TF_LSTM_LOB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using LSTMs to Predict Mid-Price Movement from the Limit Order Book\n",
    "\n",
    "### Overview\n",
    "- This notebook provides an example of how TensorFlow can be used to train and test a Long-Short Term Memory (a type of recurrent neural network) architecture for classification of mid-price movements from time series of limit order book updates. \n",
    "\n",
    "- Recurrent neural networks are well suited to classification from time series (a.k.a. sequence classification) and time series regression. Unlike typical time series methods, e.g. GARCH, there are no parameteric assumptions on the distribution of the predictors. \n",
    "\n",
    "- The goal of the LSTM predictor is to predict whether the mid-price will remain stationary, tick-up or tick-down in the next period.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quant's guide to machine learning\n",
    "- We choose to build a model which provides strong predictive power, in favor of explantory power. \n",
    "- Our choice to use a recurrent neural network is predicated on each observation in the time series being dependent on previous observations. The ordering of the observations therefore matters and $X$ is not iid.\n",
    "- Once the input data is appropriately scaled, model building starts with 'feature selection' -identifying the relevant features to include in the model. Elastic net or LARS are common techniques for automatic variable selection. Other techniques, commonly used with neural network architectures include auto-encoders. \n",
    "- In this tutorial, we assume that we've already identifed the relevant set of features. \n",
    "- Our primary concern in assessing the extent to which the classifier is over-fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disclaimer\n",
    "- This is not an engineer's tutorial on how to use TensorFlow. We believe that a more statistical approach to describing how to use TensorFlow is insightful.\n",
    "- We do not claim that the LSTM is superior in performance to any other method, including classical econometrics methods adapted for classification with a logistic link function.\n",
    "\n",
    "- Due to licensing restrictions, the example time series data is fictitious and designed to be representative of tick level data.\n",
    "\n",
    "- For the purposes of this tutorial, the training and testing phases have been simplified. A more robust approach would use time-series cross validation and typically split the dataset into three disjoint sub-sets to allow for training, verification (a.k.a. tuning) and testing.\n",
    "\n",
    "- The integration of the classifier with a strategy backtesting environment is not described here.\n",
    "\n",
    "- This notebook has been tested in a virtual environment run on a MAC and Linux. It has not been tested on Windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "\n",
    "# module_path = os.path.abspath(os.path.join('../..')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have overriden some native TensorFlow functions to allow for more flexibility. Let's import these modules here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import sys\n",
    "# if module_path not in sys.path:\n",
    "#     sys.path.append(module_path)\n",
    "\n",
    "# from core.ml_analysis import MLOperator, MLEvaluator\n",
    "from ml_analysis import MLOperator, MLEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary set of modules from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras import optimizers\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "import keras.backend as K\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Data\n",
    "- The example dataset is a chronologically ordered time series. Each observation is recorded at each limit order book update. The ordering of the observations matters and each observation is not assumed to be independent (as with cross-sectional classification data). \n",
    "\n",
    "- For simplicity, since the timestamps are not important for this tutorial, an integer index has been used for the Pandas Dataframe. A real dataset would typically be indexed by a unique timestamp. \n",
    "\n",
    "- Each observation $X$ has three variables (a.k.a. features) representing the liquidity and best limit order price quotes in the book. To predict whether the mid-price will tick-down, remain the same or tick-up, each observation is labelled by the categorical response variable $Y\\in\\{-1,0,1\\}$ respectively.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the Pandas Dataframe, viewing the first ten rows and the distribution of the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/HFT.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   feature_1  feature_2  feature_3  label\n",
       "0   0.515301       0.72   0.710953      0\n",
       "1   0.515301       0.72   0.710953      0\n",
       "2   0.515301       0.72   0.710953      0\n",
       "3   0.515301       0.72   0.710953      0\n",
       "4   0.515301       0.72   0.710953      0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11457bb90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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EvLWGyrU5DAYVSVJnrKlIPbGmoiGzpiJJ6p1BRRMxb62hcm0Og0FFktQZaypST6ypaMis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\niiSpMwYVSVJnDCqSpM4YVCRJnfn/qJIs774UT3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11457b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.label.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    1028280\n",
       " 1       2629\n",
       "-1       2623\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that there is a class imbalance problem. Proceeding without correcting for the imbalance will lead to spurious results. \n",
    "\n",
    "Our exposition includes examples of exploratory data analysis, designed to provide intuition into the level of non-linearity in the map $Y=F(X)$. Let's first view the distribution of our features by each label 0,-1, then 1. \n",
    "\n",
    "*Note: We just consider feature_1, but you should check the other features too.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11469ab10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148ddb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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pL43icDtAROwGNgC7gU3ATX7bP7kjDzury7lInIvEucgvT4+AiHicxsdBx7p0gu1XACvy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149ebed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for lab in df.label.unique():\n",
    "    print lab\n",
    "    df.feature_1[df.label==lab].hist()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Preparation of the training and testing sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a simple 80/20 splitting rule, ensuring that the training set pre-dates the testing set (to avoid look-ahead bias)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_weight = 0.8\n",
    "split = int(len(df)*train_weight)\n",
    "df_train = df[:split]\n",
    "df_test = df[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's instantiate our MLOperator class which will be used for performing high-level data processing tasks on top of Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "operator = MLOperator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sampling\n",
    "\n",
    "**Training Set**: Resolve the time series class imbalance problem by sampling in a neighborhood of the up and down ticks. We choose this neighborhood to be a lagging window of size 20. Although simplistic, this preserves at least some of the covariance structure in the time series data.\n",
    "\n",
    "**Testing Set**: Do not sample - leave imbalanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_steps = 20\n",
    "use_features = ['feature_1']\n",
    "y_train = df_train.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampled_idx = operator.get_samples_index(y_train.iloc[n_steps-1:], 'min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = df_train[use_features]\n",
    "y_test = df_test.label.iloc[n_steps-1:]\n",
    "x_test = df_test[use_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data formatting for LSTMs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the following function for reshaping the data into times series classification format. For example, consider a univariate time series of increasing integers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting the sequence length to 10, we move the window forward by one observation at a time and construct new sequences:\n",
    "\n",
    "1 2 3 4 5 6 7 8 9 10\n",
    "\n",
    "2 3 4 5 6 7 8 9 10 11\n",
    "\n",
    "3 4 5 6 7 8 9 10 11 12\n",
    "\n",
    "4 5 6 7 8 9 10 11 12 13\n",
    "\n",
    "5 6 7 8 9 10 11 12 13 14\n",
    "\n",
    "6 7 8 9 10 11 12 13 14 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_LSTM_data(x_train, y_train, x_test, y_test, sampled_idx, n_steps):\n",
    "    # training set\n",
    "    x_train_m = x_train.as_matrix()\n",
    "    x_train_list = []\n",
    "    for idx in sampled_idx:\n",
    "        int_idx = y_train.index.get_loc(idx)\n",
    "        x_train_list.append(x_train_m[(int_idx-n_steps+1):int_idx+1])\n",
    "\n",
    "    x_train = np.array(x_train_list)\n",
    "    y_train = pd.get_dummies(y_train[sampled_idx]).as_matrix()\n",
    "\n",
    "    # test set\n",
    "    x_test_m = x_test.as_matrix()\n",
    "    x_test_list = []\n",
    "    for i in range(x_test.shape[0] - n_steps+1):\n",
    "        x_test_list.append(x_test_m[i: (i + n_steps)])\n",
    "    x_test = np.array(x_test_list)\n",
    "\n",
    "    y_test = pd.get_dummies(y_test).as_matrix()\n",
    "    \n",
    "    return x_train, y_train, x_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test = get_LSTM_data(x_train, y_train, x_test, y_test, sampled_idx, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nrow = 3000\n",
    "x_valid = x_test[0:nrow]\n",
    "y_valid = y_test[0:nrow]\n",
    "x_test = x_test[nrow:]\n",
    "y_test = y_test[nrow:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6294, 20, 1) (6294, 3) (203688, 20, 1) (203688, 3) (3000, 20, 1) (3000, 3)\n"
     ]
    }
   ],
   "source": [
    "print x_train.shape,y_train.shape,x_test.shape,y_test.shape, x_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM Model Specification\n",
    "\n",
    "We now proceed with the model specification in Keras. Consistent with the window size, we specify a ten layer LSTM network and squash each unit output with a softmax function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(10, stateful=False, input_shape=(x_train.shape[1], x_train.shape[-1])))\n",
    "model.add(Dense(y_train.shape[1], activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Configurations\n",
    "initial_lrate = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adagrad = optimizers.Adagrad(lr=initial_lrate, epsilon=1e-08, decay=0.0)\n",
    "adam = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "callbacks_list = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# learning rate schedule\n",
    "# LearningRate = InitialLearningRate * DropRate^floor(Epoch / EpochDrop)\n",
    "# Or: decrease half of the learning rate after every epochs_drop (20 epochs for example)\n",
    "def step_decay(epoch):\n",
    "    lrate = initial_lrate\n",
    "    epochs_drop = 70\n",
    "    drop = 0.5\n",
    "    if (epoch % epochs_drop == 0):\n",
    "        lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n",
    "        print(\"Decreased Learning Rate to: {}\".format(lrate))\n",
    "    return lrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lrate = LearningRateScheduler(step_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "callbacks_list.append(lrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# initial settings\n",
    "n_epoch = 2000\n",
    "n_batch = 500\n",
    "last_epoch_th = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6294 samples, validate on 3000 samples\n",
      "Decreased Learning Rate to: 0.0001\n",
      "Epoch 1/2000\n",
      "6294/6294 [==============================] - 1s - loss: 1.1046 - acc: 0.2666 - val_loss: 1.0960 - val_acc: 0.6530\n",
      "Epoch 2/2000\n",
      "3000/6294 [=============>................] - ETA: 0s - loss: 1.1030 - acc: 0.3353"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-7c05cf2e07dd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'history = model.fit(x_train, y_train, epochs=n_epoch, batch_size=n_batch, verbose=1,\\n          validation_data=(x_valid, y_valid), shuffle=True, callbacks = callbacks_list, \\n                    initial_epoch = last_epoch_th)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2101\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2102\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2103\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2104\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1174\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m             \u001b[0;32mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1177\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1178\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1498\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = model.fit(x_train, y_train, epochs=n_epoch, batch_size=n_batch, verbose=1,\n",
    "          validation_data=(x_valid, y_valid), shuffle=True, callbacks = callbacks_list, \n",
    "                    initial_epoch = last_epoch_th)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Serialization\n",
    "If no further adjustments to the training are needed then we can save the model to HDF5 format and simply reload it in future notebook sessions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the model\n",
    "model.save('model/lstm.hdf5', overwrite=True)  # creates a HDF5 file 'my_model.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist = history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "history_path = \"history/lstm_history.json\"\n",
    "with open(history_path, 'w') as fp:\n",
    "    json.dump(hist, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "model = load_model('model/lstm.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 21s, sys: 56.6 s, total: 2min 17s\n",
      "Wall time: 1min 22s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred_df_train, pred_df_test, _ = operator.get_pred_dfs(x_train, x_test, y_train, y_test, \n",
    "                                                       classifier='tensorflow', model=model, \n",
    "                                                       columns=['-1', '0', '1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.77281106  0.74409251  0.76955504]\n",
      "[ 0.02784449  0.84006876  0.02666575]\n"
     ]
    }
   ],
   "source": [
    "train_f1 = f1_score(pred_df_train.true, pred_df_train.predict, average=None)\n",
    "test_f1 = f1_score(pred_df_test.true, pred_df_test.predict, average=None)\n",
    "print train_f1\n",
    "print test_f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We instantiate our MLEvaluator class to store the performance results of the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "evaluator = MLEvaluator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned, our primary objective in assessing our LSTM model, is to evaluate the extent of over-fitting. One simple method for measuring the bias-variance tradeoff compares the performance of the model on the training and test set. We use confusion matrices and ROC curves to measure the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[1677  205  216]\n",
      " [ 305 1480  313]\n",
      " [ 260  195 1643]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.7993327   0.09771211  0.1029552 ]\n",
      " [ 0.14537655  0.70543375  0.1491897 ]\n",
      " [ 0.12392755  0.09294566  0.78312679]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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64k1GDNwo75z7DOjK7U+/zdKyxIx5i3hrzscM2HT9urkxNXr5n2tt6b5pD7pt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\nYJ+efHfkcD7+6KM6vxc1bj8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ed5f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VRZ+rhr3NVcPKZ+132aQrx+zWm6/mzF3ksVc//DaDH3mnxuv+7dD+jHhtSsPf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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114eae350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XbHfqVPF9IiJ10dNPr+Hhh78GYPTobsyePYJmzfQJsiuqAc+nBLxueH3D69y9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\nd4HewI+52vQG5gCo6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149f5d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.set_pred_df(pred_df_train)\n",
    "cf_mx_train = evaluator.generate_confusion_matrix()\n",
    "\n",
    "fig = evaluator.plot_confusion_matrix(cf_mx_train.as_matrix(), [-1, 0, 1])\n",
    "fig = evaluator.plot_confusion_matrix(cf_mx_train.as_matrix(), [-1, 0, 1], \n",
    "    normalize=True)\n",
    "fig = evaluator.plot_roc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[   400     53     71]\n",
      " [ 27744 146852  28072]\n",
      " [    63     46    387]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.76335878  0.10114504  0.13549618]\n",
      " [ 0.13689384  0.72459392  0.13851225]\n",
      " [ 0.12701613  0.09274194  0.78024194]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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2qKPpueN2HDawPz//xVV1/zZw8le+VHd59Xe/9/1l9pegtEbwM3vvwa4792Tv3Xel/4BD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\nkiSpZTZckiRJLbPhkiRJatn/AJ0Yp0MxlJe2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ed88310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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alfcEbgFWBQanlE7LypsDtwHbAR8AR6WUJtSlv7X5luImETEwIkZFxJgFt7o8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\nzaYL38uOTwI2KGvfMSurVF7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/ZP/eBwBw792uoVnZ1eb9dcCBBzHkn4PZcvNNWWP1Nbjx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\nC4/ug/Xla9p/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ed88110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hSr7dTDXgEnxOn4ZOnWDnTgC+/24O/83aygsbXsi95JObPmFo66G+ilCK4fRp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3Ps2Fm2bDno65CMMcbkIzY2FofDwd69e4vtHBMnTsThcBAfH19s57hSMTExtGjRwtdhmBJy7tw5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\ncUGnMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120fe9c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluator.set_pred_df(pred_df_test)\n",
    "cf_mx_test = evaluator.generate_confusion_matrix()\n",
    "\n",
    "fig = evaluator.plot_confusion_matrix(cf_mx_test.as_matrix(), [-1, 0, 1])\n",
    "fig = evaluator.plot_confusion_matrix(cf_mx_test.as_matrix(), [-1, 0, 1], \n",
    "    normalize=True)\n",
    "fig = evaluator.plot_roc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##  Learning Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We seek to evaluate the bias-variance tradeoff as a function of the size of the training set. This can address the question of whether the sample size is 'sufficient' for our classifier - we expect the performance on the test set to converge to that on the training set with increasing sample size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_bvt = df_train.label\n",
    "x_train_bvt = df_train[use_features]\n",
    "y_test_bvt = df_test.label.iloc[n_steps-1:]\n",
    "x_test_bvt = df_test[use_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 400, 700, 1000, 1300, 1600, 1900]\n"
     ]
    }
   ],
   "source": [
    "n_exp = 1\n",
    "n_obs_list = range(100, len(sampled_idx)/3, 300)\n",
    "print n_obs_list\n",
    "\n",
    "# initial settings\n",
    "n_epoch = 2000\n",
    "n_batch = 500\n",
    "last_epoch_th = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1_df_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bvt_callbacks_list = list()\n",
    "bvt_callbacks_list.append(lrate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "experiment  0\n",
      "number of observations  100\n",
      "300\n",
      "(300, 20, 1) (300, 3) (206688, 20, 1) (206688, 3)\n",
      "Decreased Learning Rate to: 0.0001\n",
      "Epoch 1/2000\n",
      "0s - loss: 0.5929 - acc: 0.7467\n",
      "Epoch 2/2000\n",
      "0s - loss: 0.5923 - acc: 0.7500\n",
      "Epoch 3/2000\n",
      "0s - loss: 0.5915 - acc: 0.7567\n",
      "Epoch 4/2000\n",
      "0s - loss: 0.5906 - acc: 0.7567\n",
      "Epoch 5/2000\n",
      "0s - loss: 0.5899 - acc: 0.7500\n",
      "Epoch 6/2000\n",
      "0s - loss: 0.5893 - acc: 0.7467\n",
      "Epoch 7/2000\n",
      "0s - loss: 0.5890 - acc: 0.7500\n",
      "Epoch 8/2000\n",
      "0s - loss: 0.5888 - acc: 0.7500\n",
      "Epoch 9/2000\n",
      "0s - loss: 0.5887 - acc: 0.7533\n",
      "Epoch 10/2000\n",
      "0s - loss: 0.5885 - acc: 0.7533\n",
      "Epoch 11/2000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-f1540bac19e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m         model.fit(x_train_sm, y_train_sm, epochs=n_epoch, batch_size=n_batch, verbose=2,\n\u001b[1;32m     14\u001b[0m                     \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbvt_callbacks_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     initial_epoch = last_epoch_th)\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         pred_df_train, pred_df_test, _ = operator.get_pred_dfs(x_train_sm, x_test_sm, y_train_sm, y_test_sm, \n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1498\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for exp in range(n_exp):\n",
    "    print 'experiment ', exp\n",
    "    for n_obs in n_obs_list:\n",
    "        print 'number of observations ', n_obs\n",
    "        sampled_idx_sm = operator.get_samples_index(y_train_bvt.iloc[n_steps-1:], n_obs)\n",
    "        print len(sampled_idx_sm)\n",
    "        \n",
    "        x_train_sm, y_train_sm, x_test_sm, y_test_sm = get_LSTM_data(x_train_bvt, y_train_bvt, x_test_bvt, y_test_bvt, \n",
    "                                                                     sampled_idx_sm, n_steps)\n",
    "        \n",
    "        print x_train_sm.shape,y_train_sm.shape,x_test_sm.shape,y_test_sm.shape\n",
    "\n",
    "        model.fit(x_train_sm, y_train_sm, epochs=n_epoch, batch_size=n_batch, verbose=2,\n",
    "                    shuffle=True, callbacks = bvt_callbacks_list, \n",
    "                    initial_epoch = last_epoch_th)\n",
    "        \n",
    "        pred_df_train, pred_df_test, _ = operator.get_pred_dfs(x_train_sm, x_test_sm, y_train_sm, y_test_sm, \n",
    "                                                       classifier='tensorflow', model=model, \n",
    "                                                       columns=['-1', '0', '1'])\n",
    "        \n",
    "        train_f1 = f1_score(pred_df_train.true, pred_df_train.predict, average=None)\n",
    "        test_f1 = f1_score(pred_df_test.true, pred_df_test.predict, average=None)\n",
    "\n",
    "        f1_df = pd.DataFrame([train_f1, test_f1])\n",
    "        f1_df.columns = ['-1', '0', '1']\n",
    "        f1_df.index = ['train', 'test']\n",
    "        f1_df['n_obs'] = n_obs * 3\n",
    "        f1_df_list.append(f1_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_f1_df = pd.concat(f1_df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_f1_df.to_csv('history/lstm_bias_variance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_f1_df = pd.read_csv('history/lstm_bias_variance.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_f1_df['macro_avg'] = final_f1_df[['-1','0','1']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aNInw8PCANpXS4vbSSy8RHR3NK6+8Qp06dYode/rpp4mMjOTtt98u/UYrEbWd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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a2b450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MD7p2pXThNkMeMKdrV64bOrRWXq+ycbvd7Nq1i0suuaSUgCwuLmbNmjU1NLLw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a2bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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v37+fH374oULXPB6a8+1Rq/L2IiMlAE9LkwA7MhLGjYPBg2WFzFmzZLn6Tp0g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124a2bf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4zZAHLO/Th2vHjKmT56tqSktLOXToEJdccomfgCwpKWHr1q211LPQufjii3E6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\nAKBF/j83e31srh66VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e8a5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in ['macro_avg', '1', '0', '-1']:\n",
    "    evaluator.plot_learning_curve(final_f1_df, label, 'n_obs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Loss function convergence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "history_path = \"history/lstm_history.json\"\n",
    "with open(history_path, 'r') as fp:\n",
    "    hist = json.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x125deab90>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gzzd8zvXTri+1zMtXvYwz30nP5j0BOJBxgIToBC5seiGhjlBEhP3p+4mNjKVeRD2MMeWe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\nk4pSSim/+X8aIl+XIaHwlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125dd85d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4.5))\n",
    "train_line = plt.plot(range(len(hist['acc'])), hist[\"acc\"],  color=\"b\", label=\"Training\")\n",
    "vald_line = plt.plot(range(len(hist['acc'])), hist[\"val_acc\"], color=\"g\", label=\"Validattion\")\n",
    "plt.legend(loc=\"best\", fontsize=15)\n",
    "plt.ylim(-0.1, 1)\n",
    "plt.title(\"accuracy vs epoch\")\n",
    "plt.xlabel('epoch', fontsize=20)\n",
    "plt.ylabel('score', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x125e83f90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SShVWOelCugAUT7bcCTtNy6Jk604B5XCfZ4H1xpiuIlIH+E1EmhljzlxacEyy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\nBxUzM6uNg4qZmdXmLxk0XiRruf1jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125df3210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 4.5))\n",
    "train_line = plt.plot(range(len(hist['loss'])), hist[\"loss\"],  color=\"b\", label=\"Training\")\n",
    "vald_line = plt.plot(range(len(hist['loss'])), hist[\"val_loss\"], color=\"g\", label=\"Validattion\")\n",
    "plt.legend(loc=\"best\", fontsize=15)\n",
    "plt.title(\"loss vs epoch\")\n",
    "plt.xlabel('epoch', fontsize=20)\n",
    "plt.ylabel('loss', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM Model Regression\n",
    "\n",
    "An alternative approach to classification is to formulate the response as a continuous variable and predict the 'smart price' as a uni-variate time series. The smart price is the volume weighted mid-price, represented by feature_3 in our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "use_features = ['feature_3']\n",
    "target = 'feature_3'\n",
    "n_steps = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_weight = 0.8\n",
    "split = int(len(df)*train_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = df.iloc[:split]\n",
    "std_df_train = df_train[use_features].apply(lambda x: (x - x.mean()) / x.std())\n",
    "df_test = df.iloc[split:]\n",
    "std_df_test = df[use_features].apply(lambda x: (x - x.mean()) / x.std()).iloc[split:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_lagged_features(value, n_steps):\n",
    "    lag_list = []\n",
    "    for lag in range(n_steps, 0, -1):\n",
    "        lag_list.append(value.shift(lag))\n",
    "    return pd.concat(lag_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_list = []\n",
    "for use_feature in use_features:\n",
    "    x_train_reg = get_lagged_features(std_df_train, n_steps).dropna()\n",
    "    x_train_list.append(x_train_reg)\n",
    "x_train_reg = pd.concat(x_train_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_ords = []\n",
    "for i in range(n_steps):\n",
    "    for j in range(len(use_features)):\n",
    "        col_ords.append(i + j * n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train_reg = x_train_reg.iloc[:, col_ords]\n",
    "y_train_reg = df_train.loc[x_train_reg.index, [target]].as_matrix()\n",
    "x_train_reg = np.reshape(x_train_reg.as_matrix(), (x_train_reg.shape[0], x_train_reg.shape[1] / len(use_features), len(use_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test_list = []\n",
    "for use_feature in use_features:\n",
    "    x_test_reg = get_lagged_features(std_df_test, n_steps).dropna()\n",
    "    x_test_list.append(x_test_reg)\n",
    "x_test_reg = pd.concat(x_test_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_test_reg = x_test_reg.iloc[:, col_ords]\n",
    "y_test_reg = df_test.loc[x_test_reg.index, [target]].as_matrix()\n",
    "x_test_reg = np.reshape(x_test_reg.as_matrix(), (x_test_reg.shape[0], x_test_reg.shape[1]/len(use_features), len(use_features)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(826815, 10, 1) (826815, 1) (206697, 10, 1) (206697, 1)\n"
     ]
    }
   ],
   "source": [
    "print x_train_reg.shape,y_train_reg.shape,x_test_reg.shape,y_test_reg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg_model = Sequential()\n",
    "reg_model.add(LSTM(10, input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1])))\n",
    "reg_model.add(Dense(1))\n",
    "reg_model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "last_epoch_th = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      " 24000/826815 [..............................] - ETA: 104s - loss: 0.2189"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-aa321248cfa8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreg_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_reg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlast_epoch_th\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1497\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1498\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1150\u001b[0m                 \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1152\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2227\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[0;32m-> 2229\u001b[0;31m                               feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m   2230\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "reg_model.fit(x_train_reg, y_train_reg, epochs=1000, batch_size=500, verbose=1, initial_epoch = last_epoch_th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the model\n",
    "reg_model.save('model/lstm_regression_HFT.hdf5', overwrite=True)  # creates a HDF5 file 'my_model.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# returns a compiled model\n",
    "# identical to the previous one\n",
    "reg_model = load_model('model/lstm_regression_HFT.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "206528/206697 [============================>.] - ETA: 0sCPU times: user 3min 42s, sys: 2min 30s, total: 6min 12s\n",
      "Wall time: 3min 49s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# make predictions\n",
    "pred_train = reg_model.predict(x_train_reg, verbose=1)\n",
    "pred_test = reg_model.predict(x_test_reg, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xxmA1mZeIiIiIiIhIdCXU8lAiIiIiIomuffv2bNmyJdZhiERMu3bt2Lx5c0xj0NBrERER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\nAXQA8CSA53Wqo2YXfl+peYoRqazXyWR9z8RkIiQkWcnFK6d8u1t8G1v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YAAAg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\npB9J+kDee7HTOfeS6IswLJJ0k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      "text/plain": [
       "<matplotlib.figure.Figure at 0x136ddc510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ct8Pvtp137GRF4oqAjpOzKIfcz41F5Mv3l1O2t4ycRTmu7bv+vIuNEzay7bJt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\nl6tuqMKix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frQQ4rqTaeU6oeWQohKALsAfA2eDHA7gCoADQDavP/olfYtpXQB+BiAvwA4\nDOBJKeVRIcRdQogPRbpE7ZiJiIgoPXz/UX7frFkJ99EbpchUOkXcpznBvs5Yrej27jOthu9hwQKF\nGXbf9Ox0/SH0y8WLI67njnS/EacTA95tqDpXrPAfPzE1hdBHILH2287mH3XDfx0GANS/oz6LoyCi\nfJBIpvnzABYDuBOerahcAL4F4EsAroJn+6hJeLLQikgpn4Mn+A489v0obd+fwJiJiIgoTbYEBE2J\nOJFAwJkKSgp+KS0K9k6Fa4ID+QLtwC2TYknn9GwAuKW2NqwadjSX7HbovN+NPuQ7OjI5CQD4cXs7\nJIB6vT5lY1RTpC0ee68dGlO6v1UimgkS+TfFTQBekFL+WAbMWZIeOwG8HkAHgM+laIxERESUg3xT\ncjdGWd+q1IjTiaIsTT2OR8mo3hhjP+kLMbLoxywWVWNJZyGwOxoaFAXvvgrpYy4XDnvHH7oH85B3\nfXeVTof3zZqFN8T4frI9fbCoPBfK+xBRrkskaG4BsCfgvRsBa5qllP0A/gTP2mQiIiKaoYa9wVEq\n1trWFIUHL4n0unt8HN/2rrPOlF1jY1HPnbfZop4bc7n82VoltN7/TTTQ/PLZs1HP3aVwev0BbxY5\n0Moo+3PfXJv6fY9T/dhAUxL7T+GT/3oSzonEi7wR0cyQSNBsAYKWq4wCaAxp0wdPgTAiIiKaoUaT\nqBgdqjrGulc1mzmdtloVtUtlhtO3tjcR1REeFkSjdBp3NOMxipXF+v5D3dPc7H9tEAImrTasTW7O\nGwg2/1vzseA/F8Rs0/O1HvQ/3p+hERFRrkrk37498GSbfY4A2CyECOxrE4BLyQyMiIiIctu5CAFq\nosFSeYTAy2fr6GiCvcaWqsDuW/OVbBYyLXA6c5mKoNkXYKdjSrMxTkBeEnA+sG2zwRBx2nixwgBf\nSXXyQKkMxls+2YLam+Jnw3W1yh8oENHMlEjQvAXAtWK6EsMvAMwH8EchxEeFEL8CsB7AH1M0RiIi\nIspBSrO68cwxGPDa6uqw474/NJwqAqtEgyq1wRswHbx+sqUFC1XsMT0UkJkuUZE99mWaA6/oiTH9\n28c3zsBgdyxklkC8KfZ/v/JKAIB18+agfqJlqHN1jXoihGHmfBYiSkwi1Q8ehWdLqWZ4ss7fA/Aq\nALcAuMHbZhs8VbaJiIhohvp4UxPOKgja4jm7YUPM85mqbxwpNIoXLvnOv7ByJRq3bw87v7q0FHsm\nJvzv72ho8BdOe09DA65WWURtbVkZrgm4ZknIHs+R1ob7qlc/1tHhP9bhvW5BcTF6rFY0x9lje1VZ\nGVaWlsKg0eCelhZcW1mJGp0u7H7LTCZcVV6O9eXlij5PNguB/aK/Hy0GQ9yfgcbACttEhU510Cyl\n3Avg7oD3TgBvEUKsBrAAwBkAL0sp1SxBIiIiojzzsYC1remkZpuhbOUEG/R6SLM57PjuNWuiXvPo\nokWq7/PS6tVB75uNxoj3DVRRVBTWpsNkintdIL1Gg73ez2LSarG5sjLovJq+kqH056skGL/tyBFs\nKC/H9lWrYrZj0ExEKauzL6Xcg+Cq2kRERERJUxMIKw2wlWY4OTE3sanrivtOQ59qfmaGGNPjpcsz\nungVtolo5ks4aBZCtAKog+ffdwNSynMpGxUREREVPF8AnK7AlQFx/lEz60CJshgF6JyjnnXfQsvf\nFKJCp+rRmRCiVgjxn0KIiwBOAdgF4CUAp4UQvUKI/xBChFfyICIiooKQjvBCVaY5xfeuVFHdmtTL\n5ppmILiSeSiXJfoWXURUWBQHzUKIKwDsBvAJAA0AXAD6AQx4XzcCuAfAbiHEvNQPlYiIiApRqrOL\naqwqK8vavXNFNr//dGuJUQDNbWF5HiLyUBQ0e/dgfhzAHHi2nHo1gFIp5SwpZSOAMngqZ78AoA3A\nz9IyWiIiIqIYFBeKSuM63ZkmrWuas7hPMxB7ayzHgCPqOSIqLEozzTcAWAPglwCul1L+Q0pp952U\nUtqklH+DZ+upXwO4SgjxmpSPloiIiAqGCPnfdPUPRJ8mPHNzrARED5pdFhf2bdqX4dEQUa5SGjS/\nFYANwMdljEeC3nMfA+AA8Lbkh0dERESFLlNrmiNdy3x0bslUptl60goA0DfpU3xHIspHSoPmVQC2\nSSkH4jWUUvYD2Oq9hoiIiCgpzPZmTrq/a0NIkJrthxLRgmbbeRsqr6uEvo5BMxEpD5pbABxW0e9h\nAK3qh0NERETk4Z+eraIQVar3aU53H6ROJtc0c39mIvJR+m+DcgAjKvodgac4GBERERWQfNpyKhVV\noZkFVyf0O8/WgweXd7WhNsbvQPlV5ZkaDhHlOKVBsx6ebaWUcnuvISIiIkpKpvJ9zBwXjqItWwAA\nDTpd1DYN72zI1HCIKMcVqWjL/5YQERFRxqmanp3GcVDqqf3jMtnZAf9x7hxqAwJlgyb8kYzb7tmf\n2TAn+h7ORFRY1ATNDwohHkzXQIiIiIgCpTMAZiagMH361Cn/6w/Pnh2xjXPICQDQ6LimmYg81ATN\nav/bxf8eERERUdLStqY54DX/aIkund9NjJ1MI0r0QcplhwOfDQiY/6mxMWpbx6AjwbsQ0UylKGiW\nUvJRGxEREalWvXUrhp1OrCotTbiPZDPOT/T1weJy4YNdXbi9vh4/7+9XfB8G0/lv1OlE7bZtAICn\nly7FTTU1EELg7q6uiO3dU+5MDo+I8gCDYSIiIkqLczYbhp2eqa57JyYS7ieZitiisxPvOnoUH/QG\nSJEC5tuPHMFxi0XVmFxSqs6SUrgto6No27Ejbf27pETl1q0AgHfV1+Pm2tq466JdE8pq31q6LNi9\nenfSYySi3KdmejYRERFRTEIIwBtMvvXw4eT7QmKFwERnp//Yw+3tGHM6cWVpKa6uqMAxiwUrdu+G\n3e2GW0q8MDqKyqIiLDeZ4vY/f+dOfKSpCS+MjKjaViSfpXJteWhfP+vrS+r6WMadTpR7A2YA+Ofm\nZkXX1dxcg6mTU3HbjW4dxcTexB8GEVH+YNBMREREafWnZctgcSc+5TXZoO39s2YFvb/SO1XcLiX6\nHJ71qyNOJ14YHY1771NWK+47eTLJEVG6SSAoYAaAuUajomsrN1WiclNl3HZKM9JElP8YNBMREVFa\n3VhTk9T1iRT3emd9PX7c0QF9hC2FfOwKAvnACdhHVU7hnilSOQk9Wl93Hj+O9zbE3xdZ6e/CGavV\n/3p00yaUF6XhT14uciQqGPy/OxEREaXNc8uXJ3ytCPlfNZ7o748ZMAOeTDMArC8vT+AOHjOtZJRv\nDXq6WGM8qHhU5VRtJaTZnJ6AGYC0cU07UaFg0ExEREQpExgU3dHQgNdWV2f0/r4A+9VVVXHbOrxB\n8z3NzXH/IHKHFP36SUcHftrRAb2K9db54MkolcVzxUW7XXHbZ5cuTeNIALd1pj0yIaJoGDQTERFR\nWlRotUld7wtTz9lsiq/ZNT4OAHgmTsD07NKl2OjNMNfqdNgQJdt8dHISAPxrn8+sX48WgwHtxcV4\nd2OjqiJl+Srd2WdpNisqwgYo+12oKipCiUaT9gc29j7lATwR5TcGzURERJQW97S0JHV9jXda7c21\ntYqveaC1FUtNJpTECdjfVFuLIo0GjXo9lphMeH7FCvxp2bKwdhMuT7GnpSYTbq6pQavRiHMbNmB9\nRYWKT5I/NkZ4ePCO+vqU9X9PczP2rF6NYu/U+Xu9Fa3vnj0bt9bVAQDMldGLcC0oLva/bjEYIrap\n0ukwuXlz3On50WwZGcE7jxyJ2+7C/1xIqH8iyj9iJu0xKISQM+nzEBEREVF63N3VheUmE+5uago6\n/p6jR/FYXx+k2YzdK3ej/ZF2lK0sC7u+U3QCAMzSnIHREpGPEAJSyoxO82GmmYiIiIjIa9wVfysp\nt92zntm0XNm0ciLKbwyaiYiIiIi8xhWs4e7+aLfnxcxf0k5E4D7NRERERER+A96ib6HcTjfOfeUc\nzjxwJrMDIqKsY9BMRERERATAJSUOTE6GJZCdo05srdwadKz1/lYMPjuYucERUdZwejYRERERFTwp\nJa7euxcAsCmkOnpowHyt+1rUvkV5VXciym/MNBMRERFRwdNs2QIA2FxRgdKALcv2rNrjfz3nX+dg\n3lfmZXxsRJRdDJqJiIiIqKANe9cx/967V/d3L4TvwcytpYgKF6dnExEREVFB+8vwMADg9dXVQccn\n9k8AANYdX5fxMRFR7mDQTEREREQFze5245baWggReQ+pkoUlGR4RReJ2uCHdEgBg77PDNemCy+KC\nlDLLI6OZjtOziYiIiKignbZasaQkcmCsLddGPK6EY9gBXZUu4evz2eThSYy8MIKmu5tS0p/jsgPb\narcB8EyV39643X9OY9Jg88TmlNyHKBJmmomIiIiooH3hzBnMKy72vw/MW1ZurkyoT9sFG7ZVb0ty\nZPnFl/E9/PbDOPjGg+j+SHfK+j7+weP+170/7A065550wz5gT9m9iEIxaCYiIiKigvW5U6cAAM0G\nAwCE7dFcurpUVX/dH+9Gp+jEjuYdqRheXtmi2QJLlwUDvxqA9Yw1pX0PPjW9J3bXh7rCzm+v3x52\njChVOD2biIiIiArSHy5fxh+GhqAXAjeEFAEDAE2xBjWvr1Hc30uLX4Lb7g465ppyYeDXA2h8d2PS\n480H+1+1P9tDIEo5ZpqJiIiIqCD9YWgIc41G2K69NuL5zZbNKF9Xrqgv6ZKwHLVAaIJz1S+WvIhj\n7zmW9FjzRd3b6rI9BKKUY9BMRERERAXrusrE1iyHsvd71tQWVRb2RE5ddWEWPqOZjUEzERERERWs\nN9Yon34di3PUCQDQN+hT0h8R5Q4GzURERERUsEzaxLeUCuQadwEAdA3BmdY1r6yBca4xJffIC9wy\nmWYgBs1EREREVHDsbk/BrqUmU0r68wXN+vrgTLO2NDVBeb6QUqJ8vbJ14ET5gkEzERERERWcXw4M\nAABme7eaSpZj0AEAEPrQTatQcNnXQntQQDMfg2YiIiIiKjgTLhcMIkKAmyBfIbAwqbtFfiiwBwRU\nGBg0ExEREVFBqtGlrtKzo9+Rsr6IKLcwaCYiIiKiglSSoiJgADC+Z9zzgplWohmHQTMRERERFZz7\nW1vxxba2iOd8ce+yl19Gl8USsx/plhjdMeovBMagOdyOOTvgdroTvn7i0EQKR0OkXk4EzUKIG4UQ\nx4QQXUKIz0Q4f5MQ4hUhxD4hxG4hxKuyMU4iIiIimhm+OHcubm9oCDseuAT50OQk9oyPx+zn4E0H\nse/qfRh9cTTFI8xTER4a2HpskLbEnyYM/3U4iQERJa8o2wMQQmgAfAfA9QB6AbwshHhGSnksoNnf\npJTPetsvA/AUgAUZHywRERERFZRiTfQck/2iHZOvTPrfC0Pkql9SMv2clEIrpkY5JxcyzesAdEsp\nz0opHQCeBHBzYAMpZeC8mFIAgxkcHxEREREVqOIY6559xb86ftKBNQfWRN5qqdACPj4foBko65lm\nAE0AegLen4cnkA4ihLgFwFcANAJ4bWaGRkRERESFzBgj0wwAsz86G43vbczQaHKfy+LK9hCIUi4X\ngmZFpJRPA3haCLEJwGMA2iO1e/DBB/2vzWYzzGZzJoZHRERERDNQrOnZAFB6ZWnwgQLPtJ7/z/Oo\nenVVtodBM0hnZyc6OzuzOoZcCJovAJgT8L7ZeywiKeVWIUSREKJGSnk59Hxg0ExERERElAin21Pt\nWSdiz68unl/sfy3itCUi9UIToQ899FDGx5ALa5pfBrBACNEqhNADuA3As4ENhBDzA16vAoBIATMR\nERERUSpMeYPmeGFwYNBMaVLg2XvKvqxnmqWULiHExwD8BZ4g/mEp5VEhxF2e0/IHAN4qhHgPADuA\nSQDvyN6IiYiIiGims7pj7yssHZ5IztBiiN8Zgz6ivJb1oBkApJTPIWSNspTy+wGvvw7g65keFxER\nEREVpnhBs3PICQAQmuBcdOj2UpyyTZT/cmF6NhERERFRTokXNAtdbgTDw53DmDo5le1hKNYpOjFx\nYCLm+ZeWvpTBERHFx6CZiIiIiChEvKC58rpKbLy8MfxEhqdiv3LdKzj4xoOZvWmSLMctEY+7pjzb\nVVkOWzDxSvTAOhrXpAudohPOcWdS4yMKxaCZiIiIiCiAW0os3707ZhshBHTVupCDaRxUDMa5xuzc\n2Gv3yt0Y3z8es83YrjH0fLsHAOC2RH4gsaN5h//1pZ9cUj2OsZfGPNf+WP21RLEwaCYiIiIi8hIA\n+uz21Haa5uyzrkYXv1EaTeyfwJ6Ve2K2Of3AaZz81EkAwMBTAxHb+NaJA0goW6yfpQcAnPjECdXX\nEsWSE4XAiIiIiIhyxRmrFWvLymCPM0VbkUxkn7UZuEeS3FPT3+XlZ+LvHGu/oP7BhbSzTDmlBzPN\nREREREQBRl0utBmzO+VZDVGUG0XJYgkMmgFgZOsIzn75bNAx05Um/+uh54amTyiMhd3WFDzkIIqA\nQTMRERERUQhtoltFZTDZ6Zr0FM4S2uwGzfpGfdw2oUHzua+cw+nPnw46VlSe3CRYt9WN0lWlSfVB\nFAmDZiIiIiKiEPvGYxe2iijDseu2um2e22Z5+ytNcXhIMeuuWShZXOJ/76uM7R9rpIcLIce26Leo\nGofb6obGqIk4HqJk8DeKiIiIiCjED9vbsz2EmNwOtz97W7o8u9lVbWn4omptiRbGOdNT3H1j1ZQo\nDz+kQ13aXjpk1h8g0MzEoJmIiIiIKIRBk+CfyQoyqMmSUmLy4CQAoO7Wupz4i944P/YacF/QrC3x\nBNhDfxqK1TwhbrsbGn0OfBk047B6NhERERGR15jLM414TVlZajpMIvEppYSj3wF9gx72Pju2N26H\naakJk4c8AXPtm2uhLc+N0tnF84thPWmNet6XNdYY1AW1zlHlW08x00zpwqCZiIiIiMhr1OkJ0jQJ\nFgKTMvG0si8wBoDme5oxeXgSw38eDmrjnwqtBdofacfJ+04mfL+UkUBRVXhYkcx34bNn7R7M/vBs\nRW2PvOMIgMhrrImSwaCZiIiIiMhryKk8sxkmiSTnxUcu4vidxwEAzfc24/w3zwd3bRDYdHkTtKYI\nmeUc2J544NcDwQe838Wx9x+bPmSI/QVFCrKnuqdw7uvnkh4fUTIYNBMREREReVm807MzadcVuzB1\nYgpXfPcKNN3dBCkl5v/HfAgF2W4lbTIiytd26ceX/K91tbqEunb0ORK6D7IMAQAAIABJREFUjihV\nOHeBiIiIiMhricmk6A9kq8uF5y5fVtyvlBK9P+rF4O8G0ffzPgw/P4yBpwcg3RJTJ6YAwD8NWQiR\nO8GwQs2fbPa/Hv7bMFxj4VG0aYkpKJtc++bamH3O/qiyadmB5n1tnupriOJhppmIiIiIyOvt9fV4\ne3193HY/7+/H+48fhzSb47aVUmJi3wS6PtgVtc2aA2vyLlAOVLGpAue/PT2l3NZrA9zBbXTVwZnm\nWEXBrvz7lTAtMaH3f3vVjWNzhar2REow00xEREREpJI7wjFHnwM9X+sJPigA+wU79qzeAwBoeE8D\nimoiFM1SuSdxrhH64IC/6voqjO0ai9h2Xdc6dDzaEbO/ko4S6OrVT+c2LTWpvoYoHmaaiYiIiIhU\ncrgjhc2x1dxcg0WPLvK/dzvccE+5Ye+zo+SKksQHEyHedlx2wG1zwzDbkHi/AdxON5xDTujr9Yra\nG5oMEadoQwJCJyJvDRX4OYT69dptX2yD0ORvtp5yFzPNREREREQq2VVup7ToZ4uw7OllQcc0Og2K\nyouSC5gDYkQpJdx2NwafGcS22m3Y0bQj8X5DXPjvC9jesB1jL42h62NdOPC6A7D2TO/LbGieDs7r\n3lYHbUX0/aPVBMNLn16KsjXK9syOt0Y6lvG94wlfSzMfg2YiIiIiIpVsCjPNbqunndLAL1HHP3wc\nWzRbcPHhizh0yyH/8anTUynp3znm2Ypr71V70fu/vRh6bggXf3TRf14UTQfCS361BOMveYLQomrP\nxNby9eWekypnodfeXAvrWWv8hgC0xZED9U7RCemKfeM9q/dg8sikusFRwWDQTERERESkUrRM86In\nFqHsKk+ArKvVwTXumaJc0p5ENlmBi9/3BLDdH+kOOr5r3i44RpLfssk4xxh2bPKgJ8iMtL+y2+Z5\nWLDp8iYAnqnpfipnUM/+iLIq2sZ54WMc2TICALB0WeJe//KSl9UNjAoGg2YiIiIiIpUuOyIHouXr\nylH1qioAQOOdjTAtM2HRzxdFbJspPd/oid8oDv3s8LXMmuKAUCIkEK6/rR5Nn2jyv4+WBQ4SJRk8\n632z0Pj+Rsz7euztpCJN+95v3g8AeHlx/IC45qaauG2oMLEQGBERERGRSt86fz7s2GbbZmj0Gsz9\nt7lofaAVWqMnUGy4rSHTwwuWqsLcGgSVDe9/oh+LH1/sf193a51/OnrpslJc8e0r/Oe05dqEx2Js\nNaLjYU+17Tn/MgcXf3IRw38ZxuInFsM15YLb5oauMn6lbUuXBSULo2f8gx4CEAXgbwYRERERUQpo\n9J4/rYVG+APmjIgTiBqaUlNBOxK3YzqKXvLLJVj27LKwNmZphsYYPSut1qx/moXFT3iCdW2xVlHA\nDAAvtb+U3I2pYDFoJiIiIiJSwbeGt8WQvmBUsZAAtOHd4VltfaOybaISse+afaqyx5HWP/vPOQPO\ncecoyiGcnk1EREREpMKw01NJurooN/6Utl2wTb8JCTarX1edmptEiXXHdynfqsl23gb7BXtQpW0A\n6P91P47ceiSZ0RGlFTPNREREREQqvOvo0WwPIci5r53LzI1CAvK5X5mr6vLRraMAAMOs4Ax9/8/7\nkxpWNC6ry/+69YFW1N6S+D7OVNgYNBMRERERKfD88DBEZyeeGxrK9lCCSHuqKn2pY2wxovr16jLZ\n1W+Ybt//ZD9OP3Aag78dTPXQsOeqPZjqnt6juviKYmhMDH0oMbkxp4SIiIiIKEdNuVwoefHFoGP3\ntbTgrzkQPNvO2eI3SqO5/zYXQ39U/j2Erq8++6WzqR4S3FNujL80jsFnIgfjU2emAAkUzy0OOj7w\niwHgSc/r/l/0w7TMBNNi0/T53w6g+vXV0Bq1OP+d87CetMIx5MDEvgk039MM+wU7iiqLYB+ww3rS\niqlTUzAtMUE6JerfUY/iK4qhq9dhbOcYql89/fBgfP84DLMN0NdHXns+dXIKvT/oRdNHmmBsnd6L\n2jnu9BSdM2Ww6FyBYtBMRERERBTD+r17g94PbdyIs1ZrTgTNQ895xxCyHVQyJo9MwthmhLZEi0uP\nXULtTdGnNZcs8mzh5J5UdvOyVWUAgIl9EzHbBRUFS9CZ+89EPL5r7i4AQPuP2nH8A8dxjeUa/znn\nqBPju8dx5LbpNdbLn1sOXYMOh996GAu/vxCzPzQbJz5+IqjP4+87jorNFShZVAJ9nR59P+sDAIxt\nHwMAXPrxJQBA6/2tOPulszBLs//aPSv3eM59oRVtD7TBbXVj6M9D0DfoUb6hHMP/GEbP13tQemWp\nP2iWLoltddsgbTKoL0oPBs1ERERERFG8ND6OA5OT/vcH1qxBlU6Hs1ZrFkcVTD9bD/tFe0r6mjo1\nhZeXvBx0rO1LbShbXRbeWAtojVosfXYpSleWxuzXesrzfdXfXg8AcAw5AADG+UasPbQWWqMWnaJz\n+oIkY+arTl2FXfN2oXRFKSb2TwfogdW7j3/gOADg0iOX/McOvvGgf+21z4EbD/hfd93VhZEtIxHv\nOe9r81CxvgIA0PuDXjj6HWFtxvdMF07bOX+n/3sBgLMPncXZh6Jn3qVLwj5gx6l/PRU05p3zdmLV\njlXQN6SvSnqh48R+IiIiIqIo7urqAgBsX7kSI5s2YVlp7OAwGzR6TdJBps/FRy4CADb0bsD8b84H\nAEx1TUVsK7SeymC1b6r171EdzdQpTx+6Ks+eytLhGfD6E+sj7mltbDGGHVPD0GyAWZqhbwoOJN2W\n8Ix498e6/a9DA+Yg3mH2PxG5cJnQBFRKi/Dz0M/W+6eyb2/eDuspqz9T77P26FrUv7Me17quxWbH\nZqzes9p/zjnixPEPHselRy6h8vpK/3HraStOf/509HFT0hg0ExERERHFsaGiAhU5ssVUqNAtnIJI\nT4ZSKcsRCwBPheuWe1oAAH2P9cE17gpr6wualQgtVuYLmgOtO75OcX9R7+P9rL7vRNoC7uOCP/BN\nxNoDawEAc78cpWp4QGQV6fPZe6dnA9gveF4vfWppUBVyU4cJix9fDKER0BRpULaqDEWVnt+7E/98\nApefuYz1Z9djxd9WoGKzJ6ttlma0/7A98Q9GceXm//OJiIiIiLLsFe+07LPr12d5JLEFBs2uyeDg\ndui5IQw9N4QzHWcArScb6pp0QVuqhSgSKKoognGuEboaHaxnrRh8Krh41oJvL8CJT57AkXeE76Mc\nM1gP4bYHZ3gjBZUlC0vCjqnm7VYIz9iG/zYMABh/eRz9T/aj/0n121ut2b8Gu1fshqbYExWXrog8\n2yCwyNmcz83BqX855Wm/shSzPzwbXXd1hV0zeXQSGl3sPObaQ2tx+oHT/inZxjneLHx2iqYXJAbN\nRERERERRVBYVYY4xuanC6aZv0sNyzJMhjjT9uHRFKRY9vgjSJSGdEhqjBm6rG9Ip4RpzwXLcAueo\nEzVLauAad2HoT9MFzpo/0QxthRZT3VPQlmlx+v9NTwM2tin/XrRlwSneWNlvtfs/ByoqL8K6ruCM\nde1bauEY8KwvnvfVeTj1r6f85xY9sQhTXVM48+AZAEDLp1tQe3Mt9m3cN91nTZH/Myx5agmqbqiK\nW3xrzn1zMOe+OUHHDE0GCL2AtkyL4rnFsJ61omxtGc5/63zMvgxNBnQ83IGOhzuCjrc/3A7rmdxZ\nWz+TicDF8PlOCCFn0uchIiIiouwRnZ1YZjLhwNq1Yef2j4/jn44dw/4I5zLJ1muDtlSLHS074Bpz\nofL6Soz8PbhQVc0ba7Dsd8sU9SfdEhP7J/xVrgOd/fJZ/9pZ05UmrN2v/LM7J5xwDDpQ3ObZ5sl2\n0Yapk1Oo3FQZ1M7SZYGxzRh3jbRSnaIT9bfXwz3lxuDTgzBLM+yDdliOWaAt1qJ0RSmEVuDwOw5j\n4JcD/mB4fN84Jg9NovHdjQAAt9MNTVF6Vra6LC6M7RpD+VXl0JZw+6h4hBCQUiqf5pACzDQTERER\nEUUxW5/bFYkNsw2e/20ywDJmCVs7DABCrzy+EBoRMWAGpqdYd/ykA3W31qkaZ1FpEYpKp0MPwywD\nDLMMYe1SMkU7hNALlK8ox9hLnu2f9LV66DcF/1xDv7eylWUoWzn9PaQrYAYAbYkWVddVpa1/Sh4L\ngRERERERRXFFSeqDuHTwrRGOtFbYV0gqWb7CX43vbcyrjGjxgmLM+fQcXH3h6qht1DxYoMLDTDMR\nERERURSLVATNUkpMuFx4ZWIC/33hAr4xf37G1kPP+sAsnPrXU2j8p0aM7RzzH19zYM104agktdzX\ngpo31UQ9/+9nz+K2+nrMKy7GkMOB4xYLNlRUpOTee8fHsXrPHhg1Gljdnoz3hvJyTLhcODg5iQfb\n2vCFtraw6zaNbIK2NH6Av/B7C9H2hfDriQCuaSYiIiIiimjLyAg2lJdDrwmfnBlpTbPo7MSmigps\nHZ3e63fimmvwvd5e3NvSktaxuiZdOHzrYSz/4/K03icW0dmJYo0GlzduRMmLLwIApNmcsr7jSdW9\nKLdlY00zp2cTEREREUVwbWVlxIA5Eqc3+9lntwcdX7hrF+47eTLlYwulNWmzGjD7fGbOHH/ATDRT\nMGgmIiIiIkrSqMuzP3JFUfDqx96QIHqm0wmuDaaZh0EzEREREVGSbN5Mc61Ol+WREFGqMWgmIiIi\nIkrSlDdorgsJmh9sa8PcDBUDywWsLkQzEYNmIiIiIqIkWaMEze9uaMjGcIgohRg0ExEREREladTp\nBABUFhX2jq5SSrymqirbwyBKKQbNRERERERJOjI5me0hEFGaMGgmIiIiIkqSb3p2JIW0zreQPisV\nDgbNRERERERJGvNuORWKGzAR5T8GzURERERESeq12bI9hJzATDPNRAyaiYiIiIiS1O9wZHsIRJQm\nhV3ej4iIiIgoCb02G15/4AAMmui5KCmZf02Gm98fZRmDZiIiIiKiBM3ftSuoCFhoeFdoa5ojhbei\nsxOOzZtRFOPBQiw/vXQpuUERJYnTs4mIiIiIEnDaag0KmCu02iyOJndECpwdSWSLL3v3wCbKlpwI\nmoUQNwohjgkhuoQQn4lw/p1CiFe8/2wVQizLxjiJiIiIiHx8FbMfbm9H74YNEKLQ8srh0jEVnd8q\nZVvWp2cLITQAvgPgegC9AF4WQjwjpTwW0OwUgM1SylEhxI0AfghgfeZHS0REREQ07dVVVXj/rFkx\n23BFLlF+y4VM8zoA3VLKs1JKB4AnAdwc2EBKuVNKOep9uxNAU4bHSEREREQUpizOlOxCyz7/8OLF\niMeTeXBQWN8g5aJcCJqbAPQEvD+P2EHxBwD8Ka0jIiIiIiJS4M44WeZC02u3p7xPBs2UbVmfnq2G\nEOI6AO8DsClamwcffND/2mw2w2w2p31cRERERFSYqory6s/pvFRo2XoK1tnZic7OzqyOIRf+X34B\nwJyA983eY0GEEMsB/ADAjVLK4WidBQbNRERERETptKC4OOh9pGnIXNPM74ASF5oIfeihhzI+hlyY\nnv0ygAVCiFYhhB7AbQCeDWwghJgD4DcA3i2lPJmFMRIRERER+Tm9VaJrdbqY7ZgjTR6/Q8q2rGea\npZQuIcTHAPwFniD+YSnlUSHEXZ7T8gcA7gdQDeC7wjM/wyGlXJe9URMRERFRIRvy7h2s4dRhohkv\n60EzAEgpnwPQHnLs+wGvPwjgg5keFxERERFRJMPeoDnb+ux2lGu1KI5TxTvblO7f/NTAAGp1OlxT\nWZnmEREplwvTs4mIiIiI8srrq6vxo/b2+A2R3vW8jdu3476T+bN68XeDg7C73VHPv+XwYWzevx+H\nJyf9x9Tk8juHo5Y+IkoYg2YiIiIiIpXKiooUbTeVicnbz4+MZOAu0e0YHYVLYSb5pkOH8OzgYMRz\nv+7v978+MTXlf630O7S6XLjulVfQbbEovIJIGQbNRERERER5bFVpaVbvf/W+ffiv8+djtrn96FEI\n77ZB4y5XxDa3Hjnif308gcD3uDfQvrurS/W1RLEwaCYiIiIiShGla3dTSZsDxcjujTNF/FhAEPzZ\n06fj9tdvt/tfK92n+eDEBADg71nOvNPMw6CZiIiIiCiN0h1I50LQHCjS5zUEjPFSQEAcTWAbpZ9u\nXsie2USpwqCZiIiIiChNMhHO5lrQHIkjJJAedjiCin0BwIqAaeaPB6xvVvrplK6rJlKLQTMRERER\nUR4rynLQXFkUvottq8EQ9D4waK7V6fChri4sffnloDbFmuRCEzeA1Vle300zU07s00xERERENFOl\nK//5rZ4eANnPgpVoNAhdRXxTbS3OWK3+94HbTE25XJiMUgwsUL/djnq9XvE4XFJCIwRKkgy+iULx\nN4qIiIiIKE2UFrFKhG9/5qaQrG6mlWi1cdsEZpotMfZpDtSwfTsA5dOz3VLmxVR1yj8MmomIiIiI\n8oxLSrgBlGu1aFCRjU0HvRARp2gHsnuDZr0QkEhP9t0FBjeUHvy9IiIiIiJKkUyVovrd4CAA4K11\ndRm6Y2xryspinvdNxzZ5s9LPDQ0p7ltptp6ZZkoXrmkmIiIiIkqjVATSFpcL5S++iMCVwE16fUaq\ncytRFmeKtm96dolGg2EV/TrdblXVs5kRpHTg7xURERERUZqoDWq/ce4cPnT8OE5YLNg2OgrR2YnX\nvvIKTCEBMwD8Y8WKVA0zafoIGd5IDwt0Kot0ff70acVtd46NweJ2ZyzbT4WDmWYiIiIioiyTUqJ1\n50702GwAgB9evOg/95dhT27WXFmJ3y9b5p/iHHhttk2FFPfyhdBjTqf/mFGjUf0Q4Ws9PfjBwoWK\n2n753DkAyW9dRRSKQTMRERERURaNOZ3YsHcvemw2/LSjAxvKy/GNnh7cUluL+cXF2DM+jjfV1oYF\ny4D6THa6PHv5csTjlVu3+l9XxykWFs2HuroSuo4oVRg0ExERERGlUbw88Ia9e3HEYsHRtWvRYTIB\nAL7X3u4/f0VJSRpHl16Bn92o0UDZZlMezQYDznsz70TZxLkLRERERERpEpgJdrrdcLrd6Lfbg6ZU\n99rtAID2PA6OP9/a6n99ymqN+KBgifeBgM/t9fUx+9xcUaF6HF9sa1N9DVE8zDQTEREREaVItKyy\nze2G8YUXwo5/qa0NI04ndq1apXhrpVy0PCAgPm214m/Dw5hnNAa1KY1TYTvwu3tVZSXub23FE/39\nqsbx2upqfMW7tpkoVRg0ExERERGlkcXlwm8HBiKeu//MGQCe7ZLymS4k4L+ppgb7JybC2kkpcUdD\nA/q92fVonli8GKYECnrV63SqryGKh9OziYiIiIhSwOZ240tnz4YdH3O58M6jRwEA7muvxZ+XL8ej\nHR1BbeLtc5zrQreSqtHpcNhiidj2S21teG9jY9w+SxMoHFav16u+higeZpqJiIiIiFIgdNslILy6\ntRACN1RXAwDe09iISZcLGgDFSQTNuZCjDv2c1Tpdxot4/UtLC4Dc+D5oZmGmmYiIiIgozf5nwQJI\nsznsuEmrTSpgzpV10OUhn6EhRdOkTRoNFikskHZrXV3ObMFFMwuDZiIiIiKiNGsyGLI9hLSqDJhK\n3bVuXdRsr9os8MTmzZhfXKyorTGBNdBESvA3i4iIiIgoDQSmp12nezupCacTj/f1we5247/On8cf\nLl/GYJxiW2o43W6cs1px1mrFTy5exPbR0aDzgRnvK0pK0OOdmn1PczMA4G11dRHbKuGIMO09En0S\nQXOP1ZrwtTTzcU0zEREREVEKvL66Gv/S0oIdY2P47OnT+PuVV6KiqCjitOxUK9u6FQDwzZ4e7Auo\nWv2F1lY8OHdu0v0/1teH9x8/HnRs28qVuDpgL+XPt7aiy1v8672NjajT6XBrfT1mGwyo1enw56Gh\nhO79o/Z2tO3cCRcAvRCwR6k0PtdojFiF/JLNhsY4mf45O3fiDdXV+P3y5QmNkWY2Bs1ERERERCnw\nB2/AZa6qwv9rbc3KGPaFbPP00NmzuKW2FivKypLqd3aEoHPH2FhQ0PylgODcpNXi1vp6AMC9LS14\noq8PQGJFupqNRjgVPniYcrmC3l+/fz/+MTKCJxYtwu0NDTGv/fPwcAKjo0LA6dlERERERDPYr6Ps\nEa1GpFJlW0OmaCuVyWJd/xgZAQD/ll+xvKW2Nt3DoTzFoJmIiIiIKI/Fy96G7qGcKnaFa42J8h2D\nZiIiIiKiPKUkaxtpnW8qHPGuX84XMk3fA818DJqJiIiIiGYwQ5oyzWcSqDidibA1WnDMvDglikEz\nEREREdEMVpKmoLmmKLGawulc0xyr70cvXUrjnWkmY9BMRERERDRDfHj27LBjbUZj0v1Gyt2OhVSq\nznV3hmyZRaQUg2YiIiIiIlLNISUsKgNnriumfMSgmYiIiIhohrqxujqt/f82ge2sok2hZkBNuYpB\nMxERERFRHosVaqZ7T+TRPJuiTZSIxFbvExERERFR1qU7KI7nY93dqNXpku7nos2GXePjKRjRNGau\nKVWYaSYiIiIiymNTWc72DjocitoNORy4YLejWKsNOv67wUHM3rEj5eOyM2imFGHQTERERESUx3aO\njflfZzK7+rk5c1S1//PwMACgJiQz/YOLF1M2JmB6urrNPb0z8/cWLsRba2tTeh8qHAyaiYiIiIjy\n2C8TKMalVqRQfHlpKe5vbVXcR7lWi0UlJWHHry4vT2JkwYSYnrBuDQiaTRoNjGnar5pmPv7mEBER\nERHlsbKQ6c6ZIgCYVNzb4nZjcYSgOV0CM81EyWAhMCIiIiKiPDYWsKY5k6t4BYDPnz6tuL1TyqAg\n++f9/bi5thafVdGHUi+MjGBc5VrvbBdVo9zFTDMREREREakmhMDPFy1SdU1pSGb6oTNnUjgiDwng\n2v370W2xhB0nSgQzzUREREREM0SmM81XqJxuHTqd+2hIYJsKDm8xtMAq3YFrnYnUYqaZiIiIiChP\n/cVbkVqXhaBQAKgqUpeDK8/g+uthhVthEcXDoJmIiIiIKE/5tphKd4Y5Uv+JBM3V3u2muuJkmFOx\nddb/S8NaaSpMDJqJiIiIiPLUWZsNQOaC50AaIfzTrfvsdkXX+PZoPhwQNL+/sRHOa68NaqeuhNe0\noiQy7r/IwNZdlJ8YNBMRERER5bE5BoP/dSoytEoJeNYKP7VkCTZWVCi6ZpnJBGA6uP1MSwu+v3Ah\ntCHBrj3B7aK0QkCazRjauBEA0BGw5pqFwChRDJqJiIiIiPJYiVablYDQF+beUlcHgyZ2WPHKxASA\n6SDWV0X7q/PnoyjCtcmu0K7S6SDNZsw1GsP6c0sJm9sNd4wHDE63Gy4p4QgI3p1p2vdZSglXBh92\nkHqsnk1ERERElKd+uXgxmg0GbNq3LyX9bRsdxaZ9+yDN5rht1VSkvr6qCkNOJzTea769YAHub20N\narPcZMKByUkAQKs32E0H7ZYtQe9vrqnxv368rw/1Oh1uOHDAf6xUq8WEd8/nt9TW4jdLl6q+5xN9\nffjNwAAWFBfj6z09Qd+vJmA8Sr53n60jIxh0OHBLXZ3q8ZA6DJqJiIiIiPLUrfX1Qe+TzVceCynQ\n5XC7oYuSRVaTDb6huho3VFf735u02rDtp15Zu1ZFj6nzzOXL/td3HD2K+SEBuy9gBoDfDg7iks2G\nxoAp8Uo80deHPwwN+d//9NIlvKexMcERe9zd3Y1Dk5OqAm1KDKdnExERERHNEMkGzbaQKcj6F16A\n6OyM2HZBcXGSd8tN8YqJOROYSh06ff29x47hta+8orqfQJlcv17oGDQTEREREeW5VIVPoUGzv/8I\nAdrCgCJb+UBCWaAZLWhuUZldDnTaag075ttjO5l+KTMYNBMRERER5TlfBe1kg+cdY2MRj7uRfHGu\nbPKNXcn3Exo031pXh7KQqeRqXV9VFfG40+1O+HtlnjlzGDQTEREREeW5er0+Jf1Ey7I+F7AeN5/F\nqpjtE/odrC8vT/q+S71bbYXaFuUhhRIMmjMnJ4JmIcSNQohjQoguIcRnIpxvF0JsF0JYhRD3ZGOM\nREREREQz3ajTGfR+RWkpWg0GXHY4sjSi1Dpns4UdqyoKro0cGjSnIsMerQ9Ozc4PWQ+ahRAaAN8B\n8FoASwDcLoToCGl2GcDHAfxHhodHRERERJQ3xkKCXiWOTk6ix2qFS8qwoLlICLQajbhot6dqiFkj\npcRbDh0KO14aMvU6XiGwRF1pMmGZN+MszWY0GwzQCcHp2XkgF7acWgegW0p5FgCEEE8CuBn/v717\nD7Kzru84/v4m2c1uEhISAgkEQhICGIzKcEmRQhNtVXSsKFMtSAFtRxkrrVVHqZepwDgtdOq02Bax\nylToiFAvVayOIi3bgBfuiIaEQAEDSbjmQsrmspdf/zjPLmfPnmd3z+657r5fM8/sc37Pc57zO2d/\neTaf8/s9vwc2DeyQUnoBeCEi3t6YKkqSJEnNrztnIq+RnHDPPUDhuujSXleAh7u7eaGnZ1Jc0/zL\n7D7QxUYLzdV636+dM4edvb38qkwdxsPZs+un4T3NwBLgqaLHT2dlkiRJkiqQN/t1nj1FPcuvmzOn\nbKh8YZIMzc5Ter/ovPtSa+pqhp7mqrrssssG19etW8c6b/YtSZKkSW6gz3FemZ7ikcy9804A/nr5\ncj79xBNVrlVrGLWnuQrDtQd+P/YOV66rq4uunHuF10szhOatwNKix0dmZeNSHJolSZKkqeSKZcv4\n7gsvVPy84tmdV5Xcf7ktgp6UCFr/Otoz5s3jzt27h5SVTnJWq+HZeccZbyhv9d/FWJV2hF5++eV1\nr0MzjD24B1gZEUdHRDtwLnDLCPu38uUUkiRJUs10jGFocV9K/NszzwCFQPz91av5/YULc/f/xFFH\nAa0f0p7r6RkWmAG6+/qGPG6v0URgal0ND80ppT7gEuBWYANwU0ppY0RcHBEfBIiIRRHxFPBR4DMR\nsSUi5jSu1pIkSVLzqGTY7x27dnHhpk28/aGH6Elp8D7Ez51+OhsKkpUFAAAR7UlEQVROPXXY/u/M\nAnUrh+aIYFN3d9ltH8u+FBiwoK1t6HOrXI9qaeXfR6tphuHZpJR+BBxfUvblovVngaNKnydJkiRp\nbB7Ys4eT7rtv8PEPduwAYGF7OwCHtrfz/CSe9GtmTi/8rJLyQ0quC691v7P92s2vKUKzJEmSpPEb\nqdfxi1u3cs6GDWW3vXzmmRUdq5X15Mws3lkyEdhBFU6m1iiT9ffUjBo+PFuSJElS7XTt2jW4ft/J\nJwNw10knkdatY1ZJYMzr9VzW0VGr6tXN1gMHWFAmEJdO/FVqYEj1REJqLWbNdibu+mmNr1EkSZIk\n5RotPj26Zg3HdHbSD3zzhBNYk13HPFZtk2RyrBWdnezYs2dI2eJseHqt5c6eXZdX10QYmiVJkqRJ\n6u6XXgIK92+OCKYDf3DYYSM+p1wPZqvPKD1Q+3tLAjPA7FFmHG/Wd24/c/04PFuSJEmapF7s7QUY\nNgw7T15AnCwB7RurVg0rmz3KZ1PN0OyQ6tZkaJYkSZJa3GhRrHMM92+u5fMb7fG9ewH4rTLD0sf6\nhUKtNGtPtl7R2q1fkiRJ0qimTXB49QmzZ1epJo3x5L59ACzv7By2raPoC4HDy1zfXI1QW4v+Zfus\n68drmiVJkqQWlzfs96wFC5hTYU9quSONNoS5We3Khqf/zYoVfGjJkrL7FIfmAzm3paqGaPHrwqcy\nQ7MkSZI0SX139WpmVjC0Oi/YtWrc+3k2Edr8tjbmt7WV3ad4ZvCdWcguVuuwO97j29NcPw7PliRJ\nklrcOxYuBGB+yX2IJ9o/PNCD/fq5c4f0yLaKd2Wfy2g+s3QpAKeNcCuugc+iu7+f6OoathQbKJu1\nfv34Kl7iw5s3c9WWLWXro9qzp1mSJElqcVcsX84Vy5cDkNatq+qxA7hg8WIuWLy4qseth++sXj3q\nPgF8fsUKPr9iBR945JHcfQbs6esru89T+/ZxVEfHkLK9/f3MWb+ehW1tvGH+/HH3Kl+zbRsAl2bh\nXvXVel8XSZIkSaoZ+y+HGmvMHZhsDGBWUa/8y/39/Gb//nG//nXbtw+un//ww4Pr/p7qx9AsSZIk\nCWjda5cnotL33JMzWdh47/c82ut//LHHBtdvfO65wXVDc/0YmiVJkiQpR2mofbanp66v3+u1yw1n\naJYkSZI0ZY12nXHp1h0TCM3jmbyrx9DccIZmSZIkSYOMaCPrrvK9nEcbnn0gJzT7e6ofQ7MkSZIk\nwGuay24v6Ylu9K2e9lc5tGt0hmZJkiRJyuSF4oHS8V5jXK0vJJ47cKBQH4dt142hWZIkSdKgqRbG\nRu1pLnlc7Ym5xhqmrzv+eFZ0dPB8nScik6FZkiRJUmYqDs8eTa1D81jNnDaNlZ2dr/Q0N6QWU5Oh\nWZIkSdKUNdrs2aXyQnOtQ2wAh7W38+S+fXV5Pb3C0CxJkiRJOWo+PLuC0L505ky2Zz3Nqh9DsyRJ\nkqRB9mAOVRpqxxuae1KquFe71NwZM9ibzZ49UIt7X3rJGbVrzNAsSZIkCah8qPJUtHnv3nE9b2N3\n94QnWeucNu2V0Jwd69T776dj/foJHVcjMzRLkiRJUo7SrxG++fzz4zrOvpLe4J7+fp7Yt6+iydc6\np03j9p072dzdPa46aHwMzZIkSZKGeWyKBrPuGg117ivpZX62p4e1Dz444nN6Supy3fbtbOju5vi7\n7x5S/szpp1enkiprRqMrIEmSJKl5JAqB+diSYDYVPL1/P1/dvn1IWbUGrG/bv58VHR3DyjeO8OXE\n7t7eIY+XzJwJe/YAsL8ohC9qb69SLVWOPc2SJEmSgFcC4sBtjaaa0pAKw0NzueA7pmP39Y26z892\n7ya6uujt7+fFnh7e8/DDQ7Z/fvnywfVdZeqq2rCnWZIkSdIQV27Z0ugqNET/GCbqeryGXyj89gMP\nAHDttm30A7fv2jVk+6rZs0nr1hFdXTWrg4azp1mSJEnSoCf27eO/SsLaVFGuL7h0RvELFy0q+9yJ\nzoxd7Jcvv8xHHntsaD2qdnRVytAsSZIkCSgfzKbSbajact7rS319bDtwAIDXz51b83qUXldd6uxD\nDhlc//Jxx9W6OlOeoVmSJEnSMD94zWsAWDyFJpmaXiY0l5YsaGsb17EPmTG+K2OPnDlzWFnx9dEf\nPOKIcR1XY2doliRJkgQUZs4ecPrcuXx/9erCjM1TxJ27d4+6T8e0yiPU3x1zDBvWrOEHO3YMKf/N\naacNefz1VauGPP7nY4/lz5YsGXa8rik6fL5RDM2SJEmSAOjNrsvtX7uWg9vaePvChQ2uUX19eulS\nbnjVq4aUdWe9um84+GAAlo7jS4Q506ezqL2dq1euHCxL69axtKNjsEcf4D2HHsqmNWsGH3/g8MPL\nHu9P7V2uq6jmBeuNFhFpMr0fSZIkqd5e6u1l7jiHEreCvX197Ovvp33aNPb09rI4JwR39/XRnxJz\nZszgQLb/QPnsO+4Ytv89J53EKdn1zrPXr6e7v39w27XHHcfFIwTd/pTo7utjTva596fE3v5+Zk+f\nzt9u2cKljz/OjatWcV7RJGQpJXpTom0cPd+tLCJIKdX1QvvJ+69BkiRJUsUmc2AG6Jw+nc7p0wGY\nnf0sZ1bRtvYaB9NpEYOBeeDxSHWDQnjMm7hM1TW1vpaQJEmSJKkChmZJkiRJmiAvEp28DM2SJEmS\nJOUwNEuSJElSk/Pq5cYxNEuSJEmSlMPQLEmSJElSDkOzJEmSJEk5DM2SJEmSJOUwNEuSJEmSlMPQ\nLEmSJElSDkOzJEmSJI1R3q2fUl1roXoyNEuSJEmSlMPQLEmSJElNLiKvj1u1NqPRFZAkSZKkVveZ\nJ57gtp07OWvBgkZXRVVmT7MkSZIkTdBtO3cC8KMdO4Zt6+nvr3d1VEWGZkmSJEmqktWzZw8ru+zJ\nJ+tfEVWNoVmSJEmSquRAmV7ll+1pbmlNEZoj4qyI2BQRmyPi0px9vhgRj0bEgxFxYr3rKEmSJEmj\neWr/frpLQvIpBx3UoNqoGhoemiNiGvBPwFuAVwPnRcSrSvZ5K3BMSulY4GLg2rpXVKqjrq6uRldB\nmjDbsSYL27ImA9tx/ewt06vcl8Z/F+f99lI3XMNDM7AGeDSl9JuUUg9wE3B2yT5nAzcApJTuAuZF\nxKL6VlOqH/+waTKwHWuysC1rMrAd18fuM84oW94/gdC8s7cXgN4JHEMT0wyheQnwVNHjp7OykfbZ\nWmYfSZIkSaq7b6xaRf/atcydMYP/OXH4laR9Ezj2iz09APzRxo0TOIomohlCsyRJkiS1hGkRw8oW\nt7cTWfnvHHzwsO2Ht7eP+/V2ZT3NV61YMe5jaGIiNbibPyJOAy5LKZ2VPf5LIKWUrira51rg9pTS\nzdnjTcDalNKzJcdyzIIkSZIkTWIppeHfXNTQjHq+WI57gJURcTSwHTgXOK9kn1uADwM3ZyF7V2lg\nhvp/eJIkSZKkya3hoTml1BcRlwC3Uhgufl1KaWNEXFzYnP4lpfTDiHhbRDwGvAy8v5F1liRJkiRN\nDQ0fni1JkiRJUrNqmonAIuJTEbEhIh6KiK9HRHtEzI+IWyPikYj4cUTMK9n/0YjYGBFvLio/KTvG\n5oj4h6Ly9oi4KXvOzyNiadG2i7L9H4mIC+v3rjXZlGnHMyPicxHxdETcny1nlexvO1bTiYiPRMSv\nsuXPszLPyWopOe3Yc7KaXkRcFxHPRsRDRWUNPQdHxLKI+EW27RsR0fARq2p+lbTliDg6IrqLzs/X\nFD2nsW05pdTwBTgaeBxozx7fDFwEXAV8Miu7FLgyWz8BeIDC8PJlwGO80mt+F3Bqtv5D4C3Z+oeA\na7L1PwRuytbnA/8LzAMOHlhv9Gfi0nrLCO34c8DHyuy/ynbs0owL8GrgIWAmMJ3C5TPHeE52aaVl\nhHbsOdml6RfgDOBE4KGisoaegyn8v+bd2fqXgIsb/Tm5NP9SYVs+uni/kuM0tC03S0/zS8ABYHaW\n9Dsp3Iv5bOD6bJ/rgXdm6++g8IH0ppSeBB4F1kTEYuCglNI92X43FD2n+FjfAt6Yrb8FuDWltDul\ntIvCH9XBb52lCpS241kU2jFAuUnqzsZ2rOa0CrgrpbQ/pdQHrAfOoXDu9ZysVpHXjsFzsppcSulO\nYGdJcaP/X/xG4NtFr/+uCb1JTQkVtmUoc35uhrbcFKE5pbQT+AKwhULI2J1Sug1YlLJZslNKzwCH\nZU9ZAjxVdIitWdkS4Omi8qezsiHPyf547o6IBSMcS6pImXa8K2vHAJdExIMR8dWi4VS2YzWrXwNn\nZsOnZgFvA47Cc7JaS7l2fCSQ8Jys1nRYo87BEXEIsDOl1F90rCOq9L409eS1ZYBl2dDs2yPijKys\n4W25KUJzRKwAPkqhS/4ICj1151P4w1asmrOWeXsqVVWZdjwnIt4LXAOsSCmdCDxDIVhX7WWreCwJ\ngJTSJgpDp35CYQjUA0BfuV2r+LK2ZVXVCO34S3hO1uRQ73Ow7Vu1MtCWtwNLU0onAR8HboyIORUe\nqyZtuSlCM3AK8NOU0o7sG4L/AE4Hno2IRTDYLf9ctv9WCr0eA47MyvLKhzwnIqYDc1NKO7LypTnP\nkSpR2o6/A5yeUno+ZRdNAF8B1mTrtmM1rZTSv6aUTkkprQN2AY/gOVktpkw73uw5WS2sYefglNKL\nwLyImFbmWFKlyrbllNKBbOQmKaX7KVyHfBxN0JabJTQ/ApwWER0REcDvAg8DtwDvy/a5CPhetn4L\ncG42W9pyYCVwd9a9vzsi1mTHubDkORdl6+8G/jtb/zHwpoiYFxHzgTdlZVKlyrXjjdnJYMA5FIYM\ngu1YTSwiDs1+LqVwrc+NeE5WiynXjj0nq4UEQ3vEGn0Ovj3bt/T1pdGMqS1HxMKBMJuN4FwJPN4U\nbbmS2c9quQCfADZQmOnyeqANWADcRiGM3AocXLT/pyjMDrgReHNR+cnAryhMgnB1UflM4N+z8l8A\ny4q2vS8r3wxc2OjPwqV1l5J2/LWsHd+QPX4Q+C6F60IH9rcduzTlQmHSpF9TGNK6LivznOzSUktO\nO/ac7NL0C4UvKrcB+ynMlfJ+CrMBN+wcDCynMIPxZgqzD7c1+nNyaf6lkrbMK19k3g/cC7yt6DgN\nbcsD09FLkiRJkqQSzTI8W5IkSZKkpmNoliRJkiQph6FZkiRJkqQchmZJkiRJknIYmiVJkiRJymFo\nliRJkiQph6FZkqQmExFdEdHf6HpIkiRDsyRJNRMR/RUuF2ZPTYChWZKkJjCj0RWQJGkSu6xM2UeB\nucDVwK6SbQ9mPy8AZtWuWpIkaawipdToOkiSNGVExBPAUmB5SmlLo+sjSZJG5vBsSZKaTLlrmiNi\nbTaE+68i4uSI+FFE7MyWb0fEUdl+x0TEzRHxXER0R8TtEfHanNfpjIhPRcQDEfF/EbEnIn4WEefW\n431KktQKDM2SJDWflC3lrAHuoHDN81eAXwDvAm6LiBOAu4DDgeuB/wR+B7g1IoYM946IecBPgc8D\nvcB1wNeAhcCNEXFFdd+SJEmtyWuaJUlqLW8Fzk8p3TRQEBFfBf4Y+BlwZUrpyqJtnwUuB/4E+Mei\n41wNvA74ZErpC0X7twPfAz4dEd9KKT1UyzcjSVKzs6dZkqTWckdxYM5cn/18EbiqZNsNQAAnDhRE\nxALgfODe4sAMkFI6AFxK4f8I761ivSVJakn2NEuS1FruK1O2Lfv5YBo+w+fW7OeRRWWnAtOBFBGf\nK3O89uznqnHXUpKkScLQLElSa9ldpqw3b1tKqS8iANqKig/Jfp6aLeUkYPY46yhJ0qTh8GxJkqae\ngXD99yml6SMsv9fQWkqS1AQMzZIkTT13U5h9+8xGV0SSpGZnaJYkaYpJKT0PfB04JSI+GxHD/j8Q\nESsiYlm96yZJUrPxmmZJkqamS4CVFG5HdUFE3Ak8CxxBYQKwU4DzgCcbVUFJkpqBoVmSpPorneF6\nrPukEZ5b0baU0p6IWAt8kMKtpc4BOigE50eBvwB+MoZ6SpI0qcXwO1NIkiRJkiTwmmZJkiRJknIZ\nmiVJkiRJymFoliRJkiQph6FZkiRJkqQchmZJkiRJknIYmiVJkiRJymFoliRJkiQph6FZkiRJkqQc\nhmZJkiRJknIYmiVJkiRJyvH/PuMdM5r8/AwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12466b090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "train_line_real = plt.plot(df_train.index[n_steps:], df_train[use_feature][n_steps:], color=\"g\", label=\"Real (Training)\")\n",
    "train_line_pred = plt.plot(df_train.index[n_steps:], pred_train[:, 0], color=\"r\", label=\"Predict (Training)\")\n",
    "plt.legend(loc=\"best\", fontsize=15)\n",
    "plt.title('Real vs Predict (Training)', fontsize=20)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Data', fontsize=20)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(16,9))\n",
    "test_line_real = plt.plot(df_test.index[n_steps:], df_test[use_feature][n_steps:], color=\"c\", label=\"Real (Testing)\")\n",
    "test_line_pred = plt.plot(df_test.index[n_steps:], pred_test[:, 0], color=\"m\", label=\"Predict (Testing)\")\n",
    "plt.legend(loc=\"best\", fontsize=15)\n",
    "plt.title('Real vs Predict (Testing)', fontsize=20)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Data', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.53520347526e-05\n",
      "0.00685378069555\n"
     ]
    }
   ],
   "source": [
    "# calculate root mean squared error\n",
    "MSE_train = mean_squared_error(df_train[use_feature][n_steps:], pred_train[:, 0])\n",
    "print MSE_train\n",
    "MSE_test = mean_squared_error(df_test[use_feature][n_steps:], pred_test[:, 0])\n",
    "print MSE_test"
   ]
  }
 ],
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