       "\n",
       "                      ata                 atd  in_port  voyage  \\\n",
       "2742  2019-06-08 10:13:53 2019-06-07 11:27:49    False      14   \n",
       "11820 2019-05-29 12:23:39 2019-05-28 12:21:10    False      67   \n",
       "15524 2019-06-05 04:44:42 2019-06-04 00:01:23    False      88   \n",
       "16933 2019-05-19 06:17:29 2019-05-16 14:00:49    False      93   \n",
       "17582 2019-05-30 11:44:19 2019-05-28 20:35:06    False      94   \n",
       "\n",
       "       end_port_sea_area      course  \n",
       "2742                  -1  342.857462  \n",
       "11820                  1   90.670464  \n",
       "15524                  7   41.045639  \n",
       "16933                 -1  132.546724  \n",
       "17582                  7  125.601933  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take first observation of every voyage\n",
    "test_voyages['course'] = -1\n",
    "voyages = test_voyages.groupby('voyage')\n",
    "first_rows = []\n",
    "for voyage, observations in voyages:\n",
    "    course = shortest_path.angleFromCoordinatesInDeg([observations.iloc[0].lat, observations.iloc[0].lon], [observations.iloc[1].lat, observations.iloc[1].lon])\n",
    "    row = observations.iloc[1]\n",
    "    row.course = course\n",
    "    first_rows.append(row)\n",
    "\n",
    "validation_set = pd.DataFrame(data=first_rows, columns=test_voyages.columns)\n",
    "validation_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use_dirways= False\n",
      "use_turn_penalty= False\n",
      "use_shallow_penalty= True\n",
      "side_length: 5000\n",
      "Error count= 0\n",
      "[]\n",
      "CPU times: user 10min 36s, sys: 3.26 s, total: 10min 40s\n",
      "Wall time: 10min 44s\n"
     ]
    }
   ],
   "source": [
    "# Predicting routes using the adjacent model 5km\n",
    "\n",
    "start_time = None\n",
    "\n",
    "importlib.reload(shortest_path)\n",
    "graph_adj_5km.use_turn_penalty = False\n",
    "graph_adj_5km.use_shallow_penalty = True\n",
    "graph_adj_5km.use_dirways = False\n",
    "\n",
    "%time routes_and_areas = shortest_path.predict_routes(validation_set, grid_5km, graph_adj_5km, speed_model_5km, None, shallow_graph_5km)\n",
    "routes_adjacent_5km = pd.DataFrame(data=routes_and_areas[0], columns=['lat', 'lon', 'node', 'speed', 'mmsi', 'voyage', 'start_time', 'number'])\n",
    "areas_adjacent_5km = pd.DataFrame(data=routes_and_areas[1], columns=['lat', 'lon', 'voyage','g', 'h', 'f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.21 s, sys: 116 ms, total: 7.32 s\n",
      "Wall time: 7.02 s\n"
     ]
    },
    {
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      "text/plain": [
       "         lat        lon    node     speed       mmsi  voyage  \\\n",
       "0  53.992868  11.331420  3354.0  4.630000  209014000      14   \n",
       "1  54.046465  11.412650  3631.0  4.649501  209014000      14   \n",
       "2  54.099800  11.469985  3908.0  4.747096  209014000      14   \n",
       "3  54.153118  11.527464  4185.0  4.727564  209014000      14   \n",
       "4  54.206419  11.585086  4462.0  4.818929  209014000      14   \n",
       "\n",
       "           start_time  number                  timestamp  \n",
       "0 2019-06-07 11:35:43       0 2019-06-07 11:35:43.000000  \n",
       "1 2019-06-07 11:35:43       1 2019-06-07 12:04:19.275179  \n",
       "2 2019-06-07 11:35:43       2 2019-06-07 12:28:56.346183  \n",
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       "4 2019-06-07 11:35:43       4 2019-06-07 13:17:55.078619  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(shortest_path)\n",
    "%time routes_timestamp_5km_adj = shortest_path.calculate_timestamps(routes_adjacent_5km)\n",
    "routes_timestamp_5km_adj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/villehak/hobbies/gradu/pygradu/shortest_path.py:530: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  pred_route.sort_values(by=['timestamp'], inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error count= 0\n",
      "CPU times: user 6min 40s, sys: 4.31 s, total: 6min 44s\n",
      "Wall time: 6min 48s\n"
     ]
    },
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       "      <th>voyage</th>\n",
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      "text/plain": [
       "   voyage vessel_type  end_port  end_port_sea_area          start_time  \\\n",
       "0      14          GC     19942                 -1 2019-06-07 11:35:43   \n",
       "1      14          GC     19942                 -1 2019-06-07 11:35:43   \n",
       "2      14          GC     19942                 -1 2019-06-07 11:35:43   \n",
       "3      14          GC     19942                 -1 2019-06-07 11:35:43   \n",
       "4      14          GC     19942                 -1 2019-06-07 11:35:43   \n",
       "\n",
       "            pred_time  mins_to_future  actual_lat  actual_lon   pred_lat  \\\n",
       "0 2019-06-07 11:35:43        0.000000   53.992868   11.331420  53.992868   \n",
       "1 2019-06-07 11:41:43        6.000000   54.007890   11.331562  54.004092   \n",
       "2 2019-06-07 11:47:53       12.166667   54.018665   11.345753  54.015624   \n",
       "3 2019-06-07 11:52:59       17.266667   54.020092   11.367012  54.025160   \n",
       "4 2019-06-07 12:00:02       24.316667   54.029510   11.390418  54.038339   \n",
       "\n",
       "    pred_lon  actual_speed  pred_speed  acc_distance_nm  error_rate_lat  \\\n",
       "0  11.331420          4.63    4.639751         0.000000        0.000000   \n",
       "1  11.348405          4.63    4.639751         0.636599        0.007033   \n",
       "2  11.365872          4.42    4.639751         0.732831        0.005629   \n",
       "3  11.380325          4.53    4.639751         0.559560        0.009382   \n",
       "4  11.400315          4.53    4.639751         0.634643        0.016341   \n",
       "\n",
       "   error_rate_lon  error_rate_speed  correct_node  \n",
       "0        0.000000          0.210595             1  \n",
       "1        0.148645          0.210595             1  \n",
       "2        0.177325          4.971731             0  \n",
       "3        0.117123          2.422749             1  \n",
       "4        0.086885          2.422749             1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "importlib.reload(shortest_path)\n",
    "%time results_5km_adj = shortest_path.test_accuracy(grid_5km, routes_timestamp_5km_adj, test_voyages)\n",
    "results_5km_adj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <td>2.499131</td>\n",
       "      <td>33.621809</td>\n",
       "      <td>0.058432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1763.526938</td>\n",
       "      <td>1.852573e+06</td>\n",
       "      <td>3.745004</td>\n",
       "      <td>896.329937</td>\n",
       "      <td>2.662714</td>\n",
       "      <td>3.840448</td>\n",
       "      <td>2.619508</td>\n",
       "      <td>3.785522</td>\n",
       "      <td>1.703659</td>\n",
       "      <td>1.437901</td>\n",
       "      <td>24.356328</td>\n",
       "      <td>0.557729</td>\n",
       "      <td>3.387673</td>\n",
       "      <td>382.360018</td>\n",
       "      <td>0.234559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.230000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.894570</td>\n",
       "      <td>9.954022</td>\n",
       "      <td>53.885251</td>\n",
       "      <td>9.871372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736985</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1973.000000</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>340.533333</td>\n",
       "      <td>56.257503</td>\n",
       "      <td>17.492565</td>\n",
       "      <td>56.272092</td>\n",
       "      <td>17.419616</td>\n",
       "      <td>5.090000</td>\n",
       "      <td>5.571333</td>\n",
       "      <td>6.339408</td>\n",
       "      <td>0.089991</td>\n",
       "      <td>0.483069</td>\n",
       "      <td>4.637311</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2807.000000</td>\n",
       "      <td>1.073000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>754.566667</td>\n",
       "      <td>58.690872</td>\n",
       "      <td>19.529342</td>\n",
       "      <td>58.690700</td>\n",
       "      <td>19.484924</td>\n",
       "      <td>5.810000</td>\n",
       "      <td>5.891748</td>\n",
       "      <td>15.901843</td>\n",
       "      <td>0.271696</td>\n",
       "      <td>1.381082</td>\n",
       "      <td>9.976380</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3847.000000</td>\n",
       "      <td>1.594200e+04</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1420.329167</td>\n",
       "      <td>60.027463</td>\n",
       "      <td>21.488541</td>\n",
       "      <td>60.029470</td>\n",
       "      <td>21.425874</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>6.874701</td>\n",
       "      <td>34.772804</td>\n",
       "      <td>0.697539</td>\n",
       "      <td>3.233522</td>\n",
       "      <td>18.875453</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6576.000000</td>\n",
       "      <td>8.616642e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5348.783333</td>\n",
       "      <td>65.790930</td>\n",
       "      <td>29.776128</td>\n",
       "      <td>65.753231</td>\n",
       "      <td>29.801895</td>\n",
       "      <td>15.330000</td>\n",
       "      <td>11.568559</td>\n",
       "      <td>165.815944</td>\n",
       "      <td>2.956229</td>\n",
       "      <td>35.140593</td>\n",
       "      <td>19091.990535</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             voyage      end_port  end_port_sea_area  mins_to_future  \\\n",
       "count  74754.000000  7.475400e+04       74754.000000    74754.000000   \n",
       "mean    3106.411483  5.785171e+05           4.838698     1010.824679   \n",
       "std     1763.526938  1.852573e+06           3.745004      896.329937   \n",
       "min       14.000000  9.230000e+02          -1.000000        0.000000   \n",
       "25%     1973.000000  9.990000e+02           1.000000      340.533333   \n",
       "50%     2807.000000  1.073000e+03           5.000000      754.566667   \n",
       "75%     3847.000000  1.594200e+04           7.000000     1420.329167   \n",
       "max     6576.000000  8.616642e+06          13.000000     5348.783333   \n",
       "\n",
       "         actual_lat    actual_lon      pred_lat      pred_lon  actual_speed  \\\n",
       "count  74754.000000  74754.000000  74754.000000  74754.000000  74752.000000   \n",
       "mean      58.608989     19.258922     58.623045     19.271726      6.144720   \n",
       "std        2.662714      3.840448      2.619508      3.785522      1.703659   \n",
       "min       53.894570      9.954022     53.885251      9.871372      0.000000   \n",
       "25%       56.257503     17.492565     56.272092     17.419616      5.090000   \n",
       "50%       58.690872     19.529342     58.690700     19.484924      5.810000   \n",
       "75%       60.027463     21.488541     60.029470     21.425874      6.890000   \n",
       "max       65.790930     29.776128     65.753231     29.801895     15.330000   \n",
       "\n",
       "         pred_speed  acc_distance_nm  error_rate_lat  error_rate_lon  \\\n",
       "count  74754.000000     74754.000000    74754.000000    74754.000000   \n",
       "mean       6.405815        24.332909        0.491800        2.499131   \n",
       "std        1.437901        24.356328        0.557729        3.387673   \n",
       "min        0.736985         0.000000        0.000000        0.000000   \n",
       "25%        5.571333         6.339408        0.089991        0.483069   \n",
       "50%        5.891748        15.901843        0.271696        1.381082   \n",
       "75%        6.874701        34.772804        0.697539        3.233522   \n",
       "max       11.568559       165.815944        2.956229       35.140593   \n",
       "\n",
       "       error_rate_speed  correct_node  \n",
       "count      74661.000000  74754.000000  \n",
       "mean          33.621809      0.058432  \n",
       "std          382.360018      0.234559  \n",
       "min            0.000108      0.000000  \n",
       "25%            4.637311      0.000000  \n",
       "50%            9.976380      0.000000  \n",
       "75%           18.875453      0.000000  \n",
       "max        19091.990535      1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_5km_adj.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
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       "      <th>timestamp</th>\n",
       "      <th>mmsi</th>\n",
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      ],
      "text/plain": [
       "                timestamp       mmsi        lat        lon  speed   node  \\\n",
       "1230  2019-03-20 14:44:01  209185000  61.622263  17.241178   5.61  11387   \n",
       "3041  2019-02-09 01:52:51  209467000  65.222573  21.647583   2.11  16519   \n",
       "3647  2019-02-23 00:34:33  209467000  65.210282  21.631402   5.86  16519   \n",
       "5762  2019-03-22 21:31:43  209467000  65.209857  21.651402   4.22  16519   \n",
       "13242 2019-02-01 21:59:31  212018000  65.219908  21.651478   4.06  16519   \n",
       "\n",
       "      vessel_type iceclass  start_port  end_port    end_lat    end_lon  \\\n",
       "1230           GC       IA     3012222       935  65.234498  21.628285   \n",
       "3041         CONT       IA         935      6652  54.365267  10.144437   \n",
       "3647         CONT       IA         935      6652  54.374755  10.177130   \n",
       "5762         CONT       IA         935     30775  59.162025  17.663345   \n",
       "13242        CONT       IA         935      6652  54.365450  10.143257   \n",
       "\n",
       "                      ata                 atd  in_port  voyage      course  \\\n",
       "1230  2019-03-22 00:08:31 2019-03-20 14:38:32    False       5   90.725478   \n",
       "3041  2019-02-11 15:49:55 2019-02-09 01:47:32    False      15  143.856736   \n",
       "3647  2019-02-25 15:30:35 2019-02-23 00:28:32    False      16  141.574254   \n",
       "5762  2019-03-24 08:55:07 2019-03-22 21:26:10    False      21  176.249323   \n",
       "13242 2019-02-04 18:12:41 2019-02-01 21:54:20    False      43  145.443601   \n",
       "\n",
       "       end_port_sea_area  \n",
       "1230                   1  \n",
       "3041                  -1  \n",
       "3647                  -1  \n",
       "5762                   6  \n",
       "13242                 -1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dirways\n",
    "\n",
    "dirways = pd.read_csv(DATASET_DIR + 'dirways_all_2018_2019.csv', parse_dates = ['publishtime', 'deletetime', 'createtime'])\n",
    "# Load test set\n",
    "\n",
    "test_voyages_winter = pd.read_csv(DATASET_DIR + 'validation_set_winter.csv', index_col=0, parse_dates = ['timestamp', 'ata', 'atd'])\n",
    "\n",
    "# Take first observation of every voyage\n",
    "test_voyages_winter['course'] = -1\n",
    "voyages = test_voyages_winter.groupby('voyage')\n",
    "first_rows = []\n",
    "for voyage, observations in voyages:\n",
    "    course = shortest_path.angleFromCoordinatesInDeg([observations.iloc[0].lat, observations.iloc[0].lon], [observations.iloc[1].lat, observations.iloc[1].lon])\n",
    "    row = observations.iloc[1]\n",
    "    row.course = course\n",
    "    first_rows.append(row)\n",
    "\n",
    "validation_set_winter = pd.DataFrame(data=first_rows, columns=test_voyages_winter.columns)\n",
    "validation_set_winter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "use_dirways= True\n",
      "use_turn_penalty= False\n",
      "use_shallow_penalty= True\n",
      "side_length: 5000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/villehak/hobbies/gradu/pygradu/shortest_path.py:391: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  dirways.sort_values(by=['id', 'number'], inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error count= 0\n",
      "[]\n",
      "CPU times: user 5min 4s, sys: 2.05 s, total: 5min 6s\n",
      "Wall time: 5min 11s\n"
     ]
    }
   ],
   "source": [
    "# Predicting winter routes using the adjacent model 5km\n",
    "\n",
    "start_time = None\n",
    "\n",
    "importlib.reload(shortest_path)\n",
    "graph_adj_5km.use_turn_penalty = False\n",
    "graph_adj_5km.use_shallow_penalty = True\n",
    "graph_adj_5km.use_dirways = True\n",
    "\n",
    "%time routes_and_areas_winter = shortest_path.predict_routes(validation_set_winter, grid_5km, graph_adj_5km, speed_model_5km, dirways, shallow_graph_5km)\n",
    "routes_adjacent_5km_winter = pd.DataFrame(data=routes_and_areas_winter[0], columns=['lat', 'lon', 'node', 'speed', 'mmsi', 'voyage', 'start_time', 'number'])\n",
    "areas_adjacent_5km_winter = pd.DataFrame(data=routes_and_areas_winter[1], columns=['lat', 'lon', 'voyage','g', 'h', 'f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.87 s, sys: 346 ms, total: 6.21 s\n",
      "Wall time: 7.23 s\n"
     ]
    },
    {
     "data": {
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       "      <td>45959.0</td>\n",
       "      <td>5.610000</td>\n",
       "      <td>209185000</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-20 14:44:01.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.586999</td>\n",
       "      <td>17.391566</td>\n",
       "      <td>45684.0</td>\n",
       "      <td>5.708226</td>\n",
       "      <td>209185000</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-20 15:09:54.294205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61.593594</td>\n",
       "      <td>17.484973</td>\n",
       "      <td>45685.0</td>\n",
       "      <td>5.262779</td>\n",
       "      <td>209185000</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-03-20 15:25:43.543365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.644637</td>\n",
       "      <td>17.564763</td>\n",
       "      <td>45962.0</td>\n",
       "      <td>5.427669</td>\n",
       "      <td>209185000</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-03-20 15:47:26.324265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.695640</td>\n",
       "      <td>17.644814</td>\n",
       "      <td>46239.0</td>\n",
       "      <td>5.487834</td>\n",
       "      <td>209185000</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>4</td>\n",
       "      <td>2019-03-20 16:08:54.891652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lon     node     speed       mmsi  voyage  \\\n",
       "0  61.622263  17.241178  45959.0  5.610000  209185000       5   \n",
       "1  61.586999  17.391566  45684.0  5.708226  209185000       5   \n",
       "2  61.593594  17.484973  45685.0  5.262779  209185000       5   \n",
       "3  61.644637  17.564763  45962.0  5.427669  209185000       5   \n",
       "4  61.695640  17.644814  46239.0  5.487834  209185000       5   \n",
       "\n",
       "           start_time  number                  timestamp  \n",
       "0 2019-03-20 14:44:01       0 2019-03-20 14:44:01.000000  \n",
       "1 2019-03-20 14:44:01       1 2019-03-20 15:09:54.294205  \n",
       "2 2019-03-20 14:44:01       2 2019-03-20 15:25:43.543365  \n",
       "3 2019-03-20 14:44:01       3 2019-03-20 15:47:26.324265  \n",
       "4 2019-03-20 14:44:01       4 2019-03-20 16:08:54.891652  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(shortest_path)\n",
    "%time routes_timestamp_5km_adj_winter = shortest_path.calculate_timestamps(routes_adjacent_5km_winter)\n",
    "routes_timestamp_5km_adj_winter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/villehak/hobbies/gradu/pygradu/shortest_path.py:530: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  pred_route.sort_values(by=['timestamp'], inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error count= 0\n",
      "CPU times: user 2min 37s, sys: 41.2 s, total: 3min 18s\n",
      "Wall time: 4min 4s\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voyage</th>\n",
       "      <th>vessel_type</th>\n",
       "      <th>end_port</th>\n",
       "      <th>end_port_sea_area</th>\n",
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       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>2019-03-20 14:49:25</td>\n",
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       "      <td>0.438834</td>\n",
       "      <td>0.011657</td>\n",
       "      <td>0.016462</td>\n",
       "      <td>42.906889</td>\n",
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       "      <td>5</td>\n",
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       "      <td>935</td>\n",
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       "      <td>2019-03-20 14:44:01</td>\n",
       "      <td>2019-03-20 14:59:52</td>\n",
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       "      <td>61.609090</td>\n",
       "      <td>17.316947</td>\n",
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       "      <td>2019-03-20 15:05:02</td>\n",
       "      <td>21.016667</td>\n",
       "      <td>61.602707</td>\n",
       "      <td>17.337982</td>\n",
       "      <td>61.593894</td>\n",
       "      <td>17.362243</td>\n",
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       "      <td>0.014305</td>\n",
       "      <td>0.139930</td>\n",
       "      <td>23.561415</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   voyage vessel_type  end_port  end_port_sea_area          start_time  \\\n",
       "0       5          GC       935                  1 2019-03-20 14:44:01   \n",
       "1       5          GC       935                  1 2019-03-20 14:44:01   \n",
       "2       5          GC       935                  1 2019-03-20 14:44:01   \n",
       "3       5          GC       935                  1 2019-03-20 14:44:01   \n",
       "4       5          GC       935                  1 2019-03-20 14:44:01   \n",
       "\n",
       "            pred_time  mins_to_future  actual_lat  actual_lon   pred_lat  \\\n",
       "0 2019-03-20 14:44:01        0.000000   61.622263   17.241178  61.622263   \n",
       "1 2019-03-20 14:49:25        5.400000   61.622037   17.275425  61.614984   \n",
       "2 2019-03-20 14:54:42       10.683333   61.615038   17.299898  61.607856   \n",
       "3 2019-03-20 14:59:52       15.850000   61.609090   17.316947  61.600878   \n",
       "4 2019-03-20 15:05:02       21.016667   61.602707   17.337982  61.593894   \n",
       "\n",
       "    pred_lon  actual_speed  pred_speed  acc_distance_nm  error_rate_lat  \\\n",
       "0  17.241178          5.61    5.659113         0.000000        0.000000   \n",
       "1  17.272306          5.45    5.659113         0.432679        0.011444   \n",
       "2  17.302746          3.96    5.659113         0.438834        0.011657   \n",
       "3  17.332501          3.96    5.659113         0.663569        0.013329   \n",
       "4  17.362243          4.58    5.659113         0.871780        0.014305   \n",
       "\n",
       "   error_rate_lon  error_rate_speed  correct_node  \n",
       "0        0.000000          0.875451             1  \n",
       "1        0.018057          3.836932             1  \n",
       "2        0.016462         42.906889             1  \n",
       "3        0.089823         42.906889             0  \n",
       "4        0.139930         23.561415             0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(shortest_path)\n",
    "%time results_5km_adj_winter = shortest_path.test_accuracy(grid_5km, routes_timestamp_5km_adj_winter, test_voyages_winter)\n",
    "results_5km_adj_winter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>voyage</th>\n",
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       "      <td>39287.000000</td>\n",
       "      <td>39245.000000</td>\n",
       "      <td>39287.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>314.698857</td>\n",
       "      <td>3.112186e+04</td>\n",
       "      <td>1.334513</td>\n",
       "      <td>1738.344958</td>\n",
       "      <td>60.733060</td>\n",
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       "      <td>60.666018</td>\n",
       "      <td>19.393196</td>\n",
       "      <td>6.359651</td>\n",
       "      <td>6.234350</td>\n",
       "      <td>45.948526</td>\n",
       "      <td>1.037635</td>\n",
       "      <td>4.026942</td>\n",
       "      <td>42.785651</td>\n",
       "      <td>0.033624</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>180.640385</td>\n",
       "      <td>2.765535e+05</td>\n",
       "      <td>2.801052</td>\n",
       "      <td>1206.548257</td>\n",
       "      <td>3.412544</td>\n",
       "      <td>3.216125</td>\n",
       "      <td>3.285420</td>\n",
       "      <td>2.950573</td>\n",
       "      <td>1.479089</td>\n",
       "      <td>1.001174</td>\n",
       "      <td>48.777317</td>\n",
       "      <td>1.176384</td>\n",
       "      <td>5.848638</td>\n",
       "      <td>380.449862</td>\n",
       "      <td>0.180263</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.310000e+02</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>54.044950</td>\n",
       "      <td>10.140200</td>\n",
       "      <td>54.044950</td>\n",
       "      <td>10.144687</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877776</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>1.027000e+03</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>705.700000</td>\n",
       "      <td>58.145701</td>\n",
       "      <td>18.489867</td>\n",
       "      <td>58.171166</td>\n",
       "      <td>18.513612</td>\n",
       "      <td>5.610000</td>\n",
       "      <td>5.732670</td>\n",
       "      <td>11.611163</td>\n",
       "      <td>0.211195</td>\n",
       "      <td>0.767281</td>\n",
       "      <td>5.039359</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>341.000000</td>\n",
       "      <td>1.073000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1563.683333</td>\n",
       "      <td>61.022207</td>\n",
       "      <td>19.780890</td>\n",
       "      <td>60.909807</td>\n",
       "      <td>19.670226</td>\n",
       "      <td>6.330000</td>\n",
       "      <td>5.900363</td>\n",
       "      <td>29.888738</td>\n",
       "      <td>0.624719</td>\n",
       "      <td>2.074318</td>\n",
       "      <td>11.046170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>461.000000</td>\n",
       "      <td>6.652000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2622.533333</td>\n",
       "      <td>63.752575</td>\n",
       "      <td>21.619500</td>\n",
       "      <td>63.603836</td>\n",
       "      <td>21.354730</td>\n",
       "      <td>7.410000</td>\n",
       "      <td>6.503941</td>\n",
       "      <td>57.421364</td>\n",
       "      <td>1.349328</td>\n",
       "      <td>5.064831</td>\n",
       "      <td>20.695534</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>627.000000</td>\n",
       "      <td>3.012222e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5509.300000</td>\n",
       "      <td>65.755000</td>\n",
       "      <td>27.169408</td>\n",
       "      <td>65.786941</td>\n",
       "      <td>26.957523</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>10.123011</td>\n",
       "      <td>262.712483</td>\n",
       "      <td>5.798519</td>\n",
       "      <td>66.987788</td>\n",
       "      <td>12814.655684</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "             voyage      end_port  end_port_sea_area  mins_to_future  \\\n",
       "count  39287.000000  3.928700e+04       39287.000000    39287.000000   \n",
       "mean     314.698857  3.112186e+04           1.334513     1738.344958   \n",
       "std      180.640385  2.765535e+05           2.801052     1206.548257   \n",
       "min        5.000000  9.310000e+02          -1.000000        0.000000   \n",
       "25%      143.000000  1.027000e+03          -1.000000      705.700000   \n",
       "50%      341.000000  1.073000e+03           1.000000     1563.683333   \n",
       "75%      461.000000  6.652000e+03           1.000000     2622.533333   \n",
       "max      627.000000  3.012222e+06           9.000000     5509.300000   \n",
       "\n",
       "         actual_lat    actual_lon      pred_lat      pred_lon  actual_speed  \\\n",
       "count  39287.000000  39287.000000  39287.000000  39287.000000  39282.000000   \n",
       "mean      60.733060     19.426252     60.666018     19.393196      6.359651   \n",
       "std        3.412544      3.216125      3.285420      2.950573      1.479089   \n",
       "min       54.044950     10.140200     54.044950     10.144687      0.000000   \n",
       "25%       58.145701     18.489867     58.171166     18.513612      5.610000   \n",
       "50%       61.022207     19.780890     60.909807     19.670226      6.330000   \n",
       "75%       63.752575     21.619500     63.603836     21.354730      7.410000   \n",
       "max       65.755000     27.169408     65.786941     26.957523     10.650000   \n",
       "\n",
       "         pred_speed  acc_distance_nm  error_rate_lat  error_rate_lon  \\\n",
       "count  39287.000000     39287.000000    39287.000000    39287.000000   \n",
       "mean       6.234350        45.948526        1.037635        4.026942   \n",
       "std        1.001174        48.777317        1.176384        5.848638   \n",
       "min        0.877776         0.000000        0.000000        0.000000   \n",
       "25%        5.732670        11.611163        0.211195        0.767281   \n",
       "50%        5.900363        29.888738        0.624719        2.074318   \n",
       "75%        6.503941        57.421364        1.349328        5.064831   \n",
       "max       10.123011       262.712483        5.798519       66.987788   \n",
       "\n",
       "       error_rate_speed  correct_node  \n",
       "count      39245.000000  39287.000000  \n",
       "mean          42.785651      0.033624  \n",
       "std          380.449862      0.180263  \n",
       "min            0.000443      0.000000  \n",
       "25%            5.039359      0.000000  \n",
       "50%           11.046170      0.000000  \n",
       "75%           20.695534      0.000000  \n",
       "max        12814.655684      1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_5km_adj_winter.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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