learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c7ef96c-3d86-4b8f-a90b-786480f6243f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd7acecf-390d-4fd8-8cca-54b1a268c2a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anagrams',\n",
       " 'anscombe',\n",
       " 'attention',\n",
       " 'brain_networks',\n",
       " 'car_crashes',\n",
       " 'diamonds',\n",
       " 'dots',\n",
       " 'dowjones',\n",
       " 'exercise',\n",
       " 'flights',\n",
       " 'fmri',\n",
       " 'geyser',\n",
       " 'glue',\n",
       " 'healthexp',\n",
       " 'iris',\n",
       " 'mpg',\n",
       " 'penguins',\n",
       " 'planets',\n",
       " 'seaice',\n",
       " 'taxis',\n",
       " 'tips',\n",
       " 'titanic']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.get_dataset_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d9db61a-9126-4dc6-beb0-626486637521",
   "metadata": {},
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949</td>\n",
       "      <td>Jan</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949</td>\n",
       "      <td>Feb</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949</td>\n",
       "      <td>Mar</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949</td>\n",
       "      <td>Apr</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949</td>\n",
       "      <td>May</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year month  passengers\n",
       "0  1949   Jan         112\n",
       "1  1949   Feb         118\n",
       "2  1949   Mar         132\n",
       "3  1949   Apr         129\n",
       "4  1949   May         121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight_data = sns.load_dataset(\"flights\")\n",
    "flight_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ac168d8-d131-468d-8414-9d93b95e2045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad14d903-3a9b-438b-b468-c0a45d846142",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0] = 15\n",
    "fig_size[1] = 5\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4bf7b6db-d696-4d74-a5be-418639e34ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x177c116f0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Month vs Passenger')\n",
    "plt.ylabel('Total Passengers')\n",
    "plt.xlabel('Months')\n",
    "plt.grid(True)\n",
    "plt.autoscale(axis='x',tight=True)\n",
    "plt.plot(flight_data['passengers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11b652d9-2423-4ab5-9a97-104f467aabca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'month', 'passengers'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flight_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "591ed651-53a1-4f9c-a69f-153e1b7f5cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = flight_data['passengers'].values.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b993853a-0c55-4735-8e86-8e6fea91d9a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[112. 118. 132. 129. 121. 135. 148. 148. 136. 119. 104. 118. 115. 126.\n",
      " 141. 135. 125. 149. 170. 170. 158. 133. 114. 140. 145. 150. 178. 163.\n",
      " 172. 178. 199. 199. 184. 162. 146. 166. 171. 180. 193. 181. 183. 218.\n",
      " 230. 242. 209. 191. 172. 194. 196. 196. 236. 235. 229. 243. 264. 272.\n",
      " 237. 211. 180. 201. 204. 188. 235. 227. 234. 264. 302. 293. 259. 229.\n",
      " 203. 229. 242. 233. 267. 269. 270. 315. 364. 347. 312. 274. 237. 278.\n",
      " 284. 277. 317. 313. 318. 374. 413. 405. 355. 306. 271. 306. 315. 301.\n",
      " 356. 348. 355. 422. 465. 467. 404. 347. 305. 336. 340. 318. 362. 348.\n",
      " 363. 435. 491. 505. 404. 359. 310. 337. 360. 342. 406. 396. 420. 472.\n",
      " 548. 559. 463. 407. 362. 405. 417. 391. 419. 461. 472. 535. 622. 606.\n",
      " 508. 461. 390. 432.]\n"
     ]
    }
   ],
   "source": [
    "print(all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe3c097c-6c05-481a-a608-e0c12080c82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_size = 12\n",
    "\n",
    "train_data = all_data[:-test_data_size]\n",
    "test_data = all_data[-test_data_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09fac8e6-8bae-4ded-bf52-ccfae0ad2e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "132\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data))\n",
    "print(len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9418c6af-4f34-4bfd-af60-7ec0257857f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "train_data_normalized = scaler.fit_transform(train_data .reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "942530af-73f0-4684-b897-96802cc6157e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.96483516]\n",
      " [-0.93846154]\n",
      " [-0.87692308]\n",
      " [-0.89010989]\n",
      " [-0.92527473]]\n",
      "[[1.        ]\n",
      " [0.57802198]\n",
      " [0.33186813]\n",
      " [0.13406593]\n",
      " [0.32307692]]\n"
     ]
    }
   ],
   "source": [
    "print(train_data_normalized[:5])\n",
    "print(train_data_normalized[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6807236b-4ea6-47bc-9e56-8dfc1e0e606e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_normalized = torch.FloatTensor(train_data_normalized).view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d30f41d-efba-495b-bc62-78da5f45fccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_window = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae377c04-0b50-40b4-be79-88f77c15f6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_inout_sequences(input_data, tw):\n",
    "    inout_seq = []\n",
    "    L = len(input_data)\n",
    "    for i in range(L-tw):\n",
    "        train_seq = input_data[i:i+tw]\n",
    "        train_label = input_data[i+tw:i+tw+1]\n",
    "        inout_seq.append((train_seq ,train_label))\n",
    "    return inout_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e54dc25f-4ea2-41a6-996a-e97c069f30b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inout_seq = create_inout_sequences(train_data_normalized, train_window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3a5f350f-91d3-49cf-9181-7d59acf87e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor([-0.9648, -0.9385, -0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066,\n",
       "          -0.8593, -0.9341, -1.0000, -0.9385]),\n",
       "  tensor([-0.9516])),\n",
       " (tensor([-0.9385, -0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593,\n",
       "          -0.9341, -1.0000, -0.9385, -0.9516]),\n",
       "  tensor([-0.9033])),\n",
       " (tensor([-0.8769, -0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341,\n",
       "          -1.0000, -0.9385, -0.9516, -0.9033]),\n",
       "  tensor([-0.8374])),\n",
       " (tensor([-0.8901, -0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341, -1.0000,\n",
       "          -0.9385, -0.9516, -0.9033, -0.8374]),\n",
       "  tensor([-0.8637])),\n",
       " (tensor([-0.9253, -0.8637, -0.8066, -0.8066, -0.8593, -0.9341, -1.0000, -0.9385,\n",
       "          -0.9516, -0.9033, -0.8374, -0.8637]),\n",
       "  tensor([-0.9077]))]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inout_seq[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6d322c8-d311-4464-b3c5-3084c2e983d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "\n",
    "        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n",
    "\n",
    "    def forward(self, input_seq):\n",
    "        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq) ,1, -1), self.hidden_cell)\n",
    "        predictions = self.linear(lstm_out.view(len(input_seq), -1))\n",
    "        return predictions[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "710d5a1e-0825-4a86-a852-c6590759fc02",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LSTM()\n",
    "model.add_module('linear',nn.Linear(100,1))\n",
    "loss_function = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab861e6f-f141-4a64-9324-e2f4685c5dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSTM(\n",
      "  (lstm): LSTM(1, 100)\n",
      "  (linear): Linear(in_features=100, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f076a95-28ad-4788-9a83-906a91fd9fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1 loss: 0.00620962\n",
      "epoch:  26 loss: 0.00171014\n",
      "epoch:  51 loss: 0.00441343\n",
      "epoch:  76 loss: 0.00034941\n",
      "epoch: 101 loss: 0.00581620\n",
      "epoch: 126 loss: 0.00044333\n",
      "epoch: 149 loss: 0.0022045304\n"
     ]
    }
   ],
   "source": [
    "epochs = 150\n",
    "\n",
    "for i in range(epochs):\n",
    "    for seq, labels in train_inout_seq:\n",
    "        optimizer.zero_grad()\n",
    "        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),\n",
    "                        torch.zeros(1, 1, model.hidden_layer_size))\n",
    "\n",
    "        y_pred = model(seq)\n",
    "\n",
    "        single_loss = loss_function(y_pred, labels)\n",
    "        single_loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    if i%25 == 1:\n",
    "        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')\n",
    "\n",
    "print(f'epoch: {i:3} loss: {single_loss.item():10.10f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e8f1ca6-9558-4853-9910-37072d6bb8f4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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