#!/usr/bin/env python
# coding: utf-8

# # Is my model better than predicting the mean?
# 
# It is easy to be convinced that a model is good if it has a low error.
# 
# But it is always a good idea to compare your model to a baseline, to see if it is actually better than just predicting the mean.
# 
# This can be done easily in `modelskill` thanks to the `DummyModelResult` class.
# 

# In[1]:


import modelskill as ms

fn = '../tests/testdata/Oresund2D.dfsu'
mr = ms.model_result(fn, item='Surface elevation')
mr


# In[2]:


fn = '../tests/testdata/smhi_2095_klagshamn.dfs0'
obs = ms.PointObservation(fn, x=366844.15, y=6154291.6, item=0)
obs


# In[3]:


dmr = ms.DummyModelResult(data=0.0)
dmr


# In[4]:


cmp = ms.match(obs=obs, mod=[mr, dmr]).remove_bias()
cmp.skill().round(3)


# In[5]:


cmp.skill().rmse.plot.barh(title="Better than predicting 0.0");


# Above we created a DummyModelResult which always predicts 0.0.
# 
# But we can be even more lazy and just use the `DummyModelResult` with the `mean` strategy, which will predict the mean of the observed values.

# In[6]:


dmr2 = ms.DummyModelResult(strategy='mean')
dmr2


# In[7]:


cmp2 = ms.match(obs=obs, mod=[mr, dmr2]).remove_bias()
cmp2.skill().round(3)