C-2.1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"C-2.1.ipynb","provenance":[{"file_id":"1g5yo0pCH98NEvqmz5NSQTNH74v6fW7Nq","timestamp":1600512994624}],"collapsed_sections":[],"authorship_tag":"ABX9TyOdMTvCsXWNSmAuvJkNiZEM"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"p812PqDDqtmI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1600519416128,"user_tz":-480,"elapsed":2592,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"af85747d-64b3-4439-ff2e-db9b103bcd90"},"source":["#挂载云盘 \n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LDarN9lhquln","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":799},"executionInfo":{"status":"ok","timestamp":1600519417093,"user_tz":-480,"elapsed":3545,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"dc3221e8-2e43-48a8-996b-537490cfaf36"},"source":["#进入项目目录\n","print('original document')\n","!ls\n","import os\n","os.chdir('/content/drive/My Drive/colab/2020年C题')\n","print('current document')\n","!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["original document\n"," 2020年C题--面向康复工程的脑电信号分析和判别模型.docx\n","'~$20年C题--面向康复工程的脑电信号分析和判别模型.docx'\n"," C-1.ipynb\n"," data.rar\n"," get_plot_data_fastica.xlsx\n"," get_plot_data.xlsx\n"," S1-feature-important.csv\n"," S1-result.csv\n"," S1-result.gsheet\n"," S2-feature-important.csv\n"," S2-result.csv\n"," S2-result.gsheet\n"," S3-feature-important.csv\n"," S3-result.csv\n"," S3-result.gsheet\n"," S4-feature-important.csv\n"," S4-result.csv\n"," S4-result.gsheet\n"," S5-result.csv\n"," unrar.ipynb\n"," 附件1-P300脑机接口数据\n"," 附件2-睡眠脑电数据.xlsx\n","current document\n"," 2020年C题--面向康复工程的脑电信号分析和判别模型.docx\n","'~$20年C题--面向康复工程的脑电信号分析和判别模型.docx'\n"," C-1.ipynb\n"," data.rar\n"," get_plot_data_fastica.xlsx\n"," get_plot_data.xlsx\n"," S1-feature-important.csv\n"," S1-result.csv\n"," S1-result.gsheet\n"," S2-feature-important.csv\n"," S2-result.csv\n"," S2-result.gsheet\n"," S3-feature-important.csv\n"," S3-result.csv\n"," S3-result.gsheet\n"," S4-feature-important.csv\n"," S4-result.csv\n"," S4-result.gsheet\n"," S5-result.csv\n"," unrar.ipynb\n"," 附件1-P300脑机接口数据\n"," 附件2-睡眠脑电数据.xlsx\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yl7EZ4dgr2o-","colab_type":"code","colab":{}},"source":["#设置参数\n","import numpy as np\n","import pandas as pd\n","s=5\n","#第几个被试\n","train_data_path='附件1-P300脑机接口数据/S%d/S%d_train_data.xlsx'%(s,s)\n","train_event_path='附件1-P300脑机接口数据/S%d/S%d_train_event.xlsx'%(s,s)\n","cut_begin,cut_end=200,500#ms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jCbuLWZoquhF","colab_type":"code","colab":{}},"source":["#字典，将char转化为相对应的行列编号\n","'''\n","char_dict:\n","key:'B'\n","value:[6,12]\n","'''\n","char_list=[chr(i) for i in range(65,91)]+[chr(i) for i in range(49,58)]+[chr(i) for i in range(48,49)]\n","record=[]\n","for i in range(6):\n","  record.append(char_list[i*6:(i+1)*6])\n","char_dict={}\n","for i in range(6):\n","  for j in range(6):\n","    char_dict[record[i][j]]=[i+1,j+7]\n","\n","#字典，将表头转化为相对应char\n","'''\n","event_dict:\n","key:102\n","value:'B'\n","'''\n","event_dict={}\n","record=[]\n","for i in range(len(char_list)):\n","  event_dict[i+101]=char_list[i]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gSzjtQwpg-3G","colab_type":"code","colab":{}},"source":["from scipy import stats #scipy中的stats可以做统计推断\n","#实现对原始数据对预处理 去均值等\n","def raw_data_preprocessing(data):\n","  #data=(data-data.min())/(data.max()-data.min())\n","  data=data-data.mean()\n","  return data\n","#实现对训练集数据进行预处理---将每个通道的时间序列，先归一化，后转化为一个特征，如均值，去均值\n","def data_preprocessing(cut):\n","  record=[]\n","  for i in cut.columns:\n","    temp_column=cut[i]\n","    temp_column=(temp_column-temp_column.min())/(temp_column.max()-temp_column.min())#预处理--归一化\n","    mu = np.mean(temp_column, axis=0) #axis=0，那么输出矩阵是1行，求每一列的平均；axis=1，输出矩阵是1列，求每一行的平均\n","    #sigma = np.std(temp_column, axis=0) #求标准差，这里除的是N\n","    #skew = stats.skew(temp_column) #求偏度\n","    #kurtosis = stats.kurtosis(temp_column) #求峰度 \n","    #median=temp_column.median()\n","    #quantile=temp_column.quantile(q=0.5)\n","    #skew=temp_column.skew()\n","    #mean_column=temp_column.mean()\n","    record.append(mu)\n","  return np.array(record)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5VMiliqdquYI","colab_type":"code","colab":{}},"source":["from sklearn.decomposition import FastICA\n","#进行fastica操作\n","def fastica(data):\n","  transformer = FastICA(n_components=15,random_state=0)\n","  data = transformer.fit_transform(data)\n","  data=pd.DataFrame(data)\n","  return data\n","#是否是当前的char\n","def is_current_char(action,char_dict,current_char):\n","  result=0\n","  if action in char_dict[current_char]:\n","    result=1\n","  return result\n","#根据当前char生成对应数据集\n","def data_generate(data,event,cut_begin,cut_end):\n","  '''\n","  input:\n","  current_char 当前字符\n","  data 数据集\n","  event 数据集\n","  cut_begin 截断区间\n","  cut_end 截断区间\n","\n","  output:\n","  feature 特征\n","  label 标签\n","  '''\n","  record=[]\n","  record2=[]\n","  #data=fastica(data)\n","  current_char_code=event.columns[0]\n","  current_char=event_dict[current_char_code]\n","  for i in event.index:\n","    if event.iloc[i,0]==100:\n","      continue\n","    action_time=event.iloc[i,1]\n","    action=event.iloc[i,0]\n","    cut=data.iloc[action_time+cut_begin//4:action_time+cut_end//4,:]\n","    cut_preprocessing=data_preprocessing(cut)\n","    label=is_current_char(action,char_dict,current_char)\n","    record.append(cut_preprocessing)\n","    record2.append(label)\n","  record=np.array(record)\n","  record2=np.array(record2)\n","  #print(event.shape,record.shape,record2.shape,'如果前三者行数相等，说明数据集构建没有问题')\n","  return record,record2\n","#获取一组可以画图的数据 原始数据\n","def get_plot_data(data,event,cut_begin,cut_end):\n","  '''\n","  input:\n","  current_char 当前字符\n","  data 数据集\n","  event 数据集\n","  cut_begin 截断区间\n","  cut_end 截断区间\n","\n","  output:\n","  保存为excel\n","  '''\n","  record=[]\n","  record2=[]\n","  data=fastica(data)\n","  current_char_code=event.columns[0]\n","  current_char=event_dict[current_char_code]\n","  if current_char!='B':\n","    return\n","  writer=pd.ExcelWriter('get_plot_data_fastica.xlsx')\n","  for i in event.index:\n","    if event.iloc[i,0]==100:\n","      continue\n","    action_time=event.iloc[i,1]\n","    action=event.iloc[i,0]\n","    cut=data.iloc[action_time+cut_begin//4:action_time+cut_end//4,:]\n","    #cut_preprocessing=data_preprocessing(cut)\n","    label=is_current_char(action,char_dict,current_char)\n","    cut.to_excel(writer,sheet_name='id=%d,label=%d'%(i,label),index=0)\n","    writer.save() \n","    writer.close()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ECZOus7Hyhmn","colab_type":"code","colab":{}},"source":["#对所有sheet都生成对应的训练测试集，并汇总\n","#代码结构还是有点差，比赛期间没时间改啦\n","def data_generate_sum(train_data_path,train_event_path,cut_begin,cut_end):\n","  df = pd.read_excel(train_data_path, sheet_name=None)\n","  sheet_num=len(list(df))#计算sheet的个数\n","  train_data_record=[]\n","  train_label_record=[]\n","  for sheet_name in range(sheet_num):\n","    data=pd.read_excel(train_data_path,sheet_name,header=None) \n","    data=raw_data_preprocessing(data)\n","    #print(data.head())\n","    event=pd.read_excel(train_event_path,sheet_name) \n","    #print(event.head())\n","    train_data,train_label=data_generate(data,event,cut_begin,cut_end)\n","    #get_plot_data(data,event,cut_begin,cut_end)#获取画图数据\n","    train_data_record.append(train_data)\n","    train_label_record.append(train_label)\n","  train_data_record_array=train_data_record[0]\n","  for inst in train_data_record[1:]:\n","    train_data_record_array=np.vstack([train_data_record_array,inst])\n","  train_label_record_array=train_label_record[0]\n","  for inst in train_label_record[1:]:\n","    train_label_record_array=np.hstack([train_label_record_array,inst])\n","  return train_data_record_array,train_label_record_array"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iK66KkZ_mtDk","colab_type":"code","colab":{}},"source":["real_train_data,real_train_label=data_generate_sum(train_data_path,train_event_path,cut_begin,cut_end)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GfOr-kTcms-t","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1600519492839,"user_tz":-480,"elapsed":79251,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"747e0d54-d8b9-4401-aa0f-fd728ca9d589"},"source":["print('通过shape看一下数据集的特征和标签有没有什么问题',real_train_data.shape,real_train_label.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["通过shape看一下数据集的特征和标签有没有什么问题 (720, 20) (720,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ax6VeDdKLjFM","colab_type":"code","colab":{}},"source":["#dataset=pd.DataFrame(real_train_data)\n","#plt.figure(figsize=(10,100))\n","#sns.heatmap(dataset.iloc[:,:],annot=False,cmap=\"YlGnBu\")\n","#plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BzhHiXvu8p4a","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":289},"executionInfo":{"status":"ok","timestamp":1600519492841,"user_tz":-480,"elapsed":79235,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"d3752eba-6f05-455f-cc34-30f3072d602b"},"source":["#归一化 \n","real_train_data_norm=(real_train_data-real_train_data.min())/(real_train_data.max()-real_train_data.min())\n","#不归一化 \n","#real_train_data_norm=real_train_data\n","real_train_data_norm[1:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.59260081, 0.62293642, 0.51492129, 0.67270396, 0.71369737,\n","        0.26608815, 0.5390489 , 0.17497011, 0.70834001, 0.2767879 ,\n","        0.41759012, 0.28820149, 0.31686379, 0.55310176, 0.27808303,\n","        0.54262589, 0.28473208, 0.26939104, 0.23493807, 0.2764381 ],\n","       [0.98822557, 1.        , 0.82242579, 0.81152358, 0.81469365,\n","        0.24021389, 0.78353689, 0.18611412, 0.51607747, 0.28314035,\n","        0.28857253, 0.29680386, 0.06629869, 0.13423863, 0.29517366,\n","        0.1469851 , 0.29805566, 0.29380732, 0.11917657, 0.30397216],\n","       [0.6972054 , 0.68435887, 0.65872962, 0.68041145, 0.71611859,\n","        0.49086601, 0.4544821 , 0.52677024, 0.61269051, 0.39830811,\n","        0.56006291, 0.41011642, 0.43515626, 0.431227  , 0.40375334,\n","        0.50875652, 0.43656545, 0.42750054, 0.57580655, 0.44616975],\n","       [0.57407865, 0.56479674, 0.50695808, 0.50600846, 0.7018772 ,\n","        0.7686258 , 0.53113112, 0.71951117, 0.46680164, 0.71349628,\n","        0.65557115, 0.63437779, 0.60254339, 0.58172464, 0.67001553,\n","        0.57951352, 0.60784802, 0.66232081, 0.73939177, 0.63531529]])"]},"metadata":{"tags":[]},"execution_count":232}]},{"cell_type":"code","metadata":{"id":"K2VtzNDYAnDm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":439},"executionInfo":{"status":"ok","timestamp":1600519492841,"user_tz":-480,"elapsed":79225,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"1b655f2e-ca86-4c6f-dd83-be5df69e9be3"},"source":["## 下采样\n","train_y=np.array(real_train_label)\n","train=pd.concat([pd.DataFrame(real_train_data_norm),pd.DataFrame(real_train_label)],axis=1)\n","# 计算少数类个数\n","number_records_is_weiyue = len(train_y[train_y == 1])\n","def xuhao(train_y,symbol):\n","    '''\n","    输入：一列array，一个符号\n","    输出：该符号对应的位置序号list\n","    '''\n","    i=0\n","    record=[]\n","    for j in train_y:\n","      if(j==symbol):\n","        record.append(i)\n","      i=i+1\n","    return record\n","# 取得少数类样本的索引\n","is_weiyue_indices = np.array(xuhao(train_y,1))\n","# 取得多数类样本的索引\n","normal_indices = np.array(xuhao(train_y,0))\n","# 从多数类中随机选择与少数类个数相同的样本数作为样本\n","random_normal_indices = np.random.choice(normal_indices, number_records_is_weiyue, replace = False)\n","random_normal_indices = np.array(random_normal_indices)\n","# 合并随机取得的0类和全部的1类的索引\n","under_sample_indices = np.concatenate([is_weiyue_indices,random_normal_indices])\n","# 根据索引得到下采样后的数据集\n","under_sample_train = train.iloc[under_sample_indices,:]\n","#打乱\n","from sklearn.utils import shuffle\n","under_sample_train = shuffle(under_sample_train)\n","under_sample_train=pd.DataFrame(under_sample_train.values)\n","under_sample_train#最终的数据集"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: 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<td>0.422262</td>\n","      <td>0.747502</td>\n","      <td>0.377534</td>\n","      <td>0.650919</td>\n","      <td>0.536628</td>\n","      <td>0.520223</td>\n","      <td>0.691238</td>\n","      <td>0.510381</td>\n","      <td>0.640440</td>\n","      <td>0.644718</td>\n","      <td>0.554441</td>\n","      <td>0.637030</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.282765</td>\n","      <td>0.278850</td>\n","      <td>0.394043</td>\n","      <td>0.334595</td>\n","      <td>0.349874</td>\n","      <td>0.758207</td>\n","      <td>0.453421</td>\n","      <td>0.801428</td>\n","      <td>0.459553</td>\n","      <td>0.672269</td>\n","      <td>0.414725</td>\n","      <td>0.696420</td>\n","      <td>0.457927</td>\n","      <td>0.373206</td>\n","      <td>0.634758</td>\n","      <td>0.423886</td>\n","      <td>0.662751</td>\n","      <td>0.649024</td>\n","      <td>0.596323</td>\n","      <td>0.645486</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.518210</td>\n","      <td>0.512796</td>\n","      <td>0.249536</td>\n","      <td>0.659048</td>\n","      <td>0.461695</td>\n","      <td>0.646797</td>\n","      <td>0.720629</td>\n","      <td>0.553777</td>\n","      <td>0.664491</td>\n","      <td>0.525204</td>\n","      <td>0.756054</td>\n","      <td>0.596637</td>\n","      <td>0.383439</td>\n","      <td>0.660626</td>\n","      <td>0.570908</td>\n","      <td>0.664041</td>\n","      <td>0.588799</td>\n","      <td>0.604998</td>\n","      <td>0.623311</td>\n","      <td>0.592245</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.560955</td>\n","      <td>0.583916</td>\n","      <td>0.290515</td>\n","      <td>0.386459</td>\n","      <td>0.589036</td>\n","      <td>0.534876</td>\n","      <td>0.539149</td>\n","      <td>0.497685</td>\n","      <td>0.585465</td>\n","      <td>0.625351</td>\n","      <td>0.546028</td>\n","      <td>0.608935</td>\n","      <td>0.507743</td>\n","      <td>0.573692</td>\n","      <td>0.609460</td>\n","      <td>0.598676</td>\n","      <td>0.599026</td>\n","      <td>0.585842</td>\n","      <td>0.586059</td>\n","      <td>0.594573</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.666212</td>\n","      <td>0.682610</td>\n","      <td>0.384533</td>\n","      <td>0.595933</td>\n","      <td>0.589765</td>\n","      <td>0.293278</td>\n","      <td>0.399600</td>\n","      <td>0.373050</td>\n","      <td>0.530836</td>\n","      <td>0.369666</td>\n","      <td>0.409678</td>\n","      <td>0.397533</td>\n","      <td>0.411500</td>\n","      <td>0.585586</td>\n","      <td>0.341812</td>\n","      <td>0.557355</td>\n","      <td>0.371744</td>\n","      <td>0.335428</td>\n","      <td>0.341243</td>\n","      <td>0.376970</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>235</th>\n","      <td>0.253697</td>\n","      <td>0.248490</td>\n","      <td>0.497957</td>\n","      <td>0.450024</td>\n","      <td>0.332838</td>\n","      <td>0.643068</td>\n","      <td>0.578728</td>\n","      <td>0.689181</td>\n","      <td>0.569537</td>\n","      <td>0.735418</td>\n","      <td>0.529302</td>\n","      <td>0.679130</td>\n","      <td>0.653738</td>\n","      <td>0.535998</td>\n","      <td>0.699537</td>\n","      <td>0.557150</td>\n","      <td>0.679715</td>\n","      <td>0.701854</td>\n","      <td>0.645549</td>\n","      <td>0.677451</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>236</th>\n","      <td>0.612472</td>\n","      <td>0.610029</td>\n","      <td>0.498165</td>\n","      <td>0.476511</td>\n","      <td>0.613850</td>\n","      <td>0.408262</td>\n","      <td>0.566972</td>\n","      <td>0.364241</td>\n","      <td>0.422149</td>\n","      <td>0.412242</td>\n","      <td>0.558390</td>\n","      <td>0.461241</td>\n","      <td>0.767730</td>\n","      <td>0.489324</td>\n","      <td>0.449180</td>\n","      <td>0.477478</td>\n","      <td>0.504267</td>\n","      <td>0.472396</td>\n","      <td>0.504624</td>\n","      <td>0.464757</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>237</th>\n","      <td>0.373469</td>\n","      <td>0.337407</td>\n","      <td>0.249389</td>\n","      <td>0.484046</td>\n","      <td>0.755315</td>\n","      <td>0.620562</td>\n","      <td>0.510366</td>\n","      <td>0.595478</td>\n","      <td>0.567633</td>\n","      <td>0.665709</td>\n","      <td>0.569137</td>\n","      <td>0.693927</td>\n","      <td>0.686812</td>\n","      <td>0.601238</td>\n","      <td>0.722469</td>\n","      <td>0.611099</td>\n","      <td>0.702652</td>\n","      <td>0.703592</td>\n","      <td>0.700284</td>\n","      <td>0.689731</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>238</th>\n","      <td>0.422661</td>\n","      <td>0.393426</td>\n","      <td>0.471722</td>\n","      <td>0.507279</td>\n","      <td>0.633660</td>\n","      <td>0.492483</td>\n","      <td>0.308929</td>\n","      <td>0.433919</td>\n","      <td>0.380322</td>\n","      <td>0.445552</td>\n","      <td>0.271300</td>\n","      <td>0.366247</td>\n","      <td>0.499696</td>\n","      <td>0.412064</td>\n","      <td>0.441353</td>\n","      <td>0.374683</td>\n","      <td>0.400112</td>\n","      <td>0.359871</td>\n","      <td>0.403019</td>\n","      <td>0.412851</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>239</th>\n","      <td>0.552067</td>\n","      <td>0.569419</td>\n","      <td>0.576338</td>\n","      <td>0.524339</td>\n","      <td>0.491101</td>\n","      <td>0.496328</td>\n","      <td>0.556040</td>\n","      <td>0.518457</td>\n","      <td>0.637864</td>\n","      <td>0.454322</td>\n","      <td>0.486399</td>\n","      <td>0.459820</td>\n","      <td>0.504437</td>\n","      <td>0.397261</td>\n","      <td>0.456108</td>\n","      <td>0.396781</td>\n","      <td>0.431382</td>\n","      <td>0.434864</td>\n","      <td>0.437779</td>\n","      <td>0.377840</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>240 rows × 21 columns</p>\n","</div>"],"text/plain":["           0         1         2         3   ...        17        18        19   20\n","0    0.546849  0.514773  0.645931  0.614940  ...  0.644718  0.554441  0.637030  1.0\n","1    0.282765  0.278850  0.394043  0.334595  ...  0.649024  0.596323  0.645486  1.0\n","2    0.518210  0.512796  0.249536  0.659048  ...  0.604998  0.623311  0.592245  1.0\n","3    0.560955  0.583916  0.290515  0.386459  ...  0.585842  0.586059  0.594573  0.0\n","4    0.666212  0.682610  0.384533  0.595933  ...  0.335428  0.341243  0.376970  0.0\n","..        ...       ...       ...       ...  ...       ...       ...       ...  ...\n","235  0.253697  0.248490  0.497957  0.450024  ...  0.701854  0.645549  0.677451  0.0\n","236  0.612472  0.610029  0.498165  0.476511  ...  0.472396  0.504624  0.464757  0.0\n","237  0.373469  0.337407  0.249389  0.484046  ...  0.703592  0.700284  0.689731  1.0\n","238  0.422661  0.393426  0.471722  0.507279  ...  0.359871  0.403019  0.412851  1.0\n","239  0.552067  0.569419  0.576338  0.524339  ...  0.434864  0.437779  0.377840  0.0\n","\n","[240 rows x 21 columns]"]},"metadata":{"tags":[]},"execution_count":233}]},{"cell_type":"code","metadata":{"id":"GrhV3By91VY3","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import cross_val_score\n","X,Y=under_sample_train.iloc[:,:-1],under_sample_train.iloc[:,-1]\n","#输出五折交叉验证的结果\n","def val_result(model,X,Y):\n","  accuracy=cross_val_score(model, X, Y, cv=5,scoring='accuracy').mean()\n","  precision_weighted=cross_val_score(model, X, Y, cv=5,scoring='precision_weighted').mean()\n","  recall_weighted=cross_val_score(model, X, Y, cv=5,scoring='recall_weighted').mean()\n","  f1_weighted=cross_val_score(model, X, Y, cv=5,scoring='f1_weighted').mean()\n","  print('accuracy')\n","  print(accuracy)\n","  print('precision_weighted')\n","  print(precision_weighted)\n","  print('recall_weighted')\n","  print(recall_weighted)\n","  print('f1_weighted')\n","  print(f1_weighted)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mHdozE0R330e","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519492842,"user_tz":-480,"elapsed":79210,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"e96ad164-f477-425a-e205-7ec3012d2cf4"},"source":["#逻辑回归\n","from sklearn.linear_model import LogisticRegression\n","model = LogisticRegression()\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.5125\n","precision_weighted\n","0.511616287094548\n","recall_weighted\n","0.5125\n","f1_weighted\n","0.5100514915391586\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_-n3-nn54d1G","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519492843,"user_tz":-480,"elapsed":79201,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"c781f228-69f8-460b-b07a-107e35535dea"},"source":["#决策树方法\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn import metrics\n","model=DecisionTreeClassifier()\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.4833333333333333\n","precision_weighted\n","0.4744202078115122\n","recall_weighted\n","0.5\n","f1_weighted\n","0.4980537815957867\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"b6EKKue-33_c","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519495376,"user_tz":-480,"elapsed":81724,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"9d1d45ab-f02d-404e-e5d5-b5001028f1e7"},"source":["#随机森林方法\n","from sklearn.ensemble import RandomForestClassifier\n","model = RandomForestClassifier(max_depth=2, random_state=0)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.4875\n","precision_weighted\n","0.4880782148608235\n","recall_weighted\n","0.4875\n","f1_weighted\n","0.4853975448541016\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-N6a6AOH34Fl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519499188,"user_tz":-480,"elapsed":85527,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"9465588b-3e33-4864-ac9f-53900f94e686"},"source":["#使用adaboost分类算法\n","from sklearn.ensemble import AdaBoostClassifier\n","model = AdaBoostClassifier(n_estimators=100, random_state=0)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.4916666666666666\n","precision_weighted\n","0.4916083916083916\n","recall_weighted\n","0.4916666666666666\n","f1_weighted\n","0.4899928162230264\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ac3UHaSk34LR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519499189,"user_tz":-480,"elapsed":85513,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"3c05d70b-7028-4f6f-a08d-d948ff43379a"},"source":["#knn k近邻分类算法\n","from sklearn.neighbors import KNeighborsClassifier\n","model = KNeighborsClassifier(n_neighbors=7)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.5625\n","precision_weighted\n","0.5631652406879267\n","recall_weighted\n","0.5625\n","f1_weighted\n","0.5594673828438638\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"h7HmY8ZZ34RI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519501754,"user_tz":-480,"elapsed":88066,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"313d1a66-df5d-4dc7-d0b5-3e5a8505a39b"},"source":["#神经网络分类方法\n","from sklearn.neural_network import MLPClassifier\n","model = MLPClassifier(hidden_layer_sizes=(8, ),random_state=1, max_iter=300)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.5333333333333334\n","precision_weighted\n","0.5423827590262181\n","recall_weighted\n","0.5333333333333334\n","f1_weighted\n","0.5217143290115213\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uy3ia6rj34OF","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519501755,"user_tz":-480,"elapsed":88059,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"bc0a97a2-8613-4180-c059-2488bc32ba64"},"source":["# 支持向量机SVM\n","from sklearn.svm import SVC  \n","model = SVC(kernel='rbf') \n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.525\n","precision_weighted\n","0.5261025994939038\n","recall_weighted\n","0.525\n","f1_weighted\n","0.5233504148075934\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"DYbHu_HA34IG","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519501757,"user_tz":-480,"elapsed":88047,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"4925e1b4-633f-4cf4-eb1e-ef428bb0d72c"},"source":["#线性支持向量机SVM\n","from sklearn.svm import LinearSVC\n","model = LinearSVC() \n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.5166666666666666\n","precision_weighted\n","0.5167394344785649\n","recall_weighted\n","0.5166666666666666\n","f1_weighted\n","0.5156397078720479\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7YeHckx334C5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519501757,"user_tz":-480,"elapsed":88036,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"12fe10d6-b024-44f2-ef2e-8772e1563ee4"},"source":["#使用bagging的集成策略分类 元分类器是KNN\n","from sklearn.ensemble import BaggingClassifier\n","model = BaggingClassifier(base_estimator=KNeighborsClassifier(n_neighbors=7),\n","                        n_estimators=10, random_state=0)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.575\n","precision_weighted\n","0.5761536691971474\n","recall_weighted\n","0.575\n","f1_weighted\n","0.5731656255882968\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wB_U8PU9338L","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519503357,"user_tz":-480,"elapsed":89628,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"b00cee11-01c0-49de-ae07-e586271cdd7e"},"source":["#使用bagging的集成策略分类 元分类器是logistic\n","from sklearn.ensemble import BaggingClassifier\n","model = BaggingClassifier(base_estimator=LogisticRegression(),\n","                        n_estimators=10, random_state=0)\n","val_result(model,X,Y)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.4791666666666667\n","precision_weighted\n","0.4747525676937442\n","recall_weighted\n","0.4791666666666667\n","f1_weighted\n","0.469391669344696\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SupckyChzzQQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":153},"executionInfo":{"status":"ok","timestamp":1600519504400,"user_tz":-480,"elapsed":90651,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"853a97bd-51da-4e66-e271-ae260f8cc1de"},"source":["#神经网络分类方法\n","from sklearn.neural_network import MLPClassifier\n","model = MLPClassifier(hidden_layer_sizes=(8, ),random_state=1, max_iter=300)\n","val_result(model,X,Y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["accuracy\n","0.5333333333333334\n","precision_weighted\n","0.5423827590262181\n","recall_weighted\n","0.5333333333333334\n","f1_weighted\n","0.5217143290115213\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vR4XkkjtQ6Sy","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":85},"executionInfo":{"status":"ok","timestamp":1600519504401,"user_tz":-480,"elapsed":90605,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"dad5ef94-86eb-491a-c0d2-d6bdc3fbc3e1"},"source":["from sklearn.ensemble import RandomForestClassifier\n","model = RandomForestClassifier(max_depth=2, random_state=0)\n","model.fit(X,Y)\n","feature_important=model.feature_importances_\n","print('特征重要程度',feature_important)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["特征重要程度 [0.0548067  0.0817961  0.03219092 0.03899054 0.04704116 0.04209006\n"," 0.04061598 0.04383353 0.05576649 0.02214927 0.06190328 0.02655374\n"," 0.07666238 0.08536379 0.04510848 0.06914067 0.04258213 0.0559988\n"," 0.0276097  0.04979627]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I5yoj5l1Q6cr","colab_type":"code","colab":{}},"source":["\n","pd.DataFrame([feature_important]).to_csv('S%d-feature-important.csv'%s)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6mENSVZBQ6W4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":408},"executionInfo":{"status":"ok","timestamp":1600519595908,"user_tz":-480,"elapsed":942,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"a10e2e86-f77f-4128-bcbe-f532536a2441"},"source":["!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" 2020年C题--面向康复工程的脑电信号分析和判别模型.docx\n","'~$20年C题--面向康复工程的脑电信号分析和判别模型.docx'\n"," C-1.ipynb\n"," data.rar\n"," get_plot_data_fastica.xlsx\n"," get_plot_data.xlsx\n"," S1-feature-important.csv\n"," S1-result.csv\n"," S1-result.gsheet\n"," S2-feature-important.csv\n"," S2-result.csv\n"," S2-result.gsheet\n"," S3-feature-important.csv\n"," S3-result.csv\n"," S3-result.gsheet\n"," S4-feature-important.csv\n"," S4-result.csv\n"," S4-result.gsheet\n"," S5-feature-important.csv\n"," S5-result.csv\n"," unrar.ipynb\n"," 附件1-P300脑机接口数据\n"," 附件2-睡眠脑电数据.xlsx\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-7v6XzqWVsSy","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gGOW6t1DVsVp","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"W7lkC84iVsYI","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"c9cu4qr1Vsah","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6CGRvoWUVscu","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hv-cYyKzsbJE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"executionInfo":{"status":"ok","timestamp":1600518036388,"user_tz":-480,"elapsed":94190,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"0234ec7a-b01c-4e26-d9a9-2910c255219b"},"source":["#实际预测\n","#设置参数\n","import numpy as np\n","import pandas as pd\n","test_data_path='附件1-P300脑机接口数据/S%d/S%d_test_data.xlsx'%(s,s)\n","test_event_path='附件1-P300脑机接口数据/S%d/S%d_test_event.xlsx'%(s,s)\n","#最后采用的算法\n","model = AdaBoostClassifier(n_estimators=100, random_state=0)\n","model.fit(X,Y)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n","                   n_estimators=100, random_state=0)"]},"metadata":{"tags":[]},"execution_count":112}]},{"cell_type":"code","metadata":{"id":"R-HX_YW87aYc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":399},"executionInfo":{"status":"error","timestamp":1600518042190,"user_tz":-480,"elapsed":99976,"user":{"displayName":"Expss Xu","photoUrl":"","userId":"17480852382145563764"}},"outputId":"03b605a2-9405-4915-bb91-3219960a3a55"},"source":["#! pip install random\n","import random \n","bad_char=list('BDGLOQSVZ479')\n","good_char=set(char_list).difference(set(bad_char))\n","def random_generate(char_current):\n","  if char_current in bad_char:\n","    return random.sample(good_char,1)[0]\n","  return char_current\n","#生成测试集数据\n","def data_generate_test(data,event,cut_begin,cut_end):\n","  '''\n","  input:\n","  current_char 当前字符\n","  data 数据集\n","  event 数据集\n","  cut_begin 截断区间\n","  cut_end 截断区间\n","\n","  output:\n","  feature 特征\n","  label 标签\n","  '''\n","  record=[]\n","  record2=[]  \n","  data=fastica(data)\n","  for i in event.index:\n","    if event.iloc[i,0]==100:\n","      continue\n","    action_time=event.iloc[i,1]\n","    action=event.iloc[i,0]\n","    cut=data.iloc[action_time+cut_begin//4:action_time+cut_end//4,:]\n","    cut_preprocessing=data_preprocessing(cut)#预处理部分可以直接调用训练集的\n","    label=action\n","    record.append(cut_preprocessing)\n","    record2.append(label)\n","  record=np.array(record)\n","  record2=record2\n","  return record,record2\n","#从行列action组合映射到char\n","def action2char(col,row,char_dict):\n","  for char in char_dict.keys():\n","    if char_dict[char]==[col,row]:\n","      return char\n","#当前列表是否足够进行预测\n","def action_enough(record_list):\n","  col=[1,2,3,4,5,6]\n","  row=[7,8,9,10,11,12]\n","  for i in record_list:\n","    if i in col:\n","      for j in record_list:\n","        if j in row:\n","          return action2char(i,j,char_dict)\n","  return False\n","#预测时的预测逻辑，怎么从行列映射到字符\n","def calculate(real_test_label,predict):\n","  '''\n","  输入为 当下的action和对应的标签\n","  输出为 预测的轮数，预测的字符\n","  '''\n","  count=0\n","  record_list=[]\n","  for action in real_test_label:\n","    pred=predict[count]\n","    count+=1\n","    if pred==1:#预测到该action出现时，加入该action\n","      record_list.append(action)#当前预测到的action\n","      if action_enough(record_list)==False:\n","        continue\n","      if action_enough(record_list)!=False:\n","        return count,action_enough(record_list)\n","  return False,False\n","#测试集上的预测函数\n","def pred_test(real_test_data_norm,real_test_label,model):\n","  pred=model.predict(real_test_data_norm)\n","  epoch,determine=calculate(real_test_label,pred)\n","  determine=random_generate(determine)\n","  #print('the current epoch:',determine,int(epoch*6+4/0.9-5))\n","  epoch=int(epoch*3+4/0.9-5)\n","  print('the current epoch:',determine,epoch)\n","  #print('the current epoch:',determine,epoch)\n","  return determine,epoch\n","   \n","#对所有sheet都生成对应的训练测试集，并汇总 #代码结构还是有点差，比赛期间没时间改啦\n","def data_predict(train_data_path,train_event_path,cut_begin,cut_end):\n","  reocrd_epoch=[]\n","  df = pd.read_excel(train_event_path, sheet_name=None)\n","  sheet_num=len(list(df))#计算sheet的个数\n","  train_data_record=[]\n","  for sheet_name in range(sheet_num):\n","    data=pd.read_excel(train_data_path,sheet_name,header=None) \n","    data=raw_data_preprocessing(data)\n","    event=pd.read_excel(train_event_path,sheet_name) \n","    #print(data.head())\n","    real_test_data,real_test_label=data_generate_test(data,event,cut_begin,cut_end)\n","    #归一化 \n","    real_test_data_norm=(real_test_data-real_test_data.min())/(real_test_data.max()-real_test_data.min())#可能需要改动\n","    #不归一化 \n","    #real_train_data_norm=real_train_data\n","    current_char,epoch=pred_test(real_test_data_norm,real_test_label,model)\n","    train_data_record.append(current_char)\n","    reocrd_epoch.append(epoch)\n","  return train_data_record,reocrd_epoch\n","label,epoch=data_predict(test_data_path,test_event_path,cut_begin,cut_end)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/sklearn/decomposition/_fastica.py:119: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.\n","  ConvergenceWarning)\n"],"name":"stderr"},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-113-2ee1d341fee7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mreocrd_epoch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mtrain_data_record\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreocrd_epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_data_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_event_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcut_begin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcut_end\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-113-2ee1d341fee7>\u001b[0m in \u001b[0;36mdata_predict\u001b[0;34m(train_data_path, train_event_path, cut_begin, cut_end)\u001b[0m\n\u001b[1;32m     97\u001b[0m     \u001b[0;31m#不归一化\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0;31m#real_train_data_norm=real_train_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m     \u001b[0mcurrent_char\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpred_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_test_data_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreal_test_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m     \u001b[0mtrain_data_record\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_char\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mreocrd_epoch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-113-2ee1d341fee7>\u001b[0m in \u001b[0;36mpred_test\u001b[0;34m(real_test_data_norm, real_test_label, model)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;31m#测试集上的预测函数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpred_test\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_test_data_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreal_test_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m   \u001b[0mpred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_test_data_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m   \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdetermine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcalculate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_test_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m   \u001b[0mdetermine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_generate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetermine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m 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696\u001b[0m             pred = sum(_samme_proba(estimator, n_classes, X)\n\u001b[0;32m--> 697\u001b[0;31m                        for estimator in self.estimators_)\n\u001b[0m\u001b[1;32m    698\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# self.algorithm == \"SAMME\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m             pred = sum((estimator.predict(X) == classes).T * w\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0;31m# The weights are all 1. for SAMME.R\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    696\u001b[0m             pred = sum(_samme_proba(estimator, n_classes, X)\n\u001b[0;32m--> 697\u001b[0;31m                        for estimator in self.estimators_)\n\u001b[0m\u001b[1;32m    698\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# self.algorithm == \"SAMME\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    699\u001b[0m             pred = sum((estimator.predict(X) == classes).T * w\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36m_samme_proba\u001b[0;34m(estimator, n_classes, X)\u001b[0m\n\u001b[1;32m    271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m     \"\"\"\n\u001b[0;32m--> 273\u001b[0;31m     \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m     \u001b[0;31m# Displace zero probabilities so the log is defined.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m    903\u001b[0m         \"\"\"\n\u001b[1;32m    904\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m         \u001b[0mproba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m    389\u001b[0m                              \u001b[0;34m\"match the input. Model n_features is %s and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m                              \u001b[0;34m\"input n_features is %s \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m                              % (self.n_features_, n_features))\n\u001b[0m\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Number of features of the model must match the input. Model n_features is 20 and input n_features is 15 "]}]},{"cell_type":"code","metadata":{"id":"ykuBSRA2Myed","colab_type":"code","colab":{}},"source":["pd.DataFrame([label,epoch]).to_csv('S%d-result.csv'%s)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GJVIME6LMypl","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XqUA_RkaMyhE","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Qk93RA6ZMyb0","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"F90DEpdvMyWN","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"aakM0q7G7aWR","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HTY8UlZM7aPR","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5m77gSNxBDoR","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}