ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

[KingArnaiz]

• confounds version:0.1.3
• Python version: 3.10
• Operating System: mac

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)

[KingArnaiz]

---

ValueError Traceback (most recent call last)
in
14
15 resid = Residualize()
---> 16 resid.fit(C_sample)
17 graph_corr_2 = resid.transform(C_sample)

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/confounds/base.py in fit(self, X, y)
210 """
211
--> 212 return self._fit(X, y) # which itself must return self
213
214

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/confounds/base.py in _fit(self, in_features, confounds)
217
218 in_features = check_array(in_features)
--> 219 confounds = check_array(confounds, ensure_2d=False)
220
221 # turning it into 2D, in case if its just a column

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0

/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)
661
662 if force_all_finite:
--> 663 _assert_all_finite(array,
664 allow_nan=force_all_finite == 'allow-nan')
665

/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)
101 not allow_nan and not np.isfinite(X).all()):
102 type_err = 'infinity' if allow_nan else 'NaN, infinity'
--> 103 raise ValueError(
104 msg_err.format
105 (type_err,

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

[raamana]

thanks @KingArnaiz for trying to use the confounds the package - it appears you are not using it correctly. the resid.fit() requires two inputs: data X and confound variables C, so try resid.fit(X_sample, C_sample)? The same thing applies to .transform() method: resid.transform(X_test, C_test)

[raamana]

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.

[KingArnaiz]

It worked. Thanks