smash.multiple_forward_run#
- smash.multiple_forward_run(model, samples, cost_options=None, common_options=None)[source]#
Run the forward Model with multiple sets of parameters.
- Parameters:
- model
Model
Primary data structure of the hydrological model
smash
.- samples
Samples
Represents the generated samples result.
- cost_optionsdict[str, Any] or None, default None
Dictionary containing computation cost options for simulated and observed responses. The elements are:
- jobs_cmptstr or list[str, …], default ‘nse’
Type of observation objective function(s) to be computed. Should be one or a sequence of any of
'nse'
,'nnse'
,'kge'
,'mae'
,'mape'
,'mse'
,'rmse'
,'lgrm'
(classical evaluation metrics)'Crc'
,'Crchf'
,'Crclf'
,'Crch2r'
,'Cfp2'
,'Cfp10'
,'Cfp50'
,'Cfp90'
(continuous signatures-based error metrics)'Eff'
,'Ebf'
,'Erc'
,'Erchf'
,'Erclf'
,'Erch2r'
,'Elt'
,'Epf'
(flood event signatures-based error metrics)
>>> cost_options = { "jobs_cmpt": "nse", } >>> cost_options = { "jobs_cmpt": ["nse", "Epf"], }
Hint
See the Efficiency & Error Metric and Hydrological Signature sections
- wjobs_cmptstr or list[float, …], default ‘mean’
The corresponding weighting of observation objective functions in case of multi-criteria (i.e., a sequence of objective functions to compute). There are two ways to specify it:
An alias among
'mean'
A sequence of value whose size must be equal to the number of observation objective function(s) in jobs_cmpt
>>> cost_options = { "wjobs_cmpt": "mean", } >>> cost_options = { "wjobs_cmpt": [0.7, 0.3], }
- jobs_cmpt_tfmstr or list[str, …], default ‘keep’
Type of transformation applied to discharge in observation objective function(s). Should be one or a sequence of any of
'keep'
: No transformation \(f:x \rightarrow x\)'sqrt'
: Square root transformation \(f:x \rightarrow \sqrt{x}\)'inv'
: Multiplicative inverse transformation \(f:x \rightarrow \frac{1}{x}\)
>>> cost_options = { "jobs_cmpt_tfm": "inv", } >>> cost_options = { "jobs_cmpt_tfm": ["keep", "inv"], }
Note
If jobs_cmpt is a multi-criteria and only one transformation is choosen in jobs_cmpt_tfm. The transformation will be applied to each observation objective function.
- end_warmupstr,
pandas.Timestamp
or None, default None The end of the warm-up period, which must be between the start time and the end time defined in
Model.setup
.>>> cost_options = { "end_warmup": "1997-12-21", } >>> cost_options = { "end_warmup": pd.Timestamp("19971221"), }
Note
If not given, it is set to be equal to the
Model.setup
start time.- gaugestr or list[str, …], default ‘dws’
Type of gauge to be computed. There are two ways to specify it:
An alias among
'all'
(all gauge codes) or'dws'
(most downstream gauge code(s))A gauge code or any sequence of gauge codes. The gauge code(s) given must belong to the gauge codes defined in the
Model.mesh
>>> cost_options = { "gauge": "dws", } >>> cost_options = { "gauge": "V3524010", } >>> cost_options = { "gauge": ["V3524010", "V3515010"], }
- wgaugestr or list[float, …] default ‘mean’
Type of gauge weights. There are two ways to specify it:
An alias among
'mean'
,'lquartile'
(1st quantile or lower quantile),'median'
, or'uquartile'
(3rd quantile or upper quantile)A sequence of value whose size must be equal to the number of gauges optimized in gauge
>>> cost_options = { "wgauge": "mean", } >>> cost_options = { "wgauge": [0.6, 0.4]", }
- event_segdict[str, float], default {‘peak_quant’: 0.995, ‘max_duration’: 240}
A dictionary of event segmentation options when calculating flood event signatures for cost computation (i.e., jobs_cmpt includes flood events signatures).
>>> cost_options = { event_seg = { "peak_quant": 0.998, "max_duration": 120, } }
Hint
See the
hydrograph_segmentation
function and Hydrograph Segmentation section.
- common_optionsdict[str, Any] or None, default None
Dictionary containing common options with two elements:
- ncpuint, default 1
Number of CPU(s) to perform a parallel computation.
Warning
Parallel computation is not supported on
Windows
.- verbosebool, default False
Whether to display information about the running method.
- model
- Returns:
- multiple_forward_run
MultipleForwardRun
It returns an object containing the results of the multiple forward run.
- multiple_forward_run
See also
Samples
Represents the generated samples result.
MultipleForwardRun
Represents the multiple forward run result.
Examples
>>> from smash.factory import load_dataset >>> from smash.factory import generate_samples >>> setup, mesh = load_dataset("cance") >>> model = smash.Model(setup, mesh)
Define sampling problem and generate samples
>>> problem = { ... 'num_vars': 4, ... 'names': ['cp', 'ct', 'kexc', 'llr'], ... 'bounds': [[1, 2000], [1, 1000], [-20, 5], [1, 1000]] ... } >>> sr = generate_samples(problem, n=5, random_state=11)
Run Model with multiple sets of parameters
>>> mfr = smash.multiple_forward_run(model, samples=sr) </> Multiple Forward Run Forward Run 5/5 (100%)
Get the cost values through multiple forward runs
>>> mfr.cost array([1.2170078, 1.0733036, 1.2239422, 1.2506678, 1.2261102], dtype=float32)