mlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Amir Shokri\n",
    "## St code : 9811920009\n",
    "## E-mail : amirsh.nll@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## mlp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Amirsh.nll/Downloads/981018-Tavahhom-Karafarini.mp3\n",
      "/Users/Amirsh.nll/Downloads/981018-Tavahhom-Karafarini.pdf\n",
      "/Users/Amirsh.nll/Downloads/Data-shokriamir9811920009.rar\n",
      "/Users/Amirsh.nll/Downloads/dataset_choice_amirshokri[9811920009.pdf\n",
      "/Users/Amirsh.nll/Downloads/desktop.ini\n",
      "/Users/Amirsh.nll/Downloads/glass.zip\n",
      "/Users/Amirsh.nll/Downloads/NNproject- shokriamir9811920009.rar\n",
      "/Users/Amirsh.nll/Downloads/suicide-rates-overview-1985-to-2016.zip\n",
      "/Users/Amirsh.nll/Downloads/content\\content.xlsx\n",
      "/Users/Amirsh.nll/Downloads/content\\pooshak.xlsx\n",
      "/Users/Amirsh.nll/Downloads/glass\\Decision-Tree.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\glass.csv\n",
      "/Users/Amirsh.nll/Downloads/glass\\KNN.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\logistic_regression.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\mlp.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\Naive-Bayes.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\.ipynb_checkpoints\\Decision-Tree-checkpoint.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\.ipynb_checkpoints\\KNN-checkpoint.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\.ipynb_checkpoints\\logistic_regression-checkpoint.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\.ipynb_checkpoints\\mlp-checkpoint.ipynb\n",
      "/Users/Amirsh.nll/Downloads/glass\\.ipynb_checkpoints\\Naive-Bayes-checkpoint.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\Decision-Tree.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\KNN.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\logistic_regression.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\mlp.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\Naive-Bayes.ipynb\n",
      "/Users/Amirsh.nll/Downloads/suicide\\suicide.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/Users/Amirsh.nll/Downloads/'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      "RI      214 non-null float64\n",
      "Na      214 non-null float64\n",
      "Mg      214 non-null float64\n",
      "Al      214 non-null float64\n",
      "Si      214 non-null float64\n",
      "K       214 non-null float64\n",
      "Ca      214 non-null float64\n",
      "Ba      214 non-null float64\n",
      "Fe      214 non-null float64\n",
      "Type    214 non-null int64\n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 16.8 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('glass.csv', encoding ='latin1')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe  Type\n",
       "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0     1\n",
       "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0     1\n",
       "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0     1\n",
       "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0     1\n",
       "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0     1\n",
       "..       ...    ...   ...   ...    ...   ...   ...   ...  ...   ...\n",
       "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0     7\n",
       "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0     7\n",
       "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0     7\n",
       "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0     7\n",
       "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0     7\n",
       "\n",
       "[214 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          RI     Na    Mg    Al     Si     K    Ca    Ba   Fe\n",
      "0    1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.00  0.0\n",
      "1    1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.00  0.0\n",
      "2    1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.00  0.0\n",
      "3    1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.00  0.0\n",
      "4    1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.00  0.0\n",
      "..       ...    ...   ...   ...    ...   ...   ...   ...  ...\n",
      "209  1.51623  14.14  0.00  2.88  72.61  0.08  9.18  1.06  0.0\n",
      "210  1.51685  14.92  0.00  1.99  73.06  0.00  8.40  1.59  0.0\n",
      "211  1.52065  14.36  0.00  2.02  73.42  0.00  8.44  1.64  0.0\n",
      "212  1.51651  14.38  0.00  1.94  73.61  0.00  8.48  1.57  0.0\n",
      "213  1.51711  14.23  0.00  2.08  73.36  0.00  8.62  1.67  0.0\n",
      "\n",
      "[214 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "y = data['Type'].values\n",
    "y = y.reshape(-1,1)\n",
    "x_data = data.drop(['Type'],axis = 1)\n",
    "print(x_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "           RI        Na        Mg        Al        Si         K        Ca  \\\n",
       "0    0.432836  0.437594  1.000000  0.252336  0.351786  0.009662  0.308550   \n",
       "1    0.283582  0.475188  0.801782  0.333333  0.521429  0.077295  0.223048   \n",
       "2    0.220808  0.421053  0.790646  0.389408  0.567857  0.062802  0.218401   \n",
       "3    0.285777  0.372932  0.821826  0.311526  0.500000  0.091787  0.259294   \n",
       "4    0.275241  0.381955  0.806236  0.295950  0.583929  0.088567  0.245353   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "209  0.223003  0.512782  0.000000  0.806854  0.500000  0.012882  0.348513   \n",
       "210  0.250219  0.630075  0.000000  0.529595  0.580357  0.000000  0.276022   \n",
       "211  0.417032  0.545865  0.000000  0.538941  0.644643  0.000000  0.279740   \n",
       "212  0.235294  0.548872  0.000000  0.514019  0.678571  0.000000  0.283457   \n",
       "213  0.261633  0.526316  0.000000  0.557632  0.633929  0.000000  0.296468   \n",
       "\n",
       "           Ba   Fe  \n",
       "0    0.000000  0.0  \n",
       "1    0.000000  0.0  \n",
       "2    0.000000  0.0  \n",
       "3    0.000000  0.0  \n",
       "4    0.000000  0.0  \n",
       "..        ...  ...  \n",
       "209  0.336508  0.0  \n",
       "210  0.504762  0.0  \n",
       "211  0.520635  0.0  \n",
       "212  0.498413  0.0  \n",
       "213  0.530159  0.0  \n",
       "\n",
       "[214 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = (x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data)).values\n",
    "x.head(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train:  (107, 9)\n",
      "x_test:  (107, 9)\n",
      "y_train:  (107, 1)\n",
      "y_test:  (107, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.5,random_state=100)\n",
    "\n",
    "y_train = y_train.reshape(-1,1)\n",
    "y_test = y_test.reshape(-1,1)\n",
    "\n",
    "print(\"x_train: \",x_train.shape)\n",
    "print(\"x_test: \",x_test.shape)\n",
    "print(\"y_train: \",y_train.shape)\n",
    "print(\"y_test: \",y_test.shape)\n",
    "\n",
    "my_x = x_train\n",
    "my_y = y_train\n",
    "\n",
    "my_xx = x_test\n",
    "my_yy = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(7, 2), random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Amirsh.nll\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:921: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5700934579439252\n"
     ]
    }
   ],
   "source": [
    "clf.fit(my_x, my_y)\n",
    "y_pred = clf.predict(my_xx)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(my_yy, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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