non_linear_ica_time_series.ipynb

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  "metadata": {
    "colab": {
      "name": "non_linear_ica_time_series.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dauparas/tensorflow_examples/blob/master/non_linear_ica_time_series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qnklk9JPr0W6",
        "colab_type": "text"
      },
      "source": [
        "Based on Nonlinear ICA Using Auxiliary Variables\n",
        "and Generalized Contrastive Learning: https://arxiv.org/pdf/1805.08651.pdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkZ_O0Z9gKA1",
        "colab_type": "text"
      },
      "source": [
        "# Import stock prices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0RRe2I_CV6v",
        "colab_type": "code",
        "outputId": "10b8dca9-99d3-4bae-9c38-ccc58d304606",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance as yf\n",
        "import datetime as dt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "import time\n",
        "from __future__ import division\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "tfd = tfp.distributions\n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "yf.pdr_override()\n",
        "\n",
        "stocks = [\"VTI\", \"VXUS\"]\n",
        "start = dt.datetime(2016,5,1)\n",
        "end = dt.datetime(2019,5,1)\n",
        "\n",
        "f = pdr.get_data_yahoo(stocks, start=start, end=end)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  2 of 2 downloaded\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNjbX6tGCjYT",
        "colab_type": "code",
        "outputId": "83eb3760-6daa-45c5-dafc-3d4edb80d69c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "f.tail()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"2\" halign=\"left\">High</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "      <th>VTI</th>\n",
              "      <th>VXUS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-04-24</th>\n",
              "      <td>149.779999</td>\n",
              "      <td>53.200001</td>\n",
              "      <td>150.050003</td>\n",
              "      <td>53.240002</td>\n",
              "      <td>149.470001</td>\n",
              "      <td>52.919998</td>\n",
              "      <td>149.600006</td>\n",
              "      <td>53.029999</td>\n",
              "      <td>149.045700</td>\n",
              "      <td>52.422779</td>\n",
              "      <td>1924600</td>\n",
              "      <td>1056700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-25</th>\n",
              "      <td>149.539993</td>\n",
              "      <td>52.860001</td>\n",
              "      <td>149.690002</td>\n",
              "      <td>52.970001</td>\n",
              "      <td>148.600006</td>\n",
              "      <td>52.740002</td>\n",
              "      <td>149.350006</td>\n",
              "      <td>52.939999</td>\n",
              "      <td>148.796631</td>\n",
              "      <td>52.333809</td>\n",
              "      <td>2428600</td>\n",
              "      <td>942000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-26</th>\n",
              "      <td>149.410004</td>\n",
              "      <td>53.090000</td>\n",
              "      <td>150.160004</td>\n",
              "      <td>53.220001</td>\n",
              "      <td>148.990005</td>\n",
              "      <td>53.009998</td>\n",
              "      <td>150.160004</td>\n",
              "      <td>53.220001</td>\n",
              "      <td>149.603638</td>\n",
              "      <td>52.610603</td>\n",
              "      <td>2184500</td>\n",
              "      <td>710400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-29</th>\n",
              "      <td>150.220001</td>\n",
              "      <td>53.230000</td>\n",
              "      <td>150.690002</td>\n",
              "      <td>53.360001</td>\n",
              "      <td>150.130005</td>\n",
              "      <td>53.180000</td>\n",
              "      <td>150.380005</td>\n",
              "      <td>53.349998</td>\n",
              "      <td>149.822815</td>\n",
              "      <td>52.739113</td>\n",
              "      <td>2461500</td>\n",
              "      <td>1228500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>150.279999</td>\n",
              "      <td>53.279999</td>\n",
              "      <td>150.600006</td>\n",
              "      <td>53.389999</td>\n",
              "      <td>149.300003</td>\n",
              "      <td>53.090000</td>\n",
              "      <td>150.389999</td>\n",
              "      <td>53.340000</td>\n",
              "      <td>149.832779</td>\n",
              "      <td>52.729229</td>\n",
              "      <td>2224500</td>\n",
              "      <td>1284500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Open                   High  ...  Adj Close   Volume         \n",
              "                   VTI       VXUS         VTI  ...       VXUS      VTI     VXUS\n",
              "Date                                           ...                             \n",
              "2019-04-24  149.779999  53.200001  150.050003  ...  52.422779  1924600  1056700\n",
              "2019-04-25  149.539993  52.860001  149.690002  ...  52.333809  2428600   942000\n",
              "2019-04-26  149.410004  53.090000  150.160004  ...  52.610603  2184500   710400\n",
              "2019-04-29  150.220001  53.230000  150.690002  ...  52.739113  2461500  1228500\n",
              "2019-04-30  150.279999  53.279999  150.600006  ...  52.729229  2224500  1284500\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zffaLrtUDc9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close = f['Adj Close'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Chuz6tzE-Fo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close = close/close[0,:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL1BWftPrbA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "N = close.shape[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QVhT5DqGyZz",
        "colab_type": "code",
        "outputId": "f11abc18-76cc-42f6-94e7-6339ec7c9152",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.imshow(np.cov(close.T));\n",
        "plt.colorbar();"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WC+DzC/tZunjLYIcRcFXVhRFZr/f2PxOQ+wTqvXWr1UphYSHr1q3D4/Ewbdo0UlNT2bFj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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e47AAasCG3Ba",
        "colab_type": "code",
        "outputId": "9741ee18-f555-4617-c9e8-323a214e567d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "for i in range(close.shape[1]):\n",
        "  plt.plot(np.array(range(close.shape[0])), close[:,i]);\n",
        "plt.legend(['VTI', 'VXUS']);"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZSO1Hj/TY6SBBh1tpGXk7m/CG7Hmxw4A1IKqQtFJ5L6dhmMPIEaOi3leSX1T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\nEpLiLoQQSUiKuxBCJKH/BwTtjRHnK44EAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbPxYjM1iVFJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = close #input\n",
        "d = np.array(range(x.shape[0])) #time label\n",
        "d = (d-np.mean(d))/np.std(d)\n",
        "\n",
        "d_random = d[np.random.permutation(d.shape[0])] #random time\n",
        "\n",
        "c_true = 1+np.zeros([x.shape[0],1], np.int32) #true means 1\n",
        "c_false = np.zeros([x.shape[0],1], np.int32) #false means 0\n",
        "\n",
        "\n",
        "features = np.concatenate((x,x), axis=0).astype(np.float32) \n",
        "auxilary = np.concatenate((d,d_random), axis=0).astype(np.float32).reshape([-1,1])\n",
        "labels = np.concatenate((c_true, c_false), axis=0).astype(np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBmAPiHvimcx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "shuff = tf.placeholder(tf.int64, name='shuff')\n",
        "\n",
        "batch_size = tf.placeholder(tf.int64, name='batch') \n",
        "train_data = (features, auxilary, labels) #inputs and targets - they are the same for the autoencoder\n",
        "train_data = tf.data.Dataset.from_tensor_slices(train_data)\n",
        "train_data = train_data.shuffle(buffer_size=shuff)\n",
        "train_data = train_data.batch(batch_size)\n",
        "\n",
        "#Create an iterator\n",
        "iterator = tf.data.Iterator.from_structure(train_data.output_types, train_data.output_shapes)\n",
        "X, U, Y = iterator.get_next()\n",
        "train_init = iterator.make_initializer(train_data)\n",
        "\n",
        "def encoder(x, activation, regularizer, name):\n",
        "  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):\n",
        "    h = tf.layers.dense(x, 16, activation, kernel_regularizer=regularizer)\n",
        "    h = tf.layers.dense(h, 8, activation, kernel_regularizer=regularizer)\n",
        "    h = tf.layers.dense(h, 4, activation, kernel_regularizer=regularizer)\n",
        "    z = tf.layers.dense(h, 2)\n",
        "  return z\n",
        "\n",
        "\n",
        "def latent_aux(z, u, activation, regularizer, name):\n",
        "  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):\n",
        "    k = tf.concat([z, u], axis=1) #latent representation plus auxilary data\n",
        "    k = tf.layers.dense(k,16, activation, kernel_regularizer=regularizer)\n",
        "    k = tf.layers.dense(k,8, activation, kernel_regularizer=regularizer)\n",
        "    k = tf.layers.dense(k,4, activation, kernel_regularizer=regularizer)\n",
        "    k = tf.layers.dense(k,1)\n",
        "  return k\n",
        "\n",
        "z = encoder(X, tf.nn.leaky_relu, tf.contrib.layers.l2_regularizer(scale=0.00001),  'encoder')\n",
        "\n",
        "z_1 = tf.reshape(z[:,0],[-1,1])\n",
        "z_2 = tf.reshape(z[:,1],[-1,1])\n",
        "\n",
        "k_1 = latent_aux(z_1, U, tf.nn.leaky_relu, tf.contrib.layers.l2_regularizer(scale=0.00001), 'k_1')\n",
        "k_2 = latent_aux(z_2, U, tf.nn.leaky_relu, tf.contrib.layers.l2_regularizer(scale=0.00001), 'k_2')\n",
        "  \n",
        "k = k_1+k_2\n",
        "\n",
        "cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=Y, logits=k)\n",
        "loss = tf.reduce_mean(cross_entropy) + tf.losses.get_regularization_loss()\n",
        "optimizer = tf.train.AdamOptimizer(0.001).minimize(loss)\n",
        "\n",
        "predicted = tf.nn.sigmoid(k)\n",
        "correct_pred = tf.equal(tf.round(predicted), Y)\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjceYz-TjRgd",
        "colab_type": "code",
        "outputId": "d77dae5c-7f59-4bb2-d03c-3b3f78c65e3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Create a training session\n",
        "import time\n",
        "saver = tf.train.Saver()\n",
        "with tf.Session() as sess:\n",
        "    start_time = time.time()\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "    for i in range(500):\n",
        "        sess.run(train_init, feed_dict={batch_size: 16, shuff: 10000})\n",
        "        total_loss = 0 #total loss\n",
        "        total_acc = 0\n",
        "        n_batches = 0\n",
        "        try:\n",
        "            while True:\n",
        "                _, _loss, acc  = sess.run([optimizer, loss, accuracy])\n",
        "                total_loss += _loss\n",
        "                total_acc += acc\n",
        "                n_batches += 1\n",
        "        except tf.errors.OutOfRangeError:\n",
        "            pass\n",
        "        if i % 50 == 0:\n",
        "          print('Epoch: {0}, loss: {1:.4f}, acc: {2:.4f}'\n",
        "                .format(i, total_loss/n_batches, total_acc/n_batches))\n",
        "    print('Total time: {0} seconds'.format(time.time() - start_time))\n",
        "    \n",
        "    \n",
        "    #Calculate the decoder mean for the training data X\n",
        "    sess.run(train_init, feed_dict={batch_size: features.shape[0], shuff: 1})\n",
        "    try:\n",
        "      while True:\n",
        "        #latent, classes_out, labels_, grad1_out, grad2_out= sess.run([z, U, Y, grad1, grad2])\n",
        "        latent_, X_ = sess.run([z, X])\n",
        "    except tf.errors.OutOfRangeError:\n",
        "      pass"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, loss: 0.6964, acc: 0.5033\n",
            "Epoch: 50, loss: 0.3345, acc: 0.8612\n",
            "Epoch: 100, loss: 0.3276, acc: 0.8605\n",
            "Epoch: 150, loss: 0.3226, acc: 0.8658\n",
            "Epoch: 200, loss: 0.3045, acc: 0.8763\n",
            "Epoch: 250, loss: 0.2922, acc: 0.8822\n",
            "Epoch: 300, loss: 0.2985, acc: 0.8743\n",
            "Epoch: 350, loss: 0.2706, acc: 0.8901\n",
            "Epoch: 400, loss: 0.2764, acc: 0.8921\n",
            "Epoch: 450, loss: 0.2597, acc: 0.8993\n",
            "Total time: 120.63364720344543 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k31SkRt4lhOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "7260e7e7-51c0-4f13-c0b3-222c4b562e0e"
      },
      "source": [
        "plt.scatter(latent_[0:N,0], latent_[0:N,1], c=d)\n",
        "plt.colorbar()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f292556c630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Kai8pKaGkpASAE044gSVLlvRkGIUiLk725POHWRdT6/Vy77pVrKk4DJqNSLMQ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\nCUISuhBCJAhJ6EIIkSAkoQshRIL4f06XZSB2eSU2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "donnJgPrpEXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "7533e948-aac2-44a0-ada1-5ebc66d8f2eb"
      },
      "source": [
        "plt.plot(d, latent_[0:N,0])\n",
        "plt.plot(d, latent_[0:N,1])"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2925523d30>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QvtafW5gHgBgxzvjPe9qha1aLd00XRVF6jErsAejoQVH8bmoSz5w+hPFJYVhN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\n3C8gVLGxKQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOtXw0x_qhs7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "5211a713-0d72-4bc0-c25c-79b6dcf31b0e"
      },
      "source": [
        "plt.plot(d, close[:,0])\n",
        "plt.plot(d, close[:,1])"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2923462630>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IS7WwaGYB95xdyLAMKxUNAaSUBENfMiV1fr7/8p52/jD6CFZbs7CMDIu0tRFa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\nxHWTcqaY8QUlW0oN0bFwS78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+trGcqfkpHN8kxdET0AnoUJRtY05R/62pUJ57P0Zf8zHB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\nIUSBqLgTQogC/R9fSWZ+VAkPKAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TnR2_FfYrm7F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}