import numpy as np
import pandas as pd
from bgp.selection.corr import Corr
from mgetool.exports import Store
from mgetool.imports import Call
from mgetool.tool import name_to_name
# import seaborn as sns
if __name__ == "__main__":
store = Store(r'C:\Users\Administrator\Desktop\band_gap_exp\2.corr')
data = Call(r'C:\Users\Administrator\Desktop\band_gap_exp')
all_import = data.csv().all_import
name_init, abbr_init = data.pickle_pd().name_and_abbr
data_import = all_import
data225_import = data_import.iloc[np.where(
data_import['group_number'] == 225)[0]]
X_frame = data225_import.drop(['exp_gap', 'group_number'], axis=1)
y_frame = data225_import['exp_gap']
X = X_frame.values
y = y_frame.values
"""calculate corr"""
corr = Corr(threshold=0.90, muti_grade=2, muti_index=[2, len(X)])
corr.fit(X_frame)
cof_list = corr.count_cof()
"""get x_name and abbr"""
X_frame_name = corr.transform(X_frame.columns.values)
from mgetool.tool import name_to_name
from sklearn import utils
from sklearn.model_selection import GridSearchCV
warnings.filterwarnings("ignore")
"""
this is a description
"""
if __name__ == "__main__":
store = Store(r'C:\Users\Administrator\Desktop\band_gap_exp\3.sum\sub')
data = Call(
r'C:\Users\Administrator\Desktop\band_gap_exp',
r'C:\Users\Administrator\Desktop\band_gap_exp\3.sum\method',
)
data_import = data.csv().all_import
name_init, abbr_init = data.pickle_pd().name_and_abbr
select = [
'cell volume', 'electron density', 'lattice constants a',
'lattice constants c', 'radii covalent', 'radii ionic(shannon)',
'distance core electron(schubert)', 'latent heat of fusion',
'energy cohesive brewer', 'total energy',
'charge nuclear effective(slater)', 'valence electron number',
'electronegativity(martynov&batsanov)', 'volume atomic(villars,daams)'
]
select = ['cell volume', 'electron density'
] + [j + "_%i" % i for j in select[2:] for i in range(2)]
data225_import = data_import.iloc[np.where(
data_import['group_number'] == 225)[0]]