swave_arrival_tf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
       " PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),\n",
       " PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU'),\n",
       " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import h5py, os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import pickle, random\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "tf.config.list_physical_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\": \"qkRvDQVAIfzsJo40iRBbxt6sttt0hv4lh74DG7OK4MCHv4C5oohXYoHUM5W11uqS\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\": \"Sb7Mr06a9TNlet/GEBeKaf5xH3eb6AlCzwjtU82wNPyDrnfoiVl26qnvlKjmcAd+\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\": \"HaJ15vgfmcfRtB4c4YBOI4f1MUujukqInOWVqZJZZGK7Q+ivud0OKGSTn/Vm2iso\"};\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      if (url in hashes) {\n",
       "        element.crossOrigin = \"anonymous\";\n",
       "        element.integrity = \"sha384-\" + hashes[url];\n",
       "      }\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    const hashes = {\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\": \"qkRvDQVAIfzsJo40iRBbxt6sttt0hv4lh74DG7OK4MCHv4C5oohXYoHUM5W11uqS\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\": \"Sb7Mr06a9TNlet/GEBeKaf5xH3eb6AlCzwjtU82wNPyDrnfoiVl26qnvlKjmcAd+\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\": \"HaJ15vgfmcfRtB4c4YBOI4f1MUujukqInOWVqZJZZGK7Q+ivud0OKGSTn/Vm2iso\"};\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      if (url in hashes) {\n        element.crossOrigin = \"anonymous\";\n        element.integrity = \"sha384-\" + hashes[url];\n      }\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.2.1.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### seed_everythin\n",
    "seed = 1987\n",
    "random.seed(seed)\n",
    "os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "np.random.seed(seed)\n",
    "tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data from pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'selected_data.pkl'\n",
    "outfile = open(filename,'rb')\n",
    "data = pickle.load(outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182330"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdata = np.array(data['data'], dtype = 'float32')\n",
    "ylablel = np.array(data['labels'], dtype = 'float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((182330, 50, 3), (182330,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xdata.shape, ylablel.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((145864, 50, 3), (36466, 50, 3))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Split data\n",
    "X_train, X_val, y_train, y_val = train_test_split(xdata, ylablel, test_size=0.2)\n",
    "X_train.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xscalers = {}\n",
    "for i in range(xdata.shape[2]):\n",
    "    xscalers[i] = StandardScaler()\n",
    "    X_train[:, :, i] = xscalers[i].fit_transform(X_train[:, :, i])\n",
    "\n",
    "yscaler = StandardScaler()\n",
    "y_train = yscaler.fit_transform(y_train.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalers = {}\n",
    "for i in range(xdata.shape[2]):\n",
    "    X_val[:, :, i] = xscalers[i].transform(X_val[:, :, i])\n",
    "\n",
    "y_val = yscaler.transform(y_val.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.706501, 7.02627)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.max(), y_train.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scalers = {}\n",
    "# for i in range(xdata.shape[2]):\n",
    "#     scalers[i] = StandardScaler()\n",
    "#     xdata[:, :, i] = scalers[i].fit_transform(xdata[:, :, i])\n",
    "    \n",
    "# yscaler = StandardScaler()\n",
    "# ylablel = yscaler.fit_transform(ylablel.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot seismogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"4be957d3-5bb1-4e1f-8ab1-0bf7fe22cb84\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\":{\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],\"y\":{\"__ndarray__\":\"l1EWO6ASJjsv1zg7AnFkum3d47qGx8+6bhsDu8OP/bp80AG7UqEDu7w1OLuSZSq7eQzvuqQiHLhgmDM7DuZSOxzZRzuxcCc7U+gFO74I3jqvBfM6szcUO7DJPDsV6XQ7ll+BOwqDfTu+Y/46P39KusNIibrPjKi6YP+2usQ7/Lopeh67/N48u46gULtyNFG7hKpVu2Y3Ubs3mUu7g79hu2uya7vax4a7e4iKuxFqhLvh3oS79EJgu4AMbrsFJHe7rpKiuogOEzo=\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[50]}},\"selected\":{\"id\":\"1055\"},\"selection_policy\":{\"id\":\"1054\"}},\"id\":\"1033\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"1043\"}},\"id\":\"1047\",\"type\":\"CDSView\"},{\"attributes\":{\"data\":{\"x\":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],\"y\":{\"__ndarray__\":\"qWFNulEfOrtrzVC7OeDjunCmdTryNiE7mNGyOvLemjtJMQg7QU1BuybbS7uQJAu78EHTujfVC7uIj2m7EOagu2/LsLvxEa27R5uUu6XverveSUO73Vcpu0S+MbthtDW7XD8gu6TG2bpWI6K6XbfiunIY4rpUZJ+6j2ETu9sbLruGDjq7+Zgvu2PLOLsC+lm770dmu5XKUbsnAzK7kBwku01UD7tK0bo6DKWuOmOLyTrhKPI6nEgOO65CGDtd0CQ7eYczO2i4Pjs=\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[50]}},\"selected\":{\"id\":\"1059\"},\"selection_policy\":{\"id\":\"1058\"}},\"id\":\"1043\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1009\",\"type\":\"LinearScale\"},{\"attributes\":{\"line_alpha\":0.1,\"line_width\":2,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1045\",\"type\":\"Line\"},{\"attributes\":{\"text\":\"\"},\"id\":\"1048\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1053\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1055\",\"type\":\"Selection\"}],\"root_ids\":[\"1002\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.1\"}};\n",
       "  var render_items = [{\"docid\":\"59f75be2-140e-41ee-81e2-84d81ad813c5\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"4be957d3-5bb1-4e1f-8ab1-0bf7fe22cb84\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=600, plot_height=250)\n",
    "x = np.arange(50)\n",
    "i = 10\n",
    "p.line(x, X_train[i][:, 0], line_width=2, color = 'red')\n",
    "p.line(x, X_train[i][:, 1], line_width=2, color = 'blue')\n",
    "p.line(x, X_train[i][:, 2], line_width=2, color = 'black')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape=(50,3))\n",
    "x = tf.keras.layers.Flatten()(inputs)\n",
    "x = tf.keras.layers.Dense(128, activation=tf.nn.relu)(x)\n",
    "x = tf.keras.layers.Dense(64, activation=tf.nn.relu)(x)\n",
    "x = tf.keras.layers.Dropout(0.25)(x)\n",
    "x = tf.keras.layers.Dense(16, activation=tf.nn.relu)(x)\n",
    "outputs = tf.keras.layers.Dense(1)(x)\n",
    "model = tf.keras.Model(inputs=inputs, outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_15\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_8 (InputLayer)         [(None, 50, 3)]           0         \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 150)               0         \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 128)               19328     \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 16)                1040      \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 28,641\n",
      "Trainable params: 28,641\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), \n",
    "              loss=\"mean_squared_error\",\n",
    "#               metrics=[\"mean_absolute_error\"]\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.2677 - val_loss: 0.9971\n",
      "Epoch 2/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.3710 - val_loss: 0.9777\n",
      "Epoch 3/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.7746 - val_loss: 0.9695\n",
      "Epoch 4/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.1450 - val_loss: 0.9692\n",
      "Epoch 5/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 2.3334 - val_loss: 0.9659\n",
      "Epoch 6/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 2.3664 - val_loss: 0.9516\n",
      "Epoch 7/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.0877 - val_loss: 0.9501\n",
      "Epoch 8/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.0513 - val_loss: 0.9444\n",
      "Epoch 9/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.8922 - val_loss: 0.9374\n",
      "Epoch 10/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9891 - val_loss: 0.9334\n",
      "Epoch 11/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.3801 - val_loss: 0.9315\n",
      "Epoch 12/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9366 - val_loss: 0.9310\n",
      "Epoch 13/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.0055 - val_loss: 0.9230\n",
      "Epoch 14/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9463 - val_loss: 0.9106\n",
      "Epoch 15/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9263 - val_loss: 0.9061\n",
      "Epoch 16/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9183 - val_loss: 0.9011\n",
      "Epoch 17/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 1.1711 - val_loss: 0.9055\n",
      "Epoch 18/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9236 - val_loss: 0.8894\n",
      "Epoch 19/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9554 - val_loss: 0.8947\n",
      "Epoch 20/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9257 - val_loss: 0.8906\n",
      "Epoch 21/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9410 - val_loss: 0.8921\n",
      "Epoch 22/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9165 - val_loss: 0.8920\n",
      "Epoch 23/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 2.2408 - val_loss: 0.8871\n",
      "Epoch 24/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 1.0376 - val_loss: 0.8749\n",
      "Epoch 25/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 1.5468 - val_loss: 0.8761\n",
      "Epoch 26/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9407 - val_loss: 0.8734\n",
      "Epoch 27/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9839 - val_loss: 0.8732\n",
      "Epoch 28/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.8863 - val_loss: 0.8633\n",
      "Epoch 29/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.9252 - val_loss: 0.8697\n",
      "Epoch 30/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 4.2835 - val_loss: 0.8642\n",
      "Epoch 31/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 1.2495 - val_loss: 0.8728\n",
      "Epoch 32/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.9368 - val_loss: 0.8608\n",
      "Epoch 33/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.8756 - val_loss: 0.8633\n",
      "Epoch 34/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.8643 - val_loss: 0.8526\n",
      "Epoch 35/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.8538 - val_loss: 0.8534\n",
      "Epoch 36/200\n",
      "285/285 [==============================] - 0s 1ms/step - loss: 0.8516 - val_loss: 0.8504\n",
      "Epoch 37/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.8514 - val_loss: 0.8500\n",
      "Epoch 38/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.8467 - val_loss: 0.8507\n",
      "Epoch 39/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.8486 - val_loss: 0.8556\n",
      "Epoch 40/200\n",
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      "Epoch 158/200\n",
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      "Epoch 160/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7748 - val_loss: 0.8332\n",
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      "285/285 [==============================] - 0s 2ms/step - loss: 0.7716 - val_loss: 0.8351\n",
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      "Epoch 165/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7730 - val_loss: 0.8341\n",
      "Epoch 166/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7720 - val_loss: 0.8656\n",
      "Epoch 167/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7696 - val_loss: 0.8356\n",
      "Epoch 168/200\n",
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      "Epoch 170/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7685 - val_loss: 0.8362\n",
      "Epoch 171/200\n",
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      "Epoch 172/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7714 - val_loss: 0.8425\n",
      "Epoch 173/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7664 - val_loss: 0.8512\n",
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      "Epoch 177/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7659 - val_loss: 0.8351\n",
      "Epoch 178/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7670 - val_loss: 0.8392\n",
      "Epoch 179/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7663 - val_loss: 0.8341\n",
      "Epoch 180/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7647 - val_loss: 0.8395\n",
      "Epoch 181/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7630 - val_loss: 0.8328\n",
      "Epoch 182/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7640 - val_loss: 0.8373\n",
      "Epoch 183/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7673 - val_loss: 0.8427\n",
      "Epoch 184/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7664 - val_loss: 0.8372\n",
      "Epoch 185/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7633 - val_loss: 0.8366\n",
      "Epoch 186/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7639 - val_loss: 0.8374\n",
      "Epoch 187/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7650 - val_loss: 0.8382\n",
      "Epoch 188/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7625 - val_loss: 0.9132\n",
      "Epoch 189/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7643 - val_loss: 0.8464\n",
      "Epoch 190/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7655 - val_loss: 0.8393\n",
      "Epoch 191/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7646 - val_loss: 0.8524\n",
      "Epoch 192/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7636 - val_loss: 0.8365\n",
      "Epoch 193/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7645 - val_loss: 0.8458\n",
      "Epoch 194/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7622 - val_loss: 0.8416\n",
      "Epoch 195/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7808 - val_loss: 0.8391\n",
      "Epoch 196/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7944 - val_loss: 0.8773\n",
      "Epoch 197/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7677 - val_loss: 0.8449\n",
      "Epoch 198/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7627 - val_loss: 0.8417\n",
      "Epoch 199/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7621 - val_loss: 0.8396\n",
      "Epoch 200/200\n",
      "285/285 [==============================] - 0s 2ms/step - loss: 0.7610 - val_loss: 0.8322\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x=X_train,\n",
    "    y = y_train,\n",
    "    batch_size=512,\n",
    "    epochs=200,\n",
    "    validation_data=(X_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  var render_items = [{\"docid\":\"94c4d227-99df-428d-89d7-8f8a17efbddb\",\"root_ids\":[\"3073\"],\"roots\":{\"3073\":\"3d35b4b5-2d6a-4be6-9b21-a7b3b92f98f2\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "3073"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = figure(plot_width=600, plot_height=250)\n",
    "x = np.arange(len(history.history['loss']))\n",
    "p.line(x, history.history['loss'], line_width=2, color = 'blue', legend_label = 'Train MSE')\n",
    "p.line(x, history.history['val_loss'], line_width=2, color = 'red', legend_label = 'Val MSE')\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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