optimizer.Optimizer

optimizer.Optimizer(self)

Base class for optimizers

Methods

Name	Description
get_trace	Get the optimization trace
optimize	Optimize the parameters of the model
setup
update	update whole parameters one step
update_one	update one parameter

get_trace

optimizer.Optimizer.get_trace()

Get the optimization trace

Returns

Name	Type	Description
	pl.DataFrame	pl.DataFrame: the optimization trace with columns ['epoch', 'mse_train', 'mse_test', 'tt_norm', 'tt_ranks'].

optimize

optimizer.Optimizer.optimize(
    epochs=1000
    epoch_per_trace=1
    epoch_per_log=500
    epoch_per_save=100000000
    fix_coord=False
    fix_basis=False
    wf=1.0
)

Optimize the parameters of the model

Parameters

Name	Type	Description	Default
epochs	int	the number of epochs executed in this execution.	1000
epoch_per_trace	int	the number of epochs per recording the optimization trace. Defaults to 1.	1
epoch_per_log	int	the number of epochs per logging. Defaults to 500.	500
epoch_per_save	int	the number of epochs per saving the model. Defaults to 100_000_000.	100000000
fix_coord	bool	whether to fix the coordinator or not. Defaults to False.	False
fix_basis	bool	whether to fix the basis or not. Defaults to False.	False
wf	float	the weight factor for the force.	1.0

Returns

Name	Type	Description
	pl.DataFrame	pl.DataFrame: the optimization trace with columns ['epoch', 'mse_train', 'mse_test', 'tt_norm', 'tt_ranks'].

setup

optimizer.Optimizer.setup(
    model
    x_train
    y_train
    *
    batch_size=100
    shuffle=True
    x_test=None
    y_test=None
    f_train=None
    f_test=None
    jobname=None
    outdir='.'
)

Parameters

Name	Type	Description	Default
model	pompon.model.Model	the model to be optimized	required
x_train	Array	the training data	required
y_train	Array	the training target	required
batch_size	int	the batch size for stochastic method. Defaults to 100.	100
shuffle	bool	whether to shuffle the data. Defaults to True. When batch_size is large, it is recommended to set shuffle=False.	True
x_test	Array	the test data. Defaults to None.	None
y_test	Array	the test target. Defaults to None.	None
f_train	Array	the force data. Defaults to None.	None
f_test	Array	the force data for test. Defaults to None. Currently, test MSE is evaluated by only the energy term.	None
jobname	str	the name of the job. Defaults to None.	None
outdir	str	the output directory. Defaults to “.”.	'.'

Returns

Name	Type	Description
Optimizer	Optimizer	the optimizer defined with the model and data.

update

optimizer.Optimizer.update(params)

update whole parameters one step

update_one

optimizer.Optimizer.update_one(param)

update one parameter