Train-Geovex-Embeddings.ipynb

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   "id": "50d0896d-6fbb-434a-9e84-ec1e6ee2fdda",
   "metadata": {},
   "outputs": [],
   "source": [
    "from srai.embedders import GeoVexEmbedder\n",
    "from srai.joiners import IntersectionJoiner\n",
    "from srai.loaders import OSMPbfLoader\n",
    "from srai.loaders.osm_loaders.filters import GEOFABRIK_LAYERS\n",
    "from srai.neighbourhoods import H3Neighbourhood\n",
    "from srai.regionalizers import H3Regionalizer, geocode_to_region_gdf\n",
    "from srai.plotting import plot_regions\n",
    "from srai.h3 import ring_buffer_h3_regions_gdf\n",
    "\n",
    "import warnings\n",
    "\n",
    "from pytorch_lightning import seed_everything\n",
    "import pandas as pd\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99a4a9a4-b7bd-401a-993d-d06da85e90da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 71\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SEED = 71\n",
    "seed_everything(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff89b567-331b-4bbe-8fa7-b6332a8baaab",
   "metadata": {},
   "source": [
    "## Get City Boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50ff60ce-a8a1-4deb-984f-01b053d3e8cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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[-114.2689968, 51.1199393], [-114.2646265, 51.1199525], [-114.2646975, 51.1172553], [-114.2646876, 51.1163763], [-114.2646801, 51.1162402], [-114.2636892, 51.116475], [-114.2621871, 51.1167797], [-114.2615595, 51.1168555], [-114.2605244, 51.1167709], [-114.2588062, 51.1163182], [-114.2591537, 51.1155145], [-114.2616865, 51.1155497], [-114.2651835, 51.1148894], [-114.2665507, 51.1149627], [-114.2676303, 51.1152803], [-114.2683708, 51.1158376], [-114.2687999, 51.116538], [-114.2689714, 51.1170374], [-114.2690738, 51.1170398], [-114.2690651, 51.1156148], [-114.2690573, 51.1142067], [-114.269037, 51.1105612], [-114.2740005, 51.1105971], [-114.2739945, 51.1129958], [-114.2739914, 51.1145251], [-114.278005, 51.1145221], [-114.2805349, 51.1145202], [-114.2805525, 51.1159054], [-114.2805759, 51.1175957], [-114.2805261, 51.1175912], [-114.2805316, 51.1196837], [-114.2805467, 51.1250279], [-114.2806054, 51.1250323], [-114.280603, 51.1250897], [-114.2806073, 51.1252288], [-114.280662, 51.1252291], [-114.2829535, 51.1252453], [-114.2845164, 51.1257477], [-114.2851923, 51.1262326], [-114.28571, 51.1272086], [-114.2865146, 51.1289794], [-114.2869492, 51.1289258], [-114.287849, 51.1288129], [-114.2888404, 51.1288562], [-114.2895455, 51.1294689], [-114.2894516, 51.1302442], [-114.2884431, 51.1310368], [-114.2887795, 51.1312609], [-114.2902425, 51.1334603], [-114.2906118, 51.1340042], [-114.2910348, 51.1346265], [-114.291267, 51.1349681], [-114.2914552, 51.1357487], [-114.2926113, 51.1359145], [-114.2932979, 51.1362713], [-114.2943528, 51.1369626], [-114.2950507, 51.1375801], [-114.2969243, 51.1383514], [-114.2969135, 51.139139], [-114.2948751, 51.1391458], [-114.2948965, 51.1396304], [-114.2979253, 51.1396494], [-114.3041835, 51.1396886], [-114.3157587, 51.1397612]]], &quot;type&quot;: &quot;Polygon&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;__folium_color&quot;: &quot;rgb(127, 60, 141)&quot;, &quot;region_id&quot;: &quot;Calgary, Alberta, Canada&quot;}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_cf44d838ba8868890d0fffd47f840b4b.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature=&gt;typeof(feature)==&#x27;object&#x27; ? JSON.stringify(feature) : feature;\n",
       "    let fields = [&quot;region_id&quot;];\n",
       "    let aliases = [&quot;region_id&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{&quot;className&quot;: &quot;foliumtooltip&quot;, &quot;sticky&quot;: true});\n",
       "                     \n",
       "    \n",
       "                geo_json_cf44d838ba8868890d0fffd47f840b4b.addTo(map_ce09b3d3225edf8b72220ec745bb7a05);\n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x28b177f70>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_gdf = geocode_to_region_gdf(\"Calgary, Alberta\")\n",
    "plot_regions(area_gdf, tiles_style=\"CartoDB positron\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e48803b-a2fd-4f04-b52f-9c550619dee1",
   "metadata": {},
   "source": [
    "## Get H3 Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "83a382bd-a2e6-4ef7-a622-f249010708af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base regions: 59799\n",
      "Buffered regions: 64248\n"
     ]
    }
   ],
   "source": [
    "resolution = 10\n",
    "k_ring_buffer_radius = 3\n",
    "\n",
    "regionalizer = H3Regionalizer(resolution=resolution)\n",
    "base_h3_regions = regionalizer.transform(area_gdf)\n",
    "\n",
    "buffered_h3_regions = ring_buffer_h3_regions_gdf(base_h3_regions, distance=k_ring_buffer_radius)\n",
    "buffered_h3_geometry = buffered_h3_regions.unary_union\n",
    "\n",
    "print(\"Base regions:\", len(base_h3_regions))\n",
    "print(\"Buffered regions:\", len(buffered_h3_regions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8b06b56-138c-46e3-9318-c2898e8ec778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>8a12ccd40cf7fff</th>\n",
       "      <td>POLYGON ((-114.00126 50.99468, -114.00196 50.9...</td>\n",
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       "      <td>POLYGON ((-114.06325 50.84542, -114.06395 50.8...</td>\n",
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       "    <tr>\n",
       "      <th>8a12ccd50447fff</th>\n",
       "      <td>POLYGON ((-114.08356 50.97384, -114.08426 50.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8a12ccd52697fff</th>\n",
       "      <td>POLYGON ((-114.11083 50.95556, -114.11153 50.9...</td>\n",
       "    </tr>\n",
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       "      <th>8a12ccc0395ffff</th>\n",
       "      <td>POLYGON ((-114.16807 50.89519, -114.16877 50.8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                                          geometry\n",
       "region_id                                                         \n",
       "8a12ccd40cf7fff  POLYGON ((-114.00126 50.99468, -114.00196 50.9...\n",
       "8a12ccc54417fff  POLYGON ((-114.06325 50.84542, -114.06395 50.8...\n",
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       "8a12ccd52697fff  POLYGON ((-114.11083 50.95556, -114.11153 50.9...\n",
       "8a12ccc0395ffff  POLYGON ((-114.16807 50.89519, -114.16877 50.8..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buffered_h3_regions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e5d237-a420-49ff-959a-6cd9c56b19d5",
   "metadata": {},
   "source": [
    "## Get OSM Features/Tags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cc07fcb-2397-49f4-a70d-23f9cfb9061a",
   "metadata": {},
   "source": [
    "OSM pbf file can be downlaoded from https://download.geofabrik.de/ and extracting an bbox using osmium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f7c64e6-0bb9-4350-afe1-67bb04cf6a4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[calgary.osm.pbf] Counting pbf features: 2166490it [00:03, 604767.13it/s]\n",
      "[calgary.osm.pbf] Parsing pbf file #1: 100%|█| 2166490/2166490 [00:30<00:00, 712\n",
      "Grouping features: 100%|████████████████████████| 28/28 [00:04<00:00,  6.35it/s]\n"
     ]
    }
   ],
   "source": [
    "tags = GEOFABRIK_LAYERS\n",
    "\n",
    "loader = OSMPbfLoader(pbf_file=\"files/calgary.osm.pbf\")\n",
    "\n",
    "features_gdf = loader.load(buffered_h3_geometry, tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b584de-5c30-4b9f-8371-761c163d0232",
   "metadata": {},
   "source": [
    "## Join OSM Features and Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ba6165f-eb79-456b-a870-6d9f1017ed10",
   "metadata": {},
   "outputs": [
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       "\n",
       "[730173 rows x 0 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joiner = IntersectionJoiner()\n",
    "joint_gdf = joiner.transform(buffered_h3_regions, features_gdf)\n",
    "joint_gdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d28eee-4dfd-449f-9ce0-afad51581ecb",
   "metadata": {},
   "source": [
    "## Train GeoVex Embedder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a231618-92c8-40f1-af95-b0e3280cc6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbourhood = H3Neighbourhood(buffered_h3_regions)\n",
    "\n",
    "embedder = GeoVexEmbedder(\n",
    "    target_features=GEOFABRIK_LAYERS,\n",
    "    batch_size=32,\n",
    "    neighbourhood_radius=k_ring_buffer_radius,\n",
    "    convolutional_layers=2,\n",
    "    embedding_size=3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c193e4-0d8a-4711-8e05-26b657b0177d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████| 64248/64248 [00:05<00:00, 11472.67it/s]\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name    | Type       | Params\n",
      "---------------------------------------\n",
      "0 | encoder | Sequential | 2.0 M \n",
      "1 | decoder | Sequential | 1.4 M \n",
      "2 | _loss   | GeoVeXLoss | 0     \n",
      "---------------------------------------\n",
      "3.4 M     Trainable params\n",
      "0         Non-trainable params\n",
      "3.4 M     Total params\n",
      "13.493    Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
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       "model_id": "a30b45891653483e834d8ba26cd25c98",
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      },
      "text/plain": [
       "Training: |                                               | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Some regions were not able to be encoded, as they don't have r=3 neighbors.\n"
     ]
    },
    {
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    embeddings = embedder.fit_transform(\n",
    "        regions_gdf=buffered_h3_regions,\n",
    "        features_gdf=features_gdf,\n",
    "        joint_gdf=joint_gdf,\n",
    "        neighbourhood=neighbourhood,\n",
    "        trainer_kwargs={\n",
    "            \"max_epochs\": 10,\n",
    "            \"accelerator\": (\n",
    "                \"cpu\" if torch.backends.mps.is_available() else \"auto\"\n",
    "            ),  # GeoVexEmbedder does not support MPS\n",
    "        },\n",
    "        learning_rate=0.001,\n",
    "    )\n",
    "\n",
    "embeddings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d4a01f9-c700-4aa5-a76c-5c1825080a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings.to_csv(\"geovex_embeddings_3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "264bb504-19d1-446c-8bc5-0ab0f890995b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76d0833a-3d0f-4db4-905a-c358396ea439",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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