stats2 = Statistics(lambda ind: 0 if ind else 0) stats2.register("countable_number", np.sum) stats = MultiStatistics(score1=stats1, score2=stats2) population, logbook = eaSimple(pop, toolbox, cxpb=cxpb, mutpb=mutpb, ngen=ngen, stats=stats, halloffame=hof, pset=pset, store=store) return hof if __name__ == '__main__': # 输入 store = Store(r'D:\sy') data = Call(r'D:\sy') data_import = data.xlsx().featuredata name_abbr = data_import.columns.values x_name = name_abbr[:-1] # data_import = data_import.iloc[np.where(data_import['f1'] <= 1)[0]] X_frame = data_import[x_name] y_frame = data_import['y'] X = X_frame.values y = y_frame.values # 处理 # scal = preprocessing.MinMaxScaler() # X = scal.fit_transform(X) X, y = shuffle(X, y, random_state=0)
import pandas as pd import sklearn from sklearn import svm from sklearn.model_selection import GridSearchCV, LeaveOneOut from sklearn.preprocessing import MinMaxScaler from featurebox.selection.backforward import BackForward from featurebox.tools.exports import Store from featurebox.tools.imports import Call warnings.filterwarnings("ignore") # 数据导入 store = Store(r'/data/home/wangchangxin/data/zlj/') data = Call(r'/data/home/wangchangxin/data/zlj/', index_col=None) all_import = data.xlsx().data x_name = all_import.index.values y = all_import["y"].values x_frame = all_import.drop("y", axis=1) x = x_frame.values # # 预处理 # minmax = MinMaxScaler() # x = minmax.fit_transform(x) # 数据划分 xtrain, xtest = x[3:], x[:3] ytrain, ytest = y[3:], y[:3] xtrain, ytrain = sklearn.utils.shuffle(xtrain, ytrain, random_state=3) # x = minmax.inverse_transform(x_new)
# x3 = x[:, 3] # # t = expr01 # func0 = sympy.utilities.lambdify(terminals, t) # re = func0(*x.T) # p = BasePlot(font=None) # p.scatter(y, re, strx='Experimental $E_{gap}$', stry='Calculated $E_{gap}$') # import matplotlib.pyplot as plt # # plt.show() if __name__ == '__main__': store = Store(r'C:\Users\Administrator\Desktop\band_gap_exp\4.symbol') data = Call(r'C:\Users\Administrator\Desktop\c', index_col=None) data_import = data.xlsx().sr X = data_import["delt_x"].values input_x = data_import[["delt_x", "G"]].values Pexp = data_import["Pexp"].values Pmix = data_import["Pmix"].values G = data_import["G"].values y = data_import["PG_y"].values y = y * G testfunc = input_x[:, 0] * input_x[:, 1] t = np.corrcoef(y, input_x[:, 0] * input_x[:, 1]) dim1 = Dim([0, 0, 0, 0, 0, 0, 0]) target_dim = [Dim([0, 0, 0, 0, 0, 0, 0])]