smash 0.3.1 Release Notes#

Contributors#

This release was made possible thanks to the contributions of:

Ngo Nghi Truyen Huynh
François Colleoni
Maxime Jay-Allemand

Compatibilities#

In order to ensure compatibility with the latest versions of SALib, the SALib version used by smash and smash-dev has been pinned to SALib>=1.4.5.

Improvements#

Bounds definition#

The bounds argument in the optimization methods (Model.optimize(), Model.bayes_estimate(), Model.bayes_optimize(), Model.ann_optimize()) is now defined by a dictionary instead of a list, a tuple, or a set.

It can be used as follows:

>>> model.optimize(bounds={"lr": [10., 20.]})

BayesResult object#

The generated Model parameters/states in the attribute data of smash.BayesResult object is a scalar instead of a 2D-array for each generated sample.

Users can easily save the results of Model.bayes_estimate() and Model.bayes_optimize() methods into a pandas.DataFrame as follows:

>>> import pandas as pd
>>> br = model.bayes_estimate(inplace=True, return_br=True)
>>> pd.DataFrame(br.data).to_csv("br_data.csv")  # br.data[parameter/state] is a 1D-array instead of a 3D-array
>>> pd.DataFrame(br.l_curve).to_csv("br_lcurve.csv")

Signatures-based cost function#

The formula for the signatures-based cost function has been changed from a squared relative value to an absolute relative value. For example:

\[j_{k,s,i} = \left|\frac{S_{i}^{s}(k)}{S_{i}^{o}(k)}-1\right|\]

instead of:

\[j_{k,s,i} = \left(\frac{S_{i}^{s}(k)}{S_{i}^{o}(k)}-1\right)^2\]

Note

More information can be find in the Math/Num documentation section

Setting the learning rate for Net#

Instead of passing the learning_rate argument directly as a parameter in the Net.compile() method, it is now included as a key-value pair within the options dictionary argument.

The default value of the learning rate depends on the optimizer that you choose. Here are some examples:

# A default learning rate of 0.01 will be set with the adagrad optimizer
>>> net.compile(optimizer="adagrad")

# A default learning rate of 0.001 will be set with the rmsprop optimizer
>>> net.compile(optimizer="rmsprop")

# Set a custom learning rate of 0.002 with the rmsprop optimizer
>>> net.compile(optimizer="rmsprop", options={"learning_rate": 0.002})

Documentation#

Add the documentation for the options argument in the Model.optimize(), Net.add() and Net.compile() methods using a customize sphinx directive.

Add the user guide for advanced optimization techniques.

New Features#

Loading external dataset#

External datasets can be loaded using the smash.load_dataset() method.

It can be used as follows:

>>> setup, mesh = model.load_dataset("path_to_external_data")

New dataset on daily timestep#

A new dataset called Lez is available in the smash.load_dataset() method. This dataset contains 3 nested gauges with a total area of 169km². The simulation is over a period of one year between 2012-08-01 and 2013-07-31 at daily time step with the gr-a structure. Moreover, 6 descriptors are available:

“slope”
“drainage_density”
“karst”
“woodland”
“urban”
“soil_water_storage”

It can be used as follows:

>>> setup, mesh = model.load_dataset("Lez")

Adjusting additional options in ann_optimize#

Optimizer, learning rate and random state can be adjusted if the neural network is not set when using Model.ann_optimize().

It can be used as follows:

>>> model.ann_optimize(optimizer="sgd", learning_rate=0.01, random_state=11)

Fixes#

F2PY f2cmap warnings#

Fix F2PY c_int warnings by adding into kind_map file: dict("integer": dict("c_int": "int")).

Fix mw_meshing.f9O drow and dcol variable initialisation.