sin_time_series_SOLVED.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM Time Series Basic Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the time series training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create x, y data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(1, 1000, 1000)\n",
    "\n",
    "y1 = 1.5 * np.sin(x)\n",
    "y2 = 0.5 * np.sin(400*x)\n",
    "y3 = 15 - 0.04*x + 0.02*x**1.1\n",
    "\n",
    "y = y1 + y2 + y3\n",
    "training = y[:900]\n",
    "testing = y[900:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot x, y data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1215505f8>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Package x, y data into sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[16.7738],\n",
      "        [15.1145],\n",
      "        [13.3961],\n",
      "        [13.9441],\n",
      "        [14.3965],\n",
      "        [15.5029],\n",
      "        [16.8404],\n",
      "        [15.3514],\n",
      "        [13.6940],\n",
      "        [13.8297]])\n",
      "tensor([13.8297])\n",
      "torch.Size([890, 10, 1])\n",
      "torch.Size([890, 1])\n"
     ]
    }
   ],
   "source": [
    "k = 10\n",
    "inputs = []\n",
    "target = []\n",
    "for i in range(0, len(training)-k):\n",
    "    inputs.append(training[i:i+k].reshape(k, 1))\n",
    "    target.append([training[i+k]])\n",
    "inputs = torch.tensor(inputs, dtype=torch.float)\n",
    "target = torch.tensor(target, dtype=torch.float)\n",
    "print(inputs[1])\n",
    "print(target[0])\n",
    "print(inputs.shape)\n",
    "print(target.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build our LSTM network architecture\n",
    "<img src=\"./screenshots/lstm_architecture.jpeg\" alt=\"drawing\" width=\"800\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self, hidden_size, layer_dim=1):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "        self.lstm1 = nn.LSTM(input_size=1, hidden_size=hidden_size, num_layers=layer_dim, batch_first=True)\n",
    "        self.fc1 = nn.Linear(hidden_size, 40)\n",
    "        self.fc2 = nn.Linear(40, 1)\n",
    "        \n",
    "    def forward(self, inputs):\n",
    "        out, (hn, cn) = self.lstm1(inputs)\n",
    "        out = F.relu(self.fc1(hn.view(-1, self.hidden_size)))\n",
    "        out = self.fc2(out)\n",
    "        return out\n",
    "    \n",
    "net = LSTM(hidden_size=30, layer_dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define our loss criterion and step optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(net.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(189.7171, grad_fn=<MseLossBackward>)\n",
      "tensor(1.3533, grad_fn=<MseLossBackward>)\n",
      "tensor(1.3450, grad_fn=<MseLossBackward>)\n",
      "tensor(1.2828, grad_fn=<MseLossBackward>)\n",
      "tensor(0.5212, grad_fn=<MseLossBackward>)\n",
      "tensor(0.3744, grad_fn=<MseLossBackward>)\n",
      "tensor(0.3174, grad_fn=<MseLossBackward>)\n",
      "tensor(0.2682, grad_fn=<MseLossBackward>)\n",
      "tensor(0.2244, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0597, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0844, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0228, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0217, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0164, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0136, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0104, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0116, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0086, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0074, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0083, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0065, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0240, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0056, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0125, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0052, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0049, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0049, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0051, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0044, grad_fn=<MseLossBackward>)\n",
      "tensor(0.0108, grad_fn=<MseLossBackward>)\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(3000):  # loop over the dataset multiple times\n",
    "    optimizer.zero_grad()\n",
    "    outputs = net(inputs)\n",
    "    loss = criterion(outputs, target)\n",
    "    if epoch%100 == 0:\n",
    "        print(loss)\n",
    "    #print(loss)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate our trained Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prediction function to make predictions on our testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred(testing, look_ahead):\n",
    "    result = np.array([])\n",
    "    for i in range(look_ahead):\n",
    "        if i < len(testing):\n",
    "            test_ar = np.concatenate((testing[i:], result))\n",
    "        else:\n",
    "            test_ar = result[-10:]\n",
    "        tensor_input = torch.from_numpy(test_ar)\n",
    "        tensor_input = tensor_input.float()\n",
    "        prediction = net(tensor_input.view(1, len(testing), 1))\n",
    "        result = np.append(result, prediction.item())\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.3096962 , 15.49162197, 14.1918354 ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred(testing[20:30], 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### analyze function to plot the ground truth data against our model's predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(testing, ground_truth):\n",
    "    plt.plot(np.linspace(1, len(testing)+len(ground_truth), len(testing)+len(ground_truth)), np.concatenate((testing, ground_truth)), 'r')\n",
    "    plt.plot(np.linspace(len(testing)+1, len(testing)+len(ground_truth), len(ground_truth)), pred(testing, len(ground_truth)), 'b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ground Truth (RED) vs Prediction (Blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyze(testing[20:30], testing[30:40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}