mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-11-10 06:54:44 +00:00
43 lines
1002 B
Python
43 lines
1002 B
Python
# Titan Robotics Team 2022: RegressionMetric submodule
|
|
# Written by Arthur Lu
|
|
# Notes:
|
|
# this should be imported as a python module using 'from tra_analysis import RegressionMetric'
|
|
# setup:
|
|
|
|
__version__ = "1.0.1"
|
|
|
|
__changelog__ = """changelog:
|
|
1.0.1:
|
|
- optimized imports
|
|
1.0.0:
|
|
- ported analysis.RegressionMetric() here
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <learthurgo@gmail.com>",
|
|
)
|
|
|
|
__all__ = [
|
|
'RegressionMetric'
|
|
]
|
|
|
|
import numpy as np
|
|
import sklearn
|
|
|
|
class RegressionMetric():
|
|
|
|
def __new__(cls, predictions, targets):
|
|
|
|
return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
|
|
|
|
def r_squared(self, predictions, targets): # assumes equal size inputs
|
|
|
|
return sklearn.metrics.r2_score(targets, predictions)
|
|
|
|
def mse(self, predictions, targets):
|
|
|
|
return sklearn.metrics.mean_squared_error(targets, predictions)
|
|
|
|
def rms(self, predictions, targets):
|
|
|
|
return np.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) |