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

[Joel Nothman]

Sorry if I misunderstood your question.

On 29 August 2017 at 06:32, Raga Markely <raga.markely at gmail.com> wrote:

> Sounds good.. tried it and works.. thank you!
>
> On Mon, Aug 28, 2017 at 3:20 PM, Andreas Mueller <t3kcit at gmail.com> wrote:
>
>> you can also use grid.best_estimator_ (and then all the rest)
>>
>> On 08/28/2017 03:07 PM, Raga Markely wrote:
>>
>> 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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>>>>>>
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