Adaboost进行给定训练集并且最终分类器可视化.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier #adaboost引入方法\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles#造数据\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 设置属性防止中文乱码\n",
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdt = AdaBoostClassifier(RandomForestClassifier(),\n",
    "                         algorithm=\"SAMME.R\",#可以不写\n",
    "                         n_estimators=10, learning_rate=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "x_test=np.arange(10)\n",
    "print(x_test)\n",
    "y_true=[1,1,1,-1,-1,-1,1,1,1,-1]\n",
    "y_true=np.array(y_true)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-e00ebbac50ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbdt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    411\u001b[0m         \u001b[1;31m# Fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 412\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAdaBoostClassifier\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    413\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    414\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_validate_estimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         X, y = check_X_y(X, y, accept_sparse=accept_sparse, dtype=dtype,\n\u001b[1;32m--> 110\u001b[1;33m                          y_numeric=is_regressor(self))\n\u001b[0m\u001b[0;32m    111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    754\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    755\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 756\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    757\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    550\u001b[0m                     \u001b[1;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m                     \u001b[1;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 552\u001b[1;33m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[1;31m# in the future np.flexible dtypes will be handled like object dtypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
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