t81_558_class_10_5_temporal_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_10_5_temporal_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 10: Time Series in Keras**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 10 Material\n",
    "\n",
    "* Part 10.1: Time Series Data Encoding for Deep Learning [[Video]](https://www.youtube.com/watch?v=dMUmHsktl04&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_1_timeseries.ipynb)\n",
    "* Part 10.2: Programming LSTM with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=wY0dyFgNCgY&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_2_lstm.ipynb)\n",
    "* Part 10.3: Text Generation with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=6ORnRAz3gnA&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_3_text_generation.ipynb)\n",
    "* Part 10.4: Image Captioning with Keras and TensorFlow [[Video]](https://www.youtube.com/watch?v=NmoW_AYWkb4&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_4_captioning.ipynb)\n",
    "* **Part 10.5: Temporal CNN in Keras and TensorFlow** [[Video]](https://www.youtube.com/watch?v=i390g8acZwk&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_10_5_temporal_cnn.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow.\n",
    "  Running the following code will map your GDrive to ```/content/drive```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: not using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 10.5: Temporal CNN in Keras and TensorFlow\n",
    "\n",
    "Traditionally, we use Convolutional Neural Networks (CNNs) for image classification problems and Long Short Term Memory (LSTM) networks for time series.  However, recent research has shown CNN's to be beneficial at time series problems. We begin similarly to how we started for LSTM by attempting to classify simple numeric sequences.  The data preparation is nearly identical to LSTM; however, the neural network consists of a **Conv1D** layer this time around.  The following code prepares the data and constructs the neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0. 1. 0. 0.]]\n",
      "\n",
      " [[0. 0. 1. 0.]]\n",
      "\n",
      " [[0. 0. 0. 1.]]\n",
      "\n",
      " [[0. 0. 1. 0.]]\n",
      "\n",
      " [[0. 0. 0. 1.]]\n",
      "\n",
      " [[0. 1. 0. 0.]]]\n",
      "Build model...\n",
      "Train...\n",
      "Train on 6 samples\n",
      "Epoch 1/200\n",
      "6/6 [==============================] - 1s 240ms/sample - loss: 0.7315 - accuracy: 0.3333\n",
      "Epoch 2/200\n",
      "6/6 [==============================] - 0s 583us/sample - loss: 0.7166 - accuracy: 0.4167\n",
      "Epoch 3/200\n",
      "6/6 [==============================] - 0s 583us/sample - loss: 0.7021 - accuracy: 0.5833\n",
      "Epoch 4/200\n",
      "6/6 [==============================] - 0s 584us/sample - loss: 0.6882 - accuracy: 0.5833\n",
      "Epoch 5/200\n",
      "6/6 [==============================] - 0s 583us/sample - loss: 0.6746 - accuracy: 0.6667\n",
      "Epoch 6/200\n",
      "6/6 [==============================] - 0s 583us/sample - loss: 0.6616 - accuracy: 0.7917\n",
      "Epoch 7/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.6489 - accuracy: 0.7917\n",
      "Epoch 8/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.6367 - accuracy: 0.7500\n",
      "Epoch 9/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.6250 - accuracy: 0.7500\n",
      "Epoch 10/200\n",
      "6/6 [==============================] - 0s 667us/sample - loss: 0.6136 - accuracy: 0.7917\n",
      "Epoch 11/200\n",
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      "Epoch 12/200\n",
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      "Epoch 13/200\n",
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      "Epoch 14/200\n",
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      "Epoch 15/200\n",
      "6/6 [==============================] - 0s 667us/sample - loss: 0.5629 - accuracy: 0.7917\n",
      "Epoch 16/200\n",
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      "Epoch 17/200\n",
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      "Epoch 18/200\n",
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      "Epoch 19/200\n",
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      "Epoch 20/200\n",
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      "Epoch 21/200\n",
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      "Epoch 22/200\n",
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      "Epoch 23/200\n",
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      "Epoch 24/200\n",
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      "Epoch 25/200\n",
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      "Epoch 26/200\n",
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      "Epoch 27/200\n",
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      "Epoch 28/200\n",
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      "Epoch 29/200\n",
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      "Epoch 30/200\n",
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      "Epoch 31/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.4516 - accuracy: 0.8333\n",
      "Epoch 32/200\n",
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      "Epoch 39/200\n",
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      "Epoch 40/200\n",
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      "Epoch 50/200\n",
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      "Epoch 51/200\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 80/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.2895 - accuracy: 0.8750\n",
      "Epoch 81/200\n",
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      "Epoch 87/200\n",
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      "Epoch 89/200\n",
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      "Epoch 90/200\n",
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      "Epoch 102/200\n",
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      "Epoch 103/200\n",
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      "Epoch 105/200\n",
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      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
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      "Epoch 113/200\n",
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      "Epoch 114/200\n",
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      "Epoch 115/200\n",
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      "Epoch 120/200\n",
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      "Epoch 121/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
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      "Epoch 129/200\n",
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      "Epoch 130/200\n",
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      "Epoch 131/200\n",
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      "6/6 [==============================] - 0s 416us/sample - loss: 0.1885 - accuracy: 0.9583\n",
      "Epoch 143/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1873 - accuracy: 0.9583\n",
      "Epoch 144/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1861 - accuracy: 0.9583\n",
      "Epoch 145/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1849 - accuracy: 0.9583\n",
      "Epoch 146/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1837 - accuracy: 0.9583\n",
      "Epoch 147/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1826 - accuracy: 0.9583\n",
      "Epoch 148/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1814 - accuracy: 0.9583\n",
      "Epoch 149/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1803 - accuracy: 0.9583\n",
      "Epoch 150/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1791 - accuracy: 0.9583\n",
      "Epoch 151/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1780 - accuracy: 0.9583\n",
      "Epoch 152/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1769 - accuracy: 0.9583\n",
      "Epoch 153/200\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.1757 - accuracy: 0.9583\n",
      "Epoch 154/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1746 - accuracy: 0.9583\n",
      "Epoch 155/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1735 - accuracy: 0.9583\n",
      "Epoch 156/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1724 - accuracy: 0.9583\n",
      "Epoch 157/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1713 - accuracy: 0.9583\n",
      "Epoch 158/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1702 - accuracy: 0.9583\n",
      "Epoch 159/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1691 - accuracy: 0.9583\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 160/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1680 - accuracy: 0.9583\n",
      "Epoch 161/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1669 - accuracy: 0.9583\n",
      "Epoch 162/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1658 - accuracy: 0.9583\n",
      "Epoch 163/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1647 - accuracy: 0.9583\n",
      "Epoch 164/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1636 - accuracy: 0.9583\n",
      "Epoch 165/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1626 - accuracy: 0.9583\n",
      "Epoch 166/200\n",
      "6/6 [==============================] - 0s 583us/sample - loss: 0.1615 - accuracy: 0.9583\n",
      "Epoch 167/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1605 - accuracy: 0.9583\n",
      "Epoch 168/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1594 - accuracy: 0.9583\n",
      "Epoch 169/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1583 - accuracy: 0.9583\n",
      "Epoch 170/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1573 - accuracy: 0.9583\n",
      "Epoch 171/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1562 - accuracy: 0.9583\n",
      "Epoch 172/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1552 - accuracy: 0.9583\n",
      "Epoch 173/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1542 - accuracy: 0.9583\n",
      "Epoch 174/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1531 - accuracy: 0.9583\n",
      "Epoch 175/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1521 - accuracy: 0.9583\n",
      "Epoch 176/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1511 - accuracy: 0.9583\n",
      "Epoch 177/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1501 - accuracy: 0.9583\n",
      "Epoch 178/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1490 - accuracy: 0.9583\n",
      "Epoch 179/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1480 - accuracy: 0.9583\n",
      "Epoch 180/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1470 - accuracy: 0.9583\n",
      "Epoch 181/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1460 - accuracy: 0.9583\n",
      "Epoch 182/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1450 - accuracy: 0.9583\n",
      "Epoch 183/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1440 - accuracy: 0.9583\n",
      "Epoch 184/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1430 - accuracy: 0.9583\n",
      "Epoch 185/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1420 - accuracy: 0.9583\n",
      "Epoch 186/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1410 - accuracy: 0.9583\n",
      "Epoch 187/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1401 - accuracy: 0.9583\n",
      "Epoch 188/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1391 - accuracy: 0.9583\n",
      "Epoch 189/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1381 - accuracy: 0.9583\n",
      "Epoch 190/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1371 - accuracy: 0.9583\n",
      "Epoch 191/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1362 - accuracy: 0.9583\n",
      "Epoch 192/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1352 - accuracy: 0.9583\n",
      "Epoch 193/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1342 - accuracy: 0.9583\n",
      "Epoch 194/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1333 - accuracy: 0.9583\n",
      "Epoch 195/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1323 - accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "6/6 [==============================] - 0s 584us/sample - loss: 0.1314 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "6/6 [==============================] - 0s 500us/sample - loss: 0.1304 - accuracy: 1.0000\n",
      "Epoch 198/200\n",
      "6/6 [==============================] - 0s 417us/sample - loss: 0.1295 - accuracy: 1.0000\n",
      "Epoch 199/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1286 - accuracy: 1.0000\n",
      "Epoch 200/200\n",
      "6/6 [==============================] - 0s 416us/sample - loss: 0.1276 - accuracy: 1.0000\n",
      "Predicted classes: {} [[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n",
      "Expected classes: {} [[0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]\n",
      " [0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D\n",
    "import numpy as np\n",
    "\n",
    "max_features = 4 # 0,1,2,3 (total of 4)\n",
    "x = [\n",
    "    [[0],[1],[1],[0],[0],[0]],\n",
    "    [[0],[0],[0],[2],[2],[0]],\n",
    "    [[0],[0],[0],[0],[3],[3]],\n",
    "    [[0],[2],[2],[0],[0],[0]],\n",
    "    [[0],[0],[3],[3],[0],[0]],\n",
    "    [[0],[0],[0],[0],[1],[1]]\n",
    "]\n",
    "x = np.array(x,dtype=np.float32)\n",
    "y = np.array([1,2,3,2,3,1],dtype=np.int32)\n",
    "\n",
    "# Convert y2 to dummy variables\n",
    "y2 = np.zeros((y.shape[0], max_features),dtype=np.float32)\n",
    "y2[np.arange(y.shape[0]), y] = 1.0\n",
    "y2 = np.asarray(y2).astype('float32').reshape((-1,1,4))\n",
    "print(y2)\n",
    "\n",
    "print('Build model...')\n",
    "model = Sequential()\n",
    "model.add(Conv1D(128, kernel_size=x.shape[1], input_shape=(None, 1)))\n",
    "model.add(Dense(4, activation='sigmoid'))\n",
    "\n",
    "# try using different optimizers and different optimizer configs\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "print('Train...')\n",
    "model.fit(x,y2,epochs=200)\n",
    "pred = model.predict(x)\n",
    "predict_classes = np.argmax(pred,axis=1)\n",
    "print(\"Predicted classes: {}\",predict_classes)\n",
    "print(\"Expected classes: {}\",predict_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the model is trained, we will run it with sample input and see that it classifies correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "def runit(model, inp):\n",
    "    inp = np.array(inp,dtype=np.float32)\n",
    "    pred = model.predict(inp)\n",
    "    return np.argmax(pred[0])\n",
    "\n",
    "print( runit( model, [[[0],[1],[1],[0],[0],[0]]] ))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sun Spots Example - CNN\n",
    "\n",
    "We now look at an example of CNN regression to predict sunspots.  You can find the data files needed for this example at the following location.\n",
    "\n",
    "* [Sunspot Data Files](http://www.sidc.be/silso/datafiles#total)\n",
    "* [Download Daily Sunspots](http://www.sidc.be/silso/INFO/sndtotcsv.php) - 1/1/1818 to now.\n",
    "\n",
    "We use the following code to load the sunspot file:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File ./data/SN_d_tot_V2.0.csv does not exist: './data/SN_d_tot_V2.0.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-3c098c02c529>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m          'sn_error', 'obs_num']\n\u001b[0;32m     11\u001b[0m df = pd.read_csv(filename,sep=';',header=None,names=names,\n\u001b[1;32m---> 12\u001b[1;33m                  na_values=['-1'], index_col=False)\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Starting file:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File ./data/SN_d_tot_V2.0.csv does not exist: './data/SN_d_tot_V2.0.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# Replacce the following path with your own file. It can be downloaded from:\n",
    "# http://www.sidc.be/silso/INFO/sndtotcsv.php\n",
    "path = \"./data/\"\n",
    "    \n",
    "filename = os.path.join(path,\"SN_d_tot_V2.0.csv\")    \n",
    "names = ['year', 'month', 'day', 'dec_year', 'sn_value' , \n",
    "         'sn_error', 'obs_num']\n",
    "df = pd.read_csv(filename,sep=';',header=None,names=names,\n",
    "                 na_values=['-1'], index_col=False)\n",
    "\n",
    "print(\"Starting file:\")\n",
    "print(df[0:10])\n",
    "\n",
    "print(\"Ending file:\")\n",
    "print(df[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is quite a bit of missing data near the end of the file.  We want to find the starting index where the missing data no longer occurs.  This approach is somewhat sloppy; it would be better to find a use for the data between missing values.  However, the point of this example is to show how to use CNN with a somewhat simple time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_id = max(df[df['obs_num'] == 0].index.tolist())+1  # Find the last zero and move one beyond\n",
    "print(start_id)\n",
    "df = df[start_id:] # Trim the rows that have missing observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we break the data into test and training sets.  We will train the neural network in the years before 2000 and test it with years after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['sn_value'] = df['sn_value'].astype(float)\n",
    "df_train = df[df['year']<2000]\n",
    "df_test = df[df['year']>=2000]\n",
    "\n",
    "spots_train = df_train['sn_value'].tolist()\n",
    "spots_test = df_test['sn_value'].tolist()\n",
    "\n",
    "print(\"Training set has {} observations.\".format(len(spots_train)))\n",
    "print(\"Test set has {} observations.\".format(len(spots_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will make use of the same **to_sequences** function previously used in this course.  This function allows us to convert the training and test data into the 2D sequences that will train the neural network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def to_sequences(seq_size, obs):\n",
    "    x = []\n",
    "    y = []\n",
    "\n",
    "    for i in range(len(obs)-SEQUENCE_SIZE-1):\n",
    "        #print(i)\n",
    "        window = obs[i:(i+SEQUENCE_SIZE)]\n",
    "        after_window = obs[i+SEQUENCE_SIZE]\n",
    "        window = [[x] for x in window]\n",
    "        #print(\"{} - {}\".format(window,after_window))\n",
    "        x.append(window)\n",
    "        y.append(after_window)\n",
    "        \n",
    "    return np.array(x),np.array(y)\n",
    "    \n",
    "    \n",
    "SEQUENCE_SIZE = 25\n",
    "x_train,y_train = to_sequences(SEQUENCE_SIZE,spots_train)\n",
    "x_test,y_test = to_sequences(SEQUENCE_SIZE,spots_test)\n",
    "\n",
    "print(\"Shape of training set: {}\".format(x_train.shape))\n",
    "print(\"Shape of test set: {}\".format(x_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display the training data, which is essentially a 2D matrix where each row is a sequence of the same length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to build and train the neural network.  This code is similar to the previous classification example; however, now we are performing regression.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import sequence\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, \n",
    "from tensorflow.keras.layers import Dropout, MaxPooling1D, Flatten\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import numpy as np\n",
    "\n",
    "print('Build model...')\n",
    "model = Sequential()\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=64, kernel_size=3, activation='relu', \n",
    "                 input_shape=(SEQUENCE_SIZE,1)))\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=50, \n",
    "                        verbose=1, mode='auto', restore_best_weights=True)\n",
    "print('Train...')\n",
    "\n",
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),\n",
    "          callbacks=[monitor],verbose=2,epochs=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we evaluate the accuracy of the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# from sklearn import metrics\n",
    "pred = model.predict(x_test)\n",
    "score = np.sqrt(metrics.mean_squared_error(pred.flatten(),y_test))\n",
    "print(\"Score (RMSE): {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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