src/ml/feature_selection.ipynb

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   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from scipy.stats import pearsonr\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris=load_iris()\n",
    "#选择K个最好的特征，返回选择特征后的数据\n",
    "\n",
    "#第一个参数为计算评估特征是否好的函数，该函数输入特征矩阵和目标向量，输出二元组（评分，P值）的数组，数组第i项为第i个特征的评分和P值。在此定义为计算相关系数\n",
    "#参数k为选择的特征个数\n",
    "# 定义函数\n",
    "def multivariate_pearsonr(X, y):\n",
    "    scores, pvalues = [], []\n",
    "    for ret in map(lambda x:pearsonr(x, y), X.T):\n",
    "        scores.append(abs(ret[0]))\n",
    "        pvalues.append(ret[1])\n",
    "    return (np.array(scores), np.array(pvalues))\n",
    "\n",
    "transformer = SelectKBest(score_func=multivariate_pearsonr, k=2)\n",
    "Xt_pearson = transformer.fit_transform(iris.data, iris.target)\n",
    "print(Xt_pearson)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "#选择K个最好的特征，返回选择特征后的数据\n",
    "SelectKBest(mutual_info_classif, k=2).fit_transform(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#递归特征消除法，返回特征选择后的数据\n",
    "#参数estimator为基模型\n",
    "#参数n_features_to_select为选择的特征个数\n",
    "RFE(estimator=LogisticRegression(), n_features_to_select=2).fit_transform(iris.data,iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.4,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.3,  0.2],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.7,  0.4],\n",
       "       [ 1.4,  0.3],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.5,  0.1],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.6,  0.2],\n",
       "       [ 1.4,  0.1],\n",
       "       [ 1.1,  0.1],\n",
       "       [ 1.2,  0.2],\n",
       "       [ 1.5,  0.4],\n",
       "       [ 1.3,  0.4],\n",
       "       [ 1.4,  0.3],\n",
       "       [ 1.7,  0.3],\n",
       "       [ 1.5,  0.3],\n",
       "       [ 1.7,  0.2],\n",
       "       [ 1.5,  0.4],\n",
       "       [ 1. ,  0.2],\n",
       "       [ 1.7,  0.5],\n",
       "       [ 1.9,  0.2],\n",
       "       [ 1.6,  0.2],\n",
       "       [ 1.6,  0.4],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.6,  0.2],\n",
       "       [ 1.6,  0.2],\n",
       "       [ 1.5,  0.4],\n",
       "       [ 1.5,  0.1],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.5,  0.1],\n",
       "       [ 1.2,  0.2],\n",
       "       [ 1.3,  0.2],\n",
       "       [ 1.5,  0.1],\n",
       "       [ 1.3,  0.2],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.3,  0.3],\n",
       "       [ 1.3,  0.3],\n",
       "       [ 1.3,  0.2],\n",
       "       [ 1.6,  0.6],\n",
       "       [ 1.9,  0.4],\n",
       "       [ 1.4,  0.3],\n",
       "       [ 1.6,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 1.5,  0.2],\n",
       "       [ 1.4,  0.2],\n",
       "       [ 4.7,  1.4],\n",
       "       [ 4.5,  1.5],\n",
       "       [ 4.9,  1.5],\n",
       "       [ 4. ,  1.3],\n",
       "       [ 4.6,  1.5],\n",
       "       [ 4.5,  1.3],\n",
       "       [ 4.7,  1.6],\n",
       "       [ 3.3,  1. ],\n",
       "       [ 4.6,  1.3],\n",
       "       [ 3.9,  1.4],\n",
       "       [ 3.5,  1. ],\n",
       "       [ 4.2,  1.5],\n",
       "       [ 4. ,  1. ],\n",
       "       [ 4.7,  1.4],\n",
       "       [ 3.6,  1.3],\n",
       "       [ 4.4,  1.4],\n",
       "       [ 4.5,  1.5],\n",
       "       [ 4.1,  1. ],\n",
       "       [ 4.5,  1.5],\n",
       "       [ 3.9,  1.1],\n",
       "       [ 4.8,  1.8],\n",
       "       [ 4. ,  1.3],\n",
       "       [ 4.9,  1.5],\n",
       "       [ 4.7,  1.2],\n",
       "       [ 4.3,  1.3],\n",
       "       [ 4.4,  1.4],\n",
       "       [ 4.8,  1.4],\n",
       "       [ 5. ,  1.7],\n",
       "       [ 4.5,  1.5],\n",
       "       [ 3.5,  1. ],\n",
       "       [ 3.8,  1.1],\n",
       "       [ 3.7,  1. ],\n",
       "       [ 3.9,  1.2],\n",
       "       [ 5.1,  1.6],\n",
       "       [ 4.5,  1.5],\n",
       "       [ 4.5,  1.6],\n",
       "       [ 4.7,  1.5],\n",
       "       [ 4.4,  1.3],\n",
       "       [ 4.1,  1.3],\n",
       "       [ 4. ,  1.3],\n",
       "       [ 4.4,  1.2],\n",
       "       [ 4.6,  1.4],\n",
       "       [ 4. ,  1.2],\n",
       "       [ 3.3,  1. ],\n",
       "       [ 4.2,  1.3],\n",
       "       [ 4.2,  1.2],\n",
       "       [ 4.2,  1.3],\n",
       "       [ 4.3,  1.3],\n",
       "       [ 3. ,  1.1],\n",
       "       [ 4.1,  1.3],\n",
       "       [ 6. ,  2.5],\n",
       "       [ 5.1,  1.9],\n",
       "       [ 5.9,  2.1],\n",
       "       [ 5.6,  1.8],\n",
       "       [ 5.8,  2.2],\n",
       "       [ 6.6,  2.1],\n",
       "       [ 4.5,  1.7],\n",
       "       [ 6.3,  1.8],\n",
       "       [ 5.8,  1.8],\n",
       "       [ 6.1,  2.5],\n",
       "       [ 5.1,  2. ],\n",
       "       [ 5.3,  1.9],\n",
       "       [ 5.5,  2.1],\n",
       "       [ 5. ,  2. ],\n",
       "       [ 5.1,  2.4],\n",
       "       [ 5.3,  2.3],\n",
       "       [ 5.5,  1.8],\n",
       "       [ 6.7,  2.2],\n",
       "       [ 6.9,  2.3],\n",
       "       [ 5. ,  1.5],\n",
       "       [ 5.7,  2.3],\n",
       "       [ 4.9,  2. ],\n",
       "       [ 6.7,  2. ],\n",
       "       [ 4.9,  1.8],\n",
       "       [ 5.7,  2.1],\n",
       "       [ 6. ,  1.8],\n",
       "       [ 4.8,  1.8],\n",
       "       [ 4.9,  1.8],\n",
       "       [ 5.6,  2.1],\n",
       "       [ 5.8,  1.6],\n",
       "       [ 6.1,  1.9],\n",
       "       [ 6.4,  2. ],\n",
       "       [ 5.6,  2.2],\n",
       "       [ 5.1,  1.5],\n",
       "       [ 5.6,  1.4],\n",
       "       [ 6.1,  2.3],\n",
       "       [ 5.6,  2.4],\n",
       "       [ 5.5,  1.8],\n",
       "       [ 4.8,  1.8],\n",
       "       [ 5.4,  2.1],\n",
       "       [ 5.6,  2.4],\n",
       "       [ 5.1,  2.3],\n",
       "       [ 5.1,  1.9],\n",
       "       [ 5.9,  2.3],\n",
       "       [ 5.7,  2.5],\n",
       "       [ 5.2,  2.3],\n",
       "       [ 5. ,  1.9],\n",
       "       [ 5.2,  2. ],\n",
       "       [ 5.4,  2.3],\n",
       "       [ 5.1,  1.8]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "#GBDT作为基模型的特征选择\n",
    "SelectFromModel(GradientBoostingClassifier()).fit_transform(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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