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

[Andreas Mueller]

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 
> <mailto: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 <mailto: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 <mailto: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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