python - SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape data text file

Question

运行 .py 文件时，会出现“SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape data text file”的错误。请帮助我成功运行此程序而不会出错。我想完美地看到结果。

-代码示例（analysis_neuralnetwork2.py）

import numpy as np
from bayes_opt import BayesianOptimization
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasRegressor
from keras.optimizers import SGD
from sklearn.model_selection import cross_val_score, cross_validate
import warnings
warnings.filterwarnings("ignore")

matplotlib.rcParams['font.sans-serif'] = "Arial"
matplotlib.rcParams['font.family'] = "sans-serif"


parameter_label = ['GNa', 'GNaB', 'pCa', 'GCaB', 'GtoFast', 'GK1_', 'GKr_', 'GKs_', 'Gkur',
               'gkp', 'GClCa', 'GClB', 'IbarNaK', 'IbarNCX', 'ks', 'Kleak', 'Vmax_SRCaP', 'IbarSLCaP'];
phenotype_label = ['RMP (mV)', 'Vmax (V/s)', 'APD at PCL=1000 (ms)', 'APD at PCL=600 (ms)',
               'RP (ms)', 'APD alternans threshold (ms)', 'Ca alternans threshold (ms)', 'Smax']

finetuned = np.array([1.1232841, 0.8867776, 0.9441124, 0.7631924, 0.7377310, 1.7002259, 1.0733276, 0.9908650, 0.7769591, 0.8579676, 1.0879098, 0.8795566, 1.0779592, 0.9726858, 1.1786529, 1.1667818, 1.2625186, 0.9541786]);


# Import data
data =np.array(np.genfromtxt('C:\Users\sanghyeoke\Documents\Downloads\Grandi_sampling_finetuned_1.txt', delimiter='\t', encoding='utf-8',missing_values='', skip_header=1))
n_sample = len(data)
n_parameter = 18
n_phenotype = 8
parameter = data[:, 1:(n_parameter+1)]
phenotype = data[:, (n_parameter+1):]

parameter = parameter / np.tile(finetuned, (n_sample,1))



# Remove outliers
noCaAlternans = phenotype[:,6]<1000
RPgreater50 = phenotype[:,4]>50
parameter = parameter[np.logical_and(noCaAlternans, RPgreater50),:]
phenotype = phenotype[np.logical_and(noCaAlternans, RPgreater50),:]

isOutlier = np.full(len(parameter), False, dtype=bool)
for i in range(n_phenotype):
    phenotype_i = np.log10(phenotype[:,i]) if i==7 else phenotype[:,i]
    m, sd = np.mean(phenotype_i), np.std(phenotype_i)
    isOutlier_ = np.array([abs(x-m) > 2*sd for x in phenotype_i])
    isOutlier = np.logical_or(isOutlier, isOutlier_)
parameter = parameter[~isOutlier,:]
phenotype = phenotype[~isOutlier,:]
parameter_log2 = np.log2(parameter)
Smax_log10 = np.log10(phenotype[:,7])
n_sample = len(parameter)
print('# of samples:', n_sample)



# Re-scale variables into (0,1)
parameter_log2 = (parameter_log2 + 1) * 0.5
Smax_log10 = (Smax_log10 - min(Smax_log10)) / (max(Smax_log10) - min(Smax_log10))




# Smax: MLP (neural network)
np.random.seed(0) # for reproducibility

def r_square(y_true, y_pred):
    from keras import backend as K
    SS_res =  K.sum(K.square(y_true - y_pred)) 
    SS_tot = K.sum(K.square(y_true - K.mean(y_true))) 
    return (1 - SS_res/(SS_tot + K.epsilon()))

def neuralnetwork_model(n1, n2, dropout_rate, learning_rate):
    def bm():
        model = Sequential()
        model.add(Dense(n1, input_dim=n_parameter, kernel_initializer='normal', activation='relu'))
        model.add(Dropout(dropout_rate))
        model.add(Dense(n2, kernel_initializer='normal', activation='relu'))
        model.add(Dropout(dropout_rate))
        model.add(Dense(1, kernel_initializer='normal', activation='linear')) # output layer (regression)
        model.compile(loss='mse', optimizer=SGD(lr=learning_rate), metrics=[r_square])
        return model
        return bm

def mlmodel_cv(n1, n2, dropout_rate, learning_rate, batch_size, epochs):
    n1, n2, batch_size, epochs = list(map(lambda x: int(round(x)), [n1, n2, batch_size, epochs]))
    mlmodel = KerasRegressor(build_fn=neuralnetwork_model(n1=n1,
                                                      n2=n2,
                                                      dropout_rate=dropout_rate,
                                                      learning_rate=learning_rate), batch_size=batch_size, epochs=epochs, verbose=0)
    scores = cross_val_score(mlmodel, parameter_log2, Smax_log10, scoring='r2', cv=4)
    return scores.mean()

optimizer = BayesianOptimization(
    f = mlmodel_cv,
    pbounds = {
        'n1': (1, 100), # int
        'n2': (1, 100), # int
        'dropout_rate': (0.0, 0.5),
        'learning_rate': (0.001, 0.5),
        'batch_size': (5, 18), # int
        'epochs': (50, 500) # int
    },
    random_state = 0
)
optimizer.maximize(init_points=10, n_iter=100)
    print(optimizer.max)

---

-错误示例

File "<ipython-input-42-fe85d14ebe0f>", line 25
    data = np.array(np.genfromtxt('C:\Users\sanghyeoke\Documents\Downloads\Grandi_sampling_finetuned1.txt', 
delimiter='\t',missing_values='', skip_header=1))                             ^
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3:truncated\UXXXXXXXX escape

---

数据文件（Grandi_sampling_finetuned.txt）

#Idx    GNa GNaB    pCa GCaB    GtoFast GK1_    GKr_    GKs_    Gkur    gkp GClCa   GClB    IbarNaK IbarNCX ks  Kleak   Vmax_SRCaP  IbarSLCaP   RMP Vmax    APD_1000ms  APD_600ms   RP  APD_alternans_CL    Ca_alternans_CL Smax
1   5.685624e-01    1.370670e+00    9.843815e-01    5.219677e-01    1.264108e+00    2.614267e+00    1.126691e+00    5.014469e-01    9.245995e-01    1.516378e+00    1.577079e+00    9.261715e-01    6.217115e-01    1.425196e+00    1.661312e+00    8.530741e-01    8.960570e-01    1.476508e+00    -7.854786e+01   1.990175e+02    2.429400e+02    2.517600e+02    1.900000e+02    1.900000e+02    2.100000e+02    1.124607e+01
2   1.137125e+00    6.853349e-01    4.921907e-01    1.043935e+00    6.320539e-01    1.307133e+00    5.633456e-01    1.002894e+00    4.622997e-01    7.581890e-01    7.885395e-01    4.630858e-01    1.243423e+00    7.125982e-01    8.306559e-01    1.706148e+00    1.792114e+00    7.382540e-01    -7.662217e+01   2.584475e+02    3.525000e+02    3.887500e+02    2.800000e+02    3.000000e+02    3.000000e+02    8.342865e+00
5   6.761385e-01    8.150051e-01    8.277628e-01    1.241455e+00    5.314919e-01    3.108904e+00    1.894861e+00    8.433298e-01    5.497701e-01    6.375584e-01    9.377368e-01    7.788143e-01    2.091180e+00    1.694854e+00    1.975644e+00    7.173470e-01    1.506982e+00    8.779370e-01    -7.993083e+01   2.710909e+02    2.433000e+02    2.437000e+02    2.100000e+02    2.200000e+02    2.300000e+02    6.461208e+00
25  6.474724e-01    1.103725e+00    7.926684e-01    5.944109e-01    9.334337e-01    1.930409e+00    5.394616e-01    1.615151e+00    6.827364e-01    8.634170e-01    1.384874e+00    6.838971e-01    1.544155e+00    1.769891e+00    1.124920e+00    1.781686e+00    1.020420e+00    7.709393e-01    -7.848863e+01   2.231171e+02    2.854700e+02    3.224200e+02    2.400000e+02    2.500000e+02    1.000000e+03    1.456483e+01
27  9.156643e-01    7.804516e-01    5.605012e-01    8.406240e-01    1.320075e+00    1.365005e+00    7.629139e-01    5.710420e-01    9.655350e-01    1.221056e+00    9.792540e-01    4.835883e-01    5.459411e-01    1.251502e+00    1.590878e+00    1.259842e+00    7.215456e-01    1.090273e+00    -7.621383e+01   2.077098e+02    2.286200e+02    2.643400e+02    2.600000e+02    2.700000e+02    2.800000e+02    6.709694e+00
36  1.643351e+00    7.313485e-01    1.249230e+00    1.214854e+00    1.348981e+00    1.456652e+00    2.022083e+00    1.218764e+00    6.976863e-01    1.689831e+00    1.611613e+00    9.063256e-01    1.385654e+00    1.075428e+00    1.149553e+00    1.344428e+00    1.412169e+00    1.324951e+00    -7.535797e+01   2.785459e+02    2.890400e+02    3.203600e+02    1.500000e+02    1.600000e+02    1.600000e+02    1.187817e+01
39  9.771423e-01    8.697249e-01    1.485593e+00    1.021566e+00    1.134353e+00    2.449787e+00    6.011688e-01    7.246814e-01    4.148468e-01    1.420973e+00    1.916541e+00    1.524252e+00    1.647830e+00    6.394533e-01    1.367056e+00    1.130525e+00    2.374975e+00    1.114147e+00    -7.752651e+01   2.704563e+02    2.103900e+02    2.093000e+02    5.000000e+01    5.000000e+01    9.000000e+01    1.216595e+01
54  1.443069e+00    1.177830e+00    9.224479e-01    1.163348e+00    7.043525e-01    2.558248e+00    6.846042e-01    7.567658e-01    6.126566e-01    1.247794e+00    1.682965e+00    1.338485e+00    6.634535e-01    7.282021e-01    8.488449e-01    1.985487e+00    2.480124e+00    1.645401e+00    -7.632445e+01   2.944793e+02    2.162000e+02    2.192300e+02    1.300000e+02    1.400000e+02    1.500000e+02    2.412609e+00
59  9.357147e-01    1.080075e+00    1.691777e+00    5.333973e-01    4.567161e-01    2.345924e+00    2.111608e+00    1.962808e+00    4.724228e-01    4.810902e-01    6.488716e-01    1.459629e+00    2.046370e+00    6.677643e-01    7.783942e-01    1.287429e+00    6.761487e-01    1.268778e+00    -7.788357e+01   2.744703e+02    2.155900e+02    2.332900e+02    1.100000e+02    1.200000e+02    1.200000e+02    1.108297e+01
60  1.871429e+00    5.400373e-01    8.458885e-01    1.066795e+00    9.134322e-01    1.172962e+00    1.055804e+00    9.814038e-01    9.448456e-01    9.621804e-01    1.297743e+00    7.298143e-01    1.023185e+00    1.335529e+00    1.556788e+00    6.437146e-01    1.352297e+00    6.343888e-01    -7.508160e+01   2.838593e+02    3.388300e+02    3.817000e+02    2.100000e+02    2.200000e+02    2.300000e+02    1.416615e+01

标签： python