Python y_randamization_rgr示例

示例#1

文件： Tc_7model.py 项目： nakanishi-akitaka/python2018_backup

# Applicability Domain (inside: +1, outside: -1)
iappd = 1
if (iappd == 1):
    y_appd = ad_knn(X_train, X_test)
else:
    y_appd = ad_ocsvm(X_train, X_test)

data = []
for i in range(len(X_test)):
    temp = (f_test[i], int(X_test[i][0]), int(y_pred[i]), y_appd[i])
    data.append(temp)

properties = ['formula', 'P', 'Tc', 'AD']
df = pd.DataFrame(data, columns=properties)
df.sort_values('Tc', ascending=False, inplace=True)

# df.to_csv(output, index=False)
df_in_ = df[df.AD == 1]
df_in_.to_csv(output, index=False)
print('Predicted Tc is written in file {}'.format(output))

#%%
niter = 10
if (True):
    dcv_rgr(X_train, y_train, model, param_grid, niter)
    y_randamization_rgr(X_train, y_train, model, param_grid, niter)

# print(X_train[:10])
print('{:.2f} seconds '.format(time() - start))

示例#2

    y_appd = ad_knn(X, X_pred)
elif(iappd == 2):
    y_appd = ad_ocsvm(X, X_pred)
else:
    y_appd = ad_knn_list(X, X_pred, 10)

data = []
for i in range(len(X_pred)):
#    temp = (f_pred[i], int(P_pred[i]), int(y_pred[i]), int(y_pred_db[i]), y_appd[i])
    temp = (f_pred[i], int(P_pred[i]), int(y_pred[i]), int(y_pred_db[i]))
    data.append(temp)

# properties=['formula','P', 'Tc(pred)', 'Tc(DB)','AD']
properties=['formula','P', 'Tc(pred)', 'Tc(DB)']
df = pd.DataFrame(data, columns=properties)
# df.sort_values('Tc', ascending=False, inplace=True)

# df.to_csv(output, index=False)
# df_in_ = df[df.AD ==  1]
# df_in_.to_csv(output, index=False)
df.to_csv(output, index=False)
print('Predicted Tc is written in file {}'.format(output))

#%%
niter=10
if(False):
    dcv_rgr(X, y, model, param_grid, niter)
    y_randamization_rgr(X, y, model, param_grid, niter)

print('{:.2f} seconds '.format(time() - start))