How to predict using cat-boost algorithm

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Python

Python3.6

I am new in python. I am trying to predict the "time_to_failure" for given "acoustic_data" in the test CSV file using catboost algorithm.

def catbostregtest(X_train, y_train):   
    # submission format
    submission = pd.read_csv('sample_submission.csv', index_col='seg_id')
    X_test = pd.DataFrame()
    # prepare test data
    for seg_id in submission.index:
        seg = pd.read_csv('test/' + seg_id + '.csv')
        ch = gen_features(seg['acoustic_data'])
        X_test = X_test.append(ch, ignore_index=True)
    # model of choice here
    model = CatBoostRegressor(iterations=10000, loss_function='MAE', boosting_type='Ordered')
    model.fit(X_train, y_train)
    y_hat = model.predict(X_test)    #error line
    # write submission file LSTM
    submission['time_to_failure'] = y_hat
    submission.to_csv('submissionCAT.csv')
    print(model.best_score_)

This function "catbostregtest" is giving me error with the errorlog

Traceback (most recent call last):<br />
      <br />
        File "E:\dir\code.py", line 290, in main()<br />
        <br />
        File "E:\dir\code.py", line 230, in main catbostregtest(X_train, y_train)<br />
        <br />
        File "E:\dir\code.py", line 175, in catbostregtest y_hat = model.predict(X_test)<br />
        <br />
        File "C:\Users\xyz\AppData\Local\Continuum\anaconda3\lib\site-packages\catboost\core.py", line 4365, in predict return self._predict(data, "RawFormulaVal", ntree_start, ntree_end, thread_count, verbose, 'predict')<br />
        <br />
        File "C:\Users\xyz\AppData\Local\Continuum\anaconda3\lib\site-packages\catboost\core.py", line 1854, in _predict predictions = self._base_predict(data, prediction_type, ntree_start, ntree_end, thread_count, verbose)<br />
        <br />
        File "C:\Users\xyz\AppData\Local\Continuum\anaconda3\lib\site-packages\catboost\core.py", line 1271, in _base_predict return self._object._base_predict(pool, prediction_type, ntree_start, ntree_end, thread_count, verbose)<br />
        <br />
        File "_catboost.pyx", line 4015, in _catboost._CatBoost._base_predict<br />
        <br />
        File "_catboost.pyx", line 4020, in _catboost._CatBoost._base_predict<br />
        <br />
        CatBoostError: c:/goagent/pipelines/buildmaster/catboost.git/catboost/libs/data/model_dataset_compatibility.cpp:236: Feature 0 from pool must be mean.

This is gen_features function

def gen_features(X):
    strain = []
    strain.append(X.mean())
    strain.append(X.std())
    strain.append(X.min())
    strain.append(X.max())
    strain.append(X.kurtosis())
    strain.append(X.skew())
    strain.append(np.quantile(X,0.01))
    strain.append(np.quantile(X,0.05))
    strain.append(np.quantile(X,0.95))
    strain.append(np.quantile(X,0.99))
    strain.append(np.abs(X).max())
    strain.append(np.abs(X).mean())
    strain.append(np.abs(X).std())
    return pd.Series(strain)

This function is called from the main function

def main(): 
      train1 = pd.read_csv('train.csv', iterator=True, chunksize=150_000, dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})
      X_train = pd.DataFrame()
      y_train = pd.Series()
      for df in train1:
          ch = gen_features(df['acoustic_data'])
          X_train = X_train.append(ch, ignore_index=True)
          y_train = y_train.append(pd.Series(df['time_to_failure'].values[-1]))
  catbostregtest(X_train, y_train)

Here is the structure of the train.csv file
train — ImgBB[^]
Here is the structure of the sample_submission.csv file
submittion — ImgBB[^]
Here is the structure of one of the test(csv) file.
test — ImgBB[^]
How I can remove the error that occur during making predict from catboost model?
How I can remove this error please help. You can download and run the project in spyder ide from this link Link

What I have tried:

I have tried all procedure on these links
Usage examples - CatBoost. Documentation[^]
python - Catboost Regression. Function Extrapolation - Stack Overflow[^]