try to run the python script with docker image

[rogerwq]

hi @DivyaKarade
I tried to run the python code and got errors.
Would you help to check?

[rogerwq]

here is the error:

/usr/local/lib/python3.11/dist-packages/sklearn/utils/extmath.py:1163: RuntimeWarning: overflow encountered in square
  temp **= 2
/usr/local/lib/python3.11/dist-packages/sklearn/utils/extmath.py:1169: RuntimeWarning: overflow encountered in square
  new_unnormalized_variance -= correction**2 / new_sample_count
/usr/local/lib/python3.11/dist-packages/sklearn/utils/extmath.py:1169: RuntimeWarning: invalid value encountered in subtract
  new_unnormalized_variance -= correction**2 / new_sample_count
/usr/local/lib/python3.11/dist-packages/sklearn/preprocessing/_data.py:88: RuntimeWarning: overflow encountered in square
  upper_bound = n_samples * eps * var + (n_samples * mean * eps) ** 2
Traceback (most recent call last):
  File "/workspace/QSAR_workflow_sample.py", line 236, in <module>
    preds, ad, model, hist = run_qsar_workflow(
                             ^^^^^^^^^^^^^^^^^^
  File "/workspace/QSAR_workflow_sample.py", line 200, in run_qsar_workflow
    yhat_train = iso.fit_predict(pca.fit_transform(X_train))
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/utils/_set_output.py", line 316, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/base.py", line 1365, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/decomposition/_pca.py", line 466, in fit_transform
    U, S, _, X, x_is_centered, xp = self._fit(X)
                                    ^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/decomposition/_pca.py", line 503, in _fit
    X = validate_data(
        ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py", line 2954, in validate_data
    out = check_array(X, input_name="X", **check_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py", line 1105, in check_array
    _assert_all_finite(
  File "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py", line 120, in _assert_all_finite
    _assert_all_finite_element_wise(
  File "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise
    raise ValueError(msg_err)
ValueError: Input X contains NaN.
PCA does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

[rogerwq]

@DivyaKarade here is the dockerfile used to build the docker image

https://github.com/chiral-data/collab-workflows/blob/29-add-app-tensorflow/apps/t/tensorflow_2025_12_02_v2/Dockerfile