Installed SDK reference

Signatures come from the installed package; availability depends on the service capability catalogue.

Client

Client(base_url=None, api_key=None, *, timeout=60, transport=None, max_retries=2, max_retry_delay=5)

Client.causal_result(self, job_id)

Client.causal_scores(self, job_id)

Client.close(self)

Client.dataset(self, dataset_id)

Client.datasets(self)

Client.delete_causal_result(self, result_id)

Client.delete_dataset(self, dataset_id)

Client.discover(self, dataset_id, *, model='arrow', configuration='default', seed=0, constraints=None, model_version=None, idempotency_key=None, timeout=600)

Client.download_research_dataset(self, dataset_id, path, *, version=None, max_bytes=67108864)

Client.job(self, job_id)

Client.jobs(self)

Client.models(self)

Client.predict(self, predictor_id, *, columns, rows, probabilities=False)

Client.predictor(self, predictor_id)

Client.predictors(self)

Client.request(self, method, path, **kwargs)

Client.research_dataset(self, dataset_id)

Client.research_datasets(self, *, q=None, source=None, availability=None, license=None, limit=20, cursor=None)

Client.submit_discover(self, dataset_id, *, model='arrow', configuration='default', seed=0, constraints=None, model_version=None, idempotency_key=None)

Client.submit_fit(self, dataset_id, *, model, task, configuration='default', seed=0, idempotency_key=None)

Client.upload(self, *, columns, rows, name='SDK dataset', target=None)

Client.usage(self)

Job

Job(client, job_id)

Job.cancel(self)

Job.inspect(self)

Job.result(self, *, timeout=600, poll_interval=0.25)

Classifier

Classifier(model='tabicl-v2', configuration='default', base_url=None, api_key=None, random_state=0, timeout=600)

Classifier.export_metadata(self)

Classifier.fit(self, X, y)

Classifier.from_predictor(predictor_id, *, base_url=None, api_key=None)

Classifier.metadata(self)

Classifier.predict(self, X)

Classifier.predict_details(self, X, *, probabilities=False)

Classifier.predict_proba(self, X)

Classifier.set_params(self, **params)

Classifier.submit_fit(self, X, y)

Regressor

Regressor(model='tabicl-v2', configuration='default', base_url=None, api_key=None, random_state=0, timeout=600)

Regressor.export_metadata(self)

Regressor.fit(self, X, y)

Regressor.from_predictor(predictor_id, *, base_url=None, api_key=None)

Regressor.metadata(self)

Regressor.predict(self, X)

Regressor.predict_details(self, X, *, probabilities=False)

Regressor.set_params(self, **params)

Regressor.submit_fit(self, X, y)

AsyncClient

AsyncClient(base_url=None, api_key=None, *, timeout=60, transport=None, max_retries=2, max_retry_delay=5)

AsyncClient.aclose(self)

AsyncClient.causal_result(self, job_id)

AsyncClient.causal_scores(self, job_id)

AsyncClient.dataset(self, dataset_id)

AsyncClient.datasets(self)

AsyncClient.delete_causal_result(self, result_id)

AsyncClient.delete_dataset(self, dataset_id)

AsyncClient.discover(self, dataset_id, *, model='arrow', configuration='default', seed=0, constraints=None, model_version=None, idempotency_key=None, timeout=600)

AsyncClient.download_research_dataset(self, dataset_id, path, *, version=None, max_bytes=67108864)

AsyncClient.job(self, job_id)

AsyncClient.jobs(self)

AsyncClient.models(self)

AsyncClient.predict(self, predictor_id, *, columns, rows, probabilities=False)

AsyncClient.predictor(self, predictor_id)

AsyncClient.predictors(self)

AsyncClient.request(self, method, path, **kwargs)

AsyncClient.research_dataset(self, dataset_id)

AsyncClient.research_datasets(self, *, q=None, source=None, availability=None, license=None, limit=20, cursor=None)

AsyncClient.submit_discover(self, dataset_id, *, model='arrow', configuration='default', seed=0, constraints=None, model_version=None, idempotency_key=None)

AsyncClient.submit_fit(self, dataset_id, *, model, task, configuration='default', seed=0, idempotency_key=None)

AsyncClient.upload(self, *, columns, rows, name='SDK dataset', target=None)

AsyncClient.usage(self)

AsyncJob

AsyncJob(client, job_id)

AsyncJob.cancel(self)

AsyncJob.inspect(self)

AsyncJob.result(self, *, timeout=600, poll_interval=0.25)

CausalDiscovery

CausalDiscovery(model='arrow', configuration='default', base_url=None, api_key=None, random_state=0, timeout=600)

CausalDiscovery.discover(self, X=None, *, dataset_id=None, constraints=None, model_version=None, idempotency_key=None)

CausalDiscovery.set_params(self, **params)

CausalDiscovery.submit_discover(self, X=None, *, dataset_id=None, constraints=None, model_version=None, idempotency_key=None)

CausalResult

CausalResult(payload)

CausalResult.to_dict(self)

CausalResult.to_ergodic(self)