3 - Uncertainty Replaces Custom Loss.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:3em; text-align: center\">Using Uncertainty and Gaussian Distributions to Replace Custom Loss</h1>\n",
    "\n",
    "Instead of using a custom loss function that penalizes over-estimates of the true value, we will use a very accurate neural network and a Gaussian distribution to choose the lower percentiles of the predictions rather than the average prediction (which will try to get as close to the true value as possible). This way, we can select what number in the uncertainty will be used in the real world application."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas            as pd\n",
    "import math\n",
    "import keras\n",
    "from scipy.stats  import norm\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout\n",
    "from keras        import backend as K\n",
    "from datetime     import datetime\n",
    "from keras.optimizers      import RMSprop\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics       import mean_squared_error\n",
    "from matplotlib.pylab      import rcParams\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 15, 5\n",
    "\n",
    "# number of inputs to be fed\n",
    "look_back = 288\n",
    "# number of outputs to be generated\n",
    "look_forward = 24\n",
    "\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, look_back=1, look_forward=2):\n",
    "    dataX, dataY = [], []\n",
    "    np.array(dataY)\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back),0]\n",
    "        if len(dataset[i + look_back:i+look_back+look_forward, 0]) == look_forward:\n",
    "            dataX.append(a)\n",
    "            dataY.append(dataset[i + look_back:i+look_back+look_forward, 0])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "def predict_with_uncertainty(model, dataset, n_iter=100):\n",
    "    # This function activates drop-out when doing predictions\n",
    "    f = K.function([model.layers[0].input, K.learning_phase()],\n",
    "                      [model.layers[-1].output])\n",
    "    all_pred = np.zeros((n_iter,) + (1,look_forward,))\n",
    "    for iter in range(n_iter):\n",
    "        all_pred[iter] = f([dataset, 1])\n",
    "    avg_pred = all_pred.mean(axis=0)\n",
    "    std = np.std(all_pred, axis=0)\n",
    "    return all_pred, avg_pred, std\n",
    "\n",
    "# given a model and data from the original dataset, it uses predict_with_uncertainty to \n",
    "def run_predictions (model, dataset):\n",
    "    # normalizes the dataset and prepares it to be fed to the model\n",
    "    dataset = np.array(dataset).transpose().reshape(-1, 1)\n",
    "    dataset = scaler.fit_transform(dataset).transpose()\n",
    "    data = (dataset.T)\n",
    "    data = data.reshape(1, 1, data.shape[0])\n",
    "    # feeds the model and data into a function that will activate the dro-out\n",
    "    all_pred, avg_pred, std = predict_with_uncertainty(model, data)\n",
    "    \n",
    "    # All the predictions\n",
    "    all_pred = scaler.inverse_transform(all_pred.reshape(all_pred.shape[0], all_pred.shape[-1]))\n",
    "    # The Average Prediction\n",
    "    avg_pred = scaler.inverse_transform(avg_pred)\n",
    "    # The Standard Deviation At Each T Of Prediction\n",
    "    std = scaler.inverse_transform(std)\n",
    "    \n",
    "    return all_pred, avg_pred, std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict (model, day=11, total_days=1, delta=1, show_gradual_error=False, show_graph=True):\n",
    "    # creates matrices to hold the \n",
    "    avg_preds = np.zeros((288*total_days/delta, look_forward))\n",
    "    true_vals = np.zeros((288*total_days/delta, look_forward))\n",
    "    cdf_vals  = np.zeros((288*total_days/delta, look_forward))\n",
    "    \n",
    "    percent_over = np.zeros(288*total_days/delta)\n",
    "    # Runs all the predictions\n",
    "    for i in range (288*total_days/delta):\n",
    "        # the point where the model will predict\n",
    "        point = 288*day + i * delta\n",
    "        \n",
    "        # Gets predictions\n",
    "        all_pred, avg_pred, std = run_predictions (model, dataset_orig[point-look_back:point].reshape(look_back))\n",
    "        \n",
    "        # Reshapes all of the things\n",
    "        true_val = dataset_orig[point:point+look_forward].reshape(look_forward)\n",
    "        avg_pred = avg_pred.reshape(look_forward)\n",
    "        std = std.reshape(look_forward)\n",
    "        \n",
    "        percent_over[i] = 100.0 * (np.ones((len(all_pred),1) )*true_val - all_pred < 0).sum() / all_pred.size\n",
    "        \n",
    "        # converts the original values to cdf values according to the cumullative distibution functions at t+1, t+2, ..., t+24 (0.0-1.0)\n",
    "        # Adds the values to the overall matrix of cdf\n",
    "        cdf_vals[i,:] = norm.cdf(true_val, loc=avg_pred, scale=std)\n",
    "        \n",
    "        avg_preds[i], true_vals[i] = avg_pred, true_val\n",
    "        \n",
    "        if(show_graph):\n",
    "            # plots the uncertainty to the degree of half a standard deviation\n",
    "            plt.fill_between(range(point, point+look_forward), \n",
    "                             avg_pred + std/2.0, \n",
    "                             avg_pred - std/2.0, \n",
    "                             facecolor='red', alpha=0.25)\n",
    "\n",
    "            # plots the uncertainty to the degree of a full standard deviation\n",
    "            plt.fill_between(range(point, point+look_forward), \n",
    "                             avg_pred + std, \n",
    "                             avg_pred - std, \n",
    "                             facecolor='red', alpha=0.25)\n",
    "        \n",
    "            # plots the average predictions\n",
    "            plt.plot(range(point, point+look_forward), avg_pred, color='red', alpha=1)\n",
    "    \n",
    "    print \"Percent Overestimating: {}\".format(np.mean(percent_over))\n",
    "    \n",
    "    if show_graph:\n",
    "        plt.plot( np.arange(288*total_days+look_forward) + 288 * day, dataset_orig[288*day:288*day+288*total_days + look_forward,0])\n",
    "        plt.axhline(0)\n",
    "        plt.show()\n",
    "    \n",
    "    if show_gradual_error:\n",
    "        indiv_err = true_vals - avg_preds\n",
    "        for i in range(look_forward):\n",
    "            plt.scatter(np.ones(len(indiv_err[:,i]))*i,indiv_err[:,i], color='black',alpha=0.1)\n",
    "        plt.axhline(0)\n",
    "        plt.show()\n",
    "    return cdf_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The interval between each dataset (original data in 5 minute intervals)\n",
    "time_grouping = '5min'\n",
    "\n",
    "# load the dataset\n",
    "dataframe = pd.read_csv('ignored_assets/paxout_table.csv', engine='python', nrows=288*21)\n",
    "dataframe['time_bucket'] = pd.to_datetime(dataframe['time_bucket'])\n",
    "dataframe = dataframe.set_index('time_bucket')\n",
    "dataframe['total'] = dataframe.sum(axis=1)\n",
    "dataframe['day_hour'] = dataframe.index.round(time_grouping)\n",
    "\n",
    "# The data set with the sum of all the cars out at the \n",
    "dataframe = dataframe[['total','day_hour']].groupby('day_hour').sum()\n",
    "\n",
    "dataset_orig = dataframe.values\n",
    "dataset_orig = dataset_orig.astype('float32')\n",
    "\n",
    "# scale the dataset\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset_orig)\n",
    "\n",
    "# split into train and test sets\n",
    "train_size = 288*10\n",
    "test_size = len(dataset) - train_size\n",
    "test, train = dataset[0:test_size,:], dataset[test_size:len(dataset)-look_forward,:]\n",
    "\n",
    "# reshape into X=[t, t-1, t-2,..., t-look_back] and Y=[t+1, t+2,... t+look_forward]\n",
    "trainX, trainY = create_dataset(train, look_back, look_forward)\n",
    "testX, testY = create_dataset(test, look_back, look_forward)\n",
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# creates a model but does not compile the model\n",
    "def create_model(rate = 0.2):\n",
    "    model = Sequential()\n",
    "    model.add(Dropout(rate, input_shape=(1, look_back)))\n",
    "    lstm = LSTM(256, recurrent_dropout=0.3)\n",
    "    model.add(lstm)\n",
    "    model.add(Dropout(rate))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Dropout(rate))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Dropout(rate))\n",
    "    model.add(Dense(128))\n",
    "    model.add(Dense(look_forward))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4s - loss: 0.1358\n",
      "Epoch 2/10\n",
      "4s - loss: 0.0905\n",
      "Epoch 3/10\n",
      "4s - loss: 0.0767\n",
      "Epoch 4/10\n",
      "4s - loss: 0.0715\n",
      "Epoch 5/10\n",
      "4s - loss: 0.0684\n",
      "Epoch 6/10\n",
      "4s - loss: 0.0655\n",
      "Epoch 7/10\n",
      "4s - loss: 0.0632\n",
      "Epoch 8/10\n",
      "4s - loss: 0.0615\n",
      "Epoch 9/10\n",
      "4s - loss: 0.0614\n",
      "Epoch 10/10\n",
      "4s - loss: 0.0596\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0540\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0525\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "Epoch 1/1\n",
      "4s - loss: 0.0498\n",
      "Percent Overestimating: 40.0121527778\n"
     ]
    },
    {
     "data": {
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lsODmBHdNQT89zUHG47X72CVzJRrtZj3tjQFpqCLEVXL+hto99oDdskxZvau6wt1IhXQa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\nbTSH/JyekPWj4voaj2XxeZTZY9I5b53s3eAgvP7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Ps8incqg3zFjc7k2aavSOxBiLp5hT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5vu4ZgIqtiYXcjPl3v6nHPI3/gUuWs+QudIWnRncNDq77Rx2mGTOxtnBfoGRUGsqPuoGKJMMZXZ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\n5b6qiroiB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/pPTEcHxGJsLK7nrJFOqbZWUDDVZ6WUmmzGYBobWXPhCszoYkqLFsHHPw4L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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f482a0b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = 4\n",
    "seed = 12\n",
    "# create and fit the LSTM network\n",
    "# model = create_model(rate=0.2)\n",
    "model2 = create_model(rate=0.25)\n",
    "'''\n",
    "np.random.seed(seed)\n",
    "model.compile(loss='mean_squared_error', optimizer=RMSprop(lr=0.0002, rho=0.9, epsilon=1e-08, decay=0.0002))\n",
    "model.fit(trainX, trainY, epochs=5, batch_size=batch, verbose=2)\n",
    "for i in range (30):\n",
    "    if i % 10 == 9:\n",
    "        model.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=2)\n",
    "    else:\n",
    "        model.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=3)\n",
    "predict(model, total_days=1,day=11,delta=24)\n",
    "'''\n",
    "\n",
    "np.random.seed(seed)\n",
    "model2.compile(loss='mean_absolute_error', optimizer=RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0))\n",
    "model2.fit(trainX, trainY, epochs=10, batch_size=batch, verbose=2)\n",
    "for i in range (30):\n",
    "    if i % 10 == 9:\n",
    "        model2.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=2)\n",
    "    else:\n",
    "        model2.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=3)\n",
    "x = predict(model2, total_days=2,day=11,delta=24)\n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent Overestimating: 49.4143518519\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136c4d1c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zFgAAwOY68BG17n6wql6Q5G1JHpXklu6+e2WTAQAAbKhDvUetu9+a5K0rmgW+nFNmmcaa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\nABhGqAEAAAwj1AAAAIYRagAAAMP8P8xeHExEI6viAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f136c4d1e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12614952d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AQQk6AACAQQk6AACAQQk6AACAQQk6AACAQQk6AACAQQk6AACAQQk6AACAQQk6AACAQQk6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\nHQAAwKAEHQAAwKAEHQAAwKD+P6B9HoQ6pjSFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126139edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12614a7110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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k51czFgAAwPY68BG17n6yql6T5ANJnpXknu5+aGWTAQAAbKlDvUatu9+f5P0rmuXpHOrU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126138c350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iJN+T5MJqxgIAANhehz6i1t1PVdWrk7w7ybOS3Nfdj6xsMgAAgC11pNeodfe7krxrRbM8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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12630369d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aAADAMEINAABgGKEGAAAwjFADAAAYRqgBAAAMI9QAAACGEWoAAADDCDUAAIBhhBoAAMAw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126304aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1314c43690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12697bb250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126141fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6iFp3P15Vr0jyziRPS3JXdz+4sskAAAC21JFeo9bd70jyjhXNchjHckolXIH1xrpZc6yT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126123b9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6jVp3vyPJO9Y0y35WPn0SrpE1xyZYdxw1a45NsO44ak+7NVfdvekZAAAAuMwqr1EDAADg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\njl6avgHYBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12610d7750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EGgAAwDBCDQAAYBihBgAAMIxQAwAAGEaoAQAADCPUAAAAhhFqAAAAwwg1AACAYfYUalV1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\nAAwj1AAAAIb5fy4fr/2xGDh3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12611d0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f126103bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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k51czFgAAwPY68BG17n6iql6e5F1Jnpbk7u5+aGWTAQAAbKlDvUatu9+Z5J0rmmVZKzuN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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260db6890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260ef9810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260da0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260eac790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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765qPqHX3U1X16iTvSfKcJPd29yNrmwwAAGBHrfQate5+d5J3rzjDVU+LhGNm7bEt1h7b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260d4b590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ADCPUAAAAhhFqAAAAwwg1AACAYYQaAADAMEINAABgGKEGAAAwjFADAAAYRqgBAAAMs6dQ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\nADCMUAMAABjm/wGX37lbBvHqxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12607c9bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12606c2a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N8l1JLq9mLAAAgN1100fUuvvZqnpzkg8neUmSB7r7sZVNBgAAsKNO9Bq17v5Qkg+tYI4X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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12605900d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OiLXHJll/bIq1x6ZYe2zKk3rtVXdvegYAAAAuc5j3qAEAALAGaw+1qrqhqt5fVfdX1bk9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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1260480b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_matrix = predict(model2, total_days=1,day=11,delta=1, show_graph=False)\n",
    "for i in range (24):\n",
    "    plt.hist(cdf_matrix[:,i],20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We use Weights to Prioritize Accuracy in the Short Term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "11s - loss: 0.0930\n",
      "Epoch 2/5\n",
      "11s - loss: 0.0666\n",
      "Epoch 3/5\n",
      "10s - loss: 0.0601\n",
      "Epoch 4/5\n",
      "10s - loss: 0.0575\n",
      "Epoch 5/5\n",
      "10s - loss: 0.0550\n",
      "Percent Overestimating: 48.4409722222\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGyVjFwhIdcC3viUBXV0dcP/9wJo1wE9/Crz0EnDDDQAAm6aw0hhGu26Z/ZpD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Ia0NFiwgI9ddyar8l2DHkq3GcxJN9htT8qOOfdcaYT+5JPDt9XSMlj0aRPzEwIVd4qiHB3790uO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\nZfZsq2D55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YV+NrQm+KdVnXjA5ycE+dnmLj7wcjvnlftpjUFOAK4aE2RKno89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\nKLgTEREREREZBv4/BBLOfalTeLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f125b0240d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent Overestimating: 47.716724537\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f125b024150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AQQl0AAAAgxLoAAAABiXQAQAADEqgAwAAGJRABwAAMCiBDgAAYFACHQAAwKAEOgAAgEEJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f85bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f880550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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AAGB97fqIWnd/u6pemOQ9SR6Y5LruvmXfJgMAAFhTe/qMWne/O8m792kW1ovTYZnM+mQy\n65PJrE8mu6DWZ3X3qmcAAABgm718Rg0AAIADINQ4MFV1RVV9qqpuq6rNczz+O1V1a1V9vKpuqKql\nLlUK+2Gn9bltv1+pqq6qC+YqUVz4llmfVfWsxXvoLVX15sOekfW1xJ/vx6vqfVX10cWf8U9bxZys\np6q6rqruqqqbz/N4VdWfLdbvx6vq5w57xmUJNQ5EVT0wyWuSPDXJZUmurqrLvm+3jyY50d0/k+Rv\nk/zR4U7JulpyfaaqHprkRUk+dLgTss6WWZ9VdWmS30vyhO7+qSQvPvRBWUtLvn/+QZK3dffjsnVV\n8D8/3ClZc29IcsW9PP7UJJcuvk4mee0hzLQrQo2DcnmS27r7M939rSRvTXLV9h26+33d/c3F3Q9m\n62fxwWHYcX0uvCLJK5P812EOx9pbZn3+RpLXdPdXkqS77zrkGVlfy6zPTvJDi9s/nOTfD3E+1lx3\n35jky/eyy1VJ/qq3fDDJj1TVxYcz3X0j1Dgoj0jy+W33b19sO59rkvz9gU4E/2fH9bk4FeKS7j59\nmINBlnv/fHSSR1fVP1fVB6vq3v71GPbTMuvz5UmeXVW3Z+vq4L91OKPBUu7r31FXZk+X54f9UFXP\nTnIiyS+uehZIkqp6QJJXJ3n+ikeB8zmSrdN2npitsxFurKqf7u7/XOlUsOXqJG/o7ldV1c8n+euq\nekx3f2fVg8GFxBE1DsodSS7Zdv+Ri23fo6qenOT3kzy9u//7kGaDndbnQ5M8Jsk/VdXZJI9Pcr0L\ninBIlnn/vD3J9d39P939b0n+NVvhBgdtmfV5TZK3JUl3fyDJDyQ5eijTwc6W+jvqBEKNg/LhJJdW\n1aOq6sHZ+jDx9dt3qKrHJfnLbEWaz1dwmO51fXb3V7v7aHdvdPdGtj5D+fTuPrOacVkzO75/Jvm7\nbB1NS1UdzdapkJ85zCFZW8usz88leVKSVNVPZivU7j7UKeH8rk/y3MXVHx+f5KvdfeeqhzoXpz5y\nILr721X1wiTvSfLAJNd19y1V9YdJznT39Un+OMlDkvxNVSXJ57r76SsbmrWx5PqElVhyfb4nyVOq\n6tYk9yT53e7+j9VNzbpYcn2+JMnrquq3s3Vhked3d69uatZJVb0lW/+QdXTxOcmXJXlQknT3X2Tr\nc5NPS3Jbkm8m+bXVTLqz8v8NAADALE59BAAAGEaoAQAADCPUAAAAhhFqAAAAwwg1AACAYYQaAADA\nMEINAABgGKEGAAAwzP8C9HklxeqOqKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ef7fc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JqY9/mOTGqvrSqvrsJN+R5K7VjAUAALC9rvqIWnc/VlUvS/L2JE9Jcmd337+yyQAAALbU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ed12290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ec1d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ed77c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wQg0AAGAYoQYAADCMUAMAABhGqAEAAAwj1AAAAIYRagAAAMMINQAAgGGEGgAAwDBCDQAA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\nAMAwQg0AAGAYoQYAADCMUAMAABjm/wHDUW0P+xeBwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ed03650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1253754c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ed27350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12561e1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1256124b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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UAAAAhhFqAAAAwwg1AACAYYQaAADAMEINAABgGKEGAAAwjFADAAAYRqgBAAAMs69Qq6pb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f125289cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12560c0810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ef7d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f847a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f927090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f847c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124f904050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ebef9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MI9QAAACGEWoAAADDCDUAAIBhhBoAAMAwQg0AAGAYoQYAADDMgUKtqm6pqs9W1ZNVdWZV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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124ea8d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124e773710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAMBGOO9Qq6o/WVUv/+PHSb4zyaOrGgwAAGBT7ebQx0uT/FxV/fGf85Pd/V9XMhUAAMAG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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124e67e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A52umI2rd/eTk19NJfjXJ1Tvsc7y7t7p7a2NjY5a3AwAAWAt7DrWq+tKqeuE/Pk7yrUke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\nEWoAAACDEWoAAACDEWoAAACD+b8ztxFASH54RgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f124e869dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def priorities (y_true, y_pred):\n",
    "    w = np.arange(1,0, -1./24) ** 2\n",
    "    w = w / w.sum()\n",
    "    w = w[:,None]\n",
    "    W = K.variable(value = w)\n",
    "    return K.dot( K.abs(y_pred-y_true), W)\n",
    "\n",
    "batch = 1\n",
    "seed = 11\n",
    "# create and fit the LSTM network\n",
    "model = create_model(rate=0.2)\n",
    "\n",
    "np.random.seed(seed)\n",
    "model.compile(loss=priorities, optimizer=RMSprop(lr=0.0002, rho=0.9, epsilon=1e-08, decay=0.0002))\n",
    "model.fit(trainX, trainY, epochs=5, batch_size=batch, verbose=2)\n",
    "for i in range (0):\n",
    "    if i % 10 == 9:\n",
    "        model.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=2)\n",
    "    else:\n",
    "        model.fit(trainX, trainY, epochs=1, batch_size=batch, verbose=3)\n",
    "predict(model, total_days=1,day=11,delta=24)\n",
    "\n",
    "cdf_matrix = predict(model, total_days=1,day=11,delta=1, show_graph=False)\n",
    "for i in range (24):\n",
    "    plt.hist(cdf_matrix[:,i],20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "1s - loss: 0.1464\n",
      "Epoch 2/60\n",
      "1s - loss: 0.0966\n",
      "Epoch 3/60\n",
      "1s - loss: 0.0817\n",
      "Epoch 4/60\n",
      "1s - loss: 0.0759\n",
      "Epoch 5/60\n",
      "1s - loss: 0.0714\n",
      "Epoch 6/60\n",
      "1s - loss: 0.0681\n",
      "Epoch 7/60\n",
      "1s - loss: 0.0655\n",
      "Epoch 8/60\n",
      "1s - loss: 0.0623\n",
      "Epoch 9/60\n",
      "1s - loss: 0.0609\n",
      "Epoch 10/60\n",
      "1s - loss: 0.0605\n",
      "Epoch 11/60\n",
      "1s - loss: 0.0591\n",
      "Epoch 12/60\n",
      "1s - loss: 0.0572\n",
      "Epoch 13/60\n",
      "1s - loss: 0.0570\n",
      "Epoch 14/60\n",
      "1s - loss: 0.0567\n",
      "Epoch 15/60\n",
      "1s - loss: 0.0562\n",
      "Epoch 16/60\n",
      "1s - loss: 0.0553\n",
      "Epoch 17/60\n",
      "1s - loss: 0.0531\n",
      "Epoch 18/60\n",
      "1s - loss: 0.0538\n",
      "Epoch 19/60\n",
      "1s - loss: 0.0531\n",
      "Epoch 20/60\n",
      "1s - loss: 0.0528\n",
      "Epoch 21/60\n",
      "1s - loss: 0.0530\n",
      "Epoch 22/60\n",
      "1s - loss: 0.0528\n",
      "Epoch 23/60\n",
      "1s - loss: 0.0519\n",
      "Epoch 24/60\n",
      "1s - loss: 0.0510\n",
      "Epoch 25/60\n",
      "1s - loss: 0.0521\n",
      "Epoch 26/60\n",
      "1s - loss: 0.0510\n",
      "Epoch 27/60\n",
      "1s - loss: 0.0512\n",
      "Epoch 28/60\n",
      "1s - loss: 0.0511\n",
      "Epoch 29/60\n",
      "1s - loss: 0.0500\n",
      "Epoch 30/60\n",
      "1s - loss: 0.0503\n",
      "Epoch 31/60\n",
      "1s - loss: 0.0504\n",
      "Epoch 32/60\n",
      "1s - loss: 0.0495\n",
      "Epoch 33/60\n",
      "1s - loss: 0.0500\n",
      "Epoch 34/60\n",
      "1s - loss: 0.0493\n",
      "Epoch 35/60\n",
      "1s - loss: 0.0497\n",
      "Epoch 36/60\n",
      "1s - loss: 0.0493\n",
      "Epoch 37/60\n",
      "1s - loss: 0.0492\n",
      "Epoch 38/60\n",
      "1s - loss: 0.0489\n",
      "Epoch 39/60\n",
      "1s - loss: 0.0492\n",
      "Epoch 40/60\n",
      "1s - loss: 0.0480\n",
      "Epoch 41/60\n",
      "1s - loss: 0.0479\n",
      "Epoch 42/60\n",
      "1s - loss: 0.0481\n",
      "Epoch 43/60\n",
      "1s - loss: 0.0490\n",
      "Epoch 44/60\n",
      "1s - loss: 0.0476\n",
      "Epoch 45/60\n",
      "1s - loss: 0.0476\n",
      "Epoch 46/60\n",
      "1s - loss: 0.0475\n",
      "Epoch 47/60\n",
      "1s - loss: 0.0478\n",
      "Epoch 48/60\n",
      "1s - loss: 0.0475\n",
      "Epoch 49/60\n",
      "1s - loss: 0.0470\n",
      "Epoch 50/60\n",
      "1s - loss: 0.0475\n",
      "Epoch 51/60\n",
      "1s - loss: 0.0478\n",
      "Epoch 52/60\n",
      "1s - loss: 0.0473\n",
      "Epoch 53/60\n",
      "1s - loss: 0.0482\n",
      "Epoch 54/60\n",
      "1s - loss: 0.0475\n",
      "Epoch 55/60\n",
      "1s - loss: 0.0474\n",
      "Epoch 56/60\n",
      "1s - loss: 0.0478\n",
      "Epoch 57/60\n",
      "1s - loss: 0.0472\n",
      "Epoch 58/60\n",
      "1s - loss: 0.0473\n",
      "Epoch 59/60\n",
      "1s - loss: 0.0464\n",
      "Epoch 60/60\n",
      "1s - loss: 0.0468\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
      "Epoch 9/50\n",
      "Epoch 10/50\n",
      "Epoch 11/50\n",
      "Epoch 12/50\n",
      "Epoch 13/50\n",
      "Epoch 14/50\n",
      "Epoch 15/50\n",
      "Epoch 16/50\n",
      "Epoch 17/50\n",
      "Epoch 18/50\n",
      "Epoch 19/50\n",
      "Epoch 20/50\n",
      "Epoch 21/50\n",
      "Epoch 22/50\n",
      "Epoch 23/50\n",
      "Epoch 24/50\n",
      "Epoch 25/50\n",
      "Epoch 26/50\n",
      "Epoch 27/50\n",
      "Epoch 28/50\n",
      "Epoch 29/50\n",
      "Epoch 30/50\n",
      "Epoch 31/50\n",
      "Epoch 32/50\n",
      "Epoch 33/50\n",
      "Epoch 34/50\n",
      "Epoch 35/50\n",
      "Epoch 36/50\n",
      "Epoch 37/50\n",
      "Epoch 38/50\n",
      "Epoch 39/50\n",
      "Epoch 40/50\n",
      "Epoch 41/50\n",
      "Epoch 42/50\n",
      "Epoch 43/50\n",
      "Epoch 44/50\n",
      "Epoch 45/50\n",
      "Epoch 46/50\n",
      "Epoch 47/50\n",
      "Epoch 48/50\n",
      "Epoch 49/50\n",
      "Epoch 50/50\n",
      "Epoch 1/50\n",
      "Epoch 2/50\n",
      "Epoch 3/50\n",
      "Epoch 4/50\n",
      "Epoch 5/50\n",
      "Epoch 6/50\n",
      "Epoch 7/50\n",
      "Epoch 8/50\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1s - loss: 0.0386\n",
      "Epoch 38/50\n",
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      "Epoch 39/50\n",
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      "Epoch 50/50\n",
      "1s - loss: 0.0390\n",
      "Percent Overestimating: 36.1319444444\n"
     ]
    },
    {
     "data": {
      "image/png": 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14Mwdw5g4OknRtgNbWqu7rr38MmC302L+wAOAxwPs2wdYrcALL9A55udEjMgd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f410fc150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'\\ncdf_matrix = predict(model, total_days=1,day=11,delta=1, show_graph=False)\\nfor i in range (24):\\n    plt.hist(cdf_matrix[:,i],20)\\n    plt.show()\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Results from Hyper Parameter Search...\n",
    "# Best Epoch - 60\n",
    "# Best Batch - 60\n",
    "# Best Decay - 0.0\n",
    "# Best Learning Rate - 0.0001\n",
    "\n",
    "def priorities (y_true, y_pred):\n",
    "    w = np.arange(1,0, -1./24) ** 2\n",
    "    w = w / w.sum()\n",
    "    w = w[:,None]\n",
    "    W = K.variable(value = w)\n",
    "    return K.dot( K.abs(y_pred-y_true), W)\n",
    "\n",
    "batch = 10\n",
    "seed = 11\n",
    "# create and fit the LSTM network\n",
    "model = create_model(rate=0.25)\n",
    "np.random.seed(seed)\n",
    "model.compile(loss=priorities, optimizer=RMSprop(lr=0.0001, rho=0.9, epsilon=1e-08, decay=0.0))\n",
    "model.fit(trainX, trainY, epochs=60, batch_size=batch, verbose=2)\n",
    "for i in range (10):\n",
    "    if i % 10 == 9:\n",
    "        model.fit(trainX, trainY, epochs=50, batch_size=batch, verbose=2)\n",
    "    else:\n",
    "        model.fit(trainX, trainY, epochs=50, batch_size=batch, verbose=3)\n",
    "predict(model, total_days=2,day=11,delta=24)\n",
    "K.clear_session()\n",
    "'''\n",
    "cdf_matrix = predict(model, total_days=1,day=11,delta=1, show_graph=False)\n",
    "for i in range (24):\n",
    "    plt.hist(cdf_matrix[:,i],20)\n",
    "    plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}