NLP/Notebooks/6_LSTM.ipynb

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  {
   "source": [
    "## LSTM: Long Short-term Memory - 1997"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Problems with RNN:\n",
    "- Vanishing gradients  \n",
    "- Exploding Gradients"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### RNN architecture:\n",
    " <img src=\"images/rnn_arch.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### RNN problem:  \n",
    " <img src=\"images/rnn_problem.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### RNN vs LSTM\n",
    " <img src=\"images/RNN_vsLSTM.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### LSTM architecture\n",
    "\n",
    " <img src=\"images/lstm_arc.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### LSTM input gate:\n",
    " <img src=\"images/lstm_input.png\" width=\"600\"/>\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "$i_1 = \\sigma(W_{i_1} \\cdot (H_{t-1}, x_t) + bias_{i_1})$  \n",
    "$i_2 = tanh(W_{i_2} \\cdot (H_{t-1}, x_t) + bias_{i_2})$  \n",
    "$i_{input} = i_1 * i_2$"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### LSTM forget gate:\n",
    " <img src=\"images/lstm_forget.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "$f = \\sigma(W_{forget} \\cdot (H_{t-1}, x_t) + bias_{forget})$  \n",
    "$C_t = C_{t-1} * f + i_{input}$\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "#### LSTM outputt gate:\n",
    " <img src=\"images/lstm_output.png\" width=\"600\"/>"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "$O_1 = \\sigma (W_{output_1} \\cdot (H_{t-1}, x_t) + bias_{output_1})$  \n",
    "$O_2 = tanh(W_{output_2} \\cdot C_t + bias_{output_2})$  \n",
    "$H_t, O_t = O_1 * O_2$"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "df = pd.read_csv('./data/SBUX.csv', index_col = 'Date', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2019-12-11  86.260002  86.870003  85.849998  86.589996  84.145752   4921900\n",
       "2019-12-12  88.000000  88.889999  87.540001  88.209999  85.720032  10282100\n",
       "2019-12-13  88.019997  88.790001  87.580002  88.669998  86.167046   6714100\n",
       "2019-12-16  89.139999  89.300003  88.430000  88.779999  86.273941   6705600\n",
       "2019-12-17  88.870003  88.970001  87.470001  88.129997  85.642288   7296900"
      ],
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   "source": [
    "df.head(5)"
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "df['Volume'].plot(label='CLOSE', title='Star Bucks Stock Volume')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1]\n",
    "y = df.iloc[:, 5:6] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "mm = MinMaxScaler()\n",
    "ss = StandardScaler()\n",
    "\n",
    "\n",
    "X_ss = ss.fit_transform(X)\n",
    "y_mm = mm.fit_transform(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#first 200 for training\n",
    "\n",
    "X_train = X_ss[:200, :]\n",
    "X_test = X_ss[200:, :]\n",
    "\n",
    "y_train = y_mm[:200, :]\n",
    "y_test = y_mm[200:, :] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Shape (200, 5) (200, 1)\nTesting Shape (53, 5) (53, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Shape\", X_train.shape, y_train.shape)\n",
    "print(\"Testing Shape\", X_test.shape, y_test.shape) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch #pytorch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_tensors = Variable(torch.Tensor(X_train))\n",
    "X_test_tensors = Variable(torch.Tensor(X_test))\n",
    "\n",
    "y_train_tensors = Variable(torch.Tensor(y_train))\n",
    "y_test_tensors = Variable(torch.Tensor(y_test)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reshaping to rows, timestamps, features\n",
    "\n",
    "X_train_tensors_final = torch.reshape(X_train_tensors,   (X_train_tensors.shape[0], 1, X_train_tensors.shape[1]))\n",
    "\n",
    "\n",
    "X_test_tensors_final = torch.reshape(X_test_tensors,  (X_test_tensors.shape[0], 1, X_test_tensors.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training Shape torch.Size([200, 1, 5]) torch.Size([200, 1])\nTesting Shape torch.Size([53, 1, 5]) torch.Size([53, 1])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Shape\", X_train_tensors_final.shape, y_train_tensors.shape)\n",
    "print(\"Testing Shape\", X_test_tensors_final.shape, y_test_tensors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM1(nn.Module):\n",
    "    def __init__(self, num_classes, input_size, hidden_size, num_layers, seq_length):\n",
    "        super(LSTM1, self).__init__()\n",
    "        self.num_classes = num_classes #number of classes\n",
    "        self.num_layers = num_layers #number of layers\n",
    "        self.input_size = input_size #input size\n",
    "        self.hidden_size = hidden_size #hidden state\n",
    "        self.seq_length = seq_length #sequence length\n",
    "\n",
    "        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,\n",
    "                          num_layers=num_layers, batch_first=True) #lstm\n",
    "        self.fc_1 =  nn.Linear(hidden_size, 128) #fully connected 1\n",
    "        self.fc = nn.Linear(128, num_classes) #fully connected last layer\n",
    "\n",
    "        self.relu = nn.ReLU()\n",
    "        \n",
    "    def forward(self,x):\n",
    "        h_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #hidden state\n",
    "        c_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #internal state\n",
    "        # Propagate input through LSTM\n",
    "        output, (hn, cn) = self.lstm(x, (h_0, c_0)) #lstm with input, hidden, and internal state\n",
    "        hn = hn.view(-1, self.hidden_size) #reshaping the data for Dense layer next\n",
    "        out = self.relu(hn)\n",
    "        out = self.fc_1(out) #first Dense\n",
    "        out = self.relu(out) #relu\n",
    "        out = self.fc(out) #Final Output\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 1000 #1000 epochs\n",
    "learning_rate = 0.001 #0.001 lr\n",
    "\n",
    "input_size = 5 #number of features\n",
    "hidden_size = 16 #number of features in hidden state\n",
    "num_layers = 1 #number of stacked lstm layers\n",
    "\n",
    "num_classes = 1 #number of output classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm1 = LSTM1(num_classes, input_size, hidden_size, num_layers, X_train_tensors_final.shape[1]) #our lstm class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "LSTM1(\n  (lstm): LSTM(5, 16, batch_first=True)\n  (fc_1): Linear(in_features=16, out_features=128, bias=True)\n  (fc): Linear(in_features=128, out_features=1, bias=True)\n  (relu): ReLU()\n)\n"
     ]
    }
   ],
   "source": [
    "print(lstm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.MSELoss()    # mean-squared error for regression\n",
    "optimizer = torch.optim.Adam(lstm1.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch: 0, loss: 0.08815\n",
      "Epoch: 100, loss: 0.01747\n",
      "Epoch: 200, loss: 0.01574\n",
      "Epoch: 300, loss: 0.01110\n",
      "Epoch: 400, loss: 0.01015\n",
      "Epoch: 500, loss: 0.00968\n",
      "Epoch: 600, loss: 0.00927\n",
      "Epoch: 700, loss: 0.00885\n",
      "Epoch: 800, loss: 0.00848\n",
      "Epoch: 900, loss: 0.00817\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(num_epochs):\n",
    "  outputs = lstm1.forward(X_train_tensors_final) #forward pass\n",
    "  optimizer.zero_grad() #caluclate the gradient, manually setting to 0\n",
    " \n",
    "  # obtain the loss function\n",
    "  loss = criterion(outputs, y_train_tensors)\n",
    " \n",
    "  loss.backward() #calculates the loss of the loss function\n",
    " \n",
    "  optimizer.step() #improve from loss, i.e backprop\n",
    "  if epoch % 100 == 0:\n",
    "    print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X_ss = ss.transform(df.iloc[:, :-1]) #old transformers\n",
    "df_y_mm = mm.transform(df.iloc[:, -1:]) #old transformers\n",
    "\n",
    "df_X_ss = Variable(torch.Tensor(df_X_ss)) #converting to Tensors\n",
    "df_y_mm = Variable(torch.Tensor(df_y_mm))\n",
    "#reshaping the dataset\n",
    "df_X_ss = torch.reshape(df_X_ss, (df_X_ss.shape[0], 1, df_X_ss.shape[1])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "train_predict = lstm1(df_X_ss)#forward pass\n",
    "data_predict = train_predict.data.numpy() #numpy conversion\n",
    "dataY_plot = df_y_mm.data.numpy()\n",
    "\n",
    "data_predict = mm.inverse_transform(data_predict) #reverse transformation\n",
    "dataY_plot = mm.inverse_transform(dataY_plot)\n",
    "plt.figure(figsize=(10,6)) #plotting\n",
    "plt.axvline(x=200, c='r', linestyle='--') #size of the training set\n",
    "\n",
    "plt.plot(dataY_plot, label='Actuall Data') #actual plot\n",
    "plt.plot(data_predict, label='Predicted Data') #predicted plot\n",
    "plt.title('Time-Series Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}