machineLearningPart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pathlib\n",
    "import csv\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import KernelPCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "\n",
    "#PCA,Kernal PCA, Forward selection, backward selection, random selection ile feature secip ML algoritmalarının performanslarını\n",
    "#ölç"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\User\\Desktop\\features.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <th>ZCRMean</th>\n",
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       "    ZCRMean    ZCRStd    ZCRSkw     ZCRKrt    ZCRMed    ZCRMin    ZCRMax  \\\n",
       "0  0.083066  0.027690  1.428553   3.904819  0.078125  0.033203  0.242676   \n",
       "1  0.056044  0.038059  2.362931   9.193445  0.047852  0.006348  0.323730   \n",
       "2  0.076301  0.031738  4.185323  37.723069  0.073730  0.012695  0.440918   \n",
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       "4  0.101500  0.044199  1.939640   5.710074  0.092773  0.039551  0.366211   \n",
       "\n",
       "    RMSMean    RMSStd    RMSSkw  ...  Chroma3Max  Chroma4Max  Chroma5Max  \\\n",
       "0  0.130225  0.053187  0.503164  ...         1.0         1.0         1.0   \n",
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       "4  0.091563  0.047996  0.849254  ...         1.0         1.0         1.0   \n",
       "\n",
       "   Chroma6Max  Chroma7Max  Chroma8Max  Chroma9Max  Chroma10Max  Chroma11Max  \\\n",
       "0         1.0         1.0         1.0         1.0          1.0          1.0   \n",
       "1         1.0         1.0         1.0         1.0          1.0          1.0   \n",
       "2         1.0         1.0         1.0         1.0          1.0          1.0   \n",
       "3         1.0         1.0         1.0         1.0          1.0          1.0   \n",
       "4         1.0         1.0         1.0         1.0          1.0          1.0   \n",
       "\n",
       "   Labels  \n",
       "0   blues  \n",
       "1   blues  \n",
       "2   blues  \n",
       "3   blues  \n",
       "4   blues  \n",
       "\n",
       "[5 rows x 267 columns]"
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     "execution_count": 35,
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(df['Labels'])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
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   "source": [
    "labelEncoded = le.transform(df['Labels'])"
   ]
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   "execution_count": 38,
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       "    ZCRMean    ZCRStd    ZCRSkw     ZCRKrt    ZCRMed    ZCRMin    ZCRMax  \\\n",
       "0  0.083066  0.027690  1.428553   3.904819  0.078125  0.033203  0.242676   \n",
       "1  0.056044  0.038059  2.362931   9.193445  0.047852  0.006348  0.323730   \n",
       "2  0.076301  0.031738  4.185323  37.723069  0.073730  0.012695  0.440918   \n",
       "3  0.033309  0.020569  1.072661   2.017897  0.030762  0.006348  0.145020   \n",
       "4  0.101500  0.044199  1.939640   5.710074  0.092773  0.039551  0.366211   \n",
       "\n",
       "    RMSMean    RMSStd    RMSSkw  ...  Chroma4Max  Chroma5Max  Chroma6Max  \\\n",
       "0  0.130225  0.053187  0.503164  ...         1.0         1.0         1.0   \n",
       "1  0.095918  0.048718  0.299131  ...         1.0         1.0         1.0   \n",
       "2  0.175573  0.052423  0.220822  ...         1.0         1.0         1.0   \n",
       "3  0.141191  0.079617  0.525917  ...         1.0         1.0         1.0   \n",
       "4  0.091563  0.047996  0.849254  ...         1.0         1.0         1.0   \n",
       "\n",
       "   Chroma7Max  Chroma8Max  Chroma9Max  Chroma10Max  Chroma11Max  Labels  \\\n",
       "0         1.0         1.0         1.0          1.0          1.0   blues   \n",
       "1         1.0         1.0         1.0          1.0          1.0   blues   \n",
       "2         1.0         1.0         1.0          1.0          1.0   blues   \n",
       "3         1.0         1.0         1.0          1.0          1.0   blues   \n",
       "4         1.0         1.0         1.0          1.0          1.0   blues   \n",
       "\n",
       "   LabelEncoded  \n",
       "0             0  \n",
       "1             0  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  \n",
       "\n",
       "[5 rows x 268 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LabelEncoded'] = labelEncoded\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "y = df['LabelEncoded']\n",
    "X = df.drop(['LabelEncoded','Labels'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZCRMean</th>\n",
       "      <th>ZCRStd</th>\n",
       "      <th>ZCRSkw</th>\n",
       "      <th>ZCRKrt</th>\n",
       "      <th>ZCRMed</th>\n",
       "      <th>ZCRMin</th>\n",
       "      <th>ZCRMax</th>\n",
       "      <th>RMSMean</th>\n",
       "      <th>RMSStd</th>\n",
       "      <th>RMSSkw</th>\n",
       "      <th>...</th>\n",
       "      <th>Chroma2Max</th>\n",
       "      <th>Chroma3Max</th>\n",
       "      <th>Chroma4Max</th>\n",
       "      <th>Chroma5Max</th>\n",
       "      <th>Chroma6Max</th>\n",
       "      <th>Chroma7Max</th>\n",
       "      <th>Chroma8Max</th>\n",
       "      <th>Chroma9Max</th>\n",
       "      <th>Chroma10Max</th>\n",
       "      <th>Chroma11Max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.493284</td>\n",
       "      <td>-0.926543</td>\n",
       "      <td>0.024843</td>\n",
       "      <td>-0.116640</td>\n",
       "      <td>-0.404680</td>\n",
       "      <td>0.816065</td>\n",
       "      <td>-0.746026</td>\n",
       "      <td>-0.008975</td>\n",
       "      <td>0.182033</td>\n",
       "      <td>0.145133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.057595</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>0.113324</td>\n",
       "      <td>0.049328</td>\n",
       "      <td>0.100057</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>0.106991</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.139804</td>\n",
       "      <td>-0.483698</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>0.692449</td>\n",
       "      <td>-1.122855</td>\n",
       "      <td>-0.846166</td>\n",
       "      <td>-0.255064</td>\n",
       "      <td>-0.531164</td>\n",
       "      <td>0.014877</td>\n",
       "      <td>-0.183030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.057595</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>0.113324</td>\n",
       "      <td>0.049328</td>\n",
       "      <td>0.100057</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>0.106991</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.655156</td>\n",
       "      <td>-0.753669</td>\n",
       "      <td>2.765370</td>\n",
       "      <td>5.057102</td>\n",
       "      <td>-0.508931</td>\n",
       "      <td>-0.453275</td>\n",
       "      <td>0.454761</td>\n",
       "      <td>0.681293</td>\n",
       "      <td>0.153457</td>\n",
       "      <td>-0.308979</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.057595</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>0.113324</td>\n",
       "      <td>0.049328</td>\n",
       "      <td>0.100057</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>0.106991</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.683760</td>\n",
       "      <td>-1.230671</td>\n",
       "      <td>-0.328953</td>\n",
       "      <td>-0.405314</td>\n",
       "      <td>-1.528276</td>\n",
       "      <td>-0.846166</td>\n",
       "      <td>-1.337546</td>\n",
       "      <td>0.157952</td>\n",
       "      <td>1.170544</td>\n",
       "      <td>0.181728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.057595</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>0.113324</td>\n",
       "      <td>0.049328</td>\n",
       "      <td>0.100057</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>0.106991</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.052246</td>\n",
       "      <td>-0.221504</td>\n",
       "      <td>0.532919</td>\n",
       "      <td>0.159540</td>\n",
       "      <td>-0.057176</td>\n",
       "      <td>1.208956</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>-0.597457</td>\n",
       "      <td>-0.012123</td>\n",
       "      <td>0.701777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060608</td>\n",
       "      <td>0.113365</td>\n",
       "      <td>0.057595</td>\n",
       "      <td>0.078116</td>\n",
       "      <td>0.113324</td>\n",
       "      <td>0.049328</td>\n",
       "      <td>0.100057</td>\n",
       "      <td>0.044257</td>\n",
       "      <td>0.106991</td>\n",
       "      <td>0.087624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 266 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ZCRMean    ZCRStd    ZCRSkw    ZCRKrt    ZCRMed    ZCRMin    ZCRMax  \\\n",
       "0 -0.493284 -0.926543  0.024843 -0.116640 -0.404680  0.816065 -0.746026   \n",
       "1 -1.139804 -0.483698  0.953716  0.692449 -1.122855 -0.846166 -0.255064   \n",
       "2 -0.655156 -0.753669  2.765370  5.057102 -0.508931 -0.453275  0.454761   \n",
       "3 -1.683760 -1.230671 -0.328953 -0.405314 -1.528276 -0.846166 -1.337546   \n",
       "4 -0.052246 -0.221504  0.532919  0.159540 -0.057176  1.208956  0.002248   \n",
       "\n",
       "    RMSMean    RMSStd    RMSSkw  ...  Chroma2Max  Chroma3Max  Chroma4Max  \\\n",
       "0 -0.008975  0.182033  0.145133  ...    0.060608    0.113365    0.057595   \n",
       "1 -0.531164  0.014877 -0.183030  ...    0.060608    0.113365    0.057595   \n",
       "2  0.681293  0.153457 -0.308979  ...    0.060608    0.113365    0.057595   \n",
       "3  0.157952  1.170544  0.181728  ...    0.060608    0.113365    0.057595   \n",
       "4 -0.597457 -0.012123  0.701777  ...    0.060608    0.113365    0.057595   \n",
       "\n",
       "   Chroma5Max  Chroma6Max  Chroma7Max  Chroma8Max  Chroma9Max  Chroma10Max  \\\n",
       "0    0.078116    0.113324    0.049328    0.100057    0.044257     0.106991   \n",
       "1    0.078116    0.113324    0.049328    0.100057    0.044257     0.106991   \n",
       "2    0.078116    0.113324    0.049328    0.100057    0.044257     0.106991   \n",
       "3    0.078116    0.113324    0.049328    0.100057    0.044257     0.106991   \n",
       "4    0.078116    0.113324    0.049328    0.100057    0.044257     0.106991   \n",
       "\n",
       "   Chroma11Max  \n",
       "0     0.087624  \n",
       "1     0.087624  \n",
       "2     0.087624  \n",
       "3     0.087624  \n",
       "4     0.087624  \n",
       "\n",
       "[5 rows x 266 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled = pd.DataFrame(scaler.fit_transform(X),columns = X.columns)\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotVariance():\n",
    "    pca = PCA().fit(X_train)\n",
    "    plt.rcParams[\"figure.figsize\"] = (12,6)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    xi = np.arange(0, 266, step=1)\n",
    "    y = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "    plt.ylim(0.0,1.1)\n",
    "    plt.plot(xi, y, linestyle='--', color='b')\n",
    "\n",
    "    plt.xlabel('Number of Components')\n",
    "    plt.xticks(np.arange(0, 266, step=20)) #change from 0-based array index to 1-based human-readable label\n",
    "    plt.ylabel('Cumulative variance (%)')\n",
    "    plt.title('The number of components needed to explain variance')\n",
    "\n",
    "    plt.axhline(y=0.95, color='r', linestyle='-')\n",
    "    plt.text(0.5, 0.85, '95% cut-off threshold', color = 'red', fontsize=16)\n",
    "\n",
    "    ax.grid(axis='x')\n",
    "    plt.show()\n",
    "plotVariance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=100, random_state=0)\n",
    "lda = LinearDiscriminantAnalysis(n_components=9)\n",
    "kpca = KernelPCA(n_components=100, kernel='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca = pca.fit(X_train).transform(X_train) #(900,100) PCA train data\n",
    "X_test_pca = pca.fit(X_train).transform(X_test)  #(100,100) PCA test data\n",
    "X_train_lda = lda.fit(X_train,y_train).transform(X_train) #(900,9) LDA train data\n",
    "X_test_lda = lda.fit(X_test,y_test).transform(X_test)  #(100,9) LDA test data\n",
    "X_train_kpca = kpca.fit(X_train).transform(X_train)  #(900,100) KPCA train data\n",
    "X_test_kpca = kpca.fit(X_test).transform(X_test)    #(100,100) KPCA test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def showMLPerformances(X, y):\n",
    "    models = [MLPClassifier(),\n",
    "           LogisticRegression(),\n",
    "           RandomForestClassifier(),\n",
    "           LinearDiscriminantAnalysis(),\n",
    "           KNeighborsClassifier(),\n",
    "           SVC(),\n",
    "           GaussianNB(),\n",
    "           GradientBoostingClassifier(),\n",
    "           AdaBoostClassifier(),\n",
    "           LinearSVC(),\n",
    "           SVC()]\n",
    "    \n",
    "    \n",
    "    classifierNames = ['MLP', 'LogisticRegression','RandomForest','LDA','KNN','SVC','GaussianNB','GradBoost'\n",
    "                       ,'AdaBoost','LinearSVC']\n",
    "    \n",
    "    \n",
    "    param_grid = {'MLP' : [{'max_iter': [500,1000,2000], 'activation': ['tanh','logistic','identity']}],\n",
    "                  'LogisticRegression' : [{'solver': ['newton-cg', 'lbfgs','saga'], 'max_iter': [200,500,1000]}],\n",
    "                  'RandomForest' : [{'n_estimators': [50,100,150], 'criterion': ['gini', 'entropy']}],\n",
    "                  'LDA' : [{'solver': ['eigen', 'svd', 'lsqr']}],\n",
    "                  'KNN' : [{'n_neighbors': [5,10,20], 'weights': ['uniform', 'distance']}],\n",
    "                  'SVC' : [{'kernel': ['poly', 'rbf', 'sigmoid'], 'degree': [2,3], 'C': [1, 50, 100]}],\n",
    "                  'GaussianNB' : [{}],\n",
    "                  'GradBoost' : [{}],\n",
    "                  'AdaBoost' : [{}],\n",
    "                  'LinearSVC' : [{'max_iter': [500000,1000000], 'multi_class': ['ovr', 'crammer_singer']}]}\n",
    "    \n",
    "    \n",
    "    for cname, clf in zip(classifierNames, models):\n",
    "        print('SCORES FOR '+cname)\n",
    "        GridSearch = GridSearchCV(estimator=clf, param_grid=param_grid[cname], scoring='accuracy', n_jobs=-1)\n",
    "        GridSearch.fit(X, y)\n",
    "        print('Best parameters:')\n",
    "        print(GridSearch.best_params_)\n",
    "        print('gaves score: %.4f' %(GridSearch.best_score_))\n",
    "        print('++++++++++++++++++++++++++++')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCORES FOR MLP\n",
      "Best parameters:\n",
      "{'activation': 'logistic', 'max_iter': 1000}\n",
      "gaves score: 0.7756\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LogisticRegression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'max_iter': 200, 'solver': 'saga'}\n",
      "gaves score: 0.7633\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR RandomForest\n",
      "Best parameters:\n",
      "{'criterion': 'entropy', 'n_estimators': 150}\n",
      "gaves score: 0.7456\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LDA\n",
      "Best parameters:\n",
      "{'solver': 'eigen'}\n",
      "gaves score: 0.7289\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR KNN\n",
      "Best parameters:\n",
      "{'n_neighbors': 5, 'weights': 'uniform'}\n",
      "gaves score: 0.6978\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR SVC\n",
      "Best parameters:\n",
      "{'C': 50, 'degree': 2, 'kernel': 'rbf'}\n",
      "gaves score: 0.7867\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GaussianNB\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.5700\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GradBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.7311\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR AdaBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.1933\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LinearSVC\n",
      "Best parameters:\n",
      "{'max_iter': 500000, 'multi_class': 'crammer_singer'}\n",
      "gaves score: 0.7178\n",
      "++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "showMLPerformances(X_train, y_train)   #Train dataseti ile (900,266)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCORES FOR MLP\n",
      "Best parameters:\n",
      "{'activation': 'tanh', 'max_iter': 1000}\n",
      "gaves score: 0.7644\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LogisticRegression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'max_iter': 200, 'solver': 'saga'}\n",
      "gaves score: 0.7467\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR RandomForest\n",
      "Best parameters:\n",
      "{'criterion': 'entropy', 'n_estimators': 150}\n",
      "gaves score: 0.7044\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LDA\n",
      "Best parameters:\n",
      "{'solver': 'eigen'}\n",
      "gaves score: 0.7644\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR KNN\n",
      "Best parameters:\n",
      "{'n_neighbors': 5, 'weights': 'distance'}\n",
      "gaves score: 0.7044\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR SVC\n",
      "Best parameters:\n",
      "{'C': 50, 'degree': 2, 'kernel': 'rbf'}\n",
      "gaves score: 0.7811\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GaussianNB\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.6100\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GradBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.6611\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR AdaBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.1989\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LinearSVC\n",
      "Best parameters:\n",
      "{'max_iter': 500000, 'multi_class': 'crammer_singer'}\n",
      "gaves score: 0.6789\n",
      "++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "showMLPerformances(X_train_pca, y_train) #PCA uygulandıktan sonraki (900,100) data ile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCORES FOR MLP\n",
      "Best parameters:\n",
      "{'activation': 'identity', 'max_iter': 500}\n",
      "gaves score: 0.9411\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LogisticRegression\n",
      "Best parameters:\n",
      "{'max_iter': 200, 'solver': 'newton-cg'}\n",
      "gaves score: 0.9433\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR RandomForest\n",
      "Best parameters:\n",
      "{'criterion': 'gini', 'n_estimators': 100}\n",
      "gaves score: 0.9467\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LDA\n",
      "Best parameters:\n",
      "{'solver': 'eigen'}\n",
      "gaves score: 0.9500\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR KNN\n",
      "Best parameters:\n",
      "{'n_neighbors': 20, 'weights': 'uniform'}\n",
      "gaves score: 0.9433\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR SVC\n",
      "Best parameters:\n",
      "{'C': 1, 'degree': 2, 'kernel': 'rbf'}\n",
      "gaves score: 0.9467\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GaussianNB\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.9467\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GradBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.9200\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR AdaBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.4000\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LinearSVC\n",
      "Best parameters:\n",
      "{'max_iter': 500000, 'multi_class': 'ovr'}\n",
      "gaves score: 0.9411\n",
      "++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "showMLPerformances(X_train_lda, y_train)        #LDA'nın (900,9)luk datası"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised oldugu icin olabilir?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCORES FOR MLP\n",
      "Best parameters:\n",
      "{'activation': 'tanh', 'max_iter': 500}\n",
      "gaves score: 0.7800\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LogisticRegression\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:\n",
      "{'max_iter': 200, 'solver': 'saga'}\n",
      "gaves score: 0.7478\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR RandomForest\n",
      "Best parameters:\n",
      "{'criterion': 'gini', 'n_estimators': 150}\n",
      "gaves score: 0.7078\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LDA\n",
      "Best parameters:\n",
      "{'solver': 'eigen'}\n",
      "gaves score: 0.7700\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR KNN\n",
      "Best parameters:\n",
      "{'n_neighbors': 5, 'weights': 'distance'}\n",
      "gaves score: 0.6989\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR SVC\n",
      "Best parameters:\n",
      "{'C': 50, 'degree': 2, 'kernel': 'rbf'}\n",
      "gaves score: 0.7778\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GaussianNB\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.6144\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR GradBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.6767\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR AdaBoost\n",
      "Best parameters:\n",
      "{}\n",
      "gaves score: 0.2067\n",
      "++++++++++++++++++++++++++++\n",
      "SCORES FOR LinearSVC\n",
      "Best parameters:\n",
      "{'max_iter': 500000, 'multi_class': 'crammer_singer'}\n",
      "gaves score: 0.6789\n",
      "++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "showMLPerformances(X_train_kpca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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