Boost.Python.ArgumentError：〜did中的Python参数类型与C ++签名不匹配，如何处理？

Question

我想使用ppar_ligand_model预测Knoell数据。 但是一些微笑显示此错误信息。 我该如何处理？这是SMILE结构的问题吗？ 我应该添加什么代码来排除导致错误的材料的SMILE？

这是源代码和github链接 run_pparg_ligand_model.py

import numpy
import pandas
import sys
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.metrics import confusion_matrix
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem import Draw
from rdkit.Chem.Draw import SimilarityMaps
from rdkit import Chem, DataStructs
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from keras.models import load_model


def main():
    """Run the main function."""
    outfile = open(sys.argv[1] + "_predictions.txt", 'w')
    outfile.write("Chemical Name\tPrediction\n")
    model = load_model("pparg_ligand_model.h5")
    dataframe = pandas.read_csv(sys.argv[1], sep="\t")
    mols = []
    fps = []

    for index, row in dataframe.iterrows():
#         try : 
#             mol = Chem.MolFromSmiles(row['SMILES'])
#             break
#         except :
#             continue
        mol = Chem.MolFromSmiles(row['SMILES'])

        fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)
        mols.append(mol)
        fps.append(fp)

    np_fps = []
    for fp in fps:
        arr = numpy.zeros((1,))
        DataStructs.ConvertToNumpyArray(fp, arr)
        np_fps.append(arr)

    np_fps_array = numpy.array(np_fps)
    predictions = model.predict(np_fps_array, batch_size=5)
    i = 0
    for prediction in predictions:
        y_prediction = ''
        if(prediction < 0.50):
            y_prediction = "ligand"
        else:
            y_prediction = "not_ligand"
        outfile.write(dataframe['Chemical_Name'][i] + "\t" + y_prediction + "\n")
        i += 1
    outfile.close()


if __name__ == "__main__":
    main()