# MachineLearningMixin **`class MachineLearningMixin`** Bases: [`object`](https://docs.python.org/3.11/library/functions.html#object) Methods SummaryMethods Documentation **`clone_machinelearning_workflow(project_id, ml_workflow_id, name=None, type=None)`** Clone the Machine Learning Workflow. Parameters - `project_id` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – Id of the Squirro project. - `ml_workflow_id` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – Id of the Machine Learning workflow. - `name` ([`Optional`](https://docs.python.org/3.11/library/typing.html#typing.Optional)[[`str`](https://docs.python.org/3.11/library/stdtypes.html#str)]) – Optional name of Machine learning workflow. If not specified, the name of the workflow will be the same as the original one. - `type` ([`Optional`](https://docs.python.org/3.11/library/typing.html#typing.Optional)[[`str`](https://docs.python.org/3.11/library/stdtypes.html#str)]) – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the type of the workflow will be the same as the original one. **`delete_machinelearning_job(project_id, ml_workflow_id, ml_job_id)`** Delete a Machine Learning job. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `ml_job_id` – Id of the Machine Learning job. **`delete_machinelearning_workflow(project_id, ml_workflow_id)`** Delete a Machine Learning workflow. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. **`get_machinelearning_job(project_id, ml_workflow_id, ml_job_id, include_run_log=None, last_n_log_lines=None, include_results=None)`** Return a particular Machine Learning job. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `ml_job_id` – Id of the Machine Learning job. - `include_run_log` – Boolean flag to optionally fetch the last run log of the job. - `last_n_log_lines` – Integer to fetch only the last n lines of the last run log. - `include_run_log` – Boolean flag to optionally fetch the last run results. **`get_machinelearning_jobs(project_id, ml_workflow_id, include_internal_jobs=None)`** Return all the Machine Learning jobs for a particular Machine Learning workflow. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `include_internal_jobs` – Bool, whether or not to include the internal jobs. Generally, you should not need to get these jobs as these jobs are used to optimize the inference runs. **`get_machinelearning_workflow(project_id, ml_workflow_id)`** Return a specific Machine Learning Workflow in a project. Parameters - `project_id` – Id of the project. - `ml_workflow_id` – Id of the Machine Learning workflow. **`get_machinelearning_workflow_assets(project_id, ml_workflow_id, write_to_disk=None)`** Return all the binary assets like trained models associated with a Machine Learning Workflow. Parameters - `project_id` – Id of the project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `write_to_disk` – Boolean. Wheather or not to write the exported ML workflow assets to the disk. **`get_machinelearning_workflows(project_id)`** Return all Machine Learning workflows for a project. Parameters `project_id` – Id of the Squirro project. **`kill_machinelearning_job(project_id, ml_workflow_id, ml_job_id)`** Kills a Machine Learning job if it is running. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `ml_job_id` – Id of the Machine Learning job. **`modify_machinelearning_workflow(project_id, ml_workflow_id, name=None, config=None, ml_models=None, type=None)`** Modify an existing Machine Learning workflow. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `name` – Name of Machine learning workflow. - `config` – Dictionary of Machine Learning workflow config. Detailed documentation here: [https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation](https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation) # noqa - `ml_models` – Directory with ml_models to be uploaded into the workflow path. - `type` – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the default type is other. **`new_machinelearning_job(project_id, ml_workflow_id, type, scheduling_options=None)`** Create a new Machine Learning job. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine learning workflow. - `type` – Type of the Machine Learning job. Possible values are training and inference. - `scheduling_options` – Scheduling options for the job Example: ``` >>> client.new_machinelearning_job( project_id='2aEVClLRRA-vCCIvnuEAvQ', ml_workflow_id='129aVASaFNPN3NG10-ASDF', type='training', scheduling_options={"time_based":{"repeat_every":"1d"}}) '13nv0va0svSDv3333v' ``` **`new_machinelearning_workflow(project_id, name, config, ml_models=None, type=None)`** Create a new Machine Learning Workflow. Parameters - `project_id` – Id of the Squirro project. - `name` – Name of Machine learning workflow. - `config` – Dictionary of Machine learning workflow config. Detailed documentation here: [https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation](https://squirro.atlassian.net/wiki/spaces/DOC/pages/337215576/Squirro+Machine+Learning+Documentation) # noqa - `ml_models` – Directory with ml_models to be uploaded into the workflow path - `type` – Optional parameter to define type of the Machine learning workflow. Possible values are other, query, query_default, ais, published, document_embedder_queries, document_embedder_queries_default. If not specified, the default type is other. **`run_machinelearning_job(project_id, ml_workflow_id, ml_job_id)`** Schedules a Machine Learning job to run now. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `ml_job_id` – Id of the Machine Learning job. **`run_machinelearning_workflow(project_id, ml_workflow_id, data, asynchronous=False)`** Run a Machine Learning workflow directly on Squirro items. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `data` – Data to run through Machine Learning workflow. -response_format: Format of the response. Valid options are: > - standard: The default format of the response. Fields returned > > > > by the Machine Learning workflow are wrapped into a list. > - plain: Fields returned from the Machine Learning workflow > are returned as they are, without wrapping. - `asynchronous` – Whether or not to run Machine Learning workflow asynchronously (recommended for large data batches). **`wait_for_machinelearning_job(project_id, ml_workflow_id, ml_job_id, max_wait_time=600)`** Wait for the first run of the Machine Learning job to complete. Often useful in automated scripts where you would want to wait for a job to finish after setting it up to inspect logs or results. Parameters - `project_id` – Id of the Squirro project. - `ml_workflow_id` – Id of the Machine Learning workflow. - `ml_job_id` – Id of the Machine Learning job. - `max_wait_time` – Maximum time to wait for Machine Learning job (default: 600s).