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@Bin-Cao
Bin-Cao committed Jan 7, 2024
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162 changes: 141 additions & 21 deletions Bgolearn/BGOsampling.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,18 @@
import inspect
import os
import time
from typing import Any
import warnings
import numpy as np
import pandas as pd
import copy
from typing import Union
from .BGOmax import Global_max
from .BGOmin import Global_min
from .BGOclf import Boundary
from .BGO_eval import BGO_Efficient
import datetime
from art import text2art
from sklearn.utils import resample
from .BgolearnFuns.BGOmax import Global_max
from .BgolearnFuns.BGOmin import Global_min
from .BgolearnFuns.BGOclf import Boundary
from .BgolearnFuns.BGO_eval import BGO_Efficient
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import LeaveOneOut
from sklearn.metrics import r2_score
Expand All @@ -18,19 +21,49 @@
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, WhiteKernel
from sklearn.model_selection import KFold
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
from sklearn.neural_network import MLPRegressor

class Bgolearn(object):
def fit(self,data_matrix, Measured_response, virtual_samples, Mission ='Regression', Classifier = 'GaussianProcess',noise_std = None, Kriging_model = None, opt_num = 1 ,min_search = True, CV_test = False, )-> Union[Boundary, Global_max, Global_min]:
def __init__(self) -> None:
os.makedirs('Bgolearn', exist_ok=True)

now = datetime.datetime.now()
formatted_date_time = now.strftime('%Y-%m-%d %H:%M:%S')
print(text2art("Bgolearn"))
print('Package Name : Bgolearn')
print('Author : Bin CAO, HKUST(GZ)')
print('Intro : https://bgolearn.netlify.app/')
print('URL : https://github.com/Bin-Cao/Bgolearn')
print("Citation Format Suggestion:")
print('[Bin CAO et al]. "Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys", [2023], [DOI : http://dx.doi.org/10.2139/ssrn.4686075].')
print('Executed on :',formatted_date_time, ' | Have a great day.')
print('='*80)


def fit(self,data_matrix, Measured_response, virtual_samples, Mission ='Regression', Classifier = 'GaussianProcess',noise_std = None, Kriging_model = None, opt_num = 1 ,min_search = True, CV_test = False, Dynamic_W = False,seed=42):

"""
================================================================
PACKAGE: Bayesian global optimization-learn (Bgolearn) .
PACKAGE: Bayesian global optimization-learn (Bgolearn) package .
Author: Bin CAO <binjacobcao@gmail.com>
Guangzhou Municipal Key Laboratory of Materials Informatics, Advanced Materials Thrust,
Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, Guangdong, China
================================================================
Please feel free to open issues in the Github :
https://github.com/Bin-Cao/Bgolearn
or
contact Mr.Bin Cao (bcao686@connect.hkust-gz.edu.cn)
in case of any problems/comments/suggestions in using the code.
==================================================================
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.
If you need to perform multi-target optimization, here are two important reminders:
The BgoKit package is being developed to facilitate multi-task design.
If you need to perform multi-target optimization, here are two kind 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...
Expand All @@ -47,8 +80,9 @@ def fit(self,data_matrix, Measured_response, virtual_samples, Mission ='Regressi
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.
================================================================
Bin Cao, Advanced Materials Thrust, Hong Kong University of Science and Technology (Guangzhou).
Reference :
document : https://bgolearn.netlify.app/
================================================================
:param data_matrix: data matrix of training dataset, X .
Expand Down Expand Up @@ -78,12 +112,19 @@ def fit(self,data_matrix, Measured_response, virtual_samples, Mission ='Regressi
if noise_std is not None, a noise value will be estimated by maximum likelihood
on training dataset.
:param Kriging_model (default None): a user defined callable Kriging model, has an attribute of <fit_pre>
:param Kriging_model (default None):
str, Kriging_model = 'SVM', 'RF', 'AdaB', 'MLP'
The machine learning models will be implemented: Support Vector Machine (SVM),
Random Forest(RF), AdaBoost(AdaB), and Multi-Layer Perceptron (MLP).
The estimation uncertainity will be determined by Boostsrap sampling.
or
a user defined callable Kriging model, has an attribute of <fit_pre>
if user isn't applied one, Bgolearn will call a pre-set Kriging model
atribute <fit_pre> :
input -> xtrain, ytrain, xtest ;
output -> predicted mean and std of xtest
e.g. (take GaussianProcessRegressor in sklearn as an example):
e.g. (take GaussianProcessRegressor in sklearn):
class Kriging_model(object):
def fit_pre(self,xtrain,ytrain,xtest):
# instantiated model
Expand All @@ -93,6 +134,20 @@ def fit_pre(self,xtrain,ytrain,xtest):
mean,std = mdoel.predict(xtest,return_std=True)
return mean,std
e.g. (MultiModels estimations):
class Kriging_model(object):
def fit_pre(self,xtrain,ytrain,xtest):
# instantiated model
pre_1 = SVR(C=10).fit(xtrain,ytrain).predict(xtest) # model_1
pre_2 = SVR(C=50).fit(xtrain,ytrain).predict(xtest) # model_2
pre_3 = SVR(C=80).fit(xtrain,ytrain).predict(xtest) # model_3
model_1 , model_2 , model_3 can be changed to any ML models you desire
# defined the attribute's outputs
stacked_array = np.vstack((pre_1,pre_2,pre_3))
means = np.mean(stacked_array, axis=0)
std = np.sqrt(np.var(stacked_array), axis=0)
return mean, std
:param opt_num: the number of recommended candidates for next iteration, default 1.
:param min_search: default True -> searching the global minimum ;
Expand All @@ -105,11 +160,17 @@ def fit_pre(self,xtrain,ytrain,xtest):
:return: 1: array; potential of each candidate. 2: array/float; recommended candidate(s).
"""

# Fit and transform the input data matrix
Xname = data_matrix.columns
virtual_samples = preprocess_data(virtual_samples)
data_matrix = preprocess_data(data_matrix)
Measured_response = preprocess_data(Measured_response)
if Dynamic_W == False :
pass
elif Dynamic_W == True :
data_matrix, Measured_response = Resampling(data_matrix,Measured_response,min_search,seed )


row_features = copy.deepcopy(virtual_samples)
scaler = MinMaxScaler()
Expand Down Expand Up @@ -181,6 +242,14 @@ def fit_pre(self,xtrain,ytrain,xtest,ret_std = 0.0):
mean,std = mdoel.predict(xtest,return_std=True)
return mean,std
print('The internal model is instantiated with heterogenous noise')
elif type(Kriging_model) == str:
model_type = Kriging_model
class Kriging_model(object):
def fit_pre(self,xtrain,ytrain,xtest,):
mean,std = Bgolearn_model(xtrain,ytrain,xtest,model_type)
return mean,std
print('The internal model is assigned')

else:
print('The external model is instantiated')
pass
Expand Down Expand Up @@ -292,9 +361,6 @@ def fit_pre(self,xtrain,ytrain,xtest,ret_std = 0.0):
_MAE = mean_absolute_error(Y_true,_Y_pre)
_R2 = r2_score(Y_true,_Y_pre)


os.makedirs('Bgolearn', exist_ok=True)

print('Fitting goodness on training dataset: \n' + str(' RMSE = %f' % _RMSE) +' '+ str(' MAE = %f' % _MAE)
+' '+ str(' R2 = %f' % _R2))

Expand All @@ -303,17 +369,25 @@ def fit_pre(self,xtrain,ytrain,xtest,ret_std = 0.0):



results_dataset.to_csv('./Bgolearn/predictionsBy{name}_{year}.{month}.{day}_{hour}.{minute}.csv'.format(name=docu_name(CV_test),year=namey, month=nameM, day=named, hour=nameh,
results_dataset.to_csv('./Bgolearn/predictions{name}_{year}_{month}_{day}_{hour}_{minute}.csv'.format(name=docu_name(CV_test),year=namey, month=nameM, day=named, hour=nameh,
minute=namem),encoding='utf-8-sig')

_results_dataset.to_csv('./Bgolearn/predictionsOnTrainingDataset_{year}.{month}.{day}_{hour}.{minute}.csv'.format(year=namey, month=nameM, day=named, hour=nameh,
_results_dataset.to_csv('./Bgolearn/predictionsOnTrainingDataset_{year}_{month}_{day}_{hour}_{minute}.csv'.format(year=namey, month=nameM, day=named, hour=nameh,
minute=namem),encoding='utf-8-sig')

V_Xmatrix.to_csv('./Bgolearn/predictionsOfVirtualSampels_{year}.{month}.{day}_{hour}.{minute}.csv'.format(year=namey, month=nameM, day=named, hour=nameh,
V_Xmatrix.to_csv('./Bgolearn/predictionsOfVirtualSampels_{year}_{month}_{day}_{hour}_{minute}.csv'.format(year=namey, month=nameM, day=named, hour=nameh,
minute=namem),encoding='utf-8-sig')





arv_vs = pd.DataFrame(np.array(virtual_samples))
arv_vs.columns = Xname
pre,_ = Kriging_model().fit_pre(data_matrix, Measured_response, virtual_samples)
arv_vs['Y'] = np.array(pre)
arv_vs.to_csv('./Bgolearn/PredictionsByBgolearn_{year}_{month}_{day}_{hour}_{minute}.csv'.format(year=namey, month=nameM, day=named, hour=nameh,
minute=namem), encoding='utf-8-sig')

# BGO
if min_search == True:
Expand All @@ -331,8 +405,6 @@ def test(self,Ture_fun, Def_Domain,noise_std = 1e-5, Kriging_model = None, opt_n
"""
PACKAGE: Bayesian global optimization learn .
6 Apr 2023, version 1.4, Bin Cao, ZheJiang LAB, Hangzhou, CHINA.
:param Ture_fun: the true function being evaluated. e.g.,
def function(X):
X = np.array(X)
Expand Down Expand Up @@ -456,3 +528,51 @@ def preprocess_data(data):
data = np.array(data).reshape(-1, 1)
data = np.array(data)
return data


def Bgolearn_model(xtrain,ytrain,xtest,Kriging_model):
models = {
'SVM': SVR(),
'RF': RandomForestRegressor(),
'AdaB': AdaBoostRegressor(),
'MLP': MLPRegressor()
}
try:
Bgo_model = models[Kriging_model]
print('Bgolearn model : ', Bgo_model)
except:
print('Type Error: Kriging_model, please check your input of param Kriging_model')

all_predictions = []
for _ in range(10):
# Perform Bootstrap sampling
X_bootstrap, y_bootstrap = resample(xtrain, ytrain)
predictions = Bgo_model.fit(X_bootstrap, y_bootstrap).predict(xtest)
# Store the predictions
all_predictions.append(predictions)

# Convert the list of predictions to a NumPy array for easier calculations
all_predictions = np.array(all_predictions)
# Calculate mean and standard deviation across the samples
mean = np.mean(all_predictions, axis=0)
std = np.std(all_predictions, axis=0)
return mean, std

def Resampling(data_matrix,Measured_response,min_search,seed_):

np.random.seed(seed_)
max_value = max(Measured_response)
min_value = min(Measured_response)
prob = (Measured_response - min_value) / (max_value - min_value)
if min_search == True:
prob = 1 - prob
cdf = np.cumsum(prob)
cdf_ = cdf / cdf[-1]
uniform_samples = np.random.random_sample(len(Measured_response))
bootstrap_idx = cdf_.searchsorted(uniform_samples, side='right')
# searchsorted returns a scalar
bootstrap_idx = np.array(bootstrap_idx, copy=False)
print('Importance resampling is APPLIED','\n')


return data_matrix[bootstrap_idx], Measured_response[bootstrap_idx]
20 changes: 10 additions & 10 deletions Bgolearn/BGO_eval.py → Bgolearn/BgolearnFuns/BGO_eval.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ def Trail(self,trails = 100, Max_inter = 500, tol = 0.1, ini_nb = None, UTFs = '
for j in range(Max_inter):
# Max_inter = 500 is an enough large number
BGO_mdoel = Global_min(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -157,7 +157,7 @@ def Trail(self,trails = 100, Max_inter = 500, tol = 0.1, ini_nb = None, UTFs = '
for j in range(Max_inter):
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_max(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -225,7 +225,7 @@ def Opp_Cost(self,trails = 10, Max_inter = 500, threshold = 0.05, ini_nb = None,
Iter += 1
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_min(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -258,7 +258,7 @@ def Opp_Cost(self,trails = 10, Max_inter = 500, threshold = 0.05, ini_nb = None,
Iter +=1
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_max(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -340,7 +340,7 @@ def Pdf(self,trails = 200, Max_inter = 500, tol = 0.1, num_bins = 20, ini_nb = N
for j in range(Max_inter):
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_min(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand All @@ -364,7 +364,7 @@ def Pdf(self,trails = 200, Max_inter = 500, tol = 0.1, num_bins = 20, ini_nb = N
for j in range(Max_inter):
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_min(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, Ref_UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -398,7 +398,7 @@ def Pdf(self,trails = 200, Max_inter = 500, tol = 0.1, num_bins = 20, ini_nb = N
for j in range(Max_inter):
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_max(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand All @@ -422,7 +422,7 @@ def Pdf(self,trails = 200, Max_inter = 500, tol = 0.1, num_bins = 20, ini_nb = N
for j in range(Max_inter):
# Max_inter = 500 is a enough large number
BGO_mdoel = Global_max(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, Ref_UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -505,7 +505,7 @@ def Count(self,trails = 100, Max_inter = 5, tol = 0.1, ini_nb = None, UTFs = 'EI
for j in range(Max_inter):
# Max_inter is the threshold
BGO_mdoel = Global_min(self.Kriging_model,train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down Expand Up @@ -534,7 +534,7 @@ def Count(self,trails = 100, Max_inter = 5, tol = 0.1, ini_nb = None, UTFs = 'EI
for j in range(Max_inter):
# Max_inter is the threshold
BGO_mdoel = Global_max(self.Kriging_model, train_X, train_Y, self.Def_Domain,
self.opt_num, self.ret_noise)
self.opt_num, self.ret_noise,self.Def_Domain)
_, return_x = self.Call(BGO_mdoel, UTFs, param_one, param_two)
new_X = return_x
new_Y = self.Ture_fun(new_X)
Expand Down
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