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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 | ||
|
|
@@ -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 <[email protected]> | ||
| 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 ([email protected]) | ||
| 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... | ||
|
|
@@ -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 [email protected]. | ||
| ================================================================ | ||
| 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 . | ||
|
|
@@ -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 | ||
|
|
@@ -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 ; | ||
|
|
@@ -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() | ||
|
|
@@ -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 | ||
|
|
@@ -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) | ||
|
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|
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| os.makedirs('Bgolearn', exist_ok=True) | ||
|
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| print('Fitting goodness on training dataset: \n' + str(' RMSE = %f' % _RMSE) +' '+ str(' MAE = %f' % _MAE) | ||
| +' '+ str(' R2 = %f' % _R2)) | ||
|
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||
|
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@@ -303,17 +369,25 @@ def fit_pre(self,xtrain,ytrain,xtest,ret_std = 0.0): | |
|
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| 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') | ||
|
|
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| 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') | ||
|
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|
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| 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: | ||
|
|
@@ -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) | ||
|
|
@@ -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] | ||
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