# import BGOsampling after installation 
# 安装后, 通过此命令调用BGOsampling类
import Bgolearn.BGOsampling as BGOS

# import your dataset (Samples have been characterized)
# 导入研究的数据集(已经表征过的样本)
data = pd.read_csv('data.csv') 
# features 
x = data.iloc[:,:-1]
# response / target 
y = data.iloc[:,-1]

# virtual samples which have same feature dimension with x
# 设计的虚拟样本, 与x具有相同的维度
vs = pd.read_csv('virtual_data.csv') 

# instantiate class
# 实例化类 Bgolearn
Bgolearn = BGOS.Bgolearn() 

# Pass parameters to the function
# 传入参数
Mymodel = Bgolearn.fit(data_matrix = x, Measured_response = y, virtual_samples = vs)

# derive the result by EI
# 通过EI导出结果
Mymodel.EI()

from BgoKit import ToolKit
# vs is the virtual samples
# score_1,score_2 are output of Bgolearn
# score_1, _= Mymodel_1.EI() ; score_2, _= Mymodel_2.EI()

Model = ToolKit.MultiOpt(vs,[score_1,score_2])
Model.BiSearch()
Model.plot_distribution()

Thank you for choosing Bgolearn for material design. 
Bgolearn is developed to facilitate the application of machine learning in research.
Bgolearn is designed for optimizing single-target material properties.
The BgoKit package is being developed to facilitate multi-task design.


If you need to perform multi-target optimization, here are two important reminders:

1. Multi-tasks can be converted into a single task using domain knowledge. 
For example, you can use a weighted linear combination in the simplest situation. That is, y = w*y1 + y2...

2. Multi-tasks can be optimized using Pareto fronts. 
Bgolearn will return two arrays based on your dataset: 
the first array is a evaluation score for each virtual sample, 
while the second array is the recommended data considering only the current optimized target.

The first array is crucial for multi-task optimization. 
For instance, in a two-task optimization scenario, you can evaluate each candidate twice for the two separate targets. 
Then, plot the score of target 1 for each sample on the x-axis and the score of target 2 on the y-axis. 
The trade-off consideration is to select the data located in the front of the banana curve.

I am delighted to invite you to participate in the development of Bgolearn. 
If you have any issues or suggestions, please feel free to contact me at binjacobcao@gmail.com.