image_data (1).ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "model = MLPClassifier(hidden_layer_sizes=(50,), max_iter=1000, alpha=1e-4,\n",
    "                      solver='sgd', tol=1e-4, random_state=1,\n",
    "                      learning_rate_init=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [[1], [2], [3]]\n",
    "y = ['superficial_dermal', 'partial_thickness', 'full_thickness']\n",
    "# first layer 5 nodes, second is 2 nodes\n",
    "classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                            hidden_layer_sizes=(5, 2), random_state=1)\n",
    "                            \n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['superficial_dermal', 'full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[1], [4]])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>203.3000000000001</th>\n",
       "      <th>182.3000000000001</th>\n",
       "      <th>215.3000000000001</th>\n",
       "      <th>207.0</th>\n",
       "      <th>184.40149999999994</th>\n",
       "      <th>215.0999999999999</th>\n",
       "      <th>204.4999999999999</th>\n",
       "      <th>179.83249999999995</th>\n",
       "      <th>209.4999999999999</th>\n",
       "      <th>203.32999999999998</th>\n",
       "      <th>...</th>\n",
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       "<p>0 rows × 30000 columns</p>\n",
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       "Empty DataFrame\n",
       "Columns: [203.3000000000001, 182.3000000000001, 215.3000000000001, 207.0, 184.40149999999994, 215.0999999999999, 204.4999999999999, 179.83249999999995, 209.4999999999999, 203.32999999999998, 180.36350000000002, 205.63149999999996, 201.665, 178.665, 204.36500000000007, 206.3000000000001, 184.3000000000001, 215.3000000000001.1, 207.0.1, 188.0, 218.0, 212.1650000000001, 193.1650000000001, 223.1650000000001, 214.0, 194.89999999999992, 223.8999999999999, 214.7000000000001, 194.80050000000006, 224.50050000000016, 224.3000000000001, 206.40050000000014, 237.93500000000023, 224.67000000000002, 208.63650000000004, 241.26850000000007, 230.66750000000013, 216.00000000000023, 245.5000000000001, 235.1000000000001, 221.1000000000001, 246.0, 241.03500000000008, 226.7000000000001, 248.7000000000001, 241.23449999999997, 226.23449999999997, 249.0, 238.06649999999993, 223.0, 247.665, 239.66750000000013, 226.1675000000002, 245.335, 244.30149999999998, 232.0, 247.40150000000008, 239.2999999999999, 226.2999999999999, 247.7000000000001, 239.30000000000013, 225.3000000000001, 248.0, 243.9000000000001, 232.80000000000018, 248.0.1, 248.4999999999999, 241.5000000000001, 249.4999999999999, 247.335, 241.2349999999999, 248.335, 247.0, 234.63499999999993, 246.10049999999995, 247.665.1, 230.89949999999996, 248.19950000000006, 241.16649999999981, 226.0999999999999, 246.0999999999999, 240.33250000000015, 232.1650000000003, 244.1650000000001, 248.0.2, 243.59849999999992, 248.0.3, 243.96499999999992, 230.8999999999997, 243.2999999999999, 243.76550000000003, 225.665, 243.96500000000012, 242.0, 223.665, 241.0999999999999, 247.0.1, 234.00000000000023, 246.5000000000001, 247.03350000000003, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30000 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "pd.read_csv(\"/home/spk21f9/smaller.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/spk21f9'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table = pd.read_csv(\"/home/spk21f9/smaller.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array1 = table.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 203.3  ,  182.3  ,  215.3  , ...,  143.635,  122.335,  131.635])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(array1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [array1, array1, array1]\n",
    "y = ['superficial_dermal', 'partial_thickness', 'full_thickness']\n",
    "# first layer 5 nodes, second is 2 nodes\n",
    "classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                            hidden_layer_sizes=(5, 2), random_state=1)\n",
    "                            \n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['superficial_dermal'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([array1])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "table2 = pd.read_csv(\"/home/spk21f9/full_thickness.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array2 = table2.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([122, 131, 138, ..., 119, 124, 130])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(table2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "   0      1      2      3      4      5      6      7      8      9      \\\n",
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       "\n",
       "   ...    29990  29991  29992  29993  29994  29995  29996  29997  29998  29999  \n",
       "0  ...      134    120    125    131    123    128    134    119    124    130  \n",
       "1  ...      142     71     98    145     73     99    146     71     96    142  \n",
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       "3  ...       26     20     22     27     20     22     27     20     22     27  \n",
       "4  ...       61     85     56     60     85     53     57     83     46     53  \n",
       "5  ...       93    102     94     92    100     92     90     95     87     85  \n",
       "6  ...      108    141    121     92    136    116     89    124    104     79  \n",
       "7  ...       98     61     84    119     81     93    126     85     95    130  \n",
       "\n",
       "[8 rows x 30000 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 203.3  ,  182.3  ,  215.3  , ...,  143.635,  122.335,  131.635]),\n",
       " array([ 203.3  ,  182.3  ,  215.3  , ...,  143.635,  122.335,  131.635]),\n",
       " array([ 203.3  ,  182.3  ,  215.3  , ...,  143.635,  122.335,  131.635])]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'table1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-037428af80ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtable1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mY1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'full_thickness'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'table1' is not defined"
     ]
    }
   ],
   "source": [
    "X2 = table2.values\n",
    "Y2 = ['full_thickness'] * len(table2.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[122, 131, 138, ..., 119, 124, 130],\n",
       "       [ 19,  20,  24, ...,  71,  96, 142],\n",
       "       [190, 200, 227, ..., 202, 203, 227],\n",
       "       ..., \n",
       "       [ 11,  28,  33, ...,  95,  87,  85],\n",
       "       [ 34,  33,  34, ..., 124, 104,  79],\n",
       "       [ 41,  64, 103, ...,  85,  95, 130]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_thickness_table = pd.read_csv(\"/home/spk21f9/full_thickness.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X1 = full_thickness_table.values\n",
    "Y1 = ['full_thickness'] * len(full_thickness_table.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[122, 131, 138, ..., 119, 124, 130],\n",
       "       [ 19,  20,  24, ...,  71,  96, 142],\n",
       "       [190, 200, 227, ..., 202, 203, 227],\n",
       "       ..., \n",
       "       [ 11,  28,  33, ...,  95,  87,  85],\n",
       "       [ 34,  33,  34, ..., 124, 104,  79],\n",
       "       [ 41,  64, 103, ...,  85,  95, 130]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "partial_thickness_table = pd.read_csv(\"/home/spk21f9/partial_thickness.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X2 = partial_thickness_table.values\n",
    "Y2 = ['partial_thickness'] * len(partial_thickness_table.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   2,   2, ..., 147, 170, 188],\n",
       "       [  3,   3,   3, ...,   3,   3,   3]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['partial_thickness', 'partial_thickness']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "superficial_table = pd.read_csv(\"/home/spk21f9/superficial_dermal.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X3 = superficial_table.values\n",
    "Y3 = ['superficial_dermal'] * len(superficial_table.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[253, 253, 254, ..., 109,  33,  19],\n",
       "       [242, 242, 244, ..., 204, 167, 159],\n",
       "       [255, 255, 255, ..., 255, 255, 254],\n",
       "       [  0,   0,   0, ...,   0,   0,   0]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['superficial_dermal',\n",
       " 'superficial_dermal',\n",
       " 'superficial_dermal',\n",
       " 'superficial_dermal']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 2), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = []\n",
    "X.extend(X1)\n",
    "X.extend(X2)\n",
    "X.extend(X3)\n",
    "y = []\n",
    "y.extend(Y1)\n",
    "y.extend(Y2)\n",
    "y.extend(Y3)\n",
    "# first layer 5 nodes, second is 2 nodes\n",
    "classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                            hidden_layer_sizes=(5, 2), random_state=1)\n",
    "                            \n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'full_thickness',\n",
       " 'partial_thickness',\n",
       " 'partial_thickness',\n",
       " 'superficial_dermal',\n",
       " 'superficial_dermal',\n",
       " 'superficial_dermal',\n",
       " 'superficial_dermal']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([122, 131, 138, ..., 119, 124, 130]),\n",
       " array([ 19,  20,  24, ...,  71,  96, 142]),\n",
       " array([190, 200, 227, ..., 202, 203, 227]),\n",
       " array([ 59,  79, 103, ...,  20,  22,  27]),\n",
       " array([ 3,  3,  3, ..., 83, 46, 53]),\n",
       " array([11, 28, 33, ..., 95, 87, 85]),\n",
       " array([ 34,  33,  34, ..., 124, 104,  79]),\n",
       " array([ 41,  64, 103, ...,  85,  95, 130]),\n",
       " array([  2,   2,   2, ..., 147, 170, 188]),\n",
       " array([3, 3, 3, ..., 3, 3, 3]),\n",
       " array([253, 253, 254, ..., 109,  33,  19]),\n",
       " array([242, 242, 244, ..., 204, 167, 159]),\n",
       " array([255, 255, 255, ..., 255, 255, 254]),\n",
       " array([0, 0, 0, ..., 0, 0, 0])]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['full_thickness', 'full_thickness', 'full_thickness',\n",
       "       'full_thickness', 'full_thickness', 'full_thickness',\n",
       "       'full_thickness', 'full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict(X1)               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_table = pd.read_csv(\"/home/spk21f9/thirddegreeburn.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testX = test_table.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([testX])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test2_table = pd.read_csv(\"/home/spk21f9/seconddegreeburn.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test3_table = pd.read_csv(\"/home/spk21f9/sunburntest.csv\", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testX2 = test2_table.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testX3 = test3_table.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([testX2])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([testX3])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(150, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = []\n",
    "X.extend(X1)\n",
    "X.extend(X2)\n",
    "X.extend(X3)\n",
    "y = []\n",
    "y.extend(Y1)\n",
    "y.extend(Y2)\n",
    "y.extend(Y3)\n",
    "# first layer 5 nodes, second is 2 nodes\n",
    "classifier = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                            hidden_layer_sizes=(150, 100), random_state=1)\n",
    "                            \n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['full_thickness'], \n",
       "      dtype='>U18')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([testX])               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "test_set = []\n",
    "test_set.extend(testX)\n",
    "test_set.extend(testX2)\n",
    "test_set.extend(testX3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid parameters passed: {'shuffle': False}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-aff1ea53e30b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m X_train, X_test, y_train, y_test = train_test_split(\n\u001b[0;32m----> 2\u001b[0;31m     X, test_set, test_size=3, shuffle=False)\n\u001b[0m",
      "\u001b[0;32m/home/fztso1/IzODA/lib/python3.6/site-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m   1682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1683\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid parameters passed: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtrain_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid parameters passed: {'shuffle': False}"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, test_set, test_size=3, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid parameters passed: {'shuffle': False}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-034bd0771fb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m X_train, X_test, y_train, y_test = train_test_split(\n\u001b[0;32m----> 2\u001b[0;31m     X, y, test_size=0.5, shuffle=False)\n\u001b[0m",
      "\u001b[0;32m/home/fztso1/IzODA/lib/python3.6/site-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m   1682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1683\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid parameters passed: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtrain_size\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid parameters passed: {'shuffle': False}"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.5, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(150, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_test = classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42857142857142855"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predicted_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_train = classifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, predicted_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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