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Integrations

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TensorFlow

TensorFlowModel(name)

Creates a TensorFlow object.

Methods:

TensorFlowModel.contour([nbins, chart])	Draws the model's contour plot.
TensorFlowModel.deploySQL([X])	Returns the SQL code needed to deploy the model.
TensorFlowModel.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
TensorFlowModel.drop()	Drops the model from the Vertica database.
TensorFlowModel.export_models(name, path[, kind])	Exports machine learning models.
TensorFlowModel.get_attributes([attr_name])	Returns the model attributes.
TensorFlowModel.get_match_index(x, col_list)	Returns the matching index.
TensorFlowModel.get_params()	Returns the parameters of the model.
TensorFlowModel.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
TensorFlowModel.get_vertica_attributes([...])	Returns the model Vertica attributes.
TensorFlowModel.import_models(path[, ...])	Imports machine learning models.
TensorFlowModel.predict(vdf[, X, name, inplace])	Predicts using the input relation.
TensorFlowModel.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
TensorFlowModel.set_params([parameters])	Sets the parameters of the model.
TensorFlowModel.summarize()	Summarizes the model.
TensorFlowModel.to_binary(path)	Exports the model to the Vertica Binary format.
TensorFlowModel.to_pmml(path)	Exports the model to PMML.
TensorFlowModel.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
TensorFlowModel.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
TensorFlowModel.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

TensorFlowModel.object_type

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PMML

PMMLModel(name)

Creates a PMML object.

Methods:

PMMLModel.contour([nbins, chart])	Draws the model's contour plot.
PMMLModel.deploySQL([X])	Returns the SQL code needed to deploy the model.
PMMLModel.does_model_exists(name[, ...])	Checks whether the model is stored in the Vertica database.
PMMLModel.drop()	Drops the model from the Vertica database.
PMMLModel.export_models(name, path[, kind])	Exports machine learning models.
PMMLModel.get_attributes([attr_name])	Returns the model attributes.
PMMLModel.get_match_index(x, col_list[, ...])	Returns the matching index.
PMMLModel.get_params()	Returns the parameters of the model.
PMMLModel.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
PMMLModel.get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
PMMLModel.import_models(path[, schema, kind])	Imports machine learning models.
PMMLModel.predict(vdf, X[, name, inplace])	Predicts using the input relation.
PMMLModel.register(registered_name[, ...])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
PMMLModel.set_params([parameters])	Sets the parameters of the model.
PMMLModel.summarize()	Summarizes the model.
PMMLModel.to_binary(path)	Exports the model to the Vertica Binary format.
PMMLModel.to_pmml(path)	Exports the model to PMML.
PMMLModel.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
PMMLModel.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
PMMLModel.to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes:

PMMLModel.object_type