ML_LogisticRegression & L2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-13T03:09:18.020358Z",
     "start_time": "2018-06-13T03:09:17.731921Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1127e14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画一条sigmoid曲线\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def sigmoid(z):\n",
    "    return 1.0/(1.0+np.exp(-z))\n",
    "z = np.arange(-7,7,0.1)\n",
    "phi_z = sigmoid(z)\n",
    "plt.plot(z, phi_z)\n",
    "plt.yticks([0.0, 0.5, 1.0])\n",
    "plt.axvline(0.0, color = 'k', ls = 'dotted')\n",
    "# plt.axhspan(0.0, 1.0, facecolor = \"1.0\", alpha =1.0, ls = 'dotted')\n",
    "plt.axhline(y = 0.5, ls = 'dotted', color = 'k')\n",
    "plt.xlabel('z')\n",
    "plt.ylabel('$\\phi (z)$')\n",
    "plt.show()\n",
    "           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-13T03:11:46.347558Z",
     "start_time": "2018-06-13T03:11:46.266280Z"
    }
   },
   "outputs": [],
   "source": [
    "# 准备数据\n",
    "# 引入一个练习的dataset\n",
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:,[2,3]]\n",
    "y = iris.target\n",
    "# 把数据划分成训练集和验证集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "X, y, test_size=0.3, random_state=0)\n",
    "# standardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit(X_train)\n",
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)\n",
    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
    "y_combined = np.hstack((y_train, y_test))\n",
    "# 绘出决策域\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "def plot_decision_regions(X,y,classifier, \n",
    "                          test_idx=None,resolution=0.02):\n",
    "    markers = ('s','x','o','^','v')\n",
    "    colors = ('red','blue','lightgreen','gray','cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    x1_min, x1_max = X[:,0].min() -1, X[:,0].max() +1\n",
    "    x2_min, x2_max = X[:,1].min() -1, X[:,1].max() +1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                          np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha = 0.4, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n",
    "                   alpha = 0.8, c = cmap(idx),\n",
    "                   marker = markers[idx], label = cl)\n",
    "    # highlight test samples\n",
    "    if test_idx:\n",
    "        x_test, y_test = X[test_idx, :], y[test_idx]\n",
    "        plt.scatter(X_test[:,0], X_test[:, 1], c ='k',\n",
    "                   alpha = 1.0, linewidth =1, marker = 'o',\n",
    "                   s= 55, label = 'test set')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-13T03:11:48.026365Z",
     "start_time": "2018-06-13T03:11:47.843019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18d18ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LR部分\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(C = 1000.0, random_state = 0)\n",
    "lr.fit(X_train_std, y_train)\n",
    "plot_decision_regions(X_combined_std,\n",
    "                      y_combined, \n",
    "                      classifier = lr,\n",
    "                      test_idx = range(105,150))\n",
    "plt.xlabel('petal length [standardized]')\n",
    "plt.ylabel('petal width [standardized]')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-13T03:11:58.563742Z",
     "start_time": "2018-06-13T03:11:58.538104Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Expected 2D array, got 1D array instead:\narray=[0.70793846 1.50872803].\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[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-00fdde5b3453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test_std\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#这一句没搞定，不知道什么原因\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0mcalculate_ovr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmulti_class\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ovr\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcalculate_ovr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict_proba_lr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_predict_proba_lr\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0mmulticlass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mhandled\u001b[0m \u001b[0mby\u001b[0m \u001b[0mnormalizing\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mover\u001b[0m \u001b[0mall\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \"\"\"\n\u001b[0;32m--> 338\u001b[0;31m         \u001b[0mprob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m         \u001b[0mprob\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprob\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    298\u001b[0m                                  \"yet\" % {'name': type(self).__name__})\n\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0mn_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_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[1;32m    439\u001b[0m                     \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m                     \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m                     \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m    442\u001b[0m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m             \u001b[0;31m# To ensure that array flags are maintained\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0.70793846 1.50872803].\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."
     ]
    }
   ],
   "source": [
    "lr.predict_proba(X_test_std[0,:]) #这一句没搞定，不知道什么原因"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-13T03:12:18.307858Z",
     "start_time": "2018-06-13T03:12:18.121178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18d18358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 引入L2正则，看不同C的强度对权重的影响\n",
    "weights, params = [],[]\n",
    "for c in np.arange(-5,5,dtype=float):\n",
    "    lr = LogisticRegression(C = 10**c, random_state = 0)\n",
    "    lr.fit(X_train_std, y_train)\n",
    "    weights.append(lr.coef_[1])\n",
    "    params.append(10**c)\n",
    "weights = np.array(weights)\n",
    "plt.plot(params, weights[:,0],\n",
    "        label = 'petal lenghth')\n",
    "plt.plot(params, weights[:,1],\n",
    "       linestyle = '--',\n",
    "       label = 'petal width')\n",
    "plt.ylabel('weight coefficient')\n",
    "plt.xlabel('C')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}