AR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3b8aa7e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#read data\n",
    "import pandas as pd\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori,association_rules\n",
    "data_1 = pd.read_csv(\"breast_cancer_bd.csv\",dtype = str)\n",
    "data_1=data_1[data_1[\"Bare Nuclei\"] != '?']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c5340c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
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       "      <th>695</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "</table>\n",
       "<p>683 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Clump Thickness Uniformity of Cell Size Uniformity of Cell Shape  \\\n",
       "0                 5                       1                        1   \n",
       "1                 5                       4                        4   \n",
       "2                 3                       1                        1   \n",
       "3                 6                       8                        8   \n",
       "4                 4                       1                        1   \n",
       "..              ...                     ...                      ...   \n",
       "694               3                       1                        1   \n",
       "695               2                       1                        1   \n",
       "696               5                      10                       10   \n",
       "697               4                       8                        6   \n",
       "698               4                       8                        8   \n",
       "\n",
       "    Marginal Adhesion Single Epithelial Cell Size Bare Nuclei Bland Chromatin  \\\n",
       "0                   1                           2           1               3   \n",
       "1                   5                           7          10               3   \n",
       "2                   1                           2           2               3   \n",
       "3                   1                           3           4               3   \n",
       "4                   3                           2           1               3   \n",
       "..                ...                         ...         ...             ...   \n",
       "694                 1                           3           2               1   \n",
       "695                 1                           2           1               1   \n",
       "696                 3                           7           3               8   \n",
       "697                 4                           3           4              10   \n",
       "698                 5                           4           5              10   \n",
       "\n",
       "    Normal Nucleoli Mitoses  \n",
       "0                 1       1  \n",
       "1                 2       1  \n",
       "2                 1       1  \n",
       "3                 7       1  \n",
       "4                 1       1  \n",
       "..              ...     ...  \n",
       "694               1       1  \n",
       "695               1       1  \n",
       "696              10       2  \n",
       "697               6       1  \n",
       "698               4       1  \n",
       "\n",
       "[683 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ori = data_1.iloc[:,1:10]\n",
    "c1 = data_ori\n",
    "data_ori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c42fc3e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1843: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item_labels[indexer[info_axis]]] = value\n"
     ]
    }
   ],
   "source": [
    "#区分不同的属性        \n",
    "c1.iloc[:,0] = pd.Series(c1.iloc[:,0])+\" Clump thickness\"\n",
    "c1.iloc[:,1] = pd.Series(c1.iloc[:,1])+\" Uniformity of cell size\"\n",
    "c1.iloc[:,2] = pd.Series(c1.iloc[:,2])+\" Uniformity of cell shape\"\n",
    "c1.iloc[:,3] = pd.Series(c1.iloc[:,3])+\" Marginal adhesion\"\n",
    "c1.iloc[:,4] = pd.Series(c1.iloc[:,4])+\" Single epithelial cell size\"\n",
    "c1.iloc[:,5] = pd.Series(c1.iloc[:,5])+\" Bare nuclei\"\n",
    "c1.iloc[:,6] = pd.Series(c1.iloc[:,6])+\" Bland chromatin\"\n",
    "c1.iloc[:,7] = pd.Series(c1.iloc[:,7])+\" Normal nucleoli\"\n",
    "c1.iloc[:,8] = pd.Series(c1.iloc[:,8])+\" Mitoses\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "31d34c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "#在进行处理时需要二维列表\n",
    "c=c1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fc36bcda",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(1 Normal nucleoli)</td>\n",
       "      <td>(1 Mitoses)</td>\n",
       "      <td>0.632504</td>\n",
       "      <td>0.824305</td>\n",
       "      <td>0.607613</td>\n",
       "      <td>0.960648</td>\n",
       "      <td>1.165404</td>\n",
       "      <td>0.086238</td>\n",
       "      <td>4.464732</td>\n",
       "      <td>0.386205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           antecedents  consequents  antecedent support  consequent support  \\\n",
       "0  (1 Normal nucleoli)  (1 Mitoses)            0.632504            0.824305   \n",
       "\n",
       "    support  confidence      lift  leverage  conviction  zhangs_metric  \n",
       "0  0.607613    0.960648  1.165404  0.086238    4.464732       0.386205  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "te = TransactionEncoder()\n",
    "te_c = te.fit_transform(c)\n",
    "df = pd.DataFrame(te_c,columns = te.columns_)\n",
    "item = apriori(df,min_support = 0.6,use_colnames = True)\n",
    "rules = association_rules(item,min_threshold =0.8)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e5598c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Normal nucleoli->1 Mitoses\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "for i,j in rules.iterrows():\n",
    "    X = j['antecedents']\n",
    "    Y = j['consequents']\n",
    "    x = ','.join([item for item in X])\n",
    "    y = ','.join([item for item in Y])\n",
    "    print(x+'->'+ y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "01b98222",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({frozenset({'1 Mitoses'}): 1})\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counter = Counter(rules.consequents)\n",
    "print(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc45c0de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96afc268",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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