feature_importance.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 機械学習モデルにおける特徴量重要度\n",
    "機械学習を用いて簡単な分類問題に取り組み，モデルの予測に寄与している特徴量重要度を算出＆可視化してみましょう．\\\n",
    "データセットにはワインの品質を"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "df = load_breast_cancer(as_frame=True).frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype=object)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 線形モデル（ロジスティック回帰）の場合\n",
    "特徴量重要度の可視化は，線形モデルであれば非常に簡単です．\\\n",
    "ここではロジスティック回帰を試してみましょう．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9473684210526315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.9.2/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "x = df.iloc[:, 0:-1]\n",
    "y = df.iloc[:, -1]\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, train_size=0.8, random_state=0)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_test)\n",
    "\n",
    "print(accuracy_score(y_test, pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 係数の可視化\n",
    "0 or 1の二値分類において，\n",
    "ラベルが1である確率の推定値$\\hat{P}$を\n",
    "$$\n",
    "\\hat{P} = \\frac{1}{1+\\exp{-(\\hat{\\beta_0} + \\hat{\\beta_1}x_1+\\hat{\\beta_2}x_2 + \\cdots + \\hat{\\beta_n}x_n) }}\n",
    "$$\n",
    "求めるのがロジスティック回帰でした．\\\n",
    "このとき，偏回帰係数と呼ばれる$\\hat{\\beta}$をみることで，説明変数$x$がそれぞれモデルに予測にどのような影響を与えているかを確認することができます．\\\n",
    "確認は下記の1行で簡単にできます．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.53078812,  0.29248643,  0.06496636, -0.00418594, -0.06178848,\n",
       "        -0.29126313, -0.39465668, -0.16386936, -0.11398609, -0.01908855,\n",
       "         0.0832338 ,  0.61301875,  0.40341709, -0.129433  , -0.00361209,\n",
       "        -0.06291653, -0.08573028, -0.01999504, -0.02436249, -0.00532331,\n",
       "         1.67406428, -0.40528023, -0.24910565, -0.02162706, -0.10768343,\n",
       "        -0.93619314, -1.17052757, -0.32680747, -0.31638529, -0.09495482]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30個の変数にかかる係数が確認できました．\\\n",
    "このままでも良いのですが，変数名との照らし合わせや係数間の比較を行うために，\\\n",
    "グラフで表してみましょう．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 30) (30,)\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_.shape, x.columns.values.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 30 artists>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.barh(x.columns.values, model.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.956140350877193\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "model = MLPClassifier(hidden_layer_sizes=100, activation=\"tanh\", max_iter=500, random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "pred = model.predict(x_test)\n",
    "\n",
    "print(accuracy_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0       0  \n",
       "1                            3.40   1050.0       0  \n",
       "2                            3.17   1185.0       0  \n",
       "3                            3.45   1480.0       0  \n",
       "4                            2.93    735.0       0  \n",
       "..                            ...      ...     ...  \n",
       "173                          1.74    740.0       2  \n",
       "174                          1.56    750.0       2  \n",
       "175                          1.56    835.0       2  \n",
       "176                          1.62    840.0       2  \n",
       "177                          1.60    560.0       2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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