Music Sentiment Analysis and Model Comparison.ipynb

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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package punkt to\n",
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     ]
    }
   ],
   "source": [
    "%load ImportsDefinitions.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#φορτώνουμε τα σετ δεδομένων\n",
    "path1='design_matrix.csv'\n",
    "path2='mean_emotion_ratings.csv'\n",
    "data_input= pd.read_csv(path1)\n",
    "data_class= pd.read_csv(path2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Register</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Soundlevel</th>\n",
       "      <th>Articulation</th>\n",
       "      <th>Timbre</th>\n",
       "      <th>Melody</th>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Nro  Register  Mode  Tempo  Soundlevel  Articulation  Timbre  Melody\n",
       "0    1         4     1      4           4             2       2       4\n",
       "1    2         5     1      4           1             1       2       2\n",
       "2    3         2     2      5           1             1       2       1\n",
       "3    4         1     1      5           4             4       1       2\n",
       "4    5         3     2      1           3             2       2       1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_input.head(5) #vlepoume oti prepei na allaksoume to mode, kathws periexei kathgoriaka dedomena, kai na diagrapsoume to Number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nro</th>\n",
       "      <th>Scary</th>\n",
       "      <th>Happy</th>\n",
       "      <th>Sad</th>\n",
       "      <th>Peaceful</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.2889</td>\n",
       "      <td>4.4667</td>\n",
       "      <td>1.7111</td>\n",
       "      <td>3.1333</td>\n",
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       "      <td>1.0667</td>\n",
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       "      <td>2.0222</td>\n",
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       "      <td>3.7778</td>\n",
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       "      <td>2.2889</td>\n",
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       "      <td>1.2667</td>\n",
       "      <td>1.4889</td>\n",
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       "      <td>1.4000</td>\n",
       "      <td>1.4667</td>\n",
       "      <td>5.0444</td>\n",
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       "   Nro   Scary   Happy     Sad  Peaceful\n",
       "0    1  1.2889  4.4667  1.7111    3.1333\n",
       "1    2  1.0667  5.4444  1.4889    4.4889\n",
       "2    3  2.0222  1.4889  3.7778    2.7111\n",
       "3    4  2.2889  4.1111  1.2667    1.4889\n",
       "4    5  1.4000  1.4667  5.0444    3.8444"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_class.head(5) # vlepoume oti prepei na diagrapsoume to number kai na doume pws tha xeiristoume ta multiclass data afou theloume diadikh synaisthimatikh analysh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#θα μετατρέψουμε το Mode από τα δεδομένα μας, σε δυο διαφορετικά διανύσματα εισόδου, το Major και το Minor.Αν η κλιμακα ειναι\n",
    "#ματζορε τοτε το Major θα παιρνει τιμή 1, και το Minor τιμη 0. αντιθετα, αν η συγχορδία ειναι μινορε, το Major θα παιρνει την \n",
    "#τιμη 0 και το Minor την τιμή 1\n",
    "#ταυτοχρονα διαγραφουμε τις στηλες Nro από τα δεδομένα εισόδου και εξόδου μας, καθώς και το Mode από τα δεδομένα εισόδου μας\n",
    "Major=[]\n",
    "Minor=[]\n",
    "#αρχικα δημιουργουμε τις αντιστοιχες λιστες\n",
    "for i in range (0,len(data_input)):\n",
    "    if (data_input['Mode'][i]==2):\n",
    "        Major.append(1)\n",
    "        Minor.append(0)\n",
    "    else:\n",
    "        Major.append(0)\n",
    "        Minor.append(1)\n",
    "#και μετα τις περναμε στο dataframe μας\n",
    "data_input['Major']=Major\n",
    "data_input['Minor']=Minor\n",
    "if {'Nro'}.issubset(data_input.columns):\n",
    "    data_input=data_input.drop(['Nro'], axis=1)\n",
    "if {'Mode'}.issubset(data_input.columns):\n",
    "    data_input=data_input.drop(['Mode'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Register</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Soundlevel</th>\n",
       "      <th>Articulation</th>\n",
       "      <th>Timbre</th>\n",
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      "text/plain": [
       "   Register  Tempo  Soundlevel  Articulation  Timbre  Melody  Major  Minor\n",
       "0         4      4           4             2       2       4      0      1\n",
       "1         5      4           1             1       2       2      0      1\n",
       "2         2      5           1             1       2       1      1      0\n",
       "3         1      5           4             4       1       2      0      1\n",
       "4         3      1           3             2       2       1      1      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_input.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Έχουμε 22 περιπτώσεις όπου happy>sad και peaceful < scary\n",
      "Συνεπώς υπάρχει αμφισημία μεταξύ των κλάσεων από το dataset μας.\n",
      "Θα δημιουργήσουμε 3 διαφορετικές περιπτώσεις με βάση τις υπάρχουσες κλάσεις, ώστε να καλύψουμε τις διαφορετικές εκδοχές\n"
     ]
    }
   ],
   "source": [
    "#classes examination\n",
    "\n",
    "count=0\n",
    "for i in range (0,len(data_class)):\n",
    "    if ((data_class['Happy'][i]>data_class['Sad'][i]) and (data_class['Peaceful'][i]<data_class['Scary'][i])):\n",
    "        count=count+1\n",
    "print(\"Έχουμε \" + str(count) + \" περιπτώσεις όπου happy>sad και peaceful < scary\")#Παρατηρούμε ότι στο dataset εχουμε περιπτωσεις οπου η κλαση happy kai peaceful den symvadizoun san dyadika antitheta panta.\n",
    "print(\"Συνεπώς υπάρχει αμφισημία μεταξύ των κλάσεων από το dataset μας.\")\n",
    "#synepws tha ftiaksoume 3 diaforetika classiciation gia ta dedomena pou exoume\n",
    "print(\"Θα δημιουργήσουμε 3 διαφορετικές περιπτώσεις με βάση τις υπάρχουσες κλάσεις, ώστε να καλύψουμε τις διαφορετικές εκδοχές\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Η απλή κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \n",
      "Έχουμε 86 χαρούμενα τραγούδια\n",
      "Έχουμε 114 στενάχωρα τραγούδια\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#θα δημιουργήσουμε διαφορετικές κλάσεις ώστε να καλύψουμε όλο το φάσμα των περιπτώσεων, δημιουργώντας 3 περιπτώσεις:\n",
    "#Μία πιο απλή, (happy vs sad)\n",
    "#μια πιο εξειδικευμένη (happy+peaceful vs peaceful+scary)\n",
    "#και η πιο εξειδικευμένη θα ακολουθεί την εξής λογική\n",
    "#αν happy>sad και peaceful >scary ->happy\n",
    "#aν sad>happy και scary>peaceful -> sad\n",
    "#αν happy>sad scary>peaceful τότε αν |happy - sad | *150%> |scary - peaceful| *50%-> happy, else sad\n",
    "\n",
    "#τροποποιούμε το dataset των κλάσεων έτσι ώστε να έχουμε 2 κλάσεις, 1 για χαρούμενο \n",
    "#και 0 για στενάχωρο τραγούδι, αντίθετα με τις 4 κλάσεις που έχουμε ήδη φτιάξει\n",
    "music_class_simple=[]\n",
    "\n",
    "for i in range (0,len(data_class)):\n",
    "    if (data_class['Happy'][i]>data_class['Sad'])[i]:\n",
    "        music_class_simple.append(1)\n",
    "    else:\n",
    "        music_class_simple.append(0)\n",
    "print(\"Η απλή κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \")\n",
    "#print(music_class_simple)\n",
    "#τώρα στο music_class_simple εχουμε τις κλάσεις των συναισθημάτων μας\n",
    "\n",
    "print(\"Έχουμε \" + str(music_class_simple.count(1)) + \" χαρούμενα τραγούδια\")\n",
    "print(\"Έχουμε \" + str(music_class_simple.count(0)) + \" στενάχωρα τραγούδια\")\n",
    "sns.countplot(music_class_simple,label=\"Count\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Η δεύτερη κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \n",
      "Έχουμε 92 χαρούμενα τραγούδια\n",
      "Έχουμε 108 στενάχωρα τραγούδια\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "music_class_better=[]\n",
    "#print(data_class)\n",
    "for i in range (0,len(data_class)):\n",
    "    if ((data_class['Happy'][i] + data_class['Peaceful'][i]) > (data_class['Sad'] + data_class['Scary'][i])[i]):\n",
    "        music_class_better.append(1)\n",
    "    else:\n",
    "        music_class_better.append(0)\n",
    "print(\"Η δεύτερη κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \")\n",
    "#print(music_class_better)\n",
    "\n",
    "print(\"Έχουμε \" + str(music_class_better.count(1)) + \" χαρούμενα τραγούδια\")\n",
    "print(\"Έχουμε \" + str(music_class_better.count(0)) + \" στενάχωρα τραγούδια\")\n",
    "sns.countplot(music_class_better,label=\"Count\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Η κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \n",
      "Έχουμε 102 χαρούμενα τραγούδια\n",
      "Έχουμε 98 στενάχωρα τραγούδια\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "music_class_best=[]\n",
    "for i in range (0,len(data_class)):\n",
    "    if (((data_class['Happy'][i]) >= (data_class['Sad'][i])) and ((data_class['Peaceful'][i]) >= (data_class['Scary'][i]))):\n",
    "            music_class_best.append(1)\n",
    "    elif (((data_class['Happy'][i]) < (data_class['Sad'][i])) and ((data_class['Peaceful'][i]) < (data_class['Scary'][i]))):\n",
    "            music_class_best.append(0)\n",
    "    elif (((data_class['Happy'][i]) >= (data_class['Sad'][i])) and ((data_class['Peaceful'][i]) < (data_class['Scary'][i]))):\n",
    "        if (1,5*((data_class['Happy'][i]) - (data_class['Sad'][i])) >= (0.5*(data_class['Scary'][i]) - (data_class['Peaceful'][i]))):\n",
    "            music_class_best.append(1)\n",
    "        elif (1,5*(data_class['Happy'][i]) - (data_class['Sad'][i]) < 0.5*((data_class['Scary'][i]) - (data_class['Peaceful'][i]))):\n",
    "            music_class_best.append(0)\n",
    "    elif (((data_class['Happy'][i]) < (data_class['Sad'][i])) and ((data_class['Peaceful'][i]) >= (data_class['Scary'][i]))):\n",
    "        if ((data_class['Sad'][i]) - (data_class['Happy'][i]) >= ((data_class['Peaceful'][i]) - (data_class['Scary'][i]))):\n",
    "            music_class_best.append(0)\n",
    "        elif ((data_class['Sad'][i]) - (data_class['Happy'][i]) < ((data_class['Peaceful'][i]) - (data_class['Scary'][i]))):\n",
    "            music_class_best.append(1)\n",
    "\n",
    "\n",
    "print(\"Η κλάση συναισθήματος που φτιάξαμε περιλαμβάνει: \")\n",
    "#print(music_class_best)\n",
    "print(\"Έχουμε \" + str(music_class_best.count(1)) + \" χαρούμενα τραγούδια\")\n",
    "print(\"Έχουμε \" + str(music_class_best.count(0)) + \" στενάχωρα τραγούδια\")\n",
    "sns.countplot(music_class_best,label=\"Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Τα αποτελέσματα του Cross-Validation για τον LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) είναι:  0.8935539215686275 \n",
      "Τα αποτελέσματα του Cross-Validation για τον DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best') είναι:  0.8255882352941176 \n",
      "Τα αποτελέσματα του Cross-Validation για τον RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False) είναι:  0.868970588235294 \n",
      "Τα αποτελέσματα του Cross-Validation για τον LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0) είναι:  0.8998039215686274 \n",
      "Τα αποτελέσματα του Cross-Validation για τον KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform') είναι:  0.8873039215686275 \n",
      "                 params    scores\n",
      "0  Logistisc Regression  0.893554\n",
      "1         Decision Tree  0.825588\n",
      "2         Random Forest  0.868971\n",
      "3            Linear SVC  0.899804\n",
      "4                  K-nn  0.887304\n"
     ]
    }
   ],
   "source": [
    "#χωρίζουμε την πρώτη κλάση σε train/test σετ. τα ονομάζουμε X_train_s από το simple καθώς είναι προιόν της πιο απλής ανάλυσης\n",
    "\n",
    "X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(data_input, music_class_simple, test_size=0.2,random_state=5)\n",
    "#κανονικοποιούμε τα δεδομένα εκπαίδευσης και ελέγχου, για να πετύχουμε καλύτερα αποτελέσματα\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler1 = StandardScaler()\n",
    "\n",
    "X_train_scaled1 = scaler1.fit_transform(X_train_s)\n",
    "X_test_scaled1 = scaler1.transform(X_test_s)\n",
    "#print(X_scaled)\n",
    "\n",
    "classifier_finder_music(X_train_scaled1,y_train_s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Τα αποτελέσματα του Cross-Validation για τον LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) είναι:  0.8396078431372549 \n",
      "Τα αποτελέσματα του Cross-Validation για τον DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best') είναι:  0.7899264705882354 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  import sys\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Τα αποτελέσματα του Cross-Validation για τον RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False) είναι:  0.8204411764705883 \n",
      "Τα αποτελέσματα του Cross-Validation για τον LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0) είναι:  0.8396078431372549 \n",
      "Τα αποτελέσματα του Cross-Validation για τον KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform') είναι:  0.8074754901960786 \n",
      "                 params    scores\n",
      "0  Logistisc Regression  0.839608\n",
      "1         Decision Tree  0.789926\n",
      "2         Random Forest  0.820441\n",
      "3            Linear SVC  0.839608\n",
      "4                  K-nn  0.807475\n"
     ]
    }
   ],
   "source": [
    "#δημιουργούμε τα διανύσματα εκπαίδευσης και ελέγχου από τη δεύτερη ανάλυση. τα ονομάζουμε με κατάληξη _b από το better\n",
    "X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(data_input, music_class_better, test_size=0.2,random_state=6)\n",
    "\n",
    "#κανονικοποιούμε τα διανύσματα μας\n",
    "scaler2 = StandardScaler()\n",
    "X_train_scaled2 = scaler2.fit_transform(X_train_b)\n",
    "X_test_scaled2 = scaler2.transform(X_test_b)\n",
    "#print(X_scaled)\n",
    "\n",
    "classifier_finder_music(X_train_scaled2,y_train_b)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  import sys\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Τα αποτελέσματα του Cross-Validation για τον LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False) είναι:  0.8325490196078432 \n",
      "Τα αποτελέσματα του Cross-Validation για τον DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best') είναι:  0.8125490196078433 \n",
      "Τα αποτελέσματα του Cross-Validation για τον RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False) είναι:  0.8768627450980393 \n",
      "Τα αποτελέσματα του Cross-Validation για τον LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0) είναι:  0.8325490196078432 \n",
      "Τα αποτελέσματα του Cross-Validation για τον KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "           weights='uniform') είναι:  0.8384313725490197 \n",
      "                 params    scores\n",
      "0  Logistisc Regression  0.832549\n",
      "1         Decision Tree  0.812549\n",
      "2         Random Forest  0.876863\n",
      "3            Linear SVC  0.832549\n",
      "4                  K-nn  0.838431\n"
     ]
    }
   ],
   "source": [
    "#χωρίζουμε το 3ο υποσετ σε διανύσματα εκπαίδευσης και ελέγχου.η κατάληξη _B Προκύπτει από το Best καθώς είναι η πιο συγκεκριμένη μας ανάλυση για τις συναισθηματικές κλάσεις\n",
    "\n",
    "X_train_B, X_test_B, y_train_B, y_test_B = train_test_split(data_input, music_class_best, test_size=0.2,random_state=7)\n",
    "\n",
    "scaler3 = StandardScaler()\n",
    "X_train_scaled3 = scaler3.fit_transform(X_train_B)\n",
    "X_test_scaled3 = scaler3.transform(X_test_B)\n",
    "#print(X_scaled)\n",
    "\n",
    "classifier_finder_music(X_train_scaled3,y_train_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#θελουμε να κανουμε optimize : \n",
    "\n",
    "#L-SVC, L-SVC,RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.900000 using {'C': 0.5, 'dual': True, 'max_iter': 110}\n",
      "Execution time: 5.372822999954224 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.5, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=110,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Θα κάνουμε GridSearch για τον LinearSVC για την πρώτη περίπτωση\n",
    "#οριζουμε τις μεγιστες επαναληψεις\n",
    "max_iter=[110,200,500,1000,1500,2000]\n",
    "dual=[True,False]\n",
    "#οριζουμε το πεναλτυ\n",
    "C = [0.1,0.5,1.0,1.5,2.0,2.5,5,10]\n",
    "param_grid = dict(dual=dual,max_iter=max_iter,C=C)\n",
    "\n",
    "lSVC=LinearSVC()\n",
    "grid = GridSearchCV(estimator=lSVC, param_grid=param_grid, cv = 10, n_jobs=-1)\n",
    "\n",
    "start_time = time.time()\n",
    "grid_result = grid.fit(X_train_scaled1, y_train_s)\n",
    "# Summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "print(\"Execution time: \" + str((time.time() - start_time)) + ' ms')\n",
    "best_clf1 = LinearSVC(**grid_result.best_params_)\n",
    "best_clf1.fit(X_train_scaled1,y_train_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.843750 using {'C': 5, 'dual': True, 'max_iter': 200}\n",
      "Execution time: 1.9743807315826416 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=5, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=200,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Θα κάνουμε GridSearch για τον LinearSVC για την δεύτερη περίπτωση\n",
    "#οριζουμε τις μεγιστες επαναληψειςmax_iter=[110,200,500,1000,1500,2000]\n",
    "dual=[True,False]\n",
    "C = [0.1,0.5,1.0,1.5,2.0,2.5,5,10]\n",
    "param_grid = dict(dual=dual,max_iter=max_iter,C=C)\n",
    "\n",
    "lSVC=LinearSVC()\n",
    "grid = GridSearchCV(estimator=lSVC, param_grid=param_grid, cv = 10, n_jobs=-1)\n",
    "\n",
    "start_time = time.time()\n",
    "grid_result = grid.fit(X_train_scaled2, y_train_b)\n",
    "# Summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "print(\"Execution time: \" + str((time.time() - start_time)) + ' ms')\n",
    "best_clf2 = LinearSVC(**grid_result.best_params_)\n",
    "best_clf2.fit(X_train_scaled2,y_train_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 60 candidates, totalling 600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 600 out of 600 | elapsed:  5.6min finished\n",
      "C:\\Users\\dtchmnt\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.868750 using {'criterion': 'gini', 'max_depth': 7, 'max_features': 'auto', 'n_estimators': 200}\n",
      "Execution time: 337.2352831363678 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=7, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Θα κάνουμε GridSearch για τον RandomForestClassifier για την τρίτη περίπτωση\n",
    "#οριζουμε τις μεγιστες επαναληψεις\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "rfc = RandomForestClassifier()\n",
    "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 10,verbose=1)\n",
    "start_time = time.time()\n",
    "CV_rfc.fit(X_train_scaled3, y_train_B)\n",
    "\n",
    "# Summarize results\n",
    "print(\"Best: %f using %s\" % (CV_rfc.best_score_, CV_rfc.best_params_))\n",
    "print(\"Execution time: \" + str((time.time() - start_time)) + ' ms')\n",
    "best_clf3 = RandomForestClassifier(**CV_rfc.best_params_)\n",
    "best_clf3.fit(X_train_scaled3, y_train_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Επιτυχία πρώτου ταξινομητη, LinearSVC :\n",
      "Η επιτυχία του ταξινομητή για το πρώτο σετ δεδομένων είναι 0.8\n",
      "Η επιτυχία του ταξινομητή για το δεύτερο σετ δεδομένων είναι 0.85\n",
      "Η επιτυχία του ταξινομητή για το τρίτο σετ δεδομένων είναι 0.825\n",
      "\n",
      "Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\n",
      "Μέσος Όρος πρώτου ταξινομητή: 0.8249999999999998\n",
      "\n",
      "Επιτυχία δεύτερου ταξινομητη, LinearSVC :\n",
      "Η επιτυχία του ταξινομητή για το πρώτο σετ δεδομένων είναι 0.825\n",
      "Η επιτυχία του ταξινομητή για το δεύτερο σετ δεδομένων είναι 0.825\n",
      "Η επιτυχία του ταξινομητή για το τρίτο σετ δεδομένων είναι 0.875\n",
      "\n",
      "Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\n",
      "Mέσος Όρος δεύτερου ταξινομητή: 0.8416666666666667\n",
      "\n",
      "Σκορ 3ου ταξινομητη, RandomForest :\n",
      "Η επιτυχία του ταξινομητή στο πρώτο σετ δεδομένων είναι 0.875\n",
      "Η επιτυχία του ταξινομητή στο δεύτερο σετ δεδομένων είναι 0.95\n",
      "Η επιτυχία του ταξινομητή στο τρίτο σετ δεδομένων είναι 0.875\n",
      "\n",
      "Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\n",
      "Μέσος όρος τρίτου ταξινομητή: 0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#ελέγχουμε τους classifiers μας. Επειδή έχουμε εκπαιδεύσει και τους 3 στο ίδιο training data με διαφορετικά labels,\n",
    "#θα δημιουργήσουμε 3 ελέγχους ,με 3 σκορ τα οποία θα πολλαπλασιάσουμε μεταξύ τους για να βρούμε το καλύτερο\n",
    "#παίρνουμε σαν σωστή κλάση την 1η\n",
    "# make a prediction\n",
    "ypred1 = best_clf1.predict(X_test_scaled1)\n",
    "ypred2 = best_clf1.predict(X_test_scaled2)\n",
    "ypred3 = best_clf1.predict(X_test_scaled3)\n",
    "#αποθηκευουμε τα σκορ\n",
    "score1 = accuracy_score(y_test_s, ypred1)\n",
    "score2 =accuracy_score(y_test_b, ypred2)\n",
    "score3 = accuracy_score(y_test_B, ypred3)\n",
    "print(\"Επιτυχία πρώτου ταξινομητη, LinearSVC :\")\n",
    "print(\"Η επιτυχία του ταξινομητή για το πρώτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_s, ypred1)))\n",
    "print(\"Η επιτυχία του ταξινομητή για το δεύτερο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_b, ypred2)))\n",
    "print(\"Η επιτυχία του ταξινομητή για το τρίτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_B, ypred3)))\n",
    "print()\n",
    "print(\"Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\")\n",
    "print(\"Μέσος Όρος πρώτου ταξινομητή: {}\".format((score1+score2+score3)/3))\n",
    "print()\n",
    "# Φτιάχνουμε DF με τα αποτελέσματα μας \n",
    "accuracy_df1 = pd.DataFrame()\n",
    "accuracy_df1['Σετ']= ['1ο σετ δεδομένων','2ο σετ δεδομένων','3ο σετ δεδομένων',]\n",
    "accuracy_df1['scores']= [score1,score2,score3]\n",
    "\n",
    "#ελέγχουμε τους classifiers μας. Επειδή έχουμε εκπαιδεύσει και τους 3 στο ίδιο training data με διαφορετικά labels,\n",
    "#θα δημιουργήσουμε 3 ελέγχους ,με 3 σκορ τα οποία θα πολλαπλασιάσουμε μεταξύ τους για να βρούμε το καλύτερο\n",
    "#παίρνουμε σαν σωστή κλάση την 2η\n",
    "# make a prediction\n",
    "ypred1 = best_clf2.predict(X_test_scaled1)\n",
    "ypred2 = best_clf2.predict(X_test_scaled2)\n",
    "ypred3 = best_clf2.predict(X_test_scaled3)\n",
    "#αποθηκευουμε τα σκορ\n",
    "score1 = accuracy_score(y_test_s, ypred1)\n",
    "score2 =accuracy_score(y_test_b, ypred2)\n",
    "score3 = accuracy_score(y_test_B, ypred3)\n",
    "print(\"Επιτυχία δεύτερου ταξινομητη, LinearSVC :\")\n",
    "print(\"Η επιτυχία του ταξινομητή για το πρώτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_s, ypred1)))\n",
    "print(\"Η επιτυχία του ταξινομητή για το δεύτερο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_b, ypred2)))\n",
    "print(\"Η επιτυχία του ταξινομητή για το τρίτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_B, ypred3)))\n",
    "print()\n",
    "print(\"Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\")\n",
    "print(\"Mέσος Όρος δεύτερου ταξινομητή: {}\".format((score1+score2+score3)/3))\n",
    "print()\n",
    "# Φτιάχνουμε DF με τα αποτελέσματα μας \n",
    "accuracy_df2 = pd.DataFrame()\n",
    "accuracy_df2['Σετ']= ['1ο σετ δεδομένων','2ο σετ δεδομένων','3ο σετ δεδομένων',]\n",
    "accuracy_df2['scores']= [score1,score2,score3]\n",
    "\n",
    "#ελέγχουμε τους classifiers μας. Επειδή έχουμε εκπαιδεύσει και τους 3 στο ίδιο training data με διαφορετικά labels,\n",
    "#θα δημιουργήσουμε 3 ελέγχους ,με 3 σκορ τα οποία θα πολλαπλασιάσουμε μεταξύ τους για να βρούμε το καλύτερο\n",
    "#παίρνουμε σαν σωστή κλάση την 3η\n",
    "# make a prediction\n",
    "ypred1 = best_clf3.predict(X_test_scaled1)\n",
    "ypred2 = best_clf3.predict(X_test_scaled2)\n",
    "ypred3 = best_clf3.predict(X_test_scaled3)\n",
    "#αποθηκευουμε τα σκορ\n",
    "score1 = accuracy_score(y_test_s, ypred1)\n",
    "score2 =accuracy_score(y_test_b, ypred2)\n",
    "score3 = accuracy_score(y_test_B, ypred3)\n",
    "print(\"Σκορ 3ου ταξινομητη, RandomForest :\")\n",
    "print(\"Η επιτυχία του ταξινομητή στο πρώτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_s, ypred1)))\n",
    "print(\"Η επιτυχία του ταξινομητή στο δεύτερο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_b, ypred2)))\n",
    "print(\"Η επιτυχία του ταξινομητή στο τρίτο σετ δεδομένων είναι {}\".format(accuracy_score(y_test_B, ypred3)))\n",
    "print()\n",
    "print(\"Βρίσκουμε τον μέσο όρο του ταξινομητή για τα διαφορετικά σετ δεδομένων\")\n",
    "print(\"Μέσος όρος τρίτου ταξινομητή: {}\".format((score1+score2+score3)/3))\n",
    "print()\n",
    "# Φτιάχνουμε DF με τα αποτελέσματα μας \n",
    "accuracy_df3 = pd.DataFrame()\n",
    "accuracy_df3['Σετ']= ['1ο σετ δεδομένων','2ο σετ δεδομένων','3ο σετ δεδομένων',]\n",
    "accuracy_df3['scores']= [score1,score2,score3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Αποτελέσματα 1ου ταξινομητή\n",
      "                Σετ  scores\n",
      "0  1ο σετ δεδομένων   0.800\n",
      "1  2ο σετ δεδομένων   0.850\n",
      "2  3ο σετ δεδομένων   0.825\n",
      "\n",
      "Αποτελέσματα 2ου ταξινομητή\n",
      "                Σετ  scores\n",
      "0  1ο σετ δεδομένων   0.825\n",
      "1  2ο σετ δεδομένων   0.825\n",
      "2  3ο σετ δεδομένων   0.875\n",
      "\n",
      "Αποτελέσματα 3ου ταξινομητή\n",
      "                Σετ  scores\n",
      "0  1ο σετ δεδομένων   0.875\n",
      "1  2ο σετ δεδομένων   0.950\n",
      "2  3ο σετ δεδομένων   0.875\n"
     ]
    }
   ],
   "source": [
    "print(\"Αποτελέσματα 1ου ταξινομητή\")\n",
    "print(accuracy_df1)\n",
    "print()\n",
    "print(\"Αποτελέσματα 2ου ταξινομητή\")\n",
    "print(accuracy_df2)\n",
    "print()\n",
    "print(\"Αποτελέσματα 3ου ταξινομητή\")\n",
    "print(accuracy_df3)"
   ]
  }
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