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initial repo
2024-01-05 09:12:27 +08:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "70ae2cb0-c6f0-4080-b894-2246c9d880e2",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6a94278b-8f51-4edc-966b-4a32876a4536",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>Unnamed: 0_level_0</th>\n",
" <th>氢</th>\n",
" <th>碳</th>\n",
" <th>氮</th>\n",
" <th>氧</th>\n",
" <th>弹筒发热量</th>\n",
" <th>挥发分</th>\n",
" <th>固定炭</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>化验编号</th>\n",
" <th>Had</th>\n",
" <th>Cad</th>\n",
" <th>Nad</th>\n",
" <th>Oad</th>\n",
" <th>Qb,ad</th>\n",
" <th>Vad</th>\n",
" <th>Fcad</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>Unnamed: 0_level_2</th>\n",
" <th>(%)</th>\n",
" <th>(%)</th>\n",
" <th>(%)</th>\n",
" <th>(%)</th>\n",
" <th>MJ/kg</th>\n",
" <th>(%)</th>\n",
" <th>(%)</th>\n",
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" <tbody>\n",
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" <th>0</th>\n",
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" <td>3.78</td>\n",
" <td>68.93</td>\n",
" <td>0.77</td>\n",
" <td>26.512</td>\n",
" <td>27.404</td>\n",
" <td>29.96</td>\n",
" <td>54.71</td>\n",
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" <th>2</th>\n",
" <td>2720109084</td>\n",
" <td>3.48</td>\n",
" <td>69.60</td>\n",
" <td>0.76</td>\n",
" <td>26.148</td>\n",
" <td>27.578</td>\n",
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" <td>66.71</td>\n",
" <td>0.76</td>\n",
" <td>29.055</td>\n",
" <td>26.338</td>\n",
" <td>28.58</td>\n",
" <td>53.87</td>\n",
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" <td>2720062721</td>\n",
" <td>3.87</td>\n",
" <td>68.78</td>\n",
" <td>0.80</td>\n",
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" <td>27.280</td>\n",
" <td>29.97</td>\n",
" <td>54.78</td>\n",
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" <tr>\n",
" <th>...</th>\n",
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" <td>4.12</td>\n",
" <td>68.85</td>\n",
" <td>0.97</td>\n",
" <td>26.055</td>\n",
" <td>27.864</td>\n",
" <td>32.94</td>\n",
" <td>51.89</td>\n",
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" <tr>\n",
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" <td>2720028633</td>\n",
" <td>3.97</td>\n",
" <td>67.04</td>\n",
" <td>0.94</td>\n",
" <td>28.043</td>\n",
" <td>27.368</td>\n",
" <td>31.88</td>\n",
" <td>51.38</td>\n",
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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",
" </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",
" <td>27.760</td>\n",
" <td>26.616</td>\n",
" <td>31.65</td>\n",
" <td>50.56</td>\n",
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" <th>227</th>\n",
" <td>2720017678</td>\n",
" <td>3.81</td>\n",
" <td>66.74</td>\n",
" <td>0.92</td>\n",
" <td>28.530</td>\n",
" <td>26.688</td>\n",
" <td>31.02</td>\n",
" <td>50.82</td>\n",
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"<p>228 rows × 8 columns</p>\n",
"</div>"
],
"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",
"0 2720110529 3.93 70.18 0.81 25.079 27.820 32.06 55.68\n",
"1 2720096883 3.78 68.93 0.77 26.512 27.404 29.96 54.71\n",
"2 2720109084 3.48 69.60 0.76 26.148 27.578 29.31 55.99\n",
"3 2720084708 3.47 66.71 0.76 29.055 26.338 28.58 53.87\n",
"4 2720062721 3.87 68.78 0.80 26.542 27.280 29.97 54.78\n",
".. ... ... ... ... ... ... ... ...\n",
"223 2720030490 4.12 68.85 0.97 26.055 27.864 32.94 51.89\n",
"224 2720028633 3.97 67.04 0.94 28.043 27.368 31.88 51.38\n",
"225 2720028634 4.12 68.42 0.96 26.493 27.886 33.16 52.00\n",
"226 2720017683 3.88 67.42 0.94 27.760 26.616 31.65 50.56\n",
"227 2720017678 3.81 66.74 0.92 28.530 26.688 31.02 50.82\n",
"\n",
"[228 rows x 8 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_0102 = pd.read_excel('./data/20240102/20240102.xlsx', header=[0,1,2])\n",
"data_0102"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f72789a6-f3fa-4ab1-8b62-999413958608",
"metadata": {},
"outputs": [
{
"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(%)']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = [''.join([y for y in x if 'Unnamed' not in y]) for x in data_0102.columns]\n",
"cols"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6ffb1989-3f45-4d1c-84c9-59b1045b7d9e",
"metadata": {},
"outputs": [],
"source": [
"data_0102.columns = cols"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9c708cc0-9f1b-4669-a350-6d24cb720794",
"metadata": {},
"outputs": [],
"source": [
"import xgboost as xgb"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "103349e1-aa4a-427a-a489-9ab28787088b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['氢Had(%)', '碳Cad(%)', '氮Nad(%)', '氧Oad(%)', '弹筒发热量Qb,adMJ/kg']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"feature_cols = cols[1:6]\n",
"feature_cols"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "839e45dc-e9c8-4956-950b-035687469c81",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe tbody tr th:only-of-type {\n",
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"</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>2720110529</td>\n",
" <td>3.93</td>\n",
" <td>70.18</td>\n",
" <td>0.81</td>\n",
" <td>25.079</td>\n",
" <td>27.820</td>\n",
" <td>32.06</td>\n",
" <td>55.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2720096883</td>\n",
" <td>3.78</td>\n",
" <td>68.93</td>\n",
" <td>0.77</td>\n",
" <td>26.512</td>\n",
" <td>27.404</td>\n",
" <td>29.96</td>\n",
" <td>54.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2720109084</td>\n",
" <td>3.48</td>\n",
" <td>69.60</td>\n",
" <td>0.76</td>\n",
" <td>26.148</td>\n",
" <td>27.578</td>\n",
" <td>29.31</td>\n",
" <td>55.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2720084708</td>\n",
" <td>3.47</td>\n",
" <td>66.71</td>\n",
" <td>0.76</td>\n",
" <td>29.055</td>\n",
" <td>26.338</td>\n",
" <td>28.58</td>\n",
" <td>53.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2720062721</td>\n",
" <td>3.87</td>\n",
" <td>68.78</td>\n",
" <td>0.80</td>\n",
" <td>26.542</td>\n",
" <td>27.280</td>\n",
" <td>29.97</td>\n",
" <td>54.78</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 挥发分Vad(%) \\\n",
"0 2720110529 3.93 70.18 0.81 25.079 27.820 32.06 \n",
"1 2720096883 3.78 68.93 0.77 26.512 27.404 29.96 \n",
"2 2720109084 3.48 69.60 0.76 26.148 27.578 29.31 \n",
"3 2720084708 3.47 66.71 0.76 29.055 26.338 28.58 \n",
"4 2720062721 3.87 68.78 0.80 26.542 27.280 29.97 \n",
"\n",
" 固定炭Fcad(%) \n",
"0 55.68 \n",
"1 54.71 \n",
"2 55.99 \n",
"3 53.87 \n",
"4 54.78 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data = data_0102.copy()\n",
"train_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "24233d12-9468-49b8-a371-0c6c508c387e",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "54cd27a6-1a8a-47c0-93d9-c948960a7842",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "bba14f71-9d69-4c82-b6bc-b9b74c725b25",
"metadata": {},
"outputs": [],
"source": [
"train_data.reset_index(drop=True, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "e3a9ad55-0132-430f-ac57-c2e7f8e8590a",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "013c6a58-65f6-48e9-8d7f-b56c87de5b11",
"metadata": {},
"outputs": [],
"source": [
"param_xgb = {\"silent\": True,\n",
" \"obj\": 'reg:linear',\n",
" \"subsample\": 1,\n",
" \"max_depth\": 15,\n",
" \"eta\": 0.3,\n",
" \"gamma\": 0,\n",
" \"lambda\": 1,\n",
" \"alpha\": 0,\n",
" \"colsample_bytree\": 0.9,}\n",
"num_round = 1000"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "086f1901-8388-47e9-ae7c-1b2709bc1e22",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold, train_test_split\n",
"kf = KFold(n_splits=10, shuffle=True, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "fb7b06af-84bc-483c-b086-7826d7befc9c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 1.9436, RMSE: 1.3941, MAE: 1.1861, MAPE: 3.94 %, R_2: 0.6095\n",
"MSE: 1.8735, RMSE: 1.3688, MAE: 1.132, MAPE: 3.77 %, R_2: 0.495\n",
"MSE: 2.0587, RMSE: 1.4348, MAE: 1.0706, MAPE: 4.08 %, R_2: 0.7862\n",
"MSE: 1.9298, RMSE: 1.3892, MAE: 1.1469, MAPE: 3.84 %, R_2: 0.5332\n",
"MSE: 1.4583, RMSE: 1.2076, MAE: 1.097, MAPE: 3.67 %, R_2: 0.6894\n",
"MSE: 2.0822, RMSE: 1.443, MAE: 1.1645, MAPE: 3.88 %, R_2: 0.5975\n",
"MSE: 1.3521, RMSE: 1.1628, MAE: 0.9905, MAPE: 3.37 %, R_2: 0.7479\n",
"MSE: 1.4057, RMSE: 1.1856, MAE: 0.9998, MAPE: 3.3 %, R_2: 0.2946\n",
"MSE: 2.2274, RMSE: 1.4925, MAE: 1.2638, MAPE: 4.19 %, R_2: 0.6785\n",
"MSE: 1.4866, RMSE: 1.2193, MAE: 1.0797, MAPE: 3.67 %, R_2: 0.7261\n"
]
},
{
"data": {
"text/plain": [
"MSE 1.781792\n",
"RMSE 1.329760\n",
"MAE 1.113084\n",
"MAPE 0.037719\n",
"R_2 0.615796\n",
"dtype: float64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eva_list = list()\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_train, Y_train = train[feature_cols], np.log1p(train['挥发分Vad(%)'])\n",
" X_valid, Y_valid = valid[feature_cols], np.log1p(valid['挥发分Vad(%)'])\n",
" dtrain = xgb.DMatrix(X_train, Y_train)\n",
" dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
" watchlist = [(dvalid, 'eval')]\n",
" gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
" early_stopping_rounds=50, verbose_eval=False)\n",
" y_pred = np.expm1(gb_model.predict(xgb.DMatrix(X_valid)))\n",
" y_true = np.expm1(Y_valid.values)\n",
" MSE = mean_squared_error(y_true, y_pred)\n",
" RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\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('MSE:', round(MSE, 4), end=', ')\n",
" print('RMSE:', round(RMSE, 4), end=', ')\n",
" print('MAE:', round(MAE, 4), end=', ')\n",
" print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n",
" print('R_2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n",
" eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n",
"data_df = pd.DataFrame.from_records(eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R_2'])\n",
"data_df.mean()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "90841cb7-4f28-4a33-93ac-93df69f1a5a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 4.6724, RMSE: 2.1616, MAE: 1.7297, MAPE: 3.42 %, R2: 0.8346\n",
"MSE: 3.0512, RMSE: 1.7468, MAE: 1.4485, MAPE: 2.62 %, R2: 0.8011\n",
"MSE: 7.6672, RMSE: 2.769, MAE: 1.951, MAPE: 4.56 %, R2: 0.8856\n",
"MSE: 4.0334, RMSE: 2.0083, MAE: 1.487, MAPE: 2.77 %, R2: 0.8216\n",
"MSE: 2.6382, RMSE: 1.6243, MAE: 1.1551, MAPE: 2.12 %, R2: 0.846\n",
"MSE: 5.8097, RMSE: 2.4103, MAE: 1.8683, MAPE: 3.8 %, R2: 0.83\n",
"MSE: 2.3446, RMSE: 1.5312, MAE: 1.1294, MAPE: 2.28 %, R2: 0.9069\n",
"MSE: 3.0069, RMSE: 1.734, MAE: 1.3782, MAPE: 2.46 %, R2: 0.6541\n",
"MSE: 4.1652, RMSE: 2.0409, MAE: 1.5685, MAPE: 3.2 %, R2: 0.859\n",
"MSE: 4.2023, RMSE: 2.05, MAE: 1.6284, MAPE: 3.2 %, R2: 0.869\n"
]
},
{
"data": {
"text/plain": [
"MSE 4.159107\n",
"RMSE 2.007631\n",
"MAE 1.534427\n",
"MAPE 0.030424\n",
"R2 0.830794\n",
"dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eva_list = list()\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_train, Y_train = train[feature_cols], np.log1p(train['固定炭Fcad(%)'])\n",
" X_valid, Y_valid = valid[feature_cols], np.log1p(valid['固定炭Fcad(%)'])\n",
" dtrain = xgb.DMatrix(X_train, Y_train)\n",
" dvalid = xgb.DMatrix(X_valid, Y_valid)\n",
" watchlist = [(dvalid, 'eval')]\n",
" gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,\n",
" early_stopping_rounds=50, verbose_eval=False)\n",
" y_pred = np.expm1(gb_model.predict(xgb.DMatrix(X_valid)))\n",
" y_true = np.expm1(Y_valid.values)\n",
" MSE = mean_squared_error(y_true, y_pred)\n",
" RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\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('MSE:', round(MSE, 4), end=', ')\n",
" print('RMSE:', round(RMSE, 4), end=', ')\n",
" print('MAE:', round(MAE, 4), end=', ')\n",
" print('MAPE:', round(MAPE*100, 2), '%', end=', ')\n",
" print('R2:', round(R_2, 4)) #R方为负就说明拟合效果比平均值差\n",
" eva_list.append([MSE, RMSE, MAE, MAPE, R_2])\n",
"data_df = pd.DataFrame.from_records(eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R2'])\n",
"data_df.mean()"
]
},
{
"cell_type": "code",
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"id": "aa67bc97-1258-44bb-9dae-14ace1661ff6",
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"outputs": [
{
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" <th>十折交叉验证均值</th>\n",
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" MSE RMSE MAE MAPE R2\n",
"十折交叉验证均值 4.159107 2.007631 1.534427 0.030424 0.830794"
]
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