[scikit-learn] Getting weight coefficient of logistic regression from a pipeline

[Raga Markely]

Ah.. got it :D..

The pipeline was run in gridsearchcv..

It works now after calling fit..

Thanks!
Raga

On Mon, Aug 28, 2017 at 2:55 PM, Andreas Mueller <t3kcit at gmail.com> wrote:

> Have you called "fit" on the pipeline?
>
>
> On 08/28/2017 02:12 PM, Raga Markely wrote:
>
> Thank you, Andreas.
>
> When I try
>
>> pipe_lr.named_steps['clf'].coef_
>
>
> I get:
>
>> AttributeError: 'LogisticRegression' object has no attribute 'coef_'
>
>
> And when I try:
>
>> pipe_lr.named_steps['clf']
>
>
> I get:
>
>> LogisticRegression(C=0.1, class_weight=None, dual=False,
>> fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class='ovr',
>> n_jobs=1, penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
>> verbose=0, warm_start=False)
>
>
> I wonder what I am missing?
>
> Thanks,
> Raga
>
>
> On Mon, Aug 28, 2017 at 12:01 PM, Andreas Mueller <t3kcit at gmail.com>
> wrote:
>
>> Can can get the coefficients on the scaled data with
>> pipeline_lr.named_steps_['clf'].coef_
>> though
>>
>>
>> On 08/28/2017 12:08 AM, Raga Markely wrote:
>>
>> No problem, thank you!
>>
>> Best,
>> Raga
>>
>> On Mon, Aug 28, 2017 at 12:01 AM, Joel Nothman <joel.nothman at gmail.com>
>> wrote:
>>
>>> No, we do not have a way to get the coefficients with respect to the
>>> input (pre-scaling) space.
>>>
>>> On 28 August 2017 at 13:20, Raga Markely <raga.markely at gmail.com> wrote:
>>>
>>>> Hello,
>>>>
>>>> I am wondering if it's possible to get the weight coefficients of
>>>> logistic regression from a pipeline?
>>>>
>>>> For instance, I have the followings:
>>>>
>>>>> clf_lr = LogisticRegression(penalty='l1', C=0.1)
>>>>> pipe_lr = Pipeline([['sc', StandardScaler()], ['clf', clf_lr]])
>>>>> pipe_lr.fit(X, y)
>>>>
>>>>
>>>> Does pipe_lr have an attribute that I can call to get the weight
>>>> coefficient?
>>>>
>>>> Or do I have to get it from the classifier as follows?
>>>>
>>>>> X_std = StandardScaler().fit_transform(X)
>>>>> clf_lr = LogisticRegression(penalty='l1', C=0.1)
>>>>> clf_lr.fit(X_std, y)
>>>>> clf_lr.coef_
>>>>
>>>>
>>>> Thank you,
>>>> Raga
>>>>
>>>>
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