# Comparing models visually#

Martin Vonk and Davíd Brakenhoff, Artesia 2022

In this notebook we introduce the CompareModels class in Pastas that can be used to compare models (visually), and construct custom model comparison plots.

[1]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pastas as ps

ps.set_log_level("ERROR")
ps.show_versions()

Python version: 3.10.12
NumPy version: 1.23.5
Pandas version: 2.0.3
SciPy version: 1.11.1
Matplotlib version: 3.7.2
Numba version: 0.57.1
LMfit version: 1.2.2
Latexify version: Not Installed
Pastas version: 1.1.0


First load some data to construct models that we can compare with one another.

[2]:

rain = pd.read_csv("./data/rain_nb1.csv", index_col=0, parse_dates=True).squeeze()


# Create models#

• Model1a: observations series 1 with linear RechargeModel and Exponential response function

• Model1b: observations series 1 with linear RechargeModel and Gamma response function

• Model1c: observation series 1 with precipitation and evaporation as separate stresses

• Model2: has observation series 2 with linear RechargeModel and Exponential response function

[3]:

ml1a = ps.Model(obs1, name="1a_exp")
sm1a = ps.RechargeModel(rain, evap, rfunc=ps.Exponential(), name="recharge")
ml1a.solve(report=False, noise=True)

ml1b = ps.Model(obs1, name="1b_gamma")
sm1b = ps.RechargeModel(rain, evap, rfunc=ps.Gamma(), name="recharge")
ml1b.solve(report=False, noise=True)

ml1c = ps.Model(obs1, name="1c_separate")
sm2_1 = ps.StressModel(rain, rfunc=ps.Gamma(), name="Prec", settings="prec")
sm2_2 = ps.StressModel(evap, rfunc=ps.Gamma(), name="Evap", settings="evap", up=False)
ml1c.solve(report=False, noise=True)

ml2 = ps.Model(obs2, name="model_2")
ml2.solve(report=False, noise=True)


## CompareModels#

To compare models, just pass a list of models to ps.CompareModels. To plot the default comparison plot use the plot() method.

The class itself is linked to a figure and a set of axes, so for each comparison a new CompareModels class should be created.

[4]:

mc = ps.CompareModels(models=[ml1b, ml1a])
mc.plot()


The layout of the plot is controlled by a so-called mosaic, which is essentially a 2D array with labels that define the positions of the axes. The mosaic for the plot above can be accessed through the mc.mosaic attribute. The oseries and model simulations are plotted in the “sim” axes which covers a 2x2 region at the top left of the figure.

[5]:

mc.mosaic

[5]:

[['sim', 'sim', 'met'],
['sim', 'sim', 'tab'],
['res', 'res', 'tab'],
['con0', 'con0', 'rf0']]


Access to the axes or the figure is available through mc.axes dictionary (e.g. for modifying axes labels, limits, or ticks) or mc.figure (e.g. for saving the figure).

[6]:

# access the axes dictionary
mc.axes

[6]:

{'sim': <Axes: label='sim'>,
'met': <Axes: label='met'>,
'tab': <Axes: label='tab'>,
'res': <Axes: label='res'>,
'con0': <Axes: label='con0'>,
'rf0': <Axes: label='rf0'>}


## Customizing the comparison#

Perhaps you want to view all contributions on the same subplot (and the step responses as well). For this we need to customize the default plot layout and tell the plotting method we want several stresses to be plotted on the same axis.

Customizing the layout (mosaic) can either be done manually, by providing a list of lists with axes labels, or we can modify the default mosaic slightly with mc.get_default_mosaic. By setting the number of stressmodels to 1 in this method there will be only one row for the contributions and response functions.

We are now comparing models 1a and 1c (which had “prec” and “evap” as separate stresses).

[7]:

# initialize the comparison
mc = ps.CompareModels(models=[ml1a, ml1c])

[8]:

# get a custom mosaic by modifying the default mosaic slightly
mosaic = mc.get_default_mosaic(n_stressmodels=1)
mosaic

[8]:

[['sim', 'sim', 'met'],
['sim', 'sim', 'tab'],
['res', 'res', 'tab'],
['con0', 'con0', 'rf0']]


The default behavior (when no custom mosaic is provided) is shown below. Note the difference, with 3 rows showing up for plotting stress models.

[9]:

# default mosaic when no customization is applied
mc.get_default_mosaic()

[9]:

[['sim', 'sim', 'met'],
['sim', 'sim', 'tab'],
['res', 'res', 'tab'],
['con0', 'con0', 'rf0'],
['con1', 'con1', 'rf1'],
['con2', 'con2', 'rf2']]


In order to force the plot() method to plot all stressmodels on the same axes we have to pass it some extra information. This extra information is given as the smdict and is a dictionary that contains an integer index as a key (i.e, 0, 1, …) and a list of stress model names as its value. The following dictionary tells CompareModels to combine any stress models with names “recharge”, “Prec” or “Evap” from any model in the comparison list on the first row (with index 0).

[10]:

smdict = {0: ["recharge", "Prec", "Evap"]}

[11]:

# initialize the figure with our custom mosaic
mc.initialize_figure(mosaic=mosaic)

# now plot the model comparison
mc.plot(smdict=smdict)


## Using individual plotting methods#

Each component (i.e. time series or table) in the plots above is controlled by a separate method, making it easy to plot certain components separately. Check out all the methods starting with plot_* to see which options are available. When one of these methods is called separately after creating a CompareModels object, a single axis object is created on which the time series for each model are shown.

[12]:

# compare model simulations
mc = ps.CompareModels(models=[ml1b, ml1a])
ax = mc.plot_simulation()
_ = ax.legend(loc=(0, 1), frameon=False, ncol=2)

[13]:

# compare model optimal parameters
mc = ps.CompareModels(models=[ml1a, ml1b, ml1c])
ax = mc.plot_table_params()

[14]:

# compare ACF plots
mc = ps.CompareModels(models=[ml1a, ml1c])
ax = mc.plot_acf()
ax.grid(True)


## Some helper functions#

The ps.CompareModels class contains some helper methods to obtain information from the models passed to the class. Using these can be especially useful to customize the tables you wish to show on your comparison figure.

[15]:

# get minimum tmin and maximum tmax
mc.get_tmin_tmax()

[15]:

tmin tmax
1a_exp 1985-11-14 2015-06-28
1c_separate 1985-11-14 2015-06-28
[16]:

# get table with all parameters
mc.get_parameters()

[16]:

1a_exp 1c_separate
recharge_A 686.246671 NaN
recharge_a 159.386048 NaN
recharge_f -1.305359 NaN
constant_d 27.920134 28.413782
noise_alpha 49.911855 46.604725
Prec_A NaN 567.578950
Prec_n NaN 1.035391
Prec_a NaN 112.562570
Evap_A NaN -1050.931746
Evap_n NaN 1.023044
Evap_a NaN 182.918085
[17]:

# get table with parameters selected by substring
mc.get_parameters(param_selection=["_A"])

[17]:

1a_exp 1c_separate
Evap_A NaN -1050.931746
Prec_A NaN 567.578950
recharge_A 686.246671 NaN
[18]:

# get table with all p-values of statistical tests
mc.get_diagnostics()

[18]:

1a_exp 1c_separate
Shapiroo 0.00 0.00
D'Agostino 0.00 0.00
Runs test 0.64 0.08
Stoffer-Toloi 0.08 0.04
[19]:

# get table with fit metrics
mc.get_metrics()

[19]:

1a_exp 1c_separate
rmse 0.114420 0.108872
rmsn 0.079585 0.078664
sse 8.431164 7.633369
mae 0.090044 0.083524
nse 0.929136 0.935841
evp 92.913585 93.584989
rsq 0.929136 0.935841
bic -3235.196225 -3234.949501
aic -3257.534719 -3270.691090

## Equal vertical scaling between subplots#

It is possible set the vertical scale equal for all the subplots. Just initialize the figure with initialize_adjust_height_figure() instead of initialize_figure(). Note that this does require the default naming convention for the mosaic to be used (i.e. axes labels must include "sim", "res" and "con*").

Note:the scaling is not perfect, probably because space taken up by the xticklabels, the legend and perhaps some other unknown quantities are not taken into consideration in the calculations, causing some small differences in the y-scales per subplot.

[20]:

mc = ps.CompareModels(models=[ml1a, ml1c])


If you want to customize the figure yourself and use the adjusted height functionality, make sure that you provide the smdict to the initialize_adjust_height_figure() method. Keep in mind that only the first column of the mosaic is used for scaling.

[21]:

mosaic = [
["sim", "sim", "met"],
["sim", "sim", "tab"],
["res", "res", "tab"],
["con0", "con0", "rf0"],
["con1", "con1", "rf1"],
]

smdict = {0: ["Prec"], 1: ["recharge", "Evap"]}

mc = ps.CompareModels([ml1a, ml1c])
mc.plot(legend=True)


## Going a bit overboard#

Just to show you what is possible, here is an extreme example in which we do the following:

• compare 2 models that are related (ml1a and ml1c with the same oseries), and one that isn’t (ml2)

• create a custom mosaic by manually providing one

• plot just about every comparison we can think of

• combine all the contributions of the different stresses on the same subplot

• manually share the x-axes between certain plots

• choose a different qualitative colormap

Note that this comparison doesn’t make all that much sense, but it does show you how easy it is to create custom comparison plots.

[22]:

mosaic = [
["ose", "ose", "met"],
["sim", "sim", "tab"],
["res", "res", "tab"],
["con0", "con0", "dia"],
["acf", "acf", "dia"],
]

mc = ps.CompareModels(models=[ml1a, ml1c, ml2])
mc.initialize_figure(mosaic, figsize=(16, 10), cmap="Dark2")

# plot oseries on "ose" axis
mc.plot_oseries(axn="ose")

# plot simulation on "sim" axis
mc.plot_simulation()

# plot metrics
mc.plot_table_metrics()

# table of optimal parameters but only those containing the gain "_A"
mc.plot_table_params(param_selection=["_A"])

# plot residuals
mc.plot_residuals()

# plot all contributions on the same axis
mc.plot_contribution(smdict={0: ["Prec", "Evap", "Rech", "recharge"]}, axn="con{i}")

# plot p-value for diagnostic tests
mc.plot_table_diagnostics(axn="dia", diag_col=r"Reject H0 ($\alpha$=0.05)")

# plot ACF
mc.plot_acf(axn="acf")

# turn grid on
for axlbl in mc.axes:
mc.axes[axlbl].grid(True)

# share x-axes between plots
mc.share_xaxes([mc.axes["ose"], mc.axes["sim"], mc.axes["res"], mc.axes["con0"]])

# set tight layout

[23]:

mc = ps.CompareModels(models=[ml1a, ml1c, ml2])

mosaic = [
["ose", "ose", "met"],
["sim", "sim", "tab"],
["res", "res", "tab"],
["con0", "con0", "tab"],
["con1", "con1", "tab"],
["stress", "stress", "dia"],
["acf", "acf", "dia"],
]

smdict = {0: ["recharge", "Prec"], 1: ["Evap"]}

mosaic, figsize=(16, 10), cmap="Dark2", smdict=smdict
)
mc.plot_oseries(axn="ose")
mc.plot_simulation()
mc.plot_table_metrics(metric_selection=["evp", "rsq"])
mc.plot_table_params(param_selection=["_A"], param_col="stderr")
mc.plot_residuals()
mc.plot_stress()
mc.plot_contribution(axn="con{i}")
mc.plot_table_diagnostics(axn="dia", diag_col="Statistic")
mc.plot_acf(axn="acf")
for axlbl in mc.axes:
mc.axes[axlbl].grid(True)
mc.share_xaxes(
[
mc.axes["ose"],
mc.axes["sim"],
mc.axes["res"],
mc.axes["con0"],
mc.axes["con1"],
mc.axes["stress"],
]
)

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