Functions
def	validate_shape (func)

def	only_finite (func)

def	only_positive (func)

def	label (name)

def	rmse (y, y_hat)

def	rmsle (y, y_hat)

def	nrmse (y, y_hat)

def	mae (y, y_hat)

def	mape (y, y_hat)

def	leqz (y, y_hat=None)

def	leqznan (y, y_hat=None)

def	mdsa (y, y_hat)

def	msa (y, y_hat)

def	sspb (y, y_hat)

def	bias (y, y_hat)

def	r_squared (y, y_hat)

def	slope (y, y_hat)

def	intercept (y, y_hat)

def	mwr (y, y_hat, y_bench)

def	performance (key, y, y_hat, metrics=[mdsa, sspb, slope, msa, rmsle, mae, leqznan], csv=False)

Function Documentation

◆ bias()

def MDN.metrics.bias	(	y,
		y_hat
	)

Mean Bias

Definition at line 155 of file metrics.py.

◆ intercept()

def MDN.metrics.intercept	(	y,
		y_hat
	)

Locarithmic intercept

Definition at line 181 of file metrics.py.

◆ label()

def MDN.metrics.label ( name )

Label a function to aid in printing

Definition at line 43 of file metrics.py.

◆ leqz()

def MDN.metrics.leqz	(	y,
		y_hat = `None`
	)

Less than or equal to zero (y_hat)

Definition at line 111 of file metrics.py.

◆ leqznan()

def MDN.metrics.leqznan	(	y,
		y_hat = `None`
	)

Less than or equal to zero (y_hat)

Definition at line 119 of file metrics.py.

◆ mae()

def MDN.metrics.mae	(	y,
		y_hat
	)

Mean Absolute Error

Definition at line 97 of file metrics.py.

◆ mape()

def MDN.metrics.mape	(	y,
		y_hat
	)

Mean Absolute Percentage Error

Definition at line 104 of file metrics.py.

◆ mdsa()

def MDN.metrics.mdsa	(	y,
		y_hat
	)

Median Symmetric Accuracy

Definition at line 128 of file metrics.py.

◆ msa()

def MDN.metrics.msa	(	y,
		y_hat
	)

Mean Symmetric Accuracy

Definition at line 137 of file metrics.py.

◆ mwr()

def MDN.metrics.mwr	(	y,
		y_hat,
		y_bench
	)

Model Win Rate - Percent of samples in which model has a closer 
estimate than the benchmark.
    y: true, y_hat: model, y_bench: benchmark

Definition at line 189 of file metrics.py.

◆ nrmse()

def MDN.metrics.nrmse	(	y,
		y_hat
	)

Normalized Root Mean Squared Error

Definition at line 90 of file metrics.py.

◆ only_finite()

def MDN.metrics.only_finite ( func )

Decorator to remove samples which are nan in any input array

Definition at line 19 of file metrics.py.

◆ only_positive()

def MDN.metrics.only_positive ( func )

Decorator to remove samples which are zero/negative in any input array

Definition at line 31 of file metrics.py.

◆ performance()

def MDN.metrics.performance	(	key,
		y,
		y_hat,
		metrics = `[mdsa, sspb, slope, msa, rmsle, mae, leqznan]`,
		csv = `False`
	)

Return a string containing performance using various metrics. 
    y should be the true value, y_hat the estimated value.

Definition at line 208 of file metrics.py.

◆ r_squared()

def MDN.metrics.r_squared	(	y,
		y_hat
	)

Logarithmic R^2

Definition at line 163 of file metrics.py.

◆ rmse()

def MDN.metrics.rmse	(	y,
		y_hat
	)

Root Mean Squared Error

Definition at line 75 of file metrics.py.

◆ rmsle()

def MDN.metrics.rmsle	(	y,
		y_hat
	)

Root Mean Squared Logarithmic Error

Definition at line 83 of file metrics.py.

◆ slope()

def MDN.metrics.slope	(	y,
		y_hat
	)

Logarithmic slope

Definition at line 172 of file metrics.py.

◆ sspb()

def MDN.metrics.sspb	(	y,
		y_hat
	)

Symmetric Signed Percentage Bias

Definition at line 146 of file metrics.py.

◆ validate_shape()

def MDN.metrics.validate_shape ( func )

Decorator to flatten all function input arrays, and ensure shapes are the same

Definition at line 7 of file metrics.py.

Functions