machine_learning/ensemble_learning_19/ensemble_learning.ipynb

{
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   "id": "9c8d8367-9587-4e87-ad42-2debf858dfcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bece23f0-ea8b-450b-b82a-63bddc3c1de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "554e5c95-083e-45fc-9438-b2121ec20b4f",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "735d8795-1dac-4623-be2d-7fb2a2f5049e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a10a77a-d90f-4b0b-a951-d1c7c46202c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "301f9c12-aeae-45b1-8689-18cc6e00718d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.Outcome\n",
    "x = df.drop(\"Outcome\", axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa9a0f52-5647-429c-8b76-df87a3243519",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
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       "  </thead>\n",
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       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "      <td>31</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>32</td>\n",
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       "      <td>21</td>\n",
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       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7682595-b4fd-4fbc-8fca-e4a9300a4378",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4af8802-d94e-4fa7-a993-bd33e8fd56a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scalered = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "84572647-fa48-4807-b58f-eef4fb7650bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.84832379,  0.14964075,  0.90726993, -0.69289057,\n",
       "         0.20401277,  0.46849198,  1.4259954 ],\n",
       "       [-0.84488505, -1.12339636, -0.16054575,  0.53090156, -0.69289057,\n",
       "        -0.68442195, -0.36506078, -0.19067191],\n",
       "       [ 1.23388019,  1.94372388, -0.26394125, -1.28821221, -0.69289057,\n",
       "        -1.10325546,  0.60439732, -0.10558415]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_scalered[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eea39bb8-93dd-4bcd-b509-10cfff7b5fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 268)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y[y==0]), len(y[y==1])#数据还算平衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "725d9479-9a33-4d8b-93b5-256ebef8c078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.536"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y[y==1])/len(y[y==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9983a9ff-6aff-4f2e-8d3a-4dfe52c0a580",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2b049926-9155-40c9-803e-a9f8a75da355",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y, test_size=0.3)#stratify使拆分后比例一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d36e5561-5ded-473d-9161-59ee4e2f7755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5342857142857143"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train[y_train==1])/len(y_train[y_train==0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8c4fdb1-2730-46c5-99f8-d2cb4c6d1d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4cdd4a2-b7db-4e0c-bb7e-436796b38f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "62495661-ca1e-43d4-9da7-2591acfcf639",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvs = cross_val_score(DecisionTreeClassifier(), x, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3d10246e-3e29-4037-a166-10b365381b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0be2cea0-bfca-44dd-85d3-85de1e14355e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7110432051608522"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "da9f0cde-1b1f-45b2-9d18-50de178bdd65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0a7f52d2-f648-4062-946b-f178823bb29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                  n_estimators=100, oob_score=True, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=0.8,\n",
       "                  n_estimators=100, oob_score=True, random_state=0)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(estimator=DecisionTreeClassifier(), max_samples=0.8,\n",
       "                  n_estimators=100, oob_score=True, random_state=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagg = BaggingClassifier(estimator=DecisionTreeClassifier(),\n",
    "                         n_estimators=100,\n",
    "                         max_samples=0.8,\n",
    "                         oob_score=True,\n",
    "                         random_state=0\n",
    "                    )\n",
    "bagg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0e94db07-919f-4911-8c37-0516649628f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7392923649906891"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagg.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6ed41070-4666-4d84-b5c7-3be05809aec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7662337662337663"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "75149b99-951c-4566-b039-ee7142886a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7578728461081402"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagg = BaggingClassifier(estimator=DecisionTreeClassifier(),\n",
    "                         n_estimators=100,\n",
    "                         max_samples=0.8,\n",
    "                         oob_score=True,\n",
    "                         random_state=0\n",
    "                    )\n",
    "np.mean(cross_val_score(bagg, x, y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fdec9f7e-4b83-462f-ae41-0aeb1a20da7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1ca4c92b-d7c1-4c5e-b2fa-f5331ca4ccd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7657074951192598"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(RandomForestClassifier(), x, y, cv=5).mean()#随机森林就是bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f53b7a-a348-4e12-83e1-3d04d43d7907",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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