Raw File
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNTK 106: Part A - Time series prediction with LSTM (Basics)\n",
    "\n",
    "This tutorial demonstrates how to use CNTK to predict future values in a time series using LSTMs.\n",
    "\n",
    "**Goal**\n",
    "\n",
    "We use simulated data set of a continuous function (in our case a [sine wave](https://en.wikipedia.org/wiki/Sine)). From `N` previous values of the $y = sin(t)$ function where $y$ is the observed amplitude signal at time $t$, we will predict `M` values of $y$ for the corresponding future time points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://www.cntk.ai/jup/sinewave.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Figure 1\n",
    "Image(url=\"http://www.cntk.ai/jup/sinewave.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will use [LSTM](https://en.wikipedia.org/wiki/Long_short-term_memory) to implement our model. LSTMs are well suited for this task because their ability to learn from experience. For details on how LSTMs work, see [this excellent post](http://colah.github.io/posts/2015-08-Understanding-LSTMs). \n",
    "\n",
    "In this tutorial we will have following sub-sections:\n",
    "- Simulated data generation\n",
    "- LSTM network modeling\n",
    "- Model training and evaluation\n",
    "\n",
    "This model works for lots real world data. In part A of this tutorial we use a simple sin(x) function and in part B of the tutorial (currently in development) we will use real data from IOT device and try to predict daily output of solar panel. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using CNTK we can easily express our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "import cntk as C\n",
    "import cntk.axis\n",
    "from cntk.layers import Input, Dense, Dropout, Recurrence \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Select the right target device when this notebook is being tested:\n",
    "if 'TEST_DEVICE' in os.environ:\n",
    "    import cntk\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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "isFast = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data generation\n",
    "\n",
    "We need a few helper methods to generate the simulated sine wave data. Let `N` and `M` be a ordered set of past values and future (desired predicted values) of the sine wave, respectively. \n",
    "\n",
    "- **`generate_data()`**\n",
    "\n",
    "> In this tutorial, we sample `N` consecutive values of the `sin` function as the input to the model and try to predict future values that is `M` steps away from the last observed value in the input model. We generate multiple such instances of the input signal (by sampling from `sin` function) each of size `N` and  the corresponding desired output as our training data. Assuming $k$ = batch size, `generate_data` function produces the $X$ and corresponding $L$ data and returns numpy arrays of the following shape:\n",
    "\n",
    "> The input set ($X$) to the lstm: $$ X = [\\{y_{11}, y_{12},  \\cdots , y_{1N}\\},\n",
    "        \\{y_{21}, y_{22}, \\cdots, y_{2N}\\}, \\cdots,\n",
    "        \\{y_{k1}, y_{k2}, \\cdots, y_{kN}\\}]\n",
    "$$\n",
    "> In the above samples $y_{i,j}$, represents the observed function value for the $i^{th}$ batch and $j^{th}$ time point within the time window of $N$ points. \n",
    "\n",
    "The desired output ($L$) with `M` steps in the future: $$ L = [ \\{y_{1,N+M}\\},\n",
    "        \\{y_{2,N+M}\\}, \\cdots, \\{y_{k,N+M}\\}]$$\n",
    "\n",
    "> Note: `k` is a function of the length of the time series and the number of windows of size `N` one can have for the time series.\n",
    "\n",
    "- **`split_data()`**\n",
    "\n",
    "> As the name suggests, `split_data` function will split the data into training, validation and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def split_data(data, val_size=0.1, test_size=0.1):\n",
    "    \"\"\"\n",
    "    splits np.array into training, validation and test\n",
    "    \"\"\"\n",
    "    pos_test = int(len(data) * (1 - test_size))\n",
    "    pos_val = int(len(data[:pos_test]) * (1 - val_size))\n",
    "\n",
    "    train, val, test = data[:pos_val], data[pos_val:pos_test], data[pos_test:]\n",
    "\n",
    "    return {\"train\": train, \"val\": val, \"test\": test}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_data(fct, x, time_steps, time_shift):\n",
    "    \"\"\"\n",
    "    generate sequences to feed to rnn for fct(x)\n",
    "    \"\"\"\n",
    "    data = fct(x)\n",
    "    if not isinstance(data, pd.DataFrame):\n",
    "        data = pd.DataFrame(dict(a = data[0:len(data) - time_shift],\n",
    "                                 b = data[time_shift:]))\n",
    "    rnn_x = []\n",
    "    for i in range(len(data) - time_steps):\n",
    "        rnn_x.append(data['a'].iloc[i: i + time_steps].as_matrix())\n",
    "    rnn_x = np.array(rnn_x)\n",
    "\n",
    "    # Reshape or rearrange the data from row to columns\n",
    "    # to be compatible with the input needed by the LSTM model\n",
    "    # which expects 1 float per time point in a given batch\n",
    "    rnn_x = rnn_x.reshape(rnn_x.shape + (1,))\n",
    "    \n",
    "    rnn_y = data['b'].values\n",
    "    \n",
    "    # Reshape or rearrange the data from row to columns\n",
    "    # to match the input shape\n",
    "    rnn_y = rnn_y.reshape(rnn_y.shape + (1,))\n",
    "\n",
    "    return split_data(rnn_x), split_data(rnn_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us generate and visualize the generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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H3j98ZPVq+p0Gy0QC6OeXXELvjSKrVlFdqHe+c/D3ZGX1ObK+9CVrj2aU1tZWHDlyBI8+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Ny5M++88w5PP/303kJza9as4bPPPqN///57t73++uu55ZZb9nt/kyZN6NmzJ9OnT+f0008v8O/f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a5de8d630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 5 # input: N subsequent values \n",
    "M = 5 # output: predict 1 value M steps ahead\n",
    "X, Y = generate_data(np.sin, np.linspace(0, 100, 10000, dtype=np.float32), N, M)\n",
    "\n",
    "f, a = plt.subplots(3, 1, figsize=(12, 8))\n",
    "for j, ds in enumerate([\"train\", \"val\", \"test\"]):\n",
    "    a[j].plot(Y[ds], label=ds + ' raw');\n",
    "[i.legend() for i in a];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network modeling\n",
    "\n",
    "We setup our network with 1 LSTM cell for each input. We have N inputs and each input is a value in our continues function. The N outputs from the LSTM are the input into a dense layer that produces a single output. \n",
    "Between LSTM and dense layer we insert a dropout layer that randomly drops 20% of the values coming the LSTM to prevent overfitting the model to the training dataset. We want use use the dropout layer during training but when using the model to make predictions we don't want to drop values.\n",
    "![lstm](https://www.cntk.ai/jup/cntk106A_model_s3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(N))(x)\n",
    "        m = C.ops.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 the network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the `next_batch()` iterator that produces batches we can feed to the training function. \n",
    "Note that because CNTK supports variable sequence length, we must feed the batches as list of sequences. This is a convenience function to generate small batches of data often referred to as minibatch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def next_batch(x, y, ds):\n",
    "    \"\"\"get the next batch to process\"\"\"\n",
    "\n",
    "    def as_batch(data, start, count):\n",
    "        part = []\n",
    "        for i in range(start, start + count):\n",
    "            part.append(data[i])\n",
    "        return np.array(part)\n",
    "\n",
    "    for i in range(0, len(x[ds])-BATCH_SIZE, BATCH_SIZE):\n",
    "        yield as_batch(x[ds], i, BATCH_SIZE), as_batch(y[ds], i, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup everything else we need for training the model: define user specified training parameters, define inputs, outputs, model and the optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Training parameters\n",
    "\n",
    "TRAINING_STEPS = 10000\n",
    "BATCH_SIZE = 100\n",
    "EPOCHS = 20 if isFast else 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Key Insight**\n",
    "\n",
    "There are some key learnings when [working with sequences](https://www.cntk.ai/pythondocs/sequence.html) in LSTM networks. A brief recap: \n",
    "\n",
    "CNTK inputs, outputs and parameters are organized as tensors. Each tensor has a rank: A scalar is a tensor of rank 0, a vector is a tensor of rank 1, a matrix is a tensor of rank 2, and so on. We refer to these different dimensions as axes.\n",
    "\n",
    "Every CNTK tensor has some static axes and some dynamic axes. The static axes have the same length throughout the life of the network. The dynamic axes are like static axes in that they define a meaningful grouping of the numbers contained in the tensor but:\n",
    "- their length can vary from instance to instance,\n",
    "- their length is typically not known before each minibatch is presented, and\n",
    "- they may be ordered.\n",
    "\n",
    "In CNTK the axis over which you run a recurrence is dynamic and thus its dimensions are unknown at the time you define your variable. Thus the input variable only lists the shapes of the static axes. Since our inputs are a sequence of one dimensional numbers we specify the input as \n",
    "\n",
    "> `C.layers.Input(1)`\n",
    "\n",
    "The `N` instances of the observed `sin` function output and the corresponding batch are implicitly represented in the dynamic axis as shown below in the form of defaults. \n",
    "\n",
    "> ```\n",
    "x_axes = [C.Axis.default_batch_axis(), C.Axis.default_dynamic_axis()]\n",
    "C.layers.Input(1, dynamic_axes=x_axes)\n",
    "```\n",
    "The reader should be aware of the meaning of the default parameters. Specifiying the dynamic axes enables the recurrence engine handle the time sequence data in the expected order. Please take time to understand how to work with both static and dynamic axes in CNTK as described [here](https://www.cntk.ai/pythondocs/sequence.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# input sequences\n",
    "x = C.layers.Input(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.layers.Input(1, dynamic_axes=z.dynamic_axes, name=\"y\")\n",
    "\n",
    "# the learning rate\n",
    "learning_rate = 0.001\n",
    "lr_schedule = C.learning_rate_schedule(learning_rate, C.UnitType.minibatch)\n",
    "\n",
    "# loss function\n",
    "loss = C.ops.squared_error(z, l)\n",
    "\n",
    "# use squared error to determine error for now\n",
    "error = C.ops.squared_error(z, l)\n",
    "\n",
    "# use adam optimizer\n",
    "momentum_time_constant = C.learner.momentum_as_time_constant_schedule(BATCH_SIZE / -math.log(0.9)) \n",
    "learner = C.learner.adam_sgd(z.parameters, \n",
    "                             lr = lr_schedule, \n",
    "                             momentum = momentum_time_constant, \n",
    "                             unit_gain = True)\n",
    "trainer = C.Trainer(z, (loss, error), [learner])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are ready to train. 100 epochs should yield acceptable results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, loss: 0.22781\n",
      "epoch: 2, loss: 0.18071\n",
      "epoch: 4, loss: 0.16900\n",
      "epoch: 6, loss: 0.14419\n",
      "epoch: 8, loss: 0.10325\n",
      "epoch: 10, loss: 0.07130\n",
      "epoch: 12, loss: 0.07224\n",
      "epoch: 14, loss: 0.08284\n",
      "epoch: 16, loss: 0.07935\n",
      "epoch: 18, loss: 0.06039\n",
      "training took 120.3 sec\n"
     ]
    }
   ],
   "source": [
    "# train\n",
    "loss_summary = []\n",
    "start = time.time()\n",
    "for epoch in range(0, EPOCHS):\n",
    "    for x1, y1 in next_batch(X, Y, \"train\"):\n",
    "        trainer.train_minibatch({x: x1, l: y1})\n",
    "    if epoch % (EPOCHS / 10) == 0:\n",
    "        training_loss = cntk.utils.get_train_loss(trainer)\n",
    "        loss_summary.append(training_loss)\n",
    "        print(\"epoch: {}, loss: {:.5f}\".format(epoch, training_loss))\n",
    "\n",
    "print(\"training took {0:.1f} sec\".format(time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0E/zoR7DTTvD887kTSapvlgZJ9WbppdOKkautlnbGfPnl3Ikk1SdLg6R6teyy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a5de8d0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A look how the loss function shows how well the model is converging\n",
    "plt.plot(loss_summary, label='training loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally we would validate the training on the data that we set aside for validation but since the input data is small we can run validattion on all parts of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for train: 0.000097\n",
      "mse for val: 0.000095\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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for test: 0.000101\n"
     ]
    }
   ],
   "source": [
    "# Print validate and test error\n",
    "labeltxt = \"test\"\n",
    "print(\"mse for {}: {:.6f}\".format(labeltxt, get_mse(X, Y, labeltxt)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we used a simple sin(x) function we should expect that the errors are the same for train, validation and test sets. For real datasets that will be different of course. We also plot the expected output (Y) and the prediction our model made to shows how well the simple LSTM approach worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m927bCRPduROGj25gsf4T7k69G3td16eGpgSmMHrIaI6Gf0RWlu04cXbsgKHp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Ngw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\nKH5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+0MlD3P7d7Tw49UHuqXkPS+5dohpIIpIqJe8XISkJBg1yU+U7doQFC6BkyQu3t9by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\nIujRowdZsmTh1q1bbNy4kdDQUKytrfHx8aFDhw5UrFiRzp07A5A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+2vIVc4/PNTqmEEIk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5nyhctnT/DilaKi3CpQ333nbocO/evpqxml5D0TlLyLL1q0CB56yFVb/eQTeOCBzCVbpxNP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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26a5fbab9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict\n",
    "f, a = plt.subplots(3, 1, figsize = (12, 8))\n",
    "for j, ds in enumerate([\"train\", \"val\", \"test\"]):\n",
    "    results = []\n",
    "    for x1, y1 in next_batch(X, Y, ds):\n",
    "        pred = z.eval({x: x1})\n",
    "        results.extend(pred[:, 0])\n",
    "    a[j].plot(Y[ds], label = ds + ' raw');\n",
    "    a[j].plot(results, label = ds + ' predicted');\n",
    "[i.legend() for i in a];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Not perfect but close enough, considering the simplicity of the model.\n",
    "\n",
    "Here we used a simple sin wave but you can tinker yourself and try other time series data. `generate_data()` allows you to pass in a dataframe with 'a' and 'b' columns instead of a function.\n",
    "\n",
    "To improve results, we could train with more data points, let the model train for more epochs or improve on the model itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
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