KNN_NPJ.ipynb

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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import statistics"
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    "# Load in data\n",
    "da = xr.open_dataset('/network/rit/lab/elisontimmlab_rit/kf835882/python/ERA5/hourly/era5_day_ua250_1981-2010.nc')"
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    "# Select years 1990-2010\n",
    "da = da.sel(time=slice('1990-01-01', '2010-12-31'))"
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    "# Calculate monthly climatologies\n",
    "mon_clim = da.groupby('time.month').mean('time')"
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   "source": [
    "mon_clim = mon_clim.u"
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    "# Store monthly clim as variables\n",
    "jan_clim = mon_clim[0]\n",
    "feb_clim = mon_clim[1]\n",
    "mar_clim = mon_clim[2]\n",
    "apr_clim = mon_clim[3]\n",
    "nov_clim = mon_clim[4]\n",
    "dec_clim = mon_clim[5]"
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    "# Group daily data by month\n",
    "jan_day = da.groupby('time.month')[1]\n",
    "feb_day = da.groupby('time.month')[2]\n",
    "mar_day = da.groupby('time.month')[3]\n",
    "apr_day = da.groupby('time.month')[4]\n",
    "nov_day = da.groupby('time.month')[11]\n",
    "dec_day = da.groupby('time.month')[12]"
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    "# Remove monthly climatologies\n",
    "jan_anom = jan_day.u - jan_clim\n",
    "feb_anom = feb_day.u - feb_clim\n",
    "mar_anom = mar_day.u - mar_clim\n",
    "apr_anom = apr_day.u - apr_clim\n",
    "nov_anom = nov_day.u - nov_clim\n",
    "dec_anom = dec_day.u - dec_clim"
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    "# Merge dataarrays into one\n",
    "# dim='time' keeps dates in order\n",
    "ua_anom = xr.concat([jan_anom,feb_anom,mar_anom,apr_anom,nov_anom,dec_anom],dim='time')"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 3806)&gt;\n",
       "array([&#x27;1990-01-01T11:30:00.000000000&#x27;, &#x27;1990-01-02T11:30:00.000000000&#x27;,\n",
       "       &#x27;1990-01-03T11:30:00.000000000&#x27;, ..., &#x27;2010-12-29T11:30:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T11:30:00.000000000&#x27;, &#x27;2010-12-31T11:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1990-01-01T11:30:00 ... 2010-12-31T11:30:00\n",
       "    month    (time) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12 12\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      time\n",
       "    bounds:         time_bnds\n",
       "    axis:           T</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3806</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a7b08576-ca6e-4099-83dd-e65150a7d425' class='xr-array-in' type='checkbox' checked><label for='section-a7b08576-ca6e-4099-83dd-e65150a7d425' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1990-01-01T11:30:00 1990-01-02T11:30:00 ... 2010-12-31T11:30:00</span></div><div class='xr-array-data'><pre>array([&#x27;1990-01-01T11:30:00.000000000&#x27;, &#x27;1990-01-02T11:30:00.000000000&#x27;,\n",
       "       &#x27;1990-01-03T11:30:00.000000000&#x27;, ..., &#x27;2010-12-29T11:30:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T11:30:00.000000000&#x27;, &#x27;2010-12-31T11:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-ca62c523-1a18-4bc6-abc4-b9fe7a3e8206' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ca62c523-1a18-4bc6-abc4-b9fe7a3e8206' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-01-01T11:30:00 ... 2010-12-...</div><input id='attrs-f2c68db6-43e9-44e1-be2d-b8aba189e992' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2c68db6-43e9-44e1-be2d-b8aba189e992' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05375eb8-b836-4289-a97d-2fcd740e91f1' class='xr-var-data-in' type='checkbox'><label for='data-05375eb8-b836-4289-a97d-2fcd740e91f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-01-01T11:30:00.000000000&#x27;, &#x27;1990-01-02T11:30:00.000000000&#x27;,\n",
       "       &#x27;1990-01-03T11:30:00.000000000&#x27;, ..., &#x27;2010-12-29T11:30:00.000000000&#x27;,\n",
       "       &#x27;2010-12-30T11:30:00.000000000&#x27;, &#x27;2010-12-31T11:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 ... 12 12 12 12 12 12</div><input id='attrs-b0fa10c7-d30d-4c88-88a7-d7703a4638bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0fa10c7-d30d-4c88-88a7-d7703a4638bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3a4af2f-5b1a-4e7a-8c06-7ed597f59971' class='xr-var-data-in' type='checkbox'><label for='data-d3a4af2f-5b1a-4e7a-8c06-7ed597f59971' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  1,  1, ..., 12, 12, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb62aa4e-becd-4249-91ea-43adc0548375' class='xr-section-summary-in' type='checkbox'  ><label for='section-bb62aa4e-becd-4249-91ea-43adc0548375' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7a10cd0-51ca-4e51-a5ae-2616c4f25866' class='xr-index-data-in' type='checkbox'/><label for='index-c7a10cd0-51ca-4e51-a5ae-2616c4f25866' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-01-01 11:30:00&#x27;, &#x27;1990-01-02 11:30:00&#x27;,\n",
       "               &#x27;1990-01-03 11:30:00&#x27;, &#x27;1990-01-04 11:30:00&#x27;,\n",
       "               &#x27;1990-01-05 11:30:00&#x27;, &#x27;1990-01-06 11:30:00&#x27;,\n",
       "               &#x27;1990-01-07 11:30:00&#x27;, &#x27;1990-01-08 11:30:00&#x27;,\n",
       "               &#x27;1990-01-09 11:30:00&#x27;, &#x27;1990-01-10 11:30:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2010-12-22 11:30:00&#x27;, &#x27;2010-12-23 11:30:00&#x27;,\n",
       "               &#x27;2010-12-24 11:30:00&#x27;, &#x27;2010-12-25 11:30:00&#x27;,\n",
       "               &#x27;2010-12-26 11:30:00&#x27;, &#x27;2010-12-27 11:30:00&#x27;,\n",
       "               &#x27;2010-12-28 11:30:00&#x27;, &#x27;2010-12-29 11:30:00&#x27;,\n",
       "               &#x27;2010-12-30 11:30:00&#x27;, &#x27;2010-12-31 11:30:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=3806, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0087027e-0442-4eb4-b499-c43df67568cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0087027e-0442-4eb4-b499-c43df67568cc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 3806)>\n",
       "array(['1990-01-01T11:30:00.000000000', '1990-01-02T11:30:00.000000000',\n",
       "       '1990-01-03T11:30:00.000000000', ..., '2010-12-29T11:30:00.000000000',\n",
       "       '2010-12-30T11:30:00.000000000', '2010-12-31T11:30:00.000000000'],\n",
       "      dtype='datetime64[ns]')\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1990-01-01T11:30:00 ... 2010-12-31T11:30:00\n",
       "    month    (time) int64 1 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12 12\n",
       "Attributes:\n",
       "    standard_name:  time\n",
       "    long_name:      time\n",
       "    bounds:         time_bnds\n",
       "    axis:           T"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ua_anom.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27a310f4-c66a-45c5-bf18-2a8bfa187221",
   "metadata": {},
   "outputs": [],
   "source": [
    "ua_anom = ua_anom.sortby('time',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d85b2cae-b7c2-43af-91e4-16eb1d13365f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3806, 401, 681)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ua_anom.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93d579d1-47c2-4b1b-be13-f263a67ad49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get variables\n",
    "lat = ua_anom.lat.values\n",
    "lon = ua_anom.lon.values\n",
    "ua = ua_anom.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88769364-9cd8-40a3-bce9-990efbb124ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weight grid cells by cosine of latitude\n",
    "latitude = np.linspace(lat.min(), lat.max(), len(lat))\n",
    "weights_array = np.cos(np.deg2rad(latitude))[:, np.newaxis]\n",
    "\n",
    "ua=ua*weights_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3be12bad-36aa-45c4-8b17-3d66b46624c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape data into time-space format\n",
    "data = np.reshape(ua, (ua.shape[0], 401*681), order = 'F')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9aac201-d91e-44c4-a921-544bfd605556",
   "metadata": {},
   "source": [
    "# Write KNN algorithm by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d39db893-95c2-44dc-9453-5f3090e21950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply labels for disturbance types\n",
    "labels = np.load('../np_arrays/disturbance_labels.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "18dc6140-8fe8-4505-8aae-07bd81c54fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply labels for each disturbance type\n",
    "labels[np.where(labels == 4)] = 4 #KL\n",
    "labels[np.where(labels == 3)] = 3 #UL\n",
    "labels[np.where(labels == 2)] = 2 #CR\n",
    "labels[np.where(labels == 1)] = 0 #ND\n",
    "labels[np.where(labels == -1)] = 1 #NC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7c144895-b468-4c66-98da-57f83968164a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       ...,\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcf92534-7af0-4b60-83dd-263fac9864f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in composites for each disturbance type - these are the \"truth\"\n",
    "path = '/network/rit/lab/elisontimmlab_rit/kf835882/python/ERA5/hourly/'\n",
    "\n",
    "ND_truth = np.load(path+'win_map_labels_ND_truth.npy')\n",
    "UL_truth = np.load(path+'win_map_0_0_labels_UL_truth.npy')\n",
    "CR_truth = np.load(path+'win_map_0_0_labels_CR_truth.npy')\n",
    "NC_truth = np.load(path+'win_map_2_2_labels_NC_truth.npy')\n",
    "KL_truth = np.load(path+'win_map_0_2_labels_10450.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b04fa88-6be6-4004-988f-9c0ebc08a946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape into 1D arrays\n",
    "ND_truth = np.reshape(ND_truth, (401*681), order = 'F')\n",
    "NC_truth = np.reshape(NC_truth, (401*681), order = 'F')\n",
    "CR_truth = np.reshape(CR_truth, (401*681), order = 'F')\n",
    "UL_truth = np.reshape(UL_truth, (401*681), order = 'F')\n",
    "KL_truth = np.reshape(KL_truth, (401*681), order = 'F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "702ca490-38de-4a00-ae32-2ffd8434076d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array for neighbors\n",
    "X = np.zeros(shape=[5,401*681])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91b5b239-3a9c-4a8c-b33f-ff5d48a1120b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X[0] = ND_truth\n",
    "X[1] = NC_truth\n",
    "X[2] = CR_truth\n",
    "X[3] = UL_truth\n",
    "X[4] = KL_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e3fa22fd-2b01-4653-8817-f33adcf58135",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_days =  np.where(labels == 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc9a22ad-5c51-4892-9d09-7fae9774919b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_days = dist_days[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "053eba80-9e14-41e6-bbac-744a8d0ff915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  21,   22,   23,   24,   25,   26,   83,   84,   85,   86,   87,\n",
       "         88,  131,  132,  133,  142,  143,  144,  145,  146,  147,  174,\n",
       "        175,  176,  257,  258,  259,  260,  261,  262,  313,  314,  315,\n",
       "        316,  671,  672,  673,  674,  675,  845,  846,  847,  848,  849,\n",
       "       1209, 1210, 1211, 1212, 1213, 1323, 1324, 1325, 1326, 1327, 1696,\n",
       "       1697, 1698, 1699, 1776, 1777, 1778, 1779, 1808, 1809, 1810, 1811,\n",
       "       1812, 2029, 2030, 2031, 2032, 2140, 2141, 2142, 2143, 2202, 2203,\n",
       "       2204, 2205, 2206, 2304, 2305, 2306, 2443, 2444, 2445, 2446, 2447,\n",
       "       2448, 2454, 2455, 2456, 2457, 2494, 2495, 2496, 2497, 2498, 2499,\n",
       "       2500, 2503, 2504, 2505, 2506, 2507, 2532, 2533, 2534, 2535, 2536,\n",
       "       2537, 2538, 2596, 2597, 2598, 2604, 2605, 2606, 2607, 2716, 2717,\n",
       "       2718, 2719, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2958,\n",
       "       2959, 2960, 2961, 2964, 2965, 2966, 2967, 2986, 2987, 2988, 2989,\n",
       "       3088, 3089, 3090, 3091, 3201, 3202, 3203, 3204, 3205, 3403, 3404,\n",
       "       3422, 3423, 3424, 3425, 3426, 3429, 3430, 3431, 3432, 3433, 3434,\n",
       "       3435, 3436, 3437, 3469, 3470, 3471, 3472, 3513, 3514, 3515, 3516,\n",
       "       3517, 3518, 3574, 3575, 3576, 3577, 3773, 3774, 3775, 3776, 3777,\n",
       "       3778, 3779, 3782, 3783, 3784, 3785, 3786, 3791, 3792, 3793, 3794,\n",
       "       3795])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fd70fd11-5da2-4310-bc14-43fb1c19e6c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to count # of occurrences in a list\n",
    "def countX(lst, x):\n",
    "    count = 0\n",
    "    for ele in lst:\n",
    "        if (ele == x):\n",
    "            count = count + 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a610189c-8fc8-4a9b-a33e-207cdd8bc08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=1\n",
    "results = []\n",
    "\n",
    "for n in dist_days:\n",
    "    new_data_point = data[n]\n",
    "    \n",
    "    # Calculate euclidean distance between test point and each of the 5 neighbors\n",
    "    distances = np.linalg.norm(X - new_data_point, axis=1)\n",
    "    \n",
    "    # Determine nearest neighbor\n",
    "    nearest_neighbor = distances.argsort()[:k]\n",
    "    nearest_neighbor\n",
    "    results.append(nearest_neighbor)\n",
    "    \n",
    "pred = countX(results,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f4477a0f-bb5c-4bc7-8663-2d1a2b054e37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The true positive rate is: 60.30150753768844%\n"
     ]
    }
   ],
   "source": [
    "true_pos = (pred / len(dist_days))*100\n",
    "print('The true positive rate is: '+str(true_pos)+'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e14991f-c53e-4e93-8bed-6cb1b7a5059e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The false positive rate is: 39.698492462311556%\n"
     ]
    }
   ],
   "source": [
    "false_neg = ((len(dist_days) - pred) / len(dist_days))*100\n",
    "print('The false positive rate is: '+str(false_neg)+'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ecb446-f8db-454d-b089-81b21baa99ae",
   "metadata": {},
   "source": [
    "# Apply KNN method using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ab53c92d-ef9e-4d20-9b8d-8a2c29c94971",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X is the daily wind anomaly data 1990-2010\n",
    "# y is the daily disturbance classification (e.g., ND:0, NC:1, CR:2, UL:3, KL:4)\n",
    "X = data\n",
    "y = labels[:,0]\n",
    "t = da.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d7f8e1de-414a-4a2a-bca4-48f1e1301a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use sklearn to randomly split the train/test data 80/20\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test, t_train, t_test = train_test_split(X, y, t, test_size=0.2, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "40982c8c-8aba-4c29-a219-363d8561e420",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure to record dates associated with training/test data\n",
    "train_times=t_train\n",
    "test_times = t_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e8e6bbde-ca94-4710-af4f-5dc70ab4346a",
   "metadata": {},
   "outputs": [],
   "source": [
    "u_train = ua_anom.sel(time=train_times)\n",
    "u_test = ua_anom.sel(time=test_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7c7066f0-5a2a-486f-adaa-b2905653c520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weight grid cells by cosine of latitude\n",
    "latitude = np.linspace(lat.min(), lat.max(), len(lat))\n",
    "weights_array = np.cos(np.deg2rad(latitude))[:, np.newaxis]\n",
    "\n",
    "u_train=u_train*weights_array\n",
    "u_test=u_test*weights_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8aa56bc2-0cf0-487b-bbcd-2e260375db2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape data into time-space format\n",
    "u_train = np.reshape(u_train.values, (u_train.shape[0], 401*681), order = 'F')\n",
    "u_test = np.reshape(u_test.values, (u_test.shape[0], 401*681), order = 'F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "46c872a2-7411-4b6f-943b-5cbe79fc162a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3f85e01ed0>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor,KNeighborsClassifier\n",
    "\n",
    "error_rate = []\n",
    "\n",
    "for i in range(1,10):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred != y_test))\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(range(1,10),error_rate, marker='o', markersize=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ba9231ee-a935-4c3c-84a8-ab240d7fcc28",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_model = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cbaa24d2-50ec-44db-b8b9-87ca09c79506",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0549ac59-c493-49cd-8b45-44a48671bf1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5055539109717354"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "train_preds = knn_model.predict(X_train)\n",
    "mse = mean_squared_error(y_train, train_preds)\n",
    "rmse = sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1391284d-41c3-4666-bee0-7bfbd984f4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0709722812923723"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds = knn_model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, test_preds)\n",
    "rmse = sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c72c83-dca1-4198-a96e-1586008fe6bd",
   "metadata": {},
   "source": [
    "# 'Goodness of fit' test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bb0863f5-a930-42cd-80fa-c257e83ef892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square statistic: 0.34759777479037085\n",
      "P-value: 0.9865379488201709\n",
      "Degrees of freedom: 4\n",
      "Expected frequencies:\n",
      "[[71.53639419  7.53636778 12.47565026  3.64437166  4.80721611]\n",
      " [70.82103025  7.4610041  12.35089376  3.60792794  4.75914395]]\n"
     ]
    }
   ],
   "source": [
    "# Goodness of fit test for training data\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "expected_values = np.array([69, 8, 13, 4, 5])\n",
    "\n",
    "observed_values = np.zeros(shape=(5))\n",
    "\n",
    "observed_values[0] = (len(train_preds[train_preds == 0]) / len(X_train)) * 100\n",
    "observed_values[1] = (len(train_preds[train_preds == 1]) / len(X_train)) * 100\n",
    "observed_values[2] = (len(train_preds[train_preds == 2]) / len(X_train)) * 100\n",
    "observed_values[3] = (len(train_preds[train_preds == 3]) / len(X_train)) * 100\n",
    "observed_values[4] = (len(train_preds[train_preds == 4]) / len(X_train)) * 100\n",
    "\n",
    "chi2_stat, p_value, dof, expected = chi2_contingency([observed_values, expected_values])\n",
    "\n",
    "# Print results\n",
    "print(f\"Chi-square statistic: {chi2_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "print(f\"Degrees of freedom: {dof}\")\n",
    "print(\"Expected frequencies:\")\n",
    "print(expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "08197bc5-6f31-4c55-bf2e-e209d5a8d133",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square statistic: 4.738717801410996\n",
      "P-value: 0.3151721089907731\n",
      "Degrees of freedom: 4\n",
      "Expected frequencies:\n",
      "[[76.21968108  6.32822907 10.29161556  2.93330168  4.22717261]\n",
      " [75.45748427  6.26494678 10.1886994   2.90396866  4.18490088]]\n"
     ]
    }
   ],
   "source": [
    "# Goodness of fit test for test data\n",
    "observed_values = np.zeros(shape=(5))\n",
    "\n",
    "observed_values[0] = (len(test_preds[test_preds == 0]) / len(X_test)) * 100\n",
    "observed_values[1] = (len(test_preds[test_preds == 1]) / len(X_test)) * 100\n",
    "observed_values[2] = (len(test_preds[test_preds == 2]) / len(X_test)) * 100\n",
    "observed_values[3] = (len(test_preds[test_preds == 3]) / len(X_test)) * 100\n",
    "observed_values[4] = (len(test_preds[test_preds == 4]) / len(X_test)) * 100\n",
    "\n",
    "chi2_stat, p_value, dof, expected = chi2_contingency([observed_values, expected_values])\n",
    "\n",
    "# Print results\n",
    "print(f\"Chi-square statistic: {chi2_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "print(f\"Degrees of freedom: {dof}\")\n",
    "print(\"Expected frequencies:\")\n",
    "print(expected)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a090b86-45cb-40ae-86c6-c811cd8b9ac9",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2d9344bd-fcb0-4033-9a0f-05b695ae9b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "cm=confusion_matrix(y_train, train_preds,normalize='true')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=['ND','NC','CR','UL','KL'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36eb3a1d-add9-4e21-91a1-b92ceb874033",
   "metadata": {},
   "source": [
    "# Get ROC curve from TPR and FPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "284b7de9-525c-4b99-acdc-a1203f9b0d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve (training set)')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "nd_fpr, nd_tpr, nd_thresholds = roc_curve(y_train==0,train_preds==0)\n",
    "nd_auc = auc(nd_fpr, nd_tpr)\n",
    "nc_fpr, nc_tpr, nc_thresholds = roc_curve(y_train==1,train_preds==1)\n",
    "nc_auc = auc(nc_fpr, nc_tpr)\n",
    "cr_fpr, cr_tpr, cr_thresholds = roc_curve(y_train==2,train_preds==2)\n",
    "cr_auc = auc(cr_fpr, cr_tpr)\n",
    "ul_fpr, ul_tpr, ul_thresholds = roc_curve(y_train==3,train_preds==3)\n",
    "ul_auc = auc(ul_fpr, ul_tpr)\n",
    "kl_fpr, kl_tpr, kl_thresholds = roc_curve(y_train==4,train_preds==4)\n",
    "kl_auc = auc(kl_fpr, kl_tpr)\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plt.plot(nd_fpr,nd_tpr,label='ND (AUC = {:.2f})'.format(nd_auc))\n",
    "plt.plot(nc_fpr,nc_tpr,label='NC (AUC = {:.2f})'.format(nc_auc))\n",
    "plt.plot(cr_fpr,cr_tpr,label='CR (AUC = {:.2f})'.format(cr_auc))\n",
    "plt.plot(ul_fpr,ul_tpr,label='UL (AUC = {:.2f})'.format(ul_auc))\n",
    "plt.plot(kl_fpr,kl_tpr,label='KL (AUC = {:.2f})'.format(kl_auc))\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=1.5, linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('ROC curve (training set)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af40a0ab-779c-464a-a5f3-ad7496ddaf27",
   "metadata": {},
   "source": [
    "# Calculate Brier Skill Score for each disturbance type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "434f5d2b-12df-4738-955c-49460565397a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get probabilities for a single disturbance type\n",
    "y_probs_knn = knn_model.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d015dca0-97ad-4e42-a11b-c17a89c8a856",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_probs_knn = y_probs_knn[:,0] # Change 1-5 based on disturbance type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ecf12568-9308-4465-9cd5-68f7b88b2b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create binary array for occurrences\n",
    "y_binary = np.zeros(shape=(len(X_train)))\n",
    "\n",
    "true = np.where(y_train == 0)\n",
    "y_binary[true] = 1\n",
    "y_true = y_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bf2d23e5-988a-4d35-8866-539a535d297e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of probabilites from the null model\n",
    "y_probs_reference = np.zeros(shape=(len(X_train)))\n",
    "\n",
    "nd = 0.69\n",
    "nc = 0.08\n",
    "cr = 0.13\n",
    "kl = 0.05\n",
    "ul = 0.04\n",
    "\n",
    "y_probs_reference[:] = nd # This is just the climatological freq of a disturbance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f447c5aa-dc79-4c54-aeb0-1cc071011d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.748092078169484"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import brier_score_loss\n",
    "\n",
    "# Calculate Brier scores\n",
    "bs_knn = brier_score_loss(y_true, y_probs_knn)\n",
    "bs_reference = brier_score_loss(y_true, y_probs_reference)\n",
    "\n",
    "# Calculate Brier Skill Score\n",
    "bss = 1 - (bs_knn / bs_reference)\n",
    "bss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dab012f0-1198-4312-8d9c-98189b94a043",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap BSS Confidence Interval: [0.73324803 0.7673294 ]\n"
     ]
    }
   ],
   "source": [
    "observed = y_true\n",
    "predicted_probs = np.round(y_probs_knn,2)\n",
    "ref_probs = np.round(y_probs_reference,2)\n",
    "\n",
    "# Number of bootstrap samples\n",
    "n_bootstrap_samples = 1000\n",
    "\n",
    "# Function to calculate Brier Skill Score\n",
    "def calculate_bss(observed, predicted_probs,ref_probs):\n",
    "    bs_reference = brier_score_loss(observed, ref_probs)\n",
    "    bs_model = brier_score_loss(observed, predicted_probs)\n",
    "    bss = 1 - (bs_model / bs_reference)\n",
    "    return bss\n",
    "\n",
    "# Bootstrap resampling\n",
    "bss_values = []\n",
    "for _ in range(n_bootstrap_samples):\n",
    "    indices = np.random.choice(len(observed), len(observed), replace=True)\n",
    "    bootstrap_observed = observed[indices]\n",
    "    bootstrap_predicted_probs = predicted_probs[indices]\n",
    "    bootstrap_ref_probs = ref_probs[indices]\n",
    "    bss = calculate_bss(bootstrap_observed, bootstrap_predicted_probs,bootstrap_ref_probs)\n",
    "    bss_values.append(bss)\n",
    "\n",
    "# Calculate confidence interval\n",
    "confidence_interval = np.percentile(bss_values, [2.5, 97.5])\n",
    "print(f\"Bootstrap BSS Confidence Interval: {confidence_interval}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "758cf610-d2f8-4275-b4e6-d5e43a427fd1",
   "metadata": {},
   "source": [
    "# Calculate log score for train/test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "84fb9181-3033-4a7d-8d60-c3c60ea68864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a binary array for the 'true' labels\n",
    "y_train_binary = np.zeros(shape=(len(y_train),5))\n",
    "y_test_binary = np.zeros(shape=(len(y_test),5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e5711e79-975f-4c94-aa26-0d98dee14431",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(y_train)):\n",
    "    label = y_train[i]\n",
    "    if label == 0.0:\n",
    "        y_train_binary[i,0] = 1\n",
    "    elif label == 1.0:\n",
    "        y_train_binary[i,1] = 1\n",
    "    elif label == 2.0:\n",
    "        y_train_binary[i,2] = 1\n",
    "    elif label == 3.0:\n",
    "        y_train_binary[i,3] = 1\n",
    "    elif label == 4.0:\n",
    "        y_train_binary[i,4] = 1\n",
    "    elif label == 5.0:\n",
    "        y_train_binary[i,5] = 1\n",
    "\n",
    "for i in range(0,len(y_test)):\n",
    "    label = y_test[i]\n",
    "    if label == 0.0:\n",
    "        y_test_binary[i,0] = 1\n",
    "    elif label == 1.0:\n",
    "        y_test_binary[i,1] = 1\n",
    "    elif label == 2.0:\n",
    "        y_test_binary[i,2] = 1\n",
    "    elif label == 3.0:\n",
    "        y_test_binary[i,3] = 1\n",
    "    elif label == 4.0:\n",
    "        y_test_binary[i,4] = 1\n",
    "    elif label == 5.0:\n",
    "        y_test_binary[i,5] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b823db40-41bc-496e-8436-5bcd64e1528d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# True labels (ground truth)\n",
    "y_true = y_train_binary\n",
    "\n",
    "# Predicted probabilities (example values)\n",
    "y_pred_prob = knn_model.predict_proba(X_train)\n",
    "y_pred_prob = np.round(y_pred_prob,1) # Round probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a92114eb-dc71-4dae-b6e4-ec36afa7fe10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logarithmic Score (training): 0.1809\n"
     ]
    }
   ],
   "source": [
    "# Avoid log(0) by adding a small epsilon\n",
    "epsilon = 1e-15\n",
    "\n",
    "# Clip predicted probabilities to avoid log(0) or log(1)\n",
    "y_pred_prob = np.clip(y_pred_prob, epsilon, 1 - epsilon)\n",
    "\n",
    "# Calculate logarithmic score\n",
    "log_score = -np.mean(np.sum(y_true * np.log(y_pred_prob), axis=1))\n",
    "\n",
    "print(f\"Logarithmic Score (training): {log_score:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1d7990fb-e040-49e3-ba50-1cb6f2666625",
   "metadata": {},
   "outputs": [],
   "source": [
    "# True labels (ground truth)\n",
    "y_true = y_test_binary\n",
    "\n",
    "# Predicted probabilities (example values)\n",
    "y_pred_prob = knn_model.predict_proba(X_test)\n",
    "y_pred_prob = np.round(y_pred_prob,1) # Round probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5e5543ab-508f-48c1-bd81-a750a98275b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logarithmic Score (test): 1.4969\n"
     ]
    }
   ],
   "source": [
    "# Avoid log(0) by adding a small epsilon\n",
    "epsilon = 1e-15\n",
    "\n",
    "# Clip predicted probabilities to avoid log(0) or log(1)\n",
    "y_pred_prob = np.clip(y_pred_prob, epsilon, 1 - epsilon)\n",
    "\n",
    "# Calculate logarithmic score\n",
    "log_score = -np.mean(np.sum(y_true * np.log(y_pred_prob), axis=1))\n",
    "\n",
    "print(f\"Logarithmic Score (test): {log_score:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4690a580-f902-47c8-bafd-8d2a3229bd27",
   "metadata": {},
   "source": [
    "# Calculate more skill metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "32e4bcc0-4763-430c-8d5f-5a059f56961c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.94849978, 0.95269382, 0.98649278, 0.96712329],\n",
       "       [0.96244131, 0.9848883 , 0.8436214 , 0.89912281],\n",
       "       [0.93611111, 0.97568988, 0.8685567 , 0.90106952],\n",
       "       [0.98989899, 0.99605782, 0.89908257, 0.94230769],\n",
       "       [0.98561151, 0.99277267, 0.87261146, 0.92567568]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skill_metrics = np.zeros(shape=(5,4))\n",
    "\n",
    "from sklearn.metrics import precision_score\n",
    "skill_metrics[0,0] = precision_score(y_train==0, train_preds==0)\n",
    "skill_metrics[1,0] = precision_score(y_train==1, train_preds==1)\n",
    "skill_metrics[2,0] = precision_score(y_train==2, train_preds==2)\n",
    "skill_metrics[3,0] = precision_score(y_train==3, train_preds==3)\n",
    "skill_metrics[4,0] = precision_score(y_train==4, train_preds==4)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "skill_metrics[0,1] = accuracy_score(y_train==0, train_preds==0)\n",
    "skill_metrics[1,1] = accuracy_score(y_train==1, train_preds==1)\n",
    "skill_metrics[2,1] = accuracy_score(y_train==2, train_preds==2)\n",
    "skill_metrics[3,1] = accuracy_score(y_train==3, train_preds==3)\n",
    "skill_metrics[4,1] = accuracy_score(y_train==4, train_preds==4)\n",
    "\n",
    "from sklearn.metrics import recall_score\n",
    "skill_metrics[0,2] = recall_score(y_train==0, train_preds==0)\n",
    "skill_metrics[1,2] = recall_score(y_train==1, train_preds==1)\n",
    "skill_metrics[2,2] = recall_score(y_train==2, train_preds==2)\n",
    "skill_metrics[3,2] = recall_score(y_train==3, train_preds==3)\n",
    "skill_metrics[4,2] = recall_score(y_train==4, train_preds==4)\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "skill_metrics[0,3] = f1_score(y_train==0, train_preds==0)\n",
    "skill_metrics[1,3] = f1_score(y_train==1, train_preds==1)\n",
    "skill_metrics[2,3] = f1_score(y_train==2, train_preds==2)\n",
    "skill_metrics[3,3] = f1_score(y_train==3, train_preds==3)\n",
    "skill_metrics[4,3] = f1_score(y_train==4, train_preds==4)\n",
    "\n",
    "skill_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0f9e82ca-7457-4595-90dd-edfe24ea59b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "plt.imshow(skill_metrics, cmap='coolwarm', interpolation='nearest')\n",
    "plt.xticks(ticks = np.arange(4),labels=['precision','accuracy','recall','f1-score'])\n",
    "plt.yticks(ticks = np.arange(5),labels=['ND','NC','CR','UL','KL'])\n",
    "plt.colorbar()\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8b5b0329-5548-46ea-8f05-31998f21723a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test, test_preds,normalize='true')\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=['ND','NC','CR','UL','KL'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e38e85ca-401e-4536-9a24-f9a0508e326b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC curve (test set)')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nd_fpr, nd_tpr, nd_thresholds = roc_curve(y_test==0,test_preds==0)\n",
    "nd_auc = auc(nd_fpr, nd_tpr)\n",
    "nc_fpr, nc_tpr, nc_thresholds = roc_curve(y_test==1,test_preds==1)\n",
    "nc_auc = auc(nc_fpr, nc_tpr)\n",
    "cr_fpr, cr_tpr, cr_thresholds = roc_curve(y_test==2,test_preds==2)\n",
    "cr_auc = auc(cr_fpr, cr_tpr)\n",
    "ul_fpr, ul_tpr, ul_thresholds = roc_curve(y_test==3,test_preds==3)\n",
    "ul_auc = auc(ul_fpr, ul_tpr)\n",
    "kl_fpr, kl_tpr, kl_thresholds = roc_curve(y_test==4,test_preds==4)\n",
    "kl_auc = auc(kl_fpr, kl_tpr)\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "plt.plot(nd_fpr,nd_tpr,label='ND (AUC = {:.2f})'.format(nd_auc))\n",
    "plt.plot(nc_fpr,nc_tpr,label='NC (AUC = {:.2f})'.format(nc_auc))\n",
    "plt.plot(cr_fpr,cr_tpr,label='CR (AUC = {:.2f})'.format(cr_auc))\n",
    "plt.plot(ul_fpr,ul_tpr,label='UL (AUC = {:.2f})'.format(ul_auc))\n",
    "plt.plot(kl_fpr,kl_tpr,label='KL (AUC = {:.2f})'.format(kl_auc))\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=1.5, linestyle='--')\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('ROC curve (test set)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dcec32ad-8ed9-4ba0-a8b6-c9c5c832e447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.76031746, 0.77952756, 0.96572581, 0.85079929],\n",
       "       [0.77142857, 0.92650919, 0.36      , 0.49090909],\n",
       "       [0.75438596, 0.88976378, 0.38053097, 0.50588235],\n",
       "       [1.        , 0.97112861, 0.38888889, 0.56      ],\n",
       "       [0.88461538, 0.97112861, 0.54761905, 0.67647059]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skill_metrics = np.zeros(shape=(5,4))\n",
    "\n",
    "skill_metrics[0,0] = precision_score(y_test==0, test_preds==0)\n",
    "skill_metrics[1,0] = precision_score(y_test==1, test_preds==1)\n",
    "skill_metrics[2,0] = precision_score(y_test==2, test_preds==2)\n",
    "skill_metrics[3,0] = precision_score(y_test==3, test_preds==3)\n",
    "skill_metrics[4,0] = precision_score(y_test==4, test_preds==4)\n",
    "\n",
    "skill_metrics[0,1] = accuracy_score(y_test==0, test_preds==0)\n",
    "skill_metrics[1,1] = accuracy_score(y_test==1, test_preds==1)\n",
    "skill_metrics[2,1] = accuracy_score(y_test==2, test_preds==2)\n",
    "skill_metrics[3,1] = accuracy_score(y_test==3, test_preds==3)\n",
    "skill_metrics[4,1] = accuracy_score(y_test==4, test_preds==4)\n",
    "\n",
    "skill_metrics[0,2] = recall_score(y_test==0, test_preds==0)\n",
    "skill_metrics[1,2] = recall_score(y_test==1, test_preds==1)\n",
    "skill_metrics[2,2] = recall_score(y_test==2, test_preds==2)\n",
    "skill_metrics[3,2] = recall_score(y_test==3, test_preds==3)\n",
    "skill_metrics[4,2] = recall_score(y_test==4, test_preds==4)\n",
    "\n",
    "skill_metrics[0,3] = f1_score(y_test==0, test_preds==0)\n",
    "skill_metrics[1,3] = f1_score(y_test==1, test_preds==1)\n",
    "skill_metrics[2,3] = f1_score(y_test==2, test_preds==2)\n",
    "skill_metrics[3,3] = f1_score(y_test==3, test_preds==3)\n",
    "skill_metrics[4,3] = f1_score(y_test==4, test_preds==4)\n",
    "\n",
    "skill_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5bbceb4f-8e44-42e4-ae59-ba4eb2459fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "plt.imshow(skill_metrics, cmap='coolwarm', interpolation='nearest')\n",
    "plt.xticks(ticks = np.arange(4),labels=['precision','accuracy','recall','f1-score'])\n",
    "plt.yticks(ticks = np.arange(5),labels=['ND','NC','CR','UL','KL'])\n",
    "plt.colorbar()\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "db30046e-1aec-471e-805b-f3b8fd432918",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get probabilities for a single disturbance type\n",
    "y_probs_knn = knn_model.predict_proba(X_test)[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "83158bc9-1469-4e93-b572-3cceb96aba07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create binary array for occurrences\n",
    "y_binary = np.zeros(shape=(len(X_test)))\n",
    "\n",
    "true = np.where(y_test == 4)\n",
    "y_binary[true] = 1\n",
    "y_true = y_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4fe8cfaf-795e-4391-819d-2225ca0d2ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of probabilites from the null model\n",
    "y_probs_reference = np.zeros(shape=(len(X_test)))\n",
    "\n",
    "nd = 0.69\n",
    "nc = 0.08\n",
    "cr = 0.13\n",
    "kl = 0.05\n",
    "ul = 0.04\n",
    "\n",
    "y_probs_reference[:] = kl # This is just the climatological freq of a disturbance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "9ba4f9d6-f3bc-48db-b449-ed717bd7d09d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5690439211406344"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import brier_score_loss\n",
    "\n",
    "# Calculate Brier scores\n",
    "bs_knn = brier_score_loss(y_true, y_probs_knn)\n",
    "bs_reference = brier_score_loss(y_true, y_probs_reference)\n",
    "\n",
    "# Calculate Brier Skill Score\n",
    "bss = 1 - (bs_knn / bs_reference)\n",
    "bss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae086553-d32e-4c66-9099-86ea42321cde",
   "metadata": {},
   "source": [
    "# How well does the knn model represent the 'truth'?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e8679fea-8e6f-4196-aefd-558abc08b5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = knn_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "63365ced-0d55-421e-b0ee-ed47ea910778",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_ND = np.where(preds == 0)\n",
    "pred_NC = np.where(preds == 1)\n",
    "pred_CR = np.where(preds == 2)\n",
    "pred_UL = np.where(preds == 3)\n",
    "pred_KL = np.where(preds == 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5cf720d7-9b0a-4cf7-90ac-a66da718b10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ND_pattern = np.mean(u_train[pred_ND],axis=0)\n",
    "NC_pattern = np.mean(u_train[pred_NC],axis=0)\n",
    "CR_pattern = np.mean(u_train[pred_CR],axis=0)\n",
    "UL_pattern = np.mean(u_train[pred_UL],axis=0)\n",
    "KL_pattern = np.mean(u_train[pred_KL],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b62eda58-6eb2-4429-8265-b56bbe43dda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ND_pred = np.reshape(ND_pattern, (401*681), order = 'F')\n",
    "NC_pred = np.reshape(NC_pattern, (401*681), order = 'F')\n",
    "CR_pred = np.reshape(CR_pattern, (401*681), order = 'F')\n",
    "UL_pred = np.reshape(UL_pattern, (401*681), order = 'F')\n",
    "KL_pred = np.reshape(KL_pattern, (401*681), order = 'F')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e8a0c9-1688-4a67-8182-1d2ac46583fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ND:  0.959786846290852\n",
      "NC:  0.9682873811437834\n",
      "CR:  0.9712866350904812\n",
      "UL:  0.9676787664968587\n",
      "KL:  0.98682945612947\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "print('ND: ',scipy.stats.pearsonr(ND_pred, ND_truth).statistic)\n",
    "print('NC: ',scipy.stats.pearsonr(NC_pred, NC_truth).statistic)\n",
    "print('CR: ',scipy.stats.pearsonr(CR_pred, CR_truth).statistic)\n",
    "print('UL: ',scipy.stats.pearsonr(UL_pred, UL_truth).statistic)\n",
    "print('KL: ',scipy.stats.pearsonr(KL_pred, KL_truth).statistic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5500cc-a124-4689-8557-eeb65126345a",
   "metadata": {},
   "source": [
    "# Get skill metrics for train/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "38211a3a-f5ac-43a2-b1eb-67b6ff18b978",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9514301468126054\n",
      "0.778706119899655\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "print(precision_score(y_train, train_preds, average='weighted'))\n",
    "print(precision_score(y_test, test_preds, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a17e62a9-7d44-4dc8-a2e8-cfa076bb9fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9510512483574245\n",
      "0.7690288713910761\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(y_train, train_preds))\n",
    "print(accuracy_score(y_test, test_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "bbe6be78-f83b-488a-a2cf-21ab383cc10a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9510512483574245\n",
      "0.7690288713910761\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "print(recall_score(y_train, train_preds, average='weighted'))\n",
    "print(recall_score(y_test, test_preds, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f260dd3b-7496-42c9-a466-a1fcfd627240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9502490583245901\n",
      "0.7408807086726357\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "print(f1_score(y_train, train_preds, average='weighted'))\n",
    "print(f1_score(y_test, test_preds, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7a1f717-c7c3-45e7-91de-91e78d481ff8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1a67cfc-8795-4116-93c2-7dd86c046503",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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