注塑成型工艺的虚拟量测和调机优化-特征工程 - 决赛.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import time\n",
    "import datetime\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- a) 传感器高频数据：该数据来自于模温机及模具传感器采集的数据，文件夹内每一个模次对应一个csv文件，单个模次时长为40~43s，采样频率根据阶段有20Hz和50Hz两种，含有24个传感器采集的数据；\n",
    "- b) 成型机状态数据（data_spc）：该数据来自成型机机台，均为表征成型过程中的一些状态数据，每一行对应一个模次，数据维度为86维；\n",
    "- c) 机台工艺设定参数（data_set）：文件夹中含有注塑成型的81种工艺设定参数；\n",
    "- d) 产品测量尺寸(size)：文件夹内含有每个模次产品的3维尺寸；\n",
    "- e) 现场巡检数据（初赛不提供此类数据）；\n",
    "- f) 电机停机记录（初赛不提供此类数据）；\n",
    "- g) 炮筒温度记录（初赛不提供此类数据）。\n",
    "\n",
    "![pic](.\\info.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 动作阶段，包括：\n",
    "0-空闲，1-合模，2-座进，3-注射，4-保压，6-熔胶，7-后松退，8冷却，9座退，10-开模，11-顶进，12-顶退，13~16-中子1~4进，17~20-中子1~4退。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一般数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 决赛测试集\n",
    "test_data_set_taskA=pd.read_csv(r'./taskA/data_set.csv')\n",
    "test_data_spc_taskA=pd.read_csv(r'./taskA/data_spc.csv')\n",
    "sub_file_taskA =pd.read_csv('./taskA/sub_file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_data_set_taskA形状: (295, 347)\n",
      "test_data_spc_taskA形状: (3971, 86)\n",
      "sub_file_taskA形状: (3971, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"test_data_set_taskA形状:\",test_data_set_taskA.shape)\n",
    "print(\"test_data_spc_taskA形状:\",test_data_spc_taskA.shape)\n",
    "print(\"sub_file_taskA形状:\",sub_file_taskA.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决赛高频传感器数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables_sensor=['Sensor1', 'Sensor2', 'Sensor3', 'IJ', 'Sensor5',\n",
    "       'Sensor6', 'MouldTemp1', 'MouldTemp2', 'MouldTemp3', 'MouldTemp4',\n",
    "       'MouldTemp5', 'MouldTemp9', 'MouldTemp10', 'MouldTemp11', 'MouldTemp12',\n",
    "       'MouldTemp13', 'MouldTemp14', 'Sensor8', 'MouldFlow1', 'MouldFlow2',\n",
    "       'MouldFlow3', 'SP']#mean,var,max,min,median\n",
    "variables_sensor_pressure=['Sensor1', 'Sensor2', 'Sensor3']#skew,kurt,mean/sum/var(pahse 4\\6\\7\\8\\18)\n",
    "variables_sensor_temp=['Sensor5', 'Sensor6']# skew,kurt, sum/max(phase 4,6,7,8,18,10)\n",
    "variables_sensor_IJ=['IJ']# argmax/max/sum/mean/var(phase 3) mean/var(phase 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_individual_feature_cols(v,f_cols):\n",
    "    if v in variables_sensor_pressure:\n",
    "        f_cols.append(v + '_skew')\n",
    "        f_cols.append(v + '_kurt')\n",
    "        f_cols.append(v + '_mean_phase4678_18')\n",
    "        f_cols.append(v + '_sum_phase4678_18')\n",
    "        f_cols.append(v + '_var_phase4678_18')\n",
    "    elif v in variables_sensor_temp:\n",
    "        f_cols.append(v + '_skew')\n",
    "        f_cols.append(v + '_kurt')\n",
    "        f_cols.append(v + '_sum_phase4678_18_10')\n",
    "        f_cols.append(v + '_max_phase4678_18_10')\n",
    "    elif v in variables_sensor_IJ:\n",
    "        f_cols.append(v + '_argmax_phase3')\n",
    "        f_cols.append(v + '_max_phase3')\n",
    "        f_cols.append(v + '_sum_phase3')\n",
    "        f_cols.append(v + '_mean_phase3')\n",
    "        f_cols.append(v + '_var_phase3')\n",
    "        f_cols.append(v + '_mean_phase4')\n",
    "        f_cols.append(v + '_var_phase4')\n",
    "    return f_cols\n",
    "def add_individual_feature(col,avg,tmp_df):\n",
    "    if col in variables_sensor_pressure:\n",
    "        tem=tmp_df[col].skew()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col].kurt()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0,6.0,7.0,8.0,18.0])].mean()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0,6.0,7.0,8.0,18.0])].sum()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0,6.0,7.0,8.0,18.0])].var()\n",
    "        avg.append(tem)\n",
    "    elif col in variables_sensor_temp:\n",
    "        tem=tmp_df[col].skew()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col].kurt()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0,6.0,7.0,8.0,18.0,10.0])].sum()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0,6.0,7.0,8.0,18.0,10.0])].max()\n",
    "        avg.append(tem)\n",
    "    elif col in variables_sensor_IJ:\n",
    "        a = tmp_df[col][tmp_df[col].isin([3.0])]\n",
    "        tem=a.argmax() if len(a)>0 else 0\n",
    "        avg.append(tem)\n",
    "        \n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([3.0])].max()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([3.0])].sum()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([3.0])].mean()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([3.0])].var()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0])].mean()\n",
    "        avg.append(tem)\n",
    "        tem=tmp_df[col][tmp_df[\"Phase\"].isin([4.0])].var()\n",
    "        avg.append(tem)\n",
    "    return avg\n",
    "\n",
    "def feature_columns(variables):\n",
    "    f_cols = []\n",
    "    for v in variables:\n",
    "        f_cols.append(v + '_mean')\n",
    "        f_cols.append(v + '_var')\n",
    "        f_cols.append(v + '_max')\n",
    "        f_cols.append(v + '_min')\n",
    "        f_cols.append(v + '_median')\n",
    "        f_cols=add_individual_feature_cols(v,f_cols)\n",
    "    return f_cols\n",
    "\n",
    "def stage_features(df, variables):\n",
    "    avg = []\n",
    "    tmp_df = df#.loc[:, variables]\n",
    "    for v in variables:\n",
    "        tmp = tmp_df[v].mean()#均值\n",
    "        avg.append(tmp)\n",
    "        tmp=tmp_df[v].var()#方差\n",
    "        avg.append(tmp)\n",
    "        tmp=tmp_df[v].max()#最大值\n",
    "        avg.append(tmp)\n",
    "        tmp=tmp_df[v].min()#最小值\n",
    "        avg.append(tmp)\n",
    "        tmp=tmp_df[v].median()#中位数\n",
    "        avg.append(tmp)\n",
    "        avg = add_individual_feature(v,avg,tmp_df)\n",
    "#     print(avg)\n",
    "    return np.array(avg)"
   ]
  },
  {
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "name": "stdout",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "3838 mold_data_619125695_173821.csv (3971, 140)\n",
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      "3857 mold_data_619138558_174141.csv (3971, 140)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3858 mold_data_619138952_174151.csv (3971, 140)\n",
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      "3940 mold_data_619184904_175316.csv (3971, 140)\n",
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      "3950 mold_data_619190028_175446.csv (3971, 140)\n",
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      "3952 mold_data_619191801_175491.csv (3971, 140)\n",
      "3953 mold_data_619191998_175496.csv (3971, 140)\n",
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      "3955 mold_data_619193396_175526.csv (3971, 140)\n",
      "3956 mold_data_619283664_175726.csv (3971, 140)\n",
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      "3959 mold_data_619284840_175756.csv (3971, 140)\n",
      "3960 mold_data_619285036_175761.csv (3971, 140)\n",
      "3961 mold_data_619285429_175771.csv (3971, 140)\n",
      "3962 mold_data_619287587_175826.csv (3971, 140)\n",
      "3963 mold_data_619288176_175841.csv (3971, 140)\n",
      "3964 mold_data_619288961_175861.csv (3971, 140)\n",
      "3965 mold_data_619289549_175876.csv (3971, 140)\n",
      "3966 mold_data_619292103_175941.csv (3971, 140)\n",
      "3967 mold_data_619292692_175956.csv (3971, 140)\n",
      "3968 mold_data_619293281_175971.csv (3971, 140)\n",
      "3969 mold_data_619299169_176121.csv (3971, 140)\n",
      "3970 mold_data_619299758_176136.csv (3971, 140)\n",
      "Wall time: 3min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 任务A高频数据特征提取\n",
    "TEST_ZIP_taskA=zipfile.ZipFile('C:/Users/Zetlin/Downloads/IJ_data/taskA/传感器高频数据.zip')\n",
    "file_list=TEST_ZIP_taskA.namelist()\n",
    "feature_n = len(variables_sensor)\n",
    "features_ = np.empty([len(file_list), feature_n*5+30])\n",
    "times_ = []\n",
    "mold_id_ = []\n",
    "for i,f in enumerate(file_list):\n",
    "#     print(i,f)\n",
    "    df=pd.read_csv(TEST_ZIP_taskA.open(f))\n",
    "    tmp = f.split('_')\n",
    "    ti = tmp[2]\n",
    "    mold_id = tmp[3].replace('.csv', '')\n",
    "    times_.append(str(ti))\n",
    "    mold_id_.append(int(mold_id))\n",
    "    if len(df) == 0:\n",
    "        features_[i] = [None for j in range(feature_n)]\n",
    "    else:\n",
    "        features_[i] = stage_features(df, variables_sensor)\n",
    "    print(i,f,features_.shape)\n",
    "f_cols = feature_columns(variables_sensor)\n",
    "TEST_HIG_taskA = pd.DataFrame(features_, columns=f_cols)\n",
    "TEST_HIG_taskA['Time'] = times_\n",
    "TEST_HIG_taskA['Id'] = mold_id_\n",
    "TEST_HIG_taskA = TEST_HIG_taskA[['Id', 'Time'] + f_cols]\n",
    "TEST_ZIP_taskA.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3971, 142)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TEST_HIG_taskA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_HIG_taskA.rename(columns={'Time':'spcTime'}, inplace=True)\n",
    "# TEST_HIG_taskA.to_csv(\"./Middle_result/TEST_HIG_taskA_mean.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_na_col(df,drop_some_na=True):\n",
    "    '''删除全是NaN值的列\n",
    "    mc_speed_4、mc_pos_3、mo_speed_1、mo_pos_1、mc_press_4、mo_press_1、adjust_reasons这7列在data_set中也有不同程度的缺失\n",
    "    '''\n",
    "    m,n = 0,0\n",
    "    for col in df.columns:\n",
    "        if df[col].isna().sum()==len(df[col]):\n",
    "            print(f\"删掉空值列:{col}\")\n",
    "            df.drop(col,axis = 1,inplace=True)\n",
    "            m += 1\n",
    "        elif df[col].isna().sum()>0 and drop_some_na:\n",
    "            print(f\"删掉缺失值列:{col}\")\n",
    "            df.drop(col,axis = 1,inplace=True)\n",
    "            n += 1\n",
    "    print(f\"共删除{m}个空值列,{n}个缺失值列\")\n",
    "    return df\n",
    "def drop_same_val_col(df):\n",
    "    \"\"\"有些列的参数都相同，不具有表征意义，删除\"\"\"\n",
    "    m = 0\n",
    "    for col in df.columns:\n",
    "        if df[col].nunique()==1:\n",
    "            df.drop(col,axis = 1,inplace=True)\n",
    "            print(f\"删掉相同值列:{col}\")\n",
    "            m += 1\n",
    "    print(f\"共删除{m}个相同值列\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_col_unique_value(df):\n",
    "    for col in df:\n",
    "        if df[col].value_counts() is None:\n",
    "            print(col,\"**空值\")\n",
    "        elif len(df[col].value_counts())==1:\n",
    "            print(col,\"--One value\")\n",
    "def generate_profiling_report(df,name):\n",
    "    from pandas_profiling import ProfileReport\n",
    "    prof = ProfileReport(df)\n",
    "    prof.to_file(f\"{name} profile report.html\")\n",
    "def missing_no(df):\n",
    "    n = 0\n",
    "    m = 0\n",
    "    for col in df.columns:\n",
    "        if df[col].isna().sum() != 0:\n",
    "            print(f\"{col}列：{df[col].isna().sum()}/{len(df[col])}\")\n",
    "            n += 1\n",
    "        if df[col].isna().sum() == len(df[col]):\n",
    "            m += 1\n",
    "    print(f\"共有{len(df)}条数据，{df.shape[1]}列数据，存在缺失值的有{n}列，完全缺失数据的{m}列\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# taskA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"生成一个train_set,用于和train_spc合并\"\"\"\n",
    "train_set=pd.DataFrame()\n",
    "min_val = int(min(test_data_set_taskA.Id.min(),test_data_spc_taskA.Id.min()))\n",
    "max_val = int(max(test_data_set_taskA.Id.max(),test_data_spc_taskA.Id.max()))\n",
    "train_set[\"Id\"]=np.array(range(min_val,max_val+1))\n",
    "train_set = pd.merge(train_set,test_data_set_taskA,how = \"left\")\n",
    "train_set.sort_values('Id',inplace=True)\n",
    "train_set=train_set.fillna(method=\"ffill\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_set和data_spac合并\n",
    "test_taskA=pd.merge(test_data_spc_taskA,train_set,on=\"Id\",how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3971, 432)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_taskA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_taskA[\"spcTime\"]=test_taskA[\"spcTime\"].apply(int)\n",
    "test_taskA[\"spcTime\"]=test_taskA[\"spcTime\"].apply(str)\n",
    "test_taskA=test_taskA.merge(TEST_HIG_taskA,on=[\"Id\",\"spcTime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3971, 572)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_taskA.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成需要的格式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用于size1和size3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 增加一些列，为了后续列的匹配\n",
    "test_taskA=test_taskA.assign(isTrain=np.nan,size1=np.nan,size2=np.nan,size3=np.nan);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3971, 576)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_taskA.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20552, 218)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_train=pd.read_csv(\"./Middle_result/my_train_add_mean_std_max_min_median_indiv_features.csv\")\n",
    "my_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_taskA=test_taskA[[col for col in my_train.columns]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_taskA.to_csv(\"./Middle_result/test_taskA_add_mean_std_max_min_median_indiv_features.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3971, 218)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_taskA.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用于size2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_taskA[\"spcTime_x\"]=test_taskA[\"spcTime\" ]\n",
    "test_taskA[\"spcTime_y\"]=test_taskA[\"spcTime\" ]\n",
    "test_taskA.rename(lambda x:x.replace(\"_var\",\"_std\"),axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_train=pd.read_csv(\"./Middle_result/my_train_add_mean_std_max_min.csv\")\n",
    "test_taskA=test_taskA[[col for col in my_train.columns]]#主要用于size2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_taskA.to_csv(\"./Middle_result/test_taskA_add_mean_std_max_min.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3971, 170), (20552, 170))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_taskA.shape,my_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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