You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I would like to add a new trainer class in addition to scikit and xgboost called curvefit (based on scipy curve_fit function).
This will be simple trainer that based on only single main feature of the group, for example, bpf_cpu_time_us of BPFOnly group.
On Trainer instance creation, fit_func such as logarithm func, logistic func is needed to be set.
In addition, p0 and main feature can be alternatively set via p0_func and feature_index, index of the target feature in the FeatureGroup, correspondingly.
The curvefit allows more flexibility for adding a new simple model function that based on a single independent variable not limited to what provided from the scikit.
I put my PR here: #215.
What would you like to be added?
I would like to add a new trainer class in addition to scikit and xgboost called
curvefit
(based on scipy curve_fit function).This will be simple trainer that based on only single main feature of the group, for example,
bpf_cpu_time_us
of BPFOnly group.On Trainer instance creation, fit_func such as logarithm func, logistic func is needed to be set.
In addition, p0 and main feature can be alternatively set via
p0_func
andfeature_index
, index of the target feature in the FeatureGroup, correspondingly.Signed-off-by: Sunyanan Choochotkaew sunyanan.choochotkaew1@ibm.com
Why is this needed?
The main objective is to expand the range of power model training to existing data set such as SPECPower where only CPU usage metric is available.
The text was updated successfully, but these errors were encountered: