465 lines
155 KiB
Plaintext
465 lines
155 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "077f5f8a-ffe5-4405-8806-1b5559140a5d",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install pandas hyperopt xgboost scikit-learn matplotlib numpy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a3901bba-d66d-4358-89a7-50dc4b3dd91e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n",
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"from sklearn.model_selection import train_test_split\n",
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"import numpy as np"
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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": "a4713d33-c5a2-4f49-8aed-873069543bec",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>比表面积</th>\n",
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" <th>总孔体积</th>\n",
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" <th>微孔体积</th>\n",
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" <th>平均孔径</th>\n",
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" <th>氮掺杂量at</th>\n",
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" <th>氧掺杂量</th>\n",
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" <th>ID/IG</th>\n",
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" <th>电流密度</th>\n",
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" <th>比电容</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1141.8</td>\n",
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" <td>0.46</td>\n",
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" <td>0.42</td>\n",
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" <td>1.61</td>\n",
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" <td>1.74</td>\n",
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" <td>3.84</td>\n",
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" <td>1.1</td>\n",
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" <td>0.5</td>\n",
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" <td>206.5</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>1141.8</td>\n",
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" <td>0.46</td>\n",
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" <td>0.42</td>\n",
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" <td>1.61</td>\n",
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" <td>1.74</td>\n",
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" <td>3.84</td>\n",
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" <td>1.1</td>\n",
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" <td>1.0</td>\n",
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" <td>179.1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1141.8</td>\n",
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" <td>0.46</td>\n",
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" <td>0.42</td>\n",
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" <td>1.61</td>\n",
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" <td>1.74</td>\n",
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" <td>3.84</td>\n",
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" <td>1.1</td>\n",
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" <td>2.0</td>\n",
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" <td>163.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1141.8</td>\n",
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" <td>0.46</td>\n",
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" <td>0.42</td>\n",
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" <td>1.61</td>\n",
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" <td>1.74</td>\n",
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" <td>3.84</td>\n",
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" <td>1.1</td>\n",
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" <td>5.0</td>\n",
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" <td>146.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1141.8</td>\n",
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" <td>0.46</td>\n",
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" <td>0.42</td>\n",
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" <td>1.61</td>\n",
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" <td>1.74</td>\n",
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" <td>3.84</td>\n",
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" <td>1.1</td>\n",
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" <td>10.0</td>\n",
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" <td>137.8</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 比表面积 总孔体积 微孔体积 平均孔径 氮掺杂量at 氧掺杂量 ID/IG 电流密度 比电容\n",
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"0 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 0.5 206.5\n",
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"1 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 1.0 179.1\n",
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"2 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 2.0 163.3\n",
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"3 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 5.0 146.0\n",
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"4 1141.8 0.46 0.42 1.61 1.74 3.84 1.1 10.0 137.8"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data = pd.read_excel('./data/20240617/电容性能新.xlsx')\n",
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"data.head()"
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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": "24f58281-9f13-49ef-b44d-81d0644d6976",
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"metadata": {},
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"outputs": [],
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"source": [
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"out_cols = ['比电容']"
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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": 4,
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"id": "92d5da6b-f714-4a78-9aa7-7cf9dff1d0a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"feature_cols = [x for x in data.columns if x not in out_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": "e4946bd7-ae94-4981-82ed-66e2b496e035",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data = data.reset_index(drop=True)"
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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": "4109685a-4d5b-4c63-b4e2-eb9db3989d02",
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"metadata": {},
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"outputs": [],
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"source": [
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"import xgboost as xgb\n",
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"from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score"
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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": 7,
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"id": "a140942f-4206-49e5-a51b-932c55170436",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 定义超参数的搜索空间\n",
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"space = {\n",
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" 'eta': hp.loguniform('eta', -5, 0), # 学习率，搜索范围是 [1e-5, 1]\n",
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" 'max_depth': hp.choice('max_depth', range(5, 30)), # 树的最大深度，搜索范围是 [1, 10]\n",
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" 'min_child_weight': hp.uniform('min_child_weight', 0, 10), # 子节点最小的权重和\n",
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" 'gamma': hp.loguniform('gamma', -5, 0), # 叶子节点分裂所需的最小损失减少\n",
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" 'subsample': hp.uniform('subsample', 0.5, 1), # 训练集的采样率\n",
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" 'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1), # 特征的采样率\n",
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"}"
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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": 8,
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"id": "7dd2dc64-0e80-4504-b84a-1d549f2cf90d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 划分训练集和测试集\n",
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"X_train, X_test, y_train, y_test = train_test_split(train_data[feature_cols], \n",
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" train_data[out_cols], \n",
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" test_size=0.3, random_state=42)"
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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": 9,
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"id": "68669fde-52ab-4e62-8efc-b60dcf13734b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 定义目标函数，用于评估模型的性能\n",
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"def objective(params):\n",
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" # 创建决策树分类器实例\n",
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" gbr = xgb.XGBRegressor(**params)\n",
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" # 训练模型\n",
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" gbr.fit(X_train, y_train)\n",
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" # 使用模型进行预测\n",
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" y_pred = gbr.predict(X_test)\n",
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" mae = mean_absolute_error(y_test, y_pred)\n",
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" return {'loss': mae, 'status': STATUS_OK}"
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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": 10,
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"id": "e89097ea-fee2-4298-81a2-ff528688857e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"100%|██████████| 100/100 [00:08<00:00, 11.55trial/s, best loss: 12.132344347686164]\n"
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]
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}
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],
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"source": [
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"# 创建 Trials 对象来记录搜索历史\n",
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"trials = Trials()\n",
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"\n",
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"# 使用 fmin 函数进行超参数优化\n",
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"best_params = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "0ccfb873-6f5a-4606-9b17-a63cdbcf8acc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'colsample_bytree': 0.8857035476046763, 'eta': 0.11588664776521924, 'gamma': 0.007847746718601799, 'max_depth': 10, 'min_child_weight': 6.396614191886977, 'subsample': 0.7070880429614513}\n"
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]
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}
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],
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"source": [
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"print(best_params)"
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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": 12,
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"id": "2bbdcd34-16c1-43ba-b249-6c7d54db8ac2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import KFold, train_test_split\n",
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"kf = KFold(n_splits=10, shuffle=True, random_state=42)"
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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": 13,
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"id": "94af2a3a-6f61-46bf-8cd4-2b7e0da8b2c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"num_boost_round = 1000"
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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": 14,
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||
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"id": "f17eadb3-4767-4eca-bbed-880bf9cbb7a3",
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"metadata": {},
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||
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"outputs": [],
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||
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"source": [
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"import matplotlib.pyplot as plt"
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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": 15,
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||
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"id": "5bfcc8aa-f13c-4a7d-9d15-b79087e11017",
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"metadata": {},
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||
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"outputs": [],
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"source": [
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"plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"] # 设置字体\n",
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"plt.rcParams[\"axes.unicode_minus\"] = False # 正常显示负号"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "db4dbc2d-534e-4a7e-b45c-ea25ab269502",
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"metadata": {},
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"outputs": [
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{
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||
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MSE: 164.2816, RMSE: 12.8172, MAE: 9.1819, MAPE: 4.33 %, R_2: 0.9534\n",
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"MSE: 172.8146, RMSE: 13.1459, MAE: 8.4597, MAPE: 4.24 %, R_2: 0.9475\n",
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|
"MSE: 105.637, RMSE: 10.278, MAE: 7.1138, MAPE: 3.19 %, R_2: 0.9736\n",
|
||
|
|
"MSE: 306.2548, RMSE: 17.5001, MAE: 10.3353, MAPE: 4.27 %, R_2: 0.9348\n",
|
||
|
|
"MSE: 212.1827, RMSE: 14.5665, MAE: 10.452, MAPE: 4.64 %, R_2: 0.9467\n",
|
||
|
|
"MSE: 311.2193, RMSE: 17.6414, MAE: 10.62, MAPE: 3.97 %, R_2: 0.929\n",
|
||
|
|
"MSE: 479.0079, RMSE: 21.8862, MAE: 11.6752, MAPE: 5.11 %, R_2: 0.8952\n",
|
||
|
|
"MSE: 153.6563, RMSE: 12.3958, MAE: 8.8708, MAPE: 4.44 %, R_2: 0.9502\n",
|
||
|
|
"MSE: 285.905, RMSE: 16.9087, MAE: 10.4152, MAPE: 5.35 %, R_2: 0.9522\n",
|
||
|
|
"MSE: 570.9538, RMSE: 23.8946, MAE: 12.4216, MAPE: 5.98 %, R_2: 0.8954\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eva_list = list()\n",
|
||
|
|
"eva_cols = ['MSE', 'RMSE', 'MAE', 'MAPE', 'R2']\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], train[out_cols]\n",
|
||
|
|
" X_valid, Y_valid = valid[feature_cols], valid[out_cols]\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(best_params, dtrain, num_boost_round, evals=watchlist,\n",
|
||
|
|
" early_stopping_rounds=100, verbose_eval=False)\n",
|
||
|
|
" y_pred = gb_model.predict(xgb.DMatrix(X_valid))\n",
|
||
|
|
" y_true = 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])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"id": "844d8b9f-a820-4d59-85f5-df434ca3da8d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"eva_df = pd.DataFrame.from_records(eva_list, columns=eva_cols)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "6c7c4910-81a2-4703-948a-152ccc7b859d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"MSE 276.191297\n",
|
||
|
|
"RMSE 16.103459\n",
|
||
|
|
"MAE 9.954548\n",
|
||
|
|
"MAPE 0.045525\n",
|
||
|
|
"R2 0.937810\n",
|
||
|
|
"dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eva_df.mean()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "0beadfa6-eef9-47fd-adb7-8ed245fa942d",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.figure(figsize=(12, 8))\n",
|
||
|
|
"plt.plot(range(len(y_true)), y_true, 'o-', label='real')\n",
|
||
|
|
"plt.plot(range(len(y_pred)), y_pred, '*-', label='pred')\n",
|
||
|
|
"plt.legend(loc='best')\n",
|
||
|
|
"plt.title(f'{out_cols}')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "b69f612e-3548-41d6-9512-7591c47ca50e",
|
||
|
|
"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": "3.8.16"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|