from featurebox.selection.corr import Corr
from mgetool.exports import Store
from mgetool.imports import Call
from mgetool.show import corr_plot
from mgetool.tool import name_to_name
# import seaborn as sns
if __name__ == "__main__":
import os
os.chdir(r'band_gap')
store = Store()
data = Call()
all_import = data.csv().all_import
name_and_abbr = data.csv().name_and_abbr
data_import = all_import
data225_import = data_import
X_frame = data225_import.drop(['exp_gap'], 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"""
this is a description
"""
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"""
import numpy as np
from mgetool.exports import Store
from mgetool.imports import Call
from mgetool.tool import tt
from bgp.base import SymbolSet
from bgp.skflow import SymbolLearning
if __name__ == "__main__":
import os
os.chdir(r'band_gap')
data = Call()
name_and_abbr = data.csv().name_and_abbr
SL_data = data.SL_data
si_transformer = data.si_transformer
store = Store()
x, x_dim, y, y_dim, c, c_dim, X, Y = SL_data
x_g = np.arange(x.shape[1])
x_g = list(x_g[1:])
x_g = x_g.reshape(-1, 2)
pset0 = SymbolSet()
pset0.add_features(x, y, x_dim=x_dim, y_dim=y_dim, x_group=x_g)
pset0.add_constants(c, c_dim=c_dim, c_prob=0.05)
pset0.add_operations(power_categories=(2, 3, 0.5, 1 / 3, 4, 1 / 4),
# categories=("Mul",),
categories=("Add", "Mul", "Sub", "Div", "exp", "ln"),