Passengers.ipynb

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   "source": [
    "#  INF131: The case of flight passengers prediction\n",
    "\n",
    "### Build a classifier to predict number of passengers in flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ομάδα Νο 44\n",
    "## Μέλη: \n",
    "### Ξενουλέας Ευστράτιος (p3150130)\n",
    "### Σταρατζής Δημήτριος (p3150166)\n",
    "### Νάμπουρη Χρυσούλα Μαρία (t8150096)"
   ]
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   "source": [
    "## Περιγραφή Project\n",
    "---\n",
    "\n",
    "Στα πλαίσια της εργασίας του μαθήματος \"Εξόρυξη Γνώσης από Βάσεις Δεδομένων και τον Παγκόσμιο Ιστό\" ασχοληθήκαμε με ένα <u>supervised learning πρόβλημα</u> και συγκεκριμένα με ένα <u>πρόβλημα κατηγοριοποίησης</u>. \n",
    "<P> Πιο συγκεκριμένα, δίνεται ένα σύνολο δεδομένων το οποίο αποτελείται από μερικές χιλιάδες πτήσεις στην Αμερική, όπου κάθε πτήση περιγράφεται απο ένα σύνολο μεταβλητών (αεροδρόμιο αναχώρησης, αεροδρόμιο άφιξης, κτλ). Κάθε πτήση χαρακτηρίζεται επίσης από μια μεταβλητή που σχετίζεται με τον αριθμό των επιβατών της πτήσης (π.χ. κάθε τιμή της μεταβλητής σχετίζεται με ενα εύρος πλήθους επιβατών). Για κάποιες πτήσεις, η τιμή της μεταβλητής  είναι γνωστή, ενώ για άλλες όχι. Στόχος είναι να προβλέψουμε την τιμή της μεταβλητής για τις πτήσεις για τις οποίες δεν είναι διαθέσιμη.\n"
   ]
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   "source": [
    "## Dataset\n",
    "---\n",
    "Το αρχείο με όνομα `train.csv` περιέχει τα δεδομένα εκπαίδευσης (training set) του προβλήματος, ενώ το αρχείο `test.csv` περιέχει τα δεδομένα ελέγχου (test set) του προβλήματος. Κάθε γραμμή των δυο αυτών αρχείων αντιστοιχεί σε μια πτήση, η οποία χαρακτηρίζεται από τις εξής μεταβλητές:\n",
    "\n",
    "*   __DateOfDeparture:__ Ημερομηνία Αναχώρησης\n",
    "*   __Departure:__ Κωδικός Αεροδρομίου Αναχώρησης\n",
    "*   __CityDeparture:__ Όνομα Αεροδρομίου Αναχώρησης\n",
    "*   __LongitudeDeparture:__ Γεωγραφικό Μήκος Αεροδρομίου Αναχώρησης\n",
    "*   __LatitudeDeparture:__ Γεωγραφικό Πλάτος Αεροδρομίου Αναχώρησης\n",
    "*   __Arrival:__ Κωδικός Αεροδρομίου Άφιξης\n",
    "*   __CityArrival:__ Κωδικός Αεροδρομίου Άφιξης\n",
    "*   __LongitudeArrival:__ Γεωγραφικό Μήκος Αεροδρομίου Άφιξης\n",
    "*   __LatitudeArrival:__ Γεωγραφικό Πλάτος Αεροδρομίου Άφιξης\n",
    "*   __WeeksToDeparture:__ Πόσες εβδομάδες πριν την αναχώρηση της πτήσης κατά μέσο όρο έκλεισαν οι επιβάτες τα εισιτήριά τους\n",
    "*   __std_wtd:__ Τυπική απόκλιση για το WeeksToDeparture.\n",
    "\n",
    "\n",
    "Το training set περιέχει μια επιπλέον μεταβλητή (PAX), η οποία έχει σχέση με τον αριθμό των επιβατών της πτήσης. Η μεταβλητή αυτή παίρνει 8 διαφορετικές τιμές (τιμές από 0 έως 7 οπότε 8 κατηγορίες συνολικά). Κάθε κατηγορία υποδηλώνει πόσοι περίπου επιβάτες χρησιμοποίησαν την πτήση. Οι αριθμοί στις κατηγορίες έχουν ανατεθεί με τυχαίο τρόπο.\n",
    "\n",
    "<p> Η μεταβλητή `PAX` λείπει από το test set, καθώς πρόκειται για την μεταβλητή που πρέπει να προβλέψουμε στα πλαίσια της παρούσας εργασίας.</p>"
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     "text": [
      "/home/stratos/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "/home/stratos/anaconda3/lib/python3.6/site-packages/ggplot/utils.py:81: FutureWarning: pandas.tslib is deprecated and will be removed in a future version.\n",
      "You can access Timestamp as pandas.Timestamp\n",
      "  pd.tslib.Timestamp,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pprint\n",
    "import codecs\n",
    "import csv\n",
    "import time\n",
    "import math\n",
    "import datetime\n",
    "import calendar\n",
    "import keras.utils\n",
    "import geopy.distance\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "from imblearn.over_sampling import RandomOverSampler \n",
    "from ggplot import *\n",
    "from datetime import date\n",
    "from matplotlib.pyplot import hist\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier\n",
    "from keras import backend as K\n",
    "from keras import metrics, callbacks, models\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Activation, Input\n",
    "from keras.optimizers import Adam, Adadelta"
   ]
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    "id": "HjMGLHPLuD0J"
   },
   "source": [
    "# Specify Develop or Test"
   ]
  },
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   "source": [
    "DEVELOP = True\n",
    "TEST = False"
   ]
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    "id": "ggLY9e6B3JFP"
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   "source": [
    "## Write Function\n",
    "Η παρακάτω συνάρτηση είναι υπεύθυνη για τη δημιουργία του τελικού αρχείου, μορφής .csv, το οποίο είναι και το αποτέλεσμα του μοντέλου μας. Ως αποτέλεσμα θα επιστρέφεται ο αριθμός της πτήσης και η πρόβλεψη για την κατηγορία αριθμού επιβατών της (αριθμοί 0-7)."
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "colab_type": "code",
    "id": "UE2uMUIB0sal"
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   "source": [
    "def write_csv(y_pred, name):\n",
    "    with open(name, 'w', newline='') as csvfile:\n",
    "        writer = csv.writer(csvfile, delimiter=',')\n",
    "        writer.writerow(['Id', 'Label'])\n",
    "        for i in range(y_pred.shape[0]):\n",
    "            writer.writerow([i, y_pred[i]])\n",
    "        "
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Load Data\n",
    "---\n",
    "Η παρακάτω συνάρτηση φορτώνει τα δεδομένα training και test από τα αντίστοιχα αρχεία μορφής .csv. Το training set περιλαμβάνει 8899 γραμμές και 12 στήλες, ενώ το test set περιλαμβάνει 2229 γραμμές και 11 στήλες . Η στήλη που λείπει στο test είναι η PAX (label)."
   ]
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   "execution_count": 4,
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   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')"
   ]
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   "source": [
    "## Analyze Data\n",
    "---\n",
    "### Αρχικά, γίνεται μία πρώτη ανάλυση των δεδομένων για την καλύτερη κατανόησή τους. <br>\n",
    "Βρίσκουμε min, max των ποσοτικών μεταβλητών το οποίο όπως θα δούμε στην πορεία θα μας βοηθήσει στην κανονικοποίηση των δεδομένων μας.<br>\n"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>MinValue</th>\n",
       "      <th>MaxValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DateOfDeparture</td>\n",
       "      <td>2011-09-01</td>\n",
       "      <td>2013-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LongitudeDeparture</td>\n",
       "      <td>25.7933</td>\n",
       "      <td>47.449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LatitudeDeparture</td>\n",
       "      <td>-122.375</td>\n",
       "      <td>-71.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LongitudeArrival</td>\n",
       "      <td>25.7933</td>\n",
       "      <td>47.449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LatitudeArrival</td>\n",
       "      <td>-122.375</td>\n",
       "      <td>-71.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WeeksToDeparture</td>\n",
       "      <td>2.625</td>\n",
       "      <td>21.9333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>std_wtd</td>\n",
       "      <td>2.16025</td>\n",
       "      <td>15.8622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PAX</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
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      "text/plain": [
       "              Feature    MinValue    MaxValue\n",
       "0     DateOfDeparture  2011-09-01  2013-03-05\n",
       "1  LongitudeDeparture     25.7933      47.449\n",
       "2   LatitudeDeparture    -122.375    -71.0052\n",
       "3    LongitudeArrival     25.7933      47.449\n",
       "4     LatitudeArrival    -122.375    -71.0052\n",
       "5    WeeksToDeparture       2.625     21.9333\n",
       "6             std_wtd     2.16025     15.8622\n",
       "7                 PAX           0           7"
      ]
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   "source": [
    "frame = pd.DataFrame(columns=['Feature','MinValue','MaxValue'])\n",
    "\n",
    "for column in df_train:\n",
    "  if(df_train[column].dtypes=='float') or (df_train[column].dtypes=='int') or (column=='DateOfDeparture'):\n",
    "    \n",
    "    frame = frame.append({\n",
    "      'Feature': column,\n",
    "      'MinValue' : min(df_train[column]),\n",
    "      'MaxValue' : max(df_train[column])    \n",
    "    }, ignore_index=True)\n",
    "    \n",
    "frame"
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  {
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   "source": [
    "## Create New Feautures\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "Έχοντας κάνει μία πρώτη ανάλυση των χαρακτηριστικών και γνωρίζοντας, πλέον, τη μορφή τους, τον τύπο τους, το εύρος τιμών τους, συνεχίζουμε με την επεξεργασία τους. <br>\n",
    "Πιο συγκεκριμένα, προσπαθούμε να δημιουργήσουμε από αυτά τα \"απλά\" χαρακτηριστικά, πιο \"σύνθετα\" (ranges), προκειμένου να παράξουμε γνώση και να δώσουμε αξία στο μοντέλο μας."
   ]
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   "source": [
    "### Form Date Of Departure\n",
    "---\n",
    "__Η παρακάτω διαδικασία είναι υπεύθυνη για την επεξεργασία της ημερομηνίας.__ <br>\n",
    "\n",
    "Είναι σκόπιμο, αρχικά, να διαχωρίσουμε το έτος ['Year'], τον μήνα ['Month'] και την μέρα ['Day'], καθώς μπορούμε να συλλέξουμε πολλές πληροφορίες από το καθένα χωριστά. \n",
    "Αφού, διασπάσαμε αυτή τη μία μεταβλητή σε τρεις, το επόμενο βήμα είναι να βρούμε <u> σε ποιά μέρα της εβδομάδας </u> αντιστοιχεί αυτή η ημερομηνία ['WeekDay']. \n",
    "Το κέρδος από αυτήν τη νέα μεταβλητή φαίνεται συγκρίνοντάς την με την υπάρχουσα μεταβλητή 'Day'. <u>Από εκεί που είχαμε ένα εύρος τιμών 1-31</u>, τώρα έχουμε καταφέρει <u>να το περιορίσουμε σε 1-7 (ημέρες εβδομάδας)</u>. Αυτό μας βοηθάει στο ότι πλέον ομαδοποιούμε τις πτήσεις με κοινό χαρακτηριστικό την ημέρα της εβδομάδας κάτι το οποίο μπορεί να μας επιφέρει επιπλέον συσχέτιση μεταξύ αυτών. \n",
    "\n"
   ]
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       "      <th>DateOfDeparture</th>\n",
       "      <th>Departure</th>\n",
       "      <th>CityDeparture</th>\n",
       "      <th>LongitudeDeparture</th>\n",
       "      <th>LatitudeDeparture</th>\n",
       "      <th>Arrival</th>\n",
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       "      <th>LatitudeArrival</th>\n",
       "      <th>WeeksToDeparture</th>\n",
       "      <th>std_wtd</th>\n",
       "      <th>PAX</th>\n",
       "      <th>Year</th>\n",
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       "      <th>WeekDay</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>EWR</td>\n",
       "      <td>Newark</td>\n",
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       "      <td>-74.168667</td>\n",
       "      <td>8.352941</td>\n",
       "      <td>5.667243</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.636719</td>\n",
       "      <td>-84.428067</td>\n",
       "      <td>LGA</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.777245</td>\n",
       "      <td>-73.872608</td>\n",
       "      <td>10.421053</td>\n",
       "      <td>10.001754</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-06</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Boston</td>\n",
       "      <td>42.364347</td>\n",
       "      <td>-71.005181</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>7.136821</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.618972</td>\n",
       "      <td>-122.374889</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>7.404291</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-13</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.618972</td>\n",
       "      <td>-122.374889</td>\n",
       "      <td>JFK</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.639751</td>\n",
       "      <td>-73.778925</td>\n",
       "      <td>14.037037</td>\n",
       "      <td>9.858544</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DateOfDeparture Departure  CityDeparture  LongitudeDeparture  \\\n",
       "0      2011-12-05       ORD        Chicago           41.978603   \n",
       "1      2013-03-01       ATL        Atlanta           33.636719   \n",
       "2      2012-02-06       ORD        Chicago           41.978603   \n",
       "3      2012-12-10       ORD        Chicago           41.978603   \n",
       "4      2012-04-13       SFO  San Francisco           37.618972   \n",
       "\n",
       "   LatitudeDeparture Arrival    CityArrival  LongitudeArrival  \\\n",
       "0         -87.904842     EWR         Newark         40.692500   \n",
       "1         -84.428067     LGA       New York         40.777245   \n",
       "2         -87.904842     BOS         Boston         42.364347   \n",
       "3         -87.904842     SFO  San Francisco         37.618972   \n",
       "4        -122.374889     JFK       New York         40.639751   \n",
       "\n",
       "   LatitudeArrival  WeeksToDeparture    std_wtd  PAX  Year  Month  Day WeekDay  \n",
       "0       -74.168667          8.352941   5.667243    7  2011     12    5  Monday  \n",
       "1       -73.872608         10.421053  10.001754    7  2013      3    1  Friday  \n",
       "2       -71.005181          9.250000   7.136821    7  2012      2    6  Monday  \n",
       "3      -122.374889          8.666667   7.404291    7  2012     12   10  Monday  \n",
       "4       -73.778925         14.037037   9.858544    7  2012      4   13  Friday  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def split_date(date_time):\n",
    "  components = date_time.split('-')\n",
    "  return int(components[0]), int(components[1]), int(components[2])\n",
    "\n",
    "\n",
    "for dataset in [df_train, df_test]:\n",
    "   \n",
    "  years = []\n",
    "  months = []\n",
    "  days = []\n",
    "  weekDays = []\n",
    "\n",
    "  for date in dataset['DateOfDeparture']:\n",
    "      year, month, day = split_date(date)\n",
    "      years.append(year)\n",
    "      months.append(month)\n",
    "      days.append(day)\n",
    "      date_time = datetime.datetime(year, month, day)\n",
    "      week_day = calendar.day_name[date_time.weekday()]\n",
    "      weekDays.append(week_day)\n",
    "    \n",
    "  dataset['Year'], dataset['Month'],dataset['Day'], dataset['WeekDay'] = years, months, days, weekDays\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yvNa1x0A3vbP"
   },
   "source": [
    "### Season Extraction\n",
    "Στη συνέχεια με βάση το μήνα  <u> βρίσκουμε τις εποχές</u> (Χειμώνας, Άνοιξη, Καλοκαίρι, Φθινόπωρο) ['Season']. Αυτό είναι ιδιαίτερα σημαντικό, καθώς πάλι καταφέρνεις να κατηγοριοποιήσεις <u>12 τιμές σε 4</u>. Άλλωστε, είναι λογικό πως η ζήτηση για κάθε πτήση είναι διαφορετική ανά εποχή (πχ διαφορετικοί προορισμοί κυριαρχούν το χειμώνα και διαφορετικοί το καλοκαίρι)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224.0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 748.0,
     "status": "ok",
     "timestamp": 1.546739639288E12,
     "user": {
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    "id": "JTYgqSiQUKxJ",
    "outputId": "740bf3cf-58f9-4992-e425-2abe997085ce"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateOfDeparture</th>\n",
       "      <th>Departure</th>\n",
       "      <th>CityDeparture</th>\n",
       "      <th>LongitudeDeparture</th>\n",
       "      <th>LatitudeDeparture</th>\n",
       "      <th>Arrival</th>\n",
       "      <th>CityArrival</th>\n",
       "      <th>LongitudeArrival</th>\n",
       "      <th>LatitudeArrival</th>\n",
       "      <th>WeeksToDeparture</th>\n",
       "      <th>std_wtd</th>\n",
       "      <th>PAX</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-12-05</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>EWR</td>\n",
       "      <td>Newark</td>\n",
       "      <td>40.692500</td>\n",
       "      <td>-74.168667</td>\n",
       "      <td>8.352941</td>\n",
       "      <td>5.667243</td>\n",
       "      <td>7</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Monday</td>\n",
       "      <td>WINTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>ATL</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>33.636719</td>\n",
       "      <td>-84.428067</td>\n",
       "      <td>LGA</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.777245</td>\n",
       "      <td>-73.872608</td>\n",
       "      <td>10.421053</td>\n",
       "      <td>10.001754</td>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Friday</td>\n",
       "      <td>SPRING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-06</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>BOS</td>\n",
       "      <td>Boston</td>\n",
       "      <td>42.364347</td>\n",
       "      <td>-71.005181</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>7.136821</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>WINTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-10</td>\n",
       "      <td>ORD</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>41.978603</td>\n",
       "      <td>-87.904842</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.618972</td>\n",
       "      <td>-122.374889</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>7.404291</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "      <td>WINTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-13</td>\n",
       "      <td>SFO</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.618972</td>\n",
       "      <td>-122.374889</td>\n",
       "      <td>JFK</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.639751</td>\n",
       "      <td>-73.778925</td>\n",
       "      <td>14.037037</td>\n",
       "      <td>9.858544</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>Friday</td>\n",
       "      <td>SPRING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DateOfDeparture Departure  CityDeparture  LongitudeDeparture  \\\n",
       "0      2011-12-05       ORD        Chicago           41.978603   \n",
       "1      2013-03-01       ATL        Atlanta           33.636719   \n",
       "2      2012-02-06       ORD        Chicago           41.978603   \n",
       "3      2012-12-10       ORD        Chicago           41.978603   \n",
       "4      2012-04-13       SFO  San Francisco           37.618972   \n",
       "\n",
       "   LatitudeDeparture Arrival    CityArrival  LongitudeArrival  \\\n",
       "0         -87.904842     EWR         Newark         40.692500   \n",
       "1         -84.428067     LGA       New York         40.777245   \n",
       "2         -87.904842     BOS         Boston         42.364347   \n",
       "3         -87.904842     SFO  San Francisco         37.618972   \n",
       "4        -122.374889     JFK       New York         40.639751   \n",
       "\n",
       "   LatitudeArrival  WeeksToDeparture    std_wtd  PAX  Year  Month  Day  \\\n",
       "0       -74.168667          8.352941   5.667243    7  2011     12    5   \n",
       "1       -73.872608         10.421053  10.001754    7  2013      3    1   \n",
       "2       -71.005181          9.250000   7.136821    7  2012      2    6   \n",
       "3      -122.374889          8.666667   7.404291    7  2012     12   10   \n",
       "4       -73.778925         14.037037   9.858544    7  2012      4   13   \n",
       "\n",
       "  WeekDay  Season  \n",
       "0  Monday  WINTER  \n",
       "1  Friday  SPRING  \n",
       "2  Monday  WINTER  \n",
       "3  Monday  WINTER  \n",
       "4  Friday  SPRING  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in [df_train, df_test]:\n",
    "  \n",
    "  season = []\n",
    "\n",
    "  for month in dataset['Month']:\n",
    "    if month in [12, 1, 2] : season.append('WINTER')\n",
    "    if month in [3, 4, 5] : season.append('SPRING')\n",
    "    if month in [6, 7, 8] : season.append('SUMMER')\n",
    "    if month in [9, 10, 11] : season.append('FALL')\n",
    "    \n",
    "  dataset['Season'] = season\n",
    "\n",
    "  \n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3W5IYt9314F"
   },
   "source": [
    "### Vacations Extraction\n",
    "---\n",
    "Χρησιμοποιώντας πάλι την ημερομηνία, κρίναμε ότι πρέπει να δοθεί έμφαση και στις <u>σημαντικές περιόδους - εορτές</u>, καθώς η συμπεριφορά των επιβατών τότε τείνει να είναι διαφορετική σε σχέση με τον υπόλοιπο χρόνο. Για το λόγο αυτό δημιουργήσαμε ένα νέο χαρακτηριστικό ['Vacations'], το οποίο εντάσσει κάθε πτήση σε μια από τις παρακάτω κατηγορίες:\n",
    "\n",
    "*   Xmas\n",
    "*   New Year\n",
    "*   Spring Break\n",
    "*   Memorial Day\n",
    "*   Summer Vacations\n",
    "*   Labor Day\n",
    "*   Thanksgiving\n",
    "*   None\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c-VbsnVIcCkY"
   },
   "outputs": [],
   "source": [
    "for dataset in [df_train, df_test]:\n",
    "  \n",
    "  vacations = []\n",
    "\n",
    "  for index, row in dataset.iterrows():\n",
    "    month, day = row['Month'], row['Day']\n",
    "    if month == 12 and day in range(10, 31): \n",
    "        vacations.append('XMAS')\n",
    "    elif month == 1 and day in range(1, 20): \n",
    "        vacations.append('NEW_YEAR')\n",
    "    elif month in [3, 4]:\n",
    "        vacations.append('SPRING_BREAK')\n",
    "    elif month == 5 and day in range(25, 31): \n",
    "        vacations.append('MEMORIAL_DAY')\n",
    "    elif month == 7 or month == 8:\n",
    "        vacations.append('SUMMER_VACATIONS')\n",
    "    elif month == 9 and day in range(1, 7):\n",
    "        vacations.append('LABOR_DAY')\n",
    "    elif month == 11 and day in range(22, 28):\n",
    "        vacations.append('THANKSGIVING')\n",
    "    else:\n",
    "        vacations.append('NONE')\n",
    "        \n",
    "  dataset['Vacations'] = vacations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "llN3oDvj39IS"
   },
   "source": [
    "### Distance Extraction\n",
    "---\n",
    "__Οι επόμενες μεταβλητές με τις οποίες ασχοληθήκαμε είναι το longitude και το latitude.__<br>\n",
    "Μέσα από αυτές τις μεταβλητές, κρίναμε ότι χρειάζεται να δημιουργήσουμε μία πιο σχετική μετρική, μέσα από την οποία θα μπορεί να γίνει κάποια συσχέτιση των πτήσεων. Έτσι, δημιουργήσαμε ένα χαρακτηριστικό ['Distance'], το οποίο θα περιέχει την απόσταση που πρέπει να διανυθεί σε κάθε πτήση (από την πόλη αναχώρησης στην πόλη άφιξης) καθώς πτήσεις με μικρή απόσταση πιθανών να έχουν περισσότερους επιβάτες ή το αντίστροφο. Για τη δημιουργία αυτής της μεταβλητής χρησιμοποιήθηκε η βιβλιοθήκη GeoPy.\n",
    "\n",
    "*Τα ζευγάρια (Latitude, Longitude) είναι ανεστραμμένα στο dataset γι'αυτό και καλούμε τη συνάρτηση με ανεστραμένα ορίσματα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TkmVxmTBhiCY"
   },
   "outputs": [],
   "source": [
    "def calculate_distance(LatitudeDeparture, LongitudeDeparture, LatitudeArrival, LongitudeArrival):\n",
    "    return geopy.distance.distance((LatitudeDeparture, LongitudeDeparture), (LatitudeArrival, LongitudeArrival)).km\n",
    "\n",
    "\n",
    "for dataset in [df_train, df_test]:\n",
    "  \n",
    "  distances = []\n",
    "\n",
    "  for index, row in dataset.iterrows():\n",
    "    distance = calculate_distance(row['LongitudeDeparture'], row['LatitudeDeparture'], row['LongitudeArrival'], row['LatitudeArrival'])\n",
    "    distances.append(int(round(distance, 0)))\n",
    "    \n",
    "  dataset['Distance'] = distances  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0s39_Kn74EMs"
   },
   "source": [
    "### Mean +- std Extraction\n",
    "\n",
    "---\n",
    "\n",
    "Στη συνέχεια ασχολούμαστε με τις μεταβλητές ['WeeksToDeparture'] και ['std_wtd']. Δημιουργήσαμε δύο επιπλέον χαρακτηριστικά ['mean_minus_std'] και [\"mean_plus_std\"] προκειμένου να συνδυάσουμε τις 2 μεταβλητές. Υποθέτοντας πως κάθε πτήση, η διαδικασία των κρατήσεων εισιτηρίων ακολουθεί κανονική κατανομή, τότε ο ΜΟ των εβδομάδων (WeeeksToDeparture) +- την τυπική απόκληση (std_wtd), μας οδηγεί σε δύο νέες τιμές οι οποίες φαίνονται στο σχήμα και μας προσφέρουν μια νέα πληροφορία για κάθε πτήση. Το απεικονιζόμενο διάστημα αποτελεί το 68% των παρατηρήσεων, όπου στη συγκεκριμένη περίπτωση είναι το διάστημα κρατήσεων για κάθε πτήση.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/stratos/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: MatplotlibDeprecationWarning: scipy.stats.norm.pdf\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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+hb+s/YvVMc4yrnckHcOCmPN9ptVRlBtwpOhvApJEJF5EAqi5MLvMvoGI2K9QfQ2wz/Z4GTBRRAJFJB5IAjY2PrbyRAs2HKS8spo7h8dZHaVOn+z9hE/2fmJ1jLP4+/owdVgca/cfZ9ehwvp3UF6t3qJvjKkEZgArgN3AYmPMThF5VkTG25rNEJGdIpJKzXn9qbZ9dwKLgV3A58CDxhgdZqDOUl5Zzbz1BxjZNYKkDiFWx3E7kwbG0sLfl7e1t6/q4dCEa8aY5cDyWtuetnv88Hn2fQF4oaEBlXf4ZNsh8k6X8fLNcVZHcUthwf7cfHE0izZl85ux3YkICbQ6knJRekeuspwxhrfWZJLYvhWXdtVrOg01bXgc5VXVvLfhgNVRlAvToq8stynrBDsPFXLH8DhEXHfIYQv/FrTwb2F1jHPqEtGKy7u35931Byir1LOoqm5a9JXl3v4+k7AW/tzYP9rqKOf12c8/47Off2Z1jPO6c3g8+WfK+eSHw1ZHUS5Ki76yVO7JEr7YdZSJg2JoEeBaN2O5o+GJ7Uhs34q5a7Mw5qxbYpTSoq+s9e76AxhjuM0NbsZ67pvneO6b56yOcV4iwrRhcWzPPcWWgyetjqNckBZ9ZZnSiir+tfEgV/aMJLqN9StS1WdV5ipWZa6yOka9bugfRUiQH3PXZlkdRbkgLfrKMstSD3GyuIJpLnozlrtqGejHrckxfLb9MEcLS62Oo1yMFn1lCWMMb6/NontkCIPj21odx+PcPjSOKmN4b70O31T/TYu+ssTGzAJ2Hy5k2jDXHqbprmLbBTO6e3ve23BQh2+q/6JFX1li3roDhLXwZ0I/95lpu11wO9oFt7M6hsOmDYvneFE5y7fr8E31Hw5Nw6CUMx0+VcLnO49w9yXxbjVMc8ktS6yOcEGGJ7YjIaIlc9ce4AYXvwdCNR/t6atmt2DDQaqN0Tnzm5iIMHVoHD9knyQ1W4dvqhpa9FWzKqusGaY5unt7Ytq6/jBNe0+sfIInVj5hdYwLcuOAKFoG+DJvXZbVUZSL0KKvmtVn24+Qf6ac24fGWR3lgq3LWce6nHVWx7ggIUH+3HRxNJ/8cJjjZ8qsjqNcgBZ91azeWZdFQnhLLkkMtzqK17h9aGfKq6pZuCm7/sbK42nRV81mW85Jth48yW1DO+sC3s0osX0IwxPb8d76A1RWVVsdR1nMoaIvImNFJE1E0kXk8Tpe/6WI7BKRbSKySkQ6271WJSKptp9ltfdV3mPeugMEB/hy08U6kqS53T40jkOnSlm5WxdP93b1DtkUEV9gJnAFNQudbxKRZcaYXXbNtgLJxphiEbkfeAm41fZaiTGmn5NzKzfw0dZcYg+epLyyikdeXEX+mTJuHRhDaJC/1dEaJDrUff+xGt29PZ3Cgpi/PovSiqqfvpdf/ekrfn1VN67v7z73S6jGcWSc/iAg3RiTASAiC4EJ1Kx7C4Ax5mu79uuBKc4MqdzPR1tzeWLpdt623Q16xDYHTHRr112EpD7v3viu1REazM/Xh58P6czLK9JIyTrBO7bvJfdkCU8s3Q6ghd9LOHJ6JwqwvwKUY9t2LncB9itNBIlIioisF5HrG5BRuaGXV6RRUnH27f/vbjhoQRoFMHFgDABllf99Xr+kooqXV6RZEUlZwJGefl1X3OpcnUFEpgDJwKV2m2ONMYdEJAH4SkS2G2P219pvOjAdIDY21qHgyrUdOllyQdvdwSOfPwLAa2NfszhJw7Rrde7F0t35e1EXxpGefg4QY/c8GjhUu5GIjAF+B4w3xvw0INgYc8j23wxgNdC/9r7GmNnGmGRjTHJEhC6M7Qk6neM0zrm2u4PUI6mkHkm1OkajRJyj8Lvz96IujCNFfxOQJCLxIhIATAT+axSOiPQHZlFT8I/ZbW8jIoG2x+HAcOyuBSjP9eurutHC/7/n1Wnh78uvr+pmUSIF8Nuru5/1q7t+L96l3tM7xphKEZkBrAB8gTnGmJ0i8iyQYoxZBrwMtALet02Te9AYMx7oAcwSkWpq/oH5U61RP8pD/XhR0He+UFVtiAwN4vFx3fViocVuGBDNxswCWFDzPKp1Cx2942XE1RZPTk5ONikpKVbHUE5QUl7Fjq79CQsOoOsu9/9OR80dBcDqaastzdFYpRVV7Og6gNAgP7ru3mx1HOUkIrLZGJNcXzudWlk1mX+n5hJXbYgMPfcFRHfStV1XqyM4RZC/LxEhgRw+VcqRU6VEhgVZHUk1I52GQTUJY4ztDlw/Qtz0ZqzaZl83m9nXzbY6hlN0CAkCY1iwUYfQehst+qpJbDl4gl2HC+kQGljnmF9lrSB/H1oHB/CvjQcpr9T5eLyJFn3VJN5Ze4CQQD/CzzM23N1M/3g60z+ebnUMp+kQGkTe6TI+33nE6iiqGWnRV06Xd7qMz3Yc5qaLo/H1oNk09x7fy97je62O4TStg/3p3C6Y+euyrI6impEWfeV0CzcepKLKcNtQXQ7RlQkwZXBnNmWdYNehQqvjqGaiRV85VWVVNe9tOMiIpHC6RLSyOo6qx8+Sown082H++gNWR1HNRIu+cqovdx3lSGGpWy6H6I1aBwdwfb8oPtqay6niCqvjqGagRV851Tvrsohq3YLLu7e3OorT9YvsR79Iz1sa4rahnSmpqOL9zbqcojfQm7OU06QdOc36jAIeH9fdoy7g/shdZ9esT++oMJI7t2H++gPcOTxel7L0cNrTV04zb10WgX4+3JocU29b5VpuHxbHgePFfLMvz+ooqolp0VdOUVhawYdbcxnftxNtWgZYHadJTFk6hSlLPXNRuLG9IokICWTe2iyro6gmpkVfOcWSzTkUl1cxdVic1VGaTE5hDjmFOVbHaBIBfj5MHhTL6r15ZOUXWR1HNSEt+qrRqqsN89cdoH9sa3pHhVkdRzXQ5MGx+Iro8E0Pp0VfNdq3+/LIyC9imgf38r1Bh9AgxvXpyOKUbIrKKq2Oo5qIFn3VaHPXZhEREsi43h2tjqIaadqwzpwurWTp1lyro6gm4lDRF5GxIpImIuki8ngdr/9SRHaJyDYRWSUine1emyoi+2w/U50ZXlkvM7+I1Wl5/HxwLAF+nt2HGBo9lKHRQ62O0aQGxLahT1QY89Zm4WoLLCnnqPdvqYj4AjOBcUBPYJKI9KzVbCuQbIy5CPgAeMm2b1vgGWAwMAh4RkTaOC++sto7a7Pw9xUmD461OkqT++OYP/LHMX+0OkaTEhGmDotj37EzrN1/3Oo4qgk40jUbBKQbYzKMMeXAQmCCfQNjzNfGmGLb0/VAtO3xVcCXxpgCY8wJ4EtgrHOiK6udKavkg805XNOnI+1DdPUlT3HtRR1p1zKAt7/PsjqKagKOFP0owP7+7BzbtnO5C/isgfsqN7Jkcw5nyio9epimvZsW38RNi2+yOkaTC/L3ZdKgWFbtOUp2QXH9Oyi34kjRr+ue7DpP9onIFCAZePlC9hWR6SKSIiIpeXl6R6A7qK42vLMui77RYfSP9Y4zdseLj3O82DtOeUwZ0hlfEeaty7I6inIyR4p+DmB/X300cKh2IxEZA/wOGG+MKbuQfY0xs40xycaY5IiICEezKwt9sy+PjLwi7rwk3uooqglEhgUxtnckCzfp8E1P40jR3wQkiUi8iAQAE4Fl9g1EpD8wi5qCf8zupRXAlSLSxnYB90rbNuXm5qzJpL0O0/Rod14Sz+nSSpZs8cy7kL1VvUXfGFMJzKCmWO8GFhtjdorIsyIy3tbsZaAV8L6IpIrIMtu+BcBz1PzDsQl41rZNubF9R0/z3b58bh/a2eOHaXqzAbFt6BfTmre/z6K6WodvegqHplY2xiwHltfa9rTd4zHn2XcOMKehAZXreXttzWyakwZ5/jBNe6PjR1sdodndMTyOhxemsnrvMS7v3sHqOMoJtJumLsjJ4nKWbsnhhv5RtGsVaHWcZvXUpU/x1KVPWR2jWV3dpyMdQgN1+KYH0aKvLsi/NmZTWlHNtOFxVkdRzcDf14fbh8bx3b589h49bXUc5QRa9JXDKqqqmbcui+GJ7egeGWp1nGY37r1xjHtvnNUxmt3kQbEE+vkwZ02m1VGUE2jRVw77dNthDp8q5S4vHaZZUlFCSUWJ1TGaXZuWAdw4IJqlW3PJP1NW/w7KpWnRVw4xxvDmmgy6RLRkVFfPW/Rcnd9dl8RTXlnN/HU6176706KvHLI+o4AduYXcPSJBF872QontWzG6e3vmrz9AaUWV1XFUI2jRVw5587sM2rUM4Ib+OnWSt7prRDwFReV8qHPtuzUt+qpe6cfOsGrPMaYM6UyQv6/VcSxzbddrubbrtVbHsMzQhHb06hTKm99l6M1abkyLvqrXW2syCfDz4bahnetv7MEeHfYojw571OoYlhER7hmRwP68IlbvPVb/DsoladFX55V/poylW3K4sX8U4V52M5Y62zUXdSQyNIjZ32ZYHUU1kBZ9dV7vrM2ivKqau0ckWB3FcqPmjmLU3FFWx7CUv68Pd14Sx/qMAn7IPml1HNUAWvTVORWVVTJv3QGu6NGBxPatrI6jXMSkQbGEBPkx69v9VkdRDaBFX53Tok3ZnCqp4N5Lu1gdRbmQkCB/pgzpzGc7jpCZX2R1HHWBtOirOlVUVfPWmkwGxbXl4s7esTKWctwdw+Pw9/Hh/77Tc/vuRou+qtMn2w6Re7KEey/Vc/nqbO1Dgrjp4ig+2JzDsdOlVsdRF0CLvjqLMYZZ32TQtUMrLuumUy786JZet3BLr1usjuEy7hmRQEVVNXN12mW34lDRF5GxIpImIuki8ngdr48UkS0iUikiN9d6rcq2mtZPK2op1/Z12jH2HDnNvSO76JQLdh4Y+AAPDHzA6hguIyGiFWN7RTJ//QEKSyusjqMcVG/RFxFfYCYwDugJTBKRnrWaHQSmAQvqeIsSY0w/28/4Ol5XLsQYw9+/Sie6TQvG9+tkdRyXUlxRTHFFsdUxXMoDoxI5XVqpE7G5EUd6+oOAdGNMhjGmHFgITLBvYIzJMsZsA6qbIKNqRuv2H2frwZPcd2kX/H317J+9q9+7mqvfu9rqGC6lT3QYo7pFMGdNJiXlOhGbO3Dkb3UUkG33PMe2zVFBIpIiIutF5PoLSqea3T++Tqd9SCA3XxxtdRTlJmZclsjxonL+tfGg1VGUAxwp+nWd1L2Q2ZZijTHJwGTgNRE5a9C3iEy3/cOQkpeXdwFvrZxpy8ETrN1/nOkjE7x6YjV1YZLj2jI4vi2zv82grFJ7+67OkaKfA8TYPY8GDjn6AcaYQ7b/ZgCrgf51tJltjEk2xiRHREQ4+tbKyWZ+lU6bYH8mD461OopyMzMuT+RIYSlLt+i0y67OkaK/CUgSkXgRCQAmAg6NwhGRNiISaHscDgwHdjU0rGo6O3JPsWrPMe4cHk9wgJ/VcZSbuSQxnL4xrXl9dToVVXppz5XVW/SNMZXADGAFsBtYbIzZKSLPish4ABEZKCI5wM+AWSKy07Z7DyBFRH4Avgb+ZIzRou+C/rpqH6FBfkwdHmd1FJc1rd80pvWbZnUMlyQiPDw6keyCEj7U3r5Lc6hLZ4xZDiyvte1pu8ebqDntU3u/tUCfRmZUTWxH7im+3HWUX17RldAgf6vjuCwt+Od3Wbf29I0O4+9f7+OGAVE6+stF6beieG3lXsJa+DNNe/nnlV+cT35xvtUxXJaI8MiYrmQXlLBkc47VcdQ5aNH3cttzTrFy9zHuviRee/n1uHnxzdy8+Ob6G3qxUd0i6Bsdxj++Tqe8Us/tuyIt+l5Oe/nKmX7s7eecKGHJFu3tuyIt+l4sNfskq/Yc454R8YRoL185yahuEfSNac0/vkrXcfsuSIu+F3t5xR7atgxg6rA4q6MoDyIi/OqKruSeLGHBBr1L19Vo0fdS36fn8336cR4Y1UV7+crpRiSFMyShLf/4Kp2iskqr4yg7WvS9kDGGl1ak0SksiClDOlsdx23cn3w/9yffb3UMtyAi/GZsd44XlTNnTabVcZQdvfXSC63YeZQfsk/y55v66Bw7F+DW3rdaHcGtDIhtw5geHZj9bQZThnSmTcsAqyMptKfvdaqqDa98kUZCREtuGqAzaV6I7FPZZJ/Krr+h+smvr+ohBqL8AAAUrklEQVTGmfJK/vnNfqujKBst+l5myZYc9h07w6NXdsNP75i8ILd9eBu3fXib1THcSrfIEG7oF8XctVkcOllidRyFFn2vUlxeyV9WpNEvpjXjekdaHUd5iV9e2RUD/GVFmtVRFFr0vcrsbzM4drqMJ6/pgYiufauaR3SbYO4cHs/SrblszzlldRyvp0XfSxwrLGXWNxmM6x1Jclxbq+MoL/PAZV1o2zKAF5bvwpgLWYNJOZsWfS/xyhd7qayu5rGx3a2OorxQaJA/j4xJYn1GASt3H7M6jlfTIZteYPfhQhZvzuaOYfHEhbe0Oo7b+tXQX1kdwa1NGhTL3LVZ/PGz3YzqFqFTL1tE/697OGMMv1+2k7AW/jw0OtHqOG7tum7XcV2366yO4bb8fX343dU9yMgr4p21WVbH8VoOFX0RGSsiaSKSLiKP1/H6SBHZIiKVInJzrdemisg+289UZwVXjvlk22E2ZBbw6JXdaB2sN8c0Rlp+Gmn5OgKlMS7v3p7LukXw2sp9HDtdanUcr1Rv0RcRX2AmMA7oCUwSkZ61mh0EpgELau3bFngGGAwMAp4RkTaNj60cUVRWyQuf7qZXp1AmDdLFzhvr3k/u5d5P7rU6hlsTEZ6+rhflldX86bM9VsfxSo709AcB6caYDGNMObAQmGDfwBiTZYzZBtReNeEq4EtjTIEx5gTwJTDWCbmVA2Z+nc6RwlKendALXx8doqlcQ3x4S+4aEc/SLblsPlBgdRyv40jRjwLs7z3PsW1zRGP2VY2QmV/Em99lcmP/KC7urEM0lWuZcVkikaFBPLNsJ1XVOoSzOTlS9OvqIjr6LTm0r4hMF5EUEUnJy8tz8K3VuRhjeOqjHQT4+fD4OB2iqVxPy0A/fndND3bkFjJ/XZbVcbyKI0U/B4ixex4NHHLw/R3a1xgz2xiTbIxJjoiIcPCt1bl8uDWXNen5PDa2G+1Dg6yOo1Sdrr2oIyO7RvDyijSdl6cZOVL0NwFJIhIvIgHARGCZg++/ArhSRNrYLuBeadummkhBUTnPf7qb/rGt+flgnSvfmZ4c+SRPjnzS6hgeQ0R44freVBnDM8t2Wh3Ha9Rb9I0xlcAMaor1bmCxMWaniDwrIuMBRGSgiOQAPwNmichO274FwHPU/MOxCXjWtk01kReX76awpII/3tgHH71461RjEsYwJmGM1TE8SkzbYP5nTFe+3HWUz3ccsTqOV3DojlxjzHJgea1tT9s93kTNqZu69p0DzGlERuWgten5fLA5hwdGdaF7ZKjVcTxO6pFUAPpF9rM4iWe565J4/p16iGeW7WBYYjtCdfnOJqV35HqIM2WV/GbJNuLaBfPQ6CSr43ikRz5/hEc+f8TqGB7Hz9eHP93Uh7zTZTz/yS6r43g8Lfoe4sXlu8k9WcJfftZXl0BUbuei6NbcP6oLi1Ny+GrPUavjeDQt+h7g2715LNhwkHtGJOi0ycptPTQ6ie6RITy+ZDsni8utjuOxtOi7uVMlFTy2ZBuJ7Vvxyyu6Wh1HqQYL9PPllVv6UlBUzu91NE+T0aLv5n6/bCfHTpfxip7WUR6gV6cwfnF5Eh+lHuLTbYetjuORdD59N7Z0Sw4fbs3lkTFJ9I1pbXUcj/fi6BetjuAVHrisC1+nHePxpdu4KDqMmLbBVkfyKNrTd1OZ+UU8+dEOBsW35ReX62id5jAsZhjDYoZZHcPj+fv68PdJ/cHAwwu3UlFVex5H1Rha9N1QeWU1v/jXFgL8fPjrxH46g2YzWZu9lrXZa62O4RVi2gbz4o192HLwJH9duc/qOB5FT++4oT99tocduYXMvu1iOoa1sDqO1/jtqt8CsHraamuDeInr+nbiu315zFydzpCEdlySFG51JI+gPX03s+yHQ8z5PpNpw+K4slek1XGUalK/H9+LpPat+MW/tpBzotjqOB5Bi74b2XOkkMc+2MbAuDb89uoeVsdRqskFB/jxzykXU1lluP/dLZRWVFkdye1p0XcTp0oquHf+ZkKC/Jg5eQABfvrVKe+QENGKV2/tx/bcUzz10Q6M0UVXGkMrhxuorKrm4YVbOXSyhDemDNA58pXXuaJnBx66PJH3N+cwb90Bq+O4Nb2Q6+KMMTz7yS5Wp+Xx4g19dOlDC7029jWrI3i1h8d0ZdfhQv7w8U5i2wZzWff2VkdyS9rTd3Fvf5/FvHUHmD4ygcmDY62O49X6RfbTaZUt5Osj/HVif3p0DGXGgi3sOlRodSS3pEXfha3cdZTnPt3FVb068PhYXevWaiszVrIyY6XVMbxay0A/3po6kJAgf+56ZxNHC0utjuR2HCr6IjJWRNJEJF1EHq/j9UARWWR7fYOIxNm2x4lIiYik2n7+6dz4nmtjZgEz/rWFPlFhvHZrf10FywU8/+3zPP/t81bH8HqRYUHMmTaQwpIKbn9ro87IeYHqLfoi4gvMBMYBPYFJItKzVrO7gBPGmETgf4E/27223xjTz/Zzn5Nye7Qduae4a+4mOrVuwdvTBtIiQCdSU8pez06hzL49mcz8Iqa+vYkzZZVWR3IbjvT0BwHpxpgMY0w5sBCYUKvNBOAd2+MPgNEiol3TBkg/dobb52wktIU/7941mHatAq2OpJRLGp4Yzt8n92dH7immz0vRMfwOcqToRwHZds9zbNvqbGNbSP0U0M72WryIbBWRb0RkRCPzerSMvDNMeXMDPiK8e/dgOrXWKRaUOp+rekXy8s0XsXb/ce57d7MWfgc4UvTr6rHXvjviXG0OA7HGmP7AL4EFInLWit0iMl1EUkQkJS8vz4FInmfv0dPcMms9FVXVvHv3IOLDW1odSSm3cOOAaP54Yx++2ZvH3e+kUFKuhf98HCn6OUCM3fNo4NC52oiIHxAGFBhjyowxxwGMMZuB/cBZyzsZY2YbY5KNMckREREXfhRubtehQibOXo+PwKJ7h9A98qx/F5ULmHXtLGZdO8vqGKoOkwbF8tJNF/H9/nymvb2RIj3Hf06OFP1NQJKIxItIADARWFarzTJgqu3xzcBXxhgjIhG2C8GISAKQBGQ4J7pn2JhZwMTZ6wj082HRvUNJbB9idSR1Dt3Cu9EtvJvVMdQ5/Cw5htdu7UfKgRNMfnMD+WfKrI7kkuot+rZz9DOAFcBuYLExZqeIPCsi423N3gLaiUg6NadxfhzWORLYJiI/UHOB9z5jTIGzD8JdfbrtMFPe3EB4SCCL7x2qp3Rc3MdpH/Nx2sdWx1DnMaFfFG/8fAB7Dhdy0xtrycovsjqSyxFXm7woOTnZpKSkWB2jSRljeGtNJs9/upvkzm34v9uTadMywOpYTWPUqJr/rl5tZQqnGDV3FOAh8+l70PdSl80HTnD3O5sQEd6cmsyA2DZWR2pyIrLZGJNcXzu9I7eZlVZU8ej723j+092M6x3Ju3cP9tyCr5RFLu7chiX3D6NVoB8TZ69n8abs+nfyElr0m1HuyRJ+9s91LNmSw8Ojk5g5eQBB/nrjlVJNISGiFR89OJxBcW35zZJtPPnRdsordb1dnWWzmXy15yiPvr+Nispq/u/2ZK7o2cHqSEp5vLYtA5h7x0Be/iKNWd9ksPNQIX+b2J+YtsFWR7OM9vSbWGlFFc/8ewd3zk2hQ2gQH80YrgVfqWbk5+vDE+N6MHPyANKPneHqv37HR1tzrY5lGe3pN6FtOSf59fvbSDt6mrsuiec3Y7sR6Kenc9zV/BvmWx1BNcI1F3Wkb0wY/7MolUcWpfLVnmP8fnwv2nrZNTUt+k2guLySV7/Yy5zvM4kICeSdOwdxaVfvu+nM08SExdTfSLm06DbB/OueIby+ej9//2ofa9Lzefrankzo1wlvmS5Mi74TGWP4YtdRnv90F9kFJUweHMvj47oTGuRvdTTlBIt2LALg1t63WpxENYafrw8PjU7iql6RPLZkG48sSmXp1lyevrYnie1bWR2vyWnRd5Jdhwp57pNdrMs4TmL7ViyaPoTBCe3q31G5jTdS3gC06HuKbpEhLLl/GPPWZfHqF3u56rVvuW1IZx4Zk0TrYM895aNFv5Ey84v426p9/Ds1l9AW/jw7oReTB8Xi56vXyJVydb4+wh3D47mubyde/XIv89ZlsXRLDveMSGDa8DhCPPC3dC36DZR+7Az//GY/S7fkEODnw90jEnhwVCJhwZ73h0QpTxfeKpAXb+jDbUM688oXabzy5V7e+j6Te0YkMGVIZ8JaeM7fay36F8AYw/qMAt78LoNVe44R6OfDHcPjuffSBNqHBFkdTynVSD06hvLm1IH8kH2S11bu5eUVabz+dTq3DIzhzuHxHjG+X4u+A04UlbN0ay6LNh1k79EztG0ZwMOjk7htaGfCdWUrpTxO35jWvH3HIHbknuKtNZnMX3eAuWuzuLRrBBMHxjK6R3v83fQUrk64dg6lFVV8tecYH/9wiFW7j1FeVU3fmNZMHhTDhH5ROn2CozxoYq/84nwAwoPDLU7iBB70vTSHI6dKeW/DARanZHO0sIzwVoFce1FHruvbiQGxrV1iuKejE65pT9/OyeJyVqflsXL3Ub7ec4yi8irCWwUweXAstw6MoUdHXdzEm3lEsVcNEhkWxK+u7MbDo5P4Zm8ei1OyWbDxIHPXZhHVugVX9urA6O4dGBTflgA/1/4NwKuLflllFVsPnmRtej7f7z9OavZJqqoN4a0Cua5vJ67r24nB8W11JI4CYG7qXACm9ZtmaQ5lHT9fH0b36MDoHh0oLK3gy51H+XT7YRZsOMjb32cREujHkC7tGN6lHcMTw0ls38olfguw5zVF3xhDzokSduSeYmv2SbYcOMG23FOUV1bjI9AnujX3X9qF0T3a0ze6NT4+rvVFKetp0Vf2QoP8ueniaG66OJri8kq+Tz/OV3uOsiY9ny93HQWgTbA/A2LbMKBzG/pGt6ZXp1DLp1J3qOiLyFjgr4Av8KYx5k+1Xg8E5gEXA8eBW40xWbbXngDuAqqAh4wxK5yWvg7GGI4WlpGZX0R63hn2HT3N3qOn2XWokMLSmnUzA3x96B0VytShnRkY15bBCe08akiWUqp5BQf4cUXPDj9NpphdUMy6/cdJOVDA5gMnWLXn2E9to1q3oEfHEJI6hNC1Qyu6RLQiLrxls925X2/Rt61xOxO4gpoF0DeJyDJjzC67ZncBJ4wxiSIyEfgzcKuI9KRmTd1eQCdgpYh0NcY4fbn6Y4Wl3D5nIweOF1NS8Z+3bxXoR1KHVlzbtxO9OoXSu1MY3SJD9EKsUqrJxLQNJqZtMLcMrJmv6WRxOTtyC9l56BQ7DhWSdqSQb/bmUVH1n4E0bVsGMDwxnL9P6t+k2Rzp6Q8C0o0xGQAishCYANgX/QnA722PPwD+ITUnsiYAC40xZUCmbQ3dQcA658T/j9bBAUS3acHwxHDiwlsS1y6YxPatiAwNcrlzakop79I6OIBLksK5JOk/gwEqqqrJyi8iI7+IA8eLyMwvpm3Lpu/tO1L0owD7tcZygMHnamOMqRSRU0A72/b1tfaNanDa8wjw8+HNqQOb4q2VUsrp/H19SOpQc5qnOTlS9OvqJtce3H+uNo7si4hMB6YDxMbGOhBJqea3/OfLrY6gVKM5MhYxB7CfSDwaOHSuNiLiB4QBBQ7uizFmtjEm2RiTHBGh884r1xTsH0ywv/vfhq+8myNFfxOQJCLxIhJAzYXZZbXaLAOm2h7fDHxlam71XQZMFJFAEYkHkoCNzomuVPN6fdPrvL7pdatjKNUo9Z7esZ2jnwGsoGbI5hxjzE4ReRZIMcYsA94C5tsu1BZQ8w8DtnaLqbnoWwk82BQjd5RqDot3LgbggYEPWJxEqYZzaJy+MWY5sLzWtqftHpcCPzvHvi8ALzQio1JKKSfR+QWUUsqLaNFXSikvokVfKaW8iMvNpy8iecCBRrxFOJDvpDhW8pTjAD0WV+Upx+IpxwGNO5bOxph6x7y7XNFvLBFJcWQhAVfnKccBeiyuylOOxVOOA5rnWPT0jlJKeREt+kop5UU8sejPtjqAk3jKcYAei6vylGPxlOOAZjgWjzunr5RS6tw8saevlFLqHDyu6IvIcyKyTURSReQLEelkdaaGEpGXRWSP7Xg+FJHWVmdqKBH5mYjsFJFqEXG7kRYiMlZE0kQkXUQetzpPY4jIHBE5JiI7rM7SGCISIyJfi8hu25+th63O1FAiEiQiG0XkB9ux/KHJPsvTTu+ISKgxptD2+CGgpzHmPotjNYiIXEnNjKWVIvJnAGPMYxbHahAR6QFUA7OAR40xKRZHcphtydC92C0ZCkyqtWSo2xCRkcAZYJ4xprfVeRpKRDoCHY0xW0QkBNgMXO+O34ttpcGWxpgzIuIPrAEeNsasr2fXC+ZxPf0fC75NS+pYtMVdGGO+MMZU2p6up2Y9ArdkjNltjEmzOkcD/bRkqDGmHPhxyVC3ZIz5lprZcN2aMeawMWaL7fFpYDdNtDJfUzM1ztie+tt+mqR2eVzRBxCRF0QkG/g58HR97d3EncBnVofwUnUtGeqWxcVTiUgc0B/YYG2ShhMRXxFJBY4BXxpjmuRY3LLoi8hKEdlRx88EAGPM74wxMcB7wAxr055ffcdia/M7atYjeM+6pPVz5FjclEPLfipriEgrYAnwSK3f9N2KMabKGNOPmt/oB4lIk5x6c2g+fVdjjBnjYNMFwKfAM00Yp1HqOxYRmQpcC4w2Ln4B5gK+F3fj0LKfqvnZzn8vAd4zxiy1Oo8zGGNOishqYCzg9IvtbtnTPx8RSbJ7Oh7YY1WWxhKRscBjwHhjTLHVebyYI0uGqmZmu/j5FrDbGPOq1XkaQ0QifhydJyItgDE0Ue3yxNE7S4Bu1IwUOQDcZ4zJtTZVw9iWnwwEjts2rXfjkUg3AH8HIoCTQKox5iprUzlORK4GXuM/S4a67WpwIvIvYBQ1MzoeBZ4xxrxlaagGEJFLgO+A7dT8fQf4rW2lP7ciIhcB71Dz58sHWGyMebZJPsvTir5SSqlz87jTO0oppc5Ni75SSnkRLfpKKeVFtOgrpZQX0aKvlFJeRIu+Ukp5ES36SinlRbToK6WUF/l/6xIUhK77Zm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = 0\n",
    "variance = 1\n",
    "sigma = math.sqrt(variance)\n",
    "x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
    "plt.plot(x,mlab.normpdf(x, mu, sigma))\n",
    "plt.axvline(-1, color='r')\n",
    "plt.axvline(1, color='r')\n",
    "plt.axvline(0, color='g', linestyle='--')\n",
    "plt.scatter([-1, 1], [0.25, 0.25])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TDtw9h9rzFvy"
   },
   "outputs": [],
   "source": [
    "for dataset in [df_train, df_test]:\n",
    "  mean_minus_std = []\n",
    "  mean_plus_std = []\n",
    "\n",
    "  for index, row in dataset.iterrows():\n",
    "      mean_minus_std.append(row['WeeksToDeparture']-row['std_wtd'])\n",
    "      mean_plus_std.append(row['WeeksToDeparture'] + row['std_wtd'])\n",
    "    \n",
    "  dataset['mean_minus_std'] = mean_minus_std\n",
    "  dataset['mean_plus_std'] = mean_plus_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Ov2qFlM41q8"
   },
   "source": [
    "## Configure Test\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "<p>Μέσα από την παρακάτω διαδικασία επιλέγουμε ποιες μεταβλητές από το dataset θέλουμε να κρατήσουμε, προκειμένου να εκπαιδεύσουμε το μοντέλο μας. </p>\n",
    "Για να μπορεί να δουλέψει ένα αλγόριθμος ταξινόμησης είναι απαραίτητο το training και το test set να έχουν ακριβώς τον ίδιο αριθμό στηλών (ίδιες μεταβλητές)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gZ4-jTmIzwXh"
   },
   "outputs": [],
   "source": [
    "if TEST:\n",
    "    y_train = df_train[['PAX']]\n",
    "\n",
    "    df_train.drop(df_train.columns[[0,2,6,9,10,11]], axis=1, inplace=True)\n",
    "\n",
    "    df_test.drop(df_test.columns[[0,2,6,9,10]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RXCKS-Hp4mhJ"
   },
   "source": [
    "## Configure Development\n",
    "---\n",
    "Αντίστοιχα, όπως και στη διαδικασία του testing, έτσι και εδώ, επιλέγουμε τα features που θέλουμε να κρατήσουμε κατά την διαδικασία του training. Αυτή τη φορά διαχωρίζουμε όμως το training set ώστε στα δεδομένα ελέγχου να έχουμε και τις αντίστοιχες κατηγορίες με στόχο να παρατηρούμε τη πορεία του μοντέλου. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o8cvzebnzdHS"
   },
   "outputs": [],
   "source": [
    "if DEVELOP:\n",
    "    y_train = df_train[['PAX']]\n",
    "\n",
    "    df_train, df_test, y_train, y_test = train_test_split(df_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "    y_test = df_test[['PAX']]\n",
    "\n",
    "    df_train.drop(df_train.columns[[0,2,6,9,10,11]], axis=1, inplace=True)\n",
    "\n",
    "    df_test.drop(df_test.columns[[0,2,6,9,10,11]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9zoB-ZrtmLKa"
   },
   "source": [
    "## Form Data Types\n",
    "\n",
    "---\n",
    "\n",
    "Τελειώνοντας με τη δημιουργία σύνθετων χαρακτηριστικών, είναι απαραίτητο να επεξεργαστούμε τη μορφή τους, ούτως ώστε να εναρμονίζονται μεταξύ τους και να ταιριάζουν στο μοντέλο πρόβλεψης."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Osy-FMTy4YtZ"
   },
   "source": [
    "#### Normalization Function\n",
    "<p>Ξεκινώντας με τα <b>ποσοτικά χαρακτηριστικά</b> δημιουργούμαι συνάρτηση η οποία υλοποιεί την <u>κανονικοποίησή</u> τους.\n",
    "</p >\n",
    "\n",
    "Σε αυτό το σημείο κάνουμε μια κανονικοποίηση των τιμών σε συγκεκριμένα features με σκοπό οι τιμές αυτών να βρίσκονται στο ίδιο εύρος.\n",
    "Εκτελούμε αυτή τη διαδικασία καθώς γνωρίζουμε ότι είναι πολύ σημαντικό για τους classifiers κατά τη διαδικασία του trainning να διαχειρίζονται τιμές οι οποίες έχουν το ίδιο εύρος. Χρησιμοποιήσαμε των παρακάτω τύπο για την κανονικοποίησης για να 'φέρουμε' τις τιμές στο 0-1:\n",
    "\n",
    "\\begin{equation*}\n",
    "z_i = \\frac{x_i + min(x)}{max(x) - min(x)}\n",
    "\\end{equation*}\n",
    "\n",
    "όπου χ είναι το μη-κονονικοποιημένο feature, $x_i$ οι μη κανονικοποιημένες τιμές το feature x και $z_i$ οι αντίστοιχες κανονικοποιημένες τιμές για το feature x που επεξεργαζόμαστε."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rr9XWARb1M9e"
   },
   "outputs": [],
   "source": [
    "def normalize_feature(feature_name, dataset):\n",
    "    min_dataset = dataset[feature_name].min()\n",
    "    max_dataset = dataset[feature_name].max()\n",
    "    new_dataset = []\n",
    "\n",
    "    for index, row in dataset.iterrows():\n",
    "        new_dataset.append((row[feature_name] - min_dataset)/(max_dataset - min_dataset))\n",
    "        \n",
    "    dataset[feature_name] = new_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WZS_aum74eA_"
   },
   "source": [
    "### Call Normalization Function \n",
    "\n",
    "---\n",
    "\n",
    "Σε αυτό το σημείο επιλέγουμε τα ποσοτικά χαρακτηριστικά που θέλουμε να κανονικοποιήσουμε και καλούμε την παραπάνω συνάρτηση.\n",
    "<p> Τα χαρακτηριστικά αυτά είναι οι μεταβλητές 'Distance', 'LongitudeDeparture', 'LatitudeDeparture', 'LongitudeArrival', 'LatitudeArrival', 'Day', 'mean_minus_std' και 'mean_plus_std'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224.0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4833.0,
     "status": "ok",
     "timestamp": 1.546739682156E12,
     "user": {
      "displayName": "ΧΡΥΣΑ ΝΑΜΠΟΥΡΗ",
      "photoUrl": "https://lh3.googleusercontent.com/-VSvDMzx3WzQ/AAAAAAAAAAI/AAAAAAAAAAo/osqeNrmZPO4/s64/photo.jpg",
      "userId": "07430830951696944933"
     },
     "user_tz": -120.0
    },
    "id": "dIqp4k1TzUPh",
    "outputId": "8ae63014-b458-4e00-bd4b-f5a411675300"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Departure</th>\n",
       "      <th>LongitudeDeparture</th>\n",
       "      <th>LatitudeDeparture</th>\n",
       "      <th>Arrival</th>\n",
       "      <th>LongitudeArrival</th>\n",
       "      <th>LatitudeArrival</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>Season</th>\n",
       "      <th>Vacations</th>\n",
       "      <th>Distance</th>\n",
       "      <th>mean_minus_std</th>\n",
       "      <th>mean_plus_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>IAH</td>\n",
       "      <td>0.193537</td>\n",
       "      <td>0.526253</td>\n",
       "      <td>ORD</td>\n",
       "      <td>0.747393</td>\n",
       "      <td>0.671019</td>\n",
       "      <td>2011</td>\n",
       "      <td>11</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>FALL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.293958</td>\n",
       "      <td>0.375088</td>\n",
       "      <td>0.342760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>DFW</td>\n",
       "      <td>0.328023</td>\n",
       "      <td>0.493226</td>\n",
       "      <td>LAS</td>\n",
       "      <td>0.475015</td>\n",
       "      <td>0.140601</td>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>Friday</td>\n",
       "      <td>FALL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.345499</td>\n",
       "      <td>0.537907</td>\n",
       "      <td>0.265810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>LAX</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>JFK</td>\n",
       "      <td>0.685569</td>\n",
       "      <td>0.946004</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>Friday</td>\n",
       "      <td>SUMMER</td>\n",
       "      <td>SUMMER_VACATIONS</td>\n",
       "      <td>0.909001</td>\n",
       "      <td>0.878578</td>\n",
       "      <td>0.846036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4081</th>\n",
       "      <td>LAX</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>SEA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>Friday</td>\n",
       "      <td>FALL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.305302</td>\n",
       "      <td>0.568213</td>\n",
       "      <td>0.515269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5505</th>\n",
       "      <td>DFW</td>\n",
       "      <td>0.328023</td>\n",
       "      <td>0.493226</td>\n",
       "      <td>DEN</td>\n",
       "      <td>0.649638</td>\n",
       "      <td>0.344594</td>\n",
       "      <td>2011</td>\n",
       "      <td>9</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>Monday</td>\n",
       "      <td>FALL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0.181258</td>\n",
       "      <td>0.571421</td>\n",
       "      <td>0.334462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Departure  LongitudeDeparture  LatitudeDeparture Arrival  \\\n",
       "2755       IAH            0.193537           0.526253     ORD   \n",
       "1941       DFW            0.328023           0.493226     LAS   \n",
       "3424       LAX            0.376310           0.077221     JFK   \n",
       "4081       LAX            0.376310           0.077221     SEA   \n",
       "5505       DFW            0.328023           0.493226     DEN   \n",
       "\n",
       "      LongitudeArrival  LatitudeArrival  Year  Month       Day    WeekDay  \\\n",
       "2755          0.747393         0.671019  2011     11  0.266667  Wednesday   \n",
       "1941          0.475015         0.140601  2012     11  0.033333     Friday   \n",
       "3424          0.685569         0.946004  2012      8  0.066667     Friday   \n",
       "4081          1.000000         0.001277  2012      9  0.666667     Friday   \n",
       "5505          0.649638         0.344594  2011      9  0.366667     Monday   \n",
       "\n",
       "      Season         Vacations  Distance  mean_minus_std  mean_plus_std  \n",
       "2755    FALL              NONE  0.293958        0.375088       0.342760  \n",
       "1941    FALL              NONE  0.345499        0.537907       0.265810  \n",
       "3424  SUMMER  SUMMER_VACATIONS  0.909001        0.878578       0.846036  \n",
       "4081    FALL              NONE  0.305302        0.568213       0.515269  \n",
       "5505    FALL              NONE  0.181258        0.571421       0.334462  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_use = ['Distance', 'LongitudeDeparture', 'LatitudeDeparture', 'LongitudeArrival', 'LatitudeArrival', 'Day', 'mean_minus_std', 'mean_plus_std']\n",
    "\n",
    "for dataset in [df_train, df_test]:\n",
    "  for feature in features_to_use:\n",
    "    normalize_feature(feature, dataset)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JAaACzp2oDUp"
   },
   "source": [
    "### Transform String Features to Values\n",
    "\n",
    "---\n",
    "\n",
    "Στη συνέχεια μετασχηματίζουμε τα __ποιοτικά χαρακτηριστικά__. \n",
    "<p> Οι στήλες των training και test set περιέχουν κατηγορικές μεταβλητές των οποίων οι τιμές είναι αλφαριθμητικά. Οι αλγόριθμοι ταξινόμησης ωστόσο δουλεύουν μόνο με αριθμητικές τιμές, οπότε χρειάστηκε να γίνει κάποιος μετασχηματισμός.</p>\n",
    "<p><u> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nAGRPUv66h7l"
   },
   "source": [
    "#### 1) Transofrm strings to values with LabelEncoder\n",
    "\n",
    "---\n",
    "\n",
    "Η πρώτη προσέγγιση για την αριθμητική αναπαράσταση των κατηγορικών μεταβλητών ήταν με την τεχνική __LabelEncoder__. </u>\n",
    "<p>Ουσιαστικά, η συνάρτηση αυτή βάζει αριθμητικές ετικέτες σε όλες τις πιθανές τιμές ενός χαρακτηριστικού. Για παράδειγμα, στη μεταβλητή ['Departure'] θα μπορούσε να αναπαραστήσει τη τιμή 'ORD' με 1, την τιμή 'ATL' με 2 και ούτω καθεξής. Επειδή και η μεταβλητή ['Arrival'] περιέχει τις ίδιες τιμές, τότε η κωδικοποίηση των τιμών των δύο μεταβλητών θα είναι η ίδια. <p>\n",
    "  <p> Το πρόβλημα με τη συγκεκριμένη κωδικοποίηση-αναπαράσταση των κατηγορικών μεταβλητών, έγκειται στο γεγονός ότι ο ταξινομητής μας μπορεί να θεωρήσει μια ιεραρχία στα δεδομένα μας. Σύμφωνα και με το παραπάνω παράδειγμα, αν 1='ORD' και 2='ATL', ο ταξινομητής μας μπορεί 'μπερδευτεί' συγκρίνοντας αυτό το feature αλγεβρικά, δηλαδή 1< 2< 3 κλπ. </p>\n",
    "  <p>Στη πραγματικότητα όμως δεν υπάρχει τέτοια συσχέτιση καθώς η κάθε τιμή είναι ανεξάρτητη από τις υπόλοιπες και υποδηλώνει κάτι ξεχωριστό. Παρ' όλα αυτά κρατάμε την συγκεκριμένη πληρoφορία αλλά στη πορεία την 'εμπλουτίζουμε' με τη διαδικασία του one-hot encoding ώστε να λύσουμε το προαναφερθέν πρόβλημα.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Py7dZVyh6mEs"
   },
   "outputs": [],
   "source": [
    "def encode_feature(feature_name):\n",
    "    encoder = LabelEncoder()\n",
    "    encoder.fit(df_train[feature_name])\n",
    "   \n",
    "    df_train[feature_name] = encoder.transform(df_train[feature_name])\n",
    "    df_test[feature_name] = encoder.transform(df_test[feature_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cphiJAGYxYhU"
   },
   "source": [
    "Παράλληλα, η παραπάνω μέθοδος του LabelEncoder, αξιοποιείται τελικά με βάση μία διαφορετική προσέγγιση. Χρησιμοποιείται <u>βοηθητικά</u>, προκειμένου να υλοποιήσουμε μία <u>τεχνική μείωσης διάστασης</u> και συγκεκριμένα την <u> PCA</u>. Οι μεταβλητές που θέλουμε να συνδυάσουμε είναι οι 'Departure', 'Arrival', 'Year', 'Month', 'WeekDay', 'Day', 'Season' και 'Vacations'. Σκοπός είναι να δημιουργήσουμε ένα επιπλέον χαρακτηριστικό, το οποίο θα περιέχει την συμπιεσμένη πληροφορία των προηγούμενων. Τέλος, το κανονικοποιούμε, ώστε να είναι στο ίδιο εύρος τιμών με τα υπόλοιπα."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rvxYpFIVuD1-"
   },
   "outputs": [
    {
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       "      <th>LongitudeDeparture</th>\n",
       "      <th>LatitudeDeparture</th>\n",
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       "      <th>LatitudeArrival</th>\n",
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       "      <th>Distance</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>7</td>\n",
       "      <td>0.193537</td>\n",
       "      <td>0.526253</td>\n",
       "      <td>15</td>\n",
       "      <td>0.747393</td>\n",
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       "      <td>0.289542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>4</td>\n",
       "      <td>0.328023</td>\n",
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       "      <td>0.033333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.345499</td>\n",
       "      <td>0.537907</td>\n",
       "      <td>0.265810</td>\n",
       "      <td>0.370916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>10</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>8</td>\n",
       "      <td>0.685569</td>\n",
       "      <td>0.946004</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.909001</td>\n",
       "      <td>0.878578</td>\n",
       "      <td>0.846036</td>\n",
       "      <td>0.552714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4081</th>\n",
       "      <td>10</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>18</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.305302</td>\n",
       "      <td>0.568213</td>\n",
       "      <td>0.515269</td>\n",
       "      <td>0.286350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5505</th>\n",
       "      <td>4</td>\n",
       "      <td>0.328023</td>\n",
       "      <td>0.493226</td>\n",
       "      <td>3</td>\n",
       "      <td>0.649638</td>\n",
       "      <td>0.344594</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.181258</td>\n",
       "      <td>0.571421</td>\n",
       "      <td>0.334462</td>\n",
       "      <td>0.530541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Departure  LongitudeDeparture  LatitudeDeparture  Arrival  \\\n",
       "2755          7            0.193537           0.526253       15   \n",
       "1941          4            0.328023           0.493226        9   \n",
       "3424         10            0.376310           0.077221        8   \n",
       "4081         10            0.376310           0.077221       18   \n",
       "5505          4            0.328023           0.493226        3   \n",
       "\n",
       "      LongitudeArrival  LatitudeArrival  Year  Month       Day  WeekDay  \\\n",
       "2755          0.747393         0.671019     0     10  0.266667        6   \n",
       "1941          0.475015         0.140601     1     10  0.033333        0   \n",
       "3424          0.685569         0.946004     1      7  0.066667        0   \n",
       "4081          1.000000         0.001277     1      8  0.666667        0   \n",
       "5505          0.649638         0.344594     0      8  0.366667        1   \n",
       "\n",
       "      Season  Vacations  Distance  mean_minus_std  mean_plus_std  embending  \n",
       "2755       0          3  0.293958        0.375088       0.342760   0.289542  \n",
       "1941       0          3  0.345499        0.537907       0.265810   0.370916  \n",
       "3424       2          5  0.909001        0.878578       0.846036   0.552714  \n",
       "4081       0          3  0.305302        0.568213       0.515269   0.286350  \n",
       "5505       0          3  0.181258        0.571421       0.334462   0.530541  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_feature = ['Departure', 'Arrival', 'Year', 'Month', 'WeekDay', 'Season', 'Vacations']\n",
    "\n",
    "for feature_name in columns_to_feature:\n",
    "    encode_feature(feature_name)\n",
    "    \n",
    "extra_train_df = pd.concat([df_train['Departure'], df_train['Arrival'], df_train['Year'], df_train['Month'], df_train['WeekDay'], df_train['Day'], df_train['Season'], df_train['Vacations']], axis=1)\n",
    "extra_test_df = pd.concat([df_test['Departure'], df_test['Arrival'], df_test['Year'], df_test['Month'], df_test['WeekDay'], df_test['Day'], df_test['Season'], df_test['Vacations']], axis=1)\n",
    "\n",
    "pca = PCA(n_components=1)\n",
    "pca.fit(extra_train_df)\n",
    "\n",
    "X_train_extra = pca.transform(extra_train_df)\n",
    "df_train['embending'] = X_train_extra\n",
    "normalize_feature('embending', df_train)\n",
    "\n",
    "X_test_extra = pca.transform(extra_test_df)\n",
    "df_test['embending'] = X_test_extra\n",
    "normalize_feature('embending', df_test)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AISIotRKx9xf"
   },
   "source": [
    "Παρακάτω, με την ίδια λογική, υλοποιούμε διάφορα νέα χαρακτηριστικά με τη μέθοδο PCA, συνδυάζοντας διάφορες μεταβλητές, προκειμένου να δούμε κατά πόσο μπορούμε να μειώσουμε τις διαστάσεις χωρίς, όμως, να χάσουμε πληροφορία από τα δεδομένα.\n",
    "<p> Τα επικρατέστερα ήταν αυτά που συνδύασαν τις μεταβλητές 'LongitudeDeparture', 'LatitudeDeparture' και 'Distance' σε ένα χαρακτηριστικό ['ldd'], και παρόμοια για τα χαρακτηριστικά 'LongitudeDeparture', 'LatitudeDeparture' και 'Distance' σε ένα άλλο χαρακτηριστικό ['lad']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224.0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2050.0,
     "status": "ok",
     "timestamp": 1.546739709123E12,
     "user": {
      "displayName": "ΧΡΥΣΑ ΝΑΜΠΟΥΡΗ",
      "photoUrl": "https://lh3.googleusercontent.com/-VSvDMzx3WzQ/AAAAAAAAAAI/AAAAAAAAAAo/osqeNrmZPO4/s64/photo.jpg",
      "userId": "07430830951696944933"
     },
     "user_tz": -120.0
    },
    "id": "eyHmwYAjuD2E",
    "outputId": "e28aa559-9aaa-4e06-c677-4f22ffd770e8"
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=1)\n",
    "pca.fit(pd.concat([df_train['LongitudeDeparture'], df_train['LatitudeDeparture'], df_train['Distance']], axis=1))\n",
    "ldd_train = pca.transform(pd.concat([df_train['LongitudeDeparture'], df_train['LatitudeDeparture'], df_train['Distance']], axis=1))\n",
    "ldd_test = pca.transform(pd.concat([df_test['LongitudeDeparture'], df_test['LatitudeDeparture'], df_test['Distance']], axis=1))\n",
    "    \n",
    "df_train['ldd'] = ldd_train\n",
    "normalize_feature('ldd', df_train)\n",
    "\n",
    "df_test['ldd'] = ldd_test\n",
    "normalize_feature('ldd', df_test)\n",
    "\n",
    "\n",
    "pca = PCA(n_components=1)\n",
    "pca.fit(pd.concat([df_train['LongitudeArrival'], df_train['LatitudeArrival'], df_train['Distance']], axis=1))\n",
    "lad_train = pca.transform(pd.concat([df_train['LongitudeArrival'], df_train['LatitudeArrival'], df_train['Distance']], axis=1))\n",
    "lad_test = pca.transform(pd.concat([df_test['LongitudeArrival'], df_test['LatitudeArrival'], df_test['Distance']], axis=1))\n",
    "\n",
    "df_train['lad'] = lad_train\n",
    "normalize_feature('lad', df_train)\n",
    "\n",
    "df_test['lad'] = lad_test\n",
    "normalize_feature('lad', df_test)\n",
    "\n",
    "corr = df_train.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aX52ubnE2DOI"
   },
   "source": [
    "#### 2) Transform strings features to 1 hot Vectors\n",
    "\n",
    "---\n",
    "Εν τέλει, έχοντας καταλήξει στα τελικά features, για να λυθεί το πρόβλημα των ποιοτικών μεταβλητών, χρησιμοποιήσαμε την τεχνική κωδικοποίησης <b>1-hot-Vectors</b>, την οποία υλοποιούμαι στη συνέχεια. <u>Όμως παράλληλα κρατάμε την πληροφορία του label encoded feature και την κανονικοποιούμε.</u>\n",
    "Ουσιαστικά, με την τεχνική __1-hot-Vectors__ δημιουργείται μια ξεχωριστή μεταβλητή για κάθε πιθανή τιμή ενός χαρακτηριστικού. Ανάλογα με την τρέχουσα τιμή του, μια από αυτές τις μεταβλητές είναι 1, ενώ όλες οι υπόλοιπες παραμένουν 0.</p>\n",
    "  <p> Παραδείγματος χάρη, η μεταβλητή ['Season'] περιέχει 4 πιθανές τιμές (Χειμώνας, Καλοκαίρι, Άνοιξη, Φθινόπωρο). Κατά συνέπεια θα δημιουργηθούν 4 νέες μεταβλητές. Έτσι, αν η πτήση 1 πραγματοποιηθεί χειμώνα, τότε στη μεταβλητή ['Season_Winter'] θα υπάρχει η τιμή 1, ενώ στις υπόλοιπες εποχές η τιμή 0.</p>\n",
    "  <p> Παρακάτω εφαρμόζουμε one-hot encoding στις μεταβλητές 'Departure', 'Arrival',  'Year', 'Month', 'WeekDay', 'Season' και 'Vacations'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253.0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1048.0,
     "status": "ok",
     "timestamp": 1.54673975786E12,
     "user": {
      "displayName": "ΧΡΥΣΑ ΝΑΜΠΟΥΡΗ",
      "photoUrl": "https://lh3.googleusercontent.com/-VSvDMzx3WzQ/AAAAAAAAAAI/AAAAAAAAAAo/osqeNrmZPO4/s64/photo.jpg",
      "userId": "07430830951696944933"
     },
     "user_tz": -120.0
    },
    "id": "2WObLVHczm6b",
    "outputId": "057337e5-940f-498c-bfb6-590a990db91f"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Departure</th>\n",
       "      <th>LongitudeDeparture</th>\n",
       "      <th>LatitudeDeparture</th>\n",
       "      <th>Arrival</th>\n",
       "      <th>LongitudeArrival</th>\n",
       "      <th>LatitudeArrival</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>WeekDay</th>\n",
       "      <th>...</th>\n",
       "      <th>Season_2</th>\n",
       "      <th>Season_3</th>\n",
       "      <th>Vacations_0</th>\n",
       "      <th>Vacations_1</th>\n",
       "      <th>Vacations_2</th>\n",
       "      <th>Vacations_3</th>\n",
       "      <th>Vacations_4</th>\n",
       "      <th>Vacations_5</th>\n",
       "      <th>Vacations_6</th>\n",
       "      <th>Vacations_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2755</th>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.193537</td>\n",
       "      <td>0.526253</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.747393</td>\n",
       "      <td>0.671019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.328023</td>\n",
       "      <td>0.493226</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.475015</td>\n",
       "      <td>0.140601</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.685569</td>\n",
       "      <td>0.946004</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4081</th>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.376310</td>\n",
       "      <td>0.077221</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001277</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5505</th>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.328023</td>\n",
       "      <td>0.493226</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.649638</td>\n",
       "      <td>0.344594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Departure  LongitudeDeparture  LatitudeDeparture   Arrival  \\\n",
       "2755   0.368421            0.193537           0.526253  0.789474   \n",
       "1941   0.210526            0.328023           0.493226  0.473684   \n",
       "3424   0.526316            0.376310           0.077221  0.421053   \n",
       "4081   0.526316            0.376310           0.077221  0.947368   \n",
       "5505   0.210526            0.328023           0.493226  0.157895   \n",
       "\n",
       "      LongitudeArrival  LatitudeArrival  Year     Month       Day   WeekDay  \\\n",
       "2755          0.747393         0.671019   0.0  0.909091  0.266667  1.000000   \n",
       "1941          0.475015         0.140601   0.5  0.909091  0.033333  0.000000   \n",
       "3424          0.685569         0.946004   0.5  0.636364  0.066667  0.000000   \n",
       "4081          1.000000         0.001277   0.5  0.727273  0.666667  0.000000   \n",
       "5505          0.649638         0.344594   0.0  0.727273  0.366667  0.166667   \n",
       "\n",
       "         ...       Season_2  Season_3  Vacations_0  Vacations_1  Vacations_2  \\\n",
       "2755     ...              0         0            0            0            0   \n",
       "1941     ...              0         0            0            0            0   \n",
       "3424     ...              1         0            0            0            0   \n",
       "4081     ...              0         0            0            0            0   \n",
       "5505     ...              0         0            0            0            0   \n",
       "\n",
       "      Vacations_3  Vacations_4  Vacations_5  Vacations_6  Vacations_7  \n",
       "2755            1            0            0            0            0  \n",
       "1941            1            0            0            0            0  \n",
       "3424            0            0            1            0            0  \n",
       "4081            1            0            0            0            0  \n",
       "5505            1            0            0            0            0  \n",
       "\n",
       "[5 rows x 92 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_formulation(feature_name, df_train, df_test):\n",
    "    new_df_train = pd.concat([df_train, pd.get_dummies(df_train[feature_name], prefix=feature_name)], axis=1)\n",
    "    # new_df_train.drop([feature_name], axis=1, inplace=True)\n",
    "    normalize_feature(feature_name, new_df_train)\n",
    "    new_df_test = pd.concat([df_test, pd.get_dummies(df_test[feature_name], prefix=feature_name)], axis=1)\n",
    "    # new_df_test.drop([feature_name], axis=1, inplace=True)\n",
    "    normalize_feature(feature_name, new_df_test)\n",
    "    \n",
    "    return new_df_train, new_df_test\n",
    "    \n",
    "features = ['Departure', 'Arrival', 'Year', 'Month', 'WeekDay', 'Season', 'Vacations']\n",
    "\n",
    "for feature_name in features:\n",
    "    df_train, df_test = one_hot_formulation(feature_name, df_train, df_test)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "klMPQwWmuD2b"
   },
   "source": [
    "## Data Visualization\n",
    "\n",
    "Σε αυτό το σημείο, εφόσον έχουμε μετασχηματίσει τα δεδομένα του dataset μας με τρόπο που να προσφέρουν περισσότερη πληροφορία, μπορούμε να δούμε αν πλέον είναι διαχωρίσιμα. Αυτό που παρατηρούμε είναι ότι έχουν διαχωριστεί σε υποομάδες, αλλά μέσα σε αυτές υπάρχουν ακόμα παραδείγματα σχεδόν από όλες τις κατηγορίες."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "38jpSjd4uD2d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SHOW_GRAPH = True\n",
    "\n",
    "if SHOW_GRAPH:\n",
    "    tsne = TSNE(n_components=2)\n",
    "    transformed_tsne_data = tsne.fit_transform(df_train)\n",
    "\n",
    "    tsne_dataframe = pd.DataFrame(transformed_tsne_data)\n",
    "    pax_dataframe = pd.DataFrame(y_train)\n",
    "    \n",
    "    tsne_dataframe = pd.concat([tsne_dataframe, pax_dataframe], axis=1, ignore_index=True)\n",
    "\n",
    "    tsne_dataframe.columns = ['X', 'Y', 'label']\n",
    "\n",
    "    tsne_dataframe['label'] = tsne_dataframe['label'].astype(str)\n",
    "\n",
    "    chart = ggplot(tsne_dataframe, aes(x='X', y='Y', color='label')) + geom_point(alpha=0.8) + ggtitle(\"tSNE reduction\")\n",
    "    chart.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Diagram\n",
    "---\n",
    "\n",
    "Στο παρακάτω διάγραμμα φαίνεται η συσχέτιση μεταξύ των features που χρησιμοποιούμε στο μοντέλο μας. Μεγάλη συσχέτιση μεταξύ δύο features θα έδινε και μη ακριβή αποτελέσματα. Αντιθέτως παρατηρούμε ότι κανένα από τα features δεν έχει μεγάλη συσχέτιση με κάποιο άλλο."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7bf7b68978>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr, \n",
    "            cmap=\"RdBu_r\",\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "\n",
    "---\n",
    "\n",
    "Με αυτό το τρόπο παρατηρούμε ποιες κατηγορίες 'κυριαρχούν' στο dataset και κατά συνέπεια ποιες κατηγορίες έχουν περισσότερα παραδείγματα εκπαίδευσης, όπου θα μας βοηθήσουν να προβλέψουμε σωστά και με μεγαλύτερη πιθανότητα, τη συγκεκριμένη κατηγορία. \n",
    "Αντίθετα βλέπουμε ότι η κατηγορία PAX=4 έχει πολύ λιγότερα παραδείγματα εκπαίδευσης σε σχέση με τις υπόλοιπες πράγμα το οποίο σημαίνει ότι θα είναι δύσκολο για τον ταξινομητή μας να τη προβλέψει σωστά. Για αυτό το λόγω πριν δώσουμε τα δεδομένα εκπαίδευσης στους ταξινομητές μας, ενισχύουμε τα παραδείγματα στις κατηγορίες αυτές με τη μέθοδο του OverSampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pd.value_counts(y_train.values.flatten()).plot(kind='bar'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-RgWPBPNuD2g"
   },
   "source": [
    "### Scikit learn classifiers\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oQFHo8YQuD2g"
   },
   "source": [
    "Παρακάτω ξεκινήσαμε να εκπαιδεύουμε το μοντέλο μας με απλούς classifiers από το scickit learn. Πιο συγκεκριμένα ξεκινήσαμε εφαρμόζοντας Linear Regression, Logistic Regression, DecisionTreeClassifier, KNeighborsClassifier και GradientBoostingClassifier, όπου και μας έδινε την μεγαλύτερη ακρίβεια (περίπου 43%). Στη συνέχεια χρησιμοποιήσαμε ένα συνδυασμό των παραπάνω classifiers προκειμένου οι προβλέψεις μας να είναι πιο γενικές. Χρησιμοποιώντας, λοιπόν, ensembling και κάνοντας όλους τους δυνατούς συνδυασμούς καταφέραμε να αυξήσουμε την ακρίβεια του μοντέλου μας στο 46%. Μερικά από τα αποτελέσματα φαίνονται παρακάτω."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0qGK2Ns3uD2k"
   },
   "source": [
    "### Ensemble Method (Logistic Regression - Linear Regression - Decision Tree - KNeighbors) Results\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "Συνδυάσαμε τους εν λόγω ταξινομητές δίνοντάς τους κάποιο βάρος, προκειμένου να δημιουργήσουμε ένα γενικευμένο μοντέλο.\n",
    "\n",
    "Αρχικά πριν τον συνδυασμό τους, δοκιμάσαμε τον κάθε ένα ξεχωριστά και πήραμε τα εξής αποτελέσματα:\n",
    "\n",
    "\n",
    "   \n",
    "\n",
    "|<center>Classifier   </center>                                        | Time      |    Score          \n",
    "|----------------------------------------------------------------------|:--------------:|---------------------:|\n",
    "      | <p align=\"left\"><b>LogisticRegression </b> (no PCA)                                          |    0.530132    | 0.4353932584269663   |\n",
    "  |<p align=\"left\"> <b>LogisticRegression </b> (PCA 0.95%) |    1.249181    | 0.3634831460674157    |   \n",
    "      | <p align=\"left\"><b> LogisticRegression</b> (X_train + PCA (mean_minus_std, mean_plus_std )    |    0.474485    | 0.4365168539325842   |       \n",
    "      |<p align=\"left\"> <b> LogisticRegression</b> (X_train + PCA (mean_minus_std mean_plus_std)                  + PCA (LongitudeDeparture LatitudeDeparture Distance)                                      \n",
    "|&emsp;&emsp;&emsp; &emsp; &emsp;&emsp;&emsp;  &emsp; &emsp; &emsp; &emsp; &emsp;&emsp;&emsp;+ PCA (LongitudeArrival LatitudeArrival Distance) )     |    0.585910    |  0.4370786516853933 |  \n",
    "      |<p align=\"left\"><b> GradientBoostingClassifier</b> (X_train + PCA (mean_minus_std  mean_plus_std                                      + PCA (LongitudeDeparture  LatitudeDeparture  Distance)                                   \n",
    "|&emsp;&emsp;&emsp; &emsp; &emsp;&emsp;&emsp;  &emsp; &emsp; &emsp; &emsp; &emsp;&emsp;&emsp; + PCA (LongitudeArrival LatitudeArrival   Distance)        )   |    230.314923  | 0.4505617977528089   |                                             \n",
    "      |<p align=\"left\"><b> DecisionTreeClassifier</b> (X_train + PCA (mean_minus_std   mean_plus_std)            + PCA (LongitudeDeparture  LatitudeDeparture Distance)                                          \n",
    "| &emsp; &emsp; &emsp; &emsp;&emsp;  &emsp; &emsp; &emsp; &emsp; &emsp;&emsp;&emsp; + PCA (LongitudeArrival   LatitudeArrival  Distance)        )             |    0.249568    | 0.4533707865168539   |                           \n",
    "      |<p align=\"left\"> <b>KNeighborsClassifier</b> (X_train + PCA (mean_minus_std  mean_plus_std)                                 + PCA (LongitudeDeparture  LatitudeDeparture  Distance)                                       \n",
    "| &emsp; &emsp;&emsp;&emsp; &emsp; &emsp; &emsp; &emsp; &emsp;&emsp;&emsp;&emsp;+ PCA (LongitudeArrival LatitudeArrival        Distance)        )               |    3.224896    | 0.4157303370786517   |     \n",
    "      |<p align=\"left\"><b>LogisticRegression     + (x1) DecisionTreeClassifier + (x2)</b> (X_train + PCA (mean_minus_std mean_plus_std)\n",
    "|  &emsp; &emsp;&emsp;&emsp; &emsp; &emsp; &emsp;&emsp; &emsp; &emsp; &emsp;&emsp;+ PCA (LongitudeDeparture  LatitudeDeparture Distance)                           +  PCA (LongitudeArrival LatitudeArrival   Distance)        )      |    0.708108    | 0.4606741573033708  |\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kbv75AoH6wZy"
   },
   "outputs": [],
   "source": [
    "EXEC = False\n",
    "\n",
    "X_train = df_train\n",
    "X_test = df_test\n",
    "y_train = np.ravel(y_train)\n",
    "\n",
    "if EXEC:   \n",
    "    start = time.time()\n",
    "    \n",
    "    # -- Logistic Regression -- #\n",
    "    clf = LogisticRegression()    \n",
    "    # clf.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    # -- Linear Regression -- #\n",
    "    clf1 = LinearRegression()\n",
    "    # clf1.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    clf2 = GradientBoostingClassifier(n_estimators=600, max_depth=None, learning_rate = 0.1)\n",
    "    # clf2.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    # -- Desicion Tree --#\n",
    "    clf3 = DecisionTreeClassifier()\n",
    "    # clf3.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    # -- Knn Classifier --#\n",
    "    clf4 = KNeighborsClassifier(n_neighbors=15, algorithm='ball_tree')\n",
    "    clf4.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    estimators = []\n",
    "    estimators.append(('logistic', clf))\n",
    "    # estimators.append(('linear', clf1))\n",
    "    estimators.append(('DSC', clf3))\n",
    "    # estimators.append(('KNN', clf4))\n",
    "\n",
    "    # ensemble = VotingClassifier(estimators, weights=[1,2])\n",
    "    # ensemble.fit(X_train_2_fit, y_train)\n",
    "\n",
    "    # y_pred = ensemble.predict(X_test_2_fit)\n",
    "\n",
    "    y_pred = clf4.predict(X_test_2_fit)\n",
    "\n",
    "    if DEVELOP:\n",
    "        print(f1_score(y_test, y_pred, average='micro'))\n",
    "    elif TEST:\n",
    "        write_csv(y_pred=y_pred)\n",
    "    \n",
    "    end = time.time()\n",
    "    print('time: {0:f}'.format(end - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z8PPHQzB46GZ"
   },
   "source": [
    "## Calculate F1\n",
    "---\n",
    "Η παρακάτω συνάρτηση χρησιμοποιείται στο νευρωνικό μας μοντέλο το οποίο περιγράφεται παρακάτω, σαν μετρική για να μας δείχνει σε κάθε εποχή το f1_score που πετυχαίνει στα δεδομένα εκπαίδευσης.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZxETn7_lz3qe"
   },
   "outputs": [],
   "source": [
    "def f1_score_metric(y_true, y_pred):\n",
    "\n",
    "    # Count positive samples.\n",
    "    c1 = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    c2 = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    c3 = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "\n",
    "    # If there are no true samples, fix the F1 score at 0.\n",
    "    if c3 == 0:\n",
    "        return 0\n",
    "\n",
    "    # How many selected items are relevant?\n",
    "    precision = c1 / c2\n",
    "\n",
    "    # How many relevant items are selected?features_to_use = ['Distance', 'LongitudeDeparture', 'LatitudeDeparture', 'LongitudeArrival', 'LatitudeArrival', 'mean_minus_std', 'mean_plus_std', 'ticket']\n",
    "\n",
    "    recall = c1 / c3\n",
    "\n",
    "    # Calculate f1_score\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "    return f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZsEl-tdw5DVi"
   },
   "source": [
    "### Voting method\n",
    "---\n",
    "Η παρακάτω μέθοδος λαμβάνει ως ορίσματα τρεις πίνακες οι οποίοι έχουν δημιουργηθεί μετά την εκπαίδευση του Νευρωνικού μας δικτύου και περιέχουν τις προβλέψεις του μοντέλου μας για την ζητούμενη τιμή της μεταβλητής \"PAX\". Πιο συγκεκριμένα γίνεται ένας συμψηφισμός των αποτελεσμάτων με στόχο να δημιουργηθεί ένας νέος πίνακας που προβλέπει καλύτερα το \"PAX\". Έχουμε εκτιμήσει ότι η βελτίωση είναι της τάξεως του 0.5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2NJswUicuD2w"
   },
   "outputs": [],
   "source": [
    "def voting(y1, y2, y3):\n",
    "    y_best = y1.copy()\n",
    "    \n",
    "    for i in range(len(y1)):\n",
    "        if y2[i] == y3[i]:\n",
    "            y_best[i] = y2[i]\n",
    "        \n",
    "    return y_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KV8L_hF_uD20"
   },
   "source": [
    "### Neural Network\n",
    "---\n",
    "Αφού μελετήσαμε τους απλούς classifiers αποφασίσαμε να ασχοληθούμε με τα νευρωνικά δίκτυα. Παρακάτω έχουμε υλοποιήσει κάποια sequential models. Επιλέξαμε να χρησιμοποιήσουμε αρκετά μεγάλο Dropout, καθώς τα δεδομένα εκπαίδευσης κρίνονται σχετικά λίγα. Σε συνδυασμό με την μέθοδο \"__voting()__\" το μοντέλο μας παρήγαγε αποτελέσματα με ακρίβεια σχεδόν 65%. Για τη βελτιστοποίηση του Νευρωνικού δικτύου πειραματιστήκαμε αρκετά με τις παραμέτρους. Χρησιμοποιήσαμε διαφορετικούς optimizers και καταλήξαμε στον Adam και τον Adadelta. Επίσης, χρησιμοποιήσαμε τους \"zeros\" και \"Ones\" initializers οι οποίοι αρχικοποιούν με συγκεκριμένο τρόπο τα βάρη του νευρωνικού σε κάθε επίπεδο. Στον παρακάτω πίνακα φαίνονται μερικά από τα αποτελέσματα που παρήγαμε τοπικά, αλλά και στην ιστοσελίδα του διαγωνισμού."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "raw",
    "id": "2NsTBMhEuD21"
   },
   "source": [
    "| DEVELOPMENT |        |                                   |                             |          |           |              |\n",
    "|-------------|--------|-----------------------------------|-----------------------------|----------|-----------|--------------|\n",
    "| Optimizer   | Epochs | Dropout                           | Layers                      | f1 train | Acc train | f1 predict   |\n",
    "| Adam        | 30     | 0.3 – 0.3                         |(128,64)-(128,32)            | 0.7455   | 0.7525    | 0.5278       |\n",
    "| Adam        | 15     | 0.3 – 0.3                         |(128,64,32)-(64,16)          | 0.6357   | 0.6581    | 0.5359619506 |\n",
    "| Adam        | 15     | (0.3, 0.2) – (0.3, 0.2)           |(256,128,64,16) -(256,128,64,32) | 0.6145   | 0.6431 | 0.5243       |\n",
    "| sgd         | 30     | (0.3, 0.2) – (0.3, 0.2)           |(256,128,64)- (256,64,32,16)     | 0.6092   | 0.6331 | 0.5225       |\n",
    "| rm          | 30     | (0.3, 0.2) – (0.3, 0.2)           |(256,128,64,32,16)-(128,32,16)   | 0.668    | 0.686  | 0.5349       |\n",
    "| rm          | 30     | (0.3, 0.1) – (0.3, 0.1)           |(128,64,16)-(64,16)              | 0.68     | 0.7    | 0.5428       |\n",
    "| rm          | 25     | (0.3, 0.1) – (0.3, 0.1)           |(128,32,16)-(128,64,32)   | 0.66     | 0.68      | 0.5314       |\n",
    "| rm          | 35     | (0.3, 0.2, 0.1) – (0.3, 0.2 0.1)   |(256,64,32)-(128,64,16)    | 0.65     | 0.67      | 0.5452       |\n",
    "| rm          | 30     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   | (256,64,32)-(128,64,16)    | 0.6546   | 0.6731    | 0.534        |\n",
    "| sgd         | 30     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   |  (128,64,32,16)-(128,64,32,16) | 0.6      | 0.56  | 0.5222       |\n",
    "| adam        | 30     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   |(128,64,32,16)-(128,64,32)    |0.6899   | 0.707     | 0.537        |\n",
    "| adam        | 40     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   |(256,128,64,32)-(128,64,32,16) | 0.7373   | 0.7446  | 0.5443       |\n",
    "| adam        | 40     | (0.3, 0.2, 0,1) - (0.3, 0.2, 0,1) | (128,64,32) - (128,64,16)   | 0.6808   | 0.6986    | 0.5467       |\n",
    "| adam        | 50     |       (0.2, - , 0,1)             | (64,32,16)                  | 0.706    | 0.7179    | 0.57         |\n",
    "| adam        | 40     | (0.2, 0.2, 0.2, 0.2, 0.2)         | (512, 256, 128, 64, 32, 16) | 0.77     | 0.7837    | 0.5366       \n",
    "| adam        | 50     | (0.3, 0.2, 0,1) - (0.3, 0.2, 0,1) | (128,64,32) - (128,64,16)   | 0.7087   | 0.7172    | 0.555        |\n",
    "| adam        | 40     | (0.2, 0.2, 0.1, 0.2, 0.2)         | (256, 128, 64, 32, 16)      | 0.70     | 0.69      | 0.529        |\n",
    "| adam        | 40     | (0.2, 0.2, 0.2, 0.2)              | (512, 256, 128, 32, 16)     | 0.78     | 0.79      | 0.5328       |\n",
    "| adam        | 40     | (0.2, 0.2, 0.2)                   | (128, 64, 32, 16)           |0.795     |0.82       | 0.518        |\n",
    "| adam        | 40     | (0.4, 0.2, 0.2)                   | (128, 64, 32, 16)           |0.72    |  0.73       | 0.48         |\n",
    "| adam        | 40     | (0.2, 0.2, 0.2, 0.2 0.2)          | (512, 64, 128, 32, 16)      |0.69   |    0.71      | 0.552        |\n",
    "| adam        | 40     | (0.35, 0.25)                      | (256, 128, 32)              |  0.746 |   0.762     | 0.513        |\n",
    "|             |        |                               TEST|                              |          |           |              |\n",
    "| adam        | 35     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   |  (256,128,32,16)-(128,64,16)| 0.6237   | 0.6537   | 0.57             |\n",
    "| rm          | 35     | (0.3, 0.2 0.1) – (0.3, 0.2 0.1)   |(128,64,32,16)-(256,128,64,32)| 0.6545   | 0.6363  | 0.5889           |\n",
    "| adam        | 50     | (0.3,0.2,0.1)                     | (256,128,64,32) | 0.6566   | 0.6751    | 0.5988    |\n",
    "| adam        | 50     | (0.3,0.2,.0,1)                    | (128,64,32,16)  | 0.7409   | 0.7446    | 0.58083   |\n",
    "| adam        | 40     | (0.35, 0.25)                      | (256, 128, 32)              | 0.68     | 0.6912    | 0.6          |\n",
    "| adam        | 40     | (0.2, 0.2, 0.2, 0.2, 0.2)         | (512, 256, 128, 64, 32, 16) | 0.7305   | 0.7515    | 0.5          |\n",
    "| adam        | 150    | (0.2, 0.2, 0.2, 0.2, 0.2)         | (512, 256, 128, 64, 32, 16) | 0.6873  | 0.6965| 0.52         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Τέλος μετά από πολλούς πειραματισμούς καταλήξαμε στη παρακάτω αρχιτεκτονική η οποία έδωσε και τα παρακάτω αποτελέσματα\n",
    "---\n",
    "Με το υπάρχων μοντέλο και με αρκετές δοκιμές στον αριθμό των layers κάθε νευρωνικού καθώς και σε optimizers, kernel initializers καταλήξαμε να πετυχαίνουμε αποτελέσματα μεγαλύτερα του 60%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='mlp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 7806.0
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 91354.0,
     "status": "ok",
     "timestamp": 1.546741906813E12,
     "user": {
      "displayName": "ΧΡΥΣΑ ΝΑΜΠΟΥΡΗ",
      "photoUrl": "https://lh3.googleusercontent.com/-VSvDMzx3WzQ/AAAAAAAAAAI/AAAAAAAAAAo/osqeNrmZPO4/s64/photo.jpg",
      "userId": "07430830951696944933"
     },
     "user_tz": -120.0
    },
    "id": "XeNhdEyw0CaR",
    "outputId": "805b3588-8b05-4d2a-8803-759ddbed78c1",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "8763/8763 [==============================] - 1s 74us/step - loss: 1.7417 - acc: 0.3235 - f1_score_metric: nan\n",
      "Epoch 2/75\n",
      "8763/8763 [==============================] - 0s 38us/step - loss: 1.3765 - acc: 0.4404 - f1_score_metric: nan\n",
      "Epoch 3/75\n",
      "8763/8763 [==============================] - 0s 35us/step - loss: 1.2012 - acc: 0.4966 - f1_score_metric: 0.3550\n",
      "Epoch 4/75\n",
      "8763/8763 [==============================] - 0s 38us/step - loss: 1.1100 - acc: 0.5381 - f1_score_metric: 0.4067\n",
      "Epoch 5/75\n",
      "8763/8763 [==============================] - 0s 36us/step - loss: 1.0460 - acc: 0.5556 - f1_score_metric: 0.4449\n",
      "Epoch 6/75\n",
      "8763/8763 [==============================] - 0s 35us/step - loss: 1.0035 - acc: 0.5790 - f1_score_metric: 0.4813\n",
      "Epoch 7/75\n",
      "8763/8763 [==============================] - 0s 40us/step - loss: 0.9611 - acc: 0.5918 - f1_score_metric: 0.4965\n",
      "Epoch 8/75\n",
      "8763/8763 [==============================] - 0s 38us/step - loss: 0.9274 - acc: 0.6007 - f1_score_metric: 0.5290\n",
      "Epoch 9/75\n",
      "8763/8763 [==============================] - 0s 35us/step - loss: 0.8954 - acc: 0.6189 - f1_score_metric: 0.5509\n",
      "Epoch 10/75\n",
      "8763/8763 [==============================] - 0s 32us/step - loss: 0.8825 - acc: 0.6250 - f1_score_metric: 0.5639\n",
      "Epoch 11/75\n",
      "8763/8763 [==============================] - 0s 40us/step - loss: 0.8560 - acc: 0.6365 - f1_score_metric: 0.5823\n",
      "Epoch 12/75\n",
      "8763/8763 [==============================] - 0s 37us/step - loss: 0.8386 - acc: 0.6434 - f1_score_metric: 0.5952\n",
      "Epoch 13/75\n",
      "8763/8763 [==============================] - 0s 35us/step - loss: 0.8213 - acc: 0.6553 - f1_score_metric: 0.6122\n",
      "Epoch 14/75\n",
      "8763/8763 [==============================] - 0s 32us/step - loss: 0.8027 - acc: 0.6596 - f1_score_metric: 0.6180\n",
      "Epoch 15/75\n",
      "8763/8763 [==============================] - 0s 32us/step - loss: 0.7966 - acc: 0.6603 - f1_score_metric: 0.6246\n",
      "Epoch 16/75\n",
      "8763/8763 [==============================] - 0s 33us/step - loss: 0.7733 - acc: 0.6666 - f1_score_metric: 0.6343\n",
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      "Epoch 56/75\n",
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      "Epoch 57/75\n",
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      "Epoch 58/75\n",
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      "Epoch 61/75\n",
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      "Epoch 62/75\n",
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      "Epoch 63/75\n",
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      "Epoch 64/75\n",
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      "Epoch 65/75\n",
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      "Epoch 66/75\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 0s 35us/step - loss: 0.5377 - acc: 0.7744 - f1_score_metric: 0.7676\n",
      "Epoch 67/75\n",
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      "Epoch 68/75\n",
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      "Epoch 70/75\n",
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      "Epoch 73/75\n",
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      "Epoch 1/28\n",
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      "Epoch 1/20\n",
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      "Epoch 17/20\n",
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      "Epoch 19/20\n",
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      "Epoch 1/80\n",
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      "Epoch 8/80\n"
     ]
    },
    {
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     "text": [
      "8763/8763 [==============================] - 1s 67us/step - loss: 1.1244 - acc: 0.5255 - f1_score_metric: 0.4022\n",
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      "Epoch 28/80\n",
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      "Epoch 69/80\n",
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      "Epoch 71/80\n",
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      "Epoch 72/80\n",
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      "Epoch 73/80\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 0s 45us/step - loss: 0.5645 - acc: 0.7630 - f1_score_metric: 0.7565\n",
      "Epoch 74/80\n",
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      "Epoch 20/75\n"
     ]
    },
    {
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     "text": [
      "8763/8763 [==============================] - 0s 46us/step - loss: 0.7446 - acc: 0.6766 - f1_score_metric: 0.6551\n",
      "Epoch 21/75\n",
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      "Epoch 42/75\n",
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      "Epoch 44/75\n",
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      "Epoch 71/75\n",
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      "Epoch 73/75\n",
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      "Epoch 74/75\n",
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      "Epoch 75/75\n",
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      "Epoch 1/28\n",
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      "Epoch 2/28\n",
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      "Epoch 8/28\n",
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      "Epoch 10/28\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 2s 223us/step - loss: 0.9233 - acc: 0.5993 - f1_score_metric: 0.5320\n",
      "Epoch 11/28\n",
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      "Epoch 27/80\n"
     ]
    },
    {
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     "text": [
      "8763/8763 [==============================] - 0s 42us/step - loss: 0.7230 - acc: 0.6885 - f1_score_metric: 0.6646\n",
      "Epoch 28/80\n",
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      "Epoch 45/80\n",
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      "Epoch 49/80\n",
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      "Epoch 50/80\n",
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      "Epoch 51/80\n",
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      "Epoch 53/80\n",
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      "Epoch 55/80\n",
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      "Epoch 78/80\n",
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      "Epoch 79/80\n",
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      "Epoch 80/80\n",
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      "Epoch 1/38\n",
      "8763/8763 [==============================] - 1s 151us/step - loss: 1.9155 - acc: 0.2629 - f1_score_metric: nan\n",
      "Epoch 2/38\n",
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      "Epoch 4/38\n",
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      "Epoch 8/38\n",
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      "Epoch 10/38\n",
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      "Epoch 11/38\n",
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      "Epoch 12/38\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 1s 67us/step - loss: 0.9036 - acc: 0.6109 - f1_score_metric: 0.5594\n",
      "Epoch 13/38\n",
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      "Epoch 16/38\n",
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      "Epoch 39/75\n"
     ]
    },
    {
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     "text": [
      "8763/8763 [==============================] - 1s 80us/step - loss: 0.6277 - acc: 0.7339 - f1_score_metric: 0.7209\n",
      "Epoch 40/75\n",
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      "Epoch 68/75\n",
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      "Epoch 70/75\n",
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      "Epoch 71/75\n",
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      "Epoch 75/75\n",
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      "Epoch 1/28\n",
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      "Epoch 15/28\n",
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      "Epoch 16/28\n",
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      "Epoch 17/28\n",
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      "Epoch 18/28\n",
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      "Epoch 19/28\n",
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      "Epoch 20/28\n",
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      "Epoch 21/28\n",
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      "Epoch 22/28\n",
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      "Epoch 23/28\n",
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      "Epoch 24/28\n",
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      "Epoch 25/28\n",
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      "Epoch 26/28\n",
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      "Epoch 27/28\n",
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      "Epoch 28/28\n",
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      "Epoch 1/20\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 2s 184us/step - loss: 1.4051 - acc: 0.4322 - f1_score_metric: nan\n",
      "Epoch 2/20\n",
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      "Epoch 18/20\n",
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      "Epoch 20/20\n",
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      "Epoch 1/80\n",
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      "Epoch 46/80\n"
     ]
    },
    {
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     "text": [
      "8763/8763 [==============================] - 0s 44us/step - loss: 0.6046 - acc: 0.7396 - f1_score_metric: 0.7319\n",
      "Epoch 47/80\n",
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      "Epoch 80/80\n",
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      "Epoch 1/38\n",
      "8763/8763 [==============================] - 2s 205us/step - loss: 1.9266 - acc: 0.2317 - f1_score_metric: nan\n",
      "Epoch 2/38\n",
      "8763/8763 [==============================] - 1s 76us/step - loss: 1.6066 - acc: 0.3557 - f1_score_metric: nan\n",
      "Epoch 3/38\n",
      "8763/8763 [==============================] - 1s 71us/step - loss: 1.4403 - acc: 0.4093 - f1_score_metric: 0.2562\n",
      "Epoch 4/38\n",
      "8763/8763 [==============================] - 1s 84us/step - loss: 1.3046 - acc: 0.4667 - f1_score_metric: 0.2986\n",
      "Epoch 5/38\n",
      "8763/8763 [==============================] - 1s 63us/step - loss: 1.2154 - acc: 0.4893 - f1_score_metric: 0.3388\n",
      "Epoch 6/38\n",
      "8763/8763 [==============================] - 1s 73us/step - loss: 1.1334 - acc: 0.5205 - f1_score_metric: 0.3961\n",
      "Epoch 7/38\n",
      "8763/8763 [==============================] - 1s 72us/step - loss: 1.0772 - acc: 0.5370 - f1_score_metric: 0.4393\n",
      "Epoch 8/38\n",
      "8763/8763 [==============================] - 1s 65us/step - loss: 1.0281 - acc: 0.5615 - f1_score_metric: 0.4741\n",
      "Epoch 9/38\n",
      "8763/8763 [==============================] - 1s 63us/step - loss: 0.9818 - acc: 0.5723 - f1_score_metric: 0.5000\n",
      "Epoch 10/38\n",
      "8763/8763 [==============================] - 1s 68us/step - loss: 0.9458 - acc: 0.5917 - f1_score_metric: 0.5209\n",
      "Epoch 11/38\n",
      "8763/8763 [==============================] - 1s 72us/step - loss: 0.9126 - acc: 0.6060 - f1_score_metric: 0.5424\n",
      "Epoch 12/38\n",
      "8763/8763 [==============================] - 1s 64us/step - loss: 0.8796 - acc: 0.6211 - f1_score_metric: 0.5740\n",
      "Epoch 13/38\n",
      "8763/8763 [==============================] - 1s 65us/step - loss: 0.8619 - acc: 0.6282 - f1_score_metric: 0.5843\n",
      "Epoch 14/38\n",
      "8763/8763 [==============================] - 1s 70us/step - loss: 0.8283 - acc: 0.6424 - f1_score_metric: 0.6060\n",
      "Epoch 15/38\n",
      "8763/8763 [==============================] - 1s 75us/step - loss: 0.7981 - acc: 0.6614 - f1_score_metric: 0.6280\n",
      "Epoch 16/38\n",
      "8763/8763 [==============================] - 1s 63us/step - loss: 0.7813 - acc: 0.6620 - f1_score_metric: 0.6324\n",
      "Epoch 17/38\n",
      "8763/8763 [==============================] - 1s 63us/step - loss: 0.7614 - acc: 0.6739 - f1_score_metric: 0.6519\n",
      "Epoch 18/38\n",
      "8763/8763 [==============================] - 1s 62us/step - loss: 0.7451 - acc: 0.6805 - f1_score_metric: 0.6576\n",
      "Epoch 19/38\n",
      "8763/8763 [==============================] - 1s 62us/step - loss: 0.7301 - acc: 0.6836 - f1_score_metric: 0.6619\n",
      "Epoch 20/38\n",
      "8763/8763 [==============================] - 1s 61us/step - loss: 0.7213 - acc: 0.6927 - f1_score_metric: 0.6766\n",
      "Epoch 21/38\n",
      "8763/8763 [==============================] - 1s 61us/step - loss: 0.6908 - acc: 0.7066 - f1_score_metric: 0.6893\n",
      "Epoch 22/38\n",
      "8763/8763 [==============================] - 1s 75us/step - loss: 0.6870 - acc: 0.7063 - f1_score_metric: 0.6921\n",
      "Epoch 23/38\n",
      "8763/8763 [==============================] - 1s 83us/step - loss: 0.6743 - acc: 0.7107 - f1_score_metric: 0.6973\n",
      "Epoch 24/38\n",
      "8763/8763 [==============================] - 1s 73us/step - loss: 0.6643 - acc: 0.7144 - f1_score_metric: 0.7034\n",
      "Epoch 25/38\n",
      "8763/8763 [==============================] - 1s 75us/step - loss: 0.6481 - acc: 0.7249 - f1_score_metric: 0.7106\n",
      "Epoch 26/38\n",
      "8763/8763 [==============================] - 1s 76us/step - loss: 0.6449 - acc: 0.7211 - f1_score_metric: 0.7150\n",
      "Epoch 27/38\n",
      "8763/8763 [==============================] - 1s 69us/step - loss: 0.6264 - acc: 0.7345 - f1_score_metric: 0.7253\n",
      "Epoch 28/38\n",
      "8763/8763 [==============================] - 1s 67us/step - loss: 0.6191 - acc: 0.7418 - f1_score_metric: 0.7353\n",
      "Epoch 29/38\n",
      "8763/8763 [==============================] - 1s 68us/step - loss: 0.6073 - acc: 0.7384 - f1_score_metric: 0.7336\n",
      "Epoch 30/38\n",
      "8763/8763 [==============================] - 1s 78us/step - loss: 0.6012 - acc: 0.7452 - f1_score_metric: 0.7403\n",
      "Epoch 31/38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8763/8763 [==============================] - 1s 81us/step - loss: 0.5987 - acc: 0.7445 - f1_score_metric: 0.7389\n",
      "Epoch 32/38\n",
      "8763/8763 [==============================] - 1s 71us/step - loss: 0.5756 - acc: 0.7606 - f1_score_metric: 0.7566\n",
      "Epoch 33/38\n",
      "8763/8763 [==============================] - 1s 68us/step - loss: 0.5790 - acc: 0.7532 - f1_score_metric: 0.7474\n",
      "Epoch 34/38\n",
      "8763/8763 [==============================] - 1s 76us/step - loss: 0.5617 - acc: 0.7649 - f1_score_metric: 0.7612\n",
      "Epoch 35/38\n",
      "8763/8763 [==============================] - 1s 84us/step - loss: 0.5497 - acc: 0.7691 - f1_score_metric: 0.7636\n",
      "Epoch 36/38\n",
      "8763/8763 [==============================] - 1s 73us/step - loss: 0.5402 - acc: 0.7728 - f1_score_metric: 0.7701\n",
      "Epoch 37/38\n",
      "8763/8763 [==============================] - 1s 73us/step - loss: 0.5420 - acc: 0.7704 - f1_score_metric: 0.7677\n",
      "Epoch 38/38\n",
      "8763/8763 [==============================] - 1s 76us/step - loss: 0.5296 - acc: 0.7780 - f1_score_metric: 0.7749\n",
      "0.6196629213483146\n",
      "time: 437.453452\n"
     ]
    }
   ],
   "source": [
    "EXEC = True\n",
    "\n",
    "if EXEC:\n",
    "    start = time.time()\n",
    "    \n",
    "    X_train = df_train\n",
    "    X_test = df_test\n",
    "    y_train = np.ravel(y_train)\n",
    "    new_y_pred = [] # The list of the predictions from each iteration\n",
    "    \n",
    "    sampler = RandomOverSampler('minority')\n",
    "    X_train, y_train = sampler.fit_resample(X_train, y_train)\n",
    "        \n",
    "    INPUT_LEN = X_train.shape[1]\n",
    "    one_hot_labels_train = pd.get_dummies(y_train)\n",
    "                \n",
    "    for i in [1, 2, 3]:\n",
    "        \n",
    "        model1 = Sequential([\n",
    "                Dense(128, input_shape=(INPUT_LEN,), kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0.3),\n",
    "                Dense(64, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(16, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dense(8, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('softmax')\n",
    "            ])\n",
    "\n",
    "        model2 = Sequential([\n",
    "                Dense(512, input_shape=(INPUT_LEN,), kernel_initializer='he_uniform', bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0.3),\n",
    "                Dense(256, kernel_initializer='he_uniform', bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(128, kernel_initializer='he_uniform', bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(32, kernel_initializer='he_uniform', bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dense(8),\n",
    "                Activation('softmax')\n",
    "            ])\n",
    "\n",
    "        model3 = Sequential([\n",
    "                Dense(256, input_shape=(INPUT_LEN,), kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0.2),\n",
    "                Dense(128, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(64, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(16, kernel_initializer='he_uniform', bias_initializer='zeros'),\n",
    "                Activation('relu'),\n",
    "                Dense(8),\n",
    "                Activation('softmax')\n",
    "            ])\n",
    "\n",
    "        model4 = Sequential([\n",
    "                Dense(128, input_shape=(INPUT_LEN,), bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0.2),\n",
    "                Dense(64, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(32, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,1),\n",
    "                Dense(16, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dense(8),\n",
    "                Activation('softmax')\n",
    "            ])\n",
    "\n",
    "        model5 = Sequential([\n",
    "                Dense(256, input_shape=(INPUT_LEN,), bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0.2),\n",
    "                Dense(128, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(64, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,2),\n",
    "                Dense(32, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dropout(0,1),\n",
    "                Dense(16, bias_initializer='Ones'),\n",
    "                Activation('relu'),\n",
    "                Dense(8),\n",
    "                Activation('softmax')\n",
    "            ])\n",
    "\n",
    "        adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
    "        addelta = Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
    "        \n",
    "        model1.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy', f1_score_metric])\n",
    "        model1.fit(X_train, one_hot_labels_train, shuffle=True, epochs=(75))\n",
    "        y_pred1 = model1.predict(X_test)\n",
    "        \n",
    "        model2.compile(optimizer=addelta, loss='categorical_crossentropy', metrics=['accuracy', f1_score_metric])\n",
    "        model2.fit(X_train, one_hot_labels_train, epochs=(28))\n",
    "        y_pred2 = model2.predict(X_test)\n",
    "\n",
    "        model3.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy', f1_score_metric])\n",
    "        model3.fit(X_train, one_hot_labels_train, shuffle=True, epochs=(20))\n",
    "        y_pred3 = model3.predict(X_test)\n",
    "     \n",
    "        model4.compile(optimizer=addelta, loss='categorical_crossentropy', metrics=['accuracy', f1_score_metric])\n",
    "        model4.fit(X_train, one_hot_labels_train, shuffle=True, epochs=(80))\n",
    "        y_pred4 = model4.predict(X_test)\n",
    "\n",
    "        model5.compile(optimizer=addelta, loss='categorical_crossentropy', metrics=['accuracy', f1_score_metric])\n",
    "        model5.fit(X_train, one_hot_labels_train, epochs=(38))\n",
    "        y_pred5 = model5.predict(X_test)\n",
    "\n",
    "        y_pred = (y_pred1 + y_pred2 + y_pred3 + y_pred4 + y_pred5) / 5  # Compute the average of each model probalities\n",
    "        y_pred = y_pred.argmax(axis=1) # Translate the results into categories\n",
    "        new_y_pred.append(y_pred)\n",
    "\n",
    "    y_pred = voting(new_y_pred[0], new_y_pred[1], new_y_pred[2]) \n",
    "               \n",
    "    if TEST:\n",
    "        write_csv(y_pred, 'y_pred_best.csv')\n",
    "    elif DEVELOP:\n",
    "        print(f1_score(y_pred, y_test, average='micro'))\n",
    "\n",
    "    end = time.time()\n",
    "    print('time: {0:f}'.format(end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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