from featurebox.symbol.base import SymbolSet, SymbolTree
from featurebox.symbol.functions.dimfunc import Dim, dless
from featurebox.symbol.calculation.translate import group_str, compile_context, general_expr_dict
from featurebox.symbol.flow import MutilMutateLoop, OnePointMutateLoop
from featurebox.symbol.preprocess import MagnitudeTransformer
from featurebox.tools.exports import Store
from featurebox.tools.imports import Call
from featurebox.tools.tool import tt
if __name__ == "__main__":
import os
os.chdir(r'band_gap')
data = Call()
all_import = data.csv().all_import
name_and_abbr = data.csv().name_and_abbr
store = Store()
data_import = all_import
data225_import = data_import
select = [
'cell volume', 'cell density', 'lattice constants a',
'lattice constants c', 'covalent radii', 'ionic radii(shannon)',
'core electron distance(schubert)', 'fusion enthalpy',
'cohesive energy(Brewer)', 'total energy',
'effective nuclear charge(slater)', 'valence electron number',
'electronegativity(martynov&batsanov)', 'atomic volume(villars,daams)'
]
from featurebox.selection.exhaustion import Exhaustion
from featurebox.selection.quickmethod import dict_method_reg
from featurebox.tools.exports import Store
from featurebox.tools.imports import Call
from featurebox.tools.show import BasePlot
from featurebox.tools.tool import name_to_name
warnings.filterwarnings("ignore")
"""
this is a description
"""
if __name__ == "__main__":
store = Store(
r'C:\Users\Administrator\Desktop\band_gap_exp\3.sum\method', )
data = Call(r'C:\Users\Administrator\Desktop\band_gap_exp')
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)]
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
from sklearn.feature_selection import RFECV
from sklearn.linear_model import BayesianRidge
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeRegressor
from featurebox.featurizers.compositionfeaturizer import WeightedAverage
from featurebox.selection.corr import Corr
from featurebox.tools.exports import Store
from featurebox.tools.imports import Call
# 数据导入
store = Store(r'C:\Users\Administrator\Desktop\skk')
data = Call(r'C:\Users\Administrator\Desktop\skk')
all_import = data.csv().skk
# """for element site"""
element_table = pd.read_excel(
r'C:\Users\Administrator\Desktop\band_gap_exp\element_table.xlsx',
header=4,
skiprows=0,
index_col=0)
element_table = element_table.iloc[5:, 7:]
# 其他数据获取
feature_select = [
'lattice constants a',
'lattice constants b',
'lattice constants c',
'radii atomic(empirical)',