Hi Andy,
Please find attached a Jupyter notebook showing exactly where the problem
appears.
Best,
Sam
On Thu, Aug 17, 2017 at 4:03 PM, Andreas Mueller wrote:
> Hi Sam.
>
> Can you say which test fails exactly and where (i.e. give traceback)?
> The estimator checks are currently quite strict wi
gist at https://gist.github.com/jnothman/a75bac778c1eb9661017555249e50379
On 18 August 2017 at 01:26, Joel Nothman wrote:
> I don't consider LabelBinarizer the best workaround.
>
> Given a Pandas dataframe df, I'd use:
>
> DictVectorizer().fit_transform(df.to_dict(orient='records'))
>
> which wi
I don't consider LabelBinarizer the best workaround.
Given a Pandas dataframe df, I'd use:
DictVectorizer().fit_transform(df.to_dict(orient='records'))
which will handle encoding strings with one-hot and numerical features as
column vectors. Or:
class PandasVectorizer(DictVectorizer):
def f
Hi Georg.
Unfortunately this is not entirely trivial right now, but will be fixed by
https://github.com/scikit-learn/scikit-learn/pull/9151
and
https://github.com/scikit-learn/scikit-learn/pull/9012
which will be in the next release (0.20).
LabelBinarizer is probably the best work-around for now,
Hi Sam.
Can you say which test fails exactly and where (i.e. give traceback)?
The estimator checks are currently quite strict with respect to raising
helpful error messages.
That doesn't mean your estimator is broken (necessarily).
With a precomputed gram matrix, I expect the shape of X in pred
Hi,
how can I properly handle categorical values in scikit-learn?
https://stackoverflow.com/questions/45727934/pandas-categories-new-levels?noredirect=1#comment78424496_45727934
goals
- scikit-learn syle fit/transform methods to encode labels of
categorical features of X
- should handl
I am rolling classifier based on SVC which computes a custom Gram matrix
and runs this through the SVC classifier with kernel = 'precomputed'. While
this works fine with the fit method, I face a dilemma with the predict
method, shown here:
def predict(self, X):
"""Run the predict meth
