1332 lines
45 KiB
Plaintext
1332 lines
45 KiB
Plaintext
{
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6b84fefd-5936-4da4-ab6b-5b944329ad1d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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||
"os.environ['CUDA_DEVICE_ORDER'] = 'PCB_BUS_ID'\n",
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||
"os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'"
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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": 2,
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"id": "9cf130e3-62ef-46e0-bbdc-b13d9d29318d",
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"metadata": {},
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||
"outputs": [],
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"source": [
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||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
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"import matplotlib.pyplot as plt\n",
|
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"#新增加的两行\n",
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"from pylab import mpl\n",
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"# 设置显示中文字体\n",
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"mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
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"\n",
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"mpl.rcParams[\"axes.unicode_minus\"] = False"
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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": 3,
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"id": "752381a5-0aeb-4c54-bc48-f9c3f8fc5d17",
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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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" <tr>\n",
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" <th></th>\n",
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" <th>Unnamed: 0_level_0</th>\n",
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" <th>氢</th>\n",
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" <th>碳</th>\n",
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" <th>氮</th>\n",
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||
" <th>氧</th>\n",
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||
" <th>弹筒发热量</th>\n",
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||
" <th>挥发分</th>\n",
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||
" <th>固定炭</th>\n",
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||
" </tr>\n",
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" <tr>\n",
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||
" <th></th>\n",
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||
" <th>化验编号</th>\n",
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||
" <th>Had</th>\n",
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||
" <th>Cad</th>\n",
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||
" <th>Nad</th>\n",
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||
" <th>Oad</th>\n",
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||
" <th>Qb,ad</th>\n",
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" <th>Vad</th>\n",
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||
" <th>Fcad</th>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th></th>\n",
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" <th>Unnamed: 0_level_2</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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" <th>MJ/kg</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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" <td>3.93</td>\n",
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" <td>0.81</td>\n",
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" <td>25.079</td>\n",
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" <td>27.820</td>\n",
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" <td>32.06</td>\n",
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" <td>55.68</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2720096883</td>\n",
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" <td>3.78</td>\n",
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" <td>68.93</td>\n",
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" <td>3.48</td>\n",
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" <td>3.47</td>\n",
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" <td>66.71</td>\n",
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" <td>53.87</td>\n",
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" <th>223</th>\n",
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" <td>4.12</td>\n",
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" <td>68.85</td>\n",
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" <td>0.97</td>\n",
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" <td>26.055</td>\n",
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" <td>27.864</td>\n",
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" <td>32.94</td>\n",
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" <td>51.89</td>\n",
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" <td>3.81</td>\n",
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" <td>66.74</td>\n",
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" <td>0.92</td>\n",
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" <td>50.82</td>\n",
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"</div>"
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],
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"text/plain": [
|
||
" Unnamed: 0_level_0 氢 碳 氮 氧 弹筒发热量 挥发分 固定炭\n",
|
||
" 化验编号 Had Cad Nad Oad Qb,ad Vad Fcad\n",
|
||
" Unnamed: 0_level_2 (%) (%) (%) (%) MJ/kg (%) (%)\n",
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||
"0 2720110529 3.93 70.18 0.81 25.079 27.820 32.06 55.68\n",
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"1 2720096883 3.78 68.93 0.77 26.512 27.404 29.96 54.71\n",
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"2 2720109084 3.48 69.60 0.76 26.148 27.578 29.31 55.99\n",
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||
"3 2720084708 3.47 66.71 0.76 29.055 26.338 28.58 53.87\n",
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"4 2720062721 3.87 68.78 0.80 26.542 27.280 29.97 54.78\n",
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".. ... ... ... ... ... ... ... ...\n",
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"223 2720030490 4.12 68.85 0.97 26.055 27.864 32.94 51.89\n",
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"225 2720028634 4.12 68.42 0.96 26.493 27.886 33.16 52.00\n",
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"226 2720017683 3.88 67.42 0.94 27.760 26.616 31.65 50.56\n",
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"227 2720017678 3.81 66.74 0.92 28.530 26.688 31.02 50.82\n",
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"\n",
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"[228 rows x 8 columns]"
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]
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},
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"execution_count": 3,
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||
"metadata": {},
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||
"output_type": "execute_result"
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}
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||
],
|
||
"source": [
|
||
"data_0102 = pd.read_excel('./data/20240102/20240102.xlsx', header=[0,1,2])\n",
|
||
"data_0102"
|
||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 4,
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||
"id": "972f1e9c-3ebc-45cf-8d1f-7611645e5238",
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||
"metadata": {},
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||
"outputs": [
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||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['化验编号',\n",
|
||
" '氢Had(%)',\n",
|
||
" '碳Cad(%)',\n",
|
||
" '氮Nad(%)',\n",
|
||
" '氧Oad(%)',\n",
|
||
" '弹筒发热量Qb,adMJ/kg',\n",
|
||
" '挥发分Vad(%)',\n",
|
||
" '固定炭Fcad(%)']"
|
||
]
|
||
},
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||
"execution_count": 4,
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||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"cols = [''.join([y for y in x if 'Unnamed' not in y]) for x in data_0102.columns]\n",
|
||
"cols"
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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": 5,
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"id": "c95f1106-b3a4-43c6-88ec-3cdebf91d79a",
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"metadata": {},
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||
"outputs": [],
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"source": [
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||
"data_0102.columns = cols"
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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": 6,
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"id": "2e96af0a-feda-4a1f-a13e-9c8861c6f4d4",
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"metadata": {},
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"outputs": [
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{
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"text/html": [
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" <th>碳Cad(%)</th>\n",
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" <th>氮Nad(%)</th>\n",
|
||
" <th>氧Oad(%)</th>\n",
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" <th>弹筒发热量Qb,adMJ/kg</th>\n",
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" <th>挥发分Vad(%)</th>\n",
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" <td>2720110529</td>\n",
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" <td>0.81</td>\n",
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" <th>1</th>\n",
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" <td>2720096883</td>\n",
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" <td>3.78</td>\n",
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" <td>68.93</td>\n",
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" <th>2</th>\n",
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" <td>2720109084</td>\n",
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" <td>3.48</td>\n",
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" <td>27.578</td>\n",
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||
" <tr>\n",
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" <th>3</th>\n",
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" <td>2720084708</td>\n",
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" <td>3.47</td>\n",
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" <td>66.71</td>\n",
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||
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" <th>4</th>\n",
|
||
" <td>2720062721</td>\n",
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" <td>3.87</td>\n",
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" <td>68.78</td>\n",
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||
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||
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|
||
" <tr>\n",
|
||
" <th>223</th>\n",
|
||
" <td>2720030490</td>\n",
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||
" <td>4.12</td>\n",
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" <td>68.85</td>\n",
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||
" <td>0.97</td>\n",
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||
" <td>26.055</td>\n",
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||
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||
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|
||
" <td>51.89</td>\n",
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||
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||
" <tr>\n",
|
||
" <th>224</th>\n",
|
||
" <td>2720028633</td>\n",
|
||
" <td>3.97</td>\n",
|
||
" <td>67.04</td>\n",
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||
" <td>0.94</td>\n",
|
||
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|
||
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||
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||
" <td>51.38</td>\n",
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||
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|
||
" <tr>\n",
|
||
" <th>225</th>\n",
|
||
" <td>2720028634</td>\n",
|
||
" <td>4.12</td>\n",
|
||
" <td>68.42</td>\n",
|
||
" <td>0.96</td>\n",
|
||
" <td>26.493</td>\n",
|
||
" <td>27.886</td>\n",
|
||
" <td>33.16</td>\n",
|
||
" <td>52.00</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>226</th>\n",
|
||
" <td>2720017683</td>\n",
|
||
" <td>3.88</td>\n",
|
||
" <td>67.42</td>\n",
|
||
" <td>0.94</td>\n",
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||
" <td>27.760</td>\n",
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||
" <td>26.616</td>\n",
|
||
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|
||
" <td>50.56</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>227</th>\n",
|
||
" <td>2720017678</td>\n",
|
||
" <td>3.81</td>\n",
|
||
" <td>66.74</td>\n",
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" <td>0.92</td>\n",
|
||
" <td>28.530</td>\n",
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||
" <td>26.688</td>\n",
|
||
" <td>31.02</td>\n",
|
||
" <td>50.82</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>228 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 化验编号 氢Had(%) 碳Cad(%) 氮Nad(%) 氧Oad(%) 弹筒发热量Qb,adMJ/kg \\\n",
|
||
"0 2720110529 3.93 70.18 0.81 25.079 27.820 \n",
|
||
"1 2720096883 3.78 68.93 0.77 26.512 27.404 \n",
|
||
"2 2720109084 3.48 69.60 0.76 26.148 27.578 \n",
|
||
"3 2720084708 3.47 66.71 0.76 29.055 26.338 \n",
|
||
"4 2720062721 3.87 68.78 0.80 26.542 27.280 \n",
|
||
".. ... ... ... ... ... ... \n",
|
||
"223 2720030490 4.12 68.85 0.97 26.055 27.864 \n",
|
||
"224 2720028633 3.97 67.04 0.94 28.043 27.368 \n",
|
||
"225 2720028634 4.12 68.42 0.96 26.493 27.886 \n",
|
||
"226 2720017683 3.88 67.42 0.94 27.760 26.616 \n",
|
||
"227 2720017678 3.81 66.74 0.92 28.530 26.688 \n",
|
||
"\n",
|
||
" 挥发分Vad(%) 固定炭Fcad(%) \n",
|
||
"0 32.06 55.68 \n",
|
||
"1 29.96 54.71 \n",
|
||
"2 29.31 55.99 \n",
|
||
"3 28.58 53.87 \n",
|
||
"4 29.97 54.78 \n",
|
||
".. ... ... \n",
|
||
"223 32.94 51.89 \n",
|
||
"224 31.88 51.38 \n",
|
||
"225 33.16 52.00 \n",
|
||
"226 31.65 50.56 \n",
|
||
"227 31.02 50.82 \n",
|
||
"\n",
|
||
"[228 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data_0102"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "04b177a7-2f02-4e23-8ea9-29f34cf3eafc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# out_cols = ['挥发分Vad(%)']\n",
|
||
"# drop_cols = ['化验编号', '固定炭Fcad(%)']\n",
|
||
"out_cols = ['固定炭Fcad(%)']\n",
|
||
"drop_cols = ['挥发分Vad(%)', '化验编号']"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "31169fbf-d78e-42f7-87f3-71ba3dd0979d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['固定炭Fcad(%)']"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"out_cols"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "feaedd50-f999-45bf-b465-3d359b0c0110",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = data_0102.copy()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "a40bee0f-011a-4edb-80f8-4e2f40e755fd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_data = data.dropna(subset=out_cols).fillna(0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "535d37b6-b9de-4025-ac8f-62f5bdbe2451",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-01-08 18:09:21.597754: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import tensorflow as tf\n",
|
||
"from tensorflow import keras\n",
|
||
"from tensorflow.keras import layers\n",
|
||
"import tensorflow.keras.backend as K"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "1c85d462-f248-4ffb-908f-eb4b20eab179",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class TransformerBlock(layers.Layer):\n",
|
||
" def __init__(self, embed_dim, num_heads, ff_dim, name, rate=0.1):\n",
|
||
" super().__init__()\n",
|
||
" self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, name=name)\n",
|
||
" self.ffn = keras.Sequential(\n",
|
||
" [layers.Dense(ff_dim, activation=\"relu\"), layers.Dense(embed_dim),]\n",
|
||
" )\n",
|
||
" self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n",
|
||
" self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n",
|
||
" self.dropout1 = layers.Dropout(rate)\n",
|
||
" self.dropout2 = layers.Dropout(rate)\n",
|
||
"\n",
|
||
" def call(self, inputs, training):\n",
|
||
" attn_output = self.att(inputs, inputs)\n",
|
||
" attn_output = self.dropout1(attn_output, training=training)\n",
|
||
" out1 = self.layernorm1(inputs + attn_output)\n",
|
||
" ffn_output = self.ffn(out1)\n",
|
||
" ffn_output = self.dropout2(ffn_output, training=training)\n",
|
||
" return self.layernorm2(out1 + ffn_output)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "790284a3-b9d3-4144-b481-38a7c3ecb4b9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow.keras import Model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "cd9a1ca1-d0ca-4cb5-9ef5-fd5d63576cd2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow.keras.initializers import Constant"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "9bc02f29-0fb7-420d-99a8-435eadc06e29",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Custom loss layer\n",
|
||
"class CustomMultiLossLayer(layers.Layer):\n",
|
||
" def __init__(self, nb_outputs=2, **kwargs):\n",
|
||
" self.nb_outputs = nb_outputs\n",
|
||
" self.is_placeholder = True\n",
|
||
" super(CustomMultiLossLayer, self).__init__(**kwargs)\n",
|
||
" \n",
|
||
" def build(self, input_shape=None):\n",
|
||
" # initialise log_vars\n",
|
||
" self.log_vars = []\n",
|
||
" for i in range(self.nb_outputs):\n",
|
||
" self.log_vars += [self.add_weight(name='log_var' + str(i), shape=(1,),\n",
|
||
" initializer=tf.initializers.he_normal(), trainable=True)]\n",
|
||
" super(CustomMultiLossLayer, self).build(input_shape)\n",
|
||
"\n",
|
||
" def multi_loss(self, ys_true, ys_pred):\n",
|
||
" assert len(ys_true) == self.nb_outputs and len(ys_pred) == self.nb_outputs\n",
|
||
" loss = 0\n",
|
||
" for y_true, y_pred, log_var in zip(ys_true, ys_pred, self.log_vars):\n",
|
||
" mse = (y_true - y_pred) ** 2.\n",
|
||
" pre = K.exp(-log_var[0])\n",
|
||
" loss += tf.abs(tf.reduce_logsumexp(pre * mse + log_var[0], axis=-1))\n",
|
||
" return K.mean(loss)\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" ys_true = inputs[:self.nb_outputs]\n",
|
||
" ys_pred = inputs[self.nb_outputs:]\n",
|
||
" loss = self.multi_loss(ys_true, ys_pred)\n",
|
||
" self.add_loss(loss, inputs=inputs)\n",
|
||
" # We won't actually use the output.\n",
|
||
" return K.concatenate(inputs, -1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "a190207e-5a59-4813-9660-758760cf1b73",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"num_heads, ff_dim = 3, 16"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "80f32155-e71f-4615-8d0c-01dfd04988fe",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_prediction_model():\n",
|
||
" inputs = layers.Input(shape=(1,len(feature_cols)), name='input')\n",
|
||
" x = layers.Conv1D(filters=64, kernel_size=1, activation='relu')(inputs)\n",
|
||
" # x = layers.Dropout(rate=0.1)(x)\n",
|
||
" lstm_out = layers.Bidirectional(layers.LSTM(units=64, return_sequences=True))(x)\n",
|
||
" lstm_out = layers.Dense(128, activation='relu')(lstm_out)\n",
|
||
" # transformer_block = TransformerBlock(128, num_heads, ff_dim, name='first_attn')\n",
|
||
" # out = transformer_block(lstm_out)\n",
|
||
" # out = layers.GlobalAveragePooling1D()(out)\n",
|
||
" out = layers.Dropout(0.1)(lstm_out)\n",
|
||
" out = layers.Dense(64, activation='relu')(out)\n",
|
||
" bet = layers.Dense(1, activation='sigmoid', name='vad')(out)\n",
|
||
" model = Model(inputs=inputs, outputs=bet)\n",
|
||
" return model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "372011ea-9876-41eb-a4e6-83ccd6c71559",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow.python.keras.utils.vis_utils import plot_model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "1eebdab3-1f88-48a1-b5e0-bc8787528c1b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"maxs = train_data.max()\n",
|
||
"mins = train_data.min()\n",
|
||
"for col in train_data.columns:\n",
|
||
" if maxs[col] - mins[col] == 0:\n",
|
||
" continue\n",
|
||
" train_data[col] = (train_data[col] - mins[col]) / (maxs[col] - mins[col])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "7f27bd56-4f6b-4242-9f79-c7d6b3ee2f13",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>化验编号</th>\n",
|
||
" <th>氢Had(%)</th>\n",
|
||
" <th>碳Cad(%)</th>\n",
|
||
" <th>氮Nad(%)</th>\n",
|
||
" <th>氧Oad(%)</th>\n",
|
||
" <th>弹筒发热量Qb,adMJ/kg</th>\n",
|
||
" <th>挥发分Vad(%)</th>\n",
|
||
" <th>固定炭Fcad(%)</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0.996547</td>\n",
|
||
" <td>0.773973</td>\n",
|
||
" <td>0.835414</td>\n",
|
||
" <td>0.456522</td>\n",
|
||
" <td>0.171463</td>\n",
|
||
" <td>0.811249</td>\n",
|
||
" <td>0.847737</td>\n",
|
||
" <td>0.828147</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>0.851118</td>\n",
|
||
" <td>0.671233</td>\n",
|
||
" <td>0.799943</td>\n",
|
||
" <td>0.369565</td>\n",
|
||
" <td>0.210254</td>\n",
|
||
" <td>0.782038</td>\n",
|
||
" <td>0.674897</td>\n",
|
||
" <td>0.794606</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 化验编号 氢Had(%) 碳Cad(%) 氮Nad(%) 氧Oad(%) 弹筒发热量Qb,adMJ/kg \\\n",
|
||
"0 0.996547 0.773973 0.835414 0.456522 0.171463 0.811249 \n",
|
||
"1 0.851118 0.671233 0.799943 0.369565 0.210254 0.782038 \n",
|
||
"\n",
|
||
" 挥发分Vad(%) 固定炭Fcad(%) \n",
|
||
"0 0.847737 0.828147 \n",
|
||
"1 0.674897 0.794606 "
|
||
]
|
||
},
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"train_data.head(2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "baf45a3d-dc01-44fc-9f0b-456964ac2cdb",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# feature_cols = [x for x in train_data.columns if x not in out_cols and '第二次' not in x]\n",
|
||
"feature_cols = [x for x in train_data.columns if x not in out_cols and x not in drop_cols]\n",
|
||
"use_cols = feature_cols + out_cols"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "f2d27538-d2bc-4202-b0cf-d3e0949b4686",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"use_data = train_data.copy()\n",
|
||
"for col in use_cols:\n",
|
||
" use_data[col] = use_data[col].astype('float32')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "50daf170-efec-49e5-8f8e-9a45938cacfc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import KFold, train_test_split\n",
|
||
"kf = KFold(n_splits=6, shuffle=True, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "0f863423-be12-478b-a08d-e3c6f5dfb8ee",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorflow.keras import optimizers\n",
|
||
"from tensorflow.python.keras.utils.vis_utils import plot_model\n",
|
||
"from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "2c89b32a-017c-4d05-ab78-8b9b8eb0dcbb",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from keras.callbacks import ReduceLROnPlateau\n",
|
||
"reduce_lr = ReduceLROnPlateau(monitor='val_loss', patience=10, mode='auto')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "ae24eea7-7dc1-4e33-9d41-3baff07ebb88",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-01-08 18:09:35.590279: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
|
||
"2024-01-08 18:09:35.656503: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_INVALID_DEVICE: invalid device ordinal\n",
|
||
"2024-01-08 18:09:35.656548: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: zhaojh-yv621\n",
|
||
"2024-01-08 18:09:35.656557: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: zhaojh-yv621\n",
|
||
"2024-01-08 18:09:35.656758: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 520.61.5\n",
|
||
"2024-01-08 18:09:35.656795: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 520.61.5\n",
|
||
"2024-01-08 18:09:35.656802: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 520.61.5\n",
|
||
"2024-01-08 18:09:35.657280: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n",
|
||
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Model: \"model\"\n",
|
||
"_________________________________________________________________\n",
|
||
"Layer (type) Output Shape Param # \n",
|
||
"=================================================================\n",
|
||
"input (InputLayer) [(None, 1, 5)] 0 \n",
|
||
"_________________________________________________________________\n",
|
||
"conv1d (Conv1D) (None, 1, 64) 384 \n",
|
||
"_________________________________________________________________\n",
|
||
"bidirectional (Bidirectional (None, 1, 128) 66048 \n",
|
||
"_________________________________________________________________\n",
|
||
"dense (Dense) (None, 1, 128) 16512 \n",
|
||
"_________________________________________________________________\n",
|
||
"dropout (Dropout) (None, 1, 128) 0 \n",
|
||
"_________________________________________________________________\n",
|
||
"dense_1 (Dense) (None, 1, 64) 8256 \n",
|
||
"_________________________________________________________________\n",
|
||
"vad (Dense) (None, 1, 1) 65 \n",
|
||
"=================================================================\n",
|
||
"Total params: 91,265\n",
|
||
"Trainable params: 91,265\n",
|
||
"Non-trainable params: 0\n",
|
||
"_________________________________________________________________\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model = get_prediction_model()\n",
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "ca6ce434-80b6-4609-9596-9a5120680462",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def print_eva(y_true, y_pred, tp):\n",
|
||
" MSE = mean_squared_error(y_true, y_pred)\n",
|
||
" RMSE = np.sqrt(MSE)\n",
|
||
" MAE = mean_absolute_error(y_true, y_pred)\n",
|
||
" MAPE = mean_absolute_percentage_error(y_true, y_pred)\n",
|
||
" R_2 = r2_score(y_true, y_pred)\n",
|
||
" print(f\"COL: {tp}, MSE: {format(MSE, '.2E')}\", end=',')\n",
|
||
" print(f'RMSE: {round(RMSE, 3)}', end=',')\n",
|
||
" print(f'MAPE: {round(MAPE * 100, 3)} %', end=',')\n",
|
||
" print(f'MAE: {round(MAE, 3)}', end=',')\n",
|
||
" print(f'R_2: {round(R_2, 3)}')\n",
|
||
" return [MSE, RMSE, MAE, MAPE, R_2]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "503bbec7-2020-44c8-b622-05bb41082e43",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from keras.losses import mean_squared_error"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "6308b1dc-8e2e-4bf9-9b28-3b81979bf7e0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-01-08 18:03:50.956250: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
|
||
"2024-01-08 18:03:50.974801: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"COL: 挥发分Vad, MSE: 5.84E-01,RMSE: 0.764,MAPE: 2.111 %,MAE: 0.633,R_2: 0.874\n",
|
||
"COL: 挥发分Vad, MSE: 1.06E+00,RMSE: 1.028,MAPE: 2.941 %,MAE: 0.869,R_2: 0.872\n",
|
||
"COL: 挥发分Vad, MSE: 6.70E-01,RMSE: 0.819,MAPE: 2.217 %,MAE: 0.658,R_2: 0.829\n",
|
||
"COL: 挥发分Vad, MSE: 5.96E-01,RMSE: 0.772,MAPE: 2.07 %,MAE: 0.607,R_2: 0.896\n",
|
||
"WARNING:tensorflow:5 out of the last 9 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8d6f8940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||
"COL: 挥发分Vad, MSE: 8.56E-01,RMSE: 0.925,MAPE: 2.335 %,MAE: 0.717,R_2: 0.805\n",
|
||
"WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6e8f6e4160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n",
|
||
"COL: 挥发分Vad, MSE: 7.24E-01,RMSE: 0.851,MAPE: 2.435 %,MAE: 0.713,R_2: 0.851\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"vad_eva_list = list()\n",
|
||
"fcad_eva_list = list()\n",
|
||
"train_data = use_data[use_cols].copy()\n",
|
||
"for (train_index, test_index) in kf.split(train_data):\n",
|
||
" train = train_data.loc[train_index]\n",
|
||
" valid = train_data.loc[test_index]\n",
|
||
" X = np.expand_dims(train[feature_cols].values, axis=1)\n",
|
||
" Y = [x for x in train[out_cols].values.T]\n",
|
||
" X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n",
|
||
" Y_valid = [x for x in valid[out_cols].values.T]\n",
|
||
" prediction_model = get_prediction_model()\n",
|
||
" prediction_model.compile(optimizer='adam', loss=mean_squared_error)\n",
|
||
" hist = prediction_model.fit(X, Y[0], epochs=120, batch_size=8, verbose=0, \n",
|
||
" validation_data=(X_valid, Y_valid[0]),\n",
|
||
" callbacks=[reduce_lr]\n",
|
||
" )\n",
|
||
" rst = prediction_model.predict(X_valid).squeeze(axis=1)\n",
|
||
" pred_rst = pd.DataFrame.from_records(np.asarray(rst), columns=out_cols)\n",
|
||
" real_rst = valid[out_cols].copy()\n",
|
||
" for col in out_cols:\n",
|
||
" pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n",
|
||
" real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]\n",
|
||
" y_pred_vad = pred_rst[out_cols].values.reshape(-1,)\n",
|
||
" # y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)\n",
|
||
" y_true_vad = real_rst[out_cols].values.reshape(-1,)\n",
|
||
" # y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)\n",
|
||
" vad_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')\n",
|
||
" # fcad_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')\n",
|
||
" vad_eva_list.append(vad_eva)\n",
|
||
" # fcad_eva_list.append(fcad_eva)\n",
|
||
" del prediction_model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "f7132465-89e9-4193-829b-c6e7606cd266",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"2024-01-08 18:09:42.506363: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
|
||
"2024-01-08 18:09:42.522809: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2200000000 Hz\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"COL: 固定炭Fcad, MSE: 7.16E-01,RMSE: 0.846,MAPE: 1.337 %,MAE: 0.715,R_2: 0.972\n",
|
||
"COL: 固定炭Fcad, MSE: 1.04E+00,RMSE: 1.018,MAPE: 1.65 %,MAE: 0.847,R_2: 0.98\n",
|
||
"COL: 固定炭Fcad, MSE: 8.89E-01,RMSE: 0.943,MAPE: 1.294 %,MAE: 0.724,R_2: 0.936\n",
|
||
"COL: 固定炭Fcad, MSE: 5.17E-01,RMSE: 0.719,MAPE: 1.066 %,MAE: 0.545,R_2: 0.985\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"out_cols = ['固定炭Fcad(%)']\n",
|
||
"fcad_eva_list = list()\n",
|
||
"train_data = use_data[use_cols].copy()\n",
|
||
"for (train_index, test_index) in kf.split(train_data):\n",
|
||
" train = train_data.loc[train_index]\n",
|
||
" valid = train_data.loc[test_index]\n",
|
||
" X = np.expand_dims(train[feature_cols].values, axis=1)\n",
|
||
" Y = [x for x in train[out_cols].values.T]\n",
|
||
" X_valid = np.expand_dims(valid[feature_cols].values, axis=1)\n",
|
||
" Y_valid = [x for x in valid[out_cols].values.T]\n",
|
||
" prediction_model = get_prediction_model()\n",
|
||
" prediction_model.compile(optimizer='adam', loss=mean_squared_error)\n",
|
||
" hist = prediction_model.fit(X, Y[0], epochs=120, batch_size=8, verbose=0, \n",
|
||
" validation_data=(X_valid, Y_valid[0]),\n",
|
||
" callbacks=[reduce_lr]\n",
|
||
" )\n",
|
||
" rst = prediction_model.predict(X_valid).squeeze(axis=1)\n",
|
||
" pred_rst = pd.DataFrame.from_records(np.asarray(rst), columns=out_cols)\n",
|
||
" real_rst = valid[out_cols].copy()\n",
|
||
" for col in out_cols:\n",
|
||
" pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n",
|
||
" real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]\n",
|
||
" y_pred = pred_rst[out_cols].values.reshape(-1,)\n",
|
||
" y_true = real_rst[out_cols].values.reshape(-1,)\n",
|
||
" fcad_eva = print_eva(y_true, y_pred, tp='固定炭Fcad')\n",
|
||
" fcad_eva_list.append(fcad_eva)\n",
|
||
" del prediction_model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "27e0abf7-aa29-467f-bc5e-b66a1adf6165",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"MSE 0.747816\n",
|
||
"RMSE 0.859839\n",
|
||
"MAE 0.699474\n",
|
||
"MAPE 0.023513\n",
|
||
"R_2 0.854338\n",
|
||
"dtype: float64"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"vad_df = pd.DataFrame.from_records(vad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n",
|
||
"vad_df.sort_values(by='R_2').mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "070cdb94-6e7b-4028-b6d5-ba8570c902ba",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"fcad_df = pd.DataFrame.from_records(fcad_eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n",
|
||
"fcad_df.sort_values(by='R_2').mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "54c1df2c-c297-4b8d-be8a-3a99cff22545",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"train, valid = train_test_split(use_data[use_cols], test_size=0.3, random_state=42, shuffle=True)\n",
|
||
"valid, test = train_test_split(valid, test_size=0.3, random_state=42, shuffle=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "e7a914da-b9c2-40d9-96e0-459b0888adba",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"prediction_model = get_prediction_model()\n",
|
||
"trainable_model = get_trainable_model(prediction_model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "2494ef5a-5b2b-4f11-b6cd-dc39503c9106",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X = np.expand_dims(train[feature_cols].values, axis=1)\n",
|
||
"Y = [x for x in train[out_cols].values.T]\n",
|
||
"Y_valid = [x for x in valid[out_cols].values.T]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "cf869e4d-0fce-45a2-afff-46fd9b30fd1c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"trainable_model.compile(optimizer='adam', loss=None)\n",
|
||
"hist = trainable_model.fit([X, Y[0], Y[1]], epochs=120, batch_size=8, verbose=1, \n",
|
||
" validation_data=[np.expand_dims(valid[feature_cols].values, axis=1), Y_valid[0], Y_valid[1]],\n",
|
||
" callbacks=[reduce_lr]\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"id": "67bfbe88-5f2c-4659-b2dc-eb9f1b824d04",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[array([[0.73740077],\n",
|
||
" [0.89292204],\n",
|
||
" [0.7599046 ],\n",
|
||
" [0.67802393],\n",
|
||
" [0.6815233 ],\n",
|
||
" [0.88627005],\n",
|
||
" [0.6121343 ],\n",
|
||
" [0.7072234 ],\n",
|
||
" [0.8561135 ],\n",
|
||
" [0.52762157],\n",
|
||
" [0.8325021 ],\n",
|
||
" [0.50241977],\n",
|
||
" [0.8242289 ],\n",
|
||
" [0.68957335],\n",
|
||
" [0.6980361 ],\n",
|
||
" [0.82116604],\n",
|
||
" [0.8566438 ],\n",
|
||
" [0.53687835],\n",
|
||
" [0.56832707],\n",
|
||
" [0.78476715],\n",
|
||
" [0.85638577]], dtype=float32),\n",
|
||
" array([[0.68600863],\n",
|
||
" [0.78454906],\n",
|
||
" [0.8179163 ],\n",
|
||
" [0.94351083],\n",
|
||
" [0.86383885],\n",
|
||
" [0.69705516],\n",
|
||
" [0.6913491 ],\n",
|
||
" [0.80277354],\n",
|
||
" [0.93557894],\n",
|
||
" [0.82278305],\n",
|
||
" [0.82674253],\n",
|
||
" [0.93518937],\n",
|
||
" [0.8094449 ],\n",
|
||
" [0.9206344 ],\n",
|
||
" [0.7747319 ],\n",
|
||
" [0.9137207 ],\n",
|
||
" [0.9491073 ],\n",
|
||
" [0.93225 ],\n",
|
||
" [0.6185102 ],\n",
|
||
" [0.8867341 ],\n",
|
||
" [0.82890105]], dtype=float32)]"
|
||
]
|
||
},
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"rst = prediction_model.predict(np.expand_dims(test[feature_cols], axis=1))\n",
|
||
"rst"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"id": "7de501e9-05a2-424c-a5f4-85d43ad37592",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[0.9991559102070927, 0.9998196796918477]"
|
||
]
|
||
},
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"[np.exp(K.get_value(log_var[0]))**0.5 for log_var in trainable_model.layers[-1].log_vars]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"id": "5c69d03b-34fd-4dbf-aec6-c15093bb22ab",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['挥发分Vad(%)', '固定炭Fcad(%)'], dtype='object')"
|
||
]
|
||
},
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"real_rst.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "294813b8-90be-4007-9fd6-c26ee7bb9652",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for col in out_cols:\n",
|
||
" pred_rst[col] = pred_rst[col] * (maxs[col] - mins[col]) + mins[col]\n",
|
||
" real_rst[col] = real_rst[col] * (maxs[col] - mins[col]) + mins[col]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"id": "21739f82-d82a-4bde-8537-9504b68a96d5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"y_pred_vad = pred_rst['挥发分Vad(%)'].values.reshape(-1,)\n",
|
||
"y_pred_fcad = pred_rst['固定炭Fcad(%)'].values.reshape(-1,)\n",
|
||
"y_true_vad = real_rst['挥发分Vad(%)'].values.reshape(-1,)\n",
|
||
"y_true_fcad = real_rst['固定炭Fcad(%)'].values.reshape(-1,)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"id": "4ec4caa9-7c46-4fc8-a94b-cb659e924304",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"COL: 挥发分Vad, MSE: 3.35E-01,RMSE: 0.579,MAPE: 1.639 %,MAE: 0.504,R_2: 0.87\n",
|
||
"COL: 固定炭Fcad, MSE: 1.11E+00,RMSE: 1.055,MAPE: 1.497 %,MAE: 0.814,R_2: 0.876\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"pm25_eva = print_eva(y_true_vad, y_pred_vad, tp='挥发分Vad')\n",
|
||
"pm10_eva = print_eva(y_true_fcad, y_pred_fcad, tp='固定炭Fcad')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ac4a4339-ec7d-4266-8197-5276c2395288",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "f15cbb91-1ce7-4fb0-979a-a4bdc452a1ec",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.8.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|