7 - Tests with the Kalman Filter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tests with the Kalman Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3FlF68SyoCXy"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import simdkalman\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Mnnx2rURUxu"
   },
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NNiKTC-NRWO5"
   },
   "source": [
    "Load the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 788,
     "status": "ok",
     "timestamp": 1560191381580,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "Ra89js2ZQ0D-",
    "outputId": "4a1961c8-a7ab-44e4-cc85-6bcb8be4ebab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 876,
     "status": "ok",
     "timestamp": 1560191383912,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "xwYgMSETRYZT",
    "outputId": "0f135dce-066a-4135-ffc3-c7f39b4d27d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/My Drive/Colab Notebooks/aiforsea\n"
     ]
    }
   ],
   "source": [
    "cd '/content/gdrive/My Drive/Colab Notebooks/aiforsea'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13377,
     "status": "ok",
     "timestamp": 1560191397928,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "UNjXqe3BReP1",
    "outputId": "4837a9ef-938c-4b96-c5c5-0f6dc53f74ba"
   },
   "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>geohash6</th>\n",
       "      <th>day</th>\n",
       "      <th>demand</th>\n",
       "      <th>in_raw</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minutes</th>\n",
       "      <th>ds</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qp02yc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.653687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qp02z1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.664673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qp02z3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.675659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qp02z9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.686646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qp02zc</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.484924</td>\n",
       "      <td>90.697632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geohash6  day  demand  ...                   ds  latitude  longitude\n",
       "0   qp02yc    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.653687\n",
       "1   qp02z1    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.664673\n",
       "2   qp02z3    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.675659\n",
       "3   qp02z9    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.686646\n",
       "4   qp02zc    1     0.0  ...  1900-01-01 00:00:00 -5.484924  90.697632\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('full_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3sAPce7nzl3O"
   },
   "source": [
    "### Collect info for each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10094,
     "status": "ok",
     "timestamp": 1560191398256,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "pTGenbdLzpOG",
    "outputId": "d9b3d5d7-1b7c-4b0f-98fa-0705b7acf629"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1329 ['qp02yc' 'qp02z1' 'qp02z3' ... 'qp0djw' 'qp0djy' 'qp0dnn']\n"
     ]
    }
   ],
   "source": [
    "locations = df['geohash6'].unique()\n",
    "print(len(locations), locations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8hYixEOaoVnZ"
   },
   "source": [
    "## The Kalman Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dt2svEVaoRc2"
   },
   "outputs": [],
   "source": [
    "train_start = pd.to_datetime('1900-01-18 00:00:00')\n",
    "train_stepdelta = datetime.timedelta(days=1, hours=1, minutes=15)\n",
    "train_horizon = datetime.timedelta(days=14)\n",
    "location = 'qp09d2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 291
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1653,
     "status": "ok",
     "timestamp": 1560193259992,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "9NT_TseeodHB",
    "outputId": "7e0dd749-c26a-406a-af06-ec548a715f3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbbf1c4b7f0>]"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
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ZhWPGFCXjC1F9jiWfL4hWLK1vmoFi5xsXjiwPrTIN1Dv1cI2suB5C+Dbpo4JS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VwYM5FQmKvymE2us4BRz/ila968diKQZxzigbtYBQBeJfetcp0/n/c1CVPEcZ36lIewuAIpEw29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\n8RdQQAEFXGQoMP4CCiiggIsM/z8iIwgBe/froAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_loc = df[(df['geohash6'] == location)].reset_index().drop('index', axis=1)\n",
    "df_loc = df_loc.rename(columns={'demand' : 'y'}).drop(['geohash6', 'in_raw',\n",
    "                                                     'weekday', 'hour', 'minutes',\n",
    "                                                     'latitude', 'longitude'], axis=1)\n",
    "plt.plot(df_loc['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k3xqMy69oebC"
   },
   "outputs": [],
   "source": [
    "kalman0_mse_sum = 0\n",
    "kalman1_mse_sum = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1411
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 66223,
     "status": "ok",
     "timestamp": 1560193324987,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "cmWxi4Hfoj8q",
    "outputId": "442c82e4-9c3a-42c5-d0e5-d883acc39934"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900-01-18 00:00:00 1900-02-01 00:00:00\n",
      "kalman0_rmse             0.16279895710701706\n",
      "kalman1_rmse             0.2867848662148205\n",
      "done 0\n",
      "1900-01-19 01:15:00 1900-02-02 01:15:00\n",
      "kalman0_rmse             0.12225157535374899\n",
      "kalman1_rmse             0.27175298924755803\n",
      "done 1\n",
      "1900-01-20 02:30:00 1900-02-03 02:30:00\n",
      "kalman0_rmse             0.10270915336589533\n",
      "kalman1_rmse             0.2888441468455182\n",
      "done 2\n",
      "1900-01-21 03:45:00 1900-02-04 03:45:00\n",
      "kalman0_rmse             0.1372366511844568\n",
      "kalman1_rmse             0.468456853276327\n",
      "done 3\n",
      "1900-01-22 05:00:00 1900-02-05 05:00:00\n",
      "kalman0_rmse             0.12674376112061847\n",
      "kalman1_rmse             0.5849692458002957\n",
      "done 4\n",
      "1900-01-23 06:15:00 1900-02-06 06:15:00\n",
      "kalman0_rmse             0.11579509027454302\n",
      "kalman1_rmse             0.6657780379853525\n",
      "done 5\n",
      "1900-01-24 07:30:00 1900-02-07 07:30:00\n",
      "kalman0_rmse             0.10720542203114902\n",
      "kalman1_rmse             0.7213590094223356\n",
      "done 6\n",
      "1900-01-25 08:45:00 1900-02-08 08:45:00\n",
      "kalman0_rmse             0.10028148981126664\n",
      "kalman1_rmse             0.7617401201396278\n",
      "done 7\n",
      "1900-01-26 10:00:00 1900-02-09 10:00:00\n",
      "kalman0_rmse             0.09454629529738176\n",
      "kalman1_rmse             0.7916124271094177\n",
      "done 8\n",
      "1900-01-27 11:15:00 1900-02-10 11:15:00\n",
      "kalman0_rmse             0.0896944912411779\n",
      "kalman1_rmse             0.8147683250687914\n",
      "done 9\n",
      "1900-01-28 12:30:00 1900-02-11 12:30:00\n",
      "kalman0_rmse             0.08552034203994999\n",
      "kalman1_rmse             0.8333107155608578\n",
      "done 10\n",
      "1900-01-29 13:45:00 1900-02-12 13:45:00\n",
      "kalman0_rmse             0.08187949373874832\n",
      "kalman1_rmse             0.848453211995906\n",
      "done 11\n",
      "1900-01-30 15:00:00 1900-02-13 15:00:00\n",
      "kalman0_rmse             0.07905926610459141\n",
      "kalman1_rmse             0.8599606569171605\n",
      "done 12\n",
      "1900-01-31 16:15:00 1900-02-14 16:15:00\n",
      "kalman0_rmse             0.08057913135139243\n",
      "kalman1_rmse             0.865654977680024\n",
      "done 13\n",
      "1900-02-01 17:30:00 1900-02-15 17:30:00\n",
      "kalman0_rmse             0.13303597929490402\n",
      "kalman1_rmse             0.8516072630241173\n",
      "done 14\n",
      "1900-02-02 18:45:00 1900-02-16 18:45:00\n",
      "kalman0_rmse             0.15591585852824444\n",
      "kalman1_rmse             0.8340389629795917\n",
      "done 15\n",
      "1900-02-03 20:00:00 1900-02-17 20:00:00\n",
      "kalman0_rmse             0.1556305673482131\n",
      "kalman1_rmse             0.810595692989066\n",
      "done 16\n",
      "1900-02-04 21:15:00 1900-02-18 21:15:00\n",
      "kalman0_rmse             0.1514159199749189\n",
      "kalman1_rmse             0.7880180381204611\n",
      "done 17\n",
      "1900-02-05 22:30:00 1900-02-19 22:30:00\n",
      "kalman0_rmse             0.14768823733876\n",
      "kalman1_rmse             0.7673508581736277\n",
      "done 18\n",
      "1900-02-06 23:45:00 1900-02-20 23:45:00\n",
      "kalman0_rmse             0.15017529334032673\n",
      "kalman1_rmse             0.7506321520437761\n",
      "done 19\n",
      "CPU times: user 1min 4s, sys: 177 ms, total: 1min 5s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(20):\n",
    "  # collect data from location \n",
    "  start_date = train_start + i*train_stepdelta\n",
    "  end_date   = train_start + i*train_stepdelta + train_horizon\n",
    "  print(start_date, end_date)\n",
    "  \n",
    "  df_loc = df[(start_date <= pd.to_datetime(df['ds'])) &\n",
    "              (pd.to_datetime(df['ds']) <= train_start + i*train_stepdelta + train_horizon) &\n",
    "              (df['geohash6'] == location)].reset_index().drop('index', axis=1)\n",
    "  df_loc = df_loc.rename(columns={'demand' : 'y'}).drop(['geohash6', 'in_raw',\n",
    "                                                       'weekday', 'hour', 'minutes',\n",
    "                                                       'latitude', 'longitude'], axis=1)\n",
    "  \n",
    "  # train-test split\n",
    "  df_train = df_loc.iloc[:-5]\n",
    "  df_test  = df_loc.iloc[-5:]\n",
    "  \n",
    "  kf = simdkalman.KalmanFilter(\n",
    "    state_transition = np.array([[1,1],[0,1]]),\n",
    "    process_noise = np.diag([0.1, 0.01]),\n",
    "    observation_model = np.array([[1,0]]),\n",
    "    observation_noise = 0.5)\n",
    "  \n",
    "  kalman_predictions = kf.predict(df_train['y'].values, 5)\n",
    "  \n",
    "  kalman0_pred = kalman_predictions.states.mean[:,0]\n",
    "  kalman1_pred = kalman_predictions.states.mean[:,1]\n",
    "  \n",
    "  kalman0_pred = np.clip(kalman0_pred, a_min=0.0, a_max=1.0)\n",
    "  kalman1_pred = np.clip(kalman1_pred, a_min=0.0, a_max=1.0)\n",
    "  \n",
    "  kalman0_mse_sum += mean_squared_error(kalman0_pred, df_test.y)\n",
    "  kalman1_mse_sum += mean_squared_error(kalman1_pred, df_test.y)\n",
    "  \n",
    "  print(\"kalman0_rmse            \", math.sqrt(kalman0_mse_sum           /(i+1)))\n",
    "  print(\"kalman1_rmse            \", math.sqrt(kalman1_mse_sum           /(i+1)))\n",
    "  print(\"done\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KIVapdwHtZdx"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "7 - Kalman Filter.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}