abupy_lecture/12-机器学习与比特币示例(ABU量化使用文档).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# ABU量化系统使用文档 \n",
    "\n",
    "<center>\n",
    "        <img src=\"./image/abu_logo.png\" alt=\"\" style=\"vertical-align:middle;padding:10px 20px;\"><font size=\"6\" color=\"black\"><b>第12节 机器学习与比特币示例</b></font>\n",
    "</center>\n",
    "\n",
    "-----------------\n",
    "\n",
    "作者: 阿布\n",
    "\n",
    "阿布量化版权所有 未经允许 禁止转载\n",
    "\n",
    "[abu量化系统github地址](https://github.com/bbfamily/abu) (欢迎+star)\n",
    "\n",
    "[本节ipython notebook](https://github.com/bbfamily/abu/tree/master/abupy_lecture)\n",
    "\n",
    "在《量化交易之路》中我曾经编写过猪老三的世界中使用机器学习对股价和涨跌进行预测的幻想示例。\n",
    "\n",
    "![](./image/pig3.png)\n",
    "\n",
    "在猪老三的世界中实现了：**机器学习.fit(x, y) ＝ (股价预测，涨跌预测) ＝发财**\n",
    "\n",
    "在猪老三的童话中我设定的可以影响股价走势的参数是有限个的，特别是影响涨跌的因素很容易构造特征。在真实的市场中可以影响股价走势的因素是无限多的，而且这些因素之间的也可以是相关的。就像你求解一个方程组，这个方程组不是有一个两个解，它是有无限个解的系统，并且每个解都与其他任意个解相关，但又并非简单线性相关。市场是一个二级混沌系统，认为任何想通过技术对股价进行预测或者涨跌预测都是不可能的，不伦你自己认为你使用的技术本身有多高深，无异于管中窥豹。\n",
    "\n",
    "本系列教程中讲到的ump裁判系统是abupy中通过机器学习技术对回测结果进行学习，反向指导决策新的交易是否拦截的实际应用，本节讲解机器学习在量化领域很有作用的另一个方向：阀值的估计，因为无论是编写选股策略，择时策略还是任何涉及决策的代码模型中都离不开阀值，比如最常用的止盈止损策略，在代码的编写中就一定会涉及阀值，比如之前的章节一直使用的abupy中内置止盈止损策略AbuFactorCloseAtrNStop。\n",
    "\n",
    "之所以一定会涉及阀值的确定是因为就像刚刚说的类似你求解一个方程组，如果所有的参数都是未知数，那么你怎么解出你需要的答案，所以一定要把一些变量变成常数值，然后通过这些常数值来确定更多的变量，最终解出你所关心的解。\n",
    "\n",
    "对于阀值的确定传统的做法是根据经验来设定，实际上所谓的经验是个体对问题的统计模型， 在机器学习技术的帮助下，可以实现更客观，全面，适应范围更广的阀值设定。\n",
    "\n",
    "不论是个体经验对阀值进行常量定估还是通过机器学习技术通过数据模型对阀值进行定估，都达不到绝对准确预测结果的目标，量化交易的概率优势并不具有绝对的优势，即达不到预测的程度，量化交易中大多策略是基于对历史规律的总结，在规律的基础上发现概率优势，它的最大理论依据是人性的相似性以及人性很难改变的事实，如果每一个瞬间的股票价格都是全体交易者对价值所达成的一种瞬间共识，那么历史的规律在今后的交易中同样具有指导意义。\n",
    "\n",
    "按照上面解方程的说法就是，定估的常量只要能满足大多数时候解出合理的解，甚至很多时候定估的常量只要能满足有时候能解出合理的解就可以，对于有时候能解出合理的情况，可以在上层通过非均衡技术对决策进行二次逻辑过滤，我反复提及过的非均衡技术思想是量化中很很重要的一种设计思路，因为我们量化的目标结果就是非均衡，**我们想要赢的钱比输的多**。\n",
    "\n",
    "首先导入本节需要的模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "enable example env will only read RomDataBu/df_kl.h5\n"
     ]
    }
   ],
   "source": [
    "# 基础库导入\n",
    "\n",
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys\n",
    "# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题\n",
    "sys.path.insert(0, os.path.abspath('../'))\n",
    "import abupy\n",
    "\n",
    "# 使用沙盒数据，目的是和书中一样的数据环境\n",
    "abupy.env.enable_example_env_ipython()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 比特币特征的提取\n",
    "\n",
    "下面通过对比特币的短线交易决策为例，示例上述论点以及abupy中机器学习模块的使用，以及数据获取："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from abupy import abu, ml, nd, tl, pd_resample, AbuML, AbuMLPd, AbuMetricsBase \n",
    "from abupy import ABuSymbolPd, ABuScalerUtil, get_price, ABuMarketDrawing, ABuKLUtil\n",
    "\n",
    "# btc是比特币symbol代号\n",
    "btc = ABuSymbolPd.make_kl_df('btc', start='2013-09-01', end='2017-07-26')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之前在比特币, 莱特币的回测那节使用ABuKLUtil.date_week_wave对走势的日震荡做过统计如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_week\n",
       "周一    5.0108\n",
       "周二    5.5610\n",
       "周三    5.4437\n",
       "周四    5.7275\n",
       "周五    5.3008\n",
       "周六    4.7875\n",
       "周日    4.6528\n",
       "Name: wave, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ABuKLUtil.date_week_wave(btc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面可以看出大概0.055的日震荡幅度可以成做大波动的交易对比特币来说，下面对数据添加新列big_wave，可以看到结果大波动的占总交易日的1/3："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1005\n",
       "1     420\n",
       "Name: big_wave, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc['big_wave'] = (btc.high - btc.low) / btc.pre_close > 0.055\n",
    "btc['big_wave'] = btc['big_wave'].astype(int)\n",
    "btc['big_wave'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任何大的决策其实都是由很多看极起来极不起眼的小事组成的，如果我们是做比特币日内的交易者，首先你需要判断今天适不适合做交易，做出这个判断的依据里有一条即是今天的波动需要足够大，下面通过机器学习技术演示如何决策今天的波动是否足够大。\n",
    "\n",
    "备注：由于abupy中内置沙盒数据没有分时的数据，所以本示例使用日线数据做为分析对象，实际策略中应该使用的是分钟数据\n",
    "\n",
    "首先切割训练集和测试集，保留最后60天走势数据做为测试集数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "btc_train_raw = btc[:-60]\n",
    "btc_test_raw = btc[-60:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面为训练集和测试集数据都加上5，10，21，60日均线特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>date_week</th>\n",
       "      <th>p_change</th>\n",
       "      <th>key</th>\n",
       "      <th>atr21</th>\n",
       "      <th>atr14</th>\n",
       "      <th>big_wave</th>\n",
       "      <th>ma5</th>\n",
       "      <th>ma10</th>\n",
       "      <th>ma21</th>\n",
       "      <th>ma60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-05-27</th>\n",
       "      <td>15799.99</td>\n",
       "      <td>13530.03</td>\n",
       "      <td>15999.0</td>\n",
       "      <td>12355.0</td>\n",
       "      <td>28263</td>\n",
       "      <td>20170527</td>\n",
       "      <td>15749.08</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.09</td>\n",
       "      <td>1364</td>\n",
       "      <td>2289.4437</td>\n",
       "      <td>2819.2068</td>\n",
       "      <td>1</td>\n",
       "      <td>15836.082</td>\n",
       "      <td>14293.794</td>\n",
       "      <td>11937.3952</td>\n",
       "      <td>8994.2298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open     close     high      low  volume      date  pre_close  \\\n",
       "2017-05-27  15799.99  13530.03  15999.0  12355.0   28263  20170527   15749.08   \n",
       "\n",
       "            date_week  p_change   key      atr21      atr14  big_wave  \\\n",
       "2017-05-27          5    -14.09  1364  2289.4437  2819.2068         1   \n",
       "\n",
       "                  ma5       ma10        ma21       ma60  \n",
       "2017-05-27  15836.082  14293.794  11937.3952  8994.2298  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_ma(tc, ma):\n",
    "    ma_key = 'ma{}'.format(ma)\n",
    "    tc[ma_key] = nd.ma.calc_ma_from_prices(tc.close, ma, min_periods=1)\n",
    "for ma in [5, 10, 21, 60]:\n",
    "    calc_ma(btc_train_raw, ma)\n",
    "    calc_ma(btc_test_raw, ma)\n",
    "btc_train_raw.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "编写特征抽取组合函数btc_siblings_df：\n",
    "\n",
    "* 它首先将所有交易日以3个为一组，切割成多个子df，即每一个子df中有3个交易日的交易数据\n",
    "* 使用数据标准化将连续3天交易日中的连续数值特征进行标准化操作\n",
    "* 抽取第一天，第二天的大多数特征分别改名字以one，two为特征前缀，如：one_open，one_close，two_ma5，two_high....., \n",
    "* 第三天的特征只使用'open', 'low', 'pre_close', 'date_week'，该名前缀today，如today_open，today_date_week\n",
    "* 第三天的抽取了'big_wave'，其将在之后做为y\n",
    "* 将抽取改名字后的特征连接起来组合成为一条新数据，即3天的交易数据特征－>1条新的数据\n",
    "\n",
    "代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def btc_siblings_df(btc_raw):\n",
    "    # 将所有交易日以3个为一组，切割成多个子df，即每一个子df中有3个交易日的交易数据\n",
    "    btc_siblings = [btc_raw.iloc[sib_ind * 3:(sib_ind + 1) * 3, :]\n",
    "                    for sib_ind in np.arange(0, int(btc_raw.shape[0] / 3))]\n",
    "\n",
    "    btc_df = pd.DataFrame()\n",
    "    for sib_btc in btc_siblings:\n",
    "        # 使用数据标准化将连续3天交易日中的连续数值特征进行标准化操作\n",
    "        sib_btc_scale = ABuScalerUtil.scaler_std(\n",
    "            sib_btc.filter(['open', 'close', 'high', 'low', 'volume', 'pre_close',\n",
    "                            'ma5', 'ma10', 'ma21', 'ma60', 'atr21', 'atr14']))\n",
    "        # 把标准化后的和big_wave，date_week连接起来\n",
    "        sib_btc_scale = pd.concat([sib_btc['big_wave'], sib_btc_scale, sib_btc['date_week']], axis=1)\n",
    "\n",
    "        # 抽取第一天，第二天的大多数特征分别改名字以one，two为特征前缀，如：one_open，one_close，two_ma5，two_high.....\n",
    "        a0 = sib_btc_scale.iloc[0].filter(['open', 'close', 'high', 'low', 'volume', 'pre_close',\n",
    "                                           'ma5', 'ma10', 'ma21', 'ma60', 'atr21', 'atr14', 'date_week'])\n",
    "        a0.rename(index={'open': 'one_open', 'close': 'one_close', 'high': 'one_high', 'low': 'one_low',\n",
    "                         'volume': 'one_volume', 'pre_close': 'one_pre_close',\n",
    "                         'ma5': 'one_ma5', 'ma10': 'one_ma10', 'ma21': 'one_ma21',\n",
    "                         'ma60': 'one_ma60', 'atr21': 'one_atr21', 'atr14': 'one_atr14',\n",
    "                         'date_week': 'one_date_week'}, inplace=True)\n",
    "\n",
    "        a1 = sib_btc_scale.iloc[1].filter(['open', 'close', 'high', 'low', 'volume', 'pre_close',\n",
    "                                           'ma5', 'ma10', 'ma21', 'ma60', 'atr21', 'atr14', 'date_week'])\n",
    "        a1.rename(index={'open': 'two_open', 'close': 'two_close', 'high': 'two_high', 'low': 'two_low',\n",
    "                         'volume': 'two_volume', 'pre_close': 'two_pre_close',\n",
    "                         'ma5': 'two_ma5', 'ma10': 'two_ma10', 'ma21': 'two_ma21',\n",
    "                         'ma60': 'two_ma60', 'atr21': 'two_atr21', 'atr14': 'two_atr14',\n",
    "                         'date_week': 'two_date_week'}, inplace=True)\n",
    "        # 第三天的特征只使用'open', 'low', 'pre_close', 'date_week'，该名前缀today，如today_open，today_date_week\n",
    "        a2 = sib_btc_scale.iloc[2].filter(['big_wave', 'open', 'low', 'pre_close', 'date_week'])\n",
    "        a2.rename(index={'open': 'today_open', 'low': 'today_low',\n",
    "                         'pre_close': 'today_pre_close',\n",
    "                         'date_week': 'today_date_week'}, inplace=True)\n",
    "        # 将抽取改名字后的特征连接起来组合成为一条新数据，即3天的交易数据特征－>1条新的数据\n",
    "        btc_df = btc_df.append(pd.concat([a0, a1, a2], axis=0), ignore_index=True)\n",
    "    return btc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第三天的特征避免使用high是因为训练的目标y是big_wave，之所以可以使用当天的low是因为比特币市场的特点为24小时交易，即没有明确的一天的概念，也即没有明确一天中的最低，实盘使用即人工对当前最低根据24小时最低进行猜测，或直接使用24小时最低，或者使用当前的最新价格都可，组成数据后使用模型进行决策，决策的结果即为未来数小时内是否会有大的波动，实际上最终大波动的决策成立，需要其它很多模型共同生效，比如外盘的走势决策等等。\n",
    "\n",
    "下面使用训练集数据btc_train_raw做为参数抽取组合特征，重新组合好的特征如tail所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>big_wave</th>\n",
       "      <th>one_atr14</th>\n",
       "      <th>one_atr21</th>\n",
       "      <th>one_close</th>\n",
       "      <th>one_date_week</th>\n",
       "      <th>one_high</th>\n",
       "      <th>one_low</th>\n",
       "      <th>one_ma10</th>\n",
       "      <th>one_ma21</th>\n",
       "      <th>one_ma5</th>\n",
       "      <th>...</th>\n",
       "      <th>two_date_week</th>\n",
       "      <th>two_high</th>\n",
       "      <th>two_low</th>\n",
       "      <th>two_ma10</th>\n",
       "      <th>two_ma21</th>\n",
       "      <th>two_ma5</th>\n",
       "      <th>two_ma60</th>\n",
       "      <th>two_open</th>\n",
       "      <th>two_pre_close</th>\n",
       "      <th>two_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>-0.2263</td>\n",
       "      <td>0.3476</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-0.1933</td>\n",
       "      <td>-1.1190</td>\n",
       "      <td>-1.0538</td>\n",
       "      <td>-1.0275</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0825</td>\n",
       "      <td>0.3129</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>-1.1249</td>\n",
       "      <td>-1.1517</td>\n",
       "      <td>-1.1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.7435</td>\n",
       "      <td>-0.7871</td>\n",
       "      <td>-0.8908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.8698</td>\n",
       "      <td>-0.7936</td>\n",
       "      <td>-0.9451</td>\n",
       "      <td>-0.9609</td>\n",
       "      <td>-0.6258</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.2228</td>\n",
       "      <td>-0.3295</td>\n",
       "      <td>-0.1021</td>\n",
       "      <td>-0.0740</td>\n",
       "      <td>-0.5275</td>\n",
       "      <td>-0.0729</td>\n",
       "      <td>-0.1542</td>\n",
       "      <td>-0.1541</td>\n",
       "      <td>-0.8514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5464</td>\n",
       "      <td>-0.6353</td>\n",
       "      <td>-1.0217</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.8671</td>\n",
       "      <td>-0.8399</td>\n",
       "      <td>-0.9962</td>\n",
       "      <td>-0.9802</td>\n",
       "      <td>-0.9829</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.2268</td>\n",
       "      <td>-0.2663</td>\n",
       "      <td>-0.0075</td>\n",
       "      <td>-0.0386</td>\n",
       "      <td>-0.0333</td>\n",
       "      <td>-0.0622</td>\n",
       "      <td>-0.4361</td>\n",
       "      <td>-0.4349</td>\n",
       "      <td>-0.5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.9859</td>\n",
       "      <td>-0.9868</td>\n",
       "      <td>-0.5587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.7841</td>\n",
       "      <td>-1.1018</td>\n",
       "      <td>-0.9547</td>\n",
       "      <td>-0.9566</td>\n",
       "      <td>-0.9344</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.3421</td>\n",
       "      <td>0.2516</td>\n",
       "      <td>-0.0852</td>\n",
       "      <td>-0.0818</td>\n",
       "      <td>-0.1202</td>\n",
       "      <td>-0.0683</td>\n",
       "      <td>0.5856</td>\n",
       "      <td>0.5953</td>\n",
       "      <td>0.2537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.8910</td>\n",
       "      <td>-0.8987</td>\n",
       "      <td>1.0487</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.6165</td>\n",
       "      <td>0.7696</td>\n",
       "      <td>-1.0871</td>\n",
       "      <td>-1.0466</td>\n",
       "      <td>-1.0015</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5373</td>\n",
       "      <td>0.3607</td>\n",
       "      <td>0.2065</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>0.9985</td>\n",
       "      <td>0.0998</td>\n",
       "      <td>1.1385</td>\n",
       "      <td>1.1364</td>\n",
       "      <td>1.1420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     big_wave  one_atr14  one_atr21  one_close  one_date_week  one_high  \\\n",
       "450       1.0     0.1020    -0.2263     0.3476            5.0   -1.0000   \n",
       "451       1.0    -0.7435    -0.7871    -0.8908            1.0   -0.8698   \n",
       "452       1.0    -0.5464    -0.6353    -1.0217            4.0   -0.8671   \n",
       "453       1.0    -0.9859    -0.9868    -0.5587            0.0   -0.7841   \n",
       "454       1.0    -0.8910    -0.8987     1.0487            3.0    0.6165   \n",
       "\n",
       "     one_low  one_ma10  one_ma21  one_ma5     ...      two_date_week  \\\n",
       "450  -0.1933   -1.1190   -1.0538  -1.0275     ...                6.0   \n",
       "451  -0.7936   -0.9451   -0.9609  -0.6258     ...                2.0   \n",
       "452  -0.8399   -0.9962   -0.9802  -0.9829     ...                5.0   \n",
       "453  -1.1018   -0.9547   -0.9566  -0.9344     ...                1.0   \n",
       "454   0.7696   -1.0871   -1.0466  -1.0015     ...                4.0   \n",
       "\n",
       "     two_high  two_low  two_ma10  two_ma21  two_ma5  two_ma60  two_open  \\\n",
       "450    1.0000   1.0825    0.3129    0.1180   0.9700    0.1415   -1.1249   \n",
       "451   -0.2228  -0.3295   -0.1021   -0.0740  -0.5275   -0.0729   -0.1542   \n",
       "452   -0.2268  -0.2663   -0.0075   -0.0386  -0.0333   -0.0622   -0.4361   \n",
       "453   -0.3421   0.2516   -0.0852   -0.0818  -0.1202   -0.0683    0.5856   \n",
       "454    0.5373   0.3607    0.2065    0.1008   0.9985    0.0998    1.1385   \n",
       "\n",
       "     two_pre_close  two_volume  \n",
       "450        -1.1517     -1.1104  \n",
       "451        -0.1541     -0.8514  \n",
       "452        -0.4349     -0.5652  \n",
       "453         0.5953      0.2537  \n",
       "454         1.1364      1.1420  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_train0 = btc_siblings_df(btc_train_raw)\n",
    "btc_train0.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上所示这样我们只能得到454条训练集数据，但由于每3条连续交易日数据组合成一个特征，只要向前跳一条数据进行特征组合抽取即可以得到另一组新特征，下面分别跳过第一个，第二个数据，抽取btc_train1，btc_train2："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "btc_train1 = btc_siblings_df(btc_train_raw[1:])\n",
    "btc_train2 = btc_siblings_df(btc_train_raw[2:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面把上面的3组特征连起来，然后把周几这个特征使用pd.get_dummies进行离散化处理，使得所有特征值的范围都在0-1之间，最终的特征如下btc_train所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>big_wave</th>\n",
       "      <th>one_atr14</th>\n",
       "      <th>one_atr21</th>\n",
       "      <th>one_close</th>\n",
       "      <th>one_high</th>\n",
       "      <th>one_low</th>\n",
       "      <th>one_ma10</th>\n",
       "      <th>one_ma21</th>\n",
       "      <th>one_ma5</th>\n",
       "      <th>one_ma60</th>\n",
       "      <th>...</th>\n",
       "      <th>two_date_week_4.0</th>\n",
       "      <th>two_date_week_5.0</th>\n",
       "      <th>two_date_week_6.0</th>\n",
       "      <th>today_date_week_0.0</th>\n",
       "      <th>today_date_week_1.0</th>\n",
       "      <th>today_date_week_2.0</th>\n",
       "      <th>today_date_week_3.0</th>\n",
       "      <th>today_date_week_4.0</th>\n",
       "      <th>today_date_week_5.0</th>\n",
       "      <th>today_date_week_6.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.1541</td>\n",
       "      <td>-1.1538</td>\n",
       "      <td>0.6139</td>\n",
       "      <td>0.1821</td>\n",
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       "      <td>0.6302</td>\n",
       "      <td>0.6302</td>\n",
       "      <td>0.6302</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.1407</td>\n",
       "      <td>-1.1388</td>\n",
       "      <td>1.1312</td>\n",
       "      <td>0.5808</td>\n",
       "      <td>1.0308</td>\n",
       "      <td>1.0242</td>\n",
       "      <td>1.0242</td>\n",
       "      <td>0.9478</td>\n",
       "      <td>1.0242</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1030</td>\n",
       "      <td>1.0779</td>\n",
       "      <td>-0.3414</td>\n",
       "      <td>-0.3475</td>\n",
       "      <td>-1.1308</td>\n",
       "      <td>1.1400</td>\n",
       "      <td>1.1400</td>\n",
       "      <td>1.0907</td>\n",
       "      <td>1.1400</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9934</td>\n",
       "      <td>0.9856</td>\n",
       "      <td>-0.5774</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>-1.1502</td>\n",
       "      <td>1.0289</td>\n",
       "      <td>1.1275</td>\n",
       "      <td>-0.7646</td>\n",
       "      <td>1.1275</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0581</td>\n",
       "      <td>1.0542</td>\n",
       "      <td>1.1492</td>\n",
       "      <td>0.6046</td>\n",
       "      <td>0.6348</td>\n",
       "      <td>1.1539</td>\n",
       "      <td>1.0388</td>\n",
       "      <td>-0.1548</td>\n",
       "      <td>1.0388</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1358</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4186</td>\n",
       "      <td>-0.9601</td>\n",
       "      <td>0.5035</td>\n",
       "      <td>1.1462</td>\n",
       "      <td>0.7406</td>\n",
       "      <td>-1.0014</td>\n",
       "      <td>-0.9851</td>\n",
       "      <td>-1.0667</td>\n",
       "      <td>-0.9852</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1359</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.9851</td>\n",
       "      <td>-0.9910</td>\n",
       "      <td>-0.9140</td>\n",
       "      <td>0.4711</td>\n",
       "      <td>-0.9506</td>\n",
       "      <td>-0.9246</td>\n",
       "      <td>-0.9709</td>\n",
       "      <td>1.1474</td>\n",
       "      <td>-0.9081</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.1506</td>\n",
       "      <td>-1.1451</td>\n",
       "      <td>-0.7089</td>\n",
       "      <td>-0.8439</td>\n",
       "      <td>-1.1240</td>\n",
       "      <td>-0.9675</td>\n",
       "      <td>-0.9633</td>\n",
       "      <td>-0.8459</td>\n",
       "      <td>-0.9779</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0698</td>\n",
       "      <td>-1.0559</td>\n",
       "      <td>-1.1545</td>\n",
       "      <td>-1.0996</td>\n",
       "      <td>-0.9867</td>\n",
       "      <td>-0.9940</td>\n",
       "      <td>-1.0158</td>\n",
       "      <td>-1.0464</td>\n",
       "      <td>-0.9951</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0266</td>\n",
       "      <td>-1.0188</td>\n",
       "      <td>-0.3911</td>\n",
       "      <td>-1.1532</td>\n",
       "      <td>-0.8514</td>\n",
       "      <td>-1.0662</td>\n",
       "      <td>-1.0565</td>\n",
       "      <td>-1.1087</td>\n",
       "      <td>-1.0475</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1363 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      big_wave  one_atr14  one_atr21  one_close  one_high  one_low  one_ma10  \\\n",
       "0          0.0    -1.1541    -1.1538     0.6139    0.1821   0.9389    0.6302   \n",
       "1          0.0    -1.1407    -1.1388     1.1312    0.5808   1.0308    1.0242   \n",
       "2          1.0     1.1030     1.0779    -0.3414   -0.3475  -1.1308    1.1400   \n",
       "3          0.0     0.9934     0.9856    -0.5774    0.0413  -1.1502    1.0289   \n",
       "4          0.0     1.0581     1.0542     1.1492    0.6046   0.6348    1.1539   \n",
       "...        ...        ...        ...        ...       ...      ...       ...   \n",
       "1358       0.0    -0.4186    -0.9601     0.5035    1.1462   0.7406   -1.0014   \n",
       "1359       1.0    -0.9851    -0.9910    -0.9140    0.4711  -0.9506   -0.9246   \n",
       "1360       1.0    -1.1506    -1.1451    -0.7089   -0.8439  -1.1240   -0.9675   \n",
       "1361       1.0    -1.0698    -1.0559    -1.1545   -1.0996  -0.9867   -0.9940   \n",
       "1362       1.0    -1.0266    -1.0188    -0.3911   -1.1532  -0.8514   -1.0662   \n",
       "\n",
       "      one_ma21  one_ma5  one_ma60         ...           two_date_week_4.0  \\\n",
       "0       0.6302   0.6302    0.6302         ...                           0   \n",
       "1       1.0242   0.9478    1.0242         ...                           0   \n",
       "2       1.1400   1.0907    1.1400         ...                           0   \n",
       "3       1.1275  -0.7646    1.1275         ...                           0   \n",
       "4       1.0388  -0.1548    1.0388         ...                           0   \n",
       "...        ...      ...       ...         ...                         ...   \n",
       "1358   -0.9851  -1.0667   -0.9852         ...                           0   \n",
       "1359   -0.9709   1.1474   -0.9081         ...                           0   \n",
       "1360   -0.9633  -0.8459   -0.9779         ...                           1   \n",
       "1361   -1.0158  -1.0464   -0.9951         ...                           0   \n",
       "1362   -1.0565  -1.1087   -1.0475         ...                           0   \n",
       "\n",
       "      two_date_week_5.0  two_date_week_6.0  today_date_week_0.0  \\\n",
       "0                     0                  0                    0   \n",
       "1                     0                  0                    0   \n",
       "2                     0                  1                    1   \n",
       "3                     0                  0                    0   \n",
       "4                     1                  0                    0   \n",
       "...                 ...                ...                  ...   \n",
       "1358                  1                  0                    0   \n",
       "1359                  0                  0                    0   \n",
       "1360                  0                  0                    0   \n",
       "1361                  0                  0                    0   \n",
       "1362                  0                  0                    0   \n",
       "\n",
       "      today_date_week_1.0  today_date_week_2.0  today_date_week_3.0  \\\n",
       "0                       1                    0                    0   \n",
       "1                       0                    0                    0   \n",
       "2                       0                    0                    0   \n",
       "3                       0                    0                    1   \n",
       "4                       0                    0                    0   \n",
       "...                   ...                  ...                  ...   \n",
       "1358                    0                    0                    0   \n",
       "1359                    0                    1                    0   \n",
       "1360                    0                    0                    0   \n",
       "1361                    1                    0                    0   \n",
       "1362                    0                    0                    0   \n",
       "\n",
       "      today_date_week_4.0  today_date_week_5.0  today_date_week_6.0  \n",
       "0                       0                    0                    0  \n",
       "1                       1                    0                    0  \n",
       "2                       0                    0                    0  \n",
       "3                       0                    0                    0  \n",
       "4                       0                    0                    1  \n",
       "...                   ...                  ...                  ...  \n",
       "1358                    0                    0                    1  \n",
       "1359                    0                    0                    0  \n",
       "1360                    0                    1                    0  \n",
       "1361                    0                    0                    0  \n",
       "1362                    1                    0                    0  \n",
       "\n",
       "[1363 rows x 49 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_train = pd.concat([btc_train0, btc_train1, btc_train2])\n",
    "btc_train.index = np.arange(0, btc_train.shape[0])\n",
    "\n",
    "dummies_one_week = pd.get_dummies(btc_train['one_date_week'], prefix='one_date_week')\n",
    "dummies_two_week = pd.get_dummies(btc_train['two_date_week'], prefix='two_date_week')\n",
    "dummies_today_week = pd.get_dummies(btc_train['today_date_week'], prefix='today_date_week')\n",
    "btc_train.drop(['one_date_week', 'two_date_week', 'today_date_week'], inplace=True, axis=1)\n",
    "btc_train = pd.concat([btc_train, dummies_one_week, dummies_two_week, dummies_today_week], axis=1)\n",
    "\n",
    "pd.options.display.max_rows=10\n",
    "btc_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. abu中内置机器学习模块的使用\n",
    "\n",
    "下面使用abupy中内置机器学习工具AbuML对特征数据进行封装，代码如下所示，下面的y值即是big_wave列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_matrix = btc_train.as_matrix()\n",
    "y = train_matrix[:, 0]\n",
    "x = train_matrix[:, 1:]\n",
    "\n",
    "btc_ml = AbuML(x, y, btc_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AbuML会根据数据特点自动内部选择使用分类器或者回归器，下面进行特指最优分类器操作，比如特指使用随机森林做为分类器，执行random_forest_classifier_best会在内部对n_estimators参数和训练集数据进行grid search拟合，寻找最合适的参数最终做为内部分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start grid search please wait...\n"
     ]
    },
    {
     "data": {
      "image/png": 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QBOEiQcSOIAiC4DjTi1lCQR8H9hnNRtcaNBYVBEEQBLsRsSMIgiA4SqVaZW45y+RonGg4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cc01860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=260, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_ml.random_forest_classifier_best()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "所有best函数中寻找最优参数内部只根据学习器的特点寻找最少量的参数设置，如果需要自定义参数范围可使用如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start grid search please wait...\n"
     ]
    },
    {
     "data": {
      "image/png": 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pYB3S+wfi070y59DMds810cwGpNeHAePw4Fd/M2tnZp3wqV9T0jnXAgMkraqn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\nJJWIwYskSZIkSVKJGLxIkiRJkiSVyP8HYV5cLObdzLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c726d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=400, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_features': ['sqrt', 'log2'], 'n_estimators': np.arange(50, 500, 50)}\n",
    "btc_ml.random_forest_classifier_best(param_grid=param_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面使用btc_ml对训练集进行交叉准确率评分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier score mean: 0.8151620867325032\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.781 ,  0.8102,  0.7883,  0.8382,  0.8162,  0.8162,  0.8235,\n",
       "        0.8456,  0.7794,  0.8529])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_ml.cross_val_accuracy_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面使用btc_ml对训练集进行交叉roc_auc评分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier score mean: 0.8399573797130188\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.815 ,  0.8785,  0.8166,  0.8018,  0.8707,  0.8484,  0.8148,\n",
       "        0.8551,  0.8005,  0.8981])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_ml.cross_val_roc_auc_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AbuML对外的函数都支持关键子参数fiter_type，可以指定使用的学习器类型如回归，聚类等，每个函数都通过内部装饰器声明自己支持的学习器类型对不支持的类型输出不支持，如下想通过指定使用回归器进行roc_auc评分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "cross_val_roc_auc_score not support reg!\n"
     ]
    }
   ],
   "source": [
    "btc_ml.cross_val_roc_auc_score(fiter_type=ml.EMLFitType.E_FIT_REG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述输出显示函数不支持回归器，因为内部实现中通过entry_wrapper装饰器声明了只支持E_FIT_CLF，即分类器：\n",
    "    \n",
    "    @entry_wrapper(support=(EMLFitType.E_FIT_CLF,))\n",
    "    def cross_val_roc_auc_score(self, cv=10, **kwargs):\n",
    "        \"\"\"\n",
    "        被装饰器entry_wrapper(support=(EMLFitType.E_FIT_CLF,))装饰，\n",
    "        即支持有监督学习分类，使用cross_val_score对数据进行roc_auc度量，如果数据的y的\n",
    "        label标签 > 2，通过label_binarize将label标签进行二值化处理，\n",
    "        依次计算二值化的列的roc_auc，结果返回score最好的数据度量\n",
    "        :param cv: 透传cross_val_score的参数，默认10\n",
    "        :param kwargs: 外部可以传递x, y, 通过\n",
    "                                x = kwargs.pop('x', self.x)\n",
    "                                y = kwargs.pop('y', self.y)\n",
    "                       确定传递self._do_cross_val_score中参数x，y，\n",
    "                       以及装饰器使用的fiter_type，eg：\n",
    "                       ttn_abu.cross_val_roc_auc_score(fiter_type=ml.EMLFitType.E_FIT_REG)\n",
    "        :return: cross_val_score返回的score序列，\n",
    "                 eg: array([ 1.  ,  0.9 ,  1.  ,  0.9 ,  1.  ,  0.9 ,  1.  ,  0.9 ,  0.95,  1.  ])\n",
    "        \"\"\"\n",
    "        x = kwargs.pop('x', self.x)\n",
    "        y = kwargs.pop('y', self.y)\n",
    "        return self._do_cross_val_score(x, y, cv, _EMLScoreType.E_SCORE_ROC_AUC.value)\n",
    "\n",
    "更多详情实现请阅读源代码AbuML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面使用train_test_split_xy查看训练集输出的precision_score，recall_score，混淆矩阵，以及f1等度量结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "x-y:(1363, 48)-(1363,)\n",
      "train_x-train_y:(1226, 48)-(1226,)\n",
      "test_x-test_y:(137, 48)-(137,)\n",
      "accuracy = 0.77\n",
      "precision_score = 0.62\n",
      "recall_score = 0.39\n",
      "          Predicted\n",
      "         |  0  |  1  |\n",
      "         |-----|-----|\n",
      "       0 |  90 |   9 |\n",
      "Actual   |-----|-----|\n",
      "       1 |  23 |  15 |\n",
      "         |-----|-----|\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.80      0.91      0.85        99\n",
      "        1.0       0.62      0.39      0.48        38\n",
      "\n",
      "avg / total       0.75      0.77      0.75       137\n",
      "\n"
     ]
    }
   ],
   "source": [
    "btc_ml.train_test_split_xy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过plot_roc_estimator绘制roc曲线："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YY4wxxhhzBHK3pft+e8fR2lS1uV0FDkqb9uCLyER3G4bGx88WkS9F5HMRua6ZU40xxhhj\njDHGGHMI2qwH312E4ftAZaPjMcDfcFZKrAQ+FZE3VTW/rWIxxhhjjDHGNFRTU8OOHdtJSUmlW7du\nVFSU89JLL/D1Gl3Of3NyJjBmzDgqKsp58cXnGlwjISGRW2656TBHbozZl7Ycor8JOA94ptHxocBG\nVS0GcFdFPBF4uQ1jMcYY0wFUrFjO7n8/1t5hfCts9ngIHSEL6R7JwmHwBYN0pE+6OiqJNV0n4/c2\n2crZHFG++VNV66+htLyQ2Jg4UpPTqagsYfaCpyku20NxWQHllcUAnH7CpZw4/lxKyvby5ydua3Kd\nU467kJOOOd8t/zkAAwcO5Mwzz2Trlu3UVoa44trLv3G8xphvrs0SfFV9RUT6NVOUApRGvC4HUg90\nvbS0BKKjo1oputaRkZF84ErGNGLPjWmJo+W5CQZ9pOeMo/+1V7V3KMZ8Y1X+AA98uZHje3Ylp3vn\n9g4HgJpqPy8/vYIJOd0ZPCyDZx7+nO9cMpbY2LaZlblxZykvf7CBa88dQUJc83+n7XzlDTpldaPL\nuDFtEkNrKC1cAUBSZ2nXOGpra5n/6Xw+2fA5ldWV9I7rTjAYZOjQYUyZPIVgMMhf7v8L4VCIUDjs\n9LSHw4wdO44Zp88gFArx67t+Tf6efPJ376a0zPmT+zvnn8/A8cOZ0G00f3zsk/r7RUVFkZmZSVSG\nF+/AAOO6deOMPc426PVL0Xs8HHPMaEaPT6OszMsV4asYnJ1N167pAHTtmsEPfnwVCQkJh/OjMsbs\nQ3sssleGs29inWScfQD3q7i4qs0CaomOtB2IOXLYc2Na4mh6birKa/AFocRnuxi1taPpuemIAqEw\nT67Pp3dGV0ZnZRDoAF34wWCIt19dTd+BXRme0xeAELEkpaUQ2+ng/uQ7lOemtLKWp+ev4prvjqN/\nvy7N1qlcs5ouu5S+N16ONyb24N5IO0iI34DHG0Nq1iCKi4uorKykqqqKqirnv2lpXRg6dBgA//73\no1RUVFBVVUllZSU+Xw1jx+Zw8cWXAfDLX96Gz+cjcivqcePGc/nlTsPm7bf/lNraWoLBIPn5u9mx\nYztnnHE2v/nN76ipqeEX045tEt/ll1/NdTfcTCgU4s233mhSntGtO4OHjSQYDLLgk/n1x2NjY8nq\n0Z3tsfn8/OQ7yU4byD/+8Qg9e/aiT5++ZGV1Z8HKfD5cmsv3z88hNiaKk086dZ+f07Jlizj+OCe+\nbt26IzKc9PQMEhISOszPm6OlQbwtiMhU4CVgDc4QkRSc/dcvVdVaEckA7gP6AlHADuBWVd3tnj8Z\nuAuIARKBJ1T1oUb3GADMBr5Q1SuaiaEf8IKqHtvo+L3AOlV9splzvgtcoKqXNFPmAZ4AblbVioP+\nMNqAiNwNnAkEgJ+o6qJG5acA97rl76vqne7xN4CugB+oVtUZIjID6KGqjx9qHO2R4K8FskWkC1CB\nMzz/vnaIwxhjjDHmkIXDYd7YtofYKC9n9O7a3uEATkwfz1FiO0Vx7EkD2/x+oXCYx2at4YSR3Rm+\nj+Q+HAxS8MJzdL3gog6V3FdUlKO6DtV1rFu3lnXr1jB6WDduvtFJ0EePHkJNTU2Dc8477wL++U/n\n7+x77vkNVVUNO57KysrqE/wXXni2SXlNTU19gv/SSy9QVdVgiSo2btwAQK3Xz6CpwxnbexR9MvsQ\nHR1NdHQ0I0eOAsDr9fKnP/0Nr9eL1+vF4/Hg9XoZODC7vvyxx56iZ89e9OrVh6S0ZP669CGm9j6B\n7DTnubjggovq77ttdzmvzd/MHd93kvvGwuEweXm5pKdn0KlTJ7KyeuDz1SAynLS09IP9yE3HMk9V\n6x8CEXkOOEdEXgFeBe5T1TfcslOAWSIyESfpfxCYrqr5IhIPfCgim1X1nYjrTwLeVtWmcz1aQEQe\nAE4Hlu+jykxgSQdI7scBU4CJQG/gFZw15yL9GbgUJx9eICIjVXUlkA0Md3cEAEBV54jIHBF5+VC3\nyjtsCb6IXAIkqeojInIr8C7OKv7/VtXcwxWHMcYYY8w3MX93MXlVPq4b0guvp2OMRln6+XaK9lZy\n7iVj8XrbPqY5C7fh8wf5zuT++6xT+vGHRKWkkDR23CFdOxAIsHt3HsXFxZSUOP8SE5OYNu0UAO65\n5y4KCvbg99fi9wfw+/0MHizceedvAfjRj24kN3dng2sOHTqM//f//kQ4HGb06KGUlzf8e7m2enT9\n11lZ3fH5fCQkJJCQkEhCQgKDBmXXl1999fWEw2ESEhJITEyiU6dODcr/+Mf7CAQCeDwePO7z0a/f\n15/TvffeRzAYxOPxkJGRQa9efejVqxfhcJj/rH2ZX/zxLs4dOGOfn8+VV16zzzKPx8M553wXgFA4\nxCMrn2JQ2gAm9zyuSd2qGj8Pvb6Sy04bTFaXhsPrw+EQubk7UF1NWVkpIsMZNmwU3bv3onv3Xvu8\nvzmyiEgs0B0oBnKA0rrkHkBV3xeRTTgdsicCT9ctjK6q1SJyOk6Hbd31+gB3AAkishFYCPwdCAI1\nQIPd00TkfOBOoACIBdY1E+ZnwOvADft4Gz8CvutebwpwN06OmQRcAtQCbwGFOCML5uA0VHjcY1e7\n7+FfOIl5d+DNut71iFhnudess0ZVfxDxehLwnpukbxeRaBHJUNWCiDrLgC44IyDigKCIdAM6A2+J\nSGfgXlWd5dafDVzpxnvQ2jTBV9WtwLHu189FHH8L54M2xhhjjDlirCwqZ2F+KTcO602nqDbdbfig\nbVy7hzXLd3He98cRE9v26xVt3FnK3C938JsrJhDlbf4zCFZUUPjWm/S69ef1SW5zCgoKWLNmFbW1\nPk49dToAEyeOYceO7Q3qTZx4XH2CP3fuO6g2zAOKigrrv166dDEbNqxvUO73+wEnAR45chQlJSUM\nGTKUIUOGIjKUXl2/TvgXLfpqv+//rrvu2W95XU/+vlx00aXNHv9452eU11ZwZv99D5E/FLM2v0d1\noIZrRzTdIjwcDvP422sZNaArxwzt1uD4jh1bUV1NRUU5Ho+H3r370atX31aJyTQ088WbVgHDW/GS\nq1+68OERB6gzzd3GPBMIAY+o6gciMhNnkfTGNuP03vegUS+6qpY2er3dHWo/RFUfFpHFwLWqulxE\nzgX+CvwM6ndW+yswDigCmt33XVVfdKcWNOGOIugTkUQPBy5T1V0icgdwAfAskAXkuNMQFgJXq+oa\nEbkGuB14FFioqteKSBywE6fhITKOs5qLIUIKToNBnbp15iIT/JXALLfeCpwGjZ7AX4AHcJL/T0Vk\nkarucev8mI6U4BtjjDHGHC22V1TzxrYCrpaepMZ2jD+hdu8sZcHcDZxz0WgSk9t+1fyKaj//enMV\nV8wYQnpqXLN1CgoKWP7YP8n1gP+dWZSVlRETE8Mtt9wKwN13/5qFCz9lx44d7N3r/O0rMqQ+we/f\nfyC1tbWkp3clLS2Nzp3TGDJkaP3177jjbkpLS4iOjiYmJobo6BjS07+eKvHAAw9RXV3dIKaUlJT6\nr1977e0mjQ4lu+Z9g0/lm9tVsZu3t7zHbTk/JNr7zZ+txfnLWZy/jJ+P/1Gz13vvyx2UVPi48Vwn\nFwyHw/UjDrZt20xlZQV9+w5g8OBhJCXZnPa2chDJeFuYp6oXiUg6MBfY4h7PBfo1Uz/brdcDp4e7\nnoiMBryqumwf9+qhqnWNAvNx5p/XyQCKVLXQvdZnLXgvacDeiNe5wIMiUoGTOH/qHt+iqrXu10OB\nh0QEnJ70DTgNDBNE5CSc9eKa/DA9iB78/a4z5/bO/wpnKH6uiPwJuA24H/inqgaAPSKyDBBgD5AH\nHPJcmI7x28kYY4wxpgMr9vl5dmMe3+ufSY+E1kmkfTV+Ppi1jlAw1KLzA/4g+XnldOmayOcfNtfx\n5tRprVkE4XCYJ2avpV+XIJX5a3lp2fvk5e1ix44d+P21PPCAs9bWjVdfxoIvPm9wbmZmt/oEf/Pm\njSxbthSApKRkhg4dxqhRo+uTzOef/y8xMTH7jGPGjDP3G+f48cfst3x/Iwragz8U4Mk1z3PuwBl0\nS8j4xtfbXr6Tl9e/wY/GXEdybFKT8o07S5mzcBt3Xj4eryfM5s3r2bhRmTz5ZOLjExg9ejzR0dEk\nJCR+41hMx6WqhSJyGc48+jE4Q+GzRORsd7Q1IjIdGAR8jNOT/7qIvKiqBSKShDOs/R6coefN2SUi\no1R1Bc789MihNXuAzhHD2Cfg9JwfikIaJtWPAgNVtVxEnuLrzSAif8gqcLk72uAEnCH5VwIlqnqD\niAwCrhcRT6M58Qfqwf8U+JOI3Af0wmn4iGx8qMaZClA3pSEPp5HjFJxpBme4n+kInDn64DRg7DnQ\nh9CYJfjGGGMOm3CH2i3826GwppZCn7+9wzigUChEIBxos+uHw2FK8yoJtyCZDgNLCysZlRxPdEEF\nGwtaZy2ninIfu3eWMiwnq0UxrV+xh+yRmfQemNakvLKygtzc7USnb+eBf3xBYeFe7vrt7wF48t+P\n89GHHzQ554mnn6XKH83Dj/yjQXkoGKS6upof/uZf7C2rZtuCR/jdrDcbnBsbG8sf/nwfHo+HPqEQ\nw/r3p++w4WRkZJKSmkp6ejrVAWfhup/+/HZuvPlHZGRm0qtX7/qEuybocy7mgUAgeMifyTcRCAUA\nT32Mh9Obm96hS1waYzNGfeP7V/mreGTF01wo36VXco8m5eVVtfzzzVVcPn0wxQXbWPTZWny+GqKi\noiguLiQ+PoGUlAPuXm2OEu4w9QeBB1X1AhE5G7jfHd4Ozir6Z6pqENgqIrcDr4pIECexfkxVZ+/n\nFtcB/+eudB8A6heQUNWAiNwMvCsiRTgryB9q/D4R2S0ime6Q9v/gLF5XCeTjjDpo7CbgaRGJxvlR\neg1OQv2ciBwH+HB69XvgjAg42FiWiMgC4HOcNQB+CCAi04BJqnqPiNwGvCciNTi9+1eqarGInO5O\nHQgBd0Q0DEwEmv6wPgBP5BYeHVlBQXmHCtS2HzItYc+NaYmj6bnJ+9dDxA0cRNopp7V3KEe9uufm\nyfW5VPiDJEa3/dzslggTptRXxp6qvW3aABRdHab7l+DbT+4SDsG+2hjC+CHcusmfJ+QhxhdPbXzD\nFderqyuo9VU3qZ+c0gWvN4ryskKKi3ZTHChgL3n4KirxVVQx9oIZeKOiWPL8LDZ+/EWT889/8DdE\nx8ay+Nk32bTgyyblMx++B6/Xy5f/eaPZ8hk/fYa0satZ8858di3bSkKXJBK6JJGYkUJSZjI9xvSj\nX56PY5eU8N+zswi1cLG/TsDETt4DjjwIhVvvicmK8rDZH2ZJ7aE1AIWBYChEy/+cDhMiiJdoPLTO\nyIKeoVH0Co9ttmxzXilDeiWT6FuDz+cjOjqa/v2zyc4eQqdOzU+5OBgd6fdURkZyxxqiYdqUiFwM\nZKnq39o7ltYmIu8AMzvsKvrGGGO+3fyFhVSuWU2mu1WUOTzCYTitVzqDUzvecNvcijxe0NcIhoLc\nNOy79Elpu9W5S4qqmL1+JZfcMLHZ8qqaAL97chFZ3RKJjjo8+YG/NsiuXWX06tmX2poqYuOclczf\nfuxu1ixp2mnzk3tfJyG1K58ums1n7z3bpPzsGb8mKaUL+Snb2BqzjLSuPUnL7EWXjJ50yejFoNCJ\nxIbjSZ3Shwkj85ucPyh8HHExMaRM6d2w3OMhKTmN750xkXFDzoXpdzQ5FyAcCLD17l+TedUtTHS3\ndWsJX2Uue7f8l5RuTVd+BwiGQ3xUoHy4Z22rJcUATovCQV4v7MQRCjnTClq6m4KXaI5LOJvM6N4H\nrvwNhEMBAjUl9BnTk+NGZLF8WTEJCYkMHCh06tT2azcY04ZewOmRT2rvrfJak4icCbxyqMk9WIJv\njDHmMCmZN5eU4ycRFR/f3qGYVuAvKqRy1coWnRsIBlhZuIYtpduY0nU4Azv3x7N8IyVsbOUov1Ze\nDcFqKJn/UZOycDjMM+vDZHeC72XVNj25jRSW+Xls8zIWL/iIeYsX8fqf72dgz170OX0Sd237qkny\nemFGGempXhIHd8W/czhdUlJJS06hS0oKXVJS+W5WJcmJMPPi6cR8/yy8TVa4r3b+ZaThTO1srJjk\n5DjKU/YU07w3AAAgAElEQVRRvmcdJXua28XK4du6lZiMbiR+g+S+TlRMIskZTefS760u5Kk1LxDj\njeG3x/2StLjO3/heh2JjrrODwJqtRZwwsjvTxvcis3PH/Znm8/nYuHEdmzevJxAIMnncIKKjvIwf\n33zjiTFHGneefNOtIo5wqtrsrgIHwxJ8Y4wxbS5UU03pJwvo+5vftncoppWUf/EFZQs/I27AgEM6\nr6i6mC1l20iJTeHU1MHElICvZMuBT/yGfIFowv7u1GzZ0aRsUVUSuytTuKlrHjVb2n5GYGFFOQ+/\n9w7vfrWc0qpKwFn47fMFH9Nz4nFMTEvj3V/c2fTEoiJqioo4o29/zrjquqble/Kpm0TQ0mYKT1ws\nNTUtO9vj9dLt0rb5OzscDrMwbzGvb5rN6X1PYmrvSXg9h2ebwkAwxBItYO7iHZRX1XJKTm+unDGE\n+E4d989on6+G9evXsmXLBoLBIJ06dUJkBHFxLR+Gb4w5MnTcn0zGGGOOGqWffELCkKHEdP3mK0Sb\njiNxxEgyLrjwoOrurS7kpfVvUFgd5iL5MdlpA9s4uoZKiqqIfnklWVdc3eD4zj0VvP/8Mn511Ti6\np7feNAa/309xcTFffbWUZcuWsmzZEqZOncYNN/yQ+NISXvrdbwDIyuzHtddfxfnnX0DPnm03ReFg\ndaS51HUq/JU8v+4V9lTt5Zax19MzqfvhuW+1n/lf7eKDJTvJ7BzPGcf2ZcygrnhbuLbA4VC3E4Hf\n72fjRiUuLo7hw4fSr99AoqLsz35jvg3s/3RjjDFtKhwKUfLBe2Rdc317h2LagT8U4P1tH/PhzgWc\n0mcK14+8vFX2+W4NvtogD7+xigunDWqS3JeUFFNZWUl1dTXFxUXs3r2blJQUpkw5CYArrriEbdu2\nUlJSTG1tLYGAnylTpvHoo08CMGbMUAoKGu5u5PV6ueGGH5Ka2pnf//6PjBw+nq2rw1xyw7GH5f0e\nidYWruc/615mXOYorhx2MTFR+94+r7XkFVYyd/FOFq3JZ0x2V245fxR9szr2XvCVlRWsX78Gv9/P\nMcecQFJSMiecMJX09AyiojrmApvGmLbRMX7DGmOMOWpVLF9GVHIycQMHtXco5jBbV7SBF9e/RreE\nTH4x/sekxzc377t9FBUV8j9/f5ENaxfzyX/WUlCQz/jxE3n66ecBOPnkyezYsb3BOVOnTqtP8Fev\nXsn27dsalJeXf70WUnx8PMnJKYwYMZKxY3MYNy6HcePG15ffcMMPKS6sZNuaVW31Fo9o/nCIl9e/\nwfKCVXx/6EyGdMlu0/uFw2FWby1i7pc72ba7jKlje/L/rptIalLHXoCuvLyM9evXsGPHVsLhMElJ\nyQQCfqKjY8jMPPTtF40xRz5L8I0xxrSpkrnvknbK6fV7XZujX6mvjFc3zmJz6TYuyD6HURnD2zsk\nyspK2bFrA862wjDlpMnk5+1sUKe4uKj+627dsggGg8TFxZGamkq3bt0ZO3Zcffk//vEo8fFxdOmS\nTmxsJ2JiohtsM7Z4ccsWIDSQW1nAU3u206dLAncc81MSYxLa7F61/iCfr97N3MU78Xrg1PG9ufm8\nEcR00G0lI23btpmlSxcBYZKTUxAZTs+efZpZXNEYh4hMBV4C1uDs8pgCbAYuVdVaEckA7gP6AlHA\nDuBWVd3tnj8ZuAuIARKBJ1T1oUb3GADMBr5Q1SuaiaEf8IKqHtvo+L3AOlV9MuJYKs7e9ilArBvL\n543O8wBPADe39yr6InI3cCYQAH6iqosalX8U8XII8KSq/lJErgRuwvnM31DV34vIDKCHqj5+qHFY\ngm+MMabN1Gzdgr9wL0k54w9c+TAJBkMsmr+FqsrWXS09HA5RHfC16jUPRiAcZm9N0324vVEeQsEw\nlcEwS+Pz0OjW/aM/lOeBQALe15Y2OF5WW86uijwyEjI5MWk0BUVePmBtq967sWAwwI7czeTu2sIJ\nx54OwFtznmHJ8gWUlhZSUlpEja+K5KQ0bv/tpeQXV5PcfRS9evXmlGnTOOGEExk4cBCJiV8P0589\n+/393nPiRBtW39pC4RAfbJ/P3G0fckpyF04dfmmbNQwWl/uYt3Qn87/axYDuKVxySjZD+6Z1+IbI\nkpIiPB4PqalpZGZmkZbWhezsIfTo0bvDx246jHmqelHdCxF5DjhHRF4BXgXuU9U33LJTgFkiMhEn\n6X8QmK6q+SISD3woIptV9Z2I608C3lbV21oh1luBD1T1fhER4HlgXKM6M4ElHSC5HwdMwWlF7g28\nAkyIrKOqU926A3AaWv5HRAbiJPdTAR/wOxGJUdU5IjJHRF4+1K3yLME3xhjTZornvkvnk0/F00Hm\ngIbDYRa8t4GKshoGDevWatcNhoK8s+VDakI1eDmc79VLKCobT0IVhCoblHi8HsKhMBBmS7gUQqFW\nvXNPfznRgRDbQnsbHI/vFM/wrkNJim7bOcvr1i1j8eL5rN+wgk2bVuPz1eD1eJk87UyioqLYW5KP\nbviqvn5sTCd69e7HohVbeH1hPj/++e/Jkcz68lIflPr8UFTapnHXCxRTU15KXFwRm7a0bQPIocgv\niKOioubAFVtZWaCCdwoWEA6HuThjEmmBzWzKPeTtnw/I5w/y6ao8Vmws5Njh3fjVZTlkdWm7EQKt\npahoL6qr2b17FxkZ3Zg0aRrx8QlMnXpae4dmjmAiEgt0B4qBHKC0LrkHUNX3RWQTcKL772lVzXfL\nqkXkdKAi4np9gDuABBHZCCwE/g4EgRqgwfYfInI+cCdQgNND33gfzr/hJL3g5K3N/XD6EfBd93pT\ngLsBL5AEXIKzqchbQCHOyII5OA0VHvfY1e57+BdOYt4deFNVG2xlIiKz3GvWWaOqP4h4PQl4z922\nb7uIRItIhqoWNBPz/cAvVLVCRC4FFgNPuff+f6rqd+vNBq504z1oluAbY4xpE/6iIipXriSzjbbN\naonlX+xgT14Z37l0LLGtuMXV48tfYlM4jx5VUwhxeIbHhgFfjwQIh+mUF4uHhvuBR0dH4Q8E3bqt\nvzp7QsVSYv3VhKpGNjheCSwr9wNFTc4JBvx4vVF4vF4qy4soKcgl4K/BX1tDoNaHv7aG7NFTiEtI\nZuemr9jw1Uf4fTXU+qrw+6rx+6qYftmdpHTJ4rOP3ufLD56tv3Zqeg+69RnCx5t3E9Mpnswx0zk/\n+3gSk9NISO5CbFwiHo+H5+fnEgyG2ZBbwsZdhymZbyQrcS8n9l6GryaR/r1DFOaupywcaJdYGvPi\nIUTbbxUYaW+4li8DpQyPSmJEVDLBorWsrezC0uUbWv1eXo+HMdldufTUwSTGtf2Cfd9UYWEB69at\nYs+e3QCkp2eQnT20naMyreXTc89fBbTmHKbVJ7zxyogD1JnmDhXPBELAI6r6gYjMBDY1U38zTu99\nD2B5ZIGqljZ6vd0daj9EVR8WkcXAtaq6XETOBf4K/AxARGLc1+NwfmE02fddVUvculk4Q/V/Elnu\njiLoE5FEDwcuU9VdInIHcAHwLJAF5LjTEBYCV6vqGhG5BrgdeBRYqKrXikgcsBOn4SEylrOa+zAj\npOA0GNQpB1JxGi8iYx4FpKjqB+6hrjiNJ8cD8cAnInKM+95XAD/GEnxjjDEdQcm890k57niiElpv\n67FvYtO6AlYuyeW877ducv9J7kK2Vmyha8kk7rzqmFa77oG8s2Mv2yqquUZ6Et3MnNu23u5s/kML\nefuTTwiUbyYQ8LsryQf49a/vpl+//rz77hzuv/8+yspKKSsro6yslOrqaubP/4IhQ4by+OOP8Kt/\n/KzJde/8wfmMGDGSf/97Ga889EaT8ktP6sWoUWP4ckiQuSO7k5MzgZycCXTt2rVRzabTQpZoAS/O\n28Bvr5tAQjsld4HaMnbrfLr2u4jq2kzeeWUV/c6OYs76N0nt1P4rtUdFeQkGW3e0x4EkxqTy48Hf\np09yw4aoGYc1io4j7M638Xg87N69iz17dpOR0Y0hQ0aQnp5hQ/GPIgeRjLeFeap6kYikA3OBLe7x\nXKBfM/Wz3Xo9cHq464nIaMCrqsv2ca8eqlrXKDAfuDeiLAMoUtVC91qfNXcBERkJvAD8TFU/blSc\nBkQOI8sFHhSRCqAn8Kl7fIuq1s3LGwo85Iz4JwbYgNPAMEFETgLKgCarax5ED34ZEPlDPBkoaeYt\nXYbToFCnEPhIVcuBchFZCwwGFgF5QHoz19gvS/CNMca0upDPR+kn8+lzx13tHQoA+bvKmP/ues66\ncBRJKXEHPuEgbSzZwqzN73Fhn8t5Y/2eA5/QSr4sKGV1cQU3Du3dbHLfFnw+Hx9/PI8JEyaSltaF\nrzZt4In3321S76abbqZfv/7s3VvAkiVfNiiLioqiosJpdOjZsxcTJkwkISGB+PgEEhLiSUhIJDnZ\n+fvo2GOP5w9/+BNJSckkJiaSmJhEYmISAwY4uzFMmDCRCRMmHnT8e0ureebddfzo/FHtltyHw0H2\nbv0vyRnHEJfcn+q9zrSKYChATrdRXDLke+0SV6S2bhgy+xYOh8nPz2PdulWIDKd7954MGjSErKye\npKc3bsAy5ptR1UIRuQxnHv0Y4DMgS0TOVtW3AERkOjAI+BinJ/91EXlRVQtEJAlnWPs9wL4S/F0i\nMkpVV+DMT18fUbYH6BwxjH0CTs95PREZBrwMXKiqX9FUIQ2T6keBgapaLiJP4QzDB2ekQv1bBy53\nRxucgDMs/kqgRFVvEJFBwPUi4nGH29d9Xgfqwf8U+JOI3Af0wmn42NtMvZOB/2103g/dkQNRwDBg\no1uWhvM5HRJL8I0xxrS6ss8+IT57MLGZmQeu3MbKS2t499VVTD1DyGjFvawLq4t5fNV/uGLYRST4\n02nB7+AW2VhWxXs7C7l+SC8SY1o+3z8cDlNUVMTevQWUlJQQDocIh8N065bFgAEDCQaDfPLJfIqK\nCnnnnbeZO/c9KirKuf/+f3DJJd/nlHETyMvfzeBTTiMmJqb+X9++/QE4+eRTeeut90hNTSU1NZWU\nlFQSEhLqex+nTz+D6dPP2Gd8w4YNZ9iw1hm5GgiG+Nebqzl9Yh8G9kxtlWu2REnu+3ij4knpdgL+\n2iCffbiJjO7JOB0/5tsqHA6Tl7cT1dWUlBQDzpz77t170qlTJzp16thb9ZkjlztM/UHgQVW9QETO\nBu53h7eDs4r+maoaBLaKyO3AqyISxEmsH1PV2fu5xXXA/7kr3QeAayLuHRCRm4F3RaQI8Ddz/h+B\nOOABt8e9VFXPjbiGT0R2i0imqu7BGca/QEQqgXycUQeN3QQ8LSLROLPdrgHWAs+JyHE4c/43uOfm\n7ue9NaCqS0RkAfA5zhoAPwQQkWnAJFW9x62aVTdqwT1vpYg8jpPoe4Dfq2rdHLeJQN1Q/oPmCTde\ndreDKigo71CBWgu3aQl7bkxLHGnPTTgUYutvfkW3K64mYbC0ayy+mgCvP7uMISOzGH1M7wOfcLDX\nDdby1yUPcUzWOE7ucyLbdpfzxOy1/Pbqth2in1/t47F1uVw8MIsBKftfGKxr1ySWLFnFqlUrWbVq\nBVu2bGbv3r1Mnz6Da6+9kdLSErKz+zQ579prb+APf/gzNTU19OnTsIFmxIhR3HLLT/nOd86naM5s\nghXlZFxwYau+x7bw8kcb2bmnkh9fMApvOw1vripZS3HuXLLkOny+KOb8dyVpXROZMn0wn+9exPby\nndaD/y31ySfzKCjIB6Bnzz6IDCc1tfMBzupYOtJzk5GRbHMYvkVE5GKcpPlv7R1LaxORd4CZtoq+\nMcaYdlW54iu8cfHEZw9u1zhCoRBz31hN916pjJrQeovMhcNhnln7Ej2TujOt9+RWu+6BVPgDPL1h\nFzN6d202ud+8eSOLFn1Bly5dOO20GVRUVHDMMaNp3JDfs2dPAFJSUsnIyHR72DsTFRWF1+ulXz+n\nBz4qKorJk6cQGxvL5MlTOeOMs+rLjiSrNheycHU+d181od2Se39NIUU73iZj4CVUlMPbLy1j4NAM\njpnc3+ZTfwuFQiF27dpBjx698Xq9ZGZ2Jy4uHpHhJCentHd4xhxpXsDpkU9q763yWpOInAm8cqjJ\nPViCb4wxAIQDAfa8+Dxhf+vujd4aSuJiqKlpbuRax1S9YT3pZ5/brolLOBxmwdyN4PEw6dRBrRrL\nO1vnUVxTwk/G3nDY3qM/FOKZDXmM7pLMuK5fJwB5ebt47bVXePXVl1mxwlnH6OSTT+W002aQnJzM\nhAkTSUxMZOTI0QwalE1mZrf6JN3j8bB69cZm7wcQExPDK6+81bZvrI2VVPh4/O213HDOcFISYtsl\nhlDIz94tL5Pa/SRKS5OY88oyco7vy4hxPdslHtN+QqEg27ZtYf36NVRVVTJ+/HH07t2PwYNtVXxj\nWsqdJ99xtutpJaraZFeBg2UJvjGmQyjYXc5rzyyltWcNhYHQQV7UG+pJ2HrTvrnkAYQ/roWPP2rX\nMPyJ0RTkdOXzJftOYlsiHO6F19OH3y3bGnEQQiM6c+fi1t/Wy7knjO6SzCk90/H5fPVzcmfO/A6q\nzrbByckpTJ06jRNO+HpUwaxZ77VJPEeCUCjMo2+tYcqYHgzpm9YuMYTDYYp3zCYmvhvFpf34YNZK\nps4YTP/BGe0Sj2kfoVCQLVs2sWHDWqqrq/B6vfTvn016uj0HxpjWZwm+MaZD8NUE6NYjhbMuGt1q\n1yysqeWxdbmc1z+Tgcn7n68cKC1l5x/uod///qXV7t9a0jOSKewgcxsPt19+8nsGpPbF6zn0xeRi\nPdDLs7lV4/F4PJzWd2qT7by25Zfz1Jx13HXlhBZd1+/3s3nTRtatW8PGjRu46urr6dKlC/Pmvc9r\nr75Mba2Pt3w+nq8oR3UtS5euIS4ujpkzL2HZsiWcd94FnHLKacTFtd4OAUe6WZ9vJRQKc84J7Tet\noLJwGbVVuyjzn8XnH61j+vkj6N6r/Rb5M4dXOByuH+WzYcNaamt9DBwoZGcPIT5+/7+TjDGmpSzB\nN8Z0HB4PUVGts+VXVSDIM5t2c3Lvrgzp0nDl9I25pTzy5uoGowXi/VWcV+Xjjke/aJX7t6aoKA/B\nYIdaZ/SwqcmuZcuiAXhCh2dbs3A4fMBRJOvZDmxvcCwQDJGW0onyqv1P8QgGg+zatZPMzG506hTH\nG6+9yqP/up8tWzbhj5gecvzk0xkwIJZly1fy3/++0OAaXq+XuR8sIGf8sZw/8xrOn+ksSlxU6ofS\nr6dyVNeGKC6uPIh33TJlVRCsicKf3zEan4L+cir3vE046COvLJ73V/Tixkkb2LV6frvFFA7VUlQ9\nnZVLd3LuJWNI65rYbrGYw8fv97N583pyc3cwZcqpREVFMWHC8SQlJdOpkzXCGWPaliX4xpijTiAU\n5j8b8xjaOZGJmU17y0rKfWSlJ3D5aV+v8B4qK6V065v84pKxhzPUg9IlPYmiwqNm3ZhD8oflc/jJ\nBaOIi2rbP4p9/iCL1+1hwco8av1BvN5DnKrhgZLyWn73RMN930v37mDbqo8pLdxB2d6dlBflEgzU\nctqV95HWXdi8YgPr168FICG1GynpfUjp2ofH5mwlLrGEssJ0cqbfQlRULN6oaLzRsXTO7M/bq6J4\ne9WXzUVy2ITDcUAnPE+2bxzE1EB8BVFRAQglEwyn4PVAn84lfLyufYdAh4JRdErcQc45/djtyWV3\nYfP18irzD29gpk3U1tayaZOyaZPi9/uJiYmhrKyEtLR0G45vjDlsLME3xhxVwuEwr23NJyHKy/Te\nXfdZr1NMFF07x9e/DuCj3OtpcKyjyOiSgDcYbO8w2oXHA+mp8cRHt02CX1Lh44MlO/l4+S6kT2du\nPHcEgw5xn/SqqipWr17JihVfsWLFclas+Irf//6PTJp0IvPmzeWih59rUD8rqzuXntSb/I3R3PKL\nKyj94XSGDBlKUlLyPu7QMm29bVVH2CavOlDNHxfdT4oHPOEAsfFZDcoD7RRXpGqK+aRg1wHrHZM1\n7jBEY9pKaWkJ8+fPJRAIEBsby7BhoxgwIJuYmPZZ3NGY5ojIVOAlYA3OMkUpwGbgUlWtFZEM4D6g\nLxAF7ABuVdXd7vmTgbuAGCAReEJVH2p0jwHAbOALVb2imRj6AS+o6rGNjt8LrFPVJyOOJQLPAWlA\nLXCFquY2Os8DPAHc3N6r6IvI3cCZOL9+fqKqixqVfxTxcgjwpKr+UkT+CkwCQsBtqvqpiMwAeqjq\n44cahyX4xpijyod5xeypruW6Ib3abUss0/HtLKjg3UXbWb5hL8cOy+LOy3PITGt+TmwoFKKgYA+5\nuTvJzc1l166d5ORMYPz4Y1i2bAkzZpxMKBRqcM7SpUuYNOlEhg8fyU9+8jMGDcomO3swgwZlk5yc\nwsKPNtNpQC3ZQ/ri/B1lWuJFfYPspEymRVXSXa7DG93xGujM0aumppqyslIyM7NISUkhNTWN7t17\n0r//IKKjD8+0ImNaYJ6qXlT3QkSeA84RkVeAV4H7VPUNt+wUYJaITMT5ZfUgMF1V80UkHvhQRDar\n6jsR158EvK2qt7VCrNcBS1T1HhG5Ergd+HGjOjPdOu2d3I8DpgATgd7AK0CDhXlUdapbdwBOQ8v/\niMho4Hj3vEE42/7lqOocEZkjIi8f6lZ5luAbY44aXxWWs7iglBuH9ia2lebym6NHOBxmzbZi3v1i\nOzv2VHByTi/+cP2xVJUXs33TKr7I28WuXbnk5eVx3HEncPrpM8jN3ckxx4zG72+4TeGtt/6c8eOP\nYdCgbKKiohAZyujRYxg9egwjR45h+PARAHTrlsUdd9zV4NyykmrWLN/FzGtatiCfcXy5exnbyrZy\neWIUGf0vteTeHDZVVZVs2LCWrVs3ER0dw+mnn0N0dDQnnnhKe4dmzCERkVigO1AM5ACldck9gKq+\nLyKbgBPdf0+rar5bVi0ipwMVEdfrA9wBJIjIRmAh8HcgCNTgJOyR9z8fuBMoAGKBdZHlqnq/iNSt\nstsHKGnmbfwI+K57vSnA3YAXSAIuwen5fwsoxBlZMAenocLjHrvafQ//wknMuwNvquqdjWKd5V6z\nzhpV/UHE60nAe+62fdtFJFpEMlS1oJmY7wd+oaoVIpILVAGdcEZURP7BMRu40o33oFmCb4w5Kmwr\nr2bW9gKuSo+l0548fPup6y0oIqmsEN/OHfXHguUdY6Ew0/oCwRBfrMnnnS+2U1qUR6+EIr43Tjjh\n+H7s3p3HqFHS5JzaWh+nnz6DjIxMAoEAXbp0oUePXvTs2ZMePXqSk+Mk58nJKWzevKt+27qD8cX8\nLYzM6UlS8sGfYxoqrC7ivxve5KLUVDJ6TCY2oUd7h2S+BSorK1i/fg3btm0hHA6RkJDI4MHDDn3N\nDmNc99z21ipgeCtecvVdfzl7xAHqTHOHimfiDAl/RFU/EJGZwKZm6m/G6b3vASyPLFDV0kavt7tD\n7Yeo6sMishi4VlWXi8i5wF+BnwGISIz7ehxQBDS777uqBkVkHjASODWyzB1F0CciiR4OXKaqu0Tk\nDuAC4FkgC6dXvFZEFgJXq+oaEbkGZ1TAo8BCVb1WROKAnTgND5FxnNVcfBFScBoM6pQDqTiNF5Ex\njwJSVPUD91AA5/uwzq0f2QiyAmfEgiX4xhgoKaqicM+RszBbQX4FlYEgK4sOPdEOhsPM2bGXC/Zu\npuqJ16hN2/+e1/G+ACN8AfL+P3v3HV91dT9+/HVH9t6DsELCO+wNIqCIAxERRx1Vaq04vu7+1Lpq\na6u2tWpr1VZr0apULWpxgYgIiiJDBZEVchghJGTvnZs7Pr8/PjeYhEASSEiA83w8fJj7+ZzP+Zx7\nc7m573Pe55zMlvO6A1JSO31vrXcxDIOKinJqa2uJjI5n9Q95/PHRB6ivyKWsYC+1Neb7K/PSy5ky\nZQpxcfGkpKQSHBxMfHwiiYmJJCb2Yfz4iQD4+vqSlVVAQMDhR4c7E9wX5lWRn13B9PMP7VToSQ3Z\n+3EWdW6hN8eBHOzh4d3UosNze9y8lr6I00Oi6Rvah6AoPXdd615N291VVVWSlbWXoKAQRIbSt+8A\nrFadLaYdvQ4E493hc6XUVSISBXwG7PMezwUGtFE+1VsuEXOE+yBverlVKbX5MPdKVEo1dQp8BTzR\n7FwMUKaUKvXWte5wDVZKzRCRNMxOgEHNTkUAJc0e5wLPiUgN0AdY6z2+TynVtFXNEOAFEQFzLYHd\nmB0ME0TkLKAKczS9hQ6M4FcBzRfTCaHtjIN5mB0KTa4FCoCZ3mu+FpENSqkDQD4Q1UYdR6QDfE07\nSW1cm0VlWT3BoSfGljy1Lhd5oXYoO7pOidl1Rdg++Yi+Dz2Mb3zCEctuzCjim52F3HbJiKO618mq\nweVgQ8FG3J7es6Cfy+NizZerObA/h5ycbEpKinE6G+nffwAPPmimvt91161kZu6lpqaGnJxsqqoq\nSRt1OqNmP8TIQVHU5m8jN9fc1i4mJpaRI0cxfrw5Am+xWFi3btMR23Ck4L4zDMNg3ao9TDxjID6+\ntvYvOE4qvlxN6YfvEZA6uNPXBkhaN7ToyFbsX43FXceEgCAi+15wcJ9xTetqVVUVKLWDoKAQhg4d\nSXx8IqedNo34+EQsFh3Yayc2pVSpiMzDnEc/GlgHxIvIHKXUEgAROR9zXviXmCP5H4jI20qpYhEJ\nxkxrfxQ4XICfJyIjlVJbMeen72p2rggIb5bGPgFz5PwgEXkQOKCU+g9mGn3rLyiltAyqFwCDlFLV\nIvI6Zho+mCPkB586cK0322AKZkr+dUCFUupmEUkBbhIRizfdvun1am8Efy3wpIg8DSRhdnyUtFHu\nbODPzR6XAzXeTIVqwIG5gCGYHRhF7dz3EDrA17STlQEjxicxeFhcT7ekQ/ZW1VGWV8bVKUcOztvS\nsD+L3HffJPG2O9sN7rW2eQwPr6cvwulxkhDU/e8Zt8uFzW7+CdqyZhMF+/OoKC6joqiMssJSImIj\n+TpLEnMAACAASURBVL8n7ubc/tN57Oe/RamdLa4fO3bcwQB/y5YfSE/ffvCc3TcAXx9ffn/9RCJD\n/Umy/xk/Pz+GDRtOXFzLVdaPp0xVjNPpZvDwnmtDc4bHQ8n7i6n5fiN97/81vnG9/7NiX2U2qw+s\n4bqQAGKSL8dq0yuUa12voqKMjIwd5OebsUZUVMzBUfyEhKQebp2mdR1vmvpzwHNKqctFZA7wN296\nO5ir6M9WSrmBLBG5D3hPRNyYgfXLSqllR7jFjcDfvSvdu4D5ze7tEpHbgU9FpIyWc8+b/Bt43ZtK\nbwN+0ar9DhEpEJFYpVQR8AawRkRqgULMrIPWbgEWiogdcyeB+cBO4C0RmYwZYO/2XpvbxvVtUkpt\nEpE1wHrMNQBuAxCRGcBUpdSj3qLxTVkLXm8BU7wZDDbgTaWU8p6bBKyikyyGYbRfqhcoLq7uVQ3t\n7u2HtJPT8XzfrPwonX6Dok6oAP+LvDJuSOvclydncTHZf/4DsVf/jJCx4zp0zYk2gn883jdLMz9l\nV3kmd465Ebu1e/p+q6ureOONhfz73/8iMDCQL7/cAMCcOTP55pv1LcomJfXl++93APDEE49TXFxE\n3779iI2Nw8/Pj6ioaKZPn4HHMHj7w1Ws3ZJNbaOVC88ay6ypQwj0710rWLtdHha9/C1nni8kDTjy\nFJKj5ag9QH2lOvg4MNCXurrGNssabje1W7fgaWggeOx4rH69P1B2uF38PWsd0wMDmJx8EYERQ3u6\nSSelU/37zc6d28jIMDsMIyKiEBnmHbHXmSJH0pveNzExIfqXdQoRkZ9iBs3P9HRbupqILAeu0Kvo\na5p2ynDX1HDg2b8QdcGFHQ7uTyYquxyV09b0rs7Jry0kvbSAaX1msGx9TvsXdFJZSQGrlr7FV58t\npr7OnIIREhbJR2vNaX8Dh08jLD6V8MhYIqPiiIiOJyom4eD5odOuOaTOKuC9rzL5LqOIAN8wrr1i\nNuMkBlsvnQ+7bVMuEVFB3RbcA9SVp+N0lOAX1A8Aq80PSxuLfxmNjVSv/wZrQAChk8/AYu090wWO\n5OOCdJKDYjht4Fk6uNe6VElJEUFBwQQEBBITE0dxcSEiw4iNjdeBvab1foswR+SDe3qrvK4kIrOB\nxZ0N7kEH+JqmnaA8jY3kPv83gkePJXzGqbc1UX5pLf94fztnjEo8phWcqxqr2VaUwfj4MVjxweXu\n+mSp1cv/x6cfvg5A6tBxnHXhT4kdm8h+Z7p5bOaYQ66ppJhKZ1s7y/zIAoyfHEh8ZCBOSxYbCrK6\nuuldwtno5luVxZjT+rI275tuu09dZS5Wmz/+HjN7IcTwpdrjaVHGXVVN+aoV+PXrT8iYcYCl5czE\nXqq8oYJsRx0PTPgl/na9+4B27AzDoLi4kIyM7ZSWFpOcnMqoUeOJioph2rSzdWCvaScI7zz5n/V0\nO7qaUqrNXQU6Qgf4mqadcAyPh4IFL+ETHUP0pT/p6eYcd06Xmxc/2MElZyRz1pg+R11PdWMNT258\nm1+cewHj4kYdU5sqKyvYtUuxa5dCqQy+/34jt956JxdccCFT0+4ngEpuvvlWRo0aw8vb36DOmUtU\nQOQx3ROggTqyOt23fXwV5FZhTTQochdAZfvlj5ajvhKLtQ5fw/zT7u/woaHhxymNruoq6tLT8RvS\nH7+EWEqruz5bo/tYuHHEtTq417pEQUEeGRnbKS83p8HGxSXSt+8AAB3Ya5p2wtMBvqZpJxTDMChe\n9Cbu+jqSbr4FSy9Nye5Oiz7fQ3xkANNHH/3e3y6PiwXb/sPEuDFtBvc5Odk4HA5SvFsH/vGPj1Jf\nX9+izOmnT2XWrNnU1FSTmtrvkDqCgoK44IILiY2N5cUXXwZgXd53FNeX8Ktxt+Nj613z5LtDRVkd\n76/YzFU3TiAgsHvnuZcfWIHNJ4TQuMlAyzmx1Zu+o+ithcRdfwPBI4+tM0fTTkRNi+QBZGXtpby8\nlISEJNLShhEefuydjZqmab2FDvA1TTsqdbsUpR990GX1ud0eRjic5AQeeYTOcDrxNDTQ9/6HsNhP\nvY+wjRlFbNtbyu9+MeGYRpre3fUhgT4BzE4+D4D8/Dy+/vor1q5dw9dfryE7O4u5cy9lwYLXAHjt\ntZepqGg53//AgRxmzZpNcHAIAwcmExwcwuDBgkgaqanCtGlntChfVFfMh3uXcdeYm0+J4B5g/Rd7\nGT2pb7cH94djGAblK5ZTsXIFfe6+F/9+/XukHZrWUwzDQ25uDrt2pTNhwhRCQkIZNmwUQ4aMICws\nvKebp2ma1uVOvW/HmqZ1CUdONlZ/fyLOPrdL6surbSCntJoR/WLaLevXrz+2wMAuue+JpKSinv+s\nUNz1k1GdXiV+8+ZNZGfvp7y8nM37t5CRqzi970SsI80MiIsuOp/9+7MOlg8NDSMoKOjg4wce+A0O\nh6NFnbGxsQd/3rBh8xE7HNweN6/tWMSsgeeQGNzz28QZhsH273PJzTr2RQoPx+PxUFZSx7lze2ZB\nOMPtpuit/1C/axd9H3wYn8ioHmmHpvUEj8dDTk4Wu3alU1NTjcViobS0mJCQUEJCQnu6eZqmad1G\nB/iaph01n8hIAod0TfBiqaqjIq+MwE5uk3c8VDfW8Nn+1bgMV4/cPyDbl9q6Rr75KpDEQS421nzO\nxl0tyzQ2OCjIyqNgfx4FWXkU7s/D5XJxy5P3APDi7/7CjvVbWlyTE72HR37zGAAzZ85i7949TJly\nBlOnTmPEiFHYbD+urn799TcesY3tZRMs2/cZwb5BnNnn9I4+7W7j8RisW7WH3OwKxk8ZQHdOuY2O\nC8ZuP/6r1HsaGtj5p+dx1pjZLqdih5h26nK73axa9Qm1tdVYLFYGDBjE4MFDCQoK7ummaVqPEpHp\nwDtAOuYe8KFAJnCNUqpRRGKAp4H+mHuy5wB3K6UKvNdPA34L+ABBwKtKqRda3SMZWAZ8o5T6eRtt\nGAAsUkqd1ur4E0CGUuq1Nq5JA74B4pRSDa3OWYBXgdt7ehV9EXkEmA24gF8qpb5tdX51s4dpwGvA\nauAB7zELMBUYDgwAEpVSr3S2HTrA1zRNOwKXx8XL2/9DlH8k/UJ6pvMhONiPL3+oJdjfYOKIQCwW\nC40NDrJ2ZzJ4xBAA/nT/b9i8dmOL66w2K5H2cOw+PowcO4bgwGCCQ0NIiu1Lv9i+REdHHyz7+ON/\n7rb27y7PZF3+dzw48Zc9voCVy+Vm1ZIMGuqdXHzNaPw6mQlxInBVVJD73DOEpQ4iev4tp+RUFu3U\n43a7KCoqICEhCZvNRkxMLHFx8aSmDiEwMKj9CjTt1PG5Uuqqpgci8hZwkYgsBt4DnlZKfeg9dw6w\nVEQmYQb9zwHnK6UKRSQA+EJEMpVSy5vVPxX4WCl1T1c0VkRCgb8AjsMUuQLY1AuC+7HAmcAkoC+w\nGJjQvIxSarq3bDJmR8vj3nYv9x7/FbBWKbUT2Ckin4jIu53dKk//1dc0rVOKKuppbHTjrHZg1Dlp\nLOqaz9PiegeORjcHuqi+IympbGi/kNe7uz8iwO7PvCGXY7X0zIJ+2aV15GZvYs4Ig41vr+Hrr79i\n06bvcDqdZGTsIyIikvRzL6GupIbBg9NITR3M4MFCaqowtP8wbDYb0x+Z0iNtr3PWs3Dn21yT9hNC\nfUN6pA1NHA1OPlm8ncAgXy68YiQ2e+d/n66qKgoXvoqrrKwbWniMhlqgAYo3vUX42eeSct3VlJSc\nNFsCa1qbXC4X+/btZvfuDByOBs46aybh4ZGMHn1s65Ro2qlARHyBBKAcGAdUNgX3AEqplSKyFzjD\n+99CpVSh91y9iMwEaprV1w94CAgUkT3ABuB5wA00AC3SAUXkMuBhoBjwBTJanbcA//LW+SFtuwO4\nxFv+TOARwAoEA1cDjcASoBQzs+ATzI4Ki/fY9d7n8BJmYJ4AfKSUerhVW5Z662ySrpS6tdnjqcAK\n77Z92SJiF5EYpVRbe/7+Dbi/eaeEiCRhbvfXvFNgGXCdt70dpgN8TdM6rNHp5qGXNpAQHYjkFxDa\nUMF3S3Z0Sd1GkB1PXAAvfVfQJfW1Z+KQuHbLrMldz56Kfdw77rYeC+7Lqx08u2gz/kWfc+Xlfzl4\n3GKxMHz4SAoKCoiIiOTWW+/g1lvv6JE2Ho5hGCxS7zE8agjDo4f0aFtqqhpY+s5W+g6I5PSzBx3V\nF//Ggnxyn/0rIRMmEXXh3G5o5bGprtuIzRpA8Dnj8I2P18GNdlJzOp1kZu5izx5FY6MDu93O4MFD\nCAgwR+v1+187EWxa8avtwLAurHLHuPOeGt5OmRneVPFYwAP8Sym1SkSuAPa2UT4Tc/Q+Efih+Qml\nVGWrx9neVPs0pdSLIrIRuEEp9YOIzAX+CtwLICI+3sdjgTKgrX3fH8HMBtgiIoec9GYR9GsWRA8D\n5iml8kTkIeBy4E0gHhjnnYawAbheKZUuIvOB+4AFwAal1A0i4g8cwOx4aP7cLmyjfc2FYnYYNKkG\nwjA7L5q3eSQQqpRa1er6u4FnlFLNMxW2AnehA3xN07qLYYDdbuGx+ZMoX1WFs9CPi66e1CV1762q\n44u8Mm44q2cWJGttd/lePs78jLvH3UqA3R+A2gYnj7++EZfb0633djkdZGesZ8/mFaROuIRb5l9B\n/+Cr+fTjd5k5cxZTppzB6adPISKid2/t9F3hZnJr8rl/wl092o7S4hqWvbuNEeP6MGpi36P64l+3\nS5H/z38QfenlhE2d1g2tPHb1B3Zh8wnBN67nFzHUtO7StN2dy+UkI2M7NpuNtLThDBo0GF/fI+/C\nomm9TQeC8e7wuVLqKhGJAj4D9nmP52LO+24t1VsuEXOE+yARGQVYlVKbD3OvRKVUU6fAV8ATzc7F\nAGVKqVJvXevauH4ecMAbiMcDKzAzCZpEACXNHucCz4lIDdAHWOs9vk8p1ej9eQjwgrfDwAfYjdnB\nMEFEzgKqgEM+TDowgl8FNE9VDAHaWsl3HmaHQvO6rcCFwK9blc0HOr1Crg7wNU07ZuvyvqWs4dhW\nIy93OMmvrmdpZs+vblzvqmdd3neMihnGtwXfHzxe1+CkNryAqSOPPoBy1DdQXlhKQ309jroG/AL8\nGTA0BYA1H3zGgb37+f7zDdTX1AKQOCAAS/xoMuoa+d07f8FisWAAa8s3mgl1vZbBmtwN3DH6Rnx7\ncEu8vOwKPv1gB1POTmHwsPazNtpS9c16ihe9RfyN/0fQ0K4caNE0raMcjgb27MmgqqqKyZPPICAg\nkNNOO4PIyGh8fE6+tTQ0rbsppUpFZB7mPPrRwDogXkTmKKWWAIjI+UAK8CXmSP4HIvK2UqpYRIIx\n09ofBQ4X4OeJyEil1FbM+enNlwguAsKbpbFPwBw5b97GlKafRSQLOK9V/aW0DKoXAIOUUtUi8jpm\nGj6YmQoHqwWu9WYbTMFMyb8OqFBK3SwiKcBNImLxpts3taW9Efy1wJMi8jSQhNnxUdJGubOB1gsf\nDcdcYLC+1fEIzNepU3SAr2naMfto73ImJozFz3b0oycWiwUslh5Pq3R73Gwq2sKg8AHEBEa3OGex\nWLBiIci//T3Nayqrydt3ALfLxZDxIwB45Gf3kr1rX4tyQ8aP4L5//A6AL95dRtEBc4rCgCGDmHbh\nDCadNxWr93Xp6demcyxcO/RKkkISD1tib0YRFWWt/5Z1HafTzc4t+Zx70VCSBkR0+nrDMChbtpTK\nL1eTdM99+CX1bf8iTdO6VENDPbt27SQraw9utxs/P38aGurx9w8gLi6hp5unaSc0b5r6c8BzSqnL\nRWQO8DdvejuYq+jPVkq5gSwRuQ94T0TcmIH1y0qpZUe4xY3A371z6V3A/Gb3donI7cCnIlIGOI+i\n/Q4RKRCRWKVUEfAGsEZEaoFCzKyD1m4BFoqIHXMngfnATuAtEZmMuZjfbu+1uZ1oyyYRWQOsx1wD\n4DYAEZkBTFVKPeotGt+UtdCMYHagtDYJaJ3K3y6LYRjtl+oFioure1VDY2JCKC6u7ulmaCeYrn7f\nuN0eDE/b/zQ+/ziDAanRRz1q2RZHo5u7nl/D8//vDCo/X4mrsJCon17Dw2sf5/4JdxFyDIuoZVbV\ns6agnBt6cJs8wzB4Zfsb+Nn9mJd2+SEBdUlFPU+8sYnJCXk89dSfyMzcS0pKKuvWbQJg7txZrF+/\ntsU1I0eOZuXKrwC44IJz2LJlM/369Sc0NJSgoGBGjBjF73//BwBeeOF5HI4GzjtvFsOG/Zi1d7J+\n3vx3wbckJIXhH9h9o2+pQ2KJiu381liGy0Xhmwtx7N9Pnzt/iT288x0Ex1v5gRXYfEIIjZsMnLzv\nG6179ab3TX5+Lt9++zUej4eAgEBSU4cwYEAyNpsen+ptetP7JiYm5ETqDdeOkYj8FDNofqan29LV\nRGQ5cIVeRV/TThFut4d/P/M1h+v5sgBpI7t+dMMvPpDfb9rLkJxSQior2fh9JrUuN3/duh+r9dj2\n2h4W0bN7FC/PWkWFo4q7ht3c5mj5hvVrWPqv+1lY0NYaNC0FBgYxePBgRo0ae/DY66//l4iICOyH\n2basty2SdzyMmpBERHTv2sLKXV9P/j//gcVqpe99D2L19+/pJmnaKaO2tobGxkYiIiKJjIwmODiU\n5ORU+vUbiM1m6+nmaZrW+yzCHJEP7umt8rqSiMwGFnc2uAcd4GvaCcvjHbm/6d4z2inZtSw2K6fH\nhTO5KhpnoYuLxqfwwBob940eeFy2QSupL+P9PUtxeVxdWq/HMMivLeRX4+/Ax9r2R2N6+jbKCvaS\nkJDIffc9xBVX/LRFsP7BBy2z1Fp3EsTExHRpm7Wu5ywrI/fZvxKQkkrs1fOw6IBC046L6uoqlNrB\ngQP7CQuLYPr08/Dz82PGjPNPsOlJmqYdT9558j/r6XZ0NaVUW7sKdIgO8DVNO2E0uBy8tPU1RsYM\nY0Bo18+H7hOcQJjfj50Ue/bs5s9//gMzZ87iJz+5kmvmXc+XWwr530u/JyAg4JDr9ZfQE1tD9n7y\nnn+W8LPPIWLmLP371LTjoKqqgoyMHeTmZgMQEhJGamrawfP636GmaVrn6ABf07QTgsfwsHDn2wwI\n7cuFA8/rti99L7zwPGvXfsWWLT9QVFQIQHr6di677Ar8/PwZetrFbQb32omtdvtWCl5eQOy8nxEy\nfmJPN0fTTnpN290VFOSRm5tNWFgEIsNITEzSQb2madox0AG+pp2CDhTX8PKSdNyHWaDvcDyGgSWs\nZ7Yj+mTfSqobq/nFsKu75MtfdXUVS5Z8yNKlH1JfX8/775uZUF98sZIvv/wCgNDQMObMmcu99z6g\nv3CexCq+XE3ph++ReNudBKSm9nRzKN73Lq6GtnbWOTK3s4aw+OM7ZUfTOqu0tASldpCU1I9+/QYy\ncGAqoaFhxMUl6s9ZTdO0LqADfE07Ba3adIChAyM5ffih+7kbhkFWQfVhg//99Q4K86spKK2DKgfF\n+8pwuT2o7AoCbZ3e4aRD9lTsY03ubq6Uueza3+m1Rlr44fsNfLj4P6xbs5JGhwMAq9XKpp25+PsH\nMOviaznzvMsYnDaChMS+WK1WKhqhYl8ZlbWOrng6x4VhGNTv3oXhPPrfSXGZE6e7+zYwaaxtoG7P\nbnwLe+5PUd2O7dT8sJm+9z+Eb9yh/x56QmNNDlEDLsFq7/zigz5+kd3QIk07diUlRWRkbKe42MyM\n8vf3p1+/gfj4+BAf36eHW6dpmnby0AG+pp1iHE43GzOKeHT+JCJCDt23vqi8jjdW7CI1KazN6ysc\nLlyGQWhRGUF11Wz/Zj+OaDefb8rBZnR96rrD3Uh21QH6hozjy03Fnb7eMAzy9+8kJnEQPr5+rF+x\nkjUrzdH6vimjGTr+XAbIOD7/odAcPbIlQwhszYWtuTmH1DcmNfqYn9Px4CovJ/evTxGQKkd3vWFl\nuWssUZZj61A5kmAMHF9vpNzi7rZ7tMcWGkK/Bx/GFtL9C0R2ht0/GrtP72qTph2tjRvXkZOzH4CY\nmDjS0oYTHR3bw63StFOPiEwH3gHSMfeAD8Xcf/0apVSjiMQATwP9ARuQA9ytlCrwXj8N+C3gAwQB\nryqlXmh1j2RgGfCNUurnbbRhALBIKXVaq+NPABlKqdeaHbMABzD3pQdYr5R6sNV1FuBV4PaeXkVf\nRB4BZgMu4JdKqW9bnT8HeMJ7fqVS6mHv8WeBKUANcL9S6hsRmQUkKqVe6Ww7dICvaaeY73cVMzAx\ntM3gHsAwICLEj3uvGkPus3+lsSAfgFpnPR7Dg9swMDDwa3RTMiyJuJG7ySlyc8slI7p8Ff3qxhqe\n2vg8Nyafz/j4MR2+zul0sn79Wj75ZCnLly8jN/cACxa8xty5l3JgajTvjU7i0ksvJymp6xfq6zUM\nA1toKEn3/OqoLm90uLD/Yz2X3z23ixumadrJzjAMCgvziYmJw2azERUVQ2Ojk7S0YURGnhidpJp2\nEvtcKXVV0wMReQu4SEQWA+8BTyulPvSeOwdYKiKTMIP+54DzlVKFIhIAfCEimUqp5c3qnwp8rJS6\npwvaOgj4Xik15whlrgA29YLgfixwJjAJ6AssBia0KvYUcA2wE1gjIiMwX1cBJgKRwHJgvFLqExH5\nRETe7exWeTrA17RTzNdb85k+5sjpkE6Ph43FlQRnZVF/zfV4gkP4cPfLDI+ZRLnDwN9mJS08CN+Q\nIFJ87AyJGkyIT9fuYe/2uHll+xuMixvdqeA+K2sf5557JpWVFQePxcXFU1dXB0BSUl/uvPPuLm2r\npmmaZgb2eXkHUGo7lZUVjBo1nuTkVAYMSGHgwJ5f30LTtJZExBdIAMqBcUBlU3APoJRaKSJ7gTO8\n/y1UShV6z9WLyEzMUeem+voBDwGBIrIH2AA8D7iBBuDGVve/DHgYKAZ8gYxWTRwH9BGRL4B64P8p\npVSrMncAl3jrOxN4BLACwcDVQCOwBCjFzCz4BLOjwuI9dr33ObyEGZgnAB81ja43a+tSb51N0pVS\ntzZ7PBVY4d22L1tE7CISo5Rqnn66GTOI9wH8va/LUOBTpZQHKBERt4jEe7MmlgHXedvbYTrA17RT\nSElFPTlFNYxOOfwIys6KWmqcbvbXNJDmMcj2DcLlF0xFsA0jZizRVj9GRYaQEhbYrW19d/dH+Nl8\nmZM885Bz27ZtZc+eXezdu4fMzL1kZu5h4sTJPProH+nXrz/+/v7ExQmzZl3IrFmzGT16LFartVvb\nq2madqoyDIMDB/ajVDrV1ZUAJCX1O5iGrxfP07S23bjs++3AsC6scseCC8YOb6fMDBFZDcQCHuBf\nSqlVInIFsLeN8pmYo8yJwA/NTyilKls9zvam2qcppV4UkY3ADUqpH0RkLvBX4F4AEfHxPh4LlAFt\n7fueD/xJKfWuiEwF3qDZqLg3i6BfsyB6GDBPKZUnIg8BlwNvAvHAOO80hA3A9UqpdBGZD9wHLAA2\nKKVuEBF/zGkBLQJ8pdSFbb+cB4Vidhg0qQbCMDsvmmwDlnrLbcXs0EgC7hGRv2N2MAzDnP6At8xd\n6ABf07TDWbu9gElD4vCxtwx2V2Z/yfq873ATQV3DOAyjjvTC/5DiqWdXyf9oqPPBYzjJKFqE1WIl\nvbB72+nBgwUr94y9hR3bt7NmzZcYhsFtt90JwE03XcfevXtaXFNQUMDvf/8HrFYrq1evJyoqqnsb\nqWmapnkZZGTsoLa2mn79BjJ48FBCQkJ7ulGa1ut1IBjvDp8rpa4SkSjgM2Cf93guMKCN8qnecomY\nAehBIjIKsCqlNh/mXolKqaZOga8w5583iQHKlFKl3rrWtXH9Rsz56iilvhaRRBGxeEfJASKA5tvO\n5ALPiUgN0AdY6z2+TynV6P15CPCCiIA5kr4bs4NhgoicBVQBh8xj7cAIfhXQfK5qCHAwnVREwoEH\ngWFKqVwReRK4Ryn1lIhMAFYDO4BN/NhRkA90+gutDvA17RThMQzWbsvntktGHHIuv6aQYdETSK/u\nw4xIK6t21nDDiJ9R4/M4V6f9BGtYKE9tfJ5rh16Fv73tuftd6ZOPlvDVZ18wbt0wysrKAIiNjePW\nW+/AYrEwffoMRIYwaFAKgwalkJycQmrq4IOjRDq41zRN6z5ut5vs7H1kZe1l6tQZ+Pj4MG7cJPz8\n/AkK6trpWpqmdQ+lVKmIzMOcRz8aWAfEi8gcpdQSABE5H0gBvsQcyf9ARN5WShWLSDBmWvujmKnn\nbckTkZFKqa2Y89N3NTtXBIQ3S2OfgDly3twjmMHuk97OhJxmwT3ec82D6gXAIKVUtYi8jpmGD2am\nwsGnDlzrzTaYgpmSfx1QoZS6WURSgJtadSR0ZAR/rbedT2OOyluVUs07H+oxpwI0TWnIB2JEZLD3\neU0Rkb6Y0yCaOgYivK9Tp+gAX9NOESq7An9fO/3iDv3y5TZs7KyO45w+0Qy0+7LaWkhCUBx7LVbi\nAmOwB4VjwUp8UAwB9q5fKd/pdLJmzZfMmHEOAD+s38SypUsA6NMniWnTzmTatDNxu93Y7Xb+9Ken\nu7wNmqZp2pG53S6ysjLZvXsn9fV1WK1WystLiI1N0IvnadoJyJum/hzwnFLqchGZA/zNm94O5ir6\ns5VSbiBLRO4D3hMRN2Zg/bJSatkRbnEj8HfvSvcuYH6ze7tE5HbgUxEpA9ra1/cJ4A0RaVqZ/rpW\n7XeISIGIxCqlijBT+NeISC1QiJl10NotwEIRsWPuJDAfc9G7t0RkMuDAHNVPxMwI6BCl1CYRWQOs\nx1wD4DYAEZkBTFVKPSoi9wArRKQBc3T/OszA/08icivmOgW3Nat2ErCqo21oogN8TTtFfL01j6kj\nEw6ZC+nyGBxoGEhcgJPTYsMpLKs7bm1yOp28885/eeaZp8nOzmLFitWMHj2Wn/50HuPGTWDatDMZ\nODBZz988AdWVp+N0lLZfUDuEx9PYfiFNO87q6mpZvXoFDkcDNpuNlJQ0UlPT8Pfv+k5fTdO61+LL\n1wAAIABJREFUh1JqNWYqePNjf2j2cxHmwnSHu34FsKKde7zW7OfNmIvztXaa9/zHtD33vun6csxt\n547kBcyV6Z9RSh1uFeWDW/IppTYB09soM6qd+7RLKfU74Hetjn0OfO79+X3g/TYuvewwVV6AuUtA\np+gAX9NOAXUNLn7YU8qVZ7dcxdgwDD7cX4TV4mZY2PHbi7yxsZG3336LZ5/9C9nZ5t7IgwalUFNj\nZi1NnjyFyZOnHLf2aF2rriKD8tzPCIrsiamFJ77Q2MnY7EHtF9S0buZ0OqmoKCMmJo6AgEBCQ8OI\niEgmJUXw8/Pv6eZpmqYBLMIckQ/u6a3yupI3a2FxZ7fIg24M8EXEitmjMgoz1eEGpdSeZuevAe7B\n3B7g30qpF7urLZp2PFTVNbLk6yyq6g4/+ubnZ8fhcHXNDT0GuD28+MH2dosWV9Tj72PjzRW7Whyv\nLi8lumA3fagl2+ZHqc9OXG4PQ+uclK+qwXA4uqatXk6nEx8fH8rLy3jooV/hcDhITR3M3Xffx8UX\nX4bNZuvS+2nHn9NRRlnOUmKSf4pf0JG3Y9Q0rXdqbHSwd+8u9u5VeDwGM2dehJ+fH1OmnKUzqjRN\n61W88+R/1tPt6Gre7Iaj0p0j+BcD/kqpySJyGvAXYG6z809jbgNQA6SLyCJvGoamnXA2qSLeWLGL\nSUPjGCcxhy0XGhJAVXV9l9zT4/Lww94yxh7hfk3e/yqTScPiGBD/4zokB4pLGL/6A3z69KPAXUWQ\nLZBwHyv4gF+oDWdhAWFnnoUt+NgWTCooyOfjj5ewbNkSysrK+OKLtcTFxfPgg78lISGBiy66RAf2\nJwmPx0nJvv8RFn+mDu417QTkcDTwzTfp7NixA5fLha+vHyKCzWbuvKKDe03TtN6vOwP8qcByAKXU\nBhEZ3+r8Vsy9AV2YKxwaaFo3+ySnhNzahi6rz9noZt/WImrKG0gZG09NVABbW60RYtQ5cW8uAgMs\nVguG59jf6gZuGpzl+OLP4tK3j1zWMGhIcbPJx84m75Rom8vD+ct3UZgazt7TgyiorSHQbiH04AL5\nbsw1P+ph278BaPQ0YqHjX+6WLPmAF1/8Oxs3fnvwmN1uJz8/j4SERG699Y6OP+FTXOWOHeQs/G+n\nrjGcTjjOX8bLDyzHxy+K4OjWH/eapp0Iamqq2bJlC35+/qSljWDgwBTsdj2bU9M07UTSnZ/aoUBl\ns8duEbErpZryk7dj7vNXC7zXbDuANkVEBGK3965RvpiYkPYLab1K5s4cZgyIJSbQ95jr2rmnlPdW\nKYZLDLOuGoOvT9vvz5KcSjYZxYw+N+WY7wlmwL5q76eE+YWQHBfPhOh+Ryz/3c5C8IcJQ+PMAx4P\nQQsXYx+QQuqNVzDeYuGjnZ/RLzyR0QnDDluPn82XvtFHzhaoqKggNDQUq9VKQ0M1Gzd+i7+/PzNn\nzuSyyy7jwgsvJCIiotPP+VS3+63PiUhLJWLsmE5d5xMeTtBRfk45GlxYLB3/nCvJ/Q53Qy5pk+7E\ndhy2UtQ6Rv+d0o6kutoM6G02G5MnTyYmJgRf33Po16+fDuy1TtOfN5rWO3Tnp3cVLfcltDYF9yIy\nEnNFxIGYKfpviMjlSql3D1dZefnxW9m7I2JiQiguru7pZmid5HJ7iDAg2jj6kc26BheLPt9Nxv5y\nbrpwKEP6ewPWwwzMOw0I9LczrH9kl7xvvjywjobQEm4fdyU265E7vdweD//9vpD/uyyFRRkLKW8o\n54zvKgmrcvHxWVF41rwAgMvjZkLMOOKtR0irNjhs2w3D4IMPFvPrX9/PAw88zLXX/oLp08/n5Zdf\nZ8aMcwn2pvm7XIevQ2ub4fFQvmkzSQ88jDOm/ekYzTmBuqN8vRsdLowj/M5blK0roGjvUuJSfk5Z\neSOgV4HvDfTfKe1wamqq2bUrnezsfRiGQUhIKMnJQ7FarSQnJ+v3jdZpvenzRnc0aKe67gzw1wJz\ngHe8c/C3NTtXiTf/VynlFpEiQA/rab3ejqwyXlu2k+HJUfz++okE+B3fEY68mgKW7fuMe8bd2m5w\nD7A9s4yoUB8+yv0fE+LHMD69ntq6b0l44FdMCwhsUdbX5nNUbcrJyea++/4fq1Z9BsBnny3n2mt/\nQUxMDBdddMlR1an9yJG9H3tIMD6tgnu1rYBN6/d32+QmwzCwWtvvCPO4GyjJ+h8RfWbiE9C5DghN\n046/zMxdbN36PYZhEBwcgsgwkpL6Y7Vae7ppmqZ1MxGZDrwDpGN+gwgFMoFrlFKNIhKDuU5af8AG\n5AB3K6UKvNdPA34L+ABBwKtKqRda3SMZWAZ8o5T6eRttGAAsUkqd1ur4E0BG8232RMQG/BUYD/gB\nv1NKLW11nQV4Fbi9p1fRF5FHMAexXcAvlVLftjp/DvCE9/xKpdTD3uPXAbdgvuYfKqUeE5FZQKJS\n6pXOtqM7o5P3gXNFZB3mHPtfiMjVQLBS6l8i8hLwtYg0AnuB17qxLZp2TBoaXby7ei8/7C7hullp\njEiOOu5tcHpcvJb+X+YOmkVsYMcCqTVb8/BNTifCL4wp+YGUfrWcfg8+jD04rEva9OabC/n1r++j\nrq6OsLBwHnnkMa6++qRbyLRH1W7bSsS4sQcfG4bB9+uz2bkln3PmDME/8Og6ZjrC1/fInUiGYVC6\n/yP8Q5IJihzRbe3QNO3YVFZWYLfbCQoKJjIyhpCQUESG0adPXywWHdhr2inmc6XUVU0PROQt4CIR\nWQy8BzytlPrQe+4cYKmITMIM+p8DzldKFYpIAPCFiGQqpZY3q38q8LFS6p4uaOvPAB+l1BQR6QNc\n3kaZK4BNvSC4HwucCUwC+gKLgQmtij0FXAPsBNaIyAigDjO4n46589zvRcRHKfWJiHwiIu92dqu8\nbgvwlVIe4P9aHc5odv6fwD+76/6a1lV25VTw7493kpIUxmPzJxLof/iAqq7BSW1Dy23wKmoacbo8\nFFfU47ZaKas4ulX0Pz2wnFB7BCkBwynuQB0NjW521n5Pgr2Cn1jOouTdf5N07/3Yw7suWSYkJIS6\nujrmzr2Uxx//M3FxcV1Wt2aq3baV+Ovm4QQ8HoOvV+6m4EAll8wbQ1DIj3PdPe5GPK7aLr+/y3H4\n6VG15TtwO6uIHnBpl99X07RjV15ehlLbyc/PpW/fAYwfP5nw8AhmzJilV8TXNA0R8QUSgHJgHFDZ\nFNwDKKVWishe4AzvfwuVUoXec/UiMhNzunVTff2Ah4BAEdkDbACex1y9uQG4sdX9LwMeBooBX5rF\nil4zge0i8jHmgHFbKzTfAVzire9M4BHACgQDV2POG1wClGJmFnyC2VFh8R673vscXsIMzBOAj5pG\n15u1dam3zibpSqlbmz2eCqzwbtuXLSJ2EYlRShU3K7MZiMTMgPD3vi7nABuB1733/oNSqmnF7mXA\ndd72dpheQUXTDqPR6ea9rzL5Zmch184UxqQeedS8sLyOPyzchH+rUU8/l4fIBjdP/XczVpsVj9vT\n6ba4AotojP+BgKyzePqbHzp8jW/fLG6M+gmlL7xEwv/dhl+fY9u6zOPx8NJLL+Dr68v8+TcxZ87F\nLFu2kvHjJx5TvVrb3NXVNObnETp0CIXFtaxcspNGh4uLrxmDb6vpIWU5S2mozsRiPfYFJDvKavMj\nJvlKLFb9p0TTepPS0hKU2k5hYT4AERFRJCX1P3heB/ea1jvMuefD7ZjbhneVHUv+Mnd4O2VmiMhq\nIBbwAP9SSq0SkSsws6pby8QcvU8EWnwJVUpVtnqc7U21T1NKvSgiG4EblFI/iMhczHT7ewFExMf7\neCxQBrS173s0kAJciNnB8Kr3/3jrCAD6NQuihwHzlFJ5IvIQ5oj/m0A8MM47DWEDcL1SKl1E5gP3\nAQuADUqpG0TEHziA2fHQ/Lld2NaL2UwoZodBk2rMHeOaB/jbgKXeclsxOzQu8T6n04EAzAz3id4F\n6LcCd6EDfO1U56qsZN+DvzK3CWvlfMzVHzuS5+IxYAww1gJsgV0dKH+bhUM2kqvwj2VP5Fhm71pu\nbltmdH7StAcDCxYsvN6pa6xYqLL9jfj5NxEoaZ2+b3P5+XncccctfPXVF/j5+TF79hzi4xN0cN+N\natO3EyBpOJwGS97eQnCoP7Pnjjy4J3VzhsdFZN/ZBIYP6YGWaprWm2Rl7aGwMJ+oqBjS0oYTExOn\ng3pN64U6EIx3h8+VUleJSBTwGbDPezwXGNBG+VRvuUTMEe6DRGQU5kLqmw9zr0SlVFOnwFeY88+b\nxABlSqlSb13r2ri+FFjqHRX/UkQGtzofAZQ0e5wLPCciNUAfzDXhAPYppZpWAB4CvCAiYI6k78bs\nYJggImdhhgmHbAfUgRH81gvMhwAHd4kTkXDgQWCYUipXRJ4E7vE+x9VKqWqgWkR2AoOBb4F8oNPz\ngnWAr510PI0ObKGhZP3kTt7+fA82249fapxuD3arpe0vOsaP/yACfG1cNHUgo9sZtW/yzud7aHC6\n+Nl5ckjdPnnV5H6bQ+ojlxMTHUxxScenCBmGwYJt/yE2MJqLUy7o0DX1rgb+uvEfnNn3dKb2mQyA\n5RgXT1qy5EPuvfdOysvLiYqK4pln/kF8fMIx1am1r3brVhg8klf/vpY+/SOYfFay/pKuaVoLhmFQ\nVFSAUjsYMWIMERFRpKUNp3//ZKKjY3u6eZqm9VJKqVIRmYc5j340sA6IF5E5SqklACJyPuYI+peY\nI/kfiMjbSqliEQnGTGt/FDP1vC15IjJSKbUVc3568/GyIiC8WRr7BMyR8+a+Bi4AFns7E7JbnS+l\nZVC9ABiklKoWkdf5cdytefqsAq71ZhtMwUyLvw6oUErdLCIpwE0iYvF2LDS9Xu2N4K8FnhSRp4Ek\nzI6P5p0P9ZhTAZoCgXzMTo7lwG3ezAEbMBTY4y0Tgfk6dYoO8LWTkstt8MHa/Tx242nERvy4Wvyz\n2/dzZXI88YEtO+YMw2DlR+lkZ5YdHB3d/XU2u79u/TlyKKfLg8PpJijAh/+99n2bZfolR2KxWrHY\nbJ0KttfmbqC8sYLrR87r0HUew8PrO99mUNQgpvWd0uH7HMm2bVuZP99cOG/GjHN49tkX9Vz748Dw\neChU2WxtGM6Uc/ozaIheoV7TtB8ZhkFBQR5K7aC83MwKLSoqICIiiqCgYIKCgtupQdO0U503Tf05\n4Dml1OUiMgf4mze9HcxV9GcrpdxAlojcB7wnIm7MwPplpdSyI9ziRuDv3pXuXcD8Zvd2icjtwKci\nUoa5u29rC4AXvWn1Flqt76aUcohIgYjEKqWKgDcwF6+rBQoxsw5auwVYKCJ2zJ0E5mMueveWiEzG\nXOhut/fa3CM8txaUUptEZA2wHnMNgNsARGQGMFUp9aiI3AOsEJEGzNH965RS5SLyCmYHgQV4TClV\n5q12ErCqo21oYjGOIl24JxQXV/eqhvam/T61lqpz89n1xz/iuu1hJg1tGYgeLsDf8GUm+dkVTLwo\ngVd2voHH6Ng8eZfboLzaQUSIH3Zby5FVTxv/tmw2C253x9/Kda46bh5xXYdXzf8iZw37qvZzx+gb\nsR/jvOgDB3JISjIzsX7zmwfo338A8+ffrEeQvbIzS1HbC7utfnd1DbnZFZx58WgmnzGo3c+b4sx3\nCIocoVP0tYP036mTl2EYfPXVSsrKzMGhxMQkRIYRHh55zHXr9412NHrT+yYmJkR/UTmFiMhPgXil\n1DM93ZauJiLLgSt6zSr6mtYTDMPg/a8yGW2zMm5ox0aZM7bms3dnEZdeO5YcRzb+dn+uG3pVu9c5\nnG7+8f52LhyVyDhpGYDXuep5euM/CPQJaHHc4rHQmU41q8XGKzve6HD5cL8wbhs1/5iC+7KyUh5/\n/HcsWvQmn376BSNGjOKxx55o77JTzoEsc1pV/0Hds2VizaZMBvV1kDJEp9hqmgaG4aGwsIC4uAQs\nFgsREVEEBgYhMpTQ0PCebp6maVpPWYQ5Ih/c01vldSURmQ0s7mxwDzrA104yX2/NJ7+0jqkBHdsb\nPHd/OetXZ3LxNaMJCPQFB/hafdodMTcMgwUr0xkc04dZYw4dMa1wVBLqG8wfp/6mxfHe1MPdmsfj\n4a23/sPjjz9CWVkZPj4+bNnyAyNGjOrppvVa0XHBDB7WPdMV9r+/iZifXNEtdWuaduLweDzk5GSh\nVDq1tdVMnTqDmJg4RowYozOqNE075Xnnyf+sp9vR1ZRSbe0q0CE6wNdOGrkltby7ei+/On8wrldX\ntFu+vLSWzz5M59yLhhIRFdSpe335Qx4Himv49bXjj7a5vYrL5eLSSy9kwwZzAdNp06bz5z//hZSU\n1B5u2anJVVmJs7CAAP36a9opy+12k529j1270qmrq8VisTJgwKCDc+t1cK9pmqa1RQf42kmh0enm\nnx9u5/Lpg4iLtLe7IkZ9XSPL3t3GadOTSRoQ0al77S+o5r2vMnlw3lj8fGztX9CLNTQ04O/vj91u\nZ9SoMezbl8ljj/2JuXMv1V8ee1Ddju0EDhmKxa4/ojXtVOV2u9m+fTMej0Fy8mBSU9MIDOxcZ7Sm\naZp26tGL7B2l3pxqfSJauy2f1Zt/DMsthoeh+7/F5nZ16Pp6hwsDg0A/O76uRmIqc/lk4rWHlKsb\nGIz/gVrC8mpx+dmoD2252J7Dr4jqkB1El5x12HsVltcz77zBTBxy+NTsCkclT373XK9N0Xc4HCxc\n+G+eeeZpFix4jSlTplFTU4NheAgJCe3p5p0QNq9ZSWjgboKCfbu8bmdRIdaAAGze34WPjw2n033E\na1yOUiL7zSUwXLq8PdqJqbd83mgd43I52bdvD8XFhUyefCYWi4WCgjzCwyPw9w9ov4Iuot832tHo\nTe8bvciedqrTw0Nar5BVUE1yYhgTvAuKGbU1GBu2YjlrVrvXFpbXUZZbxcShcdit3s/0iMlcOfTQ\n9Ob3C0voa1jxTQxlxLQBh4xS59T5sKFkH5ePOnxqtL+vjaSYE3P7IbfbzeLF7/Dkk38kO3s/AO+8\n81+mTJlGcPCJ+Zx6iq+9HLcRRUTS5C6t13B7yH3nGRJuvAlbiLm1a3h4IBUVde1cacE3IKFL26Jp\nWvdzOhvJzNzNnj0ZNDY2YrfbqampJiQklPj4tnZ40jRN07TD0wG+1iMqHE62l/+40GWOqxFnnYvK\njAIAfBrqmGS18XXU0CPWYxgG2/KKOO/KQZRGBeJxuinbXWbuaplx6BZmPnmV4LZw0TVjsLeRXu8p\nDyKg2kZKn7Bje4K9kGEYzJ07i2+/3QBAWtoQHnroEWbObL8T5WSUf6CSorxOL0x6UGNtI8FhEfgF\n9e3CVkH97t3YPSEExv/43g8OD6He2TtGRjRN6zqlpSWsX78ap9OJj48vQ4aMIDl5ML6+XZ8ZpGma\nJiLTgXeAdMxvy6FAJnCNUqpRRGKAp4H+gA3IAe5WShV4r58G/BbwAYKAV5VSL7S6RzKwDPhGKfXz\nNtowAFiklDqt1fEngAyl1GvNjj0AnO99GI65HV58q+sswKvA7T29ir6IPALMBlzAL5VS37Y6fw7w\nhPf8SqXUw97jfwDOwfydPKCUWi0is4BEpdQrnW2HDvC1HpFRWcv3JVUMCg0EoKSojsZ6JzbvCLyv\nowHDgLKqhnbrGjOxDz4hvlQ2unAU11G8tYig/m2nmceEBXD+9JQ2g/uTkWEYbN68iTFjxmGxWDjr\nrLPJy8vlvvse4vLLr8JmOzVeh7Zs+SYHj8cgLOLoUl/DAn0Jj+z6tNnabVsI0jsXaNpJy+FooK6u\nloiIKMLCwvH3DyA1dSjJyan4+HRsBxhN07Rj8LlS6uB+0CLyFnCRiCwG3gOeVkp96D13DrBURCZh\nBv3PAecrpQpFJAD4QkQylVLLm9U/FfhYKXXPsTZUKfUEZkCMiCwF7muj2BXApl4Q3I8FzgQmAX2B\nxcCEVsWeAq4BdgJrRGQEZjx+mve//sCHwCil1Cci8omIvNvZrfJ0gK/1mP7BAczuZ25H941rHwNi\nQ7n9khEAuKqr2L/BzkOXdC7QKbBVsi6ylEsvGdnl7T2RVFZW8L//vc3Cha+xc+cOXnllIXPmXMyt\nt97Jbbfdhb+/f083sVdIGxlPshx5S8TDKc/dh83u137BTqrdtpXYq+d1eb2apvWs+vo6du/OICtr\nDwEBgZxzzgXY7XbOPvsCvaippmk9QkR8gQSgHBgHVDYF9wBKqZUishc4w/vfQqVUofdcvYjMBGqa\n1dcPeAgIFJE9wAbgecANNAA3trr/ZcDDQDHgC2Qcpp2XAuVKqba2yboDuMRb7kzgEcAKBANXA43A\nEqAUM7PgE8yOCov32PXe5/ASZmCeAHzUNLrerA1LvXU2SVdK3drs8VRghXfbvmwRsYtIjFKquFmZ\nzUAkZgaEP+BWSm0TkZlKKUNE+gMVzcovA67ztrfDdICvnbBWH1jL+3s+hmYLRfpXhxFXlcZdX7x7\nVHV6MBgedei+9p31fdFWogIij7mezqqoKOeRR37NBx8spr6+HoDo6GhKS0sBCAg4fgs1aZ3nqijH\nWVqKf/Kgnm6KpmldpK6ull270tm/PxOPx0NAQCCDBgmGARaL3u5O005lV7x9y3ZgWBdWueOdK18c\n3k6ZGSKyGogFPMC/lFKrROQKYG8b5TMxR5YTgR+an1BKVbZ6nO1NtU9TSr0oIhuBG5RSP4jIXOCv\nwL0AIuLjfTwWKAOOtO/7g8BPWx/0ZhH0axZEDwPmKaXyROQh4HLgTSAeGOedhrABuF4plS4i8zGz\nAhYAG5RSN8j/Z+++46SqzsePf2Zme++7LG1hYR86KCKCIFixYVCjwYYYW2I0Go3GVI3J1/iLxiTG\nmCgmlihBjb03RFBUlF7PLn1ZYHsvs7Mz9/fHncVh2c424Hm/XryYuefec54Zht157mkiYcAe7BsP\nga/t/FbiA3u6Q3HA80ogFvvmRaP1wFv+89bhv6FhjGnwD9P/MfYNi0brgFvRBF8dC3Ir9/Lujo/4\n5Yk/IT7s223u8vMq+KpsB7fMaOv/YMtcDudhxbalJIcPdy3hpxN/dFj1tFd1dTXZ2Vs47riJREVF\ns2TJYmpra5k27RTmzbuGc8+drfM5jxDVG9YTMWo0jmN46oRSR5v8/H3s2LGVyMgosrJGMWhQBk6n\n/h9XSkE7kvHusNgYM1dEEoEPgR3+43lARjPnD/efl47dw32AiIwHnMaY1S20lW6MabwpsBT/cHu/\nZKDEGFPsr2t5cxWIyCigzBiztZnieKAo4Hke8IiIVAH9gc/9x3cYY+r9j0cCj4kI2D3pOdg3GCaJ\nyKlABXDIEM129OBXANEBz6MJ6I0XkTjsGxWjjTF5IvJH4A7sYfsYY37pvznypYgsM8ZsA/YBic29\nL63RBF8dceq99Ty9cSEXD59NSsTBw6uDnC4cOAh29s5Hu6CmiKc3/pdrx1zR7T34+/fv48knH+fZ\nZ/9NaGgYq1dvIigoiL/85e8MGjSIzMyWdwJQfVP1+nVEjpvQ22EopQ5DRUU52dmbSEhIYujQ4Qwe\nPISgoCD69x+E03l4N5CVUqqrGGOKReRK7Hn0E4DlQJqIzDbGvAkgImcDw4BPsXvyXxORF4wxhSIS\nhT2s/T7soefN2Ssi44wx67Dnp2cHlBUAcQHD2Cdh95w3dQb2sPrmFHNwUr0AyDTGVIrIM9jD8MEe\nqXDgpQPz/KMNTsYekj8f+ybCjSIyDLhBRBz+4faN71dbvYefA38UkYeAAdg3PgJvPtRiTwVonNKw\nD0gWkdOAi40xP8KexuAJiDce+33qEE3wVe/xNuApsUeyRLgrCa31HHjurWp5nYxXt77NgOh0Tkw7\nvkfCbK/ahjoeX/c05w45k+Hx3TfEOjvb8MgjD/Pqq//D4/EAcMIJJ1JYWEC/fumceurp3da2Onxl\nn35CzcYNzZZVb9xIyuVX9XBESqmuUF5eijEbycvLBaC+3s3QocNxOl0MHJjRu8EppVQz/MPUHwEe\nMcZcIiKzgb/4h7eDvYr+ecYYL7BTRO4CXhERL3Zi/aQx5p1WmrgeeNS/0n0DcG1A2w0icjPwvoiU\nYCe2zRHsEQTNxe8Wkf0ikmKMKQCew168rhrIxx510NQPgWdFJAh71fprsRe9WygiUwA3dq9+OvaI\ngHYxxqwUkWXAF9hrAPwIwJ/ATzPG3CcidwAfiEgddu/+fOye/0tE5HPsnQv+boxpHFUxGfi4vTE0\nclgB85f7ssLCyj4VaHJyNIWFum1VR2zdU86O/fYikHlWA2lffUTGppX4gkOoq/cS5HIQFf7tUPLg\n1FQG/vRnB9WxvmgTL2a/zs8n3UZE8KHzyffnlbP8421cNK9nk3+f5eOJ9c8QGxLDZSMubvG8zn5u\nGufTh4eH88Ybr3LddVfjdDo599zZ/PCHNzNp0uROx36kKimqxl3b0u+Ctn316Q7GTRpwGIvsfYAr\nKIqY1KntvqZ+315y/98f7EX0XIf25Lkio4gYcegaEPrzRnWGfm56zvr1q9m61V4bKi4uHpEx9OvX\n/4icX6+fG9UZfelzk5wcfeT9x1OdJiKXYW+f9+fejqWrich7wKW6ir7qk/YUVvHIy+s4cWQKDoeD\nyhBIra9nz6hp5MuJmN1ljBuawHdPHdZiHeXuShZueZlrx1zZbHLfm97a/gE1njquG3P4va9er5cv\nvvic9evXsX79WjZsWEdOTjY//end3HHHzzj33NncfvudzJ17JRkZQ7og+iOHz2exa2sRa1bsobK8\njujYw1nF3tHpLfIaPJXUlGwgcUjLN3OaU/DCIuLPOZfoE4+9GzJKHW2KiwuJiYklODiEuLh4EhKS\nEBlNamq/IzKxV0qpI9Qi7B75qN7eKq8rich5wMsdTe5BE3zVjd7YVUBuVR2WZbG3qIb+J6dTHm7v\n8Vvb4CU+OhQZnEz8mVk8/2E2sdEtJ2uWZfHc5heZmn4iw+L6VlK7Mn8N3+Sv5s4TbiEslbg1AAAg\nAElEQVSoA3P/7XmahpycbIzZQlpaGj/4wc04HA7mzbuMqqpv74S7XC6++GI5lmURFBTE3Xf/ujte\nSp/lqW9gy/r9rPt6D2HhwYw/cSBDJalX5rNalpfiHS8TlTSRsKjB7b6uev06PIX5xN/8426MTinV\nnSzLoqiogC1bNlBUVMCoUeMQGc2AAYMZMGCwJvZKKdXD/PPkj7r5jcaY1nYVaJUm+KrD/vn6BvYU\nVrd5XkldPRFBLmpqG8AB3ohgSgLK9+0uZ9t+H5t3f0V5lZuLThnaYl2f7llOdUMN52ac0QWvoOvs\nrtzDi9mvc8uE64kOiWr13IaGhgOPZ82ayerVqw4qnzDhOH7wg5txOp1cfPGlAIwdO46xY8cxYsSo\nY3KLu6pKNxtW5rF57V76DYzjtPNHktY/ple/RJftXYzDGUxM2intvsZqaKDghYUkX3oZjiD9savU\nkcayLAoK9rNlywZKSuw1k1JS0khKSgV0qzullFJ9h37TVB22La+Cq88R4qNaHx79/NZ9pHscbNxc\nyI0XjCY46ODeVvdb2Tjj4pk5zd7+MzUhotl69lbt592dH3HHxB/h6sTWQpZlsbZwA/W+zs/Xbqne\nN7e/z/fkQgZEN7eGhz3c/tNPF/Pss0+zb18eK1d+A0BsbBzh4eFkZg4nKyuL4cOFMWPGHbjuwQeP\numlEHVK4v5J1X+9h17ZiskanctG8iZ0eTt+Vasq2UFO6ibQR13foC33ZJx8TnJhE5Ljx3RidUqo7\nbd68ntLSYtLS+iMymoSEDu9cpJRSSnU7TfBVp6TFR5AU13rC5coL4psN+7n7kvGkNZO8F4QHExwZ\nSnxyyz3fHq+HpzYuZE7muaREJHUq1ipPNU9t+i8Tkrt+q9GzM07j+JRxhxzfv38fCxf+h+eff5bc\n3N0ABAUFsX37dmJiUliw4Gmio2N0y6QAlmWxa1sxa1fsoby0hrETBzDtzGGEhgX3dmgAeNwllOS+\nRfLQubiCmr8Z1RxvZSUlb7/FgLvu1l4+pY4QlmWxd28uW7caTjppOqGhYYwbNxGn00lcXHxvh6eU\nUkq1SBN81S1q6jwUlddx7pTBzSb37bW6cD3RIVGc1O+ETtdhYRHuCuOa0Zd3uo62+Hw+NmxYR79+\n/UlOTubNN1/jgQd+D8DgwRlcddV8vve9K8jMzKSwsJLY2Lhui+VI4/F4yd6Qz7qvcwkKdjH+xIFk\njkjG1cwq873F5/NQtOMlYtNOITRyQIeuLXr9VaJPnExoev9uik4p1VV8Ph95ebsxZiOVlRWAg8LC\nfAYMGKw99koppY4ImuCrLmdZFv96ezPh6WGMGXp4X4gafF7iw+L6XM+nZVls376VpUs/ZdmyT/n8\n86WUlpbywAN/4vvfv55LLpnL119/xeWXz+OUU2ZqT30zaqrcbFi1l41r9pKaHsOMs4V+A2P73L81\nQGnuuwSHJhGVNKlD17n35FK18msyfveHbopMKdVV6uvrWbLkfaqrq3A4HAwaNISsrFFER8f0dmhK\nKdUlRGQm8CKwCXsP+BhgO3CFMaZeRJKBh4DB2Huy5wK3G2P2+6+fDvwGCAYigaeMMY81aWMo8A7w\nlTHm6mZiyAAWGWNOanL8AWCLMebpgGOx2KvkR2HvT39lYywB5ziAp4Cbe3sVfRG5BzgPaABuM8as\naFJ+FvD/gGrgPWPM71u6TkTOAdKNMf/qaBya4Ksu98HXuZRVuYmLjm31PF9NDST2/R4Rj8dDTk42\nmzZtIDk5hRkzTmXfvr1MmTLxoPMGDhx04HFcXDxPPPF0D0d6ZPnf0ysZPCyRC688jrjDGOXR3aqK\nV+Ou3kOaXNehmw+WZVGwaCGJs7+DK6r1BRiVUr3D6/VSWlpMUlIKISEhREXFkJycRlbWSCIj9f+t\nUuqotNgYM7fxiYgsBC4QkZeBV4CHjDGv+8vOAN4SkcnYSf8jwNnGmHwRCQc+EZHtxpj3AuqfBrxt\njLmjC2KdD6w3xtwlItcDdwJN670UWNkHkvvjgRnAZGAg8DIwKaDcCTwJzDTGbBeR50RkGlDT3HXG\nmHdF5F0ReamjW+Vpgq86zMKiss5DSM2hC97tKaji3S938at5J/DfPQUt1lG54itqNm8kcc6F3Rlq\np5WWlnDffb9hzZrVZGdvweOxF+g7//zvMGPGqaSn92fSpMmkp/dn+vQZTJ8+g4yMIX2y97mvqqtr\n4OTThxEU3L6FEy3LwtdQ081RHazBXULZ3o9JHXY1TldIh66tXrMKb0UFsTNO7abolFKd1dDQwM6d\n28jJ2Yzb7eass84nIiKSKVNO0Z/jSqljhoiEAP2AUmAiUN6Y3AMYYz4SkW3AKf4/zxpj8v1ltSIy\nC6gKqG8Q8AsgQkS2Al8CfwO8QB1wfZP2LwZ+BRQCIcCWJiGuB0b4H8cAza2YfQtwob++GcA9gBO7\n1/9yoB54EyjGHlnwLvaNCof/2Pf9r+Fx7AS7H/CGMeZXTWJ9y19no03GmJsCnk8DPvBv27dbRIJE\nJNkYU+gvTwJKjTHb/c8/919T08p172Df5HikmdfdIk3wVavqvT4qPN9u71ZcUUdplZuHX1mHs5k5\n0i6ng+/OyoIwFw2W1WydNWYL+//7PLHX3Uo14VDSctLmrvDhq3JR1so5gaoq3O06ryUlJcUkJCQS\nExPLihVfkpOTDUBGxhBGjRrDySdPP3Du229/eFhtqY4pzX2b6tINOJxt/NiyLKwWPnsd5cBBdOxU\nrAov9RX7274gIIbCF18g5cp5OFwd3/lBKdU9Gho8bN++la1b7cTe5QoiMzMLl8v+uaLJvVKqJ33+\nnYs3AKO7sMqNJ7/+clurSp8mIkuAFMAHPGGM+VhELgW2NXP+duze+3RgTWCBMaa8yfPd/qH2I4wx\n/xCRb4DrjDFrROQ7wMPATwFEJNj//HigBGhu3/di4CwR2QQkANMDC/2jCAYFJNGjsYfx7xWRXwCX\nAM8DacBE/zSEL4HvG2M2ici1wF3AAuBLY8x1IhIG7MG+8RD42s5vJr5AMf54G1UCsdg3L/D/HSEi\nI4Ac4Fzs9zOolevWAbeiCb7qSu/mFrGhtIowlxMLi9IKN64QF+nHpxIS0fzq5l+5a/kquxanw0Fk\n063x8vLY98/HqJ51NUs/2Et4RFGr7dc21OHxxfLOqvXtjrn/4I4vYLdu3RoeeugBVqz4km++WU9U\nVDR///sT1Nd7GDVqFFFR0R2uU3WdquI11FXtpv+Yn+B0tb49Y8m7b1Py7jtdNiy+hDc6dV3EqFFE\nju76nRuUUp1XXV3Nxo1rCAoKRmQ0mZlCaGjrP1OUUqq7tCMZ7w6LjTFzRSQR+BDY4T+eB2Q0c/5w\n/3np2D3cB4jIeMBpjFndQlvpxpjGmwJLgQcCypKBEmNMsb+u5c1cfw/wR2PM4yIyDnv4euD2VfFA\nYDKRBzwiIlVAf+xecoAdxph6/+ORwGMiAvZaAjnYNxgmicipQAVwyC+GdvTgVwCBCUM0UNb4xBhj\nichVwD+w1xPY4I/dauW6fUCH5zNrgq9a5bUszhqQyKTkWP7zviG8xkN9SDDXjxjQ5jZ5TXlKS8n7\n68Mkf28utSFpDHdUc8qsrFavWb73a7aV7+DykWcczsto0dq1q3nooQd4//13AQgPD2fVqpWccspM\nJkw4vlvaVB1TX5tP2d6PSBk2r83kHsBqaCDutNNImnNxD0SnlOrL3G4327YZ6uvdTJgwidjYOCZN\nmkpKSj9CQjo27UYppY4mxphiEbkSex79BGA5kCYis40xbwKIyNnAMOBT7J7810TkBWNMoYhEYQ9r\nvw9oKcHfKyLjjDHrsOeZZweUFQBxAcPRJ2H3nAcqBcoDzm+66mkxByfHC4BMY0yliDyDPQwf7JEK\nB146MM8/2uBk7CH584EyY8yNIjIMuEFEHP5h843vV1s9+J8DfxSRh4AB2Dc+mvZkzvL/8WCvd/AU\nENbKdfH+190hmuCrdvly03427izhN1dP4p5/r2j7gia8NTXk/eVPxM08lZiTpsKqvG6IsmOWLfuU\niy+eDdiJ/TXXXM9NN/2YlJSUXo5MNfJ56yja8RLx/WcREq7/Lkqp9nG768jJ2cKOHTk0NDQQGhrG\nmDETCAoKZsCAwb0dnlJK9Qn+YeqPAI8YYy4RkdnAX/zD28FeRf88Y4wX2CkidwGviIgXO7F+0hjz\nTitNXA886l/pvgG4NqDtBhG5GXhfREpofn79r4EnReQm7N72g+bwG2PcIrJfRFKMMQXAc8AyEakG\n8rFHHTT1Q+BZEQnC7j2/FtgMLBSRKdi96zn+a9udsBhjVorIMuAL7DUAfgQgIqcB04wx9wF7gRVA\nLfC8MWaj/5xDrvObDHzc3hgaaYKv2lRe5eaND3P46dwJRIR1/CNjNTSw97G/EZ6VRfw553VDhB1T\nWFhIcnIyJ588nbPOOpvhw4WbbvoxycnJvR2aCmBZFsW73iAseiiRCWN7Oxyl1BFiz55drFr1FV6v\nl7CwcEaOHEtGxjCCgvQrj1Lq2GaMWQIsaXLs/wIeF2AvTNfS9R8AH7TRxtMBj1djL87X1En+8rdp\nfu594/V7seeqt+Yx4Argz8aY21s458CWfMaYlcDMZs4Z30Y7bTLG3Avc2+TYYmCx//EC7FEGbV7n\ndy72LgEdor/tVKu8PotP1u7j4hlDGZTa8Xnols/H/qeexBkeTsplV/bqAkZVVZXce++vee21l1m6\n9EvS0/vzn/+8oIsqdUK9u4E3F62locHX9skt8Hl9rb73lYVf4fVUkJRxUafbUEodG2pqqvH5fERF\nRRMXl0BYWDjDho1g8OChuHShS6WUOpotwu6Rj+rtrfK6koicB7zc0S3yQBN81YateeUkxoRyyvjm\nRriAt7oad17T6TLfqlr5DZ6iIgbccRcO56Gr7veUvet3M+O2KeTm7iYkJISVK78mPb2/JvedVF/v\npaK8jgvmdv5mZ1CwC1dQ858Jd9VuKvI/Jy3r2rZXzVdKHbOqqirJzt7E7t07SE3tx5QpM4iKiubM\nM8/Xn+9KKXUM8M+Tv6q34+hq/tENnaLfnFWLlq3dS2VNPTPHD2jxi1LpRx9Q8flnBCc2v8CjMzKS\n/jffirOXFjPyeDzc+8tf8t4zLwIwbtwE/va3fzJy5Kheiedo4nI6SEzpmpXqA3k91RTtfIXEQbMJ\nCu34jghKqaNfRUU52dkbyc3dDVhERcXQv/+gA+Wa3CullDpWaYKvmpVbUMVLS7Zx4llDCWqhlxUA\ny0fs9FNInP2dnguuHaqrq4mMjCQoKIj9+/fhcDm566c/58c/vp3g4Oa391O9z7J8FO18hciEcYTH\ntr7DglLq2LVz5zZyc3cRExPHiBGjSU8fgMPRe6PElFJKqb5CE/yjWEVNPf96azNeX8fnSecVVXPZ\nGcPZF9b5XpDsDfspL6trtqxgXwXRMWGdrrsl+fn7+cc/HuXZZ5/i7bc/ZOTIUfzuDw8QeWYqd8z7\nWZe3p5pXvPtNvPVlbZ/YhM/rxuEMIbbfzK4PSil1xCotLWbLlo1kZmaRkpLG8OEjSEpKoV8/nWql\nlFJKBdIE/yhWXlXPvuJqrj57RIevDQ8NYmh6DK/syO9U2xtX72XtilyGjWp+a7PktGgGDU3oVN3N\n2bFjO48++ldeeOF56uvrAfjww/cZOXIUySkpJA7RLdZ6Um3ZZhIGzcbh7PjUjNBI7YlTStmKiwvZ\nsmUDBQX7AYiMjCQlJY3w8AjCwyN6OTqllFKq79EE/ygXFuJi9JCuS6TbY/f2Er7+bAdzrjiOuITu\n/wJWUVHOzJlTqK2txeFwcP753+HWW29n/Pjjur1t1bLQqAxcQeG9HYZS6gj15ZfL2LfPXsQ1KSmF\nESNGk5SU2stRKaXUkUtEZgIvApuw94CPAbYDVxhj6kUkGXgIGAy4gFzgdmPMfv/104HfYO9JHwk8\nZYx5rEkbQ4F3gK+MMVc3E0MGsMgYc1KT4w8AWwK32RORBOy97WOAYuB6/1Z+gdc5gKeAm3t7FX0R\nuQc4D2gAbjPGrGhSfgbwgL/8I2PMr0TkbOBu/ykOYBowBsgA0o0x/+poHJrgqy5VXFjFx29tZtaF\no7s8ud9YvIXVBeubLTvurMl43B5Ou/wcUgf3Yz1bWb95KwAen6dL41AdZ/l8FL64CF9dbbe35d69\nm8hx47q9HaVU17Isi6KiApKSUnA4HMTExOL1NiAyhqSk5N4OTymljhaLjTFzG5+IyELgAhF5GXgF\neMgY87q/7AzgLRGZjJ30PwKcbYzJF5Fw4BMR2W6MeS+g/mnA28aYO7og1l8Anxlj7vfHcj9wXZNz\nLgVW9oHk/nhgBjAZGAi8DExqctqDwBXAZmCZiIz1v3fv+eu4E/jcGLMZ2Cwi74rISx3dKk8TfNVl\naqrcvPvSek4+LZP0gV2/+vmGos14fB4kfhgAO7O346mvZ/iYEdx57y9bnYd5YtrxXR6Paj/L46Hs\nk49JvXJet7cVnjmMiBG6S4JSRwrLsti3Lw9jNlJWVsLkydNJTx/AyJFjdX69Ukp1IxEJAfoBpcBE\noLwxuQcwxnwkItuAU/x/njXG5PvLakVkFlAVUN8g7KQ8QkS2Al8CfwO8QB1wfZP2LwZ+BRQCIcCW\nJiGOAn7pf/w58GgzL+MW4EJ/fTOAewAnEAVcDtQDb2KPAHgHeBf7RoXDf+z7/tfwOHZi3g94wxjz\nqyaxvuWvs9EmY8xNAc+nAR/4t+3bLSJBIpJsjCkMOGc1kIA9AiLM/7401j8Ae7u/wJsC7wDz/fG2\nmyb4qkt4PF7efXkDMjaNrDFp3dbO0NgMpqafSEVFObfddT15eXtYtOgVpk+f0W1tqq7hcLmI1X8n\npZSfZVns3ZvLli0bqaiwF+VMTx9IVFQ0oFvdKaWOfvfd8eYGYHQXVrnxN3+aPaaNc04TkSVACuAD\nnjDGfCwilwLbmjl/O3bvfTqwJrDAGFPe5Plu/1D7EcaYf4jIN8B1xpg1IvId4GHgpwAiEux/fjxQ\nAjS37/sa4ALsxPgC4KDhwf5RBIMCkujRwJXGmL0i8gvgEuB5IA2Y6J+G8CXwfWPMJhG5FrgLWAB8\naYy5TkTCgD3YNx4CX9v5zcQXqHEaQaNKIBb75kWj9cBb/vPWcfANjduBPxtj3AHH1gG3ogm+6kpB\nlRVELnqSXZ76A8fmFFZT/ud3qXY5aSgrJe70s1j81mZiE8I5YVpGh9uo9tSQU9rczxPIrdxzyDHL\nsvjxj29i584djBkzjhNOOLHDbSqllOpdPp+XtWu/we2uZ+DAwWRljSYmJra3w1JKqR7TjmS8Oyw2\nxswVkUTgQ2CH/3ge9rzvpob7z0vH7uE+QETGA05jzOoW2ko3xjTeFFiKPf+8UTJQYowp9te1vJnr\n/wA8IiJLsW8A5DYpjweKAp7n+c+vAvpj9/oD7DDGNCYzI4HHRATsnvQc7BsMk0TkVKACCG0aSDt6\n8CuA6IDn0cCBLaVEJA74OTDaGJMnIn8E7gAeFBEncD7fjlZotA9IbBpLWzTBVy3y1tYyeNG/aRgz\njtSpUw4cX/S/tVx9thAXZW9zt3prPTWl1Vwwd3ynelxW5q/lo91LGBDdv9ny45PHHvT8n//8O++8\n8yYxMbH861/PEh6uC7kppVRf5/N52b17J3l5u5kyZQYuVxATJ55EZGT0gV57pZRSPcMYUywiV2LP\no58ALAfSRGS2MeZNAP8CcMOAT7F78l8TkReMMYUiEoU9rP0+7B725uwVkXHGmHXY89OzA8oKgLiA\nYeyTsHvOA50CLDDGLPcP5/+8SXkxByfVC4BMY0yliDyDPQwf7JEKB146MM8/2uBk7CH584EyY8yN\nIjIMuEFEHP7h9o3vV1s9+J8DfxSRh4AB2Dc+Am8+1GJPBWic0rAP+yYH2IvqbTHGNF2sKh77feoQ\nTfBVs6yGBvY99jdqBgwm6IxzCEv5dk59UcReggYMJiwunE1r97JjWz4XzTseV1BntzazGJU4grly\nYZtnbtuUzX33/RqARx75B0OGDO1km0oppXqC1+tl167tZGdvora2BqfTSVlZCQkJSaSmpvd2eEop\ndczyD1N/BHjEGHOJiMwG/uIf3g52j/l5xhgvsFNE7gJeEREvdmL9pDHmnVaauB541L/SfQNwbUDb\nDSJyM/C+iJQAza2KbYBn/b3teYHX++twi8h+EUnxr67/HPbiddVAPvaog6Z+6K8zCHsngWuxF71b\nKCJTADd2r366v812McasFJFlwBfYawD8CEBETgOmGWPuE5E7gA9EpA67d3++/3LBvoHS1GTg4/bG\n0MhhWVbbZ/UBhYWVfSrQ5ORoCgsrezsMat0NrDSFNPfvWFLpZqUp4L5rJzd7banbw8qiCjaVVtEQ\neL1lMeGDVwmqd/OVzCKpwE2o69vkvbCsloToMFwuB/V1Dcy58vC2w1u6Zzl7q/PbTPBfMK+SGBzP\nxwveIjQ0jHvv/X2n2+wtfeVzc7iqKt288sxK5t08tdnyPev+SL9RtxzYJs/ndrPtJ7cw/LEnejLM\no8bR8rlRPasvfG4qKsr5/PNPqKurxel0MWRIJsOHj9Q97PuwvvC5UUeevvS5SU6O1gU8jiEichmQ\nZoz5c2/H0tVE5D3gUl1F/xizNa+cV5ZuY8yQ5qdnTB938I2rBp+PTWXVrCysYE91HeMTo5mTkUK4\ny3XgnNo3XsVTV0X0LT+hemkuSeMSGDYi5UD5gwtXc8E5WcRFhxEeEUxYeHD3vLgAXq+Xmspq0tJS\n+cMfHmr2hobqOl6vj+wN+fh8vmbL3XUNPRyRUupI4fF4qKqqID4+kaioaIKDQxg4cDDDho0gLEyn\nVCmllOpSi7B75KN6e6u8riQi5wEvdzS5B03wjwoDkqP4/nkjWz0nv9bNN4UVrCmuJDU8hBOSY7hy\neD+CnQcPqy/7ZDFV61aTcfcvcUVHE+ZyEhkVQnzit70tHpeD6Lhw4uN67ovagw/ez9P/fYq7HryH\nGQOm6urK3aysuIYvPtlG5oiW954ee8KAHoxIKdXX1dfXs317Nlu3GlwuF2edNRuXy8Xpp5+Nw9HZ\nKVxKKaVUy/zz5K/q7Ti6mjGmuV0F2kUT/KOY2+tjfUklXxdWUFbv4fikGH4wcgCJYSHNnl+1ehXF\nb73BwLt/gTMq6ttecosmPeY913vu8/m4//77eOSRh3E4HNRUHjU35vq8yOhQZpwtvR2GUqqPc7vd\nbNtm2LYtm4YGD8HBIQwZMuzA7w1N7pVSSqmeown+UcayLHKr6/imsIINpVUMiQ5nZno8WbGRuFrp\n9a7dtpX8Z56i/223E5Kcwm1/+4yK6noycFC1bi9F7327TWOQy0lIiKvFurpKbW0tt9zyA95441Vc\nLhffu3M+Yycf1+3tKqWUar/CwnyM2UhoaCgi4xkyZDjBwd0/dUsppZRSh9IE/yhRUVxK9oqv2V5V\nh89nMTQmnJOjwgl3O6EIqlu51mpooOjVl0m79nrCMoYAUF3r4Yk7Z7Ls/WxS+8cwanzPr3R8//2/\n5Y03XiU6OoZ//etZ8vuV93gMSimlDlZbW0NOzmbCwyMYPnwk/fsPoKHhRAYMGExQkH6tUEoppXqT\n/iY+gvksi331HipSQnnr7Q8Ys+FrjhsylKhgF1TaGz62ltgHSrn8KiLHjuvOcDvsjjt+Rna24d57\n/4+RI0fxgnm1t0NSSqljVnV1FTk5m9m1azs+n4+4uHiGDRuBw+EkIyOzt8NTSimlFJrgH5HK3B5W\nFVewsrACy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piIzMTe234Tdv9ZDHbn6xXGmPpO1rkI+KcxZkkXhakOQ3sS/DoRScBe4f4k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\nSZLUBUzwJUmSJEnqAv8BFeF2m7QVeT4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d86438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_ml.plot_roc_estimator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过plot_confusion_matrices绘制混淆矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[[915  65]\n",
      " [183 200]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SVAGDLklSBQy6JEkVGDvaOyBJ0o4iIk4CTipPJwAHAs8FPgq0gRXArMzcGBEzgZOB9cD8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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137f07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_ml.plot_confusion_matrices()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过plot_decision_function绘制决策边界，由于绘制2d图，内部已经使用pca将数据特征降维后再绘制："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eM2HW0seFZbC5KwRWM8BrJnSRg0lDxGrm9MkVq/zmYnMVbJ5qYWWgdkMqCOit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NQWl1lpgkynH1/eIFDTdCeAbMRDMdQ6U7T5fb2Mv5BuMCLD0eVtGr7sEIJARke2BIUssPDEXVvo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3IAGcf/ojFEe7stsbkSiVZJb5tW8yYRcCj1sIwdqyQ6UsGSCGIb2qtqOaqOsq45Pd+KtYcFi8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\nSMr3pjMazYY3cK5SO1RuingWjxc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BPRFC3IX1/vVf+UTgH/m+vUsrTz8L69cASBQaA0kL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7nAMLug7fg1J4VXnlOx+Z9/F9WagzsbBI3Hn0Q5Nd3zmCCx8MTai7jzmeXO31YvcTROuu+D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\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p9RGNKswnbGzbIRxR6azxmp2NvC11g/tSN5kKdqPZFr2F+YZOB60FN4XxdZ46/zAvnBrl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\nXIQQQgghhBBN7/8Dcx4zgW4VyqIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ddc6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_ml.plot_decision_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过plot_graphviz_tree绘制逻辑决策图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier not hasattr tree_, use decision tree replace\n"
     ]
    },
    {
     "data": {
      "image/png": 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Atm3bxvTp05k/fz6tW7fmxo0bGBsb4+Pjw4gRIwgMDMTZ2Zlbt26pGwVMnTqV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\nGIZhGKZe8v9T1lQ1yQWhcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ddc64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btc_ml.plot_graphviz_tree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过feature_selection对特征的支持度进行评级："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ranking</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one_atr14</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_volume</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_open</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_ma5</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_ma21</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_ma10</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_low</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_high</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_close</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_atr21</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_atr14</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_open</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_ma60</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_volume</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_open</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_ma60</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_ma5</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_ma21</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_ma10</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_low</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_high</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_close</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_atr21</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_low</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_pre_close</th>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_pre_close</th>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_pre_close</th>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_4.0</th>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_4.0</th>\n",
       "      <td>6</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_5.0</th>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_6.0</th>\n",
       "      <td>8</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_4.0</th>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_1.0</th>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_0.0</th>\n",
       "      <td>11</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_3.0</th>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_0.0</th>\n",
       "      <td>13</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_6.0</th>\n",
       "      <td>14</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_6.0</th>\n",
       "      <td>15</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_0.0</th>\n",
       "      <td>16</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_3.0</th>\n",
       "      <td>17</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_5.0</th>\n",
       "      <td>18</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one_date_week_2.0</th>\n",
       "      <td>19</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_5.0</th>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_3.0</th>\n",
       "      <td>21</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_1.0</th>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>today_date_week_2.0</th>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_1.0</th>\n",
       "      <td>24</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_date_week_2.0</th>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ranking support\n",
       "one_atr14                  1    True\n",
       "two_volume                 1    True\n",
       "two_open                   1    True\n",
       "two_ma5                    1    True\n",
       "two_ma21                   1    True\n",
       "two_ma10                   1    True\n",
       "two_low                    1    True\n",
       "two_high                   1    True\n",
       "two_close                  1    True\n",
       "two_atr21                  1    True\n",
       "two_atr14                  1    True\n",
       "today_open                 1    True\n",
       "two_ma60                   1    True\n",
       "one_volume                 1    True\n",
       "one_open                   1    True\n",
       "one_ma60                   1    True\n",
       "one_ma5                    1    True\n",
       "one_ma21                   1    True\n",
       "one_ma10                   1    True\n",
       "one_low                    1    True\n",
       "one_high                   1    True\n",
       "one_close                  1    True\n",
       "one_atr21                  1    True\n",
       "today_low                  1    True\n",
       "two_pre_close              2   False\n",
       "today_pre_close            3   False\n",
       "one_pre_close              4   False\n",
       "one_date_week_4.0          5   False\n",
       "today_date_week_4.0        6   False\n",
       "one_date_week_5.0          7   False\n",
       "two_date_week_6.0          8   False\n",
       "two_date_week_4.0          9   False\n",
       "one_date_week_1.0         10   False\n",
       "today_date_week_0.0       11   False\n",
       "one_date_week_3.0         12   False\n",
       "two_date_week_0.0         13   False\n",
       "today_date_week_6.0       14   False\n",
       "one_date_week_6.0         15   False\n",
       "one_date_week_0.0         16   False\n",
       "two_date_week_3.0         17   False\n",
       "today_date_week_5.0       18   False\n",
       "one_date_week_2.0         19   False\n",
       "two_date_week_5.0         20   False\n",
       "today_date_week_3.0       21   False\n",
       "today_date_week_1.0       22   False\n",
       "today_date_week_2.0       23   False\n",
       "two_date_week_1.0         24   False\n",
       "two_date_week_2.0         25   False"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 48\n",
    "btc_ml.feature_selection(show=False).sort_values(by='ranking')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面通过importances_coef_pd对特征的重要程度进行量化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>1</th>\n",
       "      <td>one_atr21</td>\n",
       "      <td>0.1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one_atr14</td>\n",
       "      <td>0.0819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>two_atr21</td>\n",
       "      <td>0.0675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>two_atr14</td>\n",
       "      <td>0.0562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>one_volume</td>\n",
       "      <td>0.0357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>two_high</td>\n",
       "      <td>0.0326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>today_low</td>\n",
       "      <td>0.0325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one_close</td>\n",
       "      <td>0.0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>two_ma60</td>\n",
       "      <td>0.0314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>two_volume</td>\n",
       "      <td>0.0311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>one_ma21</td>\n",
       "      <td>0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>one_ma5</td>\n",
       "      <td>0.0294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one_ma60</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>one_high</td>\n",
       "      <td>0.0276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>two_ma10</td>\n",
       "      <td>0.0275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>two_close</td>\n",
       "      <td>0.0274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>two_ma5</td>\n",
       "      <td>0.0272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>two_low</td>\n",
       "      <td>0.0267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one_low</td>\n",
       "      <td>0.0266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one_ma10</td>\n",
       "      <td>0.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>two_ma21</td>\n",
       "      <td>0.0248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>two_open</td>\n",
       "      <td>0.0236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>two_pre_close</td>\n",
       "      <td>0.0235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one_open</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>today_open</td>\n",
       "      <td>0.0226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>today_pre_close</td>\n",
       "      <td>0.0223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>one_pre_close</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>two_date_week_4.0</td>\n",
       "      <td>0.0028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>one_date_week_6.0</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>two_date_week_5.0</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>one_date_week_5.0</td>\n",
       "      <td>0.0024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>today_date_week_0.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>today_date_week_4.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>two_date_week_6.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>one_date_week_3.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>two_date_week_3.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>one_date_week_4.0</td>\n",
       "      <td>0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>one_date_week_2.0</td>\n",
       "      <td>0.0022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>today_date_week_6.0</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>today_date_week_3.0</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>today_date_week_5.0</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>today_date_week_1.0</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>two_date_week_2.0</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>one_date_week_0.0</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>two_date_week_0.0</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>one_date_week_1.0</td>\n",
       "      <td>0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>two_date_week_1.0</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>today_date_week_2.0</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature  importance\n",
       "1             one_atr21      0.1146\n",
       "0             one_atr14      0.0819\n",
       "16            two_atr21      0.0675\n",
       "15            two_atr14      0.0562\n",
       "11           one_volume      0.0357\n",
       "18             two_high      0.0326\n",
       "12            today_low      0.0325\n",
       "2             one_close      0.0322\n",
       "23             two_ma60      0.0314\n",
       "26           two_volume      0.0311\n",
       "6              one_ma21      0.0300\n",
       "7               one_ma5      0.0294\n",
       "8              one_ma60      0.0283\n",
       "3              one_high      0.0276\n",
       "20             two_ma10      0.0275\n",
       "17            two_close      0.0274\n",
       "22              two_ma5      0.0272\n",
       "19              two_low      0.0267\n",
       "4               one_low      0.0266\n",
       "5              one_ma10      0.0265\n",
       "21             two_ma21      0.0248\n",
       "24             two_open      0.0236\n",
       "25        two_pre_close      0.0235\n",
       "9              one_open      0.0227\n",
       "13           today_open      0.0226\n",
       "14      today_pre_close      0.0223\n",
       "10        one_pre_close      0.0221\n",
       "38    two_date_week_4.0      0.0028\n",
       "33    one_date_week_6.0      0.0025\n",
       "39    two_date_week_5.0      0.0025\n",
       "32    one_date_week_5.0      0.0024\n",
       "41  today_date_week_0.0      0.0023\n",
       "45  today_date_week_4.0      0.0023\n",
       "40    two_date_week_6.0      0.0023\n",
       "30    one_date_week_3.0      0.0023\n",
       "37    two_date_week_3.0      0.0023\n",
       "31    one_date_week_4.0      0.0023\n",
       "29    one_date_week_2.0      0.0022\n",
       "47  today_date_week_6.0      0.0021\n",
       "44  today_date_week_3.0      0.0021\n",
       "46  today_date_week_5.0      0.0021\n",
       "42  today_date_week_1.0      0.0020\n",
       "36    two_date_week_2.0      0.0019\n",
       "27    one_date_week_0.0      0.0019\n",
       "34    two_date_week_0.0      0.0019\n",
       "28    one_date_week_1.0      0.0019\n",
       "35    two_date_week_1.0      0.0018\n",
       "43  today_date_week_2.0      0.0017"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_ml.importances_coef_pd().sort_values(by='importance')[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 测试集的验证与非均衡技术\n",
    "\n",
    "下面将前面保留切割的60条测试数据进行特征抽取组合，方式和抽取训练集时一样，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "btc_test0 = btc_siblings_df(btc_test_raw)\n",
    "btc_test1 = btc_siblings_df(btc_test_raw[1:])\n",
    "btc_test2 = btc_siblings_df(btc_test_raw[2:])\n",
    "btc_test = pd.concat([btc_test0, btc_test1, btc_test2])\n",
    "btc_test.index = np.arange(0, btc_test.shape[0])\n",
    "dummies_one_week = pd.get_dummies(btc_test['one_date_week'], prefix='one_date_week')\n",
    "dummies_two_week = pd.get_dummies(btc_test['two_date_week'], prefix='two_date_week')\n",
    "dummies_today_week = pd.get_dummies(btc_test['today_date_week'], prefix='today_date_week')\n",
    "btc_test.drop(['one_date_week', 'two_date_week', 'today_date_week'], inplace=True, axis=1)\n",
    "btc_test = pd.concat([btc_test, dummies_one_week, dummies_two_week, dummies_today_week], axis=1)\n",
    "matrix_test = btc_test.as_matrix()\n",
    "y_test = matrix_test[:, 0]\n",
    "x_test = matrix_test[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对测试集数据进行准确率评估，代码如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集正确率0.620690\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "y_predict = [btc_ml.predict(x_test[test_ind])[0] for test_ind in np.arange(0, matrix_test.shape[0])]\n",
    "print('测试集正确率{:3f}'.format(metrics.accuracy_score(y_test, y_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上所示上面的准确率结果为60%以上正确，下面使用predict_proba看看概率结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_prob = [btc_ml.predict_proba(x_test[test_ind])[0] for test_ind in np.arange(0, matrix_test.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.495,  0.505]),\n",
       " array([ 0.9075,  0.0925]),\n",
       " array([ 0.7875,  0.2125]),\n",
       " array([ 0.83,  0.17]),\n",
       " array([ 0.8375,  0.1625]),\n",
       " array([ 0.96,  0.04]),\n",
       " array([ 0.58,  0.42]),\n",
       " array([ 0.495,  0.505]),\n",
       " array([ 0.6575,  0.3425]),\n",
       " array([ 0.565,  0.435])]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本节开始的时候说过很多时候定估的决策只要能满足有时候能解出合理的解就可以，通过非均衡技术对决策进行二次逻辑过滤即可，下面解释这句话的意思，通过上面y_predict输出你可以看到准确率结果为60%以上，如果你只按照这个决策认定比特币今天是否有大行情还是有很多错误的可能，上面的y_prob输出了每一个决策的概率。\n",
    "\n",
    "那么我们如果希望做比特币交易只希望在决策模型有很大把握的情况下才做，否则宁可少赚点钱也不做应该怎么做呢？\n",
    "\n",
    "上面predict的决策在某一个值在0.5以上即进行了判断，如果我们希望把握更大一些就需要调整这个值，比如0.55以上才能认定决策，那首要的问题就是如何选定这个阀值，下面示例使用search_match_pos_threshold进行阀值的确定："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search pos threshold:threshold:0.57 accuracy:0.84, effect_rate:0.90:16.0%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0.580 satisfy require, accuracy:0.854, effect_rate:0.879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "search pos threshold:threshold:0.58 accuracy:0.85, effect_rate:0.88:18.0%\r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.57999999999999996"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_ml.search_match_pos_threshold(accuracy_match=0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果如上所示，search_match_pos_threshold函数的作用是对训练集数据进行非均衡结果度量，从0.5至0.99的范围内开始不断向上调整决策阀值：\n",
    "\n",
    "比如测试阀值为0.55，那么如果决策的概率为array([ 0.5378,  0.4622])，那么通过阀值二分化后结果为(0, 0)，这个结果将不计入正确率统计中，即认为未达成有效的决策，但如果决策的概率为 array([ 0.9711,  0.0289])，那么通过阀值二分化后结果为(1, 0)，认为达成有效的决策，当有效的决策正确率达到参数accuracy_match传递的值，本例中使用0.85即85%的正确率时，停止搜索，本例返回的结果为0.580，即使用阀值0.580可以达成非均衡决策的85%正确率。\n",
    "\n",
    "备注：与之对应search_match_neg_threshold进行阀值搜索，从0.5至0.01的范围内开始不断向下调整决策阀值，更多详情请阅读源代码。\n",
    "\n",
    "下面使用0.580做为阀值，通过predict_proba_threshold函数进行决策，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_prob_threshold = [btc_ml.predict_proba_threshold(x_test[test_ind], 0.580, 0)\n",
    "                    for test_ind in np.arange(0, matrix_test.shape[0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面predict_proba_threshold传递的第三个参数0为未达成有效决策的情况下返回的决策结果，即阀值二分化后结果为(0, 0)后这里返回0，因为在比特币这个示例中如果交易者想要保守的方式决策今天是否适合做日内交易，那么就希望只在有很大概率的情况下返回1，即适合交易，其它情况下没有太大把握下全部返回0即可，下面使用precision_score计算查准率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(y_test, y_prob_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上所示查准率100%正确，但召回率评分非常低，即比特币在很多有大行情的情况下为了保守，放弃了日交易，只在把握大的时候行动:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20000000000000001"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.recall_score(y_test, y_prob_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与上面的情况相反也有些激进的交易者想要的决策结果是只要今天不是很大把握说没有行情，那就进行交易。\n",
    "\n",
    "下面使用0.90做为阀值，通过predict_proba_threshold函数进行决策，第三个参数1即在为未达成有效决策的情况下返回的决策结果为1，可以看到结果的决策中大多数都被决策为1，代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    49\n",
       "0     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_prob_threshold = [btc_ml.predict_proba_threshold(x_test[test_ind], 0.90, 1)\n",
    "                    for test_ind in np.arange(0, matrix_test.shape[0])]\n",
    "pd.Series(y_prob_threshold).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 继承AbuMLPd对数据处理进行封装\n",
    "\n",
    "在abupy中不建议直接使用AbuML类进行构造，推荐使用继承AbuMLPd后实现make_xy方法，在make_xy中将数据训练集和测试集进行组装完成，AbuMLPd基类通过代理方法对AbuML中的所有方法进行代理，即可以和使用AbuML中的方法一样的接口操作，本节使用的示例内置在abupy项目中BtcBigWaveClf类，具体实现请直接阅读BtcBigWaveClf。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "小结：由于abupy中内置沙盒数据没有分时的数据，所以本示例使用日线数据做为分析对象，实际策略中应该使用的是分钟数据，本节示例主要为配合abupy中机器学习模块的使用示例所写，与真实的日内交易决策还有很大差别，在后面的章节涉及到实盘交易章节会具体详细的示例完整的一个日内交易策略，请关注公众号的更新提醒。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### abu量化文档目录章节\n",
    "\n",
    "1. [择时策略的开发](http://www.abuquant.com/lecture/lecture_1.html)\n",
    "2. [择时策略的优化](http://www.abuquant.com/lecture/lecture_2.html)\n",
    "3. [滑点策略与交易手续费](http://www.abuquant.com/lecture/lecture_3.html)\n",
    "4. [多支股票择时回测与仓位管理](http://www.abuquant.com/lecture/lecture_4.html)\n",
    "5. [选股策略的开发](http://www.abuquant.com/lecture/lecture_5.html)\n",
    "6. [回测结果的度量](http://www.abuquant.com/lecture/lecture_6.html)\n",
    "7. [寻找策略最优参数和评分](http://www.abuquant.com/lecture/lecture_7.html)\n",
    "8. [A股市场的回测](http://www.abuquant.com/lecture/lecture_8.html)\n",
    "9. [港股市场的回测](http://www.abuquant.com/lecture/lecture_9.html)\n",
    "10. [比特币，莱特币的回测](http://www.abuquant.com/lecture/lecture_10.html)\n",
    "11. [期货市场的回测](http://www.abuquant.com/lecture/lecture_11.html)\n",
    "12. [机器学习与比特币示例](http://www.abuquant.com/lecture/lecture_12.html)\n",
    "13. [量化技术分析应用](http://www.abuquant.com/lecture/lecture_13.html)\n",
    "14. [量化相关性分析应用](http://www.abuquant.com/lecture/lecture_14.html)\n",
    "15. [量化交易和搜索引擎](http://www.abuquant.com/lecture/lecture_15.html)\n",
    "16. [UMP主裁交易决策](http://www.abuquant.com/lecture/lecture_16.html)\n",
    "17. [UMP边裁交易决策](http://www.abuquant.com/lecture/lecture_17.html)\n",
    "18. [自定义裁判决策交易](http://www.abuquant.com/lecture/lecture_18.html)\n",
    "19. [数据源](http://www.abuquant.com/lecture/lecture_19.html)\n",
    "20. [A股全市场回测](http://www.abuquant.com/lecture/lecture_20.html)\n",
    "21. [A股UMP决策](http://www.abuquant.com/lecture/lecture_21.html)\n",
    "22. [美股全市场回测](http://www.abuquant.com/lecture/lecture_22.html)\n",
    "23. [美股UMP决策](http://www.abuquant.com/lecture/lecture_23.html)\n",
    "\n",
    "\n",
    "abu量化系统文档教程持续更新中，请关注公众号中的更新提醒。\n",
    "\n",
    "#### 《量化交易之路》目录章节及随书代码地址\n",
    "\n",
    "1. [第二章 量化语言——Python](https://github.com/bbfamily/abu/tree/master/ipython/第二章-量化语言——Python.ipynb)\n",
    "2. [第三章 量化工具——NumPy](https://github.com/bbfamily/abu/tree/master/ipython/第三章-量化工具——NumPy.ipynb)\n",
    "3. [第四章 量化工具——pandas](https://github.com/bbfamily/abu/tree/master/ipython/第四章-量化工具——pandas.ipynb)\n",
    "4. [第五章 量化工具——可视化](https://github.com/bbfamily/abu/tree/master/ipython/第五章-量化工具——可视化.ipynb)\n",
    "5. [第六章 量化工具——数学：你一生的追求到底能带来多少幸福](https://github.com/bbfamily/abu/tree/master/ipython/第六章-量化工具——数学.ipynb)\n",
    "6. [第七章 量化系统——入门：三只小猪股票投资的故事](https://github.com/bbfamily/abu/tree/master/ipython/第七章-量化系统——入门.ipynb)\n",
    "7. [第八章 量化系统——开发](https://github.com/bbfamily/abu/tree/master/ipython/第八章-量化系统——开发.ipynb)\n",
    "8. [第九章 量化系统——度量与优化](https://github.com/bbfamily/abu/tree/master/ipython/第九章-量化系统——度量与优化.ipynb)\n",
    "9. [第十章 量化系统——机器学习•猪老三](https://github.com/bbfamily/abu/tree/master/ipython/第十章-量化系统——机器学习•猪老三.ipynb)\n",
    "10. [第十一章 量化系统——机器学习•ABU](https://github.com/bbfamily/abu/tree/master/ipython/第十一章-量化系统——机器学习•ABU.ipynb)\n",
    "11. [附录A 量化环境部署](https://github.com/bbfamily/abu/tree/master/ipython/附录A-量化环境部署.ipynb)\n",
    "12. [附录B 量化相关性分析](https://github.com/bbfamily/abu/tree/master/ipython/附录B-量化相关性分析.ipynb)\n",
    "13. [附录C 量化统计分析及指标应用](https://github.com/bbfamily/abu/tree/master/ipython/附录C-量化统计分析及指标应用.ipynb)\n",
    "\n",
    "\n",
    "[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n",
    "\n",
    "更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n",
    "\n",
    "更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n",
    "\n",
    "更多关于abu量化系统请关注微信公众号: abu_quant\n",
    "\n",
    "![](./image/qrcode.jpg)"
   ]
  }
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