[scikit-learn] Inconsistent Logistic Regression fit results

Chris Cameron chris at upnix.com
Tue Aug 16 12:15:38 EDT 2016 

Thank you everyone for your help. The short version of this email is that changing the solver from ‘liblinear’ to ‘sag’ fixed my problem - but only if I upped “max_iter” to 1000.


Longer version - 
Without max_iter=1000, I would get the warning:
ConvergenceWarning: The max_iter was reached which means the coef_ did not converge

I have some columns in my data that have a huge range of values. Using “liblinear”, if I transformed those columns, causing the range to be smaller, the results would be consistent every time.

This is the function I ended up using -
def log_run(logreg_x, logreg_y):
    logreg_x['pass_fail'] = logreg_y
    df_train, df_test, y_train, y_test = train_test_split(logreg_x, logreg_y, random_state=0)
    del(df_train['pass_fail'])
    del(df_test['pass_fail'])
    log_reg_fit = LogisticRegression(class_weight='balanced',
                                     tol=0.00000001,
                                     random_state=8,
                                     solver='sag',
                                     max_iter=1000).fit(df_train.values, y_train)
    predicted = log_reg_fit.predict(df_test.values)
    accuracy = accuracy_score(y_test, predicted)
    kappa = cohen_kappa_score(y_test, predicted)
        
    return [kappa, accuracy]


Thank you again for the help,

Chris

> On Aug 15, 2016, at 4:26 PM, mail at sebastianraschka.com wrote:
> 
> hm, was worth a try. What happens if you change the solver to something else than liblinear, does this issue still persist?
> 
> 
> Btw. scikit-learn works with NumPy arrays, not NumPy matrices. Probably unrelated to your issue, I’d recommend setting
> 
>>   y_train = df_train.pass_fail.values
>>   y_test = df_test.pass_fail.values
> 
> instead of
> 
>>   y_train = df_train.pass_fail.as_matrix()
>>   y_test = df_test.pass_fail.as_matrix()
> 
> 
> Also, try passing NumPy arrays to the fit method:
> 
>>   log_reg_fit = LogisticRegression(...).fit(df_train.values, y_train)
> 
> and
> 
>> predicted = log_reg_fit.predict(df_test.values)
> 
> and so forth.
> 
> 
> 
> 
> 
>> On Aug 15, 2016, at 6:00 PM, Chris Cameron <chris at upnix.com> wrote:
>> 
>> Sebastian,
>> 
>> That doesn’t do it. With the function:
>> 
>> def log_run(logreg_x, logreg_y):
>>   logreg_x['pass_fail'] = logreg_y
>>   df_train, df_test = train_test_split(logreg_x, random_state=0)
>>   y_train = df_train.pass_fail.as_matrix()
>>   y_test = df_test.pass_fail.as_matrix()
>>   del(df_train['pass_fail'])
>>   del(df_test['pass_fail'])
>>   log_reg_fit = LogisticRegression(class_weight='balanced',
>>                                    tol=0.000000001,
>>                                    random_state=0).fit(df_train, y_train)
>>   predicted = log_reg_fit.predict(df_test)
>>   accuracy = accuracy_score(y_test, predicted)
>>   kappa = cohen_kappa_score(y_test, predicted)
>> 
>>   return [kappa, accuracy]
>> 
>> I’m still seeing:
>> log_run(df_save, y)
>> Out[7]: [-0.054421768707483005, 0.48333333333333334]
>> 
>> log_run(df_save, y)
>> Out[8]: [0.042553191489361743, 0.55000000000000004]
>> 
>> log_run(df_save, y)
>> Out[9]: [0.042553191489361743, 0.55000000000000004]
>> 
>> log_run(df_save, y)
>> Out[10]: [0.027777777777777728, 0.53333333333333333]
>> 
>> 
>> Chris
>> 
>>> On Aug 15, 2016, at 3:42 PM, mail at sebastianraschka.com wrote:
>>> 
>>> Hi, Chris,
>>> have you set the random seed to a specific, contant integer value? Note that the default in LogisticRegression is random_state=None. Setting it to some arbitrary number like 123 may help if you haven’t done so, yet.
>>> 
>>> Best,
>>> Sebastian
>>> 
>>> 
>>> 
>>>> On Aug 15, 2016, at 5:27 PM, Chris Cameron <chris at upnix.com> wrote:
>>>> 
>>>> Hi all,
>>>> 
>>>> Using the same X and y values sklearn.linear_model.LogisticRegression.fit() is providing me with inconsistent results.
>>>> 
>>>> The documentation for sklearn.linear_model.LogisticRegression states that "It is thus not uncommon, to have slightly different results for the same input data.” I am experiencing this, however the fix of using a smaller “tol” parameter isn’t providing me with consistent fit.
>>>> 
>>>> The code I’m using:
>>>> 
>>>> def log_run(logreg_x, logreg_y):
>>>> logreg_x['pass_fail'] = logreg_y
>>>> df_train, df_test = train_test_split(logreg_x, random_state=0)
>>>> y_train = df_train.pass_fail.as_matrix()
>>>> y_test = df_test.pass_fail.as_matrix()
>>>> del(df_train['pass_fail'])
>>>> del(df_test['pass_fail'])
>>>> log_reg_fit = LogisticRegression(class_weight='balanced',tol=0.000000001).fit(df_train, y_train)
>>>> predicted = log_reg_fit.predict(df_test)
>>>> accuracy = accuracy_score(y_test, predicted)
>>>> kappa = cohen_kappa_score(y_test, predicted)
>>>> 
>>>> return [kappa, accuracy]
>>>> 
>>>> 
>>>> I’ve gone out of my way to be sure the test and train data is the same for each run, so I don’t think there should be random shuffling going on.
>>>> 
>>>> Example output:
>>>> ---
>>>> log_run(df_save, y)
>>>> Out[32]: [0.027777777777777728, 0.53333333333333333]
>>>> 
>>>> log_run(df_save, y)
>>>> Out[33]: [0.027777777777777728, 0.53333333333333333]
>>>> 
>>>> log_run(df_save, y)
>>>> Out[34]: [0.11347517730496456, 0.58333333333333337]
>>>> 
>>>> log_run(df_save, y)
>>>> Out[35]: [0.042553191489361743, 0.55000000000000004]
>>>> 
>>>> log_run(df_save, y)
>>>> Out[36]: [-0.07407407407407407, 0.51666666666666672]
>>>> 
>>>> log_run(df_save, y)
>>>> Out[37]: [0.042553191489361743, 0.55000000000000004]
>>>> 
>>>> A little information on the problem DataFrame:
>>>> ---
>>>> len(df_save)
>>>> Out[40]: 240
>>>> 
>>>> len(df_save.columns)
>>>> Out[41]: 18
>>>> 
>>>> 
>>>> If I omit this particular column the Kappa no longer fluctuates:
>>>> 
>>>> df_save[‘abc'].head()
>>>> Out[42]: 
>>>> 0    0.026316
>>>> 1    0.333333
>>>> 2    0.015152
>>>> 3    0.010526
>>>> 4    0.125000
>>>> Name: abc, dtype: float64
>>>> 
>>>> 
>>>> Does anyone have ideas on how I can figure this out? Is there some randomness/shuffling still going on I missed?
>>>> 
>>>> 
>>>> Thanks!
>>>> Chris
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