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, [email protected] 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 <[email protected]> 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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>
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