00_-_DL_keras_rolling_forecast_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.212117Z",
     "start_time": "2018-01-07T16:22:02.294196Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.models import Sequential\n",
    "from keras.initializers import he_normal\n",
    "%matplotlib inline\n",
    "\n",
    "# Display only top 5 and bottom 5 rows\n",
    "pd.set_option('display.max_rows', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.216759Z",
     "start_time": "2018-01-07T16:22:04.213432Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set random seed for reproducibility\n",
    "# Note: CuDNN is usually non-deterministic\n",
    "# (can't determine which of the ~3000 threads finish earlier)\n",
    "# And floating points reduction is not perfectly associative due to ULP rounding\n",
    "import numpy as np\n",
    "np.random.seed(1337)\n",
    "import tensorflow as tf\n",
    "tf.set_random_seed(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.271122Z",
     "start_time": "2018-01-07T16:22:04.217846Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Junction</th>\n",
       "      <th>Vehicles</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>20151101001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-01 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>20151101011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-01 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>20151101021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-01 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>20151101031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-01 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>20151101041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>48115</th>\n",
       "      <td>2017-06-30 19:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>20170630194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48116</th>\n",
       "      <td>2017-06-30 20:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>20170630204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48117</th>\n",
       "      <td>2017-06-30 21:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>20170630214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48118</th>\n",
       "      <td>2017-06-30 22:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>20170630224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48119</th>\n",
       "      <td>2017-06-30 23:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>20170630234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48120 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DateTime  Junction  Vehicles           ID\n",
       "0     2015-11-01 00:00:00         1        15  20151101001\n",
       "1     2015-11-01 01:00:00         1        13  20151101011\n",
       "2     2015-11-01 02:00:00         1        10  20151101021\n",
       "3     2015-11-01 03:00:00         1         7  20151101031\n",
       "4     2015-11-01 04:00:00         1         9  20151101041\n",
       "...                   ...       ...       ...          ...\n",
       "48115 2017-06-30 19:00:00         4        11  20170630194\n",
       "48116 2017-06-30 20:00:00         4        30  20170630204\n",
       "48117 2017-06-30 21:00:00         4        16  20170630214\n",
       "48118 2017-06-30 22:00:00         4        22  20170630224\n",
       "48119 2017-06-30 23:00:00         4        12  20170630234\n",
       "\n",
       "[48120 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('./data/train_aWnotuB.csv', parse_dates=[0], infer_datetime_format=True)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.291202Z",
     "start_time": "2018-01-07T16:22:04.272518Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Junction</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-01 00:00:00</th>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-01 01:00:00</th>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-01 02:00:00</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-01 03:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01 04:00:00</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2017-06-30 19:00:00</th>\n",
       "      <td>105.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 20:00:00</th>\n",
       "      <td>96.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 21:00:00</th>\n",
       "      <td>90.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 22:00:00</th>\n",
       "      <td>84.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 23:00:00</th>\n",
       "      <td>78.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Junction                 1     2     3     4\n",
       "DateTime                                    \n",
       "2015-11-01 00:00:00   15.0   6.0   9.0   NaN\n",
       "2015-11-01 01:00:00   13.0   6.0   7.0   NaN\n",
       "2015-11-01 02:00:00   10.0   5.0   5.0   NaN\n",
       "2015-11-01 03:00:00    7.0   6.0   1.0   NaN\n",
       "2015-11-01 04:00:00    9.0   7.0   2.0   NaN\n",
       "...                    ...   ...   ...   ...\n",
       "2017-06-30 19:00:00  105.0  34.0  33.0  11.0\n",
       "2017-06-30 20:00:00   96.0  35.0  31.0  30.0\n",
       "2017-06-30 21:00:00   90.0  31.0  28.0  16.0\n",
       "2017-06-30 22:00:00   84.0  29.0  26.0  22.0\n",
       "2017-06-30 23:00:00   78.0  27.0  39.0  12.0\n",
       "\n",
       "[14592 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = df_train.pivot(index='DateTime',columns='Junction', values='Vehicles')\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T09:50:40.101929Z",
     "start_time": "2017-11-18T09:50:40.100033Z"
    }
   },
   "source": [
    "## Remove Nan (0 vehicle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.294541Z",
     "start_time": "2018-01-07T16:22:04.292293Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize the data on 0, 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.388352Z",
     "start_time": "2018-01-07T16:22:04.295682Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.392012Z",
     "start_time": "2018-01-07T16:22:04.389824Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "# scaler = StandardScaler()\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.403187Z",
     "start_time": "2018-01-07T16:22:04.393296Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train[train.columns] = scaler.fit_transform(train[train.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.413848Z",
     "start_time": "2018-01-07T16:22:04.404713Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Junction</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>DateTime</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-01 00:00:00</th>\n",
       "      <td>0.066225</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.044693</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01 01:00:00</th>\n",
       "      <td>0.052980</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.033520</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01 02:00:00</th>\n",
       "      <td>0.033113</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.022346</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-01 03:00:00</th>\n",
       "      <td>0.013245</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2015-11-01 04:00:00</th>\n",
       "      <td>0.026490</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 19:00:00</th>\n",
       "      <td>0.662252</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>0.178771</td>\n",
       "      <td>0.305556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 20:00:00</th>\n",
       "      <td>0.602649</td>\n",
       "      <td>0.723404</td>\n",
       "      <td>0.167598</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 21:00:00</th>\n",
       "      <td>0.562914</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.150838</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 22:00:00</th>\n",
       "      <td>0.523179</td>\n",
       "      <td>0.595745</td>\n",
       "      <td>0.139665</td>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30 23:00:00</th>\n",
       "      <td>0.483444</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>0.212291</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Junction                    1         2         3         4\n",
       "DateTime                                                   \n",
       "2015-11-01 00:00:00  0.066225  0.106383  0.044693  0.000000\n",
       "2015-11-01 01:00:00  0.052980  0.106383  0.033520  0.000000\n",
       "2015-11-01 02:00:00  0.033113  0.085106  0.022346  0.000000\n",
       "2015-11-01 03:00:00  0.013245  0.106383  0.000000  0.000000\n",
       "2015-11-01 04:00:00  0.026490  0.127660  0.005587  0.000000\n",
       "...                       ...       ...       ...       ...\n",
       "2017-06-30 19:00:00  0.662252  0.702128  0.178771  0.305556\n",
       "2017-06-30 20:00:00  0.602649  0.723404  0.167598  0.833333\n",
       "2017-06-30 21:00:00  0.562914  0.638298  0.150838  0.444444\n",
       "2017-06-30 22:00:00  0.523179  0.595745  0.139665  0.611111\n",
       "2017-06-30 23:00:00  0.483444  0.553191  0.212291  0.333333\n",
       "\n",
       "[14592 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate rolling forecast features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.418100Z",
     "start_time": "2018-01-07T16:22:04.415067Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_forecast_per_junction = 24 * (31 + 31 + 30 + 31) # Days in jul + aug + sep + oct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.423569Z",
     "start_time": "2018-01-07T16:22:04.419249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2952"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_forecast_per_junction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.428939Z",
     "start_time": "2018-01-07T16:22:04.424661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11808"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_forecast_per_junction * 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That will certainly not fit in the GPU VRAM --> We will get inspiration from seq2seq models and do a sliding window of time that matches a week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.434780Z",
     "start_time": "2018-01-07T16:22:04.429974Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_feats = 4\n",
    "seq_len = 24 * 2 # We predict 48 hours\n",
    "num_outputs = 4\n",
    "num_hidden = 4 # We keep 8 days of state\n",
    "bs = 128\n",
    "epochs = 500\n",
    "LOG_PATH = \"checkpoints/\" + time.strftime(\"%Y-%m-%d_%H%M-\")+\"s2s-concat-Conv1d.hdf5\"\n",
    "\n",
    "# pred_len = int(nb_forecast_per_junction / seq_len)\n",
    "# assert nb_forecast_per_junction % seq_len == 0 # Check that we can split forecast into even sequences\n",
    "# print(pred_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We originally had 14592 rows, we will generate sequences of predictions from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.446644Z",
     "start_time": "2018-01-07T16:22:04.435953Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_input_seqs(data, seq_len, train_split=0.9):\n",
    "    seq_len = seq_len + 1\n",
    "    result = []\n",
    "    for index in range(len(data) - seq_len):\n",
    "        result.append(data[index: index + seq_len, :])\n",
    "    result = np.array(result) # shape (14423, 168, 4)\n",
    "    train_ind = round(train_split * result.shape[0])\n",
    "    train = result[:int(train_ind), :, :]\n",
    "    x_train = train[:, :-1, :]\n",
    "    y_train = train[:, -1, :]\n",
    "    x_test = result[int(train_ind):, :-1, :]\n",
    "    y_test = result[int(train_ind):, -1, :]\n",
    "\n",
    "    return [x_train, y_train, x_test, y_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.470210Z",
     "start_time": "2018-01-07T16:22:04.447667Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train, X_test, y_test = make_input_seqs(train.values, seq_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.473965Z",
     "start_time": "2018-01-07T16:22:04.471469Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "def root_mean_squared_error(y_true, y_pred):\n",
    "        return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.483215Z",
     "start_time": "2018-01-07T16:22:04.474991Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.optimizers import adam, rmsprop, sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.508507Z",
     "start_time": "2018-01-07T16:22:04.484308Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adam = adam(clipvalue=0.5) # Clip to avoid exploding gradients\n",
    "rmsprop = rmsprop(lr = 0.005, decay = 0.05)\n",
    "# sgd = sgd(lr = 0.005, decay = 0.05)\n",
    "\n",
    "optim = adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.518806Z",
     "start_time": "2018-01-07T16:22:04.509706Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import *\n",
    "from keras.layers.core import *\n",
    "from keras.layers.recurrent import *\n",
    "from keras.models import *\n",
    "from keras.activations import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.535868Z",
     "start_time": "2018-01-07T16:22:04.519912Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def attention_n_days_ago(inputs, days_ago):\n",
    "    # inputs.shape = (batch_size, time_steps, input_dim)\n",
    "    time_steps = days_ago * 24\n",
    "    suffix = str(days_ago) +'_days'\n",
    "    \n",
    "    # We compute the attention over the seq_len\n",
    "    a = Permute((2, 1),\n",
    "                name='Attn_Permute1_' + suffix)(inputs)\n",
    "    a = Dense(time_steps,\n",
    "              activation='softmax',\n",
    "              name='Attn_DenseClf_' + suffix)(a)\n",
    "    \n",
    "    # Now we convolute so that it average over the whole time window\n",
    "    feats_depth = int(inputs.shape[2])\n",
    "    avg = Lambda(lambda x: K.expand_dims(x, axis = 1),\n",
    "                 name='Attn_Unsqueeze_' + suffix)(inputs)\n",
    "    avg = SeparableConv2D(feats_depth, (1,1),\n",
    "                          name='Attn_DepthConv_' + suffix)(avg)\n",
    "    avg = Lambda(lambda x: K.squeeze(x, 1),\n",
    "                 name='Attn_Squeeze_'+ str(days_ago) + '_days')(avg)\n",
    "    \n",
    "    \n",
    "    a_probs = Permute((2, 1),\n",
    "                      name='Attn_Permute1_' + suffix)(avg)\n",
    "    # out = Multiply(name='Attn_mul_'+ suffix)([inputs, a_probs])\n",
    "    out = Concatenate(name='Attn_cat_'+ suffix)([inputs, a_probs])\n",
    "    return out"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T21:52:05.142089Z",
     "start_time": "2017-11-18T21:52:05.139980Z"
    }
   },
   "source": [
    "# For stateful RNN\n",
    "assert X_train.shape[0] % bs == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:04.580508Z",
     "start_time": "2018-01-07T16:22:04.536912Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Net(num_feats, seq_len, num_hidden, num_outputs):\n",
    "    x = Input(shape=(seq_len, num_feats))\n",
    "\n",
    "    # Encoder RNNs\n",
    "    enc = CuDNNGRU(seq_len,\n",
    "                   return_sequences=True,\n",
    "                   stateful = False,\n",
    "                   name = 'Encoder_RNN')(x)\n",
    "    \n",
    "    # Attention decoders (lag features)\n",
    "    attention_0d = attention_n_days_ago(enc, 0)\n",
    "    attention_1d = attention_n_days_ago(enc, 1)\n",
    "    attention_2d = attention_n_days_ago(enc, 2)\n",
    "    attention_4d = attention_n_days_ago(enc, 4)\n",
    "    attention_1w = attention_n_days_ago(enc, 7)\n",
    "    attention_2w = attention_n_days_ago(enc, 14)\n",
    "    attention_1m = attention_n_days_ago(enc, 30)\n",
    "    attention_2m = attention_n_days_ago(enc, 60)\n",
    "    attention_1q = attention_n_days_ago(enc, 92)\n",
    "    attention_6m = attention_n_days_ago(enc, 184)\n",
    "    attention_3q = attention_n_days_ago(enc, 276)\n",
    "    attention_1y = attention_n_days_ago(enc, 365)\n",
    "    \n",
    "    att = Concatenate(name='attns_cat', axis = 1)([attention_0d,\n",
    "                                                   attention_1d,\n",
    "                                                   attention_2d,\n",
    "                                                   attention_4d,\n",
    "                                                   attention_1w,\n",
    "                                                   attention_2w,\n",
    "                                                   attention_1m,\n",
    "                                                   attention_2m,\n",
    "                                                   attention_1q,\n",
    "                                                   attention_6m,\n",
    "                                                   attention_3q,\n",
    "                                                   attention_1y])\n",
    "    \n",
    "    # How to merge? concat, mul, add, use Dense Layer or convolution ?\n",
    "        \n",
    "    att = Dense(seq_len, activation=None, name='Dense_merge_attns')(att)\n",
    "    # att = Lambda(lambda x: softmax(x, axis = 1),\n",
    "    #              name='Dense_merge_3D_softmax')(att) # Flatten along the concat axis\n",
    "    \n",
    "    # Decoder RNN\n",
    "    dec = CuDNNGRU(num_hidden,\n",
    "                   return_sequences=False,\n",
    "                   stateful = False,\n",
    "                   name='Decoder_RNN')(att)\n",
    "\n",
    "    # Regressor\n",
    "    # Note that Dense is automatically TimeDistributed in Keras 2\n",
    "    out = Dense(num_outputs, activation=None,\n",
    "                name = 'Classifier')(dec) # no activation for regression\n",
    "    \n",
    "    model = Model(inputs=x, outputs=out)\n",
    "                          \n",
    "    model.compile(loss= root_mean_squared_error, optimizer = optim)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.006531Z",
     "start_time": "2018-01-07T16:22:04.581591Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Net(num_feats, seq_len, num_hidden, num_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.021245Z",
     "start_time": "2018-01-07T16:22:06.008090Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 48, 4)        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Encoder_RNN (CuDNNGRU)          (None, 48, 48)       7776        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_0_days (Lambda)  (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_1_days (Lambda)  (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_2_days (Lambda)  (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_4_days (Lambda)  (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_7_days (Lambda)  (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_14_days (Lambda) (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_30_days (Lambda) (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_60_days (Lambda) (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_92_days (Lambda) (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_184_days (Lambda (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_276_days (Lambda (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Unsqueeze_365_days (Lambda (None, 1, 48, 48)    0           Encoder_RNN[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_0_days (Separabl (None, 1, 48, 48)    2400        Attn_Unsqueeze_0_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_1_days (Separabl (None, 1, 48, 48)    2400        Attn_Unsqueeze_1_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_2_days (Separabl (None, 1, 48, 48)    2400        Attn_Unsqueeze_2_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_4_days (Separabl (None, 1, 48, 48)    2400        Attn_Unsqueeze_4_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_7_days (Separabl (None, 1, 48, 48)    2400        Attn_Unsqueeze_7_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_14_days (Separab (None, 1, 48, 48)    2400        Attn_Unsqueeze_14_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_30_days (Separab (None, 1, 48, 48)    2400        Attn_Unsqueeze_30_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_60_days (Separab (None, 1, 48, 48)    2400        Attn_Unsqueeze_60_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_92_days (Separab (None, 1, 48, 48)    2400        Attn_Unsqueeze_92_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_184_days (Separa (None, 1, 48, 48)    2400        Attn_Unsqueeze_184_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_276_days (Separa (None, 1, 48, 48)    2400        Attn_Unsqueeze_276_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_DepthConv_365_days (Separa (None, 1, 48, 48)    2400        Attn_Unsqueeze_365_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_0_days (Lambda)    (None, 48, 48)       0           Attn_DepthConv_0_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_1_days (Lambda)    (None, 48, 48)       0           Attn_DepthConv_1_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_2_days (Lambda)    (None, 48, 48)       0           Attn_DepthConv_2_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_4_days (Lambda)    (None, 48, 48)       0           Attn_DepthConv_4_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_7_days (Lambda)    (None, 48, 48)       0           Attn_DepthConv_7_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_14_days (Lambda)   (None, 48, 48)       0           Attn_DepthConv_14_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_30_days (Lambda)   (None, 48, 48)       0           Attn_DepthConv_30_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_60_days (Lambda)   (None, 48, 48)       0           Attn_DepthConv_60_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_92_days (Lambda)   (None, 48, 48)       0           Attn_DepthConv_92_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_184_days (Lambda)  (None, 48, 48)       0           Attn_DepthConv_184_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_276_days (Lambda)  (None, 48, 48)       0           Attn_DepthConv_276_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Squeeze_365_days (Lambda)  (None, 48, 48)       0           Attn_DepthConv_365_days[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_0_days (Permute)  (None, 48, 48)       0           Attn_Squeeze_0_days[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_1_days (Permute)  (None, 48, 48)       0           Attn_Squeeze_1_days[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_2_days (Permute)  (None, 48, 48)       0           Attn_Squeeze_2_days[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_4_days (Permute)  (None, 48, 48)       0           Attn_Squeeze_4_days[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_7_days (Permute)  (None, 48, 48)       0           Attn_Squeeze_7_days[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_14_days (Permute) (None, 48, 48)       0           Attn_Squeeze_14_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_30_days (Permute) (None, 48, 48)       0           Attn_Squeeze_30_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_60_days (Permute) (None, 48, 48)       0           Attn_Squeeze_60_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_92_days (Permute) (None, 48, 48)       0           Attn_Squeeze_92_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_184_days (Permute (None, 48, 48)       0           Attn_Squeeze_184_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_276_days (Permute (None, 48, 48)       0           Attn_Squeeze_276_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_Permute1_365_days (Permute (None, 48, 48)       0           Attn_Squeeze_365_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_0_days (Concatenate)   (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_0_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_1_days (Concatenate)   (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_1_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_2_days (Concatenate)   (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_2_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_4_days (Concatenate)   (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_4_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_7_days (Concatenate)   (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_7_days[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_14_days (Concatenate)  (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_14_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_30_days (Concatenate)  (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_30_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_60_days (Concatenate)  (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_60_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_92_days (Concatenate)  (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_92_days[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_184_days (Concatenate) (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_184_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_276_days (Concatenate) (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_276_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "Attn_cat_365_days (Concatenate) (None, 48, 96)       0           Encoder_RNN[0][0]                \n",
      "                                                                 Attn_Permute1_365_days[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "attns_cat (Concatenate)         (None, 576, 96)      0           Attn_cat_0_days[0][0]            \n",
      "                                                                 Attn_cat_1_days[0][0]            \n",
      "                                                                 Attn_cat_2_days[0][0]            \n",
      "                                                                 Attn_cat_4_days[0][0]            \n",
      "                                                                 Attn_cat_7_days[0][0]            \n",
      "                                                                 Attn_cat_14_days[0][0]           \n",
      "                                                                 Attn_cat_30_days[0][0]           \n",
      "                                                                 Attn_cat_60_days[0][0]           \n",
      "                                                                 Attn_cat_92_days[0][0]           \n",
      "                                                                 Attn_cat_184_days[0][0]          \n",
      "                                                                 Attn_cat_276_days[0][0]          \n",
      "                                                                 Attn_cat_365_days[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Dense_merge_attns (Dense)       (None, 576, 48)      4656        attns_cat[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "Decoder_RNN (CuDNNGRU)          (None, 4)            648         Dense_merge_attns[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Classifier (Dense)              (None, 4)            20          Decoder_RNN[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 41,900\n",
      "Trainable params: 41,900\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.227143Z",
     "start_time": "2018-01-07T16:22:06.022459Z"
    }
   },
   "outputs": [
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1231\" y=\"-517.8\">Attn_Squeeze_2_days: Lambda</text>\n",
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       "<title>139868549366000-&gt;139868549308032</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2703\" y=\"-529.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2703\" y=\"-506.3\">output:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3078\" y=\"-517.8\">Attn_Squeeze_30_days: Lambda</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3203\" y=\"-529.3\">input:</text>\n",
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       "<title>139868547640792-&gt;139868547034808</title>\n",
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       "<title>139868546551704-&gt;139868546616904</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"4015\" y=\"-517.8\">Attn_Squeeze_92_days: Lambda</text>\n",
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       "<title>139868544849008-&gt;139868545649856</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"4442\" y=\"-517.8\">Attn_Squeeze_184_days: Lambda</text>\n",
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       "<title>139868544613904-&gt;139868544200152</title>\n",
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       "<polyline fill=\"none\" points=\"442.5,-438.5 539.5,-438.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"491\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<!-- 139868553292264&#45;&gt;139868552767472 -->\n",
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       "<title>139868553292264-&gt;139868552767472</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"689\" y=\"-434.8\">Attn_Permute1_1_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"815\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"815\" y=\"-423.3\">output:</text>\n",
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       "<polyline fill=\"none\" points=\"842.5,-438.5 939.5,-438.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"891\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<title>139868552572488-&gt;139868552064360</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1238\" y=\"-434.8\">Attn_Permute1_2_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1364\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1364\" y=\"-423.3\">output:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1440\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<!-- 139868552125632&#45;&gt;139868550781864 -->\n",
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       "<title>139868552125632-&gt;139868550781864</title>\n",
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       "<title>139868549938928</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1624\" y=\"-434.8\">Attn_Permute1_4_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1750\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1750\" y=\"-423.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"1777.5,-415.5 1777.5,-461.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1826\" y=\"-446.3\">(None, 48, 48)</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"1826\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<!-- 139868551032280&#45;&gt;139868549938928 -->\n",
       "<g class=\"edge\" id=\"edge41\">\n",
       "<title>139868551032280-&gt;139868549938928</title>\n",
       "<path d=\"M1726.6934,-498.3799C1722.9164,-489.907 1718.5683,-480.1531 1714.4927,-471.0107\" fill=\"none\" stroke=\"#000000\"/>\n",
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       "<title>139868548852480</title>\n",
       "<polygon fill=\"none\" points=\"2046.5,-415.5 2046.5,-461.5 2395.5,-461.5 2395.5,-415.5 2046.5,-415.5\" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2145\" y=\"-434.8\">Attn_Permute1_7_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2271\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2271\" y=\"-423.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"2298.5,-415.5 2298.5,-461.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2347\" y=\"-446.3\">(None, 48, 48)</text>\n",
       "<polyline fill=\"none\" points=\"2298.5,-438.5 2395.5,-438.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2347\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<g class=\"edge\" id=\"edge42\">\n",
       "<title>139868549308032-&gt;139868548852480</title>\n",
       "<path d=\"M2228.2144,-498.3799C2227.2258,-490.1745 2226.0925,-480.7679 2225.0215,-471.8786\" fill=\"none\" stroke=\"#000000\"/>\n",
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       "<!-- 139868547892784 -->\n",
       "<g class=\"node\" id=\"node44\">\n",
       "<title>139868547892784</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2558\" y=\"-434.8\">Attn_Permute1_14_days: Permute</text>\n",
       "<polyline fill=\"none\" points=\"2659.5,-415.5 2659.5,-461.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2687\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2687\" y=\"-423.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"2714.5,-415.5 2714.5,-461.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2763\" y=\"-446.3\">(None, 48, 48)</text>\n",
       "<polyline fill=\"none\" points=\"2714.5,-438.5 2811.5,-438.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2763\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<g class=\"edge\" id=\"edge43\">\n",
       "<title>139868548737288-&gt;139868547892784</title>\n",
       "<path d=\"M2653.479,-498.3799C2650.7518,-489.9962 2647.6166,-480.3584 2644.6698,-471.2996\" fill=\"none\" stroke=\"#000000\"/>\n",
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       "<!-- 139868547182152 -->\n",
       "<g class=\"node\" id=\"node45\">\n",
       "<title>139868547182152</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3066\" y=\"-434.8\">Attn_Permute1_30_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3195\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3195\" y=\"-423.3\">output:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3271\" y=\"-423.3\">(None, 48, 48)</text>\n",
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       "<g class=\"edge\" id=\"edge44\">\n",
       "<title>139868547034808-&gt;139868547182152</title>\n",
       "<path d=\"M3155.7074,-498.3799C3153.8087,-490.0854 3151.6289,-480.5633 3149.5746,-471.5889\" fill=\"none\" stroke=\"#000000\"/>\n",
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       "<!-- 139868546092616 -->\n",
       "<g class=\"node\" id=\"node46\">\n",
       "<title>139868546092616</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3474\" y=\"-434.8\">Attn_Permute1_60_days: Permute</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3603\" y=\"-446.3\">input:</text>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"3603\" y=\"-423.3\">output:</text>\n",
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       "<g class=\"edge\" id=\"edge45\">\n",
       "<title>139868546616904-&gt;139868546092616</title>\n",
       "<path d=\"M3574.5291,-498.3799C3571.0948,-489.9962 3567.1468,-480.3584 3563.436,-471.2996\" fill=\"none\" stroke=\"#000000\"/>\n",
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       "<!-- 139868544826280 -->\n",
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       "<title>139868544826280</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"4012\" y=\"-434.8\">Attn_Permute1_92_days: Permute</text>\n",
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       "<title>139868545649856-&gt;139868544826280</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"4419\" y=\"-434.8\">Attn_Permute1_184_days: Permute</text>\n",
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       "<title>139868544200152-&gt;139868543977512</title>\n",
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       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"4950\" y=\"-434.8\">Attn_Permute1_276_days: Permute</text>\n",
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       "<polyline fill=\"none\" points=\"2690.5,-.5 2690.5,-46.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2718\" y=\"-31.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"2690.5,-23.5 2745.5,-23.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2718\" y=\"-8.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"2745.5,-.5 2745.5,-46.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2780\" y=\"-31.3\">(None, 4)</text>\n",
       "<polyline fill=\"none\" points=\"2745.5,-23.5 2814.5,-23.5 \" stroke=\"#000000\"/>\n",
       "<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"2780\" y=\"-8.3\">(None, 4)</text>\n",
       "</g>\n",
       "<!-- 139868541622872&#45;&gt;139868540052536 -->\n",
       "<g class=\"edge\" id=\"edge88\">\n",
       "<title>139868541622872-&gt;139868540052536</title>\n",
       "<path d=\"M2698,-83.3799C2698,-75.1745 2698,-65.7679 2698,-56.8786\" fill=\"none\" stroke=\"#000000\"/>\n",
       "<polygon fill=\"#000000\" points=\"2701.5001,-56.784 2698,-46.784 2694.5001,-56.784 2701.5001,-56.784\" stroke=\"#000000\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T11:26:59.040374Z",
     "start_time": "2017-11-18T11:26:59.038563Z"
    }
   },
   "source": [
    "### Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.230530Z",
     "start_time": "2018-01-07T16:22:06.228468Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import History, ModelCheckpoint, CSVLogger, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.236089Z",
     "start_time": "2018-01-07T16:22:06.231758Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "history = History()\n",
    "checkpointer = ModelCheckpoint(filepath= LOG_PATH,\n",
    "                               verbose=1, save_best_only=False)\n",
    "csv_logger = CSVLogger(\"checkpoints/\" + time.strftime(\"%Y-%m-%d_%H%M-\")+'training.log')\n",
    "early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=1, mode='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:22:06.242305Z",
     "start_time": "2018-01-07T16:22:06.237228Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13089, 48, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.175724Z",
     "start_time": "2018-01-07T16:22:06.243381Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12434 samples, validate on 655 samples\n",
      "Epoch 1/500\n",
      "\n",
      "  128/12434 [..............................] - ETA: 216s - loss: 0.0904\n",
      "  384/12434 [..............................] - ETA: 73s - loss: 0.0849 \n",
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      "11392/12434 [==========================>...] - ETA: 1s - loss: 0.0529\n",
      "11648/12434 [===========================>..] - ETA: 0s - loss: 0.0538\n",
      "11904/12434 [===========================>..] - ETA: 0s - loss: 0.0542\n",
      "12160/12434 [============================>.] - ETA: 0s - loss: 0.0546\n",
      "12416/12434 [============================>.] - ETA: 0s - loss: 0.0548Epoch 00001: saving model to checkpoints/2018-01-07_1622-s2s-concat-Conv1d.hdf5\n",
      "\n",
      "12434/12434 [==============================] - ETA: 0s - loss: 0.0549 - val_loss: 0.0717\n",
      "Epoch 2/500\n",
      "\n",
      "  128/12434 [..............................] - ETA: 4s - loss: 0.0711\n",
      "  384/12434 [..............................] - ETA: 4s - loss: 0.0702\n",
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      "10624/12434 [========================>.....] - ETA: 1s - loss: 0.0408\n",
      "10880/12434 [=========================>....] - ETA: 0s - loss: 0.0418\n",
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      "11392/12434 [==========================>...] - ETA: 0s - loss: 0.0434\n",
      "11648/12434 [===========================>..] - ETA: 0s - loss: 0.0442\n",
      "11904/12434 [===========================>..] - ETA: 0s - loss: 0.0447\n",
      "12160/12434 [============================>.] - ETA: 0s - loss: 0.0452\n",
      "12416/12434 [============================>.] - ETA: 0s - loss: 0.0455Epoch 00002: saving model to checkpoints/2018-01-07_1622-s2s-concat-Conv1d.hdf5\n",
      "\n",
      "12434/12434 [==============================] - ETA: 0s - loss: 0.0456 - val_loss: 0.0612\n",
      "Epoch 3/500\n",
      "\n",
      "  128/12434 [..............................] - ETA: 4s - loss: 0.0649\n",
      "  384/12434 [..............................] - ETA: 4s - loss: 0.0641\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "11392/12434 [==========================>...] - ETA: 0s - loss: 0.0408\n",
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      "12160/12434 [============================>.] - ETA: 0s - loss: 0.0426\n",
      "12416/12434 [============================>.] - ETA: 0s - loss: 0.0430Epoch 00003: saving model to checkpoints/2018-01-07_1622-s2s-concat-Conv1d.hdf5\n",
      "\n",
      "12434/12434 [==============================] - ETA: 0s - loss: 0.0430 - val_loss: 0.0584\n",
      "Epoch 4/500\n",
      "\n",
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      "10368/12434 [========================>.....] - ETA: 1s - loss: 0.0338\n",
      "10624/12434 [========================>.....] - ETA: 1s - loss: 0.0352\n",
      "10880/12434 [=========================>....] - ETA: 0s - loss: 0.0361\n",
      "11136/12434 [=========================>....] - ETA: 0s - loss: 0.0370\n",
      "11392/12434 [==========================>...] - ETA: 0s - loss: 0.0375\n",
      "11648/12434 [===========================>..] - ETA: 0s - loss: 0.0383\n",
      "11904/12434 [===========================>..] - ETA: 0s - loss: 0.0388\n",
      "12160/12434 [============================>.] - ETA: 0s - loss: 0.0394\n",
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      "\n",
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      "\n",
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      "\n",
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      "Epoch 00039: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f35ae3e15c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train,\n",
    "          batch_size=bs,\n",
    "          epochs=epochs,\n",
    "          validation_split=0.05,\n",
    "          shuffle=False,\n",
    "          callbacks=[history,checkpointer,csv_logger,early_stop])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving RNN states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.181086Z",
     "start_time": "2018-01-07T16:24:42.176988Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_states(model):\n",
    "    return [K.get_value(s) for s,_ in model.state_updates]\n",
    "\n",
    "def set_states(model, states):\n",
    "    for (d,_), s in zip(model.state_updates, states):\n",
    "        K.set_value(d, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.186074Z",
     "start_time": "2018-01-07T16:24:42.182338Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RNNs_states = get_states(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T10:32:08.713366Z",
     "start_time": "2017-11-18T10:32:08.708839Z"
    }
   },
   "source": [
    "# Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T10:32:08.707276Z",
     "start_time": "2017-11-18T10:32:08.705151Z"
    }
   },
   "source": [
    "## Validation by feeding truth values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.193908Z",
     "start_time": "2018-01-07T16:24:42.187198Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_preds(y_truth, y_pred):\n",
    "    for junction in range(4):\n",
    "        plt.figure\n",
    "        plt.plot(y_truth[:,junction], color = 'blue', label = 'Real traffic')\n",
    "        plt.plot(y_pred[:,junction], color = 'orange', label = 'Predicted traffic')\n",
    "        plt.title('Traffic Forecasting at junction %i' % (junction+1))\n",
    "        plt.xlabel('Number of hours from Start')\n",
    "        plt.ylabel('Traffic')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.198732Z",
     "start_time": "2018-01-07T16:24:42.194906Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_states(model, RNNs_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.789414Z",
     "start_time": "2018-01-07T16:24:42.199864Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:42.794208Z",
     "start_time": "2018-01-07T16:24:42.790915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1454, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot & score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:43.206978Z",
     "start_time": "2018-01-07T16:24:42.795820Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uu+/eSRKlqCTmzwdWrXof48fvjm226WxpugdUEvjChSJnIUWKOBDRG8w8qa39Uk0hRZeC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G2gFmoq/oMyZ0IDBc3FZTLpaSIfmbgyXwPgMgAPAPCcyqb/5hDCg0MIXwvglQB+Y7fmo+UbQJUJ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595f4eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sYKiutiGSgp7ozHPrbiLpN1IIii/2QJNCqQCpgVIfNWVj427WzpclhVrDPN+T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\nWvWzc1Agm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SZ1NPdHUq38qOp9wH0VYfOC/ePTvH+WmFHoHUUyCkVIlyyW3jR6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\nGIZhGDH/DyrxthIofR1VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595ba4dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ffro9/cWrr77K/vvvz5FHHsmzzz5rl/XII49wxRVXALBlyxZOPfVUJkyYwIQJE/jggw+4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\nIZ8OuxoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595f2ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VUFCkgdrO8q1LsjV5LVVHczbRjQCCAT1tIanCl+A2HznaoDufB/lzXGpWvRAK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\nQSEiChEiRIgQQSEiChEiRIgQQeH/A2ED+B5qwCrNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595c35550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:43.211107Z",
     "start_time": "2018-01-07T16:24:43.208087Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "def rmse(y_true, y_pred):\n",
    "    return sqrt(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:43.217382Z",
     "start_time": "2018-01-07T16:24:43.212227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05844184420934498"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation by feeding predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:43.224536Z",
     "start_time": "2018-01-07T16:24:43.218603Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pred_seq(model, to_pred, window_size):\n",
    "    num_preds = to_pred.shape[0]\n",
    "    current = to_pred[0]\n",
    "    predicted = []\n",
    "    for i in range(num_preds):\n",
    "        predicted.append(model.predict(current[np.newaxis,:,:])[0,:])\n",
    "        current = current[1:]\n",
    "        current = np.insert(current, [window_size-1], predicted[-1], axis=0)\n",
    "    return np.asarray(predicted)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-18T20:33:09.832247Z",
     "start_time": "2017-11-18T20:33:09.824365Z"
    }
   },
   "source": [
    "def pred_seq_multi(model, to_pred, window_size, pred_len):\n",
    "    prediction_seqs = []\n",
    "    for i in range(int(len(to_pred)/pred_len)):\n",
    "        current = to_pred[i * pred_len]\n",
    "        predicted = []\n",
    "        for j in range(pred_len):\n",
    "            predicted.append(model.predict(current[np.newaxis,:,:])[0,:])\n",
    "            current = current[1:]\n",
    "            current = np.insert(current, window_size-1, predicted[-1], axis=0)\n",
    "        prediction_seqs.append(predicted)\n",
    "    return np.asarray(predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:43.233674Z",
     "start_time": "2018-01-07T16:24:43.225656Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_states(model, RNNs_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:52.599198Z",
     "start_time": "2018-01-07T16:24:43.234951Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seqpreds = pred_seq(model, X_test, seq_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:52.603706Z",
     "start_time": "2018-01-07T16:24:52.600656Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1454, 4)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seqpreds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot & score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:53.025479Z",
     "start_time": "2018-01-07T16:24:52.604935Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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48cUX8fzzzwNAATS07bfffrjwQvlKxyzLsGrVKmy44YZYvdq+leljB+7vb9X9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\n9MW/aU5B02OU+1cpSg0NK4aS2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f359594dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zv+HaRzJR0WY+naqzrq7lp1v2Q8wjZdokZtK5DADquoUhIMz9yMQ+m666ZmPR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11lKDGyLgHLi74ZKuqG1LBr9BGHmu6rjvj5SB1KLj7HPAVKQleTmlN9hVpsFIoyF9AdAFVZlBKZ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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595947a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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53UWviYYQIgzcAHwZ2As4TQixV0m294HxUspRwIPA73u2lluO87CZ8JQhiGnT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595931908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gUG5U0s9eqQPxqtV9VM6js7sLNOe6f1eX87nSq48kpgH4JoBVzHwqgLEo7HRV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3595859c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(y_test, seqpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-07T16:24:53.029926Z",
     "start_time": "2018-01-07T16:24:53.026792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2594448717047441"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse(y_test, seqpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}