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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNTK 206:  Part B - Time series prediction with LSTM (IOT Data)\n",
    "\n",
    "In [part A of this tutorial](CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb) we developed a simple LSTM network to predict future values in a time series. In part B we want to use the model on some real world internet-of-things ([IOT](https://en.wikipedia.org/wiki/Internet_of_things)) data. As an example we want to predict the daily output of a solar panel base on the initial readings of the day. \n",
    "\n",
    "[Solar power forecasting](https://en.wikipedia.org/wiki/Solar_power_forecasting) is a challenging and important problem. The solar energy generation forecasting problem is closely linked to the problem of weather variables forecasting. Indeed, this problem is usually split into two parts, on one hand focusing on the forecasting of solar PV or any other meteorological variable and on the other hand estimating the amount of energy that a concrete power plant will produce with the estimated meteorological resource. In general, the way to deal with this difficult problem is usually related to the spatial and temporal scales we are interested in. This tutorial focusses on a simplified forecasting model using previously generated data from solar panel to predict the future. \n",
    "\n",
    "**Goal**\n",
    "\n",
    "Using historic daily production of a solar panel, we want to predict the total power production of the solar panel array for a future day. We will be using the LSTM based time series prediction model developed in part A to predict the daily output of a solar panel based on the initial readings of a the day. \n",
    "\n",
    "![rooftop-solar](https://www.cntk.ai/jup/rooftop-solar-power.jpg)\n",
    "\n",
    "We train the model with historical data of the solar panel. In our example we want to predict the total power production of the solar panel array for the day starting with the initial readings of the day. We start predicting after the first 2 readings and adjust the prediction with each new reading.\n",
    "\n",
    "In this tutorial, we will use the LSTM model introduced in the CNTK 106A. This tutorial has the following sub-sections:\n",
    "- Setup\n",
    "- Data generation\n",
    "- LSTM network modeling\n",
    "- Model training and evaluation\n",
    "\n",
    "For more details on how LSTMs work, see [this excellent post](http://colah.github.io/posts/2015-08-Understanding-LSTMs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Setup\n",
    "We need a few imports and constants throughout the tutorial that we define here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import random\n",
    "import time\n",
    "\n",
    "import cntk as C\n",
    "import cntk.axis\n",
    "from cntk.layers import Dense, Dropout, Recurrence \n",
    "\n",
    "try:\n",
    "    from urllib.request import urlretrieve\n",
    "except ImportError:\n",
    "    from urllib import urlretrieve\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to make things reproduceable, seed random\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the notebook runtime environment devices / settings\n",
    "\n",
    "Set the device to cpu / gpu for the test environment. If you have both CPU and GPU on your machine, you can optionally switch the devices. By default we choose the best available device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if 'TEST_DEVICE' in os.environ:\n",
    "    if os.environ['TEST_DEVICE'] == 'cpu':\n",
    "        C.device.try_set_default_device(C.device.cpu())\n",
    "    else:\n",
    "        C.device.try_set_default_device(C.device.gpu(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two run modes:\n",
    "- *Fast mode*: `isFast` is set to `True`. This is the default mode for the notebooks, which means we train for fewer iterations or train / test on limited data. This ensures functional correctness of the notebook though the models produced are far from what a completed training would produce.\n",
    "\n",
    "- *Slow mode*: We recommend the user to set this flag to `False` once the user has gained familiarity with the notebook content and wants to gain insight from running the notebooks for a longer period with different parameters for training. \n",
    "\n",
    "For *Fast mode* we train the model for 100 epochs and results have low accuracy but is good enough for development. The model yields good accuracy after 1000-2000 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "isFast = True\n",
    "\n",
    "# we need around 2000 epochs to see good accuracy. For testing 100 epochs will do.\n",
    "EPOCHS = 100 if isFast else 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data generation\n",
    "\n",
    "Our solar panel, emits two measures at every 30 min interval:\n",
    "- `solar.current` is the current production in Watt\n",
    "- `solar.total` is the total produced for the day so far in Watt/hour\n",
    "\n",
    "Our prediction approach involves starting with the first 2 initial readings of the day. Based on these readings we start predicting and adjust the prediction with each new reading. The training data we are going to use comes as a csv file and has the following format:\n",
    "\n",
    ">```\n",
    "time,solar.current,solar.total\n",
    "7am,6.3,1.7\n",
    "7:30am,44.3,11.4\n",
    "...\n",
    ">```\n",
    "\n",
    "The training dataset we use contains data captured for 3 years and can be found [here](https://guschmueds.blob.core.windows.net/datasets/solar.csv). \n",
    "The dataset is not pre-processed: it is raw data and contains smaller gaps and errors (like a panel failed to report)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing\n",
    "Most of the code in this example is related to data preparation. Thankfully the pandas library make this easy.\n",
    "\n",
    "`generate_solar_data()` function performs the following tasks:\n",
    "- read raw data into a pandas dataframe, \n",
    "- normalize the data, \n",
    "- groups by day and \n",
    "- append the columns \"solar.current.max\" and \"solar.total.max\", and\n",
    "- generates the sequences for each day.\n",
    "\n",
    "*Sequence generation*: All sequences are concatenated into a single list of sequences. There is *no more notion of timestamp* in our train input and **only** the sequences matter.\n",
    "\n",
    "**Note** if we have less than 8 datapoints for a day we skip over the day assuming something is missing in the raw data. If we get more than 14 data points in a day we truncate the readings.\n",
    "\n",
    "## Training / Testing / Validation data preparation\n",
    "We start by reading the csv file for use with CNTK.  The raw data is sorted by time and we should randomize it before splitting into training, validation and test datasets but this would make it impractical to visualize results in the tutorial. Hence, we split the dataset in the following manner: pick in sequence, 8 values for training, 1 for validation and 1 for test until there is no more data. This will spread training, validation and test datasets across the full timeline while preserving time order.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def generate_solar_data(input_url, time_steps, normalize=1, val_size=0.1, test_size=0.1):\n",
    "    \"\"\"\n",
    "    generate sequences to feed to rnn based on data frame with solar panel data\n",
    "    the csv has the format: time ,solar.current, solar.total\n",
    "     (solar.current is the current output in Watt, solar.total is the total production\n",
    "      for the day so far in Watt hours)\n",
    "    \"\"\"\n",
    "    # try to find the data file local. If it doesn't exists download it.\n",
    "    cache_path = os.path.join(\"data\", \"iot\")\n",
    "    cache_file = os.path.join(cache_path, \"solar.csv\")\n",
    "    if not os.path.exists(cache_path):\n",
    "        os.makedirs(cache_path)\n",
    "    if not os.path.exists(cache_file):\n",
    "        urlretrieve(input_url, cache_file)\n",
    "        print(\"downloaded data successfully from \", input_url)\n",
    "    else:\n",
    "        print(\"using cache for \", input_url)\n",
    "    \n",
    "    df = pd.read_csv(cache_file, index_col=\"time\", parse_dates=['time'], dtype=np.float32)\n",
    "    \n",
    "    df[\"date\"] = df.index.date\n",
    "    \n",
    "    # normalize data\n",
    "    df['solar.current'] /= normalize\n",
    "    df['solar.total'] /= normalize\n",
    "    \n",
    "    # group by day, find the max for a day and add a new column .max\n",
    "    grouped = df.groupby(df.index.date).max()\n",
    "    grouped.columns = [\"solar.current.max\", \"solar.total.max\", \"date\"]\n",
    "\n",
    "    # merge continuous readings and daily max values into a single frame\n",
    "    df_merged = pd.merge(df, grouped, right_index=True, on=\"date\")\n",
    "    df_merged = df_merged[[\"solar.current\", \"solar.total\",\n",
    "                           \"solar.current.max\", \"solar.total.max\"]]\n",
    "    # we group by day so we can process a day at a time.\n",
    "    grouped = df_merged.groupby(df_merged.index.date)\n",
    "    per_day = []\n",
    "    for _, group in grouped:\n",
    "        per_day.append(group)\n",
    "\n",
    "    # split the dataset into train, validatation and test sets on day boundaries\n",
    "    val_size = int(len(per_day) * val_size)\n",
    "    test_size = int(len(per_day) * test_size)\n",
    "    next_val = 0\n",
    "    next_test = 0\n",
    "\n",
    "    result_x = {\"train\": [], \"val\": [], \"test\": []}\n",
    "    result_y = {\"train\": [], \"val\": [], \"test\": []}    \n",
    "\n",
    "    # generate sequences a day at a time\n",
    "    for i, day in enumerate(per_day):\n",
    "        # if we have less than 8 datapoints for a day we skip over the\n",
    "        # day assuming something is missing in the raw data\n",
    "        total = day[\"solar.total\"].values\n",
    "        if len(total) < 8:\n",
    "            continue\n",
    "        if i >= next_val:\n",
    "            current_set = \"val\"\n",
    "            next_val = i + int(len(per_day) / val_size)\n",
    "        elif i >= next_test:\n",
    "            current_set = \"test\"\n",
    "            next_test = i + int(len(per_day) / test_size)\n",
    "        else:\n",
    "            current_set = \"train\"\n",
    "        max_total_for_day = np.array(day[\"solar.total.max\"].values[0])\n",
    "        for j in range(2, len(total)):\n",
    "            result_x[current_set].append(total[0:j])\n",
    "            result_y[current_set].append([max_total_for_day])\n",
    "            if j >= time_steps:\n",
    "                break\n",
    "    # make result_y a numpy array\n",
    "    for ds in [\"train\", \"val\", \"test\"]:\n",
    "        result_y[ds] = np.array(result_y[ds])\n",
    "    return result_x, result_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data caching\n",
    "For routine testing we would like to cache the data locally when available. If it is not available from the cache locations we shall download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloaded data successfully from  https://www.cntk.ai/jup/dat/solar.csv\n"
     ]
    }
   ],
   "source": [
    "# there are 14 lstm cells, 1 for each possible reading we get per day\n",
    "TIMESTEPS = 14\n",
    "\n",
    "# 20000 is the maximum total output in our dataset. We normalize all values with \n",
    "# this so our inputs are between 0.0 and 1.0 range.\n",
    "NORMALIZE = 20000\n",
    "\n",
    "X, Y = generate_solar_data(\"https://www.cntk.ai/jup/dat/solar.csv\", \n",
    "                           TIMESTEPS, normalize=NORMALIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility for data fetching\n",
    "\n",
    "`next_batch()` yields the next batch for training. We use variable size sequences supported by CNTK and batches are a list of numpy arrays where the numpy arrays have variable length. \n",
    "\n",
    "A standard practice is to shuffle batches with each epoch. We don't do this here because we want to be able to graph the data that is easily interpretable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# process batches of 10 days\n",
    "BATCH_SIZE = TIMESTEPS * 10\n",
    "\n",
    "def next_batch(x, y, ds):\n",
    "    \"\"\"get the next batch for training\"\"\"\n",
    "\n",
    "    def as_batch(data, start, count):\n",
    "        return data[start:start + count]\n",
    "\n",
    "    for i in range(0, len(x[ds]), BATCH_SIZE):\n",
    "        yield as_batch(X[ds], i, BATCH_SIZE), as_batch(Y[ds], i, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understand the data format\n",
    "You can now see the sequence we are going to feed to the LSTM. Note if we have less than 8 datapoints for a day we skip over the day assuming something is missing in the raw data. If we get more than 14 data points in a day we truncate the readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.       ,  0.0006985], dtype=float32),\n",
       " array([ 0.       ,  0.0006985,  0.0033175], dtype=float32),\n",
       " array([ 0.       ,  0.0006985,  0.0033175,  0.010375 ], dtype=float32)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['train'][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.23899999],\n",
       "       [ 0.23899999],\n",
       "       [ 0.23899999]], dtype=float32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y['train'][0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM network setup\n",
    "\n",
    "Corresponding  to the maximum possible 14 data points in the input sequence, we model our network with 14 LSTM cells, 1 cell for each data point we take during the day. Since the input sequences can be between 8 and 14 data points per sequence, we take the advantage of CNTK support for variable sequences as input to a LSTM so we can feed our sequences as-is with no additional need for padding.\n",
    "\n",
    "The output of the neural network is the total output for the day and each sequence for a given day has the same total output.\n",
    "\n",
    "For example:\n",
    "```\n",
    "1.7,11.4 -> 10300\n",
    "1.7,11.4,67.5 -> 10300\n",
    "1.7,11.4,67.5,250.5 ... -> 10300\n",
    "1.7,11.4,67.5,250.5,573.5 -> 10300\n",
    "```\n",
    "\n",
    "The outputs from the LSTMs are feed into a dense layer and we randomly dropout 20% of the values to not overfit the model to the training set. The output of the dense layer becomes the prediction our model generates.\n",
    "\n",
    "Notice: We lost the timestamp altogether; in our model only the sequences of readings matter. \n",
    "\n",
    "Our LSTM model has the following design:\n",
    "![lstm](https://guschmueds.blob.core.windows.net/datasets/2.png)\n",
    "\n",
    "The network model is an exact translation of the network diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_model(x):\n",
    "    \"\"\"Create the model for time series prediction\"\"\"\n",
    "    with C.layers.default_options(initial_state = 0.1):\n",
    "        m = C.layers.Recurrence(C.layers.LSTM(TIMESTEPS))(x)\n",
    "        m = C.sequence.last(m)\n",
    "        m = C.layers.Dropout(0.2)(m)\n",
    "        m = cntk.layers.Dense(1)(m)\n",
    "        return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training\n",
    "Before we can start training we need to bind our input variables for the model and define what optimizer we want to use. For this example we choose the `adam` optimizer. We choose `squared_error` as our loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# input sequences\n",
    "x = C.sequence.input_variable(1)\n",
    "\n",
    "# create the model\n",
    "z = create_model(x)\n",
    "\n",
    "# expected output (label), also the dynamic axes of the model output\n",
    "# is specified as the model of the label input\n",
    "l = C.input_variable(1, dynamic_axes=z.dynamic_axes, name=\"y\")\n",
    "\n",
    "# the learning rate\n",
    "learning_rate = 0.005\n",
    "lr_schedule = C.learning_rate_schedule(learning_rate, C.UnitType.minibatch)\n",
    "\n",
    "# loss function\n",
    "loss = C.squared_error(z, l)\n",
    "\n",
    "# use squared error to determine error for now\n",
    "error = C.squared_error(z, l)\n",
    "\n",
    "# use adam optimizer\n",
    "momentum_time_constant = C.momentum_as_time_constant_schedule(BATCH_SIZE / -math.log(0.9)) \n",
    "learner = C.fsadagrad(z.parameters, \n",
    "                      lr = lr_schedule, \n",
    "                      momentum = momentum_time_constant)\n",
    "trainer = C.Trainer(z, (loss, error), [learner])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, loss: 0.0966\n",
      "epoch: 10, loss: 0.0305\n",
      "epoch: 20, loss: 0.0208\n",
      "epoch: 30, loss: 0.0096\n",
      "epoch: 40, loss: 0.0088\n",
      "epoch: 50, loss: 0.0072\n",
      "epoch: 60, loss: 0.0071\n",
      "epoch: 70, loss: 0.0075\n",
      "epoch: 80, loss: 0.0082\n",
      "epoch: 90, loss: 0.0082\n",
      "Training took 134.4 sec\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "loss_summary = []\n",
    "\n",
    "start = time.time()\n",
    "for epoch in range(0, EPOCHS):\n",
    "    for x_batch, l_batch in next_batch(X, Y, \"train\"):\n",
    "        trainer.train_minibatch({x: x_batch, l: l_batch})\n",
    "        \n",
    "    if epoch % (EPOCHS / 10) == 0:\n",
    "        training_loss = trainer.previous_minibatch_loss_average\n",
    "        loss_summary.append(training_loss)\n",
    "        print(\"epoch: {}, loss: {:.4f}\".format(epoch, training_loss))\n",
    "\n",
    "print(\"Training took {:.1f} sec\".format(time.time() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look how the loss function shows how the model is converging:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kdcHQoJb03ve+PuvwiU/k9d/8puiqJKm9GRrUsjbbLM86/OhH0NPjrIMkjTZD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\nMTRIkqSaGBokSVJNDA2SJKkm/x+Onk+eIheFPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbda5cce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_summary, label='training loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us validate the training validation and test dataset. We use mean squared error as measure which might be a little simplistic. A method that would define a ratio how many predictions have been inside a given tolerance would make a better measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# validate\n",
    "def get_mse(X,Y,labeltxt):\n",
    "    result = 0.0\n",
    "    for x1, y1 in next_batch(X, Y, labeltxt):\n",
    "        eval_error = trainer.test_minibatch({x : x1, l : y1})\n",
    "        result += eval_error\n",
    "    return result/len(X[labeltxt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for train: 0.000079\n",
      "mse for val: 0.000076\n"
     ]
    }
   ],
   "source": [
    "# Print the train and validation errors\n",
    "for labeltxt in [\"train\", \"val\"]:\n",
    "    print(\"mse for {}: {:.6f}\".format(labeltxt, get_mse(X, Y, labeltxt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for test: 0.000076\n"
     ]
    }
   ],
   "source": [
    "# Print the test error\n",
    "labeltxt = \"test\"\n",
    "print(\"mse for {}: {:.6f}\".format(labeltxt, get_mse(X, Y, labeltxt)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the prediction\n",
    "\n",
    "Our model has been trained well, given the train, validation and test errors are in the same ball park. Predicted time series data renders well with visualization of the results. Let us take our newly created model, make predictions and plot them against the actual readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LM+FNZPly6NEjdap9NsiWk99VMKIg3TWno67OKoSTaju/7t3hnnv2nvdOELLB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\npOi6jWHEd7I+6Zx8p8hfG/2Bdy1wFZ1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Sktma78qiypR9KosqafQld8eNKK4qrrJv4Iac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9G5gDYv9EX7MlrmhWPhrHOfW7j19CtF8g+bfhgbvrWBMx86kyfXPTkm8+dh0w/LWtdkV4J6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\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+poXAgVCbGqAvMM/fNA+oNLNRgdeejHcBYX7C31UCZVN1fREREREREckeCc/kO+d2AUuC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbdae794a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict\n",
    "f, a = plt.subplots(2, 1, figsize=(12, 8))\n",
    "for j, ds in enumerate([\"val\", \"test\"]):\n",
    "    results = []\n",
    "    for x_batch, _ in next_batch(X, Y, ds):\n",
    "        pred = z.eval({x: x_batch})\n",
    "        results.extend(pred[:, 0])\n",
    "    # because we normalized the input data we need to multiply the prediction\n",
    "    # with SCALER to get the real values.\n",
    "    a[j].plot((Y[ds] * NORMALIZE).flatten(), label=ds + ' raw');\n",
    "    a[j].plot(np.array(results) * NORMALIZE, label=ds + ' pred');\n",
    "    a[j].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we let the model train for 2000 epochs the predictions are close to the actual data and follow the right pattern."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Suggested activity\n",
    "\n",
    "So what we do with this model? A practical application would be to generate alerts if the actual output is not in line with the prediction, for example if one of the panels is failing. The solar array that goes with our dataset has 16 panels. If we'd want to detect failure without generating false alerts, the accuracy of our prediction would need to be at least 1 - 1/16, around 94%. Our model is close to this but would most likely generate occasional false alerts.\n",
    "\n",
    "- Improve the model by training for more epochs.\n",
    "- Further preprocess the training set to smooth-out missing values\n",
    "- Try out more complex networks.\n",
    "\n",
    "However, in our experience with time series data one can achieve significant accuracy improvement comes from higher resolution training data, for example reading a data point every 5 minutes instead of every 30 minutes.\n",
    "\n",
    "We hope this tutorial gets you started on time series prediction with neural networks."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
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