04b-time-series-prediction-answers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sequential data modelling\n",
    "In this notebook we will work with the Jena weather dataset. \"The dataset is recorded at the Weather Station at the Max Planck Institute for Biogeochemistry in Jena, Germany.\" [source](https://www.kaggle.com/pankrzysiu/weather-archive-jena). The data is collected over the years 2009-2016. Using this dataset we will try to predict the temperature in the future.\n",
    "\n",
    "We will start by preprocessing the data, reshape it, scale it, and split it to train/val/test. The reshaping step is new and very common when dealing with sequential data.\n",
    "\n",
    "After preprocessing the data we will create a baseline model (dense network) and see how well it performs on this data.\n",
    "\n",
    "As usual we start by loading our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(keras)\n",
    "library(ggplot2)\n",
    "source(\"04-helpers.R\")\n",
    "\n",
    "use_multi_cpu() # Remove this line if you are running in a local environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  `Date Time` = col_character(),\n",
      "  `p (mbar)` = col_double(),\n",
      "  `T (degC)` = col_double(),\n",
      "  `Tpot (K)` = col_double(),\n",
      "  `Tdew (degC)` = col_double(),\n",
      "  `rh (%)` = col_double(),\n",
      "  `VPmax (mbar)` = col_double(),\n",
      "  `VPact (mbar)` = col_double(),\n",
      "  `VPdef (mbar)` = col_double(),\n",
      "  `sh (g/kg)` = col_double(),\n",
      "  `H2OC (mmol/mol)` = col_double(),\n",
      "  `rho (g/m**3)` = col_double(),\n",
      "  `wv (m/s)` = col_double(),\n",
      "  `max. wv (m/s)` = col_double(),\n",
      "  `wd (deg)` = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "data <- load_jena_dataset() # This will download the dataset into a folder \"data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 1\n",
    "As usual, explore the data a bit. What are the names of the columns/features? How many examples do we have?\n",
    "\n",
    "**Hint** these column names have spaces in the name so refering to them using `data$col a` will not work, instead do `data$\"col a\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names(data)\n",
    "dim(data)\n",
    "length(data$\"T (degC)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to predict the temperature, `T (degC)`. Let us plot that feature and see how the temperature varies over those years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ggplot(data, aes(x = 1:nrow(data),y=`T (degC)`)) + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 2\n",
    "Looking at the plot above. How many years worth of data do we have? To answer this, just try counting the peaks. What do the peaks represent?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOUR ANSWER HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have about 8 years of data. The peaks (or valleys) are the summers (winters)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are quite many measurements. In fact, a new measurement is drawn every 10 minutes, 6 measurements and hour, 6*24=144 measurements a day. Let us plot 10 days (144 * 10) of measurements. Starting from day 60.\n",
    "\n",
    "Feel free change the start_day or the duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_day = 60 \n",
    "duration_days = 10\n",
    "measurements_per_day = 6*24\n",
    "start_index = 1 + (start_day - 1) * measurements_per_day\n",
    "end_index = start_index + duration_days * measurements_per_day\n",
    "ggplot(data[start_index:end_index,], aes(x = start_index:end_index, y = `T (degC)`)) + geom_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today we will need to spend some additional time preprocessing our data. This is because our sequential model will expect to get the data in dimensions `(examples, sequence_length, features)`. This will impact other preprocessing stages as well.\n",
    "\n",
    "Let us start with a basic step, removing features which we will not use and change our data object to a matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 3.1\n",
    "Remove the `Date Time` feature from `data` and store the result in a variable called `features`. Verify that you have done this correctly by calling `names()` on `features` and check the `dim()`. The data should have dimension `(420551, 14)`.\n",
    "\n",
    "**Hint** `Date Time` is the first column in `data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<ol class=list-inline>\n",
       "\t<li>420551</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 420551\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 420551\n",
       "2. 14\n",
       "\n",
       "\n"
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       "[1] 420551     14"
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     "metadata": {},
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   ],
   "source": [
    "features <- data[,-1]\n",
    "dim(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 3.2\n",
    "Now let us change `features` into a matrix. The type is important for our reshape step.. First check the type of `features` using the `typeof()` function. Then map `features` to a new variable `feature_matrix` by using the function `as.matrix()`. Verify that it has type `double`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": 4,
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       "'list'"
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       "'double'"
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       "'double'"
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   ],
   "source": [
    "typeof(features)\n",
    "feature_matrix <- as.matrix(features)\n",
    "typeof(feature_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping\n",
    "Now `feature_matrix` is of the correct type and contains all our features. Now we need to \"reshape\" this matrix (2D tensor) to an array (3D tensor). That is we need to change the dimensions from `(examples, features)` to `(examples, sequence_length, features)`. This is because sequence models expect the input to have shape `(examples, sequence_length, features)`. We need to create sequences out of multiple examples and we do this by reshaping our data. Let us see how the [`reshape()`](https://rstudio.github.io/reticulate/reference/array_reshape.html) function works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 4.1\n",
    "Reshape the vector (1D tensor) `vec` to a `(2,2)` matrix (2D tensor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec <- 1:4\n",
    "mat <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec <- 1:4\n",
    "vec\n",
    "mat <- array_reshape(vec, c(2, 2))\n",
    "mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reshape function expects our inputs to fit perfectly into the new tensor. We can not reshape the vector above to a tensor with dimensions `(2,2,2)` as it requires 8 values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 4.2\n",
    "Create a vector (1D tensor) with 8 elements (a sequence like above) and reshape it into a 3D tensor called `tensor`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec <- ?\n",
    "tensor <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec <- 1:8\n",
    "tensor <- array_reshape(vec, c(2, 2, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to verify that your solution worked and notice the ordering of elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor[1,,]\n",
    "tensor[2,,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to our task. Our data has the shape `(examples, features)` (2D tensor) and we need to reshape it to `(examples, sequence_length, features)` (3D tensor) by reducing the `examples` dimension. Run the example below to see how we might do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_1 <- 1:3\n",
    "example_2 <- 11:13\n",
    "example_3 <- 111:113\n",
    "example_4 <- 1111:1113\n",
    "x <- matrix(c(example_1, example_2, example_3, example_4), nrow = 4, byrow = TRUE)\n",
    "dim(x)\n",
    "x[1,]\n",
    "x[2,]\n",
    "x[3,]\n",
    "x[4,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x` represents our data but only has 4 examples, each with 3 features, `(4,3)`. Let us now reshape it to so that we we 2 examples, each with a sequence of length 2. That is, `(2,2,3)`. We do not change the feature dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_x <- array_reshape(x, c(2,2,3))\n",
    "reshaped_x[1,,]\n",
    "reshaped_x[2,,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can still use the `apply` function on `reshaped_x`. The cell below will sum each feature over all the examples. This will help us scale our data later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "apply(reshaped_x, 3, sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 4.3\n",
    "Reshape `feature_matrix` so that it becomes a 3D tensor with dimensions `(1752, 240, 14)`. By doing this we are create 1752 sequences of length 240. For this to work `feature_matrix` needs to fit into a 3D tensor, so only take the first 1752*240=420.480 elements from `feature_matrix`. Store the result in a variable called `feature_matrix_reshaped`. Verify the dimensions after reshaping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_length <- 240\n",
    "feature_matrix_reshaped <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\t<li>420551</li>\n",
       "\t<li>14</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 420551\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
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       "1. 420551\n",
       "2. 14\n",
       "\n",
       "\n"
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       "[1] 420551     14"
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       "\\item 1752\n",
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       "1. 1752\n",
       "2. 240\n",
       "3. 14\n",
       "\n",
       "\n"
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       "[1] 1752  240   14"
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   ],
   "source": [
    "sequence_length <- 240\n",
    "dim(feature_matrix)\n",
    "feature_matrix_reshaped <- array_reshape(x = feature_matrix[1:420480,], dim = list(1752, sequence_length, 14))\n",
    "dim(feature_matrix_reshaped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! This was a lot of work, but well worth it, as understanding how the `array_reshape()` function works is very helpful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting\n",
    "Now we will split our `feature_matrix_reshaped` into train/val/test sets. We will, like usual, only split on the `examples` axis. To make the splits easy to understand and debug we will store 6 indices, one for where each section begins and ends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 5.1\n",
    "Create the following 6 indices.\n",
    "- train_start_index\n",
    "- train_end_index\n",
    "- val_start_index\n",
    "- val_end_index\n",
    "- test_start_index\n",
    "- test_end_index\n",
    "\n",
    "Create these 6 indices so that:\n",
    "- `train_start_index = 1`\n",
    "- after the training set the validation set starts\n",
    "- after the validation set ends, the test set starts\n",
    "- `test_end_index = 1752`\n",
    "- Use the normal sizes of splits 0.8\\*0.8 / 0.8\\*0.2 / 0.2 for train/val/test.\n",
    "- Use `dim(feature_matrix_reshaped)[1]` to get the number of examples.\n",
    "\n",
    "**Hint** Be sure to return integers by using the function `as.integer( some_decimal )`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_start_index <- 1\n",
    "train_end_index <- ?\n",
    "val_start_index <- ?\n",
    "val_end_index <- ?\n",
    "test_start_index <- ?\n",
    "test_end_index <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_start_index <- 1\n",
    "train_end_index <- train_start_index + as.integer(0.8 * 0.8 * dim(feature_matrix_reshaped)[1]) - 1\n",
    "val_start_index <- train_end_index + 1\n",
    "val_end_index <- val_start_index + as.integer(0.8 * 0.2 * dim(feature_matrix_reshaped)[1]) - 1\n",
    "test_start_index <- val_end_index + 1\n",
    "test_end_index <- test_start_index + as.integer(0.2 * dim(feature_matrix_reshaped)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to verify that all the indices are correct, they should **roughly** be (it does not need to be exactly the same, off by 1 or 2 is alright):\n",
    "- train_start_index = 1\n",
    "- train_end_index = 1121\n",
    "- val_start_index = 1122\n",
    "- val_end_index = 1401\n",
    "- test_start_index = 1402\n",
    "- test_end_index = 1752\n",
    "\n",
    "If you are unable to do this, just store the indices as these numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_start_index\n",
    "train_end_index\n",
    "val_start_index\n",
    "val_end_index\n",
    "test_start_index\n",
    "test_end_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling\n",
    "Before splitting the `feature_matrix_reshaped` let us scale the features. Today we will scale all our features, even the feature we want to predict. This is because we will use previous values of that feature as input to our model, so we need to scale it. We will only use the training set to compute the mean and standard deviation and use that to scale the whole dataset. \n",
    "\n",
    "To only compute the mean and standard deviation on the training set we use the `apply()` function. Verify that this is done correctly in the cell below (not a trick question)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   "source": [
    "# We compute the mean and sd for each feature independently, The features are on the 3rd axis.\n",
    "mean <- apply(feature_matrix_reshaped[train_start_index:train_end_index,,], 3, mean)\n",
    "std <- apply(feature_matrix_reshaped[train_start_index:train_end_index,,], 3, sd)\n",
    "\n",
    "mean[2] # The mean temperature\n",
    "mean[1] # The mean air pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time we will not use the `scale()` function since it only works with (at most) 2D tensors, so we need to do this manually. We store the result in `feature_matrix_reshaped_scaled`. We provide the code for this, but we recommend reading through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty 3D tensor with same dimensions as feature_matrix_reshaped.\n",
    "feature_matrix_reshaped_scaled <- array(0, dim = dim(feature_matrix_reshaped))\n",
    "\n",
    "# For each feature we subtract the mean and divide by the std.\n",
    "for (j in 1:dim(feature_matrix_reshaped)[3]) {\n",
    "    feature_matrix_reshaped_scaled[,,j] <- (feature_matrix_reshaped[,,j] - mean[j]) / std[j]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see if this all makes sense and check the first temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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   "source": [
    "feature_matrix_reshaped[1,1,2]\n",
    "feature_matrix_reshaped_scaled[1,1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brr... A cold day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 5.2\n",
    "Now we can finish splitting the data into train/val/test sets. Create the 3 datasets `train_data`, `val_data` and `test_data` based on the indices above and the `feature_matrix_reshaped_scaled` data and verify the dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data <- ?\n",
    "val_data <- ?\n",
    "test_data <- ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\\begin{enumerate*}\n",
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       "\\item 14\n",
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       "1. 1121\n",
       "2. 240\n",
       "3. 14\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 1121  240   14"
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     "metadata": {},
     "output_type": "display_data"
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       "1. 280\n",
       "2. 240\n",
       "3. 14\n",
       "\n",
       "\n"
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       "[1] 280 240  14"
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     "metadata": {},
     "output_type": "display_data"
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       "1. 351\n",
       "2. 240\n",
       "3. 14\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 351 240  14"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "0.799657534246575"
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       "[1] 0.7996575"
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     "metadata": {},
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   ],
   "source": [
    "train_data <- feature_matrix_reshaped_scaled[train_start_index:train_end_index,,]\n",
    "val_data <- feature_matrix_reshaped_scaled[val_start_index:val_end_index,,]\n",
    "test_data <- feature_matrix_reshaped_scaled[test_start_index:test_end_index,,]\n",
    "dim(train_data)\n",
    "dim(val_data)\n",
    "dim(test_data)\n",
    "(dim(train_data)[1] + dim(val_data)[1])/(dim(test_data)[1] + dim(train_data)[1] + dim(val_data)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The predictions\n",
    "Now we have successfully scaled and split our dataset. The last step is to create the correct predictions. For each sequences we need to create a corresponding target. To do this, we need to select a temperature point somewhere in the future and predict it. Let us try to predict the temperature for the next day, that is, 144 timesteps into the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain this further let us take an example. The first training sequence we have is `train_data[1,,]`. It contains 240 measurements, each made out of 14 features. We need to select the temperature 144 timesteps into the future from the last measurement in our sequence. This temperature is in the next training example, at `train_data[2,144,2]`.\n",
    "\n",
    "Let us now do this systematically for each dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 6.1\n",
    "Create `y_train`, `y_val` and `y_test` from `train_data`, `val_data` and `test_data`, respectively. These will be our targets, `y`. Fill in the missing information to the cell below. Verify that the dimensions are correct (these will be vectors, so use the `length()` function). \n",
    "\n",
    "**Hint** There should be 1 fewer elements in the `y`'s since we cannot use the first sequence to use a prediction for anything. We will address this in the next excercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train <- ?\n",
    "y_val <- ?\n",
    "y_test <- ?\n",
    "length(y_train)\n",
    "length(y_val)\n",
    "length(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "1120"
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       "[1] 1120"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "279"
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       "[1] 279"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "[1] 350"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train <- train_data[(2:dim(train_data)[1]), 144, 2]\n",
    "length(y_train)\n",
    "y_val <- val_data[(2:dim(val_data)[1]), 144, 2]\n",
    "length(y_val)\n",
    "y_test <- test_data[(2:dim(test_data)[1]), 144, 2]\n",
    "length(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we needed to use the temperature from the next example to create our predictions, we would need to use the temperature from the first sequence in validation set as the correct prediction for the last sequence in the training set, and so on. We cannot be bothered to do that and we just skip the last sequence in the training data as input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excercise 6.2\n",
    "Create `x_train`, `x_val` and `x_test` from the `train_data`, `val_data` and `test_data`, respectively. Do not include the last sequence in `train_data` (and `val_data` and `test_data`) since we do not have the correct prediction for the last sequence in `y_train` (and `y_val` and `y_test`). Verify that `dim(x_train)[1]` is equal to `length(y_train)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train <- ?\n",
    "x_val <- ?\n",
    "x_test <- ?\n",
    "dim(x_train)[1]\n",
    "length(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "1120"
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      "text/latex": [
       "1120"
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      "text/markdown": [
       "1120"
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      "text/plain": [
       "[1] 1120"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "1120"
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       "[1] 1120"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_train <- train_data[(1:dim(train_data)[1] - 1),,]\n",
    "x_val <- val_data[(1:dim(val_data)[1] - 1),,]\n",
    "x_test <- test_data[(1:dim(test_data)[1] - 1),,]\n",
    "dim(x_train)[1]\n",
    "length(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are finally done with all of the preprocessing. Wow! This was a lot of work! This is amount of preprocessing for sequential models is quite common as you want to be able to change many of the decisions we made in these steps. For example, we might want to increase/decrease timesteps in each sequence or predict longer/shorter into the future. This would not be possible if these preprocessing steps were done for us."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The baseline model\n",
    "Now we want to create a dense network model to serve as our baseline. We then want to see the improvement we can get by adding an RNN.\n",
    "\n",
    "In this model notice a few things.\n",
    "- The dense networks expect input to be a vector, so the first layer in the network will flatten out the sequences (reversing a lot of our hard, reshape work).\n",
    "- Only a single hidden layer.\n",
    "- Since we are doing regression, the last layer has a single unit and no activation.\n",
    "- We use the mean absolute error, `mae`. It worked better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "flatten_2 (Flatten)                 (None, 3360)                    0           \n",
      "________________________________________________________________________________\n",
      "dense_5 (Dense)                     (None, 12)                      40332       \n",
      "________________________________________________________________________________\n",
      "dense_6 (Dense)                     (None, 1)                       13          \n",
      "================================================================================\n",
      "Total params: 40,345\n",
      "Trainable params: 40,345\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 80 - loss: 0.1712455, acc.: , val loss: 0.371858551297137, val acc.:   \r"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
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fGc3O37N3yD6sIIAAAggg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\nRVuT8DuZCjI07jkSq7YkVuKNis/at2+PUqzf9NMvv+DcqRMoU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hmVf5dS4FneGgzP+g2MfplJkGYxFLBMqVLozlyq1DAzMWrInCOZwvjS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\nHd4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_flatten(input_shape = c(sequence_length, 14)) %>%\n",
    "    layer_dense(units = 12, activation = 'relu') %>%\n",
    "    layer_dense(units = 1)\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"mae\"\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train,\n",
    "    y = y_train,\n",
    "    validation_data = list(x_val, y_val),\n",
    "    epochs = 80,\n",
    "    batch_size = 32,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to tweak the model a bit and then evaluate your model when you are done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>loss:</strong> 1.24498261008944"
      ],
      "text/latex": [
       "\\textbf{loss:} 1.24498261008944"
      ],
      "text/markdown": [
       "**loss:** 1.24498261008944"
      ],
      "text/plain": [
       "    loss \n",
       "1.244983 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we do the same for a simple RNN network. In this network notice:\n",
    "- A single RRN layer.\n",
    "- Very few parameters, we use the same weights for every 240 timestep.\n",
    "- Fewer epochs.\n",
    "- Same loss.\n",
    "- Slow training (because of all the timesteps we have)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "simple_rnn_5 (SimpleRNN)            (None, 12)                      324         \n",
      "________________________________________________________________________________\n",
      "dense_8 (Dense)                     (None, 1)                       13          \n",
      "================================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "________________________________________________________________________________\n",
      "Epoch 40 - loss: 0.2743628, acc.: , val loss: 0.304945211897614, val acc.:   \r"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zHrJKS8Rn/AsXOr/p8PQNUngEEEAAAQQQQCANAoMHDxZ9zK5mWr9+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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- keras_model_sequential() %>%\n",
    "    layer_simple_rnn(units = 12, input_shape = c(sequence_length, 14)) %>%\n",
    "    layer_dense(units = 1)\n",
    "\n",
    "cat(summary(model))\n",
    "\n",
    "model %>% compile(\n",
    "    optimizer = optimizer_adam(lr = 0.001),\n",
    "    loss = \"mae\"\n",
    ")\n",
    "\n",
    "history <- model %>% fit(\n",
    "    x = x_train,\n",
    "    y = y_train,\n",
    "    validation_data = list(x_val, y_val),\n",
    "    epochs = 40,\n",
    "    batch_size = 32,\n",
    "    callbacks=list(Progress$new())\n",
    ")\n",
    "\n",
    "plot(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to tweak the model a bit and then evaluate your model when you are done. You will notice that there is a lot more variation in the dense network than in the RNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>loss:</strong> 0.320749995367868"
      ],
      "text/latex": [
       "\\textbf{loss:} 0.320749995367868"
      ],
      "text/markdown": [
       "**loss:** 0.320749995367868"
      ],
      "text/plain": [
       "   loss \n",
       "0.32075 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model %>% evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are finished! In this notebook worked with the Jena weather dataset. We first preprocessed the data using the reshape function, to create sequences. Then we scaled the data \"manually\" and finally split it to train/dev/test.\n",
    "\n",
    "We then established a baseline model, a dense network and then tried a simple RNN network. We saw that the RNN performed a lot more consistently, better, had way fewer parameters, but also took longer to train due to our long sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus excercise\n",
    "Scaling the temperature in the predictions, `y`, is not as nice as having them as the correct celsius values. Repeat the processing pipeline for `y` without scaling the predictions. Then run the same model using these predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus excercise\n",
    "Try using the [`predict`](https://keras.rstudio.com/reference/predict.keras.engine.training.Model.html) function. This is the function you would run \"in production\" to get the actual predictions. Remember to shift and scale the output with the mean and std we computed earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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