# 加载数据 import torch from joblib import dump, load import torch.utils.data as Data import numpy as np import pandas as pd import torch import torch.nn as nn # 参数与配置 torch.manual_seed(100) # 设置随机种子,以使实验结果具有可重复性 device = torch.device("cuda" if torch.cuda.is_...
("创建模型") from sklearn.linear_model import LogisticRegression global model model = LogisticRegression(penalty = 'l2').fit(x_train,y_train) ### 保存模型 def save_model(): print("保存模型") from sklearn.externals import joblib joblib.dump(model,'model.pkl') ### 模型验证 def validate...
# 需要导入模块: import pydotplus [as 别名]# 或者: from pydotplus importgraph_from_dot_data[as 别名]deftest_attribute_with_implicit_value(self):d ='digraph {\na -> b[label="hi", decorate];\n}'g = pydotplus.graph_from_dot_data(d) attrs = g.get_edges()[0].get_attributes() self....
import joblib import pandas as pd from bg_utils import transform, recommend_games from tqdm import tqdm from yaml import safe_dump, safe_loadpd.options.display.max_columns = 100 pd.options.display.max_rows = 500 pd.options.display.float_format = "{:.6g}".format#...
2019-12-18 15:40 −sklearn 中模型保存的两种方法 一、 sklearn中提供了高效的模型持久化模块joblib,将模型保存至硬盘。 from sklearn.externals import joblib #lr是一个LogisticRegression模型 joblib.dump(lr, ... junneyang 0 3563 vue中的import {} from '@/api/api' ...
joblib.dump(clf,'output/CART.pkl') 开发者ID:StevenLOL,项目名称:kdd99-scikit,代码行数:17,代码来源:CART_Trainer.py 示例15: render_output_pydot ▲点赞 1▼ defrender_output_pydot(self, dotdata, **kwargs):"""Renders the image using pydot"""ifnotHAS_PYDOT:raiseCommandError("You need to ...
fromsklearn.externalsimportjoblib joblib.dump(model,'model.pkl') ### 模型验证 defvalidate_model(): print("模型验证") print(model.score(x_valid,y_valid)) ### 模型预测 defpredict_model(): print("模型预测") globalpred pred=model.predict_proba(x_test) ...
接着,我们使用joblib.dump方法将训练好的模型保存到磁盘上,并使用joblib.load方法加载模型进行预测。 这样,你就可以避免ImportError并正确地使用joblib库了。
import numpy as np import pandas as pd from joblib import Parallel, delayed from optuna import create_study from optuna.storages import RDBStorage from sklearn import model_selection from tqdm import tqdm from .utils import (OptunaObjective, init_db, run_final_classification, run_optuna_optimizatio...
('explainer3.sav', 'wb') as kernel: pickle.dump(explainer, kernel) except Exception as error: print('4th\n', error) try: joblib.dump(explainer, 'explainer4.bz2') except Exception as error: print('5th\n', error) try: joblib.dump(explainer, 'explainer5.bz2', compress=('bz2', 9))...