机器学习-ABU量化系统.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABU 量化系统  简介（版本 0.1）\n",
    "\n",
    "* #### __author__ = 'BBFamily'\n",
    "* ####  __email__ = 'bbfamily@126.com'\n",
    "* ####  __weixin__ = 'aaaabbbuu'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六部分 机器学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ZEnv\n",
    "import ZLog\n",
    "import ZCommonUtil\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本封装库的使用\n",
    "        \n",
    "        初始生成一个树回归，一个树分类，\n",
    "        自动根据y的classes确定使用分离还是回归\n",
    "        可以生成非监督学习分类器及hmm等\n",
    "        两个的数据直接生成测试数据，一个比较多，一个少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from MlFiter import MlFiterClass\n",
    "test_fiter = MlFiterClass.create_test_fiter()\n",
    "test_fiter_more = MlFiterClass.create_test_more_fiter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**学习曲线**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MIvHx2lw01ALDvDz/azlyc4P7+qwUJYFwxBFwxBFLaWsLbhX7YNjtOiJr2zbt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174c2ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.80412095046015697, 0.028989030232112856)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.estimator.logistic_regression()\n",
    "test_fiter_more.plot_learning_curve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征权重**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "******************DecisionTreeClassifier******************\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x0</td>\n",
       "      <td>0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x1</td>\n",
       "      <td>0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>x2</td>\n",
       "      <td>0.050723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>x3</td>\n",
       "      <td>0.922611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance\n",
       "0      x0    0.013333\n",
       "1      x1    0.013333\n",
       "2      x2    0.050723\n",
       "3      x3    0.922611"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter.importances_coef_pd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**树逻辑图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D0qVLoVKp0KBBAwDAuHHjUL9+fbRt2xZxcXGYMGEChEKhEUalasAUL8Nk0C7Y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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c5e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_fiter.plot_graphviz_tree()\n",
    "path = ZEnv.shell_cmd_result('pwd') + '/graphviz.png'\n",
    "!open $path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**混淆矩阵**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[[455  94]\n",
      " [ 96 246]]\n"
     ]
    },
    {
     "data": {
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xQzMrSUfHDI8BLs2PtwRuLLzmvMlmViWdOZss6RRgSURcOmDhJhwMzawkjVqG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\nMzPAwdDMDID/D48XJn8rP+baAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117079490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_fiter_more.plot_confusion_matrices()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**非均衡选择**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "scores(self, y_pre, y=None)\n",
      "accuracy = 0.79\n",
      "precision_score = 0.71\n",
      "recall_score = 0.74\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "       loss       0.83      0.82      0.82       549\n",
      "        win       0.71      0.74      0.73       342\n",
      "\n",
      "avg / total       0.79      0.79      0.79       891\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix  [[448 101]\n",
      " [ 90 252]]\n",
      "          Predicted\n",
      "         |  0  |  1  |\n",
      "         |-----|-----|\n",
      "       0 | 448 | 101 |\n",
      "Actual   |-----|-----|\n",
      "       1 |  90 | 252 |\n",
      "         |-----|-----|\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((0.78477792995499907, 448), 0.32857142857142863)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PR8SaMvs+m9Om2+guHxzHWT3dIUZwnNXWXeKslkqSxDAg+4d9bVq2E0kDgQaS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\nM7NcThJmZpbLScLMzHI5SZiZWa7/D/7fReN3DXHRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1061e0c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_fiter_more.prob_maximum_recall_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**roc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier :roc\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ad999xyuvvEJWVhZubm6MGzeO119/Ha1WC8C8efOYM2fOeQVUeygCSkFBwU6T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g0BAQFoqxB7S0VFpXIURamRq7oMGDAAjUaDu7s7devWZc6cOQDodDqKiopwcXEp18fFxYWUlBSg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\nY4wxppgsgDLGGGOMKab/DxPFepOvNvMIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f4c890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_fiter_more.plot_roc_estimator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**特征选择**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RFE selection\n",
      "             ranking support\n",
      "SibSp              1    True\n",
      "Parch              1    True\n",
      "Cabin_No           1    True\n",
      "Cabin_Yes          8   False\n",
      "Embarked_C         2   False\n",
      "Embarked_Q         7   False\n",
      "Embarked_S         4   False\n",
      "Sex_female         1    True\n",
      "Sex_male           6   False\n",
      "Pclass_1           5   False\n",
      "Pclass_2           3   False\n",
      "Pclass_3           1    True\n",
      "Age_scaled         1    True\n",
      "Fare_scaled        1    True\n",
      "\n",
      "RFECV selection\n",
      "             ranking support\n",
      "SibSp              1    True\n",
      "Parch              1    True\n",
      "Cabin_No           4   False\n",
      "Cabin_Yes          1    True\n",
      "Embarked_C         1    True\n",
      "Embarked_Q         5   False\n",
      "Embarked_S         2   False\n",
      "Sex_female         6   False\n",
      "Sex_male           1    True\n",
      "Pclass_1           3   False\n",
      "Pclass_2           1    True\n",
      "Pclass_3           1    True\n",
      "Age_scaled         1    True\n",
      "Fare_scaled        1    True\n"
     ]
    }
   ],
   "source": [
    "test_fiter_more.feature_selection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**决策边界**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nt27oamjQe+eujJG6x0JuUb8eGQn+H4oCPXvGsX//pjfWL/13yTv5D5SamkoP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\nPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116d9ecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dJJblXWDPyuWwWK3o9aeuHA0GHRbL+Vftigr35Y6bu9GnZwuGDGjDE+Nnk5SU\nwpIlG3nnnR9wcHaibdvmzJ71C6F+Dqxb8hJOjg1/d2bj4E5g++Gs3jyLbxbfR2LSEqL6P3bRXSRF\nWUdI+GkivhpvQuzC2b9iNhn715yzvbRa2PXrm+xfPgvbSujW4k4C7Zqx7ccXqSwrpLwoG2/3qmJo\nGo0Ob9fmlBdl1cm5KpdGXcnXgUMVRbhiJE5UFSq7jhDiZQo7yWaYDEEvtCSQhUlaeTNtJ4MIRo+G\n5wu38EpAO9qqlaTqxb239+C+e99i2vQHycjIY9asn1n+41MXvf3rL93Ec6/8SO+48VRUmBg6tCtz\n576IEII5cxbz0/zldZLgCwpLeeHVBezel0LzZl68OWEEXidnx56LxVTO8e2LqSjKxskvEkev5hzf\nuog+XZ7CzTmEnQcWkbT6S9qPevOiYkjduYwWoQNp2XwQALZGZ7ZvX4x3ZA8spnKOrJ9Pac5x7NwD\nadZ1FDnHtmPKS6fSVErPDmPRavV4uYWTnrufvOSdOHmHs/fIcmIjb6SsPI+jaZuJaK1q4zQEleTr\ngINGR56soFJaMAgtpdJEGRZcseEp1uEk9eRQTgQutMCNAaJq8pSTNPBd9mHaBqkkXx/G3tsXWxsD\ns9//AVsbPYvmPkZs69onrtXGYNDx7mu38u5rt/LIM3Np0Ta6eqm/jh0jmfn+j/8qrpKSCt6atYQj\nydm0aRnAL39sJ6JFKK++/iC//rqOfjfNYOPyCdjY1BxWufaPTST89A9SSsqKsvBw8MfHNYKDGxai\nc/bA37sN3u5VJaTaRo9gz2/3YrWY0GgvPDxTSis6zal0oNHowGpBWi3s+PkVnDSOtPDpytH0zez4\n+RU8wrvi5hxCQUEKZkslWq0eKSWV5nKEVkf0wMfZ+ctr7Fu6EqvFRLMut+Ie3PZffb+Uy6OSfB1w\n0xppZuPA9PLttJCubCQDieQYhQwnlDLMHKcIW/TYnfYtt0dPnrmCtcUZxNi64XgRv4zKxRNCcO8d\nPbn3jp6Xva9ruoTzxge/MWJEL1xdHZn25rdc0+Wc696ck8lkZtAt7xAQ7Ef/Ad2YO3c5e/an8ffa\nD9FoNPTq1Ya2q7ayfdcxunZsXr3dz79v5aMXvqVd5O0ArM/4H2FRtxDo05Zmgd34adnjWJwDsEor\n0mpm296f0GoNpCauwr91/wt22/i06M2uRVMxGp0w6O3YnPgtgV1GUpJ7gor8THr0fRqN0BDg246f\nVz2Lwc4e/URDAAAgAElEQVSFQ+kJNPPvzMr1bxEREkd69n7KLaW4B7dFqzfS8fZ3MJUVojXYoNWp\nomgNRSX5y7S2KIM30nbiiQ0pshRPWwNjXaLYU5ZHUb5ksAjmiCxkHencSCgLOYyzNKJHMI/9aE2C\nb9OO8K5I5L2gzgQYLm1qu3JljLqxMweSMggNvR2LxcrwQW2Z/ta9l7yfjVsPU1Bcyd/fvoxGo+GW\nW3rj6XkDKSnZBAZ6YbVaKSwqY/7CjQQFuFfP2v3of2uIDR9FsF/Vw1CzuYKk5DUE+rRFoAGhQTi4\nsHLjqxQXFeJo70276BEcTlhBccYhovo9et64XPyiaXndMxzY8gtWi5mgrrfg1+pairOPghDVCzQL\nACFwcA8k9JrR7PvzE6TVzPYDi/AM70K7oWOrq1wKIS7pwa9SP1SSvwwVVgtvpO1knIwhTDiTTRmv\nlW9hnE1Liq1mlolUrFKynSxKMfMFe3HGwKckYqvR4Ww18BLt0UgNS63JzMrYy5uB566HojQcIQST\nnh3Oy08NxWKx1hjzDlBZaWbbzmNoNIJ2McE1FgQ/nclkwd7eBo2m6sraaNRjMOi5+643GXPPIBYs\nWI2UUGrV06nfq/yz+HlCQ7zQ6TSUWk+Vz7BYTRSVZPDzymcoKc1GaLT4xw6iVdh+fv/fOvp2rbry\nDgvswY8rxhPa/c4LJly3oDa4BbWp8Z69WyB6B1fWJvyPEL+OHEvbjN7BFXu3QBw8QvBt2QeruVLN\nom3EVJK/DDnmCoxoCRNVvzwewhZ/HNhTlkd/Rz9WFqQyrvwfrEh640c6Zewlj/cCO7I4PwW3Ils0\nJ2+jI3FhiymjIU9HuQharQattmbXR3ZOEf1HvI0VDSaTGTdnW/6YPx7HWh7KdmoXSm5OARMm/I+B\nAzvx+ed/0KZlAH26hTJl8hdERAaze/fn2NvbMmnSl0yftZSZb9zGsIGteHryfMyWCpCShAO/YLGY\n6NDiZiJC+pKTf5iVy9+hz9RbsbF3rv650umMaLUGrJbKf3W+QqMl9sbJHF43j50pq7BzDyC22yPV\n492F0KgE38ipIZSXwV1npBIL+2UeUkrmyf0clPl8kLGXsckbeNEvBiuSx4nhZhHOOBFDNK7MyEgk\n2taZdSKdUmnGIq3EixSibVwa+pSUC7BYrGzZfoS1Gw9SVlaVOF96fSE9erUlYcdn7E78gmbNA3n9\nvd9r3d7e3siqn58hae8hnnz8fQyygl/mPsaLTwwl0M+NceNuxN6+6o9DeLg/WVmF9Bz6Jh989ifO\nrkYSj/zB8YpjRA98Ao1GS2SzaxFC4OEahpd7BCAorcxl98HfyS1IZuPuedi6+GJ0+PcP93VGOyJ6\nP0DsiFeI6P0AOqPdv96XcuWpK/nLYNRomegfyyspCRjQIiXMoDuOUs/PlYd5Oy0RKxJPTl3R+WJH\nhrWE61wCSaoo4qmCNWjREGV05gXvVg14NsqFlJebGH7HBxxLycPBwYbSkjJWLHiaQ4czeWHCYIQQ\nCCEYMrQbP3679Jz78fd15ZtPHjzr/cHXtmbihP8RFuZHeXklr0+dR7C/K63aRPDpp08jpeT2O6ax\n+bAD7sGxSGklvygFF0d/TOYK8gpP4BsymH5vT2DN5Lkc3LEOB68wYq5/WZUU+A9TSf4ydbT35Luw\nOD7M3Iu2UFddoTJO+jO1Ygt2Qsc8eYA7ZARZlPEXKYx0CkEjBON9WvKAVyRmacVRo68enldutXCo\nohCD0NDc6IRG1aNvFN77ZDl2To7sWfkuWq2Wl1/+nKcnfk+raH++mbeCPn3aYrVK5n+3itjoi1se\nUkrJyr/3cOx4Nr17RJFfWEqvnuPQajU8dl9fVv6zlxtuuKb6D8jNI3uw/uXf0Wh1RPR+gGWr38TH\nsyW5+UdwDYklql04ew4aaXPDhHr+blzcuR3bvJCMvX+h0egJ7Hg9PlGXVhlUuXx1kuSFEAOB9zi1\nkPe0Wtp8AAwCSoC7pZQJdXHsxsBBq6elrSu/FR3HLK3ohIa95OGts+UV/3Y8cHQtL8oNaBC0t3fn\nPs9Ty+HaaWr+L8gwlfFE8kYMVi2l0kwzGwdeC2yPXl2JNbiDhzMZfF1XtNqq/uhhw7rx8OJ1zHnn\nLobfOZPgoFFYrFY6tAnm2ccGXXB/UkoeePIr1m0+TOfO0UyctohXnhvOiV3vVLdJyyzg+/l/MnBg\nR6SEr+f9hd6pqsiaX8u+OHk3pzAzCXfHwbgGtEaIwvo5+X8hedsichL/5pqYezCZy1gT/yk6Gwc8\nQtoDVbNmywoz0eptMNpfWu1/5eJddpIXVfeBs4C+QCqwWQixSEq577Q2g4AwKWW4EKIz8DHQ5XKP\n3ZgMcPbnn6J0ppRtxk0YOSGKecu3E94GWxZFXHvR+3k/PZFOFm+G0gyztDKrfBc/5x3lZrfQeoxe\nuRito/354fs/GT26P0ajnq+/Xk5MiwAcHW1ZufBpDh/NQqvVEBLkUX1Xdj4btiQRv/YAO3d9jp2d\nDQcPnqBduwe48+Zu1fXlJz8znOF3ziS02W2YJWDjT+TAUzNHHTyCcfAIrq9TvixZ+9fSqeWteLhW\n/ey2Dh9Cyv51eIS0p6I4lx0/T8FUWojZXI5PZE8i+j50Ud835dLUxZV8J+CglPIYgBBiPjAc2Hda\nm+HA1wBSyo1CCGchhLeUsskMJ9EJDa8HdGB3WR4lVjPRNi64nFH9zyIl+8rzqZRWomycsdWc/e0/\nUVlKPxkMomqfraQbxytKr9RpKOfx6H192bTtCEFBt2BrY8DX24nF344HqqpNNg/1rtE+LT2fJat2\notfrGDYwFmenmg8s0zMLaNEiCDu7qtEp4eEBGI168gtK8faqGrH1/39ADiRl8FeJjoWLI66a/nWt\nzkh5xak7i/KKQjQ2VWPo96/6iEC3VrTrNgKTuZzl66eRvjdeFTCrB3WR5P2B46e9PkFV4j9fm5ST\n7zWZJA+gEYIYO7daP6uwWnj2+GayKiqwFTpKNSbeC+qM9xmlh5sZHdhgziBQOlCJla0ii8E25y5l\nq1w5Op2Wbz55gOQTOVRWmgkN8TprOOX/23sglb43vEXv3m0pKSnj1Rm/seb3F2rUpGnfJoQHn/qa\nNWt20b17K2bP/gUPN0c8PRxr7Euj0RAV7svBIl2DJHiLqZzS/DQMts4YHWr/+a5NUOcRbPxjBoXF\nGZjMZRw8sYYOo6p6couzjtCtU9UCLQa9LSG+HcjJOgKoJF/XGuWD18mTJ1d/HRcXR1xcXIPFUld+\nzDuCpkLDFNkJDYLfLEeYmb6H1wLb12j3uE9LnknezAvmDZRjppO9J0NdGuft+H+REILgQI8Ltnv5\n9Z957vnbGD9+BADjxs1k+swlzHjlVFnioAB3vpp1LyNumkhObjEtIv1ZNPex6olSDS1939+c2Por\nZfnpaHR6zOYKgjvcSEjnkRe1vXtwLDE3TCRz/xqE1pYO10zHzsUXAFtnX05k7KCFgw9Wq5mU7ERc\nW11Tn6fTpMTHxxMfH39RbesiyacAp1d9Cjj53pltAi/QptrpSf5qUW61kGuuwF1nPGulKICUilJa\nSvfqkTKtpDvfmLLPauemM/JJs26kVJZi1Gjx1tmofsqrUEZWIe3anVowu127cFYtObt076BrY0hN\nfJfKSnOt67yWlVWyI/E4tjZ6rIEh9RavlBJLZSlagy1CaMg4sJbDf39F59Z3gpRs3Pk1HVveSkLC\nL7gGtcbZN+qi9uvsE4Gzz9k1fiKvfZjtP03gSNpmKioKsfUMxK9V/7o+rSbrzIvfKVOmnLNtXST5\nzUBzIUQwkAaMAm49o82vwFjgeyFEFyC/KfXHxxemMT19F7boMAsrU/zbEntG+eDmNk4sL06lm/RB\nj4a1Io1wY+3lZHVCQ7DR4UqErtSTXt0iePONb4mNbU5JSTkfvL+AR+6uvVCaEKLWBH88JYf+I97B\n1s6GgoISoloG4PPIk6SvdKxlL+A3sIyeflqgkplLL74EcknuCXb9+jplRVloNDqi+j1KRuKftG9x\nM4E+VZUjTeYyktO24e0ZRXFO8kUn+XOxc/Wj8+iZFGYmodPb4OgddtU8a7jaXHaSl1JahBCPAss5\nNYRyrxDiwaqP5Rwp5R9CiMFCiENUDaEcc7nHbSyyTOXMSN/N07ItwcKRRGsuk1K280NY7+orequU\nXO8azL7yAp4pXoseDb4GO6b5nL9OzYHyAv4uSscgNAx2DsRTTR+/akx6ZjgPPzMXL68b0WgETzw8\ngHtuv7TuiPEvzWfUbf2YPPkuTCYzQ4e8gHHDHzw2buA5t3EuMtPTzwjjCmq8v+CAgdRaEr+Ukp2L\nXqNVUH8iQnqTW3CMFatm4OgVitlcUd3ObKlAYiUz+wDRnYdc0nmci85oh1tg6zrZl3JuddInL6Vc\nCkSe8d4nZ7w+fxm8q9SxymIChT3BVF1dtRRuGKWWDHMZRqFlUsp29lfk46Ix8pxvax7yisIkrXjp\nbM47yWlbSTaTUrbTU/pTSjkP5K3l45BuZz2oVRong0HH5++P4dN376qeyHSpDiSlM+m1qpmxer2O\n64Z0JXFb4smr9bM5F1VU/7en35mlfWu/ujdXFFNRkkdksz4AuLuE4OUeic4nhM3bvmXzrnloNLrq\nmvEBbYfg4t/iks9FaTiN8sFrQyqymFhacIJSq5muDl5E2Jy/cp+P3pYTsoQ8WYGrMJIiiykSJty1\nRsYd20iMyYMniSXJWsjU1B18HNIN/9PKCVdYLXydfYj9ZQX4Ge24xyMCF52BL7IOcpuMoJOoGpZn\nsGpZkHuUR7yj6/X8lbp1+kNUi8V6ztE4tWkZ6ce8eSuIiQmlvLyShQtWc9PgmOpkfj5ntunpZ2Rm\nLe20hqphnXmFJ3B1CsBkriC/4Dg6azEeLs24pv3DlFcWsmLdNNwjexDW/Y4a2xdnJ3Ni+69YTBV4\nRV6DZ9iZA+uUhqY6wU5TaKnkgaNr2ZqdS1pOBU8lb2JDceZ5twkw2HO7exiviM28IxJ4S2znSe+W\nABw3FXMdwWiFhgjhQrRwZU95fvW2Ukomp2xnb34BXcv9KCm08njyBiqsFkqtFtw41T3jipFSq7l+\nTlypVydSc+k55E1s/O7HJ3o8Cxdvuajt3n/9NlYt30R48zsICRmFn6c9D91dt0MMNRotUX0fYfn6\nN4nf+iG/rZ6AS0gbynJT6NDqNmxtnHF1CqRV8+vIT95RY9uS3BNs+/FFPCzOhNiGcXDlx6Tt/atO\n41Mun7qSP81v+ck0szhxLy1AQJR05ZPM/XRx8DrvdqPcQ+nm6EVqZSnBRgd89XZYpEQjBOmyFF/s\nMUsrR2QRM9PzWJSbzJO+rXDWGthZlss7sgc6oaGNdOd1y1Z2l+VxjaM3P+YdYrSMpAQzy0UyzznF\nXKHvhFKXRt33MdcO7Mpf/8xi27YDDBnyAlHhvrSI9D/vdt5ezmxc/jIHktKxtTFc9EzaS+UT3QtH\nr1AKM5PwdByGi39Lcg5vprA4DTfnqoFz+UUpiDMm76XuXkFEUBwxEUMBcLDzZNPWn/CNrvpDZK4o\nobQgHRsHD7V4SANSSf40JRYzntKW/18GxxNbSi7y6jnI4ECQ4dSIGK0QjPNqwYzM7cTgwQHy0aNh\nvGzP/oo8nkrexHtBVbe28uQ2BVRQICtZV5zBKLcwzFj5sGA3BqHhQY9IOtt71uXpKldAZaWZTdsO\ns3rdh2i1Wjp1iqbfte15/d3FREf40S+uJZ3anbtkhU6nveAfg7pg7x6IvfupUc5BnUeydvWnpGfv\npbQsl7TsPdi5+FGan4qdS9XkPGm1oNOcmtWt1RqQVisAOUe3kfjH29jauFBalkt4r3vwa9Wv3s9D\nOZvqrjlNZwcv/hYpHJT55MhyvhcH6XqBq/jzGewSyLSgjkS6O5BPBZPpiI+wo5fwxw97UitLibV1\n5xOxm3h5gpfZRIh0JKWgnAeOrmWQcwDzm8fxdVhPBjhfXFVDpXHR67U4Otqwe/dRAPLyili6bAtF\n5VBo0jP8zpn89Ovmhg2yFoGx1xHR5yEOHV+DVmtkSK9XiPDtRsKCyVjNVStU+UT1Ys/R5SQdX0tK\nxk7WJXyOV4teWMwVJP7xNr07jmN43FSuu2Yih1Z/SVlBegOf1X+TupI/TRs7Nx71jubzrH2USws9\nHLwZ63V544EjbZzx19vxdXYSZViwk4I0SsimDFuNjon+sczNOcTy/OP0twYyTDQD4DfrUb7KOsSL\n/m0ucASlMRNCMHvanfTv/zRDhnRl5cqtdOoYxaJfpyKEYPjw7oy+YyojhnWssV1GZgGzP/+T/MJS\nBl8bw8C+V36ooYN7AM5OfvTqWFUQzdnRjwPJ8ZTmp+DgEYKTTzjN4+5l85+fotMa0Wj1ZOz5G7eg\nWHQ6G7zdqwbcOTn44uocRGleKrbOPlf8PP7rVJI/w7XO/lzrXLe3xw5aPde7BDE9fxsmrJRiRkqY\nl5PE1ID23OcZyYGyQgLKTnX3+GPPZktRncahNIxRN3amRaQf6zYdorKoOV6BAdV9682b+5FfUFKj\nfXZOEV0HTmXg4C40j2zOQ0/PZeLTQ7jn9tonU+3ac5wZs5dRUlrJ9YNiuePmbueNx2KuJOmfL8k9\nmoDexpGwnnfVOixSZ3SgrLwAk7kCvc5IpamMiooidKeNDsvat4aYiGG0CBuIlJI12+eQsX8NJlMp\nWblJeLqFUVSSRX7hccJOljRoKqwWEyU5x9HoDNi5+jfamekqydcDKSXHTSVUWK2EGB3QCw0PeUVx\npKIYTZmGe2mBFcmc8kTm5SRxr2cEHRzcWVJ+jGayahbsEnGMgfaqMFlTEdMykJiWgXRs24zrbnuP\nYcO6ERERwFNPfsjgfjUfqM/9YR3X9GzDRx89AUDPnjHccvPkWpP8gUPp9L1hBs+/cBv+/h5MnPA/\nCovLeeSePjXaHdyfyomdh9EbHcg5shWRn0vv2IfIL05l46KptB81DXu3ml2Cdq7+uDdrz7J1b+Dn\n2YoTmQl4R/XExunUs6GKomy8gq4Dqu5avFzDOVGaQouB41m17B0c7D0pKs4krMcd1XVrmoKKkjwS\nfpoIZhNmczmOPuG0GvIcGm3jS6mNL6KrnFlaeTUlgR2ledihQ6cVvB3UCU+9DRVWC33wQyMEGgQd\npBe7yqvq14xwbUaOqYIJBRuRSIY7BzHCrVkDn41S19rHhvDxjNHcf+808vJLGHRtDLOn31mjTWlZ\nJZ5epxbR8PJyIb+glO07j9E2pmaxurk/rmPMPYN46qmbAWjWzJd77n6jRpJf9ucu7nzkM/oP7MbB\ngylkHNjHTf3excbohKtzEBk5+8k5uvWsJC+EIKr/Y2Qc+IfS3BQCw2/FK7zmXYKzXxR7jiyjh/P9\nmMzlHDy+Gu/2g/EM64zz3R9Smp+KjaMnNo5nF3WTUpK89WeOb/sNabXg16ofod1vvyrKGxz861OC\n3FvTLnokVmlh1cZ3OZHwO0Hthzd0aGdRSb6O/ZqfTEZpBW/KLujQ8Iv5CO+lJzI1sD2BRnsSKrNp\nKd2QwE6RTYix6tZXIwQPe0fzsJrs1ORdP7gd1w9ud87Phw2Mpe+NM+jatQXNm/vz+OOzCAvzZ+At\n7/DFB/cwuN+p5zRSyhoTrDQagZSyxv4ef/E7vv32Zfr164CUElubIZRXFGJzsnZSWWUh9rozZ8hW\nEULgE1l7NxFAWM+7SfxjBt/98RBSSgLaDKoeRWOwc64xdLIwI4ni7KPYOnvjGtCKtD1/krFjBf07\nPY1Wq2P1to9JNtoT3PHG83z3GoeSnOO0b90fIQRaoSPIpy2pOccvvGEDUEm+jh2tKCZWeqAXVVPP\nO0ovPq1MZGVBCimmEpIoZp/IA8DNYOROD/VgVYGjydn88MumqnVcr+/I958+xCPPzqGgqJzRowfw\n6qtjiI9P4OknZ9VI8reP6EqvodPw9/cgIMCTl178jPvvrJmUM7MLiY1tDlQl7e49olmxfgYtQgeQ\nX5xKbnEKYZFP/qu4dQZb2lw/AXNlGRqNDo3u7EJrAMcTFnNsw4/4eLbgWO4hPMK7UFGUQ0zzIbg4\nVT0Di428gYQjy66KJG/vHsjR1E24OQdjtZpJTt+OU1TjnO2rknwdCzY4sFKkESf90SHYSAZZ5jKm\npe+iPZ60xYO1pDPOO5rBzoHoroJbU6X+FBeXM3rsZ6yIT8TJyZ62bZvz9ofLiP/1OUYM64DGzoVX\nXqmq5xceHkBefs1VwqIj/Fj6wxNMn7mU4pIKnniw71mF0Hr3iGbixC94772xHDqUwo5dx/BvP5TM\n4hz0vn60H3Q/OqM9l0NnOFUXpzD9IMe3LsJqMeEV1RP34FiS1sxlWNxUHOw8qDSV8stfL+Ac0ILC\nklPFaIuKM0BosFrMjbJv+3Thve8n4aeJJMdvq+6TD4i9rqHDqlXj/k5ehYa7BpFQmsszJWvRU3U1\nL4E4/BklquqLh0pnfs5NZphaDOQ/74Env0Jv68CePV+wd28yd9zxOrff3pc33/+d++7oyS33fcLg\nwZ0JDvbm6ac+YmDfVmfto12bEOZ/9tA5j/Hpu3dx66OfY29/HQYbW0K734Nvy4tfd/hSFGUdIWHh\nZGIjrsegt2db/OeUtxuCwWCPg11Vv7xBb4eToy/uzTqyf81XlJbnoRFako6vwWh0Zst3zxB70xQM\ntrWX4gYozj5Gyo4lSKsZ7+g4XAPO/r7UJ6O9Kx3veEeNrvkv0gkNU/xiGXBwOffRgkhcGc8/+HBq\nfU9vbCkwV/JnYRod7T1w1NZ+i6s0fYuXJ5Cc/D2uro4EBXkzalRv8vNLyC8oo0eXCGZMGcltt75C\nUVEZQwbE8t5rZy7VcGFurg788OuzjHjWFoSmzpJR9pEt5CXvxGDvgn/MIHQGW1J3r6BFswFEhVb1\ny9sYndi872ekgKTjawkN6EZG9l7yC08QGdIWt+A27Fk+E0tOBkN6vYqTgw+bdn9D0uoviB7weI3j\n5RzbTmHGIYQQHNu0kJahA9HrjOz8bRrRg8bjEdK+tjDrjUarx9Hr3LOVGwuV5OvDyV+iZjihFxoC\npAOLOUqYdMYWLd9xEL3UsCgjmQ/FXmYHd621hLCUkhKrGTuN7rxliZWrl5OjLUePpuPq6oiUksOH\n09i5M4mXxg8G4NabunDrTV3q5FiilhXL/q3kbb+SsvVXIgJ7kZOxl217/6b9qOkgZY0/IkJoEAKi\n+o9j029vsG77p2g0Ouzdg9AbHdDo9BiNDoQ274CzY9UQyxC/jmw8+FON4x3d9BNpO5YS7NOB9Ow9\nONi40TpiCEJosLVxJXHzz1c8yV8tVJKvBxohGODkz8dFuxkog2mFG4s5xutsQQJ+2PMqndFKDYvk\nEeZk7meCf2yNfRytKOLFE1vJNpejFxqe943hGkc1W7Cpef3lmxhy3Qvcc89gticcZM2aXYx/sB8P\n3BV3zm2SjmSycPFWtFoNo27ohJ+v6znbXq7CjCQK0/djcHDDM7QTQmiq/hit+4ahvV7B0d4bKSUr\nNrxFVtIGfFv2JWHBJIwGB4wGB7bu/YHArjeTunMpUaH9ad18CEJoid86i+TtvxLS8Sbs3AM5dnQL\nzQK6IISWoykbsXc/NZzTYirnyMbvuaHPdOxsXbFYzfz65/Nk5h7E2z0Sg94WaVEVWs9FJfl6Mt6n\nJXN1h1hecgy90PCAYwSd7b34NHM/LUs90J584BomnfjTdJwKq4UPMvawtjgDG6GjzGriehlGT+HH\nEVnI9LQdNDc64Wuwu8CRlavJ6Fu6ExLowarVe+jXPYzvZt+Ng8O5VwBL2JXMgJFvM/Lm3pSXV9Cp\n/6us+f1FQoIuvLj4pUpNXMXh1V8R4BNLRsExMhL/otXQ54Cq2Z62RhegasSOnY0r5soynLybE3P9\nyxzZ8gtWs4ngHrfhG92blG2LCWzZH93JoZqBXjEczz0BQFCHG9iVupeFq55Fo9WhtbGnzYBTa5aa\nK8vQag3Y2lQdT6vRYWN0ISVjJ1armY2J3+Df8fo6P/+mQiX5eqITGsZ4RjDmjMKRre1dWVWWQmvp\njgbBUpKJtHFiduZeUorKeEl2IJcK3mMH3lR14TQTToTixPd5R7ARWpoZHenn5Ke6cJqInt0i6dkt\n8sINgVdm/MrkKWN45JGqSTcTJvyP6TOX8OFbd15gy0sjpZUDf81h8DUTcXH0x2I1s/ifyeQeS8A9\npB2e/9fevcdHXZ2JH/88M5NkkpArkUAIieFaBDEo4gWtQUTBWi5VW61uW/e1+utuZdX6syqiBX/W\ntba2tbpubdd1a7eurdZLQaugOF4WQRSQiyABuQQIuZB7Qkgy8/z+mJENmJCESeY7mTzvf5jvl5M5\nT/JKnjlz5pznFJzNqo1PUzh2HlW1e9lXtoEpM4IxpOeMJ33Osfs9krPy2blvFYPTCwgEWtlVupa0\n8ecB4PbEc8b8+2is2ocG/CRnjjhmdU18UhrelCw2bn+FcQUzKKvcRk3DAVrjhP112xk+ZS7DT7+s\nV7//WGJJPsKuyijAV3eQBUfeQ4BcBuGrK8UjLm7TQjLFSyZeZmouH1DGODJo0lZ2UEt9bSuTNIs/\nyS42NB7izhyrLx8rVJVDVQ0kJcaTlNTxxiSAmtrDjBnzv7WVxo7NZefWnb0eT6CthYC/jbRBwdIa\nbpeH9JThtDQFz44dP+tWit/+Lcs//DnxiWlMmreIpPSOpxNVA2QWTGbn+//FnuUfohogI++MY5Yc\nirgYNDivw68XcTFp3r1sff1XbHnrdRIHZVH4jfvCPkz8S9+zv5Vdq/9E3f5txA/KZNQFf3dMCYf+\nKqwkLyIZwJ+AfGA38E1Vre2g3W6gFggAraoanbsGIsAtwoHWJpZwNkNIJE7cPM1WtksNFRw+ugqn\nnMOsp5Ij0sZOrcWFcIdOJl7cXKojuKvhA0pbmmz6JgaUldcy/7uPs3X7AVpa2vjRgsv58Y863h4/\n6xERJr0AABXhSURBVOKJ/Pi+pxk1Kofm5hb+5cE/ctcJDvY+We44LylZ+Wzc/gqnj7mCyurPKS3f\nzJQZ3wWC6+KPX/3SEVVly6s/p7liH7lZEyk5uI68s68kb8q8Hq3y8aZkMfnqB076++mOrW/8Gqmt\nobDgUiqqd/Lxn+5i6t89Spx3UNdfHMXCHcnfBbypqg+LyJ3A3aF7xwsARapaHWZ/MUFVScB9dFes\nC+Hs5Cyeqv+UaTqMajlCiaeeXww7m/2th7kgMIS/VO4mXoPtE8RNsnhoUvuwKRZ8//8+w7SvTmbV\nmicpL6+m6KJbmXx6HnNmT/5S29t/cBk1dU1MO/9m3G4Xt9x0CddffV6Hz9va2sbh5lZSU07u8PfT\n59zNlmU/Y+Oyl4lPTGf8rFtJyuhZ0bzqko00le/m6xcuwe2Oo27ULJa+82PyzpoD0nurfcLlbztC\nWfEqrpn9BB5PAsOHTKKy5nOq9m4ge+wFTocXlnCT/FzgotDj3wM+Ok7ygh1QctTFqTk8UruBVI2n\nhQCVcpinsy5kbkYeqxvKGe1OZlHqJFLccUwAmgN+/nhoJ68H9nIWp7BGynC5hBFx4e1SNNHhw3W7\neOw3P0JEyM7O5FvXzGDNus87TPJut4sHF13Jg4uuPOFz/uKJN7j3X15ERCicmMfTL/wQ6NmI1Jty\nCmdd+zCqgZMuGtbSVENaSg7u0F6QlOShgOJvbQ57l21vklB6Cmjg6D1/wN8viqV1JdzvYIiqlgGo\n6kGgs2OUFFghImtF5MYw++y32jTA/fs38FptCfW0Mp3hXEIuboR9LY2M9abxnawxXJVRcMwGKa/L\nzS/yprLDW83PXOvY663jkbypxPfiumfjnLzcwfh8GwBoa/PzP+9vJD938Ek/35vvbOHxp1aybdvv\nqa9/lfO/OpkFN/32pJ8vnESXOnQcZRVbKavcRiDQxubiZSSl50RVggdweeLIOe1iVn74S3btX82H\nm/9Iw5EqMvMLu/7iKNflSF5EVgDZ7W8RTNqLOmiuHdwDmKaqpSJyCsFkv1VV3++sz8WLFx99XFRU\nRFFRUVdh9gsvVu+mtPEwY0nnfIZxjgR/rH5Vllbv5azkzpfBDY9P5pf550QqVBNB//rT6/jatb/i\nuf9+i337KskZksIN3z75KYIP1u7kW9fMIC8v+Pt1xx3fYvz473GmAwVOk9KHctrsH/LOisc40lRD\n2pAxTJqzsMO2GvCDSMRGz6rKwa0+qnavIy4xhfypV1Ne/D9s37+O+JTBnHXJQ3ii9DMvn8+Hz+fr\nVtsuk7yqdnr6roiUiUi2qpaJyFCgvJPnKA39WyEiLwFTgW4l+VhSfLieqZrNBirRdq+HAZSjp4eb\nAefMM05lg28JH3y0k9RBXi6a9pVjygf3VG5OBn/4y0f4/X7cbjerVm1hWE7fbZjqSlbBWVxw03+i\nAX+Hu24DAT/bVz7JgU/fAiD39NmMKfr7Pk/2e9a+SPmmN5k4cja1jaXBD1qv/wX5U+b3ab+94fjB\n75IlSzptG+6c/F+B7wE/Bb4LvHJ8AxFJAlyq2iAiycClQOcRxbDc+CQ2SRVf1Rz+g0/ZqIfw4GIj\nldyf2Xl9cRP7soeknbDGfE9cf/V5PP/KR5w95fuMHDmMd9/dyDPP38Yjf+2Vpz9pnZVV2PPhC7Qc\n3Ms3L/s1gUCAlWt/Rcn6ZeSdOadP49n78ctcPm0hqYOC5RQam6sp2/4+Iwqv6NN+Iy3cl8qfAjNF\n5DNgBvAQgIgME5FloTbZwPsish5YDSxV1eVh9tsvXZKWQ6WniWfYRhuKBxflNJHhSeC00G4+Y8IV\nF+dh6bO38ODCuVw1eyIfvXUf53Zzs5UTako2M3HUbOLjkvEmpHBawWXU7N3U5/1qoA2P+3/3JMS5\nvcEpoxgT1kheVauAL9UsDU3PXBF6vAvo/59ehGl1Qzk/OfAJeZLCQQ7zA05ngmSiqjzm38QbdfuZ\nk97xZhBjesrtdnHZxacfvf7S5pUoEp+cTlXNHnKzg4ehVNXtIT657wc9w06bwbvrfkPhuPnU1pey\n5+BHnH3xNX3eb6TZjtcICKjywIFPWKCTGE0aC3iXXIKrC0SEHE2mxt/icJTGOKPg/OtY9+e7OFS/\nF9UAh+r2cNa1D/d5v6MvuoE9a55n7c6X8HhTmHzl/SSmxV4RQEvyEdAYaKNNA4yW4HmXE8jkz+zg\neh1HBYd5lwNc3paLHlem1ZiBICl9KFOvf5TKXR+BCKNG3kacN6XP+3W53BScdw0F58Xe6L09S/IR\nMMjlIdUdz4f+MqZKNrPJ42dsYA3vkYib8xnGB7XlZHkS+Nbg6D+EwJjeFp+URs6EGU6HEZMsyUeA\niPBA7pks3Pcxzwd20CRtTErMIKspmStlFAC7tI6na7Zakjfdtq24lFsWPsuekkNMKTyVRx/8NoMz\n+3edlXC0HK6j+O3fUV+2k8T0oYyZflOnRdMGkv6/Z7efGOtN47lRRfzrqefx4ugZjPWm4W+3Vt6P\n4ra18qabqmsamXnlz/n6/Om89MpPyMgewvzvPIZqZ/sRY5tqgI0v3U9qawLTC79PbtJI1r+wiLaW\npq6/OMbZSD6CPOI6WjVyVlou/1S9ihSNI50Elsouvp1po3jTPas+3MH48fksWBDcuPPrXy9gyJD5\nlFfUkT0kzeHoIq+5roLmugqmnnMPIkJGWh57y9ZTd7CYzLwznA7PUTaSd0hufDKP5p9LfUoznyVX\ncWP2WOZm5H+pXWVbM776Uj5urMQ/QEdp5suSEuMpL6/B7w+u666tbaS5uRWvd2AeCu/yxONvO0Kb\n/wgQLDTW0tqIy9N5bf6BwkbyDipISOHunM5HGZsPV7Ow5CNGSxoVNDMsIZEHR5yFJwYq45nwXHje\nWLIyEpk/714uKirk2T++yY3fuYi01OistdLXEpIzGDJ2GitW/4yCYeewv3Iz8WmnkDZ0jNOhOc6S\nfBR7pHQz1+k4pjAEvwZ4pHkDK+oOMDstt+svNjHN43Gz7Nlb+benV7KneCe33Di907rysUZVqdjx\nAfUVu0nOHE72uAsRcfGVmT/gwKYVlJbvJHnMZL5SeEWnpRQGEkvyUay8LVixEsAtLkZqGhVtzQ5H\nZaKF1xvHbf848M42LX7nKWo/X8+I7MmUFr/Moc8/5rTZtyHiYvikgffz6Iq974+Qen8ry2v38fvK\nYj6oL+vW/Pp4bzrLKUFVqdJmPpJyxnsH3odqxnzhSEMVpVveYva0hZw5/iouO/9uaks201C52+nQ\nopaN5CNg55E6btuzhhSNp4FWFBjrTeWhvCknnF+/O2cSC0s+ZkHLe7QR4IbBYzg7uf8fLGzMyWpr\naSI+fhDxoVPRPO54EhPTaTtiSyU7Y0k+Ah4+sIn5OoqvSg6t6udh1lPS3Iiv/iCXpHZ+ZuZgj5ff\nnHo+9YFWvOK2k6DMgJeYNhQ8bjYXv8qoEdMoObiexuZqUk4pcDq0qGVJPgIOtDZxBsHj3OLEzXjN\n4FOqOdSN+XURIdUd39chGtMvuNweCr+xmK1vPMbmz18jKS2Hwm8swZMwMFcVdYcl+QgYlZDKu80H\n+DoFNGgrH1NBPS1MSOz+aT0tAT+Pl23F13AQr7i5IWs0s9NH9GHUxkSnxLShnPnNnzgdRr9hST4C\n7sqZxO17P+TNtn0048cF3DxkPBN7kOSfLP+MXfWN3KtTqKWFJ8o3kRXntTl6Y8wJWZKPgKFxifx+\n5IUcbD2MBxdD4ry4elhSeHVjBTfpBDLFSyZepmsuaxoqLMkbY07IllBGiEdc5MYnMzQ+sccJHoLl\niss5fPS6XJpIcQ/MLezGmO6zkXw/ceOQcSzZv55iraFOWtjtquP29AlOh2WMiXJhjeRF5CoR2Swi\nfhHp9Kh5EZklIttEZLuI3BlOnwPVlOQsfpV/DnlZiUw5JZPfFkwj3WOrbowxJxbuSH4TMB94srMG\nIuICHgdmAAeAtSLyiqpuC7PvAWdUQiqjElKdDsMY04+EleRV9TMAOfHBpFOBYlXdE2r7HDAXsCRv\njDF9LBIfvA4HStpd7wvdM8YY08e6HMmLyAogu/0tQIF7VHVpXwVmjDEmfF0meVWdGWYf+4G8dte5\noXudWrx48dHHRUVFFBUVhRmCMcbEDp/Ph8/n61bb3lxC2dm8/FpgtIjkA6XANcC1J3qi9kneGGPM\nsY4f/C5ZsqTTtuEuoZwnIiXAucAyEflb6P4wEVkGoKp+4GZgObAFeE5Vt4bTb3+2qamKPxzawbKa\nEo4E/E6HY4yJceGurnkZeLmD+6XAFe2uXwfGhdNXLHitpoTflm/nXM1mjVTyWk0Jj+afS5yd2WqM\n6SOWXSJEVXmifBu36RlcLaO5Vc/A3wLv1h90OjRjTAyzJB8hCjRpG9kkAsE68dkk0hBodTYwY0xM\nsyQfIS4RpiRm8d9STJ22sEWrWE8lhYmDnQ7NGAP421o4sOUt9qx7hbrynU6H02usQFmEfNJURaon\njq3uGu7xrybDncC9QwvJTxjkdGjGDHj+thbWP38PCYE40gcN45M1LzDukn9kyJjznQ4tbJbkI2Bt\nYwX/b/8nXK75nIOXFVLC4uGTGeO1OjTGRIOyz97DG4hj5rl3ICIUDD+Xd3z/ZknedM9zlbu4Rsdw\njgQ3DrtUeLFqN3fmTHI4MmMMQGtzHWmDcviiDFd6Sg4tzfUOR9U7bE4+Ato0QCLuo9eJeGhVdTAi\nY0x7Gbmns/vAGiqqdtDS2sRHn/6ZwXmFTofVK2wkHwGXpufwTPkO3OqiBT9LZRcL089wOixjTEhq\n9mjGzvg/vO17gtYj9QzOK2T8Zbc4HVavsCQfAV9Lz0OBV6t34xbh9sETmWpnsxoTVbLHXkD22Auc\nDqPXWZKPkCvS87giPa/rhsYY04tsTt4YY2KYJXljjIlhNl0TRVSV5XX7ebeujCSXh+uyRnJqQorT\nYRlj+jEbyUeRF6t3859lO5jQlEVag5d/3rOa/S2NTodlTL93pOEQNfs/5UjDIadDiTgbyUeRv1Tt\n4R/0NE6V4E7YWm1hed1+bsga63BkxvRfpZ+uZLvv30kdNJS6hoOMnX4jw8ZPdzqsiLEkH0UUcLU7\nYMuFYHumjDl5Rxqr2f7275h9wSLSU4ZTU7efv618gMH5ZxKflOZ0eBFh0zVRZE7GCJ6SrazXCt7S\nfaySUi5Jy3E6LGP6reb6CgYln0J6ynAA0lOHk5yURXNducORRY6N5KPINZkjSXJ5eK+ulES3h0ey\nppIXb1UqjTlZiWlDaWiq5FDNbgann8qhml00Hj6ENy3b6dAixpJ8FBER5mbkMzcj3+lQjIkJ8Ymp\njJ95M8uX/5TExHQON9cyfubNxCcOnAqwYSV5EbkKWAyMB85W1XWdtNsN1AIBoFVVp4bTrzHGdNeQ\nMeeTMWISzfUVeFNOIc47sN4dhzuS3wTMB57sol0AKFLV6jD7M8aYHovzDhpwyf0LYSV5Vf0MQL4o\nwtw5wT7kNcaYiItU4lVghYisFZEbI9SnMcYMeF2O5EVkBdD+o2ghmLTvUdWl3exnmqqWisgpBJP9\nVlV9v+fhGmOM6Ykuk7yqzgy3E1UtDf1bISIvAVOBTpP84sWLjz4uKiqiqKgo3BCMMSZm+Hw+fD5f\nt9r25hLKDuflRSQJcKlqg4gkA5cCS070RO2TvDHGmGMdP/hdsqTzlBrWnLyIzBOREuBcYJmI/C10\nf5iILAs1ywbeF5H1wGpgqaouD6dfY4wx3RPu6pqXgZc7uF8KXBF6vAuIjRNxjTGmn7FljcYYE8Ms\nyRtjTAyzJG+MMTHMkrwxxsQwS/LGGBPDLMkbY0wMsyRvjDExzJK8McbEMEvyxhgTwyzJG2NMDLMk\nb4wxMcySvDHGxDBL8sYYE8MsyRtjTAyzJG+MMTHMkrwxxsQwS/LGGBPDRFWdjuEYIqLRFpMxxkQz\nEUFVOzxn20byxhgTwyzJG2NMDAsryYvIwyKyVUQ2iMhfRCS1k3azRGSbiGwXkTvD6dMYY0z3hTuS\nXw5MUNVCoBi4+/gGIuICHgcuAyYA14rIV8Ls1xhjTDeEleRV9U1VDYQuVwO5HTSbChSr6h5VbQWe\nA+aG068xxpju6c05+b8H/tbB/eFASbvrfaF73eLz+cKLqo9YXD0TrXFB9MZmcfWMxdUxT1cNRGQF\nkN3+FqDAPaq6NNTmHqBVVZ/tjaAWL1589PHu3bspKirqjaftVT6fz+LqgWiNC6I3NourZwZSXD6f\nr9svHl0meVWdeaL/F5HvAZcDF3fSZD+Q1+46N3SvU+2TfPvHxhhjoKio6JgXjiVLlnTaNtzVNbOA\nO4A5qnqkk2ZrgdEiki8i8cA1wF/D6dcYY0z3hLXjVUSKgXjgUOjWalX9JxEZBvxOVa8ItZsFPErw\nReUpVX3oBM9p212NMaaHOtvxGnVlDYwxxvQe2/FqjDExzJK8McbEsH6V5EXkdhEJiEim07EAiMj9\nIvKJiKwXkddFZKjTMUH3y01EmohcJSKbRcQvImdGQTxRWW5DRJ4SkTIR2eh0LF8QkVwRWSkiW0Rk\nk4j8s9MxAYhIgoisCf0NbhKRHzsdU3si4hKRdSLi2GKTfpPkRSQXmAnscTqWdh5W1TNUdTLwKhAt\nv2BdlptwyCZgPvCO04FEebmNpwnGFU3agB+q6gTgPOAH0fDzCq3qmx76GywEZovIVIfDau8W4FMn\nA+g3SR74JcHlmlFDVRvaXSYDgc7aRlI3y01EnKp+pqrFBDfUOS1qy22o6vtAtdNxtKeqB1V1Q+hx\nA7CVHuxc70uq2hR6mEBw709UrCYJDUwvB/7dyTj6RZIXkTlAiapucjqW44nIAyKyF/g2cJ/T8XSg\ns3ITA11Y5TYGMhE5leCoeY2zkQSFpkTWAweBFaq61umYQr4YmDr6otPljtdIOUH5hEXAQoJTNe3/\nz+m47lHVpaq6CFgUmtNdACyOhrhCbXq13ERvxWX6LxEZBLwA3HLcO1nHhN61Tg599vSyiJymqo5O\nkYjI14AyVd0gIkU4+O41apJ8Z+UTRGQicCrwiYgIwamHj0VkqqqWOxVXB54FXiNCSb4Xyk30iR78\nvJzW43IbA52IeAgm+D+o6itOx3M8Va0TkbeBWTg8Dw5MA+aIyOVAIpAiIs+o6nciHUjUT9eo6mZV\nHaqqI1W1gODb6smRSPBdEZHR7S7nEZyndFw3y004zel5+WgvtyE4/zM63n8An6rqo04H8gURyRKR\ntNDjRILv+Lc5GxWo6kJVzVPVkQR/t1Y6keChHyT5DijR88v/kIhsFJENwCUEP0mPBo8Bg4AVoeVb\nTzgdEICIzBOREuBcYJmIOPZZgar6gZsJrkTaAjynqtHyIv0ssAoYKyJ7ReSGKIhpGnAdcHFoueK6\n0GDCacOAt0N/g2uAN1T1NYdjiipW1sAYY2JYfxzJG2OM6SZL8sYYE8MsyRtjTAyzJG+MMTHMkrwx\nxsQwS/LGGBPDLMkbY0wMsyRvjDEx7P8DwDCUbusR5xsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e292d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_fiter.plot_decision_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 所有接口支持参数直接设置x, y 默认不设置使用内部x， y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mean_squared_error mean: 0.0774596669241\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-0.        , -0.06666667, -0.        , -0.        , -0.        ,\n",
       "       -0.06666667, -0.06666667, -0.        , -0.        , -0.        ])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.cross_val_mean_squared_score(x=test_fiter.x, y=test_fiter.y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**train test score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.783592384519\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.75555556,  0.72222222,  0.73033708,  0.7752809 ,  0.82022472,\n",
       "        0.7752809 ,  0.83146067,  0.69662921,  0.85393258,  0.875     ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**切换使用其它学习方法**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.810433548973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.78888889,  0.78888889,  0.78651685,  0.80898876,  0.84269663,\n",
       "        0.78651685,  0.82022472,  0.82022472,  0.79775281,  0.86363636])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.estimator.adaboost_classifier()\n",
    "test_fiter_more.cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.816000737714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.8       ,  0.78888889,  0.7752809 ,  0.85393258,  0.87640449,\n",
       "        0.82022472,  0.80898876,  0.76404494,  0.84269663,  0.82954545])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.estimator.random_forest_classifier(n_estimators=200)\n",
    "test_fiter_more.cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.824927079787\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.81111111,  0.83333333,  0.78651685,  0.86516854,  0.84269663,\n",
       "        0.79775281,  0.82022472,  0.79775281,  0.86516854,  0.82954545])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fiter_more.estimator.svc()\n",
    "test_fiter_more.cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**神经网络相关**\n",
    "\n",
    "        封装类似sckit风格的接口fit，predict\n",
    "        普通多层神经网络：\n",
    "        \n",
    "        MlFiterTensorFlow:\n",
    "            基于TensorFlow实现的功能封装：\n",
    "                    MnnTF：多层神经网络\n",
    "                    CnnTF: 卷积神经网路\n",
    "                    KnnTF: TensorFlow实现knn\n",
    "                    ........"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Finished!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0]), 0.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import MlFiterTensorFlow\n",
    "reload(MlFiterTensorFlow)\n",
    "from MlFiterTensorFlow import MnnTF\n",
    "mnn_tf = MlFiterTensorFlow.MnnTF(test_fiter_more.x, test_fiter_more.y)\n",
    "mnn_tf.fit()\n",
    "mnn_tf.predict(test_fiter_more.x[100].reshape(1, -1)), test_fiter_more.y[100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Finished!\n",
      "Accuracy:0.722222\n",
      "Optimization Finished!\n",
      "Accuracy:0.730337\n",
      "Optimization Finished!\n",
      "Accuracy:0.786517\n",
      "Optimization Finished!\n",
      "Accuracy:0.786517\n",
      "Optimization Finished!\n",
      "Accuracy:0.775281\n",
      "Optimization Finished!\n",
      "Accuracy:0.719101\n",
      "Optimization Finished!\n",
      "Accuracy:0.696629\n",
      "Optimization Finished!\n",
      "Accuracy:0.775281\n",
      "Optimization Finished!\n",
      "Accuracy:0.696629\n",
      "Optimization Finished!\n",
      "Accuracy:0.786517\n",
      "acs mean = 0.747503101826\n"
     ]
    }
   ],
   "source": [
    "MlFiterTensorFlow.MnnTF.do_tf_tt(test_fiter_more.x, test_fiter_more.y, n_folds=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TensorFlow可视化窗口**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "log_proess = MlFiterTensorFlow.show_log_board()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "log_proess.kill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Accuracy: 0.744444444444\n",
      "Done!\n",
      "Accuracy: 0.797752808989\n",
      "Done!\n",
      "Accuracy: 0.752808988764\n",
      "Done!\n",
      "Accuracy: 0.786516853933\n",
      "Done!\n",
      "Accuracy: 0.820224719101\n",
      "Done!\n",
      "Accuracy: 0.741573033708\n",
      "Done!\n",
      "Accuracy: 0.707865168539\n",
      "Done!\n",
      "Accuracy: 0.786516853933\n",
      "Done!\n",
      "Accuracy: 0.752808988764\n",
      "Done!\n",
      "Accuracy: 0.674157303371\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "acs mean = 0.756466916355\n"
     ]
    }
   ],
   "source": [
    "MlFiterTensorFlow.KnnTF.do_tf_tt(test_fiter_more.x, test_fiter_more.y, n_folds=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import MlFiterSnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MlFiterSnn        \n",
    "       非高效多层神经网络实现，便于修改隐含层等参数，做初步测试使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Accuracy: 0.777777777778\n",
      "Done!\n",
      "Accuracy: 0.797752808989\n",
      "Done!\n",
      "Accuracy: 0.797752808989\n",
      "Done!\n",
      "Accuracy: 0.775280898876\n",
      "Done!\n",
      "Accuracy: 0.707865168539\n",
      "Done!\n",
      "Accuracy: 0.76404494382\n",
      "Done!\n",
      "Accuracy: 0.831460674157\n",
      "Done!\n",
      "Accuracy: 0.85393258427\n",
      "Done!\n",
      "Accuracy: 0.786516853933\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracys mean = 0.79013732834\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Accuracy: 0.808988764045\n"
     ]
    }
   ],
   "source": [
    "MlFiterSnn.SnnClass.do_snn_tt(test_fiter_more.x, test_fiter_more.y, n_folds=10, nn_hdim=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 量化结合解决由：\n",
    "\n",
    "\n",
    "**‘非均衡胜负收益’带来的必然’非均衡胜负比例‘，目标由’因子‘的能力解决一部分，’模式识别‘提升关键的一部分**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from MlFiterDegPd import MlFiterDegPdClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fn = './data/cache/golden_n6_abu'\n",
    "key = 'golden_n6_abu'\n",
    "\n",
    "orders_pd = ZCommonUtil.load_hdf5(fn, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>deg_hisWindowPd</th>\n",
       "      <th>deg_windowPd</th>\n",
       "      <th>deg_60WindowPd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.256691</td>\n",
       "      <td>3.895622</td>\n",
       "      <td>5.363046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>15.908454</td>\n",
       "      <td>4.108007</td>\n",
       "      <td>4.199374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>21.175689</td>\n",
       "      <td>3.394158</td>\n",
       "      <td>7.384808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>-7.023154</td>\n",
       "      <td>-0.075254</td>\n",
       "      <td>5.413456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>11.519025</td>\n",
       "      <td>3.621430</td>\n",
       "      <td>5.765467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  deg_hisWindowPd  deg_windowPd  deg_60WindowPd\n",
       "2015-07-28       1        -1.256691      3.895622        5.363046\n",
       "2015-07-28       0        15.908454      4.108007        4.199374\n",
       "2015-07-28       0        21.175689      3.394158        7.384808\n",
       "2015-07-28       0        -7.023154     -0.075254        5.413456\n",
       "2015-07-28       1        11.519025      3.621430        5.765467"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "deg.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ -1.25669051,   3.89562167,   5.36304612],\n",
       "        [ 15.90845415,   4.10800674,   4.19937422],\n",
       "        [ 21.17568858,   3.39415774,   7.38480771],\n",
       "        [ -7.02315417,  -0.07525363,   5.41345634],\n",
       "        [ 11.51902529,   3.62143029,   5.76546733]]),\n",
       " array([ 1.,  0.,  0.,  0.,  1.]))"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg.x[:5], deg.y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg().estimator.svc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.489222622644\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.43464707,  0.54241817,  0.51057671,  0.5154754 ,  0.4907593 ,\n",
       "        0.49988867,  0.43219773,  0.50089087,  0.44365256,  0.52171976])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg().cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**0.48的准确率，相当于是乱猜**\n",
    "\n",
    "        下面使用把数据离散化后，换种分类器测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deg = MlFiterDegPdClass(orderPd=orders_pd, dummies=True, invoke_hmm=False,\n",
    "                       invoke_pca=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result</th>\n",
       "      <th>dh_dummies_(-inf, -20]</th>\n",
       "      <th>dh_dummies_(-20, -12]</th>\n",
       "      <th>dh_dummies_(-12, -7]</th>\n",
       "      <th>dh_dummies_(-7, -3]</th>\n",
       "      <th>dh_dummies_(-3, 0]</th>\n",
       "      <th>dh_dummies_(0, 3]</th>\n",
       "      <th>dh_dummies_(3, 7]</th>\n",
       "      <th>dh_dummies_(7, 12]</th>\n",
       "      <th>dh_dummies_(12, 20]</th>\n",
       "      <th>dh_dummies_(20, inf]</th>\n",
       "      <th>dw_dummies_(-inf, -20]</th>\n",
       "      <th>dw_dummies_(-20, -12]</th>\n",
       "      <th>dw_dummies_(-12, -7]</th>\n",
       "      <th>dw_dummies_(-7, -3]</th>\n",
       "      <th>dw_dummies_(-3, 0]</th>\n",
       "      <th>dw_dummies_(0, 3]</th>\n",
       "      <th>dw_dummies_(3, 7]</th>\n",
       "      <th>dw_dummies_(7, 12]</th>\n",
       "      <th>dw_dummies_(12, 20]</th>\n",
       "      <th>dw_dummies_(20, inf]</th>\n",
       "      <th>d60_dummies_(-inf, -20]</th>\n",
       "      <th>d60_dummies_(-20, -12]</th>\n",
       "      <th>d60_dummies_(-12, -7]</th>\n",
       "      <th>d60_dummies_(-7, -3]</th>\n",
       "      <th>d60_dummies_(-3, 0]</th>\n",
       "      <th>d60_dummies_(0, 3]</th>\n",
       "      <th>d60_dummies_(3, 7]</th>\n",
       "      <th>d60_dummies_(7, 12]</th>\n",
       "      <th>d60_dummies_(12, 20]</th>\n",
       "      <th>d60_dummies_(20, inf]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            result  dh_dummies_(-inf, -20]  dh_dummies_(-20, -12]  \\\n",
       "2015-07-28       1                     0.0                    0.0   \n",
       "2015-07-28       0                     0.0                    0.0   \n",
       "2015-07-28       0                     0.0                    0.0   \n",
       "2015-07-28       0                     0.0                    0.0   \n",
       "2015-07-28       1                     0.0                    0.0   \n",
       "\n",
       "            dh_dummies_(-12, -7]  dh_dummies_(-7, -3]  dh_dummies_(-3, 0]  \\\n",
       "2015-07-28                   0.0                  0.0                 1.0   \n",
       "2015-07-28                   0.0                  0.0                 0.0   \n",
       "2015-07-28                   0.0                  0.0                 0.0   \n",
       "2015-07-28                   1.0                  0.0                 0.0   \n",
       "2015-07-28                   0.0                  0.0                 0.0   \n",
       "\n",
       "            dh_dummies_(0, 3]  dh_dummies_(3, 7]  dh_dummies_(7, 12]  \\\n",
       "2015-07-28                0.0                0.0                 0.0   \n",
       "2015-07-28                0.0                0.0                 0.0   \n",
       "2015-07-28                0.0                0.0                 0.0   \n",
       "2015-07-28                0.0                0.0                 0.0   \n",
       "2015-07-28                0.0                0.0                 1.0   \n",
       "\n",
       "            dh_dummies_(12, 20]  dh_dummies_(20, inf]  dw_dummies_(-inf, -20]  \\\n",
       "2015-07-28                  0.0                   0.0                     0.0   \n",
       "2015-07-28                  1.0                   0.0                     0.0   \n",
       "2015-07-28                  0.0                   1.0                     0.0   \n",
       "2015-07-28                  0.0                   0.0                     0.0   \n",
       "2015-07-28                  0.0                   0.0                     0.0   \n",
       "\n",
       "            dw_dummies_(-20, -12]  dw_dummies_(-12, -7]  dw_dummies_(-7, -3]  \\\n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "\n",
       "            dw_dummies_(-3, 0]  dw_dummies_(0, 3]  dw_dummies_(3, 7]  \\\n",
       "2015-07-28                 0.0                0.0                1.0   \n",
       "2015-07-28                 0.0                0.0                1.0   \n",
       "2015-07-28                 0.0                0.0                1.0   \n",
       "2015-07-28                 1.0                0.0                0.0   \n",
       "2015-07-28                 0.0                0.0                1.0   \n",
       "\n",
       "            dw_dummies_(7, 12]  dw_dummies_(12, 20]  dw_dummies_(20, inf]  \\\n",
       "2015-07-28                 0.0                  0.0                   0.0   \n",
       "2015-07-28                 0.0                  0.0                   0.0   \n",
       "2015-07-28                 0.0                  0.0                   0.0   \n",
       "2015-07-28                 0.0                  0.0                   0.0   \n",
       "2015-07-28                 0.0                  0.0                   0.0   \n",
       "\n",
       "            d60_dummies_(-inf, -20]  d60_dummies_(-20, -12]  \\\n",
       "2015-07-28                      0.0                     0.0   \n",
       "2015-07-28                      0.0                     0.0   \n",
       "2015-07-28                      0.0                     0.0   \n",
       "2015-07-28                      0.0                     0.0   \n",
       "2015-07-28                      0.0                     0.0   \n",
       "\n",
       "            d60_dummies_(-12, -7]  d60_dummies_(-7, -3]  d60_dummies_(-3, 0]  \\\n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "2015-07-28                    0.0                   0.0                  0.0   \n",
       "\n",
       "            d60_dummies_(0, 3]  d60_dummies_(3, 7]  d60_dummies_(7, 12]  \\\n",
       "2015-07-28                 0.0                 1.0                  0.0   \n",
       "2015-07-28                 0.0                 1.0                  0.0   \n",
       "2015-07-28                 0.0                 0.0                  1.0   \n",
       "2015-07-28                 0.0                 1.0                  0.0   \n",
       "2015-07-28                 0.0                 1.0                  0.0   \n",
       "\n",
       "            d60_dummies_(12, 20]  d60_dummies_(20, inf]  \n",
       "2015-07-28                   0.0                    0.0  \n",
       "2015-07-28                   0.0                    0.0  \n",
       "2015-07-28                   0.0                    0.0  \n",
       "2015-07-28                   0.0                    0.0  \n",
       "2015-07-28                   0.0                    0.0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg.df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**离散值的选择通过统计挖掘**\n",
    "\n",
    "        MlFiterBinsCs 可视化离散值选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import MlFiterBinsCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n5uxg/hpZHCRJQ5w5NMaZg6RucuYgSWqExWGEau9bmr89NWcH89fI4iBJGuLMoTHOHCR1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x138fd3cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "lowBkCnt show hist and qcuts\n",
      "[1, 3.4]        46126\n",
      "(3.4, 5.8]        659\n",
      "(5.8, 8.2]        323\n",
      "(8.2, 10.6]       101\n",
      "(10.6, 13]         68\n",
      "(13, 15.4]         39\n",
      "(15.4, 17.8]       33\n",
      "(17.8, 20.2]       18\n",
      "(20.2, 22.6]        4\n",
      "(22.6, 33]          3\n",
      "Name: lowBkCnt, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188f24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "atr_std show hist and qcuts\n",
      "(1.126, 7.425]          4738\n",
      "(0.48, 0.63]            4738\n",
      "(0.151, 0.246]          4738\n",
      "[-1.000781, -0.0941]    4738\n",
      "(0.827, 1.126]          4737\n",
      "(0.63, 0.827]           4737\n",
      "(0.356, 0.48]           4737\n",
      "(0.246, 0.356]          4737\n",
      "(0.0463, 0.151]         4737\n",
      "(-0.0941, 0.0463]       4737\n",
      "Name: atr_std, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188f2b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "jump_power show hist and qcuts\n",
      "[0, 1.0776]         11606\n",
      "(4.343, 151.515]     3995\n",
      "(1.279, 1.414]       3990\n",
      "(1.17, 1.279]        3978\n",
      "(1.585, 1.825]       3975\n",
      "(1.0776, 1.17]       3974\n",
      "(1.825, 2.184]       3973\n",
      "(2.184, 2.814]       3967\n",
      "(2.814, 4.343]       3966\n",
      "(1.414, 1.585]       3950\n",
      "Name: jump_power, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13820af50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "diff_days show hist and qcuts\n",
      "[0, 36.8]         18297\n",
      "(36.8, 73.6]       7002\n",
      "(73.6, 110.4]      5402\n",
      "(110.4, 147.2]     4358\n",
      "(147.2, 184]       3373\n",
      "(184, 220.8]       2397\n",
      "(220.8, 257.6]     2247\n",
      "(257.6, 294.4]     1784\n",
      "(294.4, 331.2]     1439\n",
      "(331.2, 368]       1075\n",
      "Name: diff_days, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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eP3/V1Ot1oPfHZ7nler2eVJ5WqeRpLvv45VtuKjPPzMwM09PTAPP1Mo9c49wlbQcebfbc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149433210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wave_score1 show hist and qcuts\n",
      "(1.201, 6.0266]      4738\n",
      "(0.513, 0.693]       4738\n",
      "(0.108, 0.227]       4738\n",
      "[-1.0169, -0.174]    4738\n",
      "(0.901, 1.201]       4737\n",
      "(0.693, 0.901]       4737\n",
      "(0.361, 0.513]       4737\n",
      "(0.227, 0.361]       4737\n",
      "(-0.0149, 0.108]     4737\n",
      "(-0.174, -0.0149]    4737\n",
      "Name: wave_score1, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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K4uY+Qt1yuMkb9uam6kY1qbzuKdSZQo2QTp15ubmbmdWQM/eKcObuzN0sD2fuZmZTzM19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\nmVkNubmbmdWQm7uZWQ25uZuZ1dA/AprT1yU+NXG8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1493bc750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wave_score2 show hist and qcuts\n",
      "(1.274, 5.558]      4738\n",
      "(0.564, 0.773]      4738\n",
      "(0.0137, 0.182]     4738\n",
      "[-1.122, -0.3]      4738\n",
      "(0.995, 1.274]      4737\n",
      "(0.773, 0.995]      4737\n",
      "(0.367, 0.564]      4737\n",
      "(0.182, 0.367]      4737\n",
      "(-0.129, 0.0137]    4737\n",
      "(-0.3, -0.129]      4737\n",
      "Name: wave_score2, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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3s82xvt3olmH0Ps9Wz9tZHqGZi0ybMveSm+YY93GPQsmapfcbHrnZ9vX1G5IpUkUa514R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e677850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wave_score3 show hist and qcuts\n",
      "(1.263, 4.992]       4738\n",
      "(0.56, 0.788]        4738\n",
      "(-0.0956, 0.0955]    4738\n",
      "[-1.126, -0.389]     4738\n",
      "(1.0024, 1.263]      4737\n",
      "(0.788, 1.0024]      4737\n",
      "(0.327, 0.56]        4737\n",
      "(0.0955, 0.327]      4737\n",
      "(-0.233, -0.0956]    4737\n",
      "(-0.389, -0.233]     4737\n",
      "Name: wave_score3, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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3400j4u8lfQzYS7Yf4PMu8mZm5SjthCkzM+uNUq51I+l3JB2Q9KSkG9um75J0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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150e46e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "deg_60WindowPd show hist and qcuts\n",
      "(13.139, 70.936]     4738\n",
      "(5.101, 6.94]        4738\n",
      "(0.166, 1.805]       4738\n",
      "[-57.515, -5.654]    4738\n",
      "(9.261, 13.139]      4737\n",
      "(6.94, 9.261]        4737\n",
      "(3.503, 5.101]       4737\n",
      "(1.805, 3.503]       4737\n",
      "(-1.813, 0.166]      4737\n",
      "(-5.654, -1.813]     4737\n",
      "Name: deg_60WindowPd, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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OzyLa8L9O9DX+Vnf/rJn9Q+B3gZ8uIo5eNonz3xCtmwvd/V1mdg3wGeBNo4+y\nZ5x3UtL6S9tsm7v74/Eyvw6cdff/WkKItWBmk8AjRMfQKxn3o5TWusLM/j6w6u4rZja/yaJVaAGy\nHXgH8Kvu/jUzu5eoq5Yqrc8porP3GeBl4DNm9gsZMfWMsajmlV0Tj5k94O63xss9YmbJV7xTwOWp\nRXexXtYpRI84/xnw+/FyXzWzH5rZG+OYfqQKcZrZjxHVtv/Yon5gdwFfN7Pdo45zs3UJYGYLRF/p\nfyo1e+TbvIeRb9tBxF/fHwEecPdH49mrZjbt7qtm1gBeKC9CrgVuMLOfJSoz/BUzewA4XaEYE98h\n+ib8tXj694gSfZXW53uAb7n7SwBm9gfAT+aJsYzSzSkz2wNgZu8mqssCPAa8P76qfAVwJdGNVmX5\nLHFSMrO3EF0QeZEozp+vQpzu/ifu3nD3N7n7FUQ779vd/YUqxWlm1xN9nb/B3dP92lZtm38VuNLM\nZszsPOD9cYxV8bvAUXf/D6l5jwEL8fjNwKOdbxoVd7/T3X/E3d9EtO6edPdfBB6nIjEm4tLH8/Gx\nDdHNnd+kQuuTqGTzLjM7Pz6RezdwlDwxlnAl+SeBrxG1tvgyUWJKXttH1OrhGHHLnLIGolYXDwDP\nxvHuqWKcHTF/i7jVTZXiJPowPwl8PR4+VrUYU/FcT9Si5Tiwt+x4UnFdC/yQqCXQM/F6vB64CDgU\nx3wQmCo71jjePay3uqlqjD9B9OG+QvTtfWfVYgWa8bHxDaILr+fmiVE3TImI1FwZpRsRERkhJXoR\nkZpTohcRqTklehGRmlOiFxGpOSV6EZGaU6IXEak5JXoRkZr7/9n5iAfq5jmQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x155c63d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "deg_hisWindowPd show hist and qcuts\n",
      "(19.737, 69.234]      4738\n",
      "(5.731, 9.188]        4738\n",
      "(-4.407, -0.456]      4738\n",
      "[-65.56, -19.177]     4738\n",
      "(13.376, 19.737]      4737\n",
      "(9.188, 13.376]       4737\n",
      "(2.534, 5.731]        4737\n",
      "(-0.456, 2.534]       4737\n",
      "(-10.134, -4.407]     4737\n",
      "(-19.177, -10.134]    4737\n",
      "Name: deg_hisWindowPd, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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XRFkfNrKYnw9mKyvzDVN/CJxoNPncEWAiv/0w8HzT+ockDUi6HbgDeK2dQaWg8Yqcgunp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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1542f1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "deg_windowPd show hist and qcuts\n",
      "(9.0906, 60.638]     4738\n",
      "(3.844, 4.962]       4738\n",
      "(0.87, 1.938]        4738\n",
      "[-32.109, -3.288]    4738\n",
      "(6.486, 9.0906]      4737\n",
      "(4.962, 6.486]       4737\n",
      "(2.878, 3.844]       4737\n",
      "(1.938, 2.878]       4737\n",
      "(-0.628, 0.87]       4737\n",
      "(-3.288, -0.628]     4737\n",
      "Name: deg_windowPd, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "MlFiterBinsCs.show_orders_hist(orders_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg().estimator.random_forest_classifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "accuracy mean: 0.496905590646\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.45134714,  0.51458473,  0.50879537,  0.51324872,  0.5092407 ,\n",
       "        0.5092407 ,  0.45290581,  0.5091314 ,  0.48017817,  0.52038316])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deg().cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**结果还是乱猜**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用hmm等方式仍然是胡猜，详情见MlFiterDhpPd\n",
    "\n",
    "自己写的类似adaboost依然效果很差 详情见MlFiterBoost及子类实现代码\n",
    "\n",
    "通过非均衡概率降低假阳，提高假阴的方式，最后统计所有测试集样本的判别\n",
    "概率发现其实还瞎猜"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Optimization Finished!\n",
      "Accuracy:0.512358\n",
      "Optimization Finished!\n",
      "Accuracy:0.515475\n",
      "Optimization Finished!\n",
      "Accuracy:0.508573\n",
      "Optimization Finished!\n",
      "Accuracy:0.522601\n",
      "Optimization Finished!\n",
      "Accuracy:0.511022\n",
      "Optimization Finished!\n",
      "Accuracy:0.497885\n",
      "Optimization Finished!\n",
      "Accuracy:0.508463\n",
      "Optimization Finished!\n",
      "Accuracy:0.501782\n",
      "Optimization Finished!\n",
      "Accuracy:0.51314\n",
      "Optimization Finished!\n",
      "Accuracy:0.518708\n",
      "acs mean = 0.511000692844\n"
     ]
    }
   ],
   "source": [
    "MlFiterTensorFlow.MnnTF.do_tf_tt(deg.x, deg.y, n_folds=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**神经网络除了慢之外一个德行**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**通过将profit cg qcut 10份分类**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n",
      " 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74\n",
      " 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>result</th>\n",
       "      <th>deg_hisWindowPd</th>\n",
       "      <th>deg_windowPd</th>\n",
       "      <th>deg_60WindowPd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.256691</td>\n",
       "      <td>3.895622</td>\n",
       "      <td>5.363046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>15.908454</td>\n",
       "      <td>4.108007</td>\n",
       "      <td>4.199374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "      <td>21.175689</td>\n",
       "      <td>3.394158</td>\n",
       "      <td>7.384808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>14</td>\n",
       "      <td>-1</td>\n",
       "      <td>-7.023154</td>\n",
       "      <td>-0.075254</td>\n",
       "      <td>5.413456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-28</th>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>11.519025</td>\n",
       "      <td>3.621430</td>\n",
       "      <td>5.765467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  result  deg_hisWindowPd  deg_windowPd  deg_60WindowPd\n",
       "2015-07-28     90       1        -1.256691      3.895622        5.363046\n",
       "2015-07-28     26      -1        15.908454      4.108007        4.199374\n",
       "2015-07-28      9      -1        21.175689      3.394158        7.384808\n",
       "2015-07-28     14      -1        -7.023154     -0.075254        5.413456\n",
       "2015-07-28     78       1        11.519025      3.621430        5.765467"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from MlFiter import MlFiterClass\n",
    "orders_pd.profit_cg.fillna(0, inplace=True)\n",
    "# order_has_ret = orders_pd[(orders_pd.result <> 0) & (np.isnan(orders_pd.profit_cg))]\n",
    "order_has_ret = orders_pd[(orders_pd.result <> 0)]\n",
    "n_class = 100\n",
    "order_has_ret['class'] = pd.qcut(order_has_ret.profit_cg, n_class, labels=range(0, n_class))\n",
    "order_has_ret['class'] = order_has_ret['class'].astype(int)\n",
    "ZLog.info(np.unique(order_has_ret['class']))\n",
    "\n",
    "deg_pd = order_has_ret.filter(['class', 'result', 'deg_hisWindowPd', 'deg_windowPd', 'deg_60WindowPd'])\n",
    "train_np = deg_pd.as_matrix()\n",
    "y = train_np[:, 0]\n",
    "x = train_np[:, 1:]\n",
    "deg = MlFiterClass(x, y, deg_pd, force_clf=True)\n",
    "deg_pd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* y为class train_test_split进行分类\n",
    "* 使用cv数据集的 predict < class阀值 使用result==0的情况查看概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>result</th>\n",
       "      <th>deg_hisWindowPd</th>\n",
       "      <th>deg_windowPd</th>\n",
       "      <th>deg_60WindowPd</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-10-14</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>7.296329</td>\n",
       "      <td>3.278012</td>\n",
       "      <td>4.534220</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>19.433193</td>\n",
       "      <td>-3.640280</td>\n",
       "      <td>-1.462732</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-26</th>\n",
       "      <td>15</td>\n",
       "      <td>-1</td>\n",
       "      <td>15.139860</td>\n",
       "      <td>4.874754</td>\n",
       "      <td>4.723976</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-05</th>\n",
       "      <td>40</td>\n",
       "      <td>-1</td>\n",
       "      <td>-13.382448</td>\n",
       "      <td>0.568413</td>\n",
       "      <td>2.621964</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-24</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>13.809379</td>\n",
       "      <td>2.636750</td>\n",
       "      <td>-5.007059</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            class  result  deg_hisWindowPd  deg_windowPd  deg_60WindowPd  \\\n",
       "2011-10-14     54       1         7.296329      3.278012        4.534220   \n",
       "2014-01-06     86       1        19.433193     -3.640280       -1.462732   \n",
       "2015-03-26     15      -1        15.139860      4.874754        4.723976   \n",
       "2012-04-05     40      -1       -13.382448      0.568413        2.621964   \n",
       "2016-03-24     66       1        13.809379      2.636750       -5.007059   \n",
       "\n",
       "            predict  \n",
       "2011-10-14     76.0  \n",
       "2014-01-06     90.0  \n",
       "2015-03-26      9.0  \n",
       "2012-04-05     22.0  \n",
       "2016-03-24      4.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "deg.estimator.random_forest_classifier()\n",
    "\n",
    "train_df, cv_df = train_test_split(deg.df, test_size=0.1, random_state=0)\n",
    "\n",
    "fiter = deg.get_fiter()\n",
    "fiter.fit(train_df.as_matrix()[:, 2:], train_df.as_matrix()[:, 0])\n",
    "\n",
    "predictions = fiter.predict(cv_df.as_matrix()[:, 2:])\n",
    "\n",
    "cv_df['predict'] = predictions\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    74\n",
       "1.0    51\n",
       "2.0    65\n",
       "3.0    46\n",
       "4.0    64\n",
       "5.0    47\n",
       "6.0    54\n",
       "7.0    40\n",
       "8.0    52\n",
       "9.0    43\n",
       "Name: predict, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df['predict'].value_counts().sort_index().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "threshold: 0, ppv: 0.608108108108\n",
      "threshold: 1, ppv: 0.568\n",
      "threshold: 2, ppv: 0.552631578947\n",
      "threshold: 3, ppv: 0.555084745763\n",
      "threshold: 4, ppv: 0.57\n",
      "threshold: 5, ppv: 0.576368876081\n",
      "threshold: 6, ppv: 0.576059850374\n",
      "threshold: 7, ppv: 0.555555555556\n",
      "threshold: 8, ppv: 0.539553752535\n",
      "threshold: 9, ppv: 0.548507462687\n"
     ]
    }
   ],
   "source": [
    "for threshold in np.arange(0, 10):\n",
    "    ppv = cv_df[cv_df['predict'] <= threshold].result.value_counts()\n",
    "    ZLog.info('threshold: {}, ppv: {}'.format(threshold, float(ppv[-1])/ppv.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 稍微有一点提高，但是没有达到0.6，这样的话在trade win high profit & loss low profit的情况下不一定能有所提高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 要放弃还是继续寻找解决方案，印钞机怎么会那么容易实现呢，所以一切正常，最后请看\n",
    "## 解决方案部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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