{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('mushrooms/train/train.tsv',sep='\\t', header=None)\n",
    "X_ver = pd.read_csv('mushrooms/dev-0/in.tsv',sep='\\t', header=None)\n",
    "y_ver = pd.read_csv('mushrooms/dev-0/expected.tsv',sep='\\t', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0  1  2  3  4  5  6  7  8  9   ... 13 14 15 16 17 18 19 20 21 22\n",
      "0  p  x  s  n  t  p  f  c  n  k  ...  s  w  w  p  w  o  p  k  s  u\n",
      "1  e  x  s  y  t  a  f  c  b  k  ...  s  w  w  p  w  o  p  n  n  g\n",
      "2  p  x  y  w  t  p  f  c  n  n  ...  s  w  w  p  w  o  p  k  s  u\n",
      "3  e  x  s  g  f  n  f  w  b  k  ...  s  w  w  p  w  o  e  n  a  g\n",
      "4  e  x  y  y  t  a  f  c  b  n  ...  s  w  w  p  w  o  p  k  n  g\n",
      "\n",
      "[5 rows x 23 columns]\n",
      "0     0\n",
      "1     0\n",
      "2     0\n",
      "3     0\n",
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      "5     0\n",
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      "20    0\n",
      "21    0\n",
      "22    0\n",
      "dtype: int64\n",
      "(6465, 23)\n"
     ]
    }
   ],
   "source": [
    "print(df.head())\n",
    "print(df.isna().sum())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2\n",
       "1      6\n",
       "2      4\n",
       "3     10\n",
       "4      2\n",
       "5      9\n",
       "6      2\n",
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       "9     12\n",
       "10     2\n",
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       "dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      6\n",
       "1      3\n",
       "2     10\n",
       "3      2\n",
       "4      9\n",
       "5      2\n",
       "6      2\n",
       "7      2\n",
       "8     12\n",
       "9      2\n",
       "10     5\n",
       "11     4\n",
       "12     4\n",
       "13     9\n",
       "14     9\n",
       "15     1\n",
       "16     4\n",
       "17     3\n",
       "18     5\n",
       "19     9\n",
       "20     6\n",
       "21     7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ver.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
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      ],
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       "     0_b  0_c  0_f  0_k  0_s  0_x  1_s  1_y  1_f  1_g  ...  20_s  20_v  20_y  \\\n",
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       "\n",
       "     21_d  21_g  21_l  21_m  21_p  21_u  21_w  \n",
       "0       0     0     0     1     0     0     0  \n",
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       "\n",
       "[792 rows x 117 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ver = pd.get_dummies(y_ver,columns=[0],drop_first=True)\n",
    "df2_unique = df[2].unique()\n",
    "X_ver[1] = pd.Categorical(X_ver[1],categories=df2_unique)\n",
    "X_ver = pd.get_dummies(X_ver)\n",
    "X_ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>6465 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0_p  1_b  1_c  1_f  1_k  1_s  1_x  2_f  2_g  2_s  ...  21_s  21_v  21_y  \\\n",
       "0       1    0    0    0    0    0    1    0    0    1  ...     1     0     0   \n",
       "1       0    0    0    0    0    0    1    0    0    1  ...     0     0     0   \n",
       "2       1    0    0    0    0    0    1    0    0    0  ...     1     0     0   \n",
       "3       0    0    0    0    0    0    1    0    0    1  ...     0     0     0   \n",
       "4       0    0    0    0    0    0    1    0    0    0  ...     0     0     0   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n",
       "6460    1    0    0    0    1    0    0    0    0    0  ...     0     1     0   \n",
       "6461    0    0    0    0    1    0    0    0    0    1  ...     0     0     0   \n",
       "6462    0    0    0    1    0    0    0    0    0    1  ...     0     0     0   \n",
       "6463    1    0    0    0    1    0    0    0    0    0  ...     0     1     0   \n",
       "6464    0    0    0    0    0    0    1    0    0    1  ...     0     0     0   \n",
       "\n",
       "      22_d  22_g  22_l  22_m  22_p  22_u  22_w  \n",
       "0        0     0     0     0     0     1     0  \n",
       "1        0     1     0     0     0     0     0  \n",
       "2        0     0     0     0     0     1     0  \n",
       "3        0     1     0     0     0     0     0  \n",
       "4        0     1     0     0     0     0     0  \n",
       "...    ...   ...   ...   ...   ...   ...   ...  \n",
       "6460     1     0     0     0     0     0     0  \n",
       "6461     0     0     1     0     0     0     0  \n",
       "6462     0     0     1     0     0     0     0  \n",
       "6463     0     0     1     0     0     0     0  \n",
       "6464     0     0     1     0     0     0     0  \n",
       "\n",
       "[6465 rows x 118 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.get_dummies(df,columns=[0],drop_first=True)\n",
    "df = pd.get_dummies(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4848, 117)\n",
      "(1617, 117)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.loc[:, df.columns != '0_p']\n",
    "y = df['0_p']\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>792 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     1_b  1_c  1_f  1_k  1_s  1_x  2_f  2_g  2_s  2_y  ...  21_s  21_v  21_y  \\\n",
       "0      1    0    0    0    0    0    1    0    0    0  ...     0     0     0   \n",
       "1      0    0    0    0    0    1    0    1    0    0  ...     0     1     0   \n",
       "2      1    0    0    0    0    0    0    1    0    0  ...     0     0     0   \n",
       "3      1    0    0    0    0    0    1    0    0    0  ...     1     0     0   \n",
       "4      0    0    0    0    0    1    0    1    0    0  ...     0     0     0   \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n",
       "787    0    0    0    0    0    1    1    0    0    0  ...     0     1     0   \n",
       "788    0    0    1    0    0    0    1    0    0    0  ...     0     1     0   \n",
       "789    0    0    0    1    0    0    1    0    0    0  ...     0     1     0   \n",
       "790    0    0    0    1    0    0    0    1    0    0  ...     0     1     0   \n",
       "791    0    0    0    0    0    1    1    0    0    0  ...     0     1     0   \n",
       "\n",
       "     22_d  22_g  22_l  22_m  22_p  22_u  22_w  \n",
       "0       0     0     0     1     0     0     0  \n",
       "1       0     1     0     0     0     0     0  \n",
       "2       0     0     0     1     0     0     0  \n",
       "3       0     0     0     1     0     0     0  \n",
       "4       0     0     0     1     0     0     0  \n",
       "..    ...   ...   ...   ...   ...   ...   ...  \n",
       "787     1     0     0     0     0     0     0  \n",
       "788     0     0     1     0     0     0     0  \n",
       "789     0     0     1     0     0     0     0  \n",
       "790     0     0     1     0     0     0     0  \n",
       "791     0     0     1     0     0     0     0  \n",
       "\n",
       "[792 rows x 117 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ver.columns = X_test.columns\n",
    "X_ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.00\n",
      "1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "poly = PolynomialFeatures(degree=2,include_bias=False)\n",
    "X_poly = poly.fit_transform(X_train)\n",
    "lr = LogisticRegression(C=10).fit(X_poly,y_train)\n",
    "print('{:.2f}'.format(lr.score(X_poly,y_train)))\n",
    "print('{:.2f}'.format(lr.score(poly.fit_transform(X_test),y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n",
      "0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train,y_train)\n",
    "\n",
    "print('{:.2f}'.format(gnb.score(X_train,y_train)))\n",
    "print('{:.2f}'.format(gnb.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.00\n",
      "1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC(kernel='rbf',C=10, gamma=0.1).fit(X_train,y_train)\n",
    "\n",
    "print('{:.2f}'.format(svc.score(X_train,y_train)))\n",
    "print('{:.2f}'.format(svc.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.00\n",
      "1.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=3).fit(X_train,y_train)\n",
    "print('{:.2f}'.format(knn.score(X_train,y_train)))\n",
    "print('{:.2f}'.format(knn.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.00\n",
      "1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier(activation='relu', hidden_layer_sizes=[10],solver='lbfgs').fit(X_train,y_train)\n",
    "\n",
    "print('{:.2f}'.format(mlp.score(X_train,y_train)))\n",
    "print('{:.2f}'.format(mlp.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bayes raport:\n",
      "Accuracy score 0.91\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     jadalne       1.00      0.82      0.90       406\n",
      "     trujące       0.84      1.00      0.91       386\n",
      "\n",
      "    accuracy                           0.91       792\n",
      "   macro avg       0.92      0.91      0.91       792\n",
      "weighted avg       0.92      0.91      0.91       792\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,accuracy_score\n",
    "\n",
    "pred_bayes = gnb.predict(X_ver)\n",
    "print('Bayes raport:')\n",
    "print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_bayes)))\n",
    "print(classification_report(y_ver,pred_bayes,target_names=['jadalne','trujące']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression raport:\n",
      "Accuracy score 1.00\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     jadalne       1.00      1.00      1.00       406\n",
      "     trujące       1.00      1.00      1.00       386\n",
      "\n",
      "    accuracy                           1.00       792\n",
      "   macro avg       1.00      1.00      1.00       792\n",
      "weighted avg       1.00      1.00      1.00       792\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_log = lr.predict(poly.fit_transform(X_ver))\n",
    "print('Logistic Regression raport:')\n",
    "print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_log)))\n",
    "print(classification_report(y_ver,pred_log,target_names=['jadalne','trujące']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machines raport:\n",
      "Accuracy score 1.00\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     jadalne       1.00      1.00      1.00       406\n",
      "     trujące       1.00      1.00      1.00       386\n",
      "\n",
      "    accuracy                           1.00       792\n",
      "   macro avg       1.00      1.00      1.00       792\n",
      "weighted avg       1.00      1.00      1.00       792\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_svc = svc.predict(X_ver)\n",
    "print('Support Vector Machines raport:')\n",
    "print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_svc)))\n",
    "print(classification_report(y_ver,pred_svc,target_names=['jadalne','trujące']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-nearest neighbors raport:\n",
      "Accuracy score 1.00\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     jadalne       1.00      1.00      1.00       406\n",
      "     trujące       1.00      1.00      1.00       386\n",
      "\n",
      "    accuracy                           1.00       792\n",
      "   macro avg       1.00      1.00      1.00       792\n",
      "weighted avg       1.00      1.00      1.00       792\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tonywesoly/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "pred_knn = knn.predict(X_ver)\n",
    "print('K-nearest neighbors raport:')\n",
    "print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_knn)))\n",
    "print(classification_report(y_ver,pred_knn,target_names=['jadalne','trujące']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural network raport:\n",
      "Accuracy score 1.00\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     jadalne       1.00      1.00      1.00       406\n",
      "     trujące       1.00      1.00      1.00       386\n",
      "\n",
      "    accuracy                           1.00       792\n",
      "   macro avg       1.00      1.00      1.00       792\n",
      "weighted avg       1.00      1.00      1.00       792\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_mlp = mlp.predict(X_ver)\n",
    "print('Neural network raport:')\n",
    "print('Accuracy score {:.2f}'.format(accuracy_score(y_ver,pred_mlp)))\n",
    "print(classification_report(y_ver,pred_mlp,target_names=['jadalne','trujące']))"
   ]
  }
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