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Analysis cleanup API
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dataikuapi/dss/ml.py

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@@ -877,6 +877,15 @@ def get_trained_model_details(self, id):
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else:
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return DSSTrainedPredictionModelDetails(ret, snippet, mltask=self, mltask_model_id=id)
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def delete_model(self, model_id):
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"""
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Deletes a trained model
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:param str model_id: Model identifier, as returend by :meth:`get_trained_models_ids`
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"""
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self.client._perform_empty(
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"DELETE", "/projects/%s/models/lab/%s/%s/models/%s" % (self.project_key, self.analysis_id, self.mltask_id, model_id))
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def deploy_to_flow(self, model_id, model_name, train_dataset, test_dataset=None, redo_optimization=True):
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"""
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Deploys a trained model from this ML Task to a saved model + train recipe in the Flow.
@@ -921,3 +930,30 @@ def redeploy_to_flow(self, model_id, recipe_name=None, saved_model_id=None, acti
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"POST", "/projects/%s/models/lab/%s/%s/models/%s/actions/redeployToFlow" % (self.project_key, self.analysis_id, self.mltask_id, model_id),
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body = obj)
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def remove_all_splits(self):
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"""
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Deletes all stored splits data that are not anymore in use for this ML Task.
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It is generally not needed to call this method
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"""
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self.client._perform_empty(
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"POST", "/projects/%s/models/lab/%s/%s/actions/removeUnusedSplits" % (self.project_key, self.analysis_id, self.mltask_id))
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def remove_all_splits(self):
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"""
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Deletes all stored splits data for this ML Task. This operation saves disk space.
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After performing this operation, it will not be possible anymore to:
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* Ensemble already trained models
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* View the "predicted data" or "charts" for already trained models
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* Resume training of models for which optimization had been previously interrupted
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Training new models remains possible
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"""
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self.client._perform_empty(
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"POST", "/projects/%s/models/lab/%s/%s/actions/removeAllSplits" % (self.project_key, self.analysis_id, self.mltask_id))
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