Repository: XuhaoWan/DMCP
Branch: main
Commit: 2572a3b252c2
Files: 24
Total size: 78.3 KB
Directory structure:
gitextract_ekw9y8z4/
├── .gitignore
├── DMCP.py
├── LICENSE
├── MANIFEST.in
├── Models/
│ ├── ENR.py
│ ├── ETR.py
│ ├── FNN.py
│ ├── GBR.py
│ ├── GPR.py
│ ├── KNR.py
│ ├── KRR.py
│ ├── Lasso.py
│ ├── MLP.py
│ ├── RFR.py
│ └── SVR.py
├── README.md
├── Visualization/
│ ├── Violin.py
│ ├── bar.py
│ ├── pearson.py
│ ├── pie.py
│ └── scatter.py
├── manual
├── requirements.txt
└── setup.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
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================================================
FILE: DMCP.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
from Models.RFR import RFR
from Models.KRR import KRR
from Models.GBR import GBR
from Models.KNR import KNR
from Models.FNN import FNN
from Models.SVR import SVR
from Models.Lasso import LSO
from Models.ENR import ENR
from Models.GPR import GPR
from Models.ETR import ETR
from Models.MLP import MLP
from Visualization.Violin import plot_Violin
from Visualization.bar import plot_bar
from Visualization.scatter import plot_scatter
from Visualization.pearson import plot_pearson
from Visualization.pie import plot_pie
import os
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer
import f90nml
from multidict import CIMultiDict
from statistics import mean
input_file = f90nml.read('DMCP_input_file')
data = CIMultiDict(input_file['data'])
general = CIMultiDict(input_file['general'])
visualization = CIMultiDict(input_file['visualization'])
def parse_data():
# intrn
if 'intrn' not in data.keys():
print('No train data file')
else:
data_file = data['intrn']
data_train = np.loadtxt(data_file, delimiter=",", dtype="float")
# grept
if 'grept' not in general.keys():
iteration = 1
else:
iteration = general['grept']
train_set_RMSE = {}
train_set_R2 = {}
test_set_RMSE = {}
test_set_R2 = {}
estimator_dict = {}
for i in range(iteration):
x = preprocessing_data(data_train)
x = add_noise(x)
y = data_train[..., -1]
##psplt
if 'psplt' not in general.keys():
test_size = 0.2
else:
test_size = 1 - general['psplt']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=16)
# model_load
# gmodl
if 'gmodl' not in general:
print("Not choose model")
else:
model_list = general['gmodl']
if type(model_list) is str:
model_list = [model_list]
for model in model_list:
param = get_params(model)
model_obj = eval(model)
ML_model = model_obj()
if 'modpr' in general:
if general['modpr'] == 'ON':
ML_model.auto_tune_params(x_train, y_train)
ML_model.modify_params(param)
ML_model.build_model()
# gcrva
if 'gcrva' in general.keys():
if general['gcrva'] == 'ON':
train_rmse, train_r2, test_rmse, test_r2, estimator = ML_model.model_evaluate(x, y, general[
'gcvrn'])
if i == 0:
train_set_RMSE[model] = [train_rmse]
train_set_R2[model] = [train_r2]
test_set_RMSE[model] = [test_rmse]
test_set_R2[model] = [test_r2]
estimator_dict[model] = [estimator]
else:
train_set_RMSE[model].append(train_rmse)
train_set_R2[model].append(train_r2)
test_set_RMSE[model].append(test_rmse)
test_set_R2[model].append(test_r2)
estimator_dict[model].append(estimator)
# ML_model.calculate(x_train, x_test, y_train, y_test)
result_visualize(train_set_RMSE, train_set_R2, test_set_RMSE, test_set_R2)
optimal_model, optimal_model_name = choose_optimal_model(train_set_RMSE, estimator_dict)
predict(optimal_model, optimal_model_name)
def predict(optimal_model, optimal_model_name):
if 'intrn' not in data.keys():
print('No train data file')
else:
data_file = data['intrn']
data_train = np.loadtxt(data_file, delimiter=",", dtype="float")
x = preprocessing_data(data_train)
x = add_noise(x)
y = data_train[..., -1]
##psplt
if 'psplt' not in general.keys():
test_size = 0.2
else:
test_size = 1 - general['psplt']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size, random_state=16)
y_train_true, y_train_pred = y_train, optimal_model.predict(x_train)
y_test_true, y_test_pred = y_test, optimal_model.predict(x_test)
plot_scatter(y_train_true, y_train_pred, y_test_true, y_test_pred)
plot_pearson(optimal_model_name, x_train)
if optimal_model_name in ['GBR', 'RFR', 'ETR']:
plot_pie(optimal_model_name, optimal_model.feature_importances_)
def choose_optimal_model(model_evaluate_data, estimator_dict):
if 'fmodl' in visualization:
optimal_model_name = visualization['fmodl']
else:
model_list = []
data_list = []
for key in model_evaluate_data.keys():
model_list.append(key)
data_list.append(mean(model_evaluate_data[key]))
optimal_model_name = model_list[data_list.index(min(data_list))]
optmal_index = model_evaluate_data[optimal_model_name].index(min(model_evaluate_data[optimal_model_name]))
optimal_model = estimator_dict[optimal_model_name][optmal_index][0]
return optimal_model, optimal_model_name
# def parse_DMCP_input_file():
# input_file = f90nml.read('DMCP_input_file')
# return input_file
def result_visualize(train_set_RMSE, train_set_R2, test_set_RMSE, test_set_R2):
if 'vvoln' in visualization.keys():
if visualization['vvoln'] == 'ON':
plot_Violin('RMSE', test_set_RMSE)
plot_Violin('R2', test_set_R2)
if 'vcomp' in visualization.keys():
if visualization['vcomp'] == 'ON':
plot_bar('DMCP', train_set_RMSE, train_set_R2, test_set_RMSE, test_set_R2)
def preprocessing_data(data_train):
# pscal
if 'pscal' not in general.keys():
X = data_train[..., 0:(data_train.shape[1] - 1)]
else:
if general['pscal'] == 'OFF':
X = data_train[..., 0:(data_train.shape[1] - 1)]
elif general['pscal'] == 'NOR':
scaler = MinMaxScaler()
X = scaler.fit_transform(data_train[..., 0:(data_train.shape[1] - 1)])
elif general['pscal'] == 'STA':
scaler = StandardScaler()
X = scaler.fit_transform(data_train[..., 0:(data_train.shape[1] - 1)])
else:
scaler = Normalizer(norm='l2')
X = scaler.fit_transform(data_train[..., 0:(data_train.shape[1] - 1)])
return X
def add_noise(X):
# pnose
if 'pnose' not in general.keys():
X = X
else:
scale = general['pnose']
x_noise = np.random.normal(loc=0.0, scale=scale, size=X.shape)
X = X + x_noise
return X
def get_params(model):
if ('PR' + model) not in general.keys():
param = {}
print("Not set" + 'PR' + model)
else:
param = general['PR' + model]
param_key = param[1:(len(param) - 1):3]
param_val = param[3:(len(param) - 1):3]
param = dict(zip(param_key, param_val))
return param
def main():
parse_data()
if __name__ == "__main__":
main()
================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
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Also add information on how to contact you by electronic and paper mail.
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Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
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consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
================================================
FILE: MANIFEST.in
================================================
include requirements.txt
================================================
FILE: Models/ENR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.linear_model import ElasticNet as enr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class ENR(object):
def __init__(self):
self.params_defualt = {'alpha': 1.0,
'fit_intercept': True,
'l1_ratio': 0.5,
'normalize': False,
'precompute': False,
'max_iter': 1000,
'tol': 1e-4,
'warm_start': False,
'positive': False,
'selection': 'cyclic',
'random_state': 2}
self.tuned_parameters = {'alpha': [1.0] ,
'fit_intercept': [True],
'l1_ratio':[0.5],
'normalize': [False],
'precompute': [False],
'max_iter': [1000],
'tol': [1e-4],
'warm_start': [False],
'positive': [False],
'selection': ['cyclic'],
'random_state': [2]}
def auto_tune_params(self, x_train, y_train):
#use RMSE as the scoring
clf = GridSearchCV(
enr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = enr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/ETR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.tree import ExtraTreeRegressor as etr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class ETR(object):
def __init__(self):
self.params_defualt = {'criterion': 'mse',
'splitter': 'random',
'max_depth': None,
'min_samples_split': 2,
'min_samples_leaf': 1,
'min_weight_fraction_leaf': 0.0,
'max_features': 'auto',
'random_state': 16,
'min_impurity_decrease': 0,
'max_leaf_nodes': None,
'ccp_alpha': 0.0}
self.tuned_parameters = {'criterion': ['mse'],
'splitter': ['random'],
'max_depth': [None],
'min_samples_split': [2],
'min_samples_leaf': [1],
'min_weight_fraction_leaf': [0.0],
'max_features': ['auto'],
'random_state': [16],
'min_impurity_decrease': [0],
'max_leaf_nodes': [None],
'ccp_alpha': [0.0]}
def auto_tune_params(self, x_train, y_train):
# use RMSE as the scoring
clf = GridSearchCV(
etr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = etr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
print(self.model.feature_importances_)
return r2
================================================
FILE: Models/FNN.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data as Data
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np
from torchvision import datasets, transforms
from torch.nn import init
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
class FNN(object):
def __init__(self, x_train, x_test, y_train, y_test, params, model_evaluation, x, y):
self.x = x
self.y = y
self.x_train = torch.from_numpy(x_train).type(torch.FloatTensor)
y_train = torch.from_numpy(y_train).type(torch.FloatTensor)
self.x_test = torch.from_numpy(x_test).type(torch.FloatTensor)
y_test = torch.from_numpy(y_test).type(torch.FloatTensor)
self.y_test = torch.unsqueeze(y_test, 1)
self.y_train = torch.unsqueeze(y_train, 1)
self.params_defualt = {'BATCH_SIZE': 32,
'LR': 0.05,
'EPOCH': 50}
self.params_modify = params
self.modify_params()
torch_data = Data.TensorDataset(self.x_train, self.y_train)
self.loader = Data.DataLoader(dataset=torch_data, batch_size=self.params_defualt['BATCH_SIZE'], shuffle=True)
self.calculate()
def modify_params(self):
for key in self.params_modify:
self.params_defualt[key] = self.params_modify[key]
def calculate(self):
adam_net = Net()
opt_adam = torch.optim.Adam(adam_net.parameters(), lr=self.params_defualt['LR'])
loss_func = nn.MSELoss()
all_loss = {}
for epoch in range(self.params_defualt['EPOCH']):
print('epoch', epoch)
for step, (b_x, b_y) in enumerate(self.loader):
print('step', step)
pre = adam_net(b_x)
loss = loss_func(pre, b_y)
opt_adam.zero_grad()
loss.backward()
opt_adam.step()
all_loss[epoch + 1] = loss
print(all_loss)
yt = self.y_train.numpy()
yp = adam_net(self.x_train)
yp = yp.detach().numpy()
rmse = np.sqrt(mse(yt, yp))
r2 = r2_score(yt, yp)
yt1 = self.y_test.numpy()
yp1 = adam_net(self.x_test)
yp1 = yp1.detach().numpy()
rmset = np.sqrt(mse(yt1, yp1))
r2t = r2_score(yt1, yp1)
print(rmse)
print(r2)
print(rmset)
print(r2t)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hidden = nn.Linear(20, 32)
self.predict = nn.Linear(32, 1)
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
def weights_init(m):
if isinstance(m, nn.Linear):
init.kaiming_normal(m.weight.data)
================================================
FILE: Models/GBR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor as gbr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class GBR(object):
def __init__(self):
self.params_defualt = {'n_estimators': 500,
'max_depth': 5,
'min_samples_split': 5,
'learning_rate': 0.005,
'loss': 'huber'}
self.tuned_parameters ={'n_estimators': [500],
'max_depth': [5],
'min_samples_split': [5],
'learning_rate': [0.005, 0.01],
'loss': ['huber']}
def auto_tune_params(self, x_train, y_train):
#use RMSE as the scoring
clf = GridSearchCV(
gbr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = gbr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True, return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(),-scores['test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
#scores1 = cross_val_score(self.model, x, y, cv=cv, scoring='neg_root_mean_squared_error')
#return scores1,scores1
def calculate(self, x_train, x_test, y_train, y_test):
return self.model.feature_importances_
================================================
FILE: Models/GPR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor as gpr
from sklearn.gaussian_process.kernels import RBF, ConstantKernel
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class GPR(object):
def __init__(self):
self.params_defualt = {}
self.tuned_parameters = {}
def auto_tune_params(self, x_train, y_train):
# use RMSE as the scoring
clf = GridSearchCV(
gpr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = gpr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/KNR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.neighbors import KNeighborsRegressor as knr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class KNR(object):
def __init__(self):
self.params_defualt = {'n_neighbors': 4}
self.tuned_parameters = {'n_neighbors': [4,5]}
def auto_tune_params(self, x_train, y_train):
# use RMSE as the scoring
clf = GridSearchCV(
knr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = knr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/KRR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.kernel_ridge import KernelRidge as krr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class KRR(object):
def __init__(self):
self.params_defualt = {'alpha': 1,
'kernel': 'linear',
'gamma': 0,
'degree': 3,
'coef0': '1'}
self.tuned_parameters = {'alpha': [1,2],
'kernel': ['linear'],
'gamma': [0,0.1],
'degree': [3,4],
'coef0': ['1']}
def auto_tune_params(self, x_train, y_train):
#use RMSE as the scoring
clf = GridSearchCV(
krr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = krr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/Lasso.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.linear_model import Lasso as lso
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class LSO(object):
def __init__(self):
self.params_defualt = {'alpha': 1.0,
'fit_intercept': True,
'normalize': False,
'precompute': False,
'max_iter': 1000,
'tol': 1e-4,
'warm_start': False,
'positive': False,
'selection': 'cyclic',
'random_state': 8}
self.tuned_parameters = {'alpha': [1.0],
'fit_intercept': [True],
'normalize': [False],
'precompute': [False],
'max_iter': [1000],
'tol': [1e-4],
'warm_start': [False],
'positive': [False],
'selection': ['cyclic'],
'random_state': [8]}
def auto_tune_params(self, x_train, y_train):
# use RMSE as the scoring
clf = GridSearchCV(
lso(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = lso(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/MLP.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.neural_network import MLPRegressor as mlp
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class MLP(object):
def __init__(self):
self.params_defualt = {'learning_rate': 'constant',
'learning_rate_init': 0.001,
'batch_size': 4,
'hidden_layer_sizes': (20, 32),
'random_state': 1,
'max_iter': 100000,
'activation': 'logistic'}
self.tuned_parameters = {'learning_rate':['constant'],
'learning_rate_init':[0.001],
'batch_size':[4],
'hidden_layer_sizes':[(20, 32)],
'random_state':[1],
'max_iter':[100000],
'activation':['logistic']}
def auto_tune_params(self, x_train, y_train):
#use RMSE as the scoring
clf = GridSearchCV(
mlp(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = mlp(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: Models/RFR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.ensemble import RandomForestRegressor as rfr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class RFR(object):
def __init__(self):
self.params_defualt = {'n_estimators': 500,
'max_depth': None,
'min_samples_split': 2,
'min_samples_leaf': 1,
'max_features': 'auto',
'max_leaf_nodes': None,
'warm_start': False,
'verbose': 0,
'ccp_alpha': 0.0,
'max_samples': None}
self.tuned_parameters = {'n_estimators': [500],
#'criterion': [mse],
'max_depth': [None],
'min_samples_split': [2],
'min_samples_leaf': [1],
'max_features' : ['auto'],
'max_leaf_nodes': [None],
'warm_start': [False],
'verbose': [0],
'ccp_alpha': [0.0],
'max_samples': [None]}
def auto_tune_params(self, x_train, y_train):
# use RMSE as the scoring
clf = GridSearchCV(
rfr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = rfr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
print(self.model.feature_importances_)
return r2
================================================
FILE: Models/SVR.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.svm import SVR as svr
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split,cross_validate,GridSearchCV
from sklearn.preprocessing import MinMaxScaler
class SVR(object):
def __init__(self):
self.params_defualt = {}
self.tuned_parameters = {}
def auto_tune_params(self, x_train, y_train):
#use RMSE as the scoring
clf = GridSearchCV(
svr(), self.tuned_parameters, scoring='neg_root_mean_squared_error'
)
clf.fit(x_train, y_train)
print("Best parameters set found on development set:")
print()
print(clf.best_params_)
self.params_defualt = clf.best_params_
def modify_params(self, params):
for key in params:
self.params_defualt[key] = params[key]
def build_model(self):
self.model = svr(**self.params_defualt)
def model_evaluate(self, x, y, cv):
scoring = ['neg_root_mean_squared_error', 'r2']
scores = cross_validate(self.model, x, y, scoring=scoring, cv=cv, return_train_score=True,
return_estimator=True)
self.estimator = scores['estimator']
return -scores['train_neg_root_mean_squared_error'].mean(), scores['train_r2'].mean(), -scores[
'test_neg_root_mean_squared_error'].mean(), scores['test_r2'].mean(), scores['estimator']
def calculate(self, x_train, x_test, y_train, y_test):
self.model.fit(x_train, y_train)
rmse = np.sqrt(mse(y_train, self.model.predict(x_train)))
r2 = r2_score(y_train, self.model.predict(x_train))
rmset = np.sqrt(mse(y_test, self.model.predict(x_test)))
r2t = r2_score(y_test, self.model.predict(x_test))
print('pre:', self.model.predict(x_test))
print(y_test)
print(rmse)
print(r2)
print(rmset)
print(r2t)
return r2
================================================
FILE: README.md
================================================
# DMCP:DFT-based Machine learning method for Captureing Property Relationship with Structures
DMCP is aimed to implement DFT-based and Machine-learning-accelerated (DFT-ML) scheme for captureing QSPR in intricate system . It is possible to predict the property of intricate system such as HEAs and to reveal the intrinsic descriptors which determine the underlying property of them with appropriate algorithm and train data features.
# Developer:
DMCP is developed within Prof. Yuzheng Guo's group in Wuhan University, in colloboration with Prof. John Robertson's group in Cambridge University.
Core developer: Xuhao Wan, Yuzheng Guo
Email: xhwanrm@whu.edu.cn, yguo@whu.edu.cn
# Major Features
1. Ten machine learning algorithms: GBR, KNR, SVR, GPR, FNN, RFR, ETR, KRR, LASSO, and ENR.
2. Multiple methods to improve model accuracy: dataset split, cross validation, repeated trails.
3. Visualization module for research.
# Prerequisites
1. Generally, you need some data obtained from DFT calculations such as VASP, QE, and CP2K or available material database.
2. DMCP requires Python 3 with the packages specified in requirements.txt. This is taken care of by pip.
# Citation
If you use DMCP in your research, please cite the following paper:
1. X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. doi.org/10.1016/j.matre.2021.100046.
# Reference
The work applied DMCP are listed as following:
1. Dou B, Zhu Z, Merkurjev E, et al. Machine learning methods for small data challenges in molecular science. Chemical Reviews, 2023, 123(13): 8736-8780.
2. Tamtaji M, Gao H, Hossain M D, et al. Machine learning for design principles for single atom catalysts towards electrochemical reactions. Journal of Materials Chemistry A, 2022, 10(29): 15309-15331.
3. Liu X, Zhang Y, Wang W, et al. Transition metal and N doping on AlP monolayers for bifunctional oxygen electrocatalysts: density functional theory study assisted by machine learning description. ACS Applied Materials & Interfaces, 2021, 14(1): 1249-1259.
4. Huang Y, Rehman F, Tamtaji M, et al. Mechanistic understanding and design of non-noble metal-based single-atom catalysts supported on two-dimensional materials for CO 2 electroreduction. Journal of Materials Chemistry A, 2022, 10(11): 5813-5834.
5. Liu T, Zhao X, Liu X, et al. Understanding the hydrogen evolution reaction activity of doped single-atom catalysts on two-dimensional GaPS4 by DFT and machine learning. Journal of Energy Chemistry, 2023, 81: 93-100.
6. X. Wan, Z. Zhang*, H. Niu, Y. Yin, C. Kuai, J. Wang, C. Shao, Y. Guo*, Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Sites Catalysts for CO2 Reduction. The Journal of Physical Chemistry Letters, 2021.
7. H. Niu#, X. Wan#, X. Wang, C. Chen, J. Robertson, Z. Zhang*, Y. Guo*, Single-Atom Rhodium on Defective g-C3N4: A Promising Bifunctional Oxygen Electrocatalyst. ACS Sustainable Chem. Eng., 9(9), 3590-3599, 2021.
8. Wan X, Yu W, Niu H, ea al. Revealing the Oxygen Reduction/Evoluti on Reaction Activity Origin of Carbon-Nitride-Related Single-Atom catalysts: Quantum Chemistry in Artificial Intelligence. Chemical Engineering Journal. 2022，440:135946.
16. Khrabrov K, Shenbin I, Ryabov A, et al. nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset. Physical Chemistry Chemical Physics, 2022, 24(42): 25853-25863.
17. Pant D, Pokharel S, Mandal S, et al. DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides. Scientific Reports, 2023, 13(1): 3277.
# Tips
Welcome to join the DMCP exchange Wechat group.
欢迎加入DMCP微信交流群。
If it is invalid, please contact us by Email.
================================================
FILE: Visualization/Violin.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
class plot_Violin(object):
def __init__(self, title, data_dict):
model_list = []
data_list = []
#for key in data_dict.keys():
#model_list.append(key)
#data_list.append(data_dict[key])
tips = pd.DataFrame.from_dict(data_dict)
#tips = np.array(data_list).T
sns.set(style='ticks', font='Times New Roman', font_scale=1.8)
fig = plt.figure(figsize=(9, 6))
ax=sns.violinplot(data=tips,
split=True,
linewidth = 2, #线宽
width = 0.8, #箱之间的间隔比例
palette = 'pastel', #设置调色板
#order = model_list, #筛选类别
# scale = 'count', #测度小提琴图的宽度： area-面积相同,count-按照样本数量决定宽度,width-宽度一样
gridsize = 50, #设置小提琴图的平滑度，越高越平滑
# inner = 'box', #设置内部显示类型 --> 'box','quartile','point','stick',None
#bw = 0.8 #控制拟合程度，一般可以不设置
)
ax.set_ylabel(title + 'Score', fontsize=28, fontfamily='Times New Roman')
ax.set_xlabel('model', fontsize=28, fontfamily='Times New Roman')
#ax = fig.add_subplot(111)
fig.savefig('volin_' + title + '.jpg', bbox_inches='tight')
plt.show()
#data_dict = {'GBR': [-0.32744250093837, -0.3958306749566164], 'KNR': [-0.3522280594983168, -0.3593963196775159], 'SVR': [-0.34374401871141413, -0.34763844616449313], 'GPR': [-0.3802567562102421, -0.3864921408452695], 'MLP': [-0.3912175478421521, -0.39099797659443214], 'RFR': [-0.34453840142807285, -0.3616486858237452], 'ETR': [-0.41477900933292655, -0.49834959923665945], 'KRR': [-0.4050557476038392, -0.41178328305680595], 'LSO': [-0.3890709246795953, -0.3890709246795953], 'ENR': [-0.3890709246795953, -0.3890709246795953]}
#data_show = plot_Violin(data_dict)
================================================
FILE: Visualization/bar.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statistics import mean
class plot_bar(object):
def __init__(self, title, train_set_RMSE, train_set_R2, test_set_RMSE, test_set_R2):
data_list2 = []
for item in [train_set_RMSE, train_set_R2, test_set_RMSE, test_set_R2]:
data_list1 = []
model_list = []
for i in item.keys():
data_list1.append(mean(item[i]))
model_list.append(i)
data_list2.append(data_list1)
font={'family':'Times New Roman', 'weight':'normal', 'size':20}
##取出 4个dict中的数据，注意dict中的数据存放是无序的
R2train = data_list2[1] # train set R2 score
R2test = data_list2[3]# test set R2 score
RMSEtrain = data_list2[0] # train set RMSE
RMSEtest = data_list2[2] # test set RMSE
#for item in [R2train, R2test, RMSEtrain, RMSEtest]:
#for i in range(len(item)):
#if item[i] < 0:
#item[i] = 0.5
label = model_list
bar_width = 0.4
bar_x = np.arange(len(label))
fig1 = plt.figure(figsize=(9, 6))
ax1 = fig1.add_subplot(111)
#ax1.set_title('RMSE')
bar1 = ax1.bar(x=bar_x - bar_width/2, # 设置不同的x起始位置
height= RMSEtrain, width=bar_width, color='royalblue')
bar2 = ax1.bar(x=bar_x + bar_width/2, # 设置不同的x起始位置
height= RMSEtest, width=bar_width, color='darkorange'
)
ax1.set_ylabel('RMSE /eV', fontsize=24, fontfamily='Times New Roman')
ax1.set_xticks(range(len(label)))
ax1.set_xticklabels(label, fontsize=20, fontfamily='Times New Roman')
#ax1.set_yticklabels(np.around((np.arange(0, 0.4, 0.05)), decimals=2), fontsize=20, fontfamily='Times New Roman')
ax1.legend((bar1, bar2), ('Train set', 'Test set'), prop=font)
fig2 = plt.figure(figsize=(9, 6))
ax2 = fig2.add_subplot(111)
#ax1.set_title('RMSE')
bar1 = ax2.bar(x=bar_x - bar_width/2, # 设置不同的x起始位置
height= R2train, width=bar_width, color='royalblue')
bar2 = ax2.bar(x=bar_x + bar_width/2, # 设置不同的x起始位置
height= R2test, width=bar_width, color='darkorange'
)
ax2.set_ylabel('Score', fontsize=24, fontfamily='Times New Roman')
ax2.set_xticks(range(len(label)))
ax2.set_xticklabels(label, fontsize=20, fontfamily='Times New Roman')
#ax2.set_yticklabels(np.around((np.arange(0, 1.0, 0.2)), decimals=2), fontsize=20, fontfamily='Times New Roman')
ax2.legend((bar1, bar2), ('Train set', 'Test set'), prop=font)
fig1.savefig('bar_RMSE.jpg')
fig2.savefig('bar2_R2.jpg')
plt.show()
================================================
FILE: Visualization/pearson.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor as GBR
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import pearsonr
import seaborn as sns
import matplotlib.pyplot as plt
class plot_pearson(object):
def __init__(self, optimal_model_name,corelation):
pearson_r2 = []
for i in range(corelation.shape[1]):
pearson_r1 = []
for j in range(corelation.shape[1]):
r, _ = pearsonr(corelation[:][i], corelation[:][j])
pearson_r1.append(r)
pearson_r2.append(pearson_r1)
ax1 = sns.heatmap(pearson_r2, vmin=-1, vmax=1, cmap='RdBu')
plt.title(optimal_model_name)
plt.savefig('Pcdac_pearson.jpg')
plt.show()
================================================
FILE: Visualization/pie.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import numpy as np
import matplotlib.pyplot as plt
class plot_pie(object):
def __init__(self, optimal_model_name, feature_importance):
font={'family':'Times New Roman', 'weight':'normal', 'size': 18}
cmap = plt.get_cmap("tab20")
colors = cmap(np.arange(len(feature_importance)))
labels = ['e_dp1', 'H_f.ox1', 'N_m1', 'χ_1', 'I_m1', 'r_1', 'N_d1', 'Q_1', 'ΔG_COOH*1', 'ΔG_Max1', 'e_dp2','H_f.ox2', 'N_m2', 'χ_2', 'I_m2', 'r_2', 'N_d2', 'Q_2', 'ΔG_COOH*2', 'ΔG_Max2']
#for i in range(1,len(feature_importance) + 1):
#labels.append(str('x')+str(i))
fig = plt.figure(figsize=(9, 6))
ax = fig.add_subplot(111)
wedges, text = ax.pie(feature_importance, colors=colors, shadow=True,
startangle=90, textprops=font)
ax.legend(wedges, labels, bbox_to_anchor=(1, 0, 0, 1), fontsize=8)
plt.title(optimal_model_name)
fig.savefig('Pcdac_fipie.jpg')
plt.show()
================================================
FILE: Visualization/scatter.py
================================================
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#author: xuhao wan, wei yu
#If you use DMCP in your research, please cite the following paper:X. Wan, Z. Zhang*, W. Yu, Y. Guo*, A State-of-the-art Density-functional-theory-based and Machine-learning-accelerated Hybrid Method for Intricate System Catalysis. Materials Reports: Energy. 2021.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
class plot_scatter(object):
def __init__(self, y_train_true, y_train_pred, y_test_true, y_test_pred):
font={'family':'Times New Roman', 'weight':'normal', 'size': 24}
fig = plt.figure(figsize=(9, 6))
ax = fig.add_subplot(111)
dot1 = ax.scatter(y_train_true, y_train_pred,
s=80, c='white', edgecolors='royalblue', marker='o', linewidth=2)
dot2 = ax.scatter(y_test_true, y_test_pred,
s=80, c='white', edgecolors='darkorange', marker='s', linewidth=2)
line = ax.plot([0,1,2.2], [0,1,2.2], color='k')
ax.set_xlabel('$\mathregular{G_{DFT}}$ /eV', fontsize=24, fontfamily='Times New Roman')
ax.set_ylabel('$\mathregular{G_{ML}}$ /eV' , fontsize=24, fontfamily='Times New Roman')
ax.set_xlim(xmin=0, xmax=2.2)
ax.set_ylim(ymin=0, ymax=2.2)
ax.set_xticklabels(np.around((np.arange(0, 2.2, 0.25)), decimals=2), fontsize=20, fontfamily='Times New Roman')
ax.set_yticklabels(np.around((np.arange(0, 2.2, 0.25)), decimals=2), fontsize=20, fontfamily='Times New Roman')
ax.legend((dot1, dot2), ('Train set', 'Test set'), prop=font)
fig.savefig('Pcdac_scatter.jpg', bbox_inches='tight')
plt.show()
================================================
FILE: manual
================================================
The keywords INTRN and OUTDAT are the filename of the input and output data files, respectively.
OTFIG is the filename prefix of the visualization results generated by DMCP and the format of these figures is optional, including jpg, png, and pdf.
The keyword PSCAL controls the feature scaling: OFF, NOR, STA, and REG means no data scaling, normalization, standardization, and regularization processing, respectively.
The noise processing is controlled by the keyword PNOSE, and its value determines the distribution range of noises while 0 means the noise processing is not employed.
Original data is reproduced with randomly distributed noises in the scale of -x to x (x is the values of the keyword PNOSE). The keyword PSPLT controls the dataset split and its values are the percentage of the training dataset. The keyword GCRVA controls whether the cross-validation is employed (ON or OFF) while the value of the keyword GREPT is the number of repeated trials.
When the cross-validation and repeated trails are applied together, the value of GREPT is the repeat times of the training procedure in each dataset split and the value of the keyword GCVRN is the number of the rounds of cross-validation.
The selected algorithm is determined by the keyword GMODL: the corresponding GMODL values of are GBR (for Gradient Boosted Regression), KNR (k-Neighbor Regression), SVR (Support Vector Regression), GPR (Gaussian Process Regression), FNN (Feedforward Neural Network), RFR (Random Forest Regression), ETR (Extra Trees Regression), KRR (Kernel Ridge Regression), LASSO (Least Absolute Shrinkage and Selection Operator Regression) and ENR (Elastic Net Regression). Several algorithms can be selected at the same time to establish different machine learning models by simply enumerating the corresponding values of GMODL.
The model parameters can be provided by the keyword PRX where X represents the abbreviations (also the values of GMODL) of the algorithms.
The keyword VVOLN in the visualization module controls the draw of the violin plot.
The keyword VCOMP is related to the histogram The keywords that control the switch of the scatter plot and the pie chart are respectively VSCAM and VFTIM and their values are the selected machine learning model which is usually the best performing model.
The keyword VPRAS controls whether the Pearson correlation map is drawn.
To predict the catalytic performance, the corresponding feature values should be generated and transported into the model at first, which is controlled by the keyword INPRE and its value is the filename of the input data used for prediction.
And the keyword GPREM determines the model used in the prediction process which is usually the best performing model and it is also the switch of the prediction function in DMCP.
================================================
FILE: requirements.txt
================================================
#The DMCP Program can be run in either Linux or Windows operation systems utilizing command lines.
#Please install the following compiler in your operation system to run DMCP.
numpy>=1.20.1
scipy
torch
sklearn
pandas
matplotlib
f90nml
multidict
statistics
pytest>=4.6
================================================
FILE: setup.py
================================================
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
with open("requirements.txt", "r") as fh:
dependencies = fh.readlines()
setuptools.setup(
name="DMCP",
packages=setuptools.find_packages(exclude=["tests"]),
version="0.1.2",
author="Xuhao Wan",
author_email="xhwanrm@whu.edu.cn",
description="DMCP",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/XuhaoWan/DMCP",
python_requires=">=3.7",
install_requires=dependencies,
license="GNU",
classifiers=[
"License :: OSI Approved :: GNU License",
"Topic :: Scientific/Engineering :: Physics",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Development Status :: 4 - Beta",
],
)
gitextract_ekw9y8z4/ ├── .gitignore ├── DMCP.py ├── LICENSE ├── MANIFEST.in ├── Models/ │ ├── ENR.py │ ├── ETR.py │ ├── FNN.py │ ├── GBR.py │ ├── GPR.py │ ├── KNR.py │ ├── KRR.py │ ├── Lasso.py │ ├── MLP.py │ ├── RFR.py │ └── SVR.py ├── README.md ├── Visualization/ │ ├── Violin.py │ ├── bar.py │ ├── pearson.py │ ├── pie.py │ └── scatter.py ├── manual ├── requirements.txt └── setup.py
SYMBOL INDEX (96 symbols across 17 files)
FILE: DMCP.py
function parse_data (line 36) | def parse_data():
function predict (line 105) | def predict(optimal_model, optimal_model_name):
function choose_optimal_model (line 128) | def choose_optimal_model(model_evaluate_data, estimator_dict):
function result_visualize (line 147) | def result_visualize(train_set_RMSE, train_set_R2, test_set_RMSE, test_s...
function preprocessing_data (line 157) | def preprocessing_data(data_train):
function add_noise (line 176) | def add_noise(X):
function get_params (line 187) | def get_params(model):
function main (line 199) | def main():
FILE: Models/ENR.py
class ENR (line 13) | class ENR(object):
method __init__ (line 14) | def __init__(self):
method auto_tune_params (line 38) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 50) | def modify_params(self, params):
method build_model (line 54) | def build_model(self):
method model_evaluate (line 57) | def model_evaluate(self, x, y, cv):
method calculate (line 65) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/ETR.py
class ETR (line 14) | class ETR(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 39) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 51) | def modify_params(self, params):
method build_model (line 55) | def build_model(self):
method model_evaluate (line 58) | def model_evaluate(self, x, y, cv):
method calculate (line 66) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/FNN.py
class FNN (line 19) | class FNN(object):
method __init__ (line 20) | def __init__(self, x_train, x_test, y_train, y_test, params, model_eva...
method modify_params (line 38) | def modify_params(self):
method calculate (line 42) | def calculate(self):
class Net (line 77) | class Net(nn.Module):
method __init__ (line 78) | def __init__(self):
method forward (line 83) | def forward(self, x):
function weights_init (line 89) | def weights_init(m):
FILE: Models/GBR.py
class GBR (line 13) | class GBR(object):
method __init__ (line 14) | def __init__(self):
method auto_tune_params (line 26) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 38) | def modify_params(self, params):
method build_model (line 42) | def build_model(self):
method model_evaluate (line 45) | def model_evaluate(self, x, y, cv):
method calculate (line 53) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/GPR.py
class GPR (line 14) | class GPR(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 19) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 31) | def modify_params(self, params):
method build_model (line 35) | def build_model(self):
method model_evaluate (line 38) | def model_evaluate(self, x, y, cv):
method calculate (line 46) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/KNR.py
class KNR (line 14) | class KNR(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 19) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 31) | def modify_params(self, params):
method build_model (line 35) | def build_model(self):
method model_evaluate (line 38) | def model_evaluate(self, x, y, cv):
function calculate (line 47) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/KRR.py
class KRR (line 14) | class KRR(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 27) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 39) | def modify_params(self, params):
method build_model (line 43) | def build_model(self):
method model_evaluate (line 46) | def model_evaluate(self, x, y, cv):
method calculate (line 54) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/Lasso.py
class LSO (line 14) | class LSO(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 37) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 49) | def modify_params(self, params):
method build_model (line 53) | def build_model(self):
method model_evaluate (line 56) | def model_evaluate(self, x, y, cv):
method calculate (line 64) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/MLP.py
class MLP (line 14) | class MLP(object):
method __init__ (line 15) | def __init__(self):
method auto_tune_params (line 31) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 43) | def modify_params(self, params):
method build_model (line 47) | def build_model(self):
method model_evaluate (line 50) | def model_evaluate(self, x, y, cv):
method calculate (line 58) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/RFR.py
class RFR (line 13) | class RFR(object):
method __init__ (line 14) | def __init__(self):
method auto_tune_params (line 37) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 49) | def modify_params(self, params):
method build_model (line 53) | def build_model(self):
method model_evaluate (line 56) | def model_evaluate(self, x, y, cv):
method calculate (line 64) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Models/SVR.py
class SVR (line 13) | class SVR(object):
method __init__ (line 14) | def __init__(self):
method auto_tune_params (line 18) | def auto_tune_params(self, x_train, y_train):
method modify_params (line 30) | def modify_params(self, params):
method build_model (line 34) | def build_model(self):
method model_evaluate (line 37) | def model_evaluate(self, x, y, cv):
method calculate (line 45) | def calculate(self, x_train, x_test, y_train, y_test):
FILE: Visualization/Violin.py
class plot_Violin (line 11) | class plot_Violin(object):
method __init__ (line 12) | def __init__(self, title, data_dict):
FILE: Visualization/bar.py
class plot_bar (line 11) | class plot_bar(object):
method __init__ (line 12) | def __init__(self, title, train_set_RMSE, train_set_R2, test_set_RMSE,...
FILE: Visualization/pearson.py
class plot_pearson (line 16) | class plot_pearson(object):
method __init__ (line 17) | def __init__(self, optimal_model_name,corelation):
FILE: Visualization/pie.py
class plot_pie (line 9) | class plot_pie(object):
method __init__ (line 10) | def __init__(self, optimal_model_name, feature_importance):
FILE: Visualization/scatter.py
class plot_scatter (line 10) | class plot_scatter(object):
method __init__ (line 11) | def __init__(self, y_train_true, y_train_pred, y_test_true, y_test_pred):
Condensed preview — 24 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (85K chars).
[
{
"path": ".gitignore",
"chars": 2981,
"preview": "# https://github.com/github/gitignore/blob/main/Python.gitignore\r\n\r\n# Byte-compiled / optimized / DLL files\r\n__pycache__"
},
{
"path": "DMCP.py",
"chars": 8019,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "LICENSE",
"chars": 18092,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 2, June 1991\n\n Copyright (C) 1989, 1991 Fr"
},
{
"path": "MANIFEST.in",
"chars": 25,
"preview": "include requirements.txt\n"
},
{
"path": "Models/ENR.py",
"chars": 3508,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/ETR.py",
"chars": 3653,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/FNN.py",
"chars": 3314,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/GBR.py",
"chars": 2587,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/GPR.py",
"chars": 2489,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/KNR.py",
"chars": 2447,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/KRR.py",
"chars": 2831,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/Lasso.py",
"chars": 3403,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/MLP.py",
"chars": 3158,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/RFR.py",
"chars": 3552,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Models/SVR.py",
"chars": 2388,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "README.md",
"chars": 3968,
"preview": "# DMCP:DFT-based Machine learning method for Captureing Property Relationship with Structures\nDMCP is aimed to implement"
},
{
"path": "Visualization/Violin.py",
"chars": 2335,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Visualization/bar.py",
"chars": 3193,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Visualization/pearson.py",
"chars": 1274,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Visualization/pie.py",
"chars": 1349,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "Visualization/scatter.py",
"chars": 1690,
"preview": "#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n#author: xuhao wan, wei yu\r\n#If you use DMCP in your research, please cit"
},
{
"path": "manual",
"chars": 2800,
"preview": "The keywords INTRN and OUTDAT are the filename of the input and output data files, respectively. \nOTFIG is the filename "
},
{
"path": "requirements.txt",
"chars": 270,
"preview": "#The DMCP Program can be run in either Linux or Windows operation systems utilizing command lines. \n#Please install the "
},
{
"path": "setup.py",
"chars": 828,
"preview": "import setuptools\n\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nwith open(\"requirements.txt\", \""
}
]
About this extraction
This page contains the full source code of the XuhaoWan/DMCP GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 24 files (78.3 KB), approximately 19.3k tokens, and a symbol index with 96 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.