2017-09-predicting-calls-from-network-anomalies/Predict_calls_from_anomalies.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Predicting Subscribers Calls following a Network Element Anomaly\n",
    "\n",
    "Assume some anomaly detection algorithm is able to detect anomalies in network elements in a fairly reliable way. It is natural to think that some subscribers will be impacted by this anomaly. The goal is to use these anomalies and recognize their configurations to predict an increase of calls.\n",
    "\n",
    "The following is assumed about the prediction model:\n",
    "\n",
    "- each network element has a subscribers population: the larger the network element, the more subscribers will call following an anomaly on that element\n",
    "- an anomaly likeliness is estimated by a probability (e.g. could be the z-score of a measurement after removal of the expected signal) and we call rating the negative log of that probability: the higher the rating, the more unlikely the anomaly is\n",
    "- at peak hour (e.g. from 6:30PM to 10:00PM), more subscribers are using their home devices (e.g. set top box) and will react quickly if a network anomaly occurs.\n",
    "- during off peak hours, less subscribers are using their devices: calls of those subscribers will be deferred to when they turn them on later in the day: the calls increase is less sudden and spread over more time\n",
    "- subscribers are assumed to behave the same in different regions at at the same time local to their time zone\n",
    "\n",
    "### Example of high increase following an severe anomaly at peak hour on a large network element\n",
    "\n",
    "![High Increase at Peak Hour ](predict_peak_hour.png)\n",
    "\n",
    "### Example of smaller increase following an severe anomaly at off peak hour on a large network element\n",
    "\n",
    "![Smaller Increase at Off Peak Hour ](predict_offpeak_hour.png)\n",
    "\n",
    "** The proof of concept in this notebook uses synthetic data with the assumptions made above to compare logistic regression with a simple neural network **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple crafted data set\n",
    "Each row in the data set corresponds to one network element anomaly along with the expected number of calls (if there was no anomaly) followed by the actual calls following the \n",
    "- the regions of the network elements (not the elements themselves) are one-hot encoded. As the timezone of the network element is assumed to be not available, the model is expected to \"learn the local time\" given the region and the UTC time\n",
    "- the UTC time of the anomaly is encoded: in this first limited data set only four values are considered\n",
    "- rating which the unlikeliness of the anomaly: the higher the more severely the subscribers are impacted\n",
    "- the population covered by the network element: the larger, the more calls are expected to follow the anomaly\n",
    "- the expected number of calls (if there was no anomaly, from the median of the calls over the 5 previous weeks at the same time)\n",
    "- the actual calls following the anomaly after 1h, 2h and 3h.\n",
    "Note that the calls increase were crafted to respect the assumptions above: more calls in case of higher rating, larger population. Also ** calls spikes are observed during peak hour while during off peak hour calls are shifted and spread over a longer period **. The model is expected to learn this pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_east</th>\n",
       "      <th>region_pacific</th>\n",
       "      <th>ano_18h</th>\n",
       "      <th>ano_23h</th>\n",
       "      <th>ano_21h</th>\n",
       "      <th>ano_2h</th>\n",
       "      <th>rating</th>\n",
       "      <th>population</th>\n",
       "      <th>calls</th>\n",
       "      <th>calls+1</th>\n",
       "      <th>calls+2</th>\n",
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       "  </thead>\n",
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       "      <td>600</td>\n",
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       "      <td>600</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "    region_east  region_pacific  ano_18h  ano_23h  ano_21h  ano_2h  rating  \\\n",
       "0             1               0        1        0        0       0      30   \n",
       "1             1               0        0        1        0       0      30   \n",
       "2             1               0        1        0        0       0      30   \n",
       "3             1               0        0        1        0       0      30   \n",
       "4             1               0        0        0        1       0      30   \n",
       "5             1               0        0        0        0       1      30   \n",
       "6             1               0        0        0        1       0      30   \n",
       "7             1               0        0        0        0       1      30   \n",
       "8             0               1        0        0        1       0      30   \n",
       "9             0               1        0        0        0       1      30   \n",
       "10            0               1        0        0        1       0      30   \n",
       "11            0               1        0        0        0       1      30   \n",
       "12            0               1        1        0        0       0      30   \n",
       "13            0               1        0        1        0       0      30   \n",
       "14            0               1        1        0        0       0      30   \n",
       "15            0               1        0        1        0       0      30   \n",
       "\n",
       "    population  calls  calls+1  calls+2  calls+3  \n",
       "0          300      2        4       10       25  \n",
       "1          300      4       32       34       30  \n",
       "2          600      4        8       20       50  \n",
       "3          600      8       64       68       60  \n",
       "4          300      3       27       34       32  \n",
       "5          300      2       15       15        7  \n",
       "6          600      6       54       68       64  \n",
       "7          600      4       30       30       14  \n",
       "8          300      2        4       10       25  \n",
       "9          300      4       32       34       30  \n",
       "10         600      4        8       20       50  \n",
       "11         600      8       64       68       60  \n",
       "12         300      2        4        5        6  \n",
       "13         300      4       10       30       31  \n",
       "14         600      4        8       10       12  \n",
       "15         600      8       20       60       62  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('data.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   0,   1,   0,   0,   0,  30, 300,   2],\n",
       "       [  1,   0,   0,   1,   0,   0,  30, 300,   4],\n",
       "       [  1,   0,   1,   0,   0,   0,  30, 600,   4]], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data.iloc[:,0:9].values\n",
    "X[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4, 10, 25],\n",
       "       [32, 34, 30],\n",
       "       [ 8, 20, 50]], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=data.iloc[:,9:12].values\n",
    "Y[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X)  \n",
    "X_std = scaler.transform(X)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Neural Network to Predict Calls (using Multilayer Perceptron Regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "regressor=MLPRegressor(hidden_layer_sizes=(100,100), activation='relu', solver='adam', alpha=0.0001, \n",
    "                       batch_size='auto', learning_rate='constant', learning_rate_init=0.001, \n",
    "                       power_t=0.5, max_iter=2000, shuffle=True, random_state=None, tol=0.0001, \n",
    "                       verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, \n",
    "                       early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, \n",
    "                       epsilon=1e-08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=2000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_std,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.01751597,   9.94850869,  25.08601922],\n",
       "       [ 32.04990559,  33.97456191,  30.0105897 ],\n",
       "       [  8.00254789,  20.00550341,  49.92479102],\n",
       "       [ 63.93866273,  68.0465592 ,  59.98397908],\n",
       "       [ 26.81987163,  34.25348114,  31.88744452],\n",
       "       [ 14.98573513,  15.07208359,   6.84246168],\n",
       "       [ 54.09600577,  67.85686358,  64.07628671],\n",
       "       [ 30.16376266,  29.70065861,  14.35385183],\n",
       "       [  3.99906557,   9.91756426,  25.01490579],\n",
       "       [ 32.20282539,  33.60058413,  30.40821066],\n",
       "       [  8.00978862,  20.06799759,  49.98196747],\n",
       "       [ 63.81116069,  68.33516923,  59.64402979],\n",
       "       [  4.04399499,   5.42093679,   5.5806298 ],\n",
       "       [  9.83637234,  30.22110125,  30.80109953],\n",
       "       [  8.00026023,   9.68145261,  12.33458018],\n",
       "       [ 20.14031563,  59.81124926,  62.17101375]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicted calls 1h, 2h, 3h following the anomaly : on training set\n",
    "regressor.predict(X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4, 10, 25],\n",
       "       [32, 34, 30],\n",
       "       [ 8, 20, 50],\n",
       "       [64, 68, 60],\n",
       "       [27, 34, 32],\n",
       "       [15, 15,  7],\n",
       "       [54, 68, 64],\n",
       "       [30, 30, 14],\n",
       "       [ 4, 10, 25],\n",
       "       [32, 34, 30],\n",
       "       [ 8, 20, 50],\n",
       "       [64, 68, 60],\n",
       "       [ 4,  5,  6],\n",
       "       [10, 30, 31],\n",
       "       [ 8, 10, 12],\n",
       "       [20, 60, 62]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actual calls 1h, 2h, 3h following the anomaly\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Without any SSE error measurement, it can be seen above that expected +1h, +2h, +3h calls match fairly the actual calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>region_east</th>\n",
       "      <th>region_pacific</th>\n",
       "      <th>ano_18h</th>\n",
       "      <th>ano_23h</th>\n",
       "      <th>ano_21h</th>\n",
       "      <th>ano_2h</th>\n",
       "      <th>rating</th>\n",
       "      <th>population</th>\n",
       "      <th>calls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>550</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>590</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>599</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>600</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1200</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region_east  region_pacific  ano_18h  ano_23h  ano_21h  ano_2h  rating  \\\n",
       "0            1               0        1        0        0       0      30   \n",
       "1            1               0        1        0        0       0      30   \n",
       "2            1               0        1        0        0       0      30   \n",
       "3            1               0        1        0        0       0      30   \n",
       "4            1               0        1        0        0       0      30   \n",
       "5            1               0        1        0        0       0      30   \n",
       "6            1               0        1        0        0       0      30   \n",
       "\n",
       "   population  calls  \n",
       "0         450      3  \n",
       "1         500      4  \n",
       "2         550      4  \n",
       "3         590      4  \n",
       "4         599      4  \n",
       "5         600      4  \n",
       "6        1200      8  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicted calls 1h, 2h, 3h following the anomaly : on test set\n",
    "data_test=pd.read_csv('test.csv')\n",
    "data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   4.51249882,   13.08295886,   36.02928949],\n",
       "       [   6.32404407,   16.17408245,   41.9181388 ],\n",
       "       [   6.98815235,   17.9068261 ,   45.93589914],\n",
       "       [   7.75989066,   19.55189425,   49.13138509],\n",
       "       [   7.97808945,   19.960199  ,   49.84559481],\n",
       "       [   8.00254789,   20.00550341,   49.92479102],\n",
       "       [  54.09307877,   78.02645002,  109.72637183]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test=data_test.values\n",
    "X_test_std = scaler.transform(X_test)\n",
    "regressor.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On a test set with manually crafted data, the predicted subscriber behavior is compliant with the expectation. For example:\n",
    "- row 1: the increase is small on as the anomaly happens during off peak hour (18:00 UTC = 11:00 in region east)\n",
    "- row 2-6: with increasing population and calls, all predicted +1h, +2h, +3h are increasing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Linear Regression (Ridge) to Predict Calls: not good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge=Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, \n",
    "            tol=0.001, solver='auto', random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.fit(X_std,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  8.69965638,   9.36386019,  16.93831994],\n",
       "       [ 25.17902848,  35.83736074,  31.61931116],\n",
       "       [ 17.33395168,  22.56131906,  33.26643043],\n",
       "       [ 52.01615376,  68.97535282,  63.68444194],\n",
       "       [ 27.7582666 ,  32.96736863,  37.79655416],\n",
       "       [ 23.59912147,  19.43631401,   9.96254422],\n",
       "       [ 45.49397689,  56.1350941 ,  61.99317479],\n",
       "       [ 32.23341678,  32.63377288,  26.2906547 ],\n",
       "       [  5.16542798,  14.28064144,  26.33949488],\n",
       "       [ 28.31052782,  30.66038664,  22.11101536],\n",
       "       [ 13.79972329,  27.47810031,  42.66760537],\n",
       "       [ 55.14765309,  63.79837871,  54.17614613],\n",
       "       [ -4.79176725,   0.64739961,  13.3497708 ],\n",
       "       [ 11.68760485,  27.12090016,  28.03076202],\n",
       "       [  3.84252805,  13.84485847,  29.67788129],\n",
       "       [ 38.52473013,  60.25889223,  60.09589279]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.predict(X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4, 10, 25],\n",
       "       [32, 34, 30],\n",
       "       [ 8, 20, 50],\n",
       "       [64, 68, 60],\n",
       "       [27, 34, 32],\n",
       "       [15, 15,  7],\n",
       "       [54, 68, 64],\n",
       "       [30, 30, 14],\n",
       "       [ 4, 10, 25],\n",
       "       [32, 34, 30],\n",
       "       [ 8, 20, 50],\n",
       "       [64, 68, 60],\n",
       "       [ 4,  5,  6],\n",
       "       [10, 30, 31],\n",
       "       [ 8, 10, 12],\n",
       "       [20, 60, 62]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem is not linearly well separable, partly due to the local time that is expected to be inferred from the UTC time and the region (assumed the timezone of each region is not available). The linear model learns coefficients for features but can't learn a coefficient for a combination of features. It is possible to add combined features (region + time) but this adds many features to the model which makes it more complex while the neural network better handles these combinations.\n",
    "### At this step the simple synthetic data gives fairly good results. Let's generate much more data (still synthetic) respecting the subscribers behavior for random combinations a larger range for population, rating, time, and calls (baseline and post-anomaly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region_idx: 3, ano_hour_utc: 6, ano_hour_local: 23\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 1.0]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume we have 4 regions from 0 to 3, each in its timezone from UTC-4 (e.g. Boston) to UTC-7 (Pacific)\n",
    "REGIONS_NB=4\n",
    "region_names= [ 'utc-' + str(4+i) for i in range(0,REGIONS_NB) ]\n",
    "\n",
    "columns=[]\n",
    "\n",
    "regions=np.zeros(REGIONS_NB)\n",
    "region_idx = np.random.randint(REGIONS_NB)\n",
    "regions[region_idx]=1\n",
    "columns += regions.tolist()\n",
    "\n",
    "# select the UTC time of the anomaly (1 hour between 0-24) and local to the region\n",
    "ano_hour_utc = np.random.randint(24)\n",
    "ano_hour_local = (ano_hour_utc - 4 - region_idx + 24) % 24\n",
    "print('region_idx: %d, ano_hour_utc: %d, ano_hour_local: %d' % (region_idx, ano_hour_utc, ano_hour_local))\n",
    "\n",
    "\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['utc-4', 'utc-5', 'utc-6', 'utc-7']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_names= [ 'utc-' + str(4+i) for i in range(0,REGIONS_NB) ]\n",
    "region_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  1.,  0.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=np.zeros(4)\n",
    "tmp[2]=1\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class CallsFunction:\n",
    "\n",
    "    def __init__(self):\n",
    "        from sklearn.preprocessing import PolynomialFeatures\n",
    "        from sklearn.linear_model import LinearRegression\n",
    "        self.base_pop = 500 # numbers below are for this base population\n",
    "        self.X=np.array([0,2,4,6,6.75,7.5,8.2,9,13,15,17,19.5,21,23,23.99]).reshape(-1,1)\n",
    "        self.y=np.array([10,3,3,3,14,30,30,20,21,23,40,60,47,13,10])\n",
    "        self.quadratic = PolynomialFeatures(degree=10)\n",
    "        X_quad = self.quadratic.fit_transform(self.X)\n",
    "        self.pr = LinearRegression()\n",
    "        self.pr.fit(X_quad, self.y)\n",
    "\n",
    "    def plot(self, population):\n",
    "        fig = plt.figure(figsize=(12,10)) \n",
    "        ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "        hours,calls_for_pop = self.__get_plot_calls(population)\n",
    "        ax.plot(hours,calls_for_pop)\n",
    "        \n",
    "        support_hours,support_points = self.__get_plot_support_points(population)\n",
    "        ax.scatter(support_hours, support_points)\n",
    "        plt.xlim(0,24)\n",
    "        plt.ylim(0,self.get_ylim(population))\n",
    "        plt.grid(True)\n",
    "        plt.xticks(np.arange(24), [ str(i) for i in np.arange(24)])\n",
    "        return ax\n",
    "    \n",
    "    def __get_plot_support_points(self, population):\n",
    "        return (self.X, [ self.adjust_for_pop(calls, population) for calls in self.y ])\n",
    "    \n",
    "    def __get_plot_calls(self, population):\n",
    "        hours = np.arange(0,24,0.1).reshape(-1,1)\n",
    "        all_calls = self.pr.predict(self.quadratic.fit_transform(hours))\n",
    "        calls_for_pop=[ self.adjust_for_pop(calls, population) for calls in all_calls ]\n",
    "        return (hours,calls_for_pop)\n",
    "\n",
    "    def adjust_for_pop(self, calls, population):\n",
    "        return calls * population / self.base_pop\n",
    "    \n",
    "    def get_ylim(self,population):\n",
    "        hours,calls_for_pop = self.__get_plot_calls(population)\n",
    "        support_hours,support_points = self.__get_plot_support_points(population)\n",
    "        return max( max(calls_for_pop), max(support_points) )*1.05\n",
    "    \n",
    "    def get_calls_for_local_hour(self,hour,population):\n",
    "        calls = self.pr.predict(self.quadratic.fit_transform(np.array([hour]).reshape(-1,1)))[0]\n",
    "        return self.adjust_for_pop(calls, population)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use interpolation to generate a typical expected calls baseline during a normal day\n",
    "The graph below shows the points used to generate a polynomial curve for the expected call rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.8406614987115617"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jR+vKK6+MTL08Du7x8uWFmjlTKi0tUJUq9uux9bjs18XFxaqQ7/tpfUhqL7Mz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\niGSaCl4RERERybT/BSyrLOXwO7s+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f2e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calls_function = CallsFunction()\n",
    "calls_function.plot(100)\n",
    "calls_function.get_calls_for_local_hour(21.3,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class PostAnomalyCallsFunction:\n",
    "\n",
    "    def __init__(self):\n",
    "        from sklearn.preprocessing import PolynomialFeatures\n",
    "        from sklearn.linear_model import LinearRegression\n",
    "        \n",
    "        # percentage of calls in ano_hour +1\n",
    "        self.X1=np.array( [0, 1,   2,  3,  5,   6,  7, 8.2,  9,  10, 13, 15, 16, 17.5, 19,  22, 23.99    ]).reshape(-1,1)\n",
    "        self.y1=np.array([.4,.2, .05, .05,.05, .2, .8, .8,   .6, .2, .2, .2, .55, .85, .85, .6, .3])\n",
    "        self.quadratic1 = PolynomialFeatures(degree=8)\n",
    "        X_quad = self.quadratic1.fit_transform(self.X1)\n",
    "        self.pr1 = LinearRegression()\n",
    "        self.pr1.fit(X_quad, self.y1)\n",
    "        \n",
    "        # percentage of calls in ano_hour +2\n",
    "        self.X2=np.array( [0,   1,   2,  3,   4,   6, 7.5, 8.2, 9,  13, 15,  17, 19, 21,  23,  23.99    ]).reshape(-1,1)\n",
    "        self.y2=np.array([.05,.05, .05, .05,.15, .3, .15,  .15, .2, .2, .3, .3, .1, .1,  .1, .05])\n",
    "        self.X2=np.array( [0,   1,   2,  3,   4,   6, 7.5, 8.2, 9,  13, 15,  17, 19, 21,  23,  23.99    ]).reshape(-1,1)\n",
    "        self.y2=np.array([.05,.05, .05, .05,.15, .3, .15,  .15, .2, .2, .3, .3, .1, .1,  .1, .05])\n",
    "        self.quadratic2 = PolynomialFeatures(degree=12)\n",
    "        X_quad = self.quadratic2.fit_transform(self.X2)\n",
    "        self.pr2 = LinearRegression()\n",
    "        self.pr2.fit(X_quad, self.y2)\n",
    "        \n",
    "        # percentage of calls in ano_hour +3\n",
    "        self.X3=np.array( [0,   1,   2,  3,   4,   6, 7.5, 8.2,  9,   13,  15,  17,  19,  21,  23,  23.99    ]).reshape(-1,1)\n",
    "        self.y3=np.array([.05,.05, .05, .15,.25, .2, .05,  .05, .05, .05, .35, .15, .05, .05, .05, .05])\n",
    "        self.X3=np.array( [0,   1,   2,  3,   4,   6, 7.5, 8.2,  9,   13, 14, 16, 17,  19,  21,  23,  23.99    ]).reshape(-1,1)\n",
    "        self.y3=np.array([.05,.05, .05, .15,.25, .2, .05,  .05, .05, .05, .5, .2, .15, .05, .05, .05, .05])\n",
    "        self.quadratic3 = PolynomialFeatures(degree=8)\n",
    "        X_quad = self.quadratic3.fit_transform(self.X3)\n",
    "        self.pr3 = LinearRegression()\n",
    "        self.pr3.fit(X_quad, self.y3)\n",
    "        \n",
    "        self.cf = CallsFunction()\n",
    "        \n",
    "    def plot_ano_calls_rate(self):\n",
    "        fig = plt.figure(figsize=(15,13)) \n",
    "        X_fit = np.arange(0,24,0.1).reshape(-1,1)\n",
    "        title_fmt='Percent of calls due to anomaly for hour + %d'\n",
    "        \n",
    "        ax1 = fig.add_subplot(3, 1, 1)\n",
    "        y_quad_fit = self.pr1.predict(self.quadratic1.fit_transform(X_fit))\n",
    "        ax1.plot(X_fit,y_quad_fit)\n",
    "        ax1.scatter(self.X1,self.y1)\n",
    "        plt.xlim(0,24)\n",
    "        plt.ylim(0,1)\n",
    "        plt.grid(True)\n",
    "        plt.title(title_fmt % 1)\n",
    "        plt.xticks(np.arange(24), [ str(i) for i in np.arange(24)])\n",
    "\n",
    "        ax2 = fig.add_subplot(3, 1, 2)\n",
    "        y_quad_fit = self.pr2.predict(self.quadratic2.fit_transform(X_fit))\n",
    "        ax2.plot(X_fit,y_quad_fit)\n",
    "        ax2.scatter(self.X2,self.y2)\n",
    "        plt.xlim(0,24)\n",
    "        plt.ylim(0,1)\n",
    "        plt.grid(True)\n",
    "        plt.title(title_fmt % 2)\n",
    "        plt.xticks(np.arange(24), [ str(i) for i in np.arange(24)])\n",
    "\n",
    "        ax3 = fig.add_subplot(3, 1, 3)\n",
    "        y_quad_fit = self.pr3.predict(self.quadratic3.fit_transform(X_fit))\n",
    "        ax3.plot(X_fit,y_quad_fit)\n",
    "        ax3.scatter(self.X3,self.y3)\n",
    "        plt.xlim(0,24)\n",
    "        plt.ylim(0,1)\n",
    "        plt.grid(True)\n",
    "        plt.title(title_fmt % 3)\n",
    "        plt.xticks(np.arange(24), [ str(i) for i in np.arange(24)])\n",
    "        \n",
    "    def __get_percent(self, pr, quadratic, ano_hour_local):\n",
    "        return max(0.0, pr.predict(quadratic.fit_transform(np.array([ano_hour_local]).reshape(-1,1)))[0])\n",
    "        \n",
    "    def get_predicted_calls(self,population, ano_rating, ano_hour_local, debug=None):\n",
    "        # Get the normal calls adjusted for the population for the hour of the anomaly\n",
    "        normal_calls_for_ano = self.cf.get_calls_for_local_hour(ano_hour_local, population)\n",
    "        if (debug is True):\n",
    "            print('normal_calls at ano hour(%d): %s' % (ano_hour_local, str(normal_calls_for_ano)))\n",
    "\n",
    "        # Compute the calls added by the anomaly for hours +1, +2, +3.\n",
    "        # Assuming only ratings > 20 trigger additional calls.\n",
    "        # With the following formula, the rate of anomaly_calls/normal_calls is:\n",
    "        # rating = 100 ==> anomaly_calls = 1.90 * normal_calls\n",
    "        # rating =  50 ==> anomaly_calls = 1.49 * normal_calls\n",
    "        # rating =  25 ==> anomaly_calls = 0.77 * normal_calls\n",
    "        # rating =  23 ==> anomaly_calls = 0.60 * normal_calls\n",
    "        # rating =  21 ==> anomaly_calls = 0.30 * normal_calls\n",
    "        # rating =  20 ==> anomaly_calls = 0.00 * normal_calls\n",
    "        ano_calls_rate = np.log10(max(ano_rating-19,1))\n",
    "        if (debug is True):\n",
    "            print('ano_calls_rate for rating: ' + str(ano_calls_rate))\n",
    "        ano_calls = ano_calls_rate * normal_calls_for_ano\n",
    "        if (debug is True):\n",
    "            print('ano_calls for rating: ' + str(ano_calls))\n",
    "\n",
    "        # distribute anomaly_calls for hour +1, +2, +3\n",
    "        ano_calls_plus_1h = max(0,int(round(ano_calls * self.__get_percent(self.pr1, self.quadratic1, ano_hour_local))))\n",
    "        ano_calls_plus_2h = max(0,int(round(ano_calls * self.__get_percent(self.pr2, self.quadratic2, ano_hour_local))))\n",
    "        ano_calls_plus_3h = max(0,int(round(ano_calls * self.__get_percent(self.pr3, self.quadratic3, ano_hour_local))))\n",
    "        calls_due_to_ano = [ano_calls_plus_1h, ano_calls_plus_2h, ano_calls_plus_3h]\n",
    "\n",
    "        # compute normal calls for hour +1, +2, +3\n",
    "        next_hours = [ (ano_hour_local + 1 + i+24) % 24 for i in range(3)]\n",
    "        normal_calls = [self.cf.get_calls_for_local_hour(hour, population) for hour in next_hours]\n",
    "\n",
    "        predicted_calls = [a+b for a,b in zip(normal_calls, calls_due_to_ano)]\n",
    "        return normal_calls_for_ano, normal_calls, predicted_calls\n",
    "\n",
    "    \n",
    "    def plot_predicted_calls(self, population, ano_rating, ano_hour_local):\n",
    "        # plot the normal calls\n",
    "        ax = self.cf.plot(population)\n",
    "        ylim = self.cf.get_ylim(population) # max of the normal graph\n",
    "        \n",
    "        normal_calls_for_ano, normal_calls, predicted_calls = \\\n",
    "            self.get_predicted_calls(population, ano_rating, ano_hour_local, debug=True)\n",
    "        print('normal_calls: ' + str(normal_calls))\n",
    "        print('predicted calls: ' + str(predicted_calls))\n",
    "\n",
    "        # mark calls at the time of the anomaly with green cross\n",
    "        ax.scatter([ano_hour_local],[normal_calls_for_ano], c='g',marker='o', s=300)\n",
    "        \n",
    "        # plot the calls (normal calls + predicted calls (normal + due to ano)) for the 3 next hours\n",
    "        next_hours = [ (ano_hour_local + 1 + i+24) % 24 for i in range(3)]\n",
    "        ax.scatter(next_hours, normal_calls, c='g',marker='o', s=60)\n",
    "        ax.scatter(next_hours, predicted_calls, c='r',marker='o', s=60)\n",
    "        plt.grid(True)\n",
    "        plt.ylim(0, max(ylim, max(predicted_calls))*1.05)\n",
    "        plt.xticks(np.arange(24), [ str(i) for i in np.arange(24)])\n",
    "        plt.ylabel('Calls per hour')\n",
    "        plt.xlabel('Hour of the day (local to timezone)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(57.48116087835124,\n",
       " [51.594436320354795, 43.486797281200417, 37.818159447284962],\n",
       " [75.594436320354788, 50.486797281200417, 40.818159447284962])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postAnoCallFunction = PostAnomalyCallsFunction()\n",
    "postAnoCallFunction.get_predicted_calls(population=1000, ano_rating=29, ano_hour_local=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine +1, +2, +3h increases on a single graph, respecting spikes in peak hour in +1h while more spread during off peak hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal_calls at ano hour(6): 37.0669672704\n",
      "ano_calls_rate for rating: 1.0\n",
      "ano_calls for rating: 37.0669672704\n",
      "normal_calls: [69.439612107610827, 98.212675338657576, 114.41381967777382]\n",
      "predicted calls: [85.439612107610827, 106.21267533865758, 120.41381967777382]\n"
     ]
    },
    {
     "data": {
      "image/png": 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UF9W8OK9NecqLWl7lISzGQElJCY888gglJSWBZm5L31v85x18cGqNdKasLwhq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      "text/plain": [
       "<matplotlib.figure.Figure at 0xec77828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LNMoSTFmC+ZLFlxygLNGkapaFC+Guu2C33Vwh9PPPcMstUFwMb74J11wD++9f88Kssiz1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\nxgh6118fo4YOGAAHHwz77BMH4FLdXnwxC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LNnDCIyePCC959xRjQPfO01eOONKJzq66O5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf16f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "postAnoCallFunction = PostAnomalyCallsFunction()\n",
    "postAnoCallFunction.plot_predicted_calls(population=2000, ano_rating=29, ano_hour_local=6)\n",
    "postAnoCallFunction.plot_ano_calls_rate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: off peak: +1h, +2h, +3h calls increase is moderate relatively to the expected call rate (blue curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal_calls at ano hour(12): 43.4867972812\n",
      "ano_calls_rate for rating: 1.0\n",
      "ano_calls for rating: 43.4867972812\n",
      "normal_calls: [37.818159447284962, 38.382455814834977, 46.947469961860044]\n",
      "predicted calls: [43.818159447284962, 44.382455814834977, 53.947469961860044]\n"
     ]
    },
    {
     "data": {
      "image/png": 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0UUpKStKaWZW+t/jPO+igxBrpTFlfOvickd4OaGyMqQfkAmuA3sCYis+PAfp4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\nDv4/DyVRedEg11oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd446be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "postAnoCallFunction.plot_predicted_calls(population=1000, ano_rating=29, ano_hour_local=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class RowGenerator:\n",
    "    REGIONS_NB=4\n",
    "    POPULATION_MIN=200\n",
    "    POPULATION_MAX=700\n",
    "    region_names= [ 'utc-' + str(4+i) for i in range(0,REGIONS_NB) ]\n",
    "    RATINGS = [21,30,50,80,120,180]\n",
    "    RATINGS_PROBAS = [0.3, 0.25, 0.2, 0.12, 0.08, 0.05]\n",
    "    utc_cols= [ 'hr=%d' % i for i in range(0,24) ]\n",
    "\n",
    "    def __init__(self):\n",
    "        self.cf = PostAnomalyCallsFunction()\n",
    "        \n",
    "    @staticmethod\n",
    "    def get_header_columns():\n",
    "        header = []\n",
    "        header += RowGenerator.region_names\n",
    "        header += RowGenerator.utc_cols\n",
    "        header += ['rating', 'population', 'calls', 'calls+1', 'calls+2', 'calls+3']\n",
    "        return header\n",
    "\n",
    "    def __gen_region(self):\n",
    "        regions_cols=np.zeros(RowGenerator.REGIONS_NB)\n",
    "        region_idx = np.random.randint(RowGenerator.REGIONS_NB)\n",
    "        regions_cols[region_idx]=1\n",
    "        return (region_idx, RowGenerator.region_names[region_idx], regions_cols)\n",
    "    \n",
    "    def __gen_hour(self, region_idx):\n",
    "        ano_hour_utc = np.random.randint(24)\n",
    "        ano_hour_local = (ano_hour_utc - 4 - region_idx + 24) % 24\n",
    "        ano_utc_cols=np.zeros(24)\n",
    "        ano_utc_cols[ano_hour_utc] = 1\n",
    "        return (ano_hour_utc, ano_hour_local, ano_utc_cols)\n",
    "    \n",
    "    def __gen_population(self):\n",
    "        return np.random.randint(RowGenerator.POPULATION_MIN, RowGenerator.POPULATION_MAX)\n",
    "\n",
    "    def __gen_rating(self):\n",
    "        return np.random.choice(RowGenerator.RATINGS, p=RowGenerator.RATINGS_PROBAS)\n",
    "\n",
    "    def get_row(self, debug=None):\n",
    "        columns=[]\n",
    "\n",
    "        # pick region\n",
    "        region_idx, region_name, regions_cols = self.__gen_region()\n",
    "        columns += regions_cols.tolist()\n",
    "        if (debug is True):\n",
    "            print('region: %s' % region_name)\n",
    "\n",
    "        # select the UTC time of the anomaly (1 hour between 0-24) and local to the region\n",
    "        ano_hour_utc, ano_hour_local, ano_utc_cols = self.__gen_hour(region_idx)\n",
    "        if (debug is True):\n",
    "            print('ano_hour_utc: %d, ano_hour_local: %d' % (ano_hour_utc, ano_hour_local))\n",
    "        columns += ano_utc_cols.tolist()\n",
    "\n",
    "        # pick rating\n",
    "        rating = self.__gen_rating()\n",
    "        columns.append(rating)\n",
    "        \n",
    "        # pick population\n",
    "        population = self.__gen_population()\n",
    "        if (debug is True):\n",
    "            print('population: %d, rating: %d' % (population, rating))\n",
    "        columns.append(population)\n",
    "\n",
    "        # calls for the anomaly plus predicted calls\n",
    "        normal_calls_for_ano, _, predicted_calls = self.cf.get_predicted_calls(population, rating, ano_hour_local)\n",
    "\n",
    "        if (debug is True):\n",
    "            print('normal_calls_for_ano: %d, predicted_calls: %s' % (normal_calls_for_ano, str(predicted_calls)))\n",
    "        columns.append(normal_calls_for_ano)\n",
    "        columns += predicted_calls\n",
    "\n",
    "        return columns\n",
    "    \n",
    "    def plot_random(self):\n",
    "        region_idx, region_name, _ = self.__gen_region()\n",
    "        _, ano_hour_local,_ = self.__gen_hour(region_idx)\n",
    "        population = self.__gen_population()\n",
    "        rating = self.__gen_rating()\n",
    "        print('region_name: %s, rating: %d, population: %d, ano_hour_local: %d' % \\\n",
    "              (region_name, rating, population, ano_hour_local))\n",
    "        self.cf.plot_predicted_calls(population, rating, ano_hour_local)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region: utc-5\n",
      "ano_hour_utc: 3, ano_hour_local: 22\n",
      "population: 585, rating: 21\n",
      "normal_calls_for_ano: 37, predicted_calls: [21.547515866340923, 13.685990641645127, 4.6384210025850621]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 21,\n",
       " 585,\n",
       " 37.035056721760277,\n",
       " 21.547515866340923,\n",
       " 13.685990641645127,\n",
       " 4.6384210025850621]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = RowGenerator()\n",
    "generator.get_row(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use a random data generator to generate various configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region_name: utc-6, rating: 50, population: 455, ano_hour_local: 16\n",
      "normal_calls at ano hour(16): 28.5522393729\n",
      "ano_calls_rate for rating: 1.49136169383\n",
      "ano_calls for rating: 42.581716074\n",
      "normal_calls: [37.607379823811868, 46.288891283601956, 51.92869087454249]\n",
      "predicted calls: [59.607379823811868, 60.288891283601956, 62.92869087454249]\n"
     ]
    },
    {
     "data": {
      "image/png": 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lNV7po0uX8EYxMpGa5Ri+z+L4nOfz2pSnPOW5y/N5bYnmVY1gRJWXTJma17Vr\nODf5pcr6oqZmWURERHaorAyefBL693ddidSEriwnVyQzy8aYLGAx8Jm19nfGmGbAdCAXKAL6WWs3\n7ODrNLMsIiLiSGEhPPggzJrluhKpCWuhRQt4801o3dp1NenD9czyn4CVMY+vAOZaaw8C5gFXRlSH\niIiIJGjKFBg82HUVUlPG6OpyMoXeLBtjWgOnAvfEPP17YHLl+5OBXmHXkQjfZ3F8zvN5bcpTnvLc\n5fm8tp3llZQEx1v3SuL/oVNpfb7nhXE4SSqtL0pRXFkeD1wGxM5TtLTWrgOw1q4FWkRQh4iIiCRo\nxgz47W+hUSPXlUhtdOkCL73kugo/hDqzbIzpCZxirb3YGNMNGFk5s/y1tbZZzOd9Za3dcwdfr5ll\nERERB44+Gq69Fk4+2XUlUhubNkHz5rB6NTRt6rqa9BBvZrleyLnHAb8zxpwKZAN7GGMeBNYaY1pa\na9cZY/YGSuJ9g6FDh5KXlwdA06ZN6dChA926dQN+vjyvx3qsx3qsx3qsx8l7PGXKAt5/H3r0SI16\n9Lh2jzt37sbrr8Ouu6ZGPan2uOr9oqIiqmWtjeQNOAF4svL9m4HLK9+/HLgxztfYKM2fP195aZrn\n89qUpzzlucvzeW3V5Y0ZY+2f/hRdXlgyPe+qq6wdPTq6vGSLOq+y79yuH82qvpUOzY1AgTHmPeDX\nlY9FRETEMWth6tTkHUQi7hx7LLzyiusq0l8k+yzXlmaWRUREovX663DmmbBqVbAFmaSv9eshLy/4\ns17Yg7cecL3PsoiIiKSBquOt1Sinv+bNg0NJli93XUl6U7McI3bgW3nplefz2pSnPOW5y/N5bTvK\n27Qp2DIurINIXK8vE/OSOYqRiuuLgpplERERAWDOHPjlL2H//V1XIsly3HHB4TJSe5pZFhEREQAG\nDYLjj4fhw11XIsny3ntw0kmws93RJP7MspplERER4bvvoE0b+PBD2Gsv19VIslgLOTnw9tvQqpXr\nalKbbvBLgO+zOD7n+bw25SlPee7yfF7btnmPPw5du4bbKGfS65kqecYkb245FdcXBTXLIiIiwpQp\n4d3YJ25pv+W60RiGiIhIhlu7Fg4+GD7/HHbbzXU1kmwLFsBVV6lh3hmNYYiIiMgOTZsGvXqpUfZV\n587BzPJPP7muJD2pWY7h+yyOz3k+r015ylOeuzyf1xabF9UIRqa8nqmWt/vucMABQcMcRV6yaGZZ\nREREnFtHljJyAAAgAElEQVS1Cr74Arp3d12JhOnoo+G111xXkZ40sywiIpLBRo+G8nK45RbXlUiY\n7r0Xnn8epk51XUnq0syyiIiIUFFRwdSpU+l8XGdatGnJzf/4nGbNZlFRUeG6NAnR0UfD66+7riI9\nqVmO4fssjs95Pq9NecpTnrs839ZWUVFBn/59OP+a81nSegml++fw067fcP1d/eg7oG/oDbNvr2c6\n5bVrB19+CaWl0eQlg2aWRUREJFKFhYXMXTyXssFlcAiwtgccOYUfzihjzhtzmDZtmusSJSRZWXDU\nUbq6XBuaWRYREckQnY/rzJLWS4JGeXN9GPc5/L/O0PRTWAmdPuvE4pcXuy5TQjJ6dPDndde5rSNV\naWZZREQkw61evRpaVj748GTY692gUQZoAZ+t/sxZbRI+7YhRO2qWY/g+i+Nzns9rU57ylOcuz7e1\ntWnTBtZVPlg+GPa9+ecPlkDrNq1Dzfft9Uy3vF/9Ct54A2o7mp7q6wuLmmUREZEMMWL4CBq93gi+\nawwfngT7LQg+sAkavd6IkReNdFqfhGuvvSAnJ9hbWxKnmWUREZEMUbUbxrPz9+anhidB7z5QEjTK\nBUcVMHPaTLKydB3NZ4MHQ48ecPbZritJPfFmltUsi4iIZJCKigry80so3zyOsh8eoHWb1oy8aCQD\nBgxQo5wBbrsNPvgA7rjDdSWpp1Y3+BljdjHGZMyZPr7P4vic5/PalKc85bnL83Fta9ZksWbN3rzz\n1s1Me2Aai19ezKBBgyJplH18PdMtr3NnWFzLDU/SYX1hqPbfDGvtFuD4iGoRERGRkBUWQp8+0LCh\n60rEhY4dYcUK+Okn15Wkj52OYRhjJgKtgIeBsqrnrbWPhluaxjBERESSrUOH4Ffx3bq5rkRcOfxw\nmDwZjjjCdSWpJd4YRr0EvrYh8BVwYsxzFgi9WRYREZHkWbECvvoKunZ1XYm41LkzLFqkZjlROx1Q\nstaevYO3c6IoLmq+z+L4nOfz2pSnPOW5y/NtbVOnBrshVI0n+7Y+5SXmyCNrN7ecLutLtp1eWTbG\n3EdwJXkrvjbMIiIiPqqoCJrlZ55xXYm41rkz3HWX6yrSRyIzy31jHjYEegNfWGsvCbOwymzNLIuI\niCTBwoXwxz/C228Hj0tLSykqKiIvL4+cnBy3xUmkfvwRmjULRnKys11XkzpqtXUcgLV2ZszbVKAf\n0DmMIkVERCQcU6bAkCHB+4WF08nNbUdBwQXk5rajsHC62+IkUrvuCgcf/PMPTlK92myq2BZokexC\nUoHvszg+5/m8NuUpT3nu8nxZ28aNMHMmDBwYXFEeNmw45eXz2bDhVsrL5zNs2HBKS0tDyY7ly+vp\nQ17VTX5R5dVGqsws77RZNsZ8Z4z5tupP4Cng8vBLExERkWSYNQvat4fWraGoqIgGDfKA/MqP5lO/\nfi5FRUXuCpTI1fYmv0yk465FREQ817cv9OwJ55wTXFnOzW1Hefl8goZ5GdnZ3SkuXqXZ5Qzy1lsw\naBCsXOm6ktQRb2Y5oWbZGPM7oGpXxgXW2qeTXF+8XDXLIiIidfD117DfflBcDE2aBM8VFk5n2LDh\n1K+fy6ZNxUyaNIGBA/u7LVQitWkTNG0Ka9fCHnu4riY11PoGP2PMjcCfgJWVb38yxlyf/BLd830W\nx+c8n9emPOUpz12eD2t75BEoKPi5UQYYOLA/xcWruOmm/0dx8arIGmUfXk9f8urXh/x8WLo0mrza\nSJuZZeBUoMBae6+19l7gZOC0cMsSERGRZJg69eddMGLl5OTQrl07jV5kMM0tJyaRfZaXAd2stesr\nHzcnGMXIr/YLk1GcxjBERERq7dNPgyONv/gCGjRwXY2kmgceCG7+nDbNdSWpodZjGMANwJvGmPuN\nMZOBJcDfk12giIiIJNdDD8Ef/qBGWXasc2ddWU5EIoeSFAJHA48CM4FjrLVe7l7u+yyOz3k+r015\nylOeu7x0Xpu1wUEkgwdHk5cI5aVW3kEHQUlJcBNoFHk1lU4zy1Wf9yXwDXCgMabrTj5fREREHFq2\nDMrK4NhjXVciqWqXXaBjR11d3plEZpZvAvoD7wAVlU9ba+3vQq5NM8siIiK1dNllwbHGf/ub60ok\nlY0aBXvuCVde6boS9+LNLNdL4Gt7AQdZa39MflkiIiKSbFu2BPPKc+e6rkRSXefOMGOG6ypSWyJj\nGB8D9cMuJBX4Povjc57Pa1Oe8pTnLi9d1/bCC7D33nDwwdHkJUp5qZdXk+3j0nF9yRD3yrIx5l+A\nBX4A3jLGPA/87+qytfaS8MsTERGRmpoyZcd7K4tsa//94bvvYN06aNnSdTWpKe7MsjHmrOq+0Fo7\nOZSKtq5BM8siIiI1UF4OrVrBO+/APvu4rkbSQUEBXHop9OzpuhK3ajyzHEUzLCIiIsn19NPBHKoa\nZUlU1ShGpjfL8SS6dVxG8H0Wx+c8n9emPOUpz11eOq6tJiMY6bg+5SU/r3NnWLQourxEpcrMsppl\nERERT3z1VXBzX+/eriuRdKKT/KpX7T7LxphdgJustaOiK2mrfM0si4iIJGjiRFi4EAoLXVci6cRa\nyMmB5csze3wn3sxytVeWrbVbgONDq0pERESS5sEH4YwzXFch6caY4CS/N990XUlqSmQM401jzJPG\nmDOMMX2q3kKvzAHfZ3F8zvN5bcpTnvLc5aXT2j78ED7+GH7zm2jyakN5qZuXSLOczuuri0RO8GsI\nfAWcGPOcBR4NpSIRERGpsSlTYMAAqJfI/9lFttGxIzzyiOsqUlO1M8uuaWZZRERk56yFtm1h+nTo\n1Ml1NZKOVq2CU08NfjuRqWo1s1z5hQcaY543xqyofJxvjLkmjCJFRESk5l59FRo0gCOOcF2JpKu2\nbaGkBL75xnUlqSeRmeW7gSuBTQDW2mXAgDCLcsX3WRyf83xem/KUpzx3eemytqob+8x218TCyast\n5aVu3i67QH4+vPVWNHmJSJWZ5USa5d2stW9s89zmMIoRERGRmvnxR3j4YRg82HUlku6OOEI7YuzI\nTmeWjTHPAhcDD1trjzDGnA4Ms9aeEnpxmlkWERGp1mOPwT//CfPnu65E0t2kScGhNg884LoSN2o9\nswxcBPwHaGeM+Ry4FLggyfWJiIhILWhvZUmWjh1h6VLXVaSenTbL1tqPrbU9gBygnbX2eGttcfil\nRc/3WRyf83xem/KUpzx3eam+tvXrYd48OP30aPLqSnmpnXfoofDRR1BeHk3ezqTNzLIxZk9jzD+B\nF4EFxpjbjTF7hl+aiIiIVGfGDDj5ZGjc2HUl4oNdd4WDDgqOvZafJTKzPAdYCEypfGow0K3yanOo\nNLMsIiIS33HHwZVXwmmnua5EfHH22XD00XD++a4riV68meVEzvnZx1p7Xczjvxlj+ievNBEREamp\njz4Kjrg+6STXlYhPEjn2OtMkcoPfbGPMAGNMVuVbP+C5sAtzwfdZHJ/zfF6b8pSnPHd5qby2KVOg\nf3+oXz+avGRQXurnVbd9nA/rq41EmuXzgIeAHyvfpgHnG2O+M8Z8G2ZxIiIisj1rtQuGhKN9e1ix\nAjbrRI3/2enMskuaWRYREdneq68Gs6XvvlvzU/tEdubAA+HRR+Gww1xXEq267LMsIiIiKaS2x1uL\nJEJzy1tTsxzD91kcn/N8XpvylKc8d3mpuLaffgq2jEvG8dapuD7luc+L1yz7sr6aUrMsIiKSRmbN\nCg6PyMtzXYn4SleWt5bIPsu/BD6z1v5ojOkG5AMPWGu/Cb04zSyLiIhspU8fOPVUOPdc15WIr0pK\ngsNJ1q/PrFGfuswszwS2GGMOAO4C2hDsjiEiIiIRWr8enn++9sdbiySiRQto1AiKilxXkhoSaZYr\nrLWbgd7Av6y1lwH7hFuWG77P4vic5/PalKc85bnLS7W1zZgRHELStGk0ecmmvPTJ69gRli6NLm9H\n0mlmeZMxZiBwFvB05XN12AJdREREakN7K0tUNLf8s0Rmlg8BLgBetdYWGmP2A/pZa28KvTjNLIuI\niADw/vvQtSusXl23U/tEEvHYY3DPPfDMM64riU68mWUdSiIiIpIGrr4aNm6EW291XYlkgqIiOPZY\n+OIL15VEp8Y3+BljlhtjlsV7C7dcN3yfxfE5z+e1KU95ynOXlypr27IFHngAhg6NJi8sykufvNzc\n4IezdeuiyduRVJlZrlfNx06LrAoRERGJ6/nnoWVLOPxw15VIpjAGOnQI5pZPPtl1NW5pDENERCTF\nDRoU/Er84otdVyKZZNQo2HNPuPJK15VEozZjGN8ZY77dwdt3xphvEwzd1RjzujHmzcqxjjGVzzcz\nxsw2xrxnjHnOGNOk9ksTERHx1zffBKf2DRzouhLJNO3bw9tvu67CvbjNsrV2D2tt4x287WGtbZzI\nN7fW/gh0t9Z2BDoApxhjjgKuAOZaaw8C5gEp8TOL77M4Puf5vDblKU957vJSYW0zZkBBQXCFL4q8\nMCkvvfK2bZZ9W1+iEtlnGQBjTAtjzC+q3hL9OmvtD5Xv7kowI22B3wOTK5+fDPRK9PuJiIhkkvvu\nS/6NfSKJaNcOiouhvNx1JW4lss/y74BbgX2BEiAXeNdae2hCAcZkAUuAXwJ3WGuvNMZ8ba1tFvM5\n6621zXfwtZpZFhGRjLVqFXTvHuytXK+6W/JFQtKhA9x9Nxx5pOtKwhdvZjmRf/WuA44mGJvoaIzp\nDgxJNNhaWwF0NMY0Bh4zxhxKcHV5q0+L9/VDhw4lLy8PgKZNm9KhQwe6desG/Hx5Xo/1WI/1WI/1\n2MfHzz3XjSFD4KWXUqMePc68x+3bw4wZCygrS416kvm46v2ioiKqZa2t9g1YXPnn20BW1fs7+7o4\n32s08GfgXaBl5XN7E1yp3tHn2yjNnz9feWma5/PalKc85bnLc7m2zZut3Xdfa1esiCYvCspLv7xb\nb7X24oujy4sVdV5l37ldP5pVfSsNwDfGmN2BhcBUY8ztQFkCX4cxZq+qnS6MMdlAQWWj/CQwtPLT\nzgKeSOT7iYiIZIo5c6BVKzg0oaFHkXBoR4zEZpYbAeUENwMOBpoAU621X+30mxtzOMENfFmVb9Ot\ntX83xjQHZgBtgGKgn7X2mx18vd1ZfSIiIj4aMAC6doXhw11XIpnsyy/hgAPg66+Dg0p8Fm9mOW6z\nbIw5gGBU4uVtnj8eWGOt/SiUSrfOUrMsIiJeKykp4f7J97Ns5TI2fLuBJo2bcEDekYwf90c++SSL\n5tvd/i4SrVat4OWXofIWMm/V+FAS4DZgR4ePbKj8mHdiB76Vl155Pq9NecpTnru8MLMWLVpEn/59\nyP1lLmMeHsPU0qk8Xfo0U0uncv19H/P9D49x7oV9WLRoUWg1+Px3p7zkqRrF8HV9O1Nds9zSWrt8\n2ycrn8sLrSIRERHPTbxzIt1O6sbj3z7Oxos2srHnRjgS2B84Ejb9eBYVff7D4xsep9tJ3Zh450TX\nJUsGy/S55erGMD6w1raN87EPrbUHhFoZGsMQERH/TLxzIqPGjOKH/j/Ajk7l+6IjTH8U/rQ/ZFn4\nCnabvhu3jL2FCy+4MPJ6RaZNg4cfhpkzXVcSrtqMYSw2xpy3g290LsEhIyIiIlIDixYtYtRV1TTK\nAEvPhSMmBY0ywJ7wQ/8fGHXVKBYvXhxZrSJVMv3KcnXN8qXA2caYBcaYWyvfXgCGAX+Kprxo+T6L\n43Oez2tTnvKU5y4v2Vk33HID5b8qj98of9AAVgyADvdv/fyeUH5UOTfcckNS6/H57055ydO2LaxZ\nA7NmRZNXJeVnlq2166y1xwJjgaLKt7HW2mOstWujKU9ERMQPJSUlPDvrWWz7asYLi06ANq9Ck8+2\n+5Btb5n1zCxKS0tDrFJke/XqwSGHwMcfu67EjZ3us+ySZpZFRMQXN//jZsY8PCa4mS+eexfCMePg\n4Md3+OHsZ7IZ228sl426LKQqRXbs3HOhUye40OOx+drMLIuIiEiSLFu5jI0tqmmUSw+C9QfAgU/H\n/ZTyFuUsX7ndRlUiocvkuWU1yzF8nTXKhDyf16Y85SnPXV4yszZ8uwF2reYT3hwGeWNgl83xP6cB\nfP3t10mryee/O+UlV34+LFwYXR6kwcyyiIiIJE+Txk3gxzgf3Fwf3j4T2j5T/Tf5CZo1bpb02kR2\nJj8fPvkEKipcVxI9zSyLiIhEoNqZ5ZV94PU/wtndq/0emlkWl3JzYe7cYHcMH2lmWURExKGhZw2F\nd4HyHXxw6bnQ6e7qv8EPYN+1wfcRcSBT55bVLMfwedbI9zyf16Y85SnPXV4ys1q0aMEpp56CeXub\nC1ff/AI+PxIOfhQ+if/15m3DqT1PJScnJ2k1+fx3p7zka9JkQaTNsmaWRUREMsyVo64k+/Vs+Crm\nyTfPhsMLoX41O2V8BdlvZHPlqCtDr1EkngMOyMwry5pZFhERidDEOycyakzlkdfNsuC2T2Dg72Cf\nOF3IV7Db9N24ZewtXHiBx5vcSsr74APo0QOKi11XEo54M8v1XBQjIiKSqaoa3lFXjeKHvB7Q6Isd\nN8o/BKMX2W9kc8v1apTFvV/+Etavh6+/hmYZtCmLxjBi+D5r5HOez2tTnvKU5y4vrKwLL7iQF2a/\nQMs1Y6i//m6yn8mGRcBcYFGw60XDCQ3p3bQ3L8x+IbRG2ee/O+Ul38KFCzjsMFi2LJq8VJlZ1pVl\nERERB5o168yWzfDRBzcybXo7lq9czgfffUDbFm05vNvhDD1raFJv5hNJhqodMU44wXUl0dHMsoin\nSktLKSoqIi8vLyn/w0329xPJdH/5C1gL//iH60pEEjdxIixeDJMmua4k+bTPskgGKSycTm5uOwoK\nLiA3tx2FhdNT6vuJZLqNG+G+++CCC1xXIlIzmbjXsprlGL7PGvmc5/PaappXWlrKsGHDKS+fz4YN\nSygvn8+wYcMpLS2tVV4yvl9N8qKgPOW5znr4YejUKbhhKoq8eJSnvJrmHX44rFwJmzdHk5cK1CyL\neKaoqIgGDfKA/Mpn8qlfP5eioqKU+H4iAhMmwPDhrqsQqbk99oB99w22kcsUmlkW8UxpaSm5ue0o\nL59P0OAuIzu7O8XFq2o1a5zs7yeS6d58E37/e/jkE9hlF9fViNRc377Qrx/07++6kuTSzLJIhsjJ\nyWHSpAlkZ3enceMjyM7uzqRJE2rd2Cb7+4lkuokT4fzz1ShL+srPj277uFSgZjlGJswa+Zrn89pq\nkzdwYH+Ki1cxd+5/KC5excCBNfvxf9u8un6/muZty1r4+GN49FG48Ub485/hrLNgwIDgz/POg2uu\nCe7Onj8fNmyoW16yKS9985KdtWFDMK88bFg0eTujPOXVJi+qZjlVZpa1z7KIp3JycpJ69TfZ329n\nSkth1ix4+mmYOxd23x06dIB27WDvveHww2HXXeGnn+DHH2HNGnjxxWCHgbfegv32gy5dgl93d+8O\nDRpEVrpIXA88ACedFPwzLJKu8vMza0cMzSyLSMqoqIDnn4c774R58+DXv4bTToOTT65Zc7FpU3DV\nY/58eOwxePdd6NULLrwQjjwyvPpFqmMtHHpoMIaRSQc6iH8qKqBJE/j0U7+OvdbMsoikrIqK4FfT\nhx0Go0ZBQUHwH+FHHoGhQ2t+Fa5+/WBbrlGj4OWXYcWK4Ip0//7QuTM89BBs2RLKUkTieuEFMAa6\ndnVdiUjdZGUF/71evtx1JdFQsxwjU2aNfMzzeW2+5z3/PBx44AJuugnGjQtGKC64INieKFn23Tc4\nLe3DD+Gvf4UbbljAoYdG1zT7/Pfne14ys/71r2C7OLPddatw8hKhPOXVNq99+/DnljWzLCIZrbQU\nRo4M5ozPOQdGj66+iUiGrCw49VTIzg6uZo8eDbfeCv/8Jxx3XLjZktk++QQWLIDJk11XIpIc+fnB\nxY1MoJllEYnczJnBFbYhQ2Ds2ODmPReshWnTgqvOJ5wQXNlu0cJNLeK3kSODreL+8Q/XlYgkx0sv\nBaNur73mupLkiTezrGZZRCKzcWOw7duzzwZN6lFHua4o8P33wXjGAw/A7bcHm+2HfZVbMse330Je\nXnAV7he/cF2NSHJs2ACtWgV/+rJnuG7wS0AmzRr5lufz2nzJKy6GY44Jxi/efHPrRtn1+nbfHW6+\nGZ58MmiaTz8d1q8PLy9sykutrPvugx49EmuUfX4tledXXpMmsNdewR74UeS5pGZZREL3+utBo3zm\nmTB9evAf2VR01FGwdCnk5kLHjsGvGUXqYsuWYCZ+xAjXlYgkXxQ3+aUCjWGISKgeeSTY3/jee+G3\nv3VdTeKeeSY4Ze1Pf4IrrtBYhtTO44/DDTcEc536Z0h8M3p0cOP02LGuK0kOjWGISOTuvTdoNmfP\nTq9GGaBnT1iyBJ54Av7wh2CuWaSmbrstuKqsRll8FNWx166pWY6RSbNGvuX5vLZ0zbvjDrj22uAU\nvY4dw8+riUTzWrUKDpJo2hSOPho++ijcvGRRXmpkvflm8M9M377R5NWG8pRXl7ywj73WzLKIeGv8\n+GD/4hdegAMPdF1N3ey6K9x9dzBKcvzx8MorriuSdHHbbXDxxcGJkiI+OuAAWLcu2PHFZ5pZFpGk\nuuce+PvfYeFCaNPGdTXJ9eyzwU2KEyYEoxki8axZA4ccElxZbt7cdTUi4TnqqOAHw2OPdV1J3Wlm\nWURC9/DDMGZMMKPsW6MMcMopMGdOcMDEv/7luhpJZRMnwsCBapTFf5kwt6xmOYbr2R/lpUeW8nZs\nzpzgV87PPgtt24afVxd1yevQITii+/bb4frrg1MAw8yrDeW5zSovh//8J7i5NYq8ulCe8uqaF2az\nrJllEfHGO+/A4MHBNnH5+a6rCV9eXtAwP/QQXHllYg2zZI777gt+NX3QQa4rEQlf2Df5pQLNLItI\nnZSUwK9+BdddB0OGuK4mWl9+CSefHDRG//53sN+oZLZNm4KbWh96KDiIR8R369cHFxC++Sb9/xuo\nmWURSbqNG6FXr6BJzrRGGYKjXufNgxUrYOhQ2LzZdUXi2rRpwQmQapQlUzRvHpzKWlzsupLwqFmO\nkQqzP8pL/Szl/ezii4O9iOt6elOqri8RjRvDf/8bXGEfMCC4shhmXiKU5yarogJuvBGuuiqavGRQ\nnvKSkRfW3LJmlkUkrd1zT7Dn8L33pv+v3upqt92Ck/7Ky4Ot5bZscV2RuPDkk9CwIRQUuK5EJFq+\n74ihmWURqbElS4JZ3RdfhHbtXFeTOsrLg2O9W7fWDxGZxtrglMe//KVmJ/aJ+KCwEB59NNg+NJ1p\nZllEkuLrr+H00+HOO9Uobys7O7jC/PHHMHy4dsnIJPPmwYYN0Lu360pEouf7jhhqlmOkyuyP8lI7\nK5PzrIXzzw+unibz6lmqrC8ZGjWCp5+Gt96CESOC18yn9WVaXqJZN9wAV1xR998m+PxaKs/fvIMO\ngs8+g7KyaPKipmZZRBJ2772wahXcfLPrSlJb1U1/CxcGN3vpCrPf3ngD3n8fBg1yXYmIG/XqBb9p\nfOcd15WEQzPLIpKQVaugSxd44QU45BDX1aSHL7+E7t2DXTKuvtp1NRKW3r3hxBPhj390XYmIO0OH\nwvHHw7nnuq6k9uLNLNdzUYyIpJdNm4IT+q67To1yTey1F8yeDccdBy1awHnnua5Ikm3lymBXmKlT\nXVci4pbPO2JoDCNGqsz+KC+1szIx7/rroWXLYF45irywRZm3zz7w178uYMwYeOyxaDJ9fj2jzttZ\n1g03wCWXBNsHRpGXbMpTXrLywrjJL1VmlnVlWUSqtXQp3HEHvPkmmO1+OSWJaN0annoKTjklOO3q\nhBNcVyTJ8N57wWz6v//tuhIR99q3D64sW+vf/ys0sywicf34Ixx5JFx2GZxxhutq0t+8ecH88uzZ\n0KGD62qkrgYPhkMPrduJfSI+2Wef4IbXNm1cV1I72mdZRGrsb3+D/feHIUNcV+KHE0+ECROgZ89g\nL2ZJXytXwty5uqlPJJavc8tqlmOk0uyP8lI3K1Pyli+H//wHJk4M/1dqmfB6Vjn9dLjmGvjNb6Ck\nJPy8KPicFy9r7Fj4859hjz2iyQuL8pSXzLxkN8upMrOsZllEtrNlS7Bzw9/+FvxaTZLrwguDX+H3\n7Anff++6Gqmp5cuDLRQvush1JSKpxdeT/DSzLCLb+de/4OGHYcGCup9IJjtmbbAf6Zo1wRHZ9eu7\nrkgS1asXdO0KI0e6rkQktSxbFtyXsXKl60pqJ97MspplEdnK6tXQsSO89FJwIpOEZ9OmoPFq2RIm\nTfLvDnIfvfJK0Ay8/z40bOi6GpHU8tNP0KQJfP11ev77oRv8EpBqsz/KS80s3/NGjICePRdE2ij7\n/HpWl1e/PsyYAStWwJgx4eeFxee82Cxr4YorgnnlsBoBn19L5fmf16ABtG2bvCvLmlkWkZQze3aw\nr/KgQa4ryRyNGsHTT0NhYXBDpaSuZ58NjjDXNooi8fm4I4bGMEQECPZUzs+HW2+F005zXU3m+egj\n6NIl2H3k9793XY1sq6IiGE8aOzYYnRGRHbv5Zli7FsaNc11JzWkMQ0SqNX48HHigGmVXfvlLePLJ\nYBeSV191XY1sa8qU4Ehr/SAjUr327f3bEUPNcoxUm/1RXmpm+Zj32Wdwyy1w++3R5G1LeYHOneGB\nB6B37+Ao5bDzksXnvAULFvDDD3D11cFvXbTnuPKUV72q7eOSMRigmWURSRlXXw3nnx+c1idunXwy\n3Hhj8OeaNa6rEQh+kDz22OBNRKq3997BD5Vr17quJHk0syyS4ZYuDQ7HeP/95J9GJrX397/DI48E\nh180buy6msz1xRdw+OGweDHst5/rakTSw69/DX/5C5x0kutKakYzyyKyHWuDI3uvvVaNcqq56io4\n5hjo2zfYu1TcuOaaYI5cjbJI4nzbEUPNcoxUnP1RXupl+ZT31FNQUgLDhkWTF4/ytmdMcJLi7rvD\nOaM45q4AACAASURBVOcEuzGEmVcXvuYtXgyPP76AK6+MJA7w97VUXmblJesmP80si4hTmzYFvya7\n5RaoV891NbIju+wCDz0En3xCpA2bBD+cXHRRcFW5SRPX1YikF9+uLGtmWSRD3XEHPP54cBCJjllO\nbV99BccfDxdeCJdc4rqazDBpEtxzD7z8MmTpspJIjWzcCM2awYYNwal+6SLezLKuJ4lkoA0b4K9/\nVaOcLvbcE/77XzjuONh3Xzj9dNcV+W39+mCHmFmz1CiL1EbDhsGc/6pVwVXmdKf/DMRI1dkf5aVW\nlg95118fHD7Svn00eTujvJ3LzQ2OxR4+HBYuDD+vJnzLGz0a+vSBI47wb23KU15UeckYxUiVmWVd\nWRbJMEVFwa+Xly93XYnUVIcOUFgIf/gDzJsHhx7quiL/vPYaPPoovPOO60pE0lvVTX5DhriupO40\nsyySYQYPDo61HjPGdSVSWw89BFdcAa+8Aq1bu67GHz/9BJ06Bdv2DRzouhqR9PbMM/DPf8Jzz7mu\nJHFO9lk2xrQ2xswzxrxjjFlujLmk8vlmxpjZxpj3jDHPGWN0r7FIBJYvh+efD/ZWlvQ1aFBwo98p\np8A337iuxh//+Af84hcwYIDrSkTSn087YoQ9s7wZGGmtPRQ4BrjIGNMOuAKYa609CJgHpMSmSKk8\n+6O81MlK57wxY+Dyy4O9e6PIS5Tyau7Pfw5OyerVK7jzPOy86viQ9957MH48TJy49U2vPqxNecpz\nkde6dfDfppKSaPLCFGqzbK1da619q/L974F3gdbA74HJlZ82GegVZh0iAkuWwBtvwAUXuK5EksEY\nGDcOWrSAM8+s2aElsrUtW4KDeUaPDq4sVyktLWXVqlWUlpa6K04kTRkTXF324f6YyGaWjTF5wALg\nMGC1tbZZzMfWW2ub7+BrNLMskiSnnhrsgDF8uOtKJJk2boSTTw5u/hs/XlsB1sY//hHMV86b9/NW\ncYWF0xk2bDgNGuTx009FTJo0gYED+7stVCTNXHJJsIXciBGuK0lMvJnlSJplY8zuBI3yddbaJ7Zt\njo0xX1lr99zB16lZFkmCV14J5lzfew923dV1NZJs33wTHFpy9tmaR6+p5cuhe3dYtCj4nzoEV5Rz\nc9tRXj4fyAeWkZ3dneLiVeTk5LgsVySt3HMPvPQS3H+/60oS4+xQEmNMPeAR4EFr7ROVT68zxrS0\n1q4zxuwNxJ1oGTp0KHl5eQA0bdqUDh060K1bN+DnWZZkPb7ttttC/f7KCy8vdq5Jeds/Hj0a+vVb\nwKuv+rm+TM9r2hTGjFnAxRfDPvt0Y999/VpfWHnBke/duPFGKC5eQHFx8PGioiKysvYE1hNc5+mG\nMc2ZOXMmF1TOMYW1vm3XGPbrqTzlhZmXn9+NCRNSd31V7xcVFVEta22ob8ADwLhtnrsJuLzy/cuB\nG+N8rY3S/PnzlZemeT6vra55zz9v7QEHWLtpUzR5taG85FixwtoWLay96aZo8qqk6+t5+eXW9uxp\nbUXF1s+XlJTY7OzmFt62MN/C2zY7u7ktKSlJSm510vW1VJ7yduT7763Nzq7Z/3/qkldXlX3ndv1o\nqGMYxpjjgIXAcsBWvl0FvAHMANoAxUA/a+12GyBpDEOkbqwNfj0/fHiwv7L479VX4fe/h6lToaDA\ndTWp67nngpv6li4NbpLcVtXMcv36uWzaVKyZZZFaOvBAePxxOOQQ15XsnNOZ5dpSsyxSN88+C6NG\nBXtd7rKL62okKi+9FBzXXFgYbC8nW1uzJjjK+qGHgnnleEpLSykqKiIvL0+zyiK19Ic/QN++6bF/\nuZNDSdJN7AyL8tIrz+e11TbPWrjmGvjrX2veKKfD+pQX3+bNC3jkkeAUuiii0+n13LIlOH73/POr\nb5QBcnJyKCsri7RRTqfXUnnKS0R+fnDsdVR5YVCzLOKpJ58M9t7t3dt1JeJC164wYwb06wcLF4aX\nk257EY8eHfx7MXq060pEMoMPJ/lpDEPEQ9bCUUfBVVepWc50zz8fXGF+9NFgfj2Z0m0v4hkz4LLL\nYPFi0FSFSDQ++ST44X31ateV7JxmlkUyyHPPBfvtLlv28yELkrnmzAlu8EzmTX/pthfx229Djx7B\nvxtHHOG6GpHMUVEBTZtCURE03+74udSimeUEpMPsj/LcZ6V6nrVw3XVw9dW1b5RTeX3Kq3leQUFw\nZXnIEHj44eRkFBUV0aBBHkGjvADIp3793J3vV5oENX09160LfsNy++01b5Rd/90pT3npnpeVVftj\nrzWzLCKhWLgQSkqCWVWRKscfH1xhvvRSuPPOun+/vLxg9AKqhhGXsWlT8f8OkUoV338fHPM+ZEhw\niqWIRC/d55Y1hiHimYKCoCk4+2zXlUgq+vhj+M1vYOjQ4LcPZrtfOCYu1fci3rw52HO6ZUuYNKlu\naxWR2rvzzuBegXvu+f/t3Xmck9XZ//HPxeoogqIjiMtMpSpugCiKiAJuaPsILrjAzx1rLYoWkWq1\ntu5rsa74uCBP3Ubc1yqLgloVBVkVQQRmRCw4FsUFRHTO749zj8RhMmSY3EuS7/v1yotMSPI9J5lJ\nrty57nPHPZK6qWdZpABMnuzXspw/H5o2jXs0klRLl8Lhh0PPnvCPfzSsrz2paxFXVcHvfgeffeZX\nhtHfg0h83nrLf6v17rtxj6Ru6lnOQC70/igv/qwk511zDVx0UcMLg6TOT3nZyWvb1q+/PH06nHAC\nrFy54VlJXIvYOTj3XJg71/doN+TvIWnPnfKUl4t5e+wBH3zg1zmPIi/bVCyL5Inp0/2he9V+IZnY\nbDMYNw6KivyyTkuWxD2i7HAOzj/f/y289BK0aBH3iERk0039h/QFC+IeyYZRG4ZInjjuOOjeHYYO\njXsk9ZPUr/ELhXNwww1wxx3wxBPQrVvcI9pwVVW+UH7nHb8zY6tWcY9IRKodfbRfwrJ//7hHkp7a\nMETy2Jw5fhWMs86KeyT1U1Y2hpKSDhx66NmUlHSgrGxM3EMqOGZw8cUwciT07Qu33+4L6FyzerU/\n+MqsWX6LuQplkWRpyGGv46ZiOUWu9P4oL96sJOZdd53forbJJtHkZUNlZSWDBg1m1aqJrFgxglWr\nJjJo0OBIDpuctOcvCXl9+8Lbb8P99/uic8WKcPMaombeihXwm9/41S/GjvUtJmFlhU15ysvXvA1Z\nPk49yyKSFeXl8K9/wTnnxD2S+vnlQS0gyoNaSO3at/d7rbduDZ06+W8rkm7OHNh3X9h1V3846402\nintEIlKbXF5rWT3LIjnuvPNg443h+uvjHkn95NrhkgvNiy/6pddOOgkuv9z/jiXNE0/AH/4AN96o\nHVtFkq6qyrdHLV6c3W9/skk9yyJ56Isv4KGHfMGca4qLixk1aiRFRb1p2bILRUW9GTVqpArlhPjt\nb31/4eLFsPvu8PLLcY9ora+/9v35w4f7calQFkm+6sNez5gR90jqT8Vyilzq/VFefFlJyhs5Eo45\nBtq1iyYv2wYMOIGKirnccMNZVFTMjezob0l5/pKeV1wMZWX+92zwYL8X+/z54eVl4pVXYKedJuGc\nL+b32ivcvFx97pSnvCTm7bmnX+Y0qrxsUbEskqNWroQ774QLL4x7JA1TXFxMhw4dtEU5wQ4/HN5/\n3xem++0HQ4b4owBGacECX6yfcQZccAHcey+0bBntGESkYbp0qV+xnBTqWRbJUXfeCRMmwNNPxz0S\nKSSVlf5IkQ884Nf2HjYMdtopvLyKCrj5Znj4YV8kDx3qD6QiIrln+nQ4+WT/4TuJ1LMskkd+/BFG\njIA//SnukUihKS6GW26BefP8Ebn23x8OOcT3zjfksNmpfvoJXnvNH4q7Sxdo1sy/uV5yiQplkVy2\n226wcCGsWhX3SOpHxXKKXOv9UV48WUnIe/JJ2GYb/5V4FHlhU17u5RUXwxVX+B0Au3efxMMPw9Zb\nQ79+cNdd8NFHfu/3TC1f7lfgOO882G47v254t26waBHcdJMvzKvptUV5ysvNvGbNYOedYfbsaPKy\npUncAxCR+qk+PPEVV8Q9EhG/rvFBB8GVV/rVWcaPh5de8ksZLl8Oe+wBO+4Ibdr4U9Om/puR1avh\ns898m8X8+bBkCeyzD/TqBRMn+jdUEck/1Tv57bNP3CPJnHqWRXLMhAl+q9vs2X4pHpGk+vJLv2LF\nokXw+eewbJkvlJs08UVzu3ZQUgI77OAPKtJEm29E8t4dd/j3r7vvjnsk60rXs6xiWSTHHHYYDBwI\np50W90hERETq5803/Y66774b90jWpR38MpBrvT/Kiycrzrzp0/3hfQcOjCYvKspTXlLz8nluylNe\nHHmdOsEHH/hvmaLIywYVyyI5ZMQI34LRrFncIxEREam/Fi1g221h7ty4R5I5tWGI5IglS/zOUgsX\nwmabxT0aERGRDTNgABxxBJxyStwj+SW1YYjkuDvu8Iu5q1AWEZFcVt/DXsdNxXKKXOz9UV70WXHk\nvfTSJO69169BG4V8fzyVp7wkZilPeYWSl2mxrJ5lEcnY2LFwwAHQvn3cIxEREWmYPfeEGTPqd+Ci\nOKlnWSThqqqgQwcYNcoXzCIiIrluu+1g0qRkbQRSz7JIjnrxRWjZEnr0iHskIiIi2dGlS+70LatY\nTpGrvT/Ky++5/eMf0KfPJGydz7rhyefHU3nKS2qW8pRXSHmZ9C2rZ1lE1mvGDPjoI+jVK+6RiIiI\nZE8urYihnmWRBDv1VNhlF7j44rhHIiIikj2ffAL77ANLl8Y9krXS9SyrWBZJqP/8B3bbDT7+GFq3\njns0IiIi2eMcFBfD7Nmw9dZxj8bTDn4ZyOXen0LPy8e53XmnP8pR69b5OT/lKS8X8vJ5bspTXpx5\nZutvxVDPsoiktWoV3HMPnH9+3CMREREJR670LasNQySB7rkHXngBnnsu7pGIiIiEo6wMnnwSnngi\n7pF4asMQyRFVVX65uKFD4x6JiIhIePbcE6ZNi3sU66diOUUu9/4Uel4+zW3sWGje/JfLxeXT/JSn\nvFzKy+e5KU95ceftuCNUVsJXX0WTt6FULIskzM03wwUXEOlBSERERKLWuDF07OiPKZBk6lkWSZDZ\ns6FPHygvh2bN4h6NiIhIuM49F9q3T0broXqWRXLALbfA4MEqlEVEpDB06QLvvRf3KOqmYjlFrvf+\nFHJePsxt2TJ46ik4++xo8uqiPOUpL/os5SmvEPO6doUpU6LL2xAqlkUS4q674PjjYcst4x6JiIhI\nNHbZBT77LP1OfkmgnmWRBPj+eygthYkT/QuHiIhIoTjgALj8cjj44HjHoZ5lkQR7+GHft6VCWURE\nCk1drRhJoGI5RT70/hRqXi7Pzbn1H4Qkl+enPOXlcl4+z015yktKXrpiWT3LIgLA+PF+TeVDDol7\nJCIiItFL+pZl9SyLxOyII+C44+CMM+IeiYiISPScgy22gA8/hDZt4huHepZFEmjOHJg+HQYOjHsk\nIiIi8TCDvfdO7tZlFcsp8qX3pxDzcnVut9wCf/gDbLRRNHmZUp7ylBd9lvKUV8h5tbViJKVnuUnc\nAxApVJWV8PjjMG9e3CMRERGJV9eucM89cY+idupZFonJVVdBRQXcd1/cIxEREYnXkiXQuTN8/rlv\ny4hDup5lFcsiMVi92h+EZPx42H33uEcjIiISv3bt4K23/PtjHLSDXwbyqfen0PJybW6PPgp77JF5\noZxr81Oe8vIlL5/npjzlJS2vZt9yUnqWVSyLRMw5uPlmuOCCuEciIiKSHPvuC5Mnxz2KdakNQyRi\nr74K554LH3wQX1+WiIhI0kycCJde6lsx4qCeZZGEOPJIfzrrrLhHIiIikhzffusPSrJ8OTRvHn2+\nepYzkG+9P4WUlytzmzcP3nkHTj75l5dXVlYyZcoUKisrs5q3oZSnPOVFn6U85RV6XosWsNNO/mBd\nUeRlSsWySIRuvRV+/3soKlp7WVnZGEpKOnDooWdTUtKBsrIx8Q1QREQkRvvtB2+/HfcofkltGCIR\nWb4c2rf3h7jeemt/WWVlJSUlHVi1aiLQEZhFUVFvKirmUlxcHOdwRUREIvfgg/D88/DYY9Fnqw1D\nJGb/+7/Qr9/aQhmgvLycZs1K8YUyQEeaNi2hvLw8+gGKiIjELIlbllUsp8i33p9Cykv63Favhjvu\nWHe5uNLSUn74oRyYFVwyizVrKiitsSJ70uenPOXla14+z015yktiXvv28P338Omn6lkWKShlZf4A\nJB07/vLy4uJiRo0aSVFRb1q27EJRUW9GjRqpFgwRESlIZtCtW7LWW1bPskjInINOneCmm6BPn9qv\nU1lZSXl5OaWlpSqURUSkoF17Lfz3vzBiRLS56XqWm0Q7DJHCM2GCL5gPOyz9dYqLi1Uki4iI4PuW\nL7007lGspTaMFPnY+1MoeUme24gRvle5IUfrS/L8lKe8fM7L57kpT3lJzevaFWbOhHHjoslbHxXL\nIiF6/33/Bz9wYNwjERERyQ0tWkD79msYP74i7cG6oqSeZZEQnXEG7LAD/OUvcY9EREQkN5SVjeHk\nk7+kSZM1NGp0OaNGjWTAgBNCz03Xs6xiWSQkS5fCLrvAxx/DFlvEPRoREZHkW3uwrveAUqI8WJcO\nSpKBfO39KYS8JM7tzjthwIDsFMpJnJ/ylFcIefk8N+UpL4l5aw/WVQpMIgkH69JqGCIhWLkS7r4b\n3nwz7pGIiIjkjnUP1jW/1oN1RUltGCIhuOsuGDsWnnkm7pGIiIjklrKyMQwaNJgff7wL52bxwAN7\nqGc5HRXLkouqqqBDBxg1Cg44IO7RiIiI5J7Kykruvfdr3nhjW156qXkkmbH0LJvZKDNbZmazUi7b\n3MzGmdk8MxtrZq3CHEN95GPvT6HkJWluzz8Pm20GPXpEkxcG5SlPedFnKU95yluruLiYnXdezDvv\nNKeqKrLYWoW9g99ooOYBfi8GJjjndgZeBf4c8hhEInXzzQ0/CImIiEih22IL2HJLf8yCOIXehmFm\nJcDzzrmOwc9zgZ7OuWVm1haY5JzrkOa2asOQnDJ1Khx7LCxYAE20+6yIiEiDnHkmdOoEQ4aEn5Wk\npeO2cs4tA3DOLQW2imEMIqEYMQLOP1+FsoiISDb07Amvvx7vGJLwll7npuPTTjvt5+VCNttsMzp3\n7kyvXr2Atb0z2fr5lltuCfX+lRdeXmofVVx5Y8ZM4sUX4e6783N+ylNeIebVzFSe8pQXbd6BB/Zi\nyJBJvPoqHHRQ9u9/0qRJ61/D2TkX6gkoAWal/Pwh0CY43xb4sI7buihNnDhReTmal4S5XXCBc8OG\nRZcXJuUpT3nRZylPecqrPW/HHZ2bPj38vKDuXKcejaJnuRTfs7xH8PMNwHLn3A1mdhGwuXPu4jS3\ndWGPTyQbli+HHXeEmTNh223jHo2IiEj+GDLEv7dedFG4OXEtHfcI8Bawk5l9YmanA9cDh5rZPODg\n4GeRnDZyJPTrp0JZREQk2w47zB/oKy6hFsvOuYHOuXbOuebOue2dc6Odc1865w5xzu3snDvMOfdV\nmGOoj9QeFuXlVl6cc1u5Em6/HYYPjyYvCspTnvKiz1Ke8pRXe17v3jBlCnz7baTxPwu1WBYpBPff\nD927wy67xD0SERGR/NOiBey9N0Rcq/8s8Ye7/vzzzykuLo57KCK1WrPG9yo/+ih06xb3aERERPLT\n9dfDkiX+m9xsq6yspLy8nH322Scx6yzXS0lJB8rKxsQ9DJFaPfYY/OpXKpRFRETC1KdPOH3LZWVj\nKCnpwKGHnp32OokvlletmsigQYOprKwMPatQen/yMS+OuTnnP+leXOtaLtnPi5LylKe86LOUpzzl\npc/r1AlWrIBFi7J3/5WVlQwaNJhVqyayYsV7aa+X+GIZOtK0acn6F4wWidi//uWP1HfYYXGPRERE\nJL81auTfb8eNy959lpeX06xZKdCxzuslvmcZZlJU1JuKirnqXZZEOeAAOOccOPHEuEciIiKS/x56\nCJ54Ap55Jjv3V1lZSUlJB1atmogvmGNYZzkbmjcfxKhRI1UoS6K8+SZ89hn07x/3SERERArDb34D\nEydmbwm54uJiRo0aSVFRb1q27JL2eokvlm+77RUGDDghkqxC6v3Jt7yo5zZ8+CSGD/dtGFHI5+dO\necpLcl4+z015ysu1vNatYb/9fBtktgwYcAIVFXOZMOHutNdJfLE8f37LuIcg8guzZ8PcuXDqqXGP\nREREpLAcdxw8/nh277O4uJiuXbum/f/E9yz37u149dW4RyKy1gknQNeucOGFcY9ERESksHzxBbRv\n71shN9kku/dtlqM9y9OmQYLreSkwc+b4IwidnX45RhEREQnJllvCvvvCSy9Fl5n4YrllS1iwIJqs\nuHtxlJf8rKuvhqFDYerUaPKq5fNzpzzlJTkvn+emPOXlal7//tlvxahL4ovlvfaC99KvEy0Smblz\nYcIEv1yciIiIxOPoo/3R/FaubNj9ZNq5kPie5auucnzzDdxwQ9yjkUJ38snQoQNcemncIxERESls\nhxwCgwfDMcds+H307Qt//rNfYQNyuGdZW5YlCebP9/1R554b90hERERkwAAYPXrDb790KbzxBnTu\nvP7r5kSxHNVOfknpxVFe8rKuvRaGDIFWraLJq0l5ylNePHn5PDflKS+X8wYMgMmTYeHCDbvvxx7z\nW5aLitZ/3cQXy1tt5ZcGWbQo7pFIoVq4EJ5/Hs4/P+6RiIiICMDGG8Ppp8Odd27Y7R95xBfcmUh8\nz7Jzjv794aij4KST4h6RFKIzz4R27eDKK+MeiYiIiFQrL4e994aKivqtubxwIXTrBkuWQNOmay/P\n2Z5lgF69/Nq2IlErL4enn4Y//jHukYiIiEiq0lLo0QMeeqh+tysr80cCTC2U65ITxXLv3tEUy0nq\nxVFeMrKuuw5+/3t/PPoo8tJRnvKUF09ePs9NecrLh7whQ+D22zPft80534IxcGDm48iJYnnXXeHr\nr2Hx4rhHIoXk44/hySdh2LC4RyIiIiK1OeggXwCPHZvZ9WfPhm+/XbtcXCZyomcZ/Obyvn39Wrci\nURg40H9Q+8tf4h6JiIiIpPPii34n/Nmz17+6xeDBfmWr665b9/9yumcZomvFEAGYMQMmTlSvsoiI\nSNL99rd+R78rrqj7em+9Bc88A8OH1+/+c6ZY7tXLFy9hSmIvjvLiybrkEn9q0SKavPVRnvKUF09e\nPs9NecrLp7xbb4X774fp02v//++/h0GD4Lbb1t0PaX1ypljeZRffY1JREfdIJN+9/jp8+CGcdVbc\nIxEREZFMtGkDN97ol3v99tt1//+aa3wteeyx9b/vnOlZBjj+ePif/4FTTolxUJLXnPPL0Jx9tvrj\nRUREcolzfgWr8ePhvvvg4INhxQp49FG47DKYORO23jr97XO+Zxl833LYrRhS2F54wf9h1WdJGRER\nEYmfGdxzjz+q32mn+RbekhK/UsYzz9RdKNclp4rlsA9OkuReHOWFn/XTT75P+dproXHj8PPqQ3nK\nU148efk8N+UpL1/zfvMbeP99/y3xxx/DU09B9+4bPo6cKpY7dIA1a2Du3LhHIvmorAw23RSOPDLu\nkYiIiEhDtGoFJ54IW27Z8PvKqZ5l8EdqadsWLr00pkFJXvrhB9h5Z/i//4OePeMejYiIiEQtL3qW\nwe/F+NRTcY9C8s1dd/lvLlQoi4iISKqcK5Z79PCHvV60KPv3nSu9OMrLbtby5X5JmZtuiiZvQyhP\necqLJy+f56Y85SkvMzlXLDdpAv36aeuyZM8VV0D//rD77nGPRERERJIm53qWAV5+Ga680h+2UKQh\n5s3z31bMmQPFxXGPRkREROKSrmc5J4vlH37wO/m9/z60axfDwCRvHHmk71O+8MK4RyIiIiJxypsd\n/ACaNYPf/haefjq795vvvTj5nLchWWPH+sNaDxkSTV5DKE95yosnL5/npjzlKS8zOVksg18V4/HH\n4x6F5KrVq32RfOut0Lx53KMRERGRpMrJNgzwxU5JiT/89S67RDwwyXnXXQdvvw3PPRf3SERERCQJ\n8qpnudpll/llv+68M8JBSc775BPo0gXefRd22CHu0YiIiEgS5FXPcrWzz4ZHHoEVK7Jzf/nei5PP\nefXJGjYMzj23YYVyPj+WylOe8uLJUp7ylBdvXjo5XSxvsw306eMPUSySiRdfhOnT4aKL4h6JiIiI\n5IKcbsMAePNNOO00v15uo5wu/SVs334Lu+0G998PBx8c92hEREQkSfKyDQOge3fYdFN/oBKRulx2\nGfTurUJZREREMpfzxbIZDB0K11wDDd1Inu+9OPmct76sKVOgrAz+/vdo8rJNecpTXjx5+Tw35SlP\neZnJ+WIZYOBA+P57XwyJ1LR6NQwa5AvlLbeMezQiIiKSS3K+Z7nav/8NAwbA3LmwySYhD0xyyqWX\n+kOjP/OM/yZCREREpKa8XGe5poEDoX17uOqqEAclOeWdd6BfP5gxA9q2jXs0IiIiklR5u4Nfqhtv\nhJEjYeHCDbt9vvfi5HNebVmrVsGpp8Jtt2W/UM7nx1J5ylNePFnKU57y4s1LJ6+K5W239V+5n3CC\nL5SksF18MXTqBMcfH/dIREREJFflVRsG+BUxTjoJqqr80f3Uo1qYXngBzjnHt19svnncoxEREZGk\nK4g2DPDF8X33wYIFcN11cY9G4vDZZ3DmmfDwwyqURUREpGHyrlgGKCryKx/cdReMGJH5+sv53ouT\nz3nVWT/95L9ZOOcc6NEj/LyoKE95yosnL5/npjzlKS8zTeIeQFjatfPLyR19NEybBvfeCxtvHE7W\nf//rl6ybNw8+/dRv2Vy2zB9eeeVK+PFHaN4cNtrIb+ls1w622QZ++AFKS6GkRO0i2XLFFf7D0SWX\nxD0SERERyQd517Nc08qVcNZZMHs2XH89HH54wwrTzz6DqVP96b33/On776FDB9h5Z9h+e18MEZCm\nZgAAFSZJREFUt2kDLVr4NZ8bN/YHxvj+e1i+3N/Hp5/6Anv2bF9U778/9OwJBx0EXbpAo7zc5h+u\n557zW5SnTvWPv4iIiEimCmKd5XScgzFj4Oqr/RbeP/4Revf2q2ekU1UFFRX+YBbVRfHUqX4r8V57\nwd57+9Nee/n7aUgBXlkJb7wBkybBuHG+eD7qKOjfHw48UIVzJubNgwMOgOefh333jXs0IiIikmsK\nZge/2pjBiSfCrFnw17/Ck0/Cnnv69ofDD4fjjoMzzoCDDprEIYdAx46w6aa+UL3jDt8ucfrpMHky\nfP45vPyyL7yPOgq2227DC+XqXpziYjjmGL8e8Ny5MGGCb9MYMgR+/WvfWrB4ccMfh3ztNVqxAvr0\nmcQ110RXKOfrY6k85SkvvizlKU958ealk7c9y7Vp1Mgfza1fP7+1ef58f/ruO/jmG1+gHnigL17b\nt/cFcxw6dIA//9mvEzx9OoweDZ07+xaNIUP8FlT1OHtr1vgPO507w+9+F/doREREJN8URBtGPvjm\nG3jgAbj1VthyS7jssob3X+c652DQIN/G8vTT0KSgPvqJiIhINhV0z3I++eknePxx3wZSVOSL5iOP\nLMyi+aqr4Nln4bXX/I6UIiIiIhuqoHuWM5ULvTiNG6/tv774Yt+D3aULPPXU+teTzoX5Zeq22+Cf\n//RH6ttkk/yam/KUp7zk5OXz3JSnPOVlRsVyTCorK5k7dy6VlZUbdPtGjeDYY31P85VX+i3NXbv6\n1TSSsDG+ofOry913w803wyuvQNu2Wb/79QpzbiIiIpIsasOIQVnZGAYNGkyzZqX88EM5o0aNZMCA\nExp0n1VVfpWPyy7zBeS110L37lkacD2FMb9qo0f7remTJvmdMKMW5txEREQkPupZTojKykpKSjqw\natVEoCMwi6Ki3lRUzKW4uLjB9//jj/Dgg3D55bDHHnDNNdCpU4PvNmNhzu/mm/0OjuPG+QPARC3s\n505ERETio57lDETRG1NeXk6zZqX4YmsS0JGmTUsoLy/Pyv03aeLXhP7oIzjsMOjTBwYM8Evk5er8\nnPNL6d17rz+EeW2Fcq7OLVP53iemPOUlNS+f56Y85SkvMyqWI1Za6r++h1nBJbNYs6aC0tLSrOY0\nbw7nnQcff+y3MHfvDn//e3YOblKXbM9v1So45RTfn/zGG/4gMHGJ6rkTERGR5FAbRgyq+16bNi1h\nzZqKSPpev/wSbrrJ7xx36ql+S21YnQPZml9FBRx9tD9Iy333wcYbhzDYeorjuRMREZHwqWc5YSor\nKykvL6e0tDTSftelS30f8yOPwDnnwLBh0KpV9nMaOr8XX4Qzz4Thw2Ho0GStIx3XcyciIiLhUc9y\nBqLsjSkuLua7776LtNiaNGkSbdvC7bfDe+/5lowdd/RbnFeuzG7Whs7v66/9UfmGDIExY+CCCzIr\nlAvhuYuS8pSnvOizlKc85cWbl46K5QJVWuqXYXvtNXjnHV8033KLL1bj4Bw89pjvr27SBGbOhAMP\njGcsIiIiItXUhiGA39J8000wfrzfoe7ss6Nbnu3tt+HCC/3W7X/8A3r1iiZXREREpJraMKROe+0F\njz4KM2b4lTR69oQePWDUKFi+PPt5VVXw7LN+6/GJJ/r+5KlTVSiLiIhIsqhYTpHvvTiZ5G23HVx/\nve9nvugi+Ne/4Fe/gkMPhTvugDlzMj+cds28qirf8jFsmL/Pq6+GwYNhwQK/NnTjxvWfU7qssClP\necorjLx8npvylKe8zDSJewCSTE2bwpFH+tN338HYsfD88zBihP95v/1gt938qbQUttjCnxo39kcR\n/P57X1gvW+YPkDJ5sm+3aNMGjj/eF+G77Rb3LEVERETqpp5lqbdPPoF33/XF8AcfwKefwhdf+FNV\nlS+0mzWDdu2gpAR22AG6dfMFdtu2cY9eREREZF1aZ1lEREREJI3E7eBnZoeb2Vwz+8jMLoprHKny\nvRcnn/PyeW7KU57y4svL57kpT3nKy0wsxbKZNQLuAPoAuwEDzKxDHGNJNWPGDOXlaF4+z015ylNe\nfHn5PDflKU95mYlry/I+wHznXIVzbg3wKNAvprH87KuvvlJejubl89yUpzzlxZeXz3NTnvKUl5m4\niuVtgMUpP38aXCYiIiIikhhaZzlFeXm58nI0L5/npjzlKS++vHyem/KUp7zMxLIahpl1Ay53zh0e\n/Hwx4JxzN9S4npbCEBEREZFIJGbpODNrDMwDDgb+A7wLDHDOfRj5YERERERE0ojlCH7OuZ/M7Fxg\nHL4VZJQKZRERERFJmkQflEREREREJE6J3MEv6gOWmNkoM1tmZrMiyNrWzF41sw/MbLaZnRdyXnMz\ne8fMpgd5fwszLyW3kZlNM7PnIsgqN7OZwRzfjSCvlZk9bmYfBs/jviFm7RTMa1rw74oIfmeGmtn7\nZjbLzB42s2YhZp0f/F6G9rdQ29+3mW1uZuPMbJ6ZjTWzViHn9Q8e05/MrEvIWTcGv5szzOxJM2sZ\nct6VKX9/L5tZ1g5qX9drs5kNM7MqM2sdZp6Z/c3MPg3+BqeZ2eFh5gWXDwmew9lmdn2YeWb2aMrc\nFpnZtJDzOpnZ29Wv12a2d8h5Hc3sreB39Fkza5GlrFrfy8N6bakjL6zXlpp5Q4LLQ3l9qSMvtNeX\nenHOJeqEL+A/BkqApsAMoEPImT2AzsCsCObXFugcnG+B790Oe34bB/82BiYD+0Qwz6HAQ8BzEWQt\nBDYPOycl7/+A04PzTYCWEeU2Aj4Dtgsxo13weDYLfh4DnBJS1m7ALKB58Ls5DtghhJx1/r6BG4A/\nBecvAq4POW9nYEfgVaBLyFmHAI2C89cD14Wc1yLl/BDgrjDzgsu3BV4GFgGtQ57f34ALsv17WUde\nr+BvoUnw85ZhP54p//934C8hz28scFhw/ghgYsh57wI9gvOnAVdmKavW9/KwXlvqyAvrtSVdXiiv\nL3Xkhfb6Up9TErcsR37AEufcv4Evw8xIyVrqnJsRnP8W+JCQ15h2zq0MzjbHF3eh9t6Y2bbAb4D7\nwsxJjSSib0mCT9EHOOdGAzjnfnTOfR1FNv5FaoFzbvF6r9kwjYFNzKwJsDG+QA/DLsA7zrnVzrmf\ngNeBY7Idkubvux/wz+D8P4Gjwsxzzs1zzs3H/65mTZqsCc65quDHyfjCMsy8b1N+3ASoIkvqeG3+\nBzA8WzkZ5GX1eVtP3h/wBdaPwXW+CDkv1fFAWch5VUD11tbNgCUh5+0YXA4wATg2S1m1vZdvS0iv\nLelqhxBfW9LlhfL6UkdeaK8v9ZHEYrlgDlhiZqX4T8HvhJzTyMymA0uB8c65KWHmsfaNLKqGeAeM\nN7MpZva7kLN+BXxhZqODry3vMbOikDOrnUAW38hq45z7DBgBfIJ/E/vKOTchpLj3gQOCry03xn/A\n2i6krJq2cs4tA/8iDWwVUW7UzgBeCjvEzK42s0+AgcBfQ87qCyx2zs0OM6eGc4Ovne/LZstOGjsB\nB5rZZDObmM02hbqY2QHAUufcgpCjhgJ/D35fbgT+HHLeB8HvDPgPA1n78Fgt5b18MtAm7NeWqGqH\nDPJCeX2pmRfl60s6SSyWC0LQN/UEcH6NT05Z55yrcs7tiX+R2NfMdg0ry8x+CywLPiEaIW2RqWF/\n51wXfLF1jpn1CDGrCdAFuDPIXAlcHGIeAGbWFOgLPB5yzmb4LSMl+JaMFmY2MIws59xc/FeW44F/\nAdOBn8LIymQ4MeWGxswuBdY45x4JO8s59xfn3PbAw/ivSkMRfDC9BN8a8fPFYeUFRuLbgzrjNzjc\nHHJeE3xbWTfgT8BjIedVG0DIH8YDf8C/722PL5zvDznvDPz7whT8lskfsnnntbyX13wtyeprS5S1\nQ115Yb2+1JYX1etLXZJYLC8Btk/5eVuy+DVNEgRfbz8BPOicezaq3KBdYCKQtR1UarE/0NfMFuJf\neHub2QMh5uGc+0/wbyXwNL6VJyyf4rdqTQ1+fgJfPIftCOC9YI5hOgRY6JxbHrRGPAV0DyvMOTfa\nObe3c64X8BXwUVhZNSwzszYAwQ4jn0eUGwkzOw3/4TGUDzp1eIQsfc2dRnugFJhpZovw7w/vmVlo\n3ww45ypd0DAJ3At0DSsrsBj/d0fwLWCVmW0RZqD5Yx8cg99HIWynOueeAXDOPUG4r9c45z5yzvVx\nznXFt3Vmbct5mvfy0F5boq4d0uWF9fqSwfzCfn1JK4nF8hTg12ZWYn4v/BOB0FdUILqtoOA/Sc9x\nzt0adpCZbVn9tWGwVeZQYG5Yec65S5xz2zvndsA/d686504JK8/MNq7eu9nMNgEOw3+9H4rg67XF\nZrZTcNHBwJyw8lJEtdXnE6CbmW1kZoafX2hroJtZcfDv9sDR+BfDUKL45d/3c/idfQBOBbL9xlPX\n60m2X2d+kWV+tYbhQF/n3OosZ9WW9+uU/zuK7P++/JznnHvfOdfWObeDc+5X+A+vezrnsvlhp+b8\nUve+P4bsv77U/F15BjgoyN4JaOqc+2+IeeDfFz4M2rCyrWbeEjPrCWBmB5P9D8g1n7/q15hGwF+A\n/81iVm3v5WG+tqyvdsj2a8s6eSG/vtSWF/brS2bS7fkX5wm/5XMeMB+4OIK8R/A7Ma3GFwunh5i1\nP/6r5hn4r52nAYeHmLdHkDEDv/LApRE+jz0JeTUMfA9x9WM5O6Lfl074D3Uz8FuAWoWctzFQCWwa\n0fP2N/wL0iz8DipNQ8x6HV98TAd6hZSxzt83sDl+Z595+JUHNgs57yj8FsNV+KOWvhRi1nygIvi7\nnwaMDHluTwR/ezPwhcHWYebV+P+FZHc1jNrm90DwtzADX8i2CTmvCfBg8JhOBXqG/XgCo4GzspWz\nnvl1D+Y1HXgb/2EnzLzzgr/zucC1Wcyq9b0caB3Ga0sdeWG9ttSWd0RYry91zC+015f6nHRQEhER\nERGRNJLYhiEiIiIikggqlkVERERE0lCxLCIiIiKShoplEREREZE0VCyLiIiIiKShYllEREREJA0V\nyyISGzP7psbPp5rZ7RGPob+ZzTGzV2pcXmJmA8IYW3Dfs7N0X23N7PngfM/q89liZhPNbJ2jVJrZ\n+Wa2UT3v6xe3MbMXzKxlNsYZJjO7ycx6xz0OEYmHimURiVNtC703ePH34PC9mRoEnOmcO7jG5b9i\n3cO5ZnNh+mzd1wXAPSHc7/r8EX/AnA2+jXPuf5xzX2d1VOG4Hbg47kGISDxULItIIgVbX18xsxlm\nNt7Mtg0uH21mx6Rc75vg355m9rqZPQt8UMv9DTCzWcHpuuCyy4AewCgzu6HGTa4DepjZNDM7P7hs\nGzN7yczmpV7fzA41s7fMbKqZjTGzdYpIM9srmMt04Jwa83w9uO1UM+sWXP5PM+ubcr2HzOzIWh6q\nY4GXa8nb3MyeNrOZwdj2CC7fxMzuDx6HGWZ2dHD5SDN718xmm9nfaslJve8hQDtgYvUW+doe3wxu\ns8jMWgePwYfBczsvmOvBZvbv4Oe9g+tvbGajzGyymb1X/XiY2b1mNj04fR48r9VbhGcHj8HxwWU9\ng63ljweZD6aMsYuZTTKzKcHz3AbAOfcJ0NrMtqrrcRGRPBXHYQN10kknnZxzAD+y9rCp04Fy4Lbg\n/54DTgrOnw48HZwfDRyTch9fB//2BL4Btq8lZ2v8IVpb4zcSvAL0Df5vIrUccpcah2sHTgU+BloA\nzYOxbgNsAbwGFAXX+xNwWS33NxPYPzh/IzArOF8ENAvO/xqYEpw/MGXOLYEFQKMa91laff2aYwZu\nqx4H0BuYHpy/Hrg55Tatgn83C/5tFDwmu6c8Pl1qmc9CYPP1Pb7pbpPyc2ugBPgB2DW4fCpwX3C+\nL/BUcP4aYGD1uPGHFC5Kub/t8R+UtgOOAcYGl28VjK9N8Bh9GYzZgLfwh2BuArwJbBHc5nhgVMp9\n3wMcHfffjE466RT9qQkiIvFZ6Zz7uR/WzE4F9gp+3A84Ojj/IFBzy29t3nV+K2BNXYGJzrnlQc7D\n+GL0ueroDMf7inPu2+A+PsAXeZsDuwJvmpkBTYG3U29kZq3wRembKfM5PDjfFLjbzDoDPwE7Ajjn\nXjezO81sC6A/8KRzrqrGeLYGKtOMtQe+YMQ5NzHYgrspcAhwQvWVnHMrgrMnmtnv8EVj22BO79fx\nWBhrH7f1Pb613YYa5xc55+YE5z/AF9wAs/EfCgAOA440s+HBz83wBfK8oBf6ceBc59xiMxsKlAVz\n/NzMJgXj/Ab/e/KfYKwzgvtfAewOjA+ex0bAf1LG9zl+y7iIFBgVyyKSVOl6b38kaCELippmKf/3\nXR33l2lBXJfVKeer8K+hBoxzzv2/DbzPocBS51zHoNd6Vcr/PQCcDJwInFbLbVcB9dnJrtbH1MxK\ngWHAXs65r81sdD3vFxr++NZ8bFennK9+rzLgWOfc/FpufxfwhHNuYgbjS836ibXP4/vOuf3T3H4j\nfvnciEiBUM+yiMSprgLrLaB6NYqTgDeC8+XA3sH5fvgts+vzLnBgsHW1cXC/k9Zzm2+ATTO478nA\n/mbWHn7uq90x9QrB1tuvzKx7cNFJKf/dirVbME8BUndO/Cd+pzjnnJtbS/ZHrN3qWtMb1Tlm1gv4\nItgqPp5f9kxvhm/z+Bb4JujTPaKO+Vb7Orgd1P74vrae29SUSbE9FjgvZeydg3/PAVo4525Kue4b\nwAlm1sjMioEDgnGmMw8oTukZb2Jmu6b8/07UvaVdRPKUimURiVNdKzecB5wefE3+/4DqnezuBXoG\nO8p1o+6tyT7EuaX41Qwm4XujpzjnXljPGGYBVcFOY+fXcj0X3PcX+K2+ZWY2E1/k71zL/Z0BjDSz\naTXuayRwWjCfnVLn45z7HPgQ36dd27xWAgvMbIda/vtyYK9gTNfie64BrsbvrDY7yOzlnJsFzAiy\nHgL+XXOetbgXeNnMXgke3z/zy8e3tiXsfr5NLfed7nyqq4CmwU6E7wNXBpcPA/YInqtpZnaWc+5p\nfAvHTGACMDx4PGuqfh7X4Ntdbgh+56bjW4EwsyZAe3wvtYgUGHMuqlWGRESkPsyvqjETv4PdN2mu\n0w/fPvHXSAdXQMzsKPxOoHWuEiIi+UlblkVEEsjMDgbm4FcHqbVQBnDOPYtvTZHwNAZGxD0IEYmH\ntiyLiIiIiKShLcsiIiIiImmoWBYRERERSUPFsoiIiIhIGiqWRURERETSULEsIiIiIpKGimURERER\nkTT+P0Um8Nmgzi1hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebfb780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generator.plot_random()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the generator to generate a larger data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>utc-4</th>\n",
       "      <th>utc-5</th>\n",
       "      <th>utc-6</th>\n",
       "      <th>utc-7</th>\n",
       "      <th>hr=0</th>\n",
       "      <th>hr=1</th>\n",
       "      <th>hr=2</th>\n",
       "      <th>hr=3</th>\n",
       "      <th>hr=4</th>\n",
       "      <th>hr=5</th>\n",
       "      <th>...</th>\n",
       "      <th>hr=20</th>\n",
       "      <th>hr=21</th>\n",
       "      <th>hr=22</th>\n",
       "      <th>hr=23</th>\n",
       "      <th>rating</th>\n",
       "      <th>population</th>\n",
       "      <th>calls</th>\n",
       "      <th>calls+1</th>\n",
       "      <th>calls+2</th>\n",
       "      <th>calls+3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "      <td>356</td>\n",
       "      <td>1.404014</td>\n",
       "      <td>0.593245</td>\n",
       "      <td>2.215413</td>\n",
       "      <td>6.597920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180</td>\n",
       "      <td>639</td>\n",
       "      <td>40.453677</td>\n",
       "      <td>68.890363</td>\n",
       "      <td>24.764697</td>\n",
       "      <td>7.974275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>635</td>\n",
       "      <td>72.471909</td>\n",
       "      <td>169.388491</td>\n",
       "      <td>73.583908</td>\n",
       "      <td>46.200446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   utc-4  utc-5  utc-6  utc-7  hr=0  hr=1  hr=2  hr=3  hr=4  hr=5    ...      \\\n",
       "0    0.0    0.0    1.0    0.0   0.0   0.0   0.0   0.0   0.0   0.0    ...       \n",
       "1    0.0    0.0    1.0    0.0   0.0   0.0   0.0   0.0   1.0   0.0    ...       \n",
       "2    0.0    0.0    0.0    1.0   0.0   0.0   1.0   0.0   0.0   0.0    ...       \n",
       "\n",
       "   hr=20  hr=21  hr=22  hr=23  rating  population      calls     calls+1  \\\n",
       "0    0.0    0.0    0.0    0.0      30         356   1.404014    0.593245   \n",
       "1    0.0    0.0    0.0    0.0     180         639  40.453677   68.890363   \n",
       "2    0.0    0.0    0.0    0.0      50         635  72.471909  169.388491   \n",
       "\n",
       "     calls+2    calls+3  \n",
       "0   2.215413   6.597920  \n",
       "1  24.764697   7.974275  \n",
       "2  73.583908  46.200446  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame([generator.get_row() for i in range(1000)], columns=RowGenerator.get_header_columns())\n",
    "data[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X=data.iloc[:,0:-3].values\n",
    "Y=data.iloc[:,-3:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X_train)  \n",
    "X_train_std = scaler.transform(X_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "regressor=MLPRegressor(hidden_layer_sizes=(100,100), activation='relu', solver='adam', alpha=0.0001, \n",
    "                       batch_size='auto', learning_rate='constant', learning_rate_init=0.001, \n",
    "                       power_t=0.5, max_iter=2000, shuffle=True, random_state=None, tol=0.0001, \n",
    "                       verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, \n",
    "                       early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, \n",
    "                       epsilon=1e-08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=2000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train_std,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_std = scaler.transform(X_test)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing expected calls increase to actual: results are faily good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  95.77760772,   91.33078777,   92.63042552],\n",
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       "       [  53.82579461,   48.00299591,   44.28928141],\n",
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       "       [   4.42831227,    2.02965742,    3.38091323],\n",
       "       [  22.79588773,    6.452679  ,    4.28743177],\n",
       "       [  72.32338095,   30.21235203,   10.36035201],\n",
       "       [   4.87065432,   12.44383845,   18.80666854],\n",
       "       [  32.08022369,   22.47956704,   20.96144132],\n",
       "       [   2.88715291,    0.5944984 ,   -0.20718592],\n",
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       "       [  18.97525981,    6.06524908,    5.29761779],\n",
       "       [   0.74853797,   10.0771864 ,   18.41741059],\n",
       "       [   1.27071078,    0.63485427,    1.43406096],\n",
       "       [  33.0265194 ,   43.066848  ,   52.24924686],\n",
       "       [  60.99842066,   53.80103983,   51.00976989],\n",
       "       [ 114.44384128,   38.76613863,   12.59875591],\n",
       "       [  42.87408559,   16.17630799,    6.99922817],\n",
       "       [  21.0386988 ,   28.73046915,   38.39485377],\n",
       "       [   1.91706288,    5.70930777,   10.34193233],\n",
       "       [  87.4916332 ,   34.08271781,   13.34899071],\n",
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       "       [  20.6685311 ,   15.26980863,   14.38192148],\n",
       "       [  27.18473619,   34.7347665 ,   45.13767295],\n",
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       "       [  25.13265064,   20.02764639,   14.7458526 ],\n",
       "       [  52.17965585,   11.85320795,    4.68929282],\n",
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       "       [  54.33943563,   11.18401909,    5.06545209],\n",
       "       [  18.92195641,    7.30316965,    2.74388936],\n",
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       "       [  29.91378545,   36.39155442,   43.9872199 ],\n",
       "       [  66.24814764,   27.44404613,    9.25394385],\n",
       "       [  69.52235911,   30.51067276,   20.18442284],\n",
       "       [ 108.82427105,   87.65747188,   69.3265929 ],\n",
       "       [  14.58956172,   17.66893996,   22.65577996],\n",
       "       [ 134.1542494 ,   38.5849274 ,   13.06235319],\n",
       "       [  17.17589236,   21.42581452,   26.19003881],\n",
       "       [   1.89670328,    8.09740915,   16.27517825],\n",
       "       [  23.56067722,   18.01666058,   15.00520012],\n",
       "       [  52.25736582,   58.66601617,   63.11046725],\n",
       "       [   6.51150004,    2.90739378,    4.3939617 ],\n",
       "       [  20.97247647,   22.43917934,   27.77995329],\n",
       "       [  26.88760379,   19.92123137,   20.20087825],\n",
       "       [  33.27383885,   41.48187257,   47.60183977],\n",
       "       [  67.96664037,   67.93847722,   71.14148931],\n",
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       "       [  36.33338257,   49.59262366,   60.93180424],\n",
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       "       [  49.57144433,   49.20374984,   52.36550271],\n",
       "       [  18.28627525,   24.71142385,   29.00540127],\n",
       "       [  10.98870289,   14.3503526 ,   19.37014275],\n",
       "       [  71.89295094,   80.53022436,   86.65469001],\n",
       "       [   3.62091659,    8.8077225 ,   16.25724098],\n",
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       "       [  41.90067264,   29.61911589,   24.16108268],\n",
       "       [   3.14955254,    5.57887798,   10.40256229],\n",
       "       [ 163.52908621,   74.34750472,   46.00460569],\n",
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       "       [   5.66882161,   16.27988872,   26.70293908],\n",
       "       [  39.7081429 ,   54.03465903,   65.65831099],\n",
       "       [ 136.40160669,   35.57247187,   13.45107979],\n",
       "       [   5.71139862,    1.84487644,    1.64158443],\n",
       "       [  40.89536114,   47.23908767,   52.84994405],\n",
       "       [  27.24316438,   24.86445362,   20.91536389],\n",
       "       [  22.06521949,   26.70743172,   30.59012082],\n",
       "       [   9.53114601,   14.54519032,   20.04405807],\n",
       "       [ 100.40881182,   70.29437278,   57.54739338],\n",
       "       [   0.31395677,   -0.1230206 ,    1.67373786],\n",
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       "       [  94.81018735,   41.91962937,   26.30349385],\n",
       "       [  17.55257045,   22.02678516,   26.16510186],\n",
       "       [ 105.25361934,   34.55620392,   15.14219136],\n",
       "       [  52.20615081,   29.65253162,   23.64712321],\n",
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       "       [   1.42656578,    5.54043056,    9.63729921],\n",
       "       [  15.08541713,   14.59479939,   13.28372664],\n",
       "       [  23.73595061,   18.53227193,   14.93439226],\n",
       "       [  34.56753435,   42.55270819,   49.91631027],\n",
       "       [  67.40435913,   49.95687686,   45.32318485],\n",
       "       [  15.5527653 ,    4.49212824,    4.36382219],\n",
       "       [  37.73961995,   21.65212477,   16.19679525],\n",
       "       [  78.20712049,   73.26166197,   71.24059863],\n",
       "       [  14.91057514,   21.35885648,   27.97312424],\n",
       "       [   1.98883622,    0.39476992,    0.26396197],\n",
       "       [  40.54909461,   26.81864715,   20.08634541],\n",
       "       [  11.32905951,    3.93081608,    2.83565091],\n",
       "       [  31.86122429,   40.07736111,   47.72428306],\n",
       "       [  53.14113716,   39.48946557,   37.52477754],\n",
       "       [  35.34914107,   23.45369782,   18.7318586 ],\n",
       "       [  28.89043246,   22.68923149,   21.76769818],\n",
       "       [  87.5188856 ,   48.43368451,   35.39732868],\n",
       "       [   1.2658741 ,    9.80084562,   18.23204216],\n",
       "       [  22.90697841,    9.14076245,    2.78436977],\n",
       "       [  32.88561205,   21.63848764,   17.36403731],\n",
       "       [   5.35918972,    2.28777111,    1.90567608],\n",
       "       [  54.17952664,   40.80372933,   31.9966304 ],\n",
       "       [  44.88620657,   55.58484247,   60.2149086 ]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "regressor.predict(X_test_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [   6.36766445,    2.47674418,    1.04651095],\n",
       "       [  22.10253297,   22.41741034,   27.19668824],\n",
       "       [  28.39550426,   21.60653445,   21.88416826],\n",
       "       [  32.47334803,   41.23489699,   46.67619253],\n",
       "       [  68.2278234 ,   69.20679152,   71.68946226],\n",
       "       [  12.55126649,   13.70870517,   16.09834412],\n",
       "       [  18.45862519,   14.87228833,   12.06399286],\n",
       "       [  35.95248584,   50.0358873 ,   62.73298534],\n",
       "       [  34.02796194,   26.30931602,   23.0101688 ],\n",
       "       [  51.48285774,   51.98139401,   53.85269344],\n",
       "       [  17.81758275,   23.15115998,   28.26466564],\n",
       "       [  10.51884904,   14.64297255,   18.56253926],\n",
       "       [  80.36158593,   85.60327594,   90.20551971],\n",
       "       [   2.93728908,    8.74780428,   16.38774846],\n",
       "       [   2.35854356,    7.0241903 ,   13.15880649],\n",
       "       [   0.73322423,    2.73815084,    8.1547328 ],\n",
       "       [   9.25643947,   11.26322457,   12.95624416],\n",
       "       [  45.90728094,   30.87746047,   25.0150348 ],\n",
       "       [   1.53709831,    4.57777046,    8.5757921 ],\n",
       "       [ 169.38849074,   73.58390791,   46.20044619],\n",
       "       [  51.44034413,   51.73307913,   50.67698511],\n",
       "       [  88.84721091,   38.19820067,   24.01818604],\n",
       "       [  37.34833095,   23.26443046,   16.39432624],\n",
       "       [  20.03975423,   26.06531088,   33.49352199],\n",
       "       [   4.34992599,   12.95490506,   24.26914443],\n",
       "       [  40.58855249,   52.50949564,   62.93856237],\n",
       "       [ 129.35119187,   35.2985818 ,   12.11579417],\n",
       "       [   5.97819119,    2.25588801,    0.9531915 ],\n",
       "       [  41.2767655 ,   48.88195598,   53.47217491],\n",
       "       [  27.45483303,   24.56727596,   21.15371889],\n",
       "       [  23.15158419,   27.25853081,   30.26021346],\n",
       "       [  10.17340694,   14.22044532,   18.99776969],\n",
       "       [ 106.68946226,   70.62075694,   57.72186431],\n",
       "       [   1.71557917,    0.72489214,    2.70703549],\n",
       "       [   3.11428343,    1.19893349,    0.50659129],\n",
       "       [  99.86310633,   43.46543208,   26.80372876],\n",
       "       [  10.85981329,   19.47093682,   27.12457164],\n",
       "       [ 115.35765036,   36.26112327,   12.88668818],\n",
       "       [  50.551429  ,   29.22194975,   23.19489714],\n",
       "       [  41.53503879,   25.62389612,   18.71659737],\n",
       "       [  38.63076745,   39.31668045,   41.0104393 ],\n",
       "       [ 100.03912862,   96.9755347 ,   98.37945585],\n",
       "       [  11.45183531,    4.85995823,    3.18884239],\n",
       "       [  10.862235  ,   14.286134  ,   18.75886537],\n",
       "       [  82.62054108,   43.52990976,   16.31311181],\n",
       "       [   4.38073412,    1.54398329,    1.87553696],\n",
       "       [  59.77268699,   60.49235894,   63.15694886],\n",
       "       [   4.84097883,    2.05864439,    2.16738261],\n",
       "       [  20.69814481,   14.04290256,   14.2076771 ],\n",
       "       [   1.78602111,    5.3191098 ,    9.96458434],\n",
       "       [  11.36143725,   12.07065798,   12.12852495],\n",
       "       [  24.51487995,   19.13399447,   15.77017249],\n",
       "       [  34.42270023,   43.11961535,   50.4576824 ],\n",
       "       [  67.33401872,   49.15172921,   45.11126795],\n",
       "       [  18.06673302,    4.44561579,    4.85300335],\n",
       "       [  37.17588747,   23.10964715,   16.26386585],\n",
       "       [  92.47961225,   78.48287988,   73.40865712],\n",
       "       [  15.78845304,   20.76693495,   27.03587834],\n",
       "       [   3.20469687,    1.25020367,    0.52825473],\n",
       "       [  42.62929885,   29.60984698,   19.67579783],\n",
       "       [  14.16708046,    5.6597694 ,    4.64238631],\n",
       "       [  32.85793066,   42.37573311,   49.54207795],\n",
       "       [  51.12825527,   38.18986076,   35.15741293],\n",
       "       [  35.45034935,   24.5435947 ,   18.54210835],\n",
       "       [  29.22136074,   21.58544414,   21.84784195],\n",
       "       [  76.78180118,   47.94854581,   35.36941728],\n",
       "       [   3.66538828,   10.91622186,   20.44996577],\n",
       "       [  13.7978141 ,    8.87056464,    2.45049209],\n",
       "       [  33.28879649,   21.78629643,   17.33744488],\n",
       "       [   5.66672428,    2.64633017,    1.11816696],\n",
       "       [  45.62734011,   36.67146121,   29.22312777],\n",
       "       [  46.57475176,   54.17211498,   59.40688244]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### This notebook with synthetic data shows the ability of a simple neural network to predict increases of subscriber calls following network element anomalies of various of configurations. However nothing replace real wild data that might require much more cleansing, feature engineering and tuning\n"
   ]
  }
 ],
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