Hi all,
I am having problems when trying to deal with missing values. I am using
Imputer like this:
Pipeline([('imputerNA', Imputer(missing_values='NA', strategy='mean',
axis=0, verbose=4)), ('minmax', MinMaxScaler())]))]
My data looks like this:
24881956.0|NA|1840.0|NA|NA|48.0|1.4|NA|-1.0|0.0|0.0|1.0
and I am getting this exception:
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/pipeline.py", line 119, in _pre_transform
Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/base.py", line 429, in fit_transform
return self.fit(X, y, **fit_params).transform(X)
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/preprocessing/imputation.py", line 181, in fit
X = atleast2d_or_csc(X, dtype=np.float64, force_all_finite=False)
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/utils/validation.py", line 154, in atleast2d_or_csc
force_all_finite)
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/utils/validation.py", line 142, in
_atleast2d_or_sparse
force_all_finite=force_all_finite)
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/sklearn/utils/validation.py", line 120, in array2d
X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
File
"/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/
site-packages/numpy/core/numeric.py", line 460, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: NA
It fails when it tries to convert X(which is a list of list) into numpy
array. That is fair because a numpy array elements must be of the same
time and I have strings and floats.
Does it make sense?
Thanks in advance,
Zoraida.-
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