transformer for TimeSeries regression

regression.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95bc4378-b4ad-433c-8bf1-f96a99cee8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -Uqq tsai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "51571e98-a734-494c-b54d-a1273f21d18f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torch import nn\n",
    "from fastai.data.core import DataLoader, DataLoaders\n",
    "from fastai.learner import Learner\n",
    "from fastai.losses import LabelSmoothingCrossEntropyFlat, MSELossFlat\n",
    "from fastai.metrics import RocAucBinary, accuracy, rmse\n",
    "from fastai.torch_core import Module\n",
    "from tsai.models.TST import TST\n",
    "from tsai.models.RNN import LSTM\n",
    "from tsai.data.external import get_UCR_data, get_Monash_regression_data as get_regression_data\n",
    "from tsai.callback.core import ShowGraph as ShowGraphCallback2\n",
    "from tsai.learner import plot_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9d196866-2fc7-447c-8b3f-32c625e65e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = get_regression_data('AppliancesEnergy', split_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "abf6aea3-54b1-404d-a702-efc9b37d9315",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data[0]\n",
    "y_train = data[1]\n",
    "X_valid = data[2]\n",
    "y_valid = data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "38434bec-1414-44b5-9f46-46501f3c8392",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TSDataset(Dataset):\n",
    "    \"\"\"TimeSeries DataSet\"\"\"\n",
    "    def __init__(self, X, y):\n",
    "        super(TSDataset, self).__init__()\n",
    "        self.X = torch.tensor(X)\n",
    "        self.Y = torch.tensor(y)\n",
    "    \n",
    "    def __len__(self): return len(self.X)\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        return self.X[i], self.Y[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2cf65cbb-7082-4f46-9665-34c88e981ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size, c_in, c_out, seq_len = 64, 24, 1, 144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4dc82eb5-4479-4ad2-8a91-c8d7ebca3189",
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_train = TSDataset(X_train, y_train)\n",
    "dset_valid = TSDataset(X_valid, y_valid)\n",
    "\n",
    "dl_train = DataLoader(dset_train, batch_size=batch_size, shuffle=True)\n",
    "dl_valid = DataLoader(dset_valid, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "dls = DataLoaders(dl_train, dl_valid) \n",
    "dls = dls.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b97ff569-f0b1-4911-960d-b5ea2c56f7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = next(iter(dl_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e57c2da5-0fd3-4f2d-b141-cd7203656fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([64, 24, 144]), torch.Size([64]))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b36beeb2-9f78-4b16-9efd-83e566b2e7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OurTST(Module):\n",
    "    def __init__(self, c_in, c_out, seq_len):\n",
    "        self.c_in, self.c_out, self.seq_len = c_in, c_out, seq_len\n",
    "        self.encoder = nn.MultiheadAttention(embed_dim=c_in, num_heads=1, batch_first=True)\n",
    "        self.head = nn.Linear(seq_len*c_in, c_out)\n",
    "    def forward(self, x):\n",
    "        o = x.swapaxes(1,2)\n",
    "        o = self.encoder(o,o,o)[0]\n",
    "        o = o.reshape(o.shape[0], -1)\n",
    "        o = self.head(o)\n",
    "        return o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d635561c-65f0-4254-ae4e-83564a3a5d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = OurTST(c_in, c_out, seq_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c7016e4e-8489-4691-9e96-d3713402a379",
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(dls, model, loss_func=MSELossFlat(), \n",
    "                    metrics=rmse,  cbs=ShowGraphCallback2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0c4b6f3f-da1a-41a3-8bd7-e45ad8dd12ab",
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "51a6a32c-b79e-4789-821c-103ffe52e659",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model):\n",
    "    learn = Learner(dls, model, loss_func=MSELossFlat(), \n",
    "                    metrics=rmse,  cbs=ShowGraph())\n",
    "    learn.fit_one_cycle(50, 1e-4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e43fc3d8-3f36-4544-b909-116e51e894bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>_rmse</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1564.331909</td>\n",
       "      <td>1447.761353</td>\n",
       "      <td>38.049458</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1525.897705</td>\n",
       "      <td>1307.784790</td>\n",
       "      <td>36.163307</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1452.980591</td>\n",
       "      <td>1086.628418</td>\n",
       "      <td>32.964050</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1351.157104</td>\n",
       "      <td>784.179321</td>\n",
       "      <td>28.003202</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1220.855591</td>\n",
       "      <td>444.824829</td>\n",
       "      <td>21.090870</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1067.047607</td>\n",
       "      <td>154.653015</td>\n",
       "      <td>12.435957</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>915.344360</td>\n",
       "      <td>16.154976</td>\n",
       "      <td>4.019326</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>787.792725</td>\n",
       "      <td>76.567673</td>\n",
       "      <td>8.750296</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>700.954651</td>\n",
       "      <td>243.951782</td>\n",
       "      <td>15.618955</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>646.997375</td>\n",
       "      <td>322.248199</td>\n",
       "      <td>17.951271</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>608.785339</td>\n",
       "      <td>227.979034</td>\n",
       "      <td>15.098974</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>564.889771</td>\n",
       "      <td>74.408463</td>\n",
       "      <td>8.626034</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>515.373169</td>\n",
       "      <td>12.771502</td>\n",
       "      <td>3.573724</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>470.886230</td>\n",
       "      <td>41.395557</td>\n",
       "      <td>6.433938</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>436.424866</td>\n",
       "      <td>65.354439</td>\n",
       "      <td>8.084208</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>406.510651</td>\n",
       "      <td>44.978752</td>\n",
       "      <td>6.706620</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>378.299713</td>\n",
       "      <td>17.701365</td>\n",
       "      <td>4.207299</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>351.404022</td>\n",
       "      <td>14.546522</td>\n",
       "      <td>3.813990</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>327.208710</td>\n",
       "      <td>26.894241</td>\n",
       "      <td>5.185966</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>306.325928</td>\n",
       "      <td>31.844805</td>\n",
       "      <td>5.643120</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>287.544250</td>\n",
       "      <td>23.944973</td>\n",
       "      <td>4.893360</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>270.032196</td>\n",
       "      <td>15.194571</td>\n",
       "      <td>3.898021</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>253.634171</td>\n",
       "      <td>12.485308</td>\n",
       "      <td>3.533456</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>238.973190</td>\n",
       "      <td>13.642256</td>\n",
       "      <td>3.693542</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>225.596481</td>\n",
       "      <td>14.258251</td>\n",
       "      <td>3.776010</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>213.230835</td>\n",
       "      <td>13.325858</td>\n",
       "      <td>3.650460</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>201.847275</td>\n",
       "      <td>12.479998</td>\n",
       "      <td>3.532704</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>191.262558</td>\n",
       "      <td>12.623893</td>\n",
       "      <td>3.553012</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>181.421783</td>\n",
       "      <td>13.291010</td>\n",
       "      <td>3.645684</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>172.361557</td>\n",
       "      <td>13.941193</td>\n",
       "      <td>3.733791</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>163.947784</td>\n",
       "      <td>14.220186</td>\n",
       "      <td>3.770966</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>156.160217</td>\n",
       "      <td>13.976988</td>\n",
       "      <td>3.738581</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>148.861664</td>\n",
       "      <td>13.585153</td>\n",
       "      <td>3.685804</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>142.082260</td>\n",
       "      <td>13.162868</td>\n",
       "      <td>3.628067</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>135.780807</td>\n",
       "      <td>12.835033</td>\n",
       "      <td>3.582602</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>129.714844</td>\n",
       "      <td>12.644345</td>\n",
       "      <td>3.555889</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>124.088997</td>\n",
       "      <td>12.520739</td>\n",
       "      <td>3.538465</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>118.846100</td>\n",
       "      <td>12.460324</td>\n",
       "      <td>3.529918</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>113.840454</td>\n",
       "      <td>12.429818</td>\n",
       "      <td>3.525595</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>109.180046</td>\n",
       "      <td>12.410569</td>\n",
       "      <td>3.522863</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>104.771690</td>\n",
       "      <td>12.404599</td>\n",
       "      <td>3.522016</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>100.686768</td>\n",
       "      <td>12.403231</td>\n",
       "      <td>3.521822</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>96.788315</td>\n",
       "      <td>12.409785</td>\n",
       "      <td>3.522752</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>93.218086</td>\n",
       "      <td>12.415319</td>\n",
       "      <td>3.523538</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>89.687485</td>\n",
       "      <td>12.419098</td>\n",
       "      <td>3.524074</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>86.434891</td>\n",
       "      <td>12.422072</td>\n",
       "      <td>3.524496</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>83.319641</td>\n",
       "      <td>12.425077</td>\n",
       "      <td>3.524922</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>80.419121</td>\n",
       "      <td>12.426616</td>\n",
       "      <td>3.525141</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>77.604324</td>\n",
       "      <td>12.427412</td>\n",
       "      <td>3.525254</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>74.950562</td>\n",
       "      <td>12.427510</td>\n",
       "      <td>3.525268</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
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