openprotein.svd.SVDModel#

class openprotein.svd.SVDModel(session, job=None, metadata=None)[source]#

SVD model that can be used to create reduced embeddings.

The model is also implemented as a Future to allow waiting for a fit job.

Parameters:

__init__(session, job=None, metadata=None)[source]#

Construct the SVD model using either job get or svd metadata get.

Parameters:

Methods

`__init__`(session[, job, metadata])	Construct the SVD model using either job get or svd metadata get.
`cancelled`()	Check if the job has been cancelled.
`create`(session[, job_id, job, response])	Create an instance of the appropriate Future class based on the job type.
`delete`()	Delete this SVD model.
`done`()	Check if the job has completed.
`embed`(sequences, **kwargs)	Use this SVD model to get reduced embeddings from input sequences.
`fit_gp`(assay, properties[, name, description])	Fit a GP on assay using this embedding model and hyperparameters.
`fit_umap`([sequences, assay, n_components])	Fit an UMAP on the embedding results of this model.
`get`([verbose])	Retrieve this SVD model itself.
`get_inputs`()	Get sequences used for svd job.
`get_model`()
`refresh`()	Refresh the job status and internal job object.
`wait`([interval, timeout, verbose])	Wait for the job to complete, then fetch results.
`wait_until_done`([interval, timeout, verbose])	Wait for the job to complete.

Attributes

`created_date`	The creation timestamp of the job.
`end_date`	The end timestamp of the job.
`id`	The unique identifier of the job.
`job_id`	The unique identifier of the job.
`job_type`	The type of the job.
`metadata`	Metadata of the SVD.
`model`	Base embeddings model used for the SVD.
`n_components`	Number of components of the SVD.
`progress_counter`	The progress counter of the job.
`reduction`	Reduction of embeddings used to fit the SVD.
`sequence_length`	Sequence length constraint of the SVD.
`start_date`	The start timestamp of the job.
`status`	The current status of the job.
`job`
`session`