When I upgraded from numpy 1.9 to 1.10 I started to see that the following regression model gives different results on different machines with the same hardware configuration:
fitted_model = pd.ols(y=lhs_unpickled, x=rhs_unpickled, intercept=False)
print fitted_model.beta
lhs_unpickled
and rhs_unpickled
look like this:
> lhs_unpickled[1:5]
2008-04-24 00:18:00+00:00 -0.465517
2008-04-24 00:33:00+00:00 -0.519584
2008-04-24 00:48:00+00:00 -0.607410
2008-04-24 01:03:00+00:00 -0.705983
Freq: 15T, Name: AI_Index, dtype: float64
> rhs_unpickled[1:5]
CPM XQH FOD EX
2008-04-24 00:18:00+00:00 -0.301556 0.148582 0.079320 -0.707586
2008-04-24 00:33:00+00:00 -0.274421 0.071747 0.130182 -0.659409
2008-04-24 00:48:00+00:00 -0.273960 -0.001447 0.148643 -0.703215
2008-04-24 01:03:00+00:00 -0.238426 -0.008732 0.130801 -0.698489
Is there something specific about this pd.ols()
function which results in this inconsistent behavior when using numpy 1.10?