Add BTTF prediction results

xinychen · Jan 5, 2021 · a1f9f1b · a1f9f1b
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diff --git a/images/NYC_time_series_volume_1321.pdf b/images/NYC_time_series_volume_1321.pdf
diff --git a/images/NYC_time_series_volume_1713.pdf b/images/NYC_time_series_volume_1713.pdf
diff --git a/images/NYC_time_series_volume_1721.pdf b/images/NYC_time_series_volume_1721.pdf
diff --git a/images/NYC_time_series_volume_1727.pdf b/images/NYC_time_series_volume_1727.pdf
diff --git a/images/NYC_time_series_volume_2621.pdf b/images/NYC_time_series_volume_2621.pdf
diff --git a/images/NYC_time_series_volume_2727.pdf b/images/NYC_time_series_volume_2727.pdf
diff --git a/images/Ndata_heatmap_predicted_values_32.pdf b/images/Ndata_heatmap_predicted_values_32.pdf
diff --git a/images/Ndata_heatmap_predicted_values_32_0.pdf b/images/Ndata_heatmap_predicted_values_32_0.pdf
diff --git a/images/Ndata_heatmap_predicted_values_33.pdf b/images/Ndata_heatmap_predicted_values_33.pdf
diff --git a/images/Ndata_heatmap_predicted_values_33_0.pdf b/images/Ndata_heatmap_predicted_values_33_0.pdf
diff --git a/predictor/.ipynb_checkpoints/BTTF-analysis-checkpoint.ipynb b/predictor/.ipynb_checkpoints/BTTF-analysis-checkpoint.ipynb
@@ -700,6 +700,20 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter: 500\n",
+      "MAPE: 0.479928\n",
+      "RMSE: 5.28403\n",
+      "\n",
+      "Iter: 1000\n",
+      "MAPE: 0.488142\n",
+      "RMSE: 5.48013\n",
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -718,6 +732,66 @@
     "print()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.style.use('classic')\n",
+    "plt.style.use('ggplot')\n",
+    "plt.style.use('bmh')\n",
+    "\n",
+    "plt.rcParams['font.family'] = 'Arial'\n",
+    "fig = plt.figure(figsize = (4, 3.1))\n",
+    "ax = fig.add_axes([0.12, 0.14, 0.80, 0.83])\n",
+    "ax = sns.heatmap(tensor_hat[:, :, 24 * 3 + 8], cmap = 'OrRd', vmin = 0, vmax = 200, linewidth = 0.01,\n",
+    "                cbar_kws={'label': 'Volume'})\n",
+    "plt.xticks(np.arange(0.5, 30, 2), [\"1\", \"3\", \"5\", \"7\", \"9\", \n",
+    "                                \"11\", \"13\", \"15\", \"17\", \"19\", \n",
+    "                                \"21\", \"23\", \"25\", \"27\", \"29\"], rotation = 0)\n",
+    "plt.yticks(np.arange(0.5, 30, 2), [\"1\", \"3\", \"5\", \"7\", \"9\", \n",
+    "                                \"11\", \"13\", \"15\", \"17\", \"19\", \n",
+    "                                \"21\", \"23\", \"25\", \"27\", \"29\"], rotation = 0)\n",
+    "ax.set_xlabel(\"Zone\")\n",
+    "ax.set_ylabel(\"Zone\")\n",
+    "plt.show()\n",
+    "fig.savefig(\"../images/Ndata_heatmap_predicted_values_32.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.style.use('classic')\n",
+    "plt.style.use('ggplot')\n",
+    "plt.style.use('bmh')\n",
+    "\n",
+    "plt.rcParams['font.family'] = 'Arial'\n",
+    "fig = plt.figure(figsize = (4, 3.1))\n",
+    "ax = fig.add_axes([0.12, 0.14, 0.80, 0.83])\n",
+    "ax = sns.heatmap(tensor_hat[:, :, 24 * 3 + 9], cmap = 'OrRd', vmin = 0, vmax = 150, linewidth = 0.01,\n",
+    "                cbar_kws={'label': 'Volume'})\n",
+    "plt.xticks(np.arange(0.5, 30, 2), [\"1\", \"3\", \"5\", \"7\", \"9\", \n",
+    "                                \"11\", \"13\", \"15\", \"17\", \"19\", \n",
+    "                                \"21\", \"23\", \"25\", \"27\", \"29\"], rotation = 0)\n",
+    "plt.yticks(np.arange(0.5, 30, 2), [\"1\", \"3\", \"5\", \"7\", \"9\", \n",
+    "                                \"11\", \"13\", \"15\", \"17\", \"19\", \n",
+    "                                \"21\", \"23\", \"25\", \"27\", \"29\"], rotation = 0)\n",
+    "ax.set_xlabel(\"Zone\")\n",
+    "ax.set_ylabel(\"Zone\")\n",
+    "plt.show()\n",
+    "fig.savefig(\"../images/Ndata_heatmap_predicted_values_33.pdf\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/predictor/BTTF-analysis.ipynb b/predictor/BTTF-analysis.ipynb