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ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). #19
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ValueError Traceback (most recent call last) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/confounds/base.py in fit(self, X, y) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/confounds/base.py in _fit(self, in_features, confounds) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator) /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in _assert_all_finite(X, allow_nan, msg_dtype) ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). |
thanks @KingArnaiz for trying to use the confounds the package - it appears you are not using it correctly. the |
let me know if that works for you or not? if it doesn't, try it with some simulated data (not your own real data) first, and see how it goes? and share the output and logs. |
It worked. Thanks |
Having the error eventhough the data is clean, has no Na nor NaN values
C_sample = graph_corr_1[confound_cols]
X_sample = graph_corr_1.drop(confound_cols, axis=1)
resid = Residualize()
resid.fit(C_sample)
graph_corr_2 = resid.transform(C_sample)
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