lzh
424 lines
120 KiB
Plaintext
424 lines
120 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 封装上述的建模预测过程"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" <th>diff_close</th>\n",
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" <th>max_min</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>date</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>1999-11-10</td>\n",
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" <td>3.3904</td>\n",
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" <td>174085100.0</td>\n",
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" <td>NaN</td>\n",
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" <td>0.3421</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1999-11-11</td>\n",
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" <td>3.3856</td>\n",
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" <td>29403500.0</td>\n",
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" <td>-0.0048</td>\n",
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" <td>0.1038</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1999-11-12</td>\n",
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" <td>3.4271</td>\n",
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" <td>15008000.0</td>\n",
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" <td>0.0415</td>\n",
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" <td>0.0647</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1999-11-15</td>\n",
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" <td>3.3904</td>\n",
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" <td>11921100.0</td>\n",
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" <td>-0.0367</td>\n",
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" <td>0.0672</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>1999-11-16</td>\n",
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" <td>3.2438</td>\n",
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" <td>23223100.0</td>\n",
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" <td>-0.1466</td>\n",
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" <td>0.1820</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2016-06-02</td>\n",
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" <td>18.0200</td>\n",
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" <td>17786525.0</td>\n",
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" <td>-0.1700</td>\n",
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" <td>0.3700</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2016-06-03</td>\n",
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" <td>18.0100</td>\n",
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" <td>18003512.0</td>\n",
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" <td>-0.0100</td>\n",
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" <td>0.2300</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2016-06-06</td>\n",
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" <td>18.0200</td>\n",
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" <td>17856247.0</td>\n",
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" <td>0.0100</td>\n",
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" <td>0.1400</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2016-06-07</td>\n",
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" <td>18.0200</td>\n",
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" <td>16533983.0</td>\n",
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" <td>0.0000</td>\n",
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" <td>0.1600</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2016-06-08</td>\n",
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" <td>17.9900</td>\n",
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" <td>23758300.0</td>\n",
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" <td>-0.0300</td>\n",
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" <td>0.1000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>4013 rows × 4 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Close Volume diff_close max_min\n",
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"date \n",
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"1999-11-10 3.3904 174085100.0 NaN 0.3421\n",
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"1999-11-11 3.3856 29403500.0 -0.0048 0.1038\n",
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"1999-11-12 3.4271 15008000.0 0.0415 0.0647\n",
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"1999-11-15 3.3904 11921100.0 -0.0367 0.0672\n",
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"1999-11-16 3.2438 23223100.0 -0.1466 0.1820\n",
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"... ... ... ... ...\n",
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"2016-06-02 18.0200 17786525.0 -0.1700 0.3700\n",
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"2016-06-03 18.0100 18003512.0 -0.0100 0.2300\n",
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"2016-06-06 18.0200 17856247.0 0.0100 0.1400\n",
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"2016-06-07 18.0200 16533983.0 0.0000 0.1600\n",
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"2016-06-08 17.9900 23758300.0 -0.0300 0.1000\n",
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"\n",
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"[4013 rows x 4 columns]"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd \n",
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"import numpy as np\n",
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"from datetime import datetime\n",
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"from sklearn.preprocessing import Imputer, PolynomialFeatures\n",
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"from sklearn.pipeline import Pipeline, FeatureUnion\n",
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"from sklearn.linear_model import LinearRegression\n",
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"from sklearn.metrics import r2_score, median_absolute_error,mean_squared_error \n",
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"from timeseriesutil import TimeSeriesDiff, TimeSeriesEmbedder, ColumnExtractor\n",
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"from sklearn.neural_network import MLPRegressor \n",
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"from sklearn.tree import DecisionTreeRegressor\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.svm import SVR\n",
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"import matplotlib.pyplot as plt \n",
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"import matplotlib \n",
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"import warnings \n",
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"warnings.filterwarnings('ignore')\n",
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"plt.rcParams['font.sans-serif']='SimHei'#设置中文字符的正常显示\n",
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"plt.rcParams['axes.unicode_minus'] = False#设置中文字体符号的正常显示\n",
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"\n",
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"data = pd.read_csv(\"600000.SH(1).csv\",encoding='gbk')#读取数据\n",
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"data['日期']=pd.to_datetime(data['日期'])#将日期转换为时间序列格式\n",
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"\n",
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"\n",
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"data['diff_close']=data[\"收盘价(元)\"]-data['前收盘价(元)']#新建特征变量\n",
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"data['max_min']=data[\"最高价(元)\"]-data[\"最低价(元)\"]#新建特征变量\n",
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"data=data[['日期','收盘价(元)',\"成交量(股)\",\"diff_close\",\"max_min\"]]#取时间列与收盘价数据作为特征列\n",
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"data.rename(columns={\"日期\":\"date\",'收盘价(元)':\"Close\",\"成交量(股)\":\"Volume\"},inplace=True)#修改两列的名称方便调用\n",
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"data=data.set_index(['date'])#将日期列作为索引\n",
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"data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"date\n",
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"1999-11-24 -0.000743\n",
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"1999-11-25 -0.001146\n",
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"1999-11-26 0.001891\n",
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"1999-11-29 -0.004518\n",
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"1999-11-30 0.002642\n",
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" ... \n",
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"2015-08-06 0.004572\n",
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"2015-08-07 0.005852\n",
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"2015-08-10 0.020039\n",
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"2015-08-11 -0.013308\n",
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"2015-08-12 -0.011561\n",
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"Name: Close, Length: 3803, dtype: float64"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"y\n",
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"y_train=y[10:n_train]\n",
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"y_train"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = data[\"Close\"].diff() / data[\"Close\"].shift()#股价变化率\n",
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"y[np.isnan(y)]=0#将缺失值赋值为0\n",
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"\n",
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"n_total = data.shape[0]#样本个数\n",
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"n_train = int(n_total*0.95)#划分95%的数据作为训练集\n",
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"\n",
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"data_train = data[:n_train]\n",
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"data_test = data[n_train:]\n",
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"y_train = y[10:n_train]\n",
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"y_test = y[n_train+10:]\n",
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"\n",
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"#选择特征变量收盘价\n",
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"def build_model(name,model):\n",
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" '''对收盘价列作数据预处理\n",
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" 1.选取特征列\n",
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" 2.作时间序列差分\n",
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" 3.将2中的结果转换为列表形式\n",
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" 4.填补上述计算中产生的缺失值\n",
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" '''\n",
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" pipeline_closing_price = Pipeline([(\"ColumnEx\", ColumnExtractor(\"Close\")),\n",
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" (\"Diff\", TimeSeriesDiff()),\n",
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" (\"Embed\", TimeSeriesEmbedder(10)),\n",
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" (\"ImputerNA\", Imputer())])\n",
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" pipeline_volume = Pipeline([(\"ColumnEx\", ColumnExtractor(\"Volume\")),\n",
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" (\"Diff\", TimeSeriesDiff()),\n",
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" (\"Embed\", TimeSeriesEmbedder(10)),\n",
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" (\"ImputerNA\", Imputer())])\n",
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" pipeline_diff_close = Pipeline([(\"ColumnEx\", ColumnExtractor(\"diff_close\")),\n",
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" (\"Diff\", TimeSeriesDiff()),\n",
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" (\"Embed\", TimeSeriesEmbedder(10)),\n",
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" (\"ImputerNA\", Imputer())])\n",
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" pipeline_max_min= Pipeline([(\"ColumnEx\", ColumnExtractor(\"max_min\")),\n",
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" (\"Diff\", TimeSeriesDiff()),\n",
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" (\"Embed\", TimeSeriesEmbedder(10)),\n",
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" (\"ImputerNA\", Imputer())])\n",
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" #将处理完毕的数据作为特征\n",
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" merged_features = FeatureUnion([(\"ClosingPriceFeature\", pipeline_closing_price),\n",
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" (\"VolumePriceFeature\", pipeline_volume),\n",
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" (\"DiffClosePriceFeature\", pipeline_diff_close),\n",
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" (\"MaxMinPriceFeature\", pipeline_max_min)])\n",
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" \n",
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" #建立模型，标准化特征值\n",
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" pipeline_2 = Pipeline([(\"MergedFeatures\", merged_features),\n",
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" ('StandardScaler',StandardScaler()),\n",
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" (\"Moder Method\",model)])\n",
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" if (name==\"逻辑回归\"):\n",
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" pipeline_2.fit(data_train, y_train.astype('int'))\n",
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" else:\n",
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" pipeline_2.fit(data_train, y_train)\n",
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" y_pred_2 = pipeline_2.predict(data_test)#预测结果\n",
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"\n",
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"\n",
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" #可视化结果\n",
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" plt.figure(figsize=(8,6))\n",
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" plot_train=(y_train+1)* data[\"Close\"].shift()\n",
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" plot_train.plot(style=\"b\", rot=10)#绘制测试集的数据\n",
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"\n",
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" plot_test=(y_test+1)* data[\"Close\"].shift()\n",
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" plot_test.plot(style=\"y\", rot=10)#绘制测试集的数据\n",
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"\n",
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" cc_2 = np.sign(y_pred_2)*y_test#每笔交易的理论收益\n",
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" cumulative_return_2 = (cc_2+1).cumprod()* data[\"Close\"].shift()\n",
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" cumulative_return_2.plot(style=\"r\", rot=10)#绘制预测的数据\n",
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" \n",
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" plt.legend(['训练集','测试集','预测数据'])\n",
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" plt.title('股价变化预测模型--'+name)\n",
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" plt.show()\n",
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" \n",
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" \n",
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" #模型评估\n",
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" print(\"预测值：\",cumulative_return_2.drop_duplicates().tolist()[1:])\n",
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" print(\"=============================================\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 分别使用支持向量机、决策树、神经网络、逻辑回归对股价进行预测"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 576x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"预测值： [11.760000000000002, 11.446779661016949, 12.062197922361948, 11.665301567262771, 11.952018951289597, 12.599153667235088, 12.55806053720497, 11.756841093931314, 12.129816742428446, 12.137871069614654, 11.961727821902567, 11.914665286209837, 11.838092887198206, 11.511114803684887, 11.250513886395773, 10.958860901654468, 10.857322812559062, 10.871770347905116, 10.85724560462134, 11.015956171599194, 11.487542255199426, 11.97653390325965, 12.268457115782978, 12.815605642931589, 12.735357642662759, 12.176319093105104, 12.322097330514682, 12.37634555775594, 12.25304448121541, 12.199336709538198, 12.490893184357349, 12.820841306208296, 12.844466935150939, 13.056967307239834, 13.121011291579453, 12.978649505414035, 13.034079979201648, 13.375618942702971, 14.391385873345662, 15.28016504842164, 15.65979647819609, 16.088951974522494, 15.716645647839329, 14.85252958336912, 14.72687366472809, 15.049236106697942, 14.89514836499114, 14.955002577709244, 15.349457920928636, 16.30273075328073, 17.359220567459815, 17.904339567412535, 18.477353582948826, 18.78773432554954, 17.698867160774427, 17.04098464369866, 17.160538232672263, 16.074777636717663, 17.616784709050172, 16.605039894525376, 15.50091256647382, 15.575476015484114, 16.792685452169767, 16.03241959338994, 16.624360202402254, 16.175752661911904, 16.16705602069582, 15.930464956978323, 16.087072477036003, 16.078223702296157, 16.069528070223523, 16.146415764339427, 15.927080962942965, 16.045059340446247, 16.41899080812614, 16.125794543695317, 15.6416868400026, 15.532947840580125, 15.143380845645618, 14.51546467703242, 14.26917053089777, 14.509662169058968, 14.146112727183885, 13.669365281682104, 13.997242689923475, 13.324990896925438, 13.684492117604949, 13.221807283829193, 13.883277803491726, 13.542393750280986, 13.550097045929613, 14.017606050342343, 14.135931368718897, 13.226314915427418, 13.585292099501142, 13.297941620310755, 13.024642761408302, 12.96209945883251, 13.133062401335097, 13.069694187094639, 12.961424531106875, 13.04783402798092, 13.468229635087704, 14.903435321668391, 15.894126108946924, 15.954658689340086, 14.674826989598984, 13.410167102690453, 13.449678496628845, 14.383019175220529, 14.957663889624168, 14.800040143699121, 15.10105790933368, 15.211099250601244, 15.303747141939345, 14.941179027832794, 14.58681019264426, 14.841997052673591, 14.599371396231914, 14.950760277975034, 15.362013282756008, 15.62850595861803, 15.837699568772427, 15.64604423845725, 15.232720009813361, 14.927387092022002, 14.910510453138992, 14.927454215017558, 14.986589574103647, 15.139687537970765, 15.26770067949814, 15.276278039430439, 15.28481704336528, 15.413549359845057, 15.602989631306842, 15.846243367206945, 15.636359349098242, 15.362037255254418, 15.344853768391717, 15.327727815525204, 15.301981352229248, 15.49005039678179, 15.707241232406885, 15.655230499849244, 15.352170174907474, 14.8846345383516, 14.674755314742582, 14.624470602070701, 14.56590578844685, 14.390614085548636, 14.291425476113494, 14.299715166064836, 14.523018988785338, 14.607210403213083, 14.515707376769265, 14.683035415694558, 14.767178885125473, 14.868151048442572, 14.995229262531822, 15.05463311360014, 15.267269174526696, 15.155389524431516, 15.358024259020016, 15.131306569029016, 14.98157401364555, 14.981574013645554, 14.989892488944633, 14.964937063047389]\n",
|
||
"=============================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 576x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"预测值： [13.02, 14.690847457627118, 16.067409200968523, 17.193736884584343, 18.509830508474575, 18.42322033898305, 17.425746569814365, 17.62469733656174, 18.183825665859562, 18.413236481033085, 18.899073855202825, 19.741786656615147, 19.614911421032787, 18.9992519288472, 18.56912656176463, 18.087749333980714, 17.92015933353351, 17.944005187603416, 17.920031900779634, 18.181985855475414, 18.96034511617804, 19.767432498333623, 20.249255740756123, 21.152331844772252, 21.019881426207615, 20.09718067809387, 20.337789646514658, 20.427327085210006, 20.22381697726271, 20.135171567844527, 20.616389504686094, 21.16097337839478, 21.199967801082234, 21.550702562497197, 21.656408031841146, 21.421437962779578, 21.51292671551872, 22.07664143140373, 23.753178599645207, 25.220120745941582, 25.846707596772426, 26.555034594670552, 25.940537926364126, 24.51430258038548, 24.306905773444818, 24.3909158164406, 24.19667644133812, 24.155005930819165, 24.79212191293939, 26.331828161701583, 28.038248310728054, 28.9187130654775, 29.844233263270855, 30.34555371703708, 28.586838351636864, 27.524240333403586, 27.717340778268262, 29.50076216882708, 30.433530104497393, 28.685710239553966, 28.983743592692186, 31.136998728653822, 30.784806455116346, 29.513593054233432, 28.546689069617358, 27.331936343250664, 26.90579324972686, 26.512049933877197, 26.77268177375147, 27.700447973831963, 28.674233327157587, 28.811430615804277, 28.42005277044852, 28.63057167985925, 29.297809579178015, 28.774634408121265, 27.910799627753235, 27.71676733087581, 27.021629623004323, 25.90118509905638, 25.461701389110704, 25.890831187803585, 25.24211882508022, 24.391417582909003, 23.750637909114705, 23.69631969777311, 24.16885627825656, 24.81982260781419, 25.090999508072073, 24.754959336088966, 25.022504402861262, 24.413925393063167, 22.779002983510818, 22.475282943730676, 23.085287607462817, 22.596997152780876, 22.13258441035731, 22.0263054936209, 22.316820314280168, 22.209139637251077, 22.02515859883598, 22.17199298949489, 22.88636507865522, 25.325189051516134, 27.00865537574986, 27.11151750177285, 24.93671826585828, 22.787700268963174, 22.85484140116401, 24.440853526867567, 25.417338861666035, 25.149491142103273, 25.661006216179953, 26.10690935110236, 26.208098922230665, 25.58719079349462, 25.036776871295892, 24.655290993460124, 24.25224508657718, 24.835966744821448, 25.519133738375277, 25.961827160809314, 26.309336280641677, 25.990961474165264, 25.30435379625666, 24.797139577623454, 24.769104371769725, 24.797251081283097, 24.89548580543079, 25.14980972398797, 25.3624631320262, 25.37671170681947, 25.390896565235803, 25.604744655341666, 25.91943984104978, 26.323529103605114, 25.974873088987824, 25.519173561110854, 25.4906286242416, 25.462179261937752, 25.41940964391658, 25.73182696914908, 26.09262095357349, 26.006221546442447, 25.502782523142955, 24.726119710976366, 24.377471660906195, 24.29393949073747, 24.196652547696758, 23.905460740532497, 23.740690189764898, 23.75446089173112, 24.125409673730392, 24.265267121114338, 24.11326373937615, 24.39122643377818, 24.53100423569095, 24.69873759798627, 24.909837919336578, 25.008518658122853, 25.361746322785606, 26.14804623696442, 26.497657997235812, 26.106495194652233, 25.84815714434781, 25.848157144347812, 25.86250925825361, 25.819452916536207]\n",
|
||
"=============================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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\n",
|
||
"text/plain": [
|
||
"<Figure size 576x432 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"预测值： [11.760000000000002, 11.446779661016949, 12.062197922361948, 11.665301567262771, 12.55822142037488, 12.499459689129809, 11.82271138241241, 11.957691969048414, 12.337039438411328, 12.492686018311602, 12.31139374100101, 12.262955470544613, 12.924965920111285, 12.56796748917287, 12.803718496408328, 12.47180097219813, 12.356244907598017, 12.372687016590165, 12.356157040616097, 12.53677860532942, 13.073470130908738, 13.06490859185772, 13.383360501088374, 13.980231478209776, 15.22330778998547, 14.500132014033063, 14.381353364894798, 14.607475273147857, 14.461946254237295, 14.398556383367126, 14.742672825233763, 15.057924948540649, 15.08567296748759, 15.33525211584676, 15.786565482836734, 15.577219828453124, 15.643748528147555, 16.05367003132266, 17.272812652170373, 18.339542174632196, 18.79518297400194, 18.847742210506418, 19.740803273924357, 19.450655376049582, 18.803397244528472, 19.21499234550317, 19.018251809542388, 19.094674176170862, 19.598318105129493, 20.81546494547106, 20.79220744273869, 21.44513003704234, 22.131464209266678, 23.20028032855935, 23.83719702288685, 22.95114736586679, 23.112164589696402, 21.649839966863063, 23.726646694659685, 23.65492887089749, 22.082029704417035, 23.722543936867048, 23.454216961745672, 22.392359377905446, 21.658756340563773, 21.07429586847799, 20.745718137195265, 20.4421222620168, 20.643082502713852, 21.358436846867303, 21.346885501898743, 21.993760820138103, 21.74095897163076, 21.533902219519995, 21.3970341969383, 21.0149443005644, 20.297042476632075, 20.15594004230255, 20.295762465221408, 19.454204193911746, 19.12411096456787, 19.44642744149879, 20.236844592517393, 19.59955169650994, 20.069672296883102, 19.105777229451327, 19.486772200595812, 20.01163082147146, 20.230274286455906, 19.73354880174381, 19.946823788793377, 20.635034338067356, 20.80921864592949, 20.5317623973171, 21.089017709668894, 21.5598653436098, 21.066127969328658, 20.96497009192492, 21.241486484024644, 22.16391533013428, 21.298342305407143, 21.827573841480902, 22.530848893652717, 24.931788239935997, 26.589103643168734, 26.69036791166829, 24.549351949106278, 22.726446140419252, 22.793406794928444, 21.64427300040358, 23.348346197850166, 22.73970933079583, 23.202211893456084, 23.605389342277352, 23.69688309941796, 23.13546934003781, 22.586751644714244, 22.242596851030676, 21.87899182922691, 22.40559220563237, 22.867827918757172, 22.624689289514645, 22.952766211727347, 22.67500996494077, 22.749886167356642, 22.293875086051052, 22.092234610428203, 22.11733942248551, 22.20495763185585, 22.431796018560394, 22.39386175246362, 22.607735713020862, 22.392905356888186, 22.43062501779254, 22.3053142635032, 22.653058583869615, 22.353018072824984, 21.960859861021046, 22.083683462033473, 22.059036493883873, 22.194898420777896, 21.748520575443816, 22.151045072914183, 22.126595795791754, 21.698260147090036, 21.566100201565124, 21.33499140075873, 21.334991400758735, 21.24955379217446, 21.407859923577483, 21.260304312386825, 21.371291979910886, 21.70502528439764, 22.359321698767882, 21.96459563233178, 21.964595632331775, 22.090467240539983, 22.241513170389826, 22.355572212289267, 22.34292050192577, 22.658498475116815, 23.360988563127766, 23.67333604252335, 23.76393929692339, 23.52878211861409, 23.528782118614092, 23.54184640629794, 23.50265354324639]\n",
|
||
"=============================================\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "ValueError",
|
||
"evalue": "This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||
"\u001b[1;32m<ipython-input-17-478c39f1bdec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mbuild_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||
"\u001b[1;32m<ipython-input-16-44e8d120525b>\u001b[0m in \u001b[0;36mbuild_model\u001b[1;34m(name, model)\u001b[0m\n\u001b[0;32m 45\u001b[0m (\"Moder Method\",model)])\n\u001b[0;32m 46\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m\"逻辑回归\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m \u001b[0mpipeline_2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'int'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 48\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[0mpipeline_2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m 354\u001b[0m self._log_message(len(self.steps) - 1)):\n\u001b[0;32m 355\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'passthrough'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 356\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 357\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m 1547\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdual\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1548\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_iter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1549\u001b[1;33m sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m 1550\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_iter_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn_iter_\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;32mE:\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_fit_liblinear\u001b[1;34m(X, y, C, fit_intercept, intercept_scaling, class_weight, penalty, dual, verbose, max_iter, tol, random_state, multi_class, loss, epsilon, sample_weight)\u001b[0m\n\u001b[0;32m 877\u001b[0m raise ValueError(\"This solver needs samples of at least 2 classes\"\n\u001b[0;32m 878\u001b[0m \u001b[1;34m\" in the data, but the data contains only one\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 879\u001b[1;33m \" class: %r\" % classes_[0])\n\u001b[0m\u001b[0;32m 880\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 881\u001b[0m \u001b[0mclass_weight_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompute_class_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclasses_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||
"\u001b[1;31mValueError\u001b[0m: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"models={\"支持向量机\":SVR(kernel='rbf',C=1000),\n",
|
||
" \"决策树\":DecisionTreeRegressor(max_depth=4),\n",
|
||
" \"神经网络\":MLPRegressor(random_state=0),\n",
|
||
" \"逻辑回归\":LogisticRegression()\n",
|
||
" }\n",
|
||
"\n",
|
||
"for name,model in models.items():\n",
|
||
" build_model(name,model)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|