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      "14.701517343521118\n",
      "0.8576779026217228\n",
      "[[42  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 41  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 35  1  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 44  0  3  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 39  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 41  0  0  0  2  0  0  0]\n",
      " [ 0  0  0  0  0  0 27  0  0 14  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 18  0  2  0  0  1]\n",
      " [ 0  0  0  0  0  5  0  0 35  2  0  0  0]\n",
      " [ 0  0  0  0  0  0  2  0  0 49  0  0  2]\n",
      " [ 0  0  0  0  0  1  0  1  0 26 12  3  2]\n",
      " [ 0  0  0  0  0  0  0  0  1  4  0 38  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  4  0  0 37]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        42\n",
      "           1       1.00      1.00      1.00        41\n",
      "           2       1.00      0.97      0.99        36\n",
      "           3       0.98      0.94      0.96        47\n",
      "           4       1.00      1.00      1.00        39\n",
      "           5       0.82      0.95      0.88        43\n",
      "           6       0.93      0.66      0.77        41\n",
      "           7       0.95      0.86      0.90        21\n",
      "           8       0.97      0.83      0.90        42\n",
      "           9       0.48      0.92      0.63        53\n",
      "          10       1.00      0.27      0.42        45\n",
      "          11       0.93      0.88      0.90        43\n",
      "          12       0.88      0.90      0.89        41\n",
      "\n",
      "    accuracy                           0.86       534\n",
      "   macro avg       0.92      0.86      0.86       534\n",
      "weighted avg       0.91      0.86      0.85       534\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "import time\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import sys, os\n",
    "import numpy as np \n",
    "from sklearn.metrics import classification_report, confusion_matrix \n",
    "#df = pd.read_excel (r'C:\\Users\\pc\\Documents\\DF features\\features\\full\\df13.xlsx')\n",
    "#dfn=shuffle(df)#shuffle\n",
    "#dfn.to_excel(r'C:\\Users\\pc\\Documents\\DF features\\features\\full\\shuffle13.xlsx')\n",
    "sdf=pd.read_excel(r'C:\\Users\\pc\\Documents\\DF features\\features\\full\\shuffle13.xlsx')\n",
    "X=sdf.iloc[:,:-1]\n",
    "Y=sdf.iloc[:,-1]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, shuffle=False)\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(271), max_iter=1000) \n",
    "#mlp=KNeighborsClassifier(n_neighbors=10)\n",
    "start=time.time()\n",
    "mlp.fit(X_train,Y_train)  \n",
    "stop=time.time()\n",
    "print(stop-start)\n",
    "#file3='df13.sav'\n",
    "#joblib.dump(mlp,file3)\n",
    "#load=joblib.load(file3)\n",
    "result=mlp.score(X_test,Y_test)\n",
    "print(result)\n",
    "predictions = mlp.predict(X_test) \n",
    "print(confusion_matrix(Y_test,predictions))  \n",
    "print(classification_report(Y_test,predictions)) "
   ]
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