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 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": 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 0x1db60b32e80>"
      ]
     },
     "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.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": 12,
   "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.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: 0.22781\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 30.8 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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0E/zoR7DTTvD887kTSapvlgZJ9WbppdOKkautlnbGfPnl3Ikk1SdLg6R6teyy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db63280320>"
      ]
     },
     "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: 0.000097\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": 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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b3bttJ0x0504YPrqBxfpPuDv1bux1XZ8amhKYwughozka/hFZWbbjxNmxA4am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btnM3v3bNYdWYfFUr9sfW6rfBttKrehTWgbyhYqm/VfQrzCjBnw+OOQkABjxkDPnln/GUre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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db630be588>"
      ]
     },
     "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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