Custom Metrics

ModelSkill comes with many metrics to choose from, but you can also define your own.

import numpy as np
import modelskill as ms

fn = '../data/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ms.model_result(fn, name='HKZN_local', item=0)
o1 = ms.PointObservation('../data/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name="HKNA")
o2 = ms.PointObservation("../data/SW/eur_Hm0.dfs0", item=0, x=3.2760, y=51.9990, name="EPL")
o3 = ms.TrackObservation("../data/SW/Alti_c2_Dutch.dfs0", item=3, name="c2")
cc = ms.match([o1, o2, o3], mr)
cc

<ComparerCollection>
Comparers:
0: HKNA - Significant wave height [m]
1: EPL - Significant wave height [m]
2: c2 - Significant wave height [m]

Standard set of metrics

cc.skill()

	n	bias	rmse	urmse	mae	cc	si	r2
observation
HKNA	386	-0.202413	0.355195	0.291877	0.255866	0.971708	0.093967	0.903554
EPL	67	-0.071238	0.224923	0.213344	0.189455	0.969760	0.082482	0.931793
c2	113	-0.004701	0.352470	0.352439	0.294758	0.975050	0.128010	0.899121

Some metrics has parameters, which require a bit special treatment.

from modelskill.metrics import hit_ratio

def hit_ratio_05_pct(obs, model):
    return hit_ratio(obs, model, 0.5) * 100

def hit_ratio_01_pct(obs, model):
    return hit_ratio(obs, model, 0.1) * 100


cc.skill(metrics=[hit_ratio_05_pct, hit_ratio_01_pct])

	n	hit_ratio_05_pct	hit_ratio_01_pct
observation
HKNA	386	86.528497	27.720207
EPL	67	98.507463	26.865672
c2	113	85.840708	17.699115

And you are always free to specify your own special metric or import metrics from other libraries, e.g. scikit-learn.

def my_special_metric_with_long_descriptive_name(obs, model):

    res = obs - model

    res_clipped = np.clip(res,0,np.inf)

    return np.mean(np.abs(res_clipped))

# short alias to avoid long column names in output
def mcae(obs, model): return my_special_metric_with_long_descriptive_name(obs, model)

cc.skill(metrics=mcae)

	n	mcae
observation
HKNA	386	0.229139
EPL	67	0.130347
c2	113	0.149729

Other Formats