Raw File
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 2,
     "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": 3,
   "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 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": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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 0x1e51dddaf98>"
      ]
     },
     "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": 9,
   "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": 10,
   "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": 11,
   "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.sequence.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.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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# input sequences\n",
    "x = C.sequence.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.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.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",
    "                      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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, loss: 0.22282\n",
      "epoch: 2, loss: 0.19430\n",
      "epoch: 4, loss: 0.16113\n",
      "epoch: 6, loss: 0.15103\n",
      "epoch: 8, loss: 0.11810\n",
      "epoch: 10, loss: 0.07352\n",
      "epoch: 12, loss: 0.06572\n",
      "epoch: 14, loss: 0.07864\n",
      "epoch: 16, loss: 0.09846\n",
      "epoch: 18, loss: 0.06972\n",
      "training took 11.1 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 = trainer.previous_minibatch_loss_average\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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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QYGb9gKuAscDuwKvALDNrGLWw9NQBXgGGAUnf+9oBuB5oBxwAVAMe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\nRERSotAgIiIiKfl//ovgR31wd4oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e51e322518>"
      ]
     },
     "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": 15,
   "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for train: 0.000098\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": 17,
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9jQivCK4Kv4pjfq+TlWU9YaIHDsCo0XWsTl/FPQn3dLjq3kbSoCRGBYyiKnQV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xVrqVkze+9x5LcjTIucxrdbvmXxnsWkpKdQP7g+vev1pnf93lxe63KKBRTLmx9A8pW1lh0x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\niIiIl/t/MUwPlIkUCiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e51e316518>"
      ]
     },
     "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 [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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