Decomposition & Preprocessing

Decomposition

PCA

decomposition.PCA([name, overwrite_model])

Creates an PCA object using scikit-learn for training and the scalability of VAST DataBase for the inferences.

Methods:

PCA.contour([nbins, chart])	Draws the model's contour plot.
PCA.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
PCA.deploySQL([X, n_components, cutoff])	Returns the SQL code needed to deploy the model.
PCA.drop()	Drops the model from the VAST DataBase.
PCA.export_models(name, path[, kind])	Exports machine learning models.
PCA.fit(input_relation[, X, return_report])	Trains the model.
PCA.get_attributes([attr_name])	Returns the model attributes.
PCA.get_match_index(x, col_list[, str_check])	Returns the matching index.
PCA.get_params()	Returns the parameters of the model.
PCA.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
PCA.import_models(path[, schema, kind])	Imports machine learning models.
PCA.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
PCA.plot([dimensions, chart])	Draws a decomposition scatter plot.
PCA.plot_circle([dimensions, chart])	Draws a decomposition circle.
PCA.plot_scree([chart])	Draws a decomposition scree plot.
PCA.score([X, input_relation, metric, p])	Returns the decomposition score on a dataset for each transformed column.
PCA.set_params([parameters])	Sets the parameters of the model.
PCA.summarize()	Summarizes the model.
PCA.to_binary(path)	Exports the model to the VAST Binary format.
PCA.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
PCA.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
PCA.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
PCA.transform([vdf, X, n_components, cutoff])	Applies the model on a VastFrame.

Attributes:

PCA.object_type

SVD

decomposition.SVD([name, overwrite_model])

Creates an SVD object using scikit-learn for training and the scalability of VAST DataBase for the inferences.

Methods:

SVD.contour([nbins, chart])	Draws the model's contour plot.
SVD.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
SVD.deploySQL([X, n_components, cutoff])	Returns the SQL code needed to deploy the model.
SVD.drop()	Drops the model from the VAST DataBase.
SVD.export_models(name, path[, kind])	Exports machine learning models.
SVD.fit(input_relation[, X, return_report])	Trains the model.
SVD.get_attributes([attr_name])	Returns the model attributes.
SVD.get_match_index(x, col_list[, str_check])	Returns the matching index.
SVD.get_params()	Returns the parameters of the model.
SVD.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
SVD.import_models(path[, schema, kind])	Imports machine learning models.
SVD.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
SVD.plot([dimensions, chart])	Draws a decomposition scatter plot.
SVD.plot_circle([dimensions, chart])	Draws a decomposition circle.
SVD.plot_scree([chart])	Draws a decomposition scree plot.
SVD.score([X, input_relation, metric, p])	Returns the decomposition score on a dataset for each transformed column.
SVD.set_params([parameters])	Sets the parameters of the model.
SVD.summarize()	Summarizes the model.
SVD.to_binary(path)	Exports the model to the VAST Binary format.
SVD.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
SVD.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
SVD.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
SVD.transform([vdf, X, n_components, cutoff])	Applies the model on a VastFrame.

Attributes:

SVD.object_type

MCA (Beta)

decomposition.MCA([name, overwrite_model])

Creates a MCA (multiple correspondence analysis) object using the VAST PCA algorithm.

Methods:

MCA.contour([nbins, chart])	Draws the model's contour plot.
MCA.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
MCA.deploySQL([X, n_components, cutoff])	Returns the SQL code needed to deploy the model.
MCA.drop()	Drops the model from the VAST DataBase.
MCA.export_models(name, path[, kind])	Exports machine learning models.
MCA.fit(input_relation[, X, return_report])	Trains the model.
MCA.get_attributes([attr_name])	Returns the model attributes.
MCA.get_match_index(x, col_list[, str_check])	Returns the matching index.
MCA.get_params()	Returns the parameters of the model.
MCA.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
MCA.import_models(path[, schema, kind])	Imports machine learning models.
MCA.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
MCA.plot([dimensions, chart])	Draws a decomposition scatter plot.
MCA.plot_circle([dimensions, chart])	Draws a decomposition circle.
MCA.plot_contrib([dimension, chart])	Draws a decomposition contribution plot of the input dimension.
MCA.plot_cos2([dimensions, chart])	Draws a MCA (multiple correspondence analysis) cos2 plot of the two input dimensions.
MCA.plot_scree([chart])	Draws a decomposition scree plot.
MCA.plot_var([dimensions, method, chart])	Draws the MCA (multiple correspondence analysis) graph.
MCA.score([X, input_relation, metric, p])	Returns the decomposition score on a dataset for each transformed column.
MCA.set_params([parameters])	Sets the parameters of the model.
MCA.summarize()	Summarizes the model.
MCA.to_binary(path)	Exports the model to the VAST Binary format.
MCA.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
MCA.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
MCA.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
MCA.transform([vdf, X, n_components, cutoff])	Applies the model on a VastFrame.

Attributes:

MCA.object_type

---

Preprocessing

One-Hot Encoder

preprocessing.OneHotEncoder([name, ...])

Creates a VAST OneHotEncoder object.

Methods:

OneHotEncoder.deployInverseSQL([...])	Returns the SQL code needed to deploy the inverse model.
OneHotEncoder.deploySQL([X, key_columns, ...])	Returns the SQL code needed to deploy the model.
OneHotEncoder.drop()	Drops the model from the VAST DataBase.
OneHotEncoder.export_models(name, path[, kind])	Exports machine learning models.
OneHotEncoder.fit(input_relation[, X, ...])	Trains the model.
OneHotEncoder.get_attributes([attr_name])	Returns the model attributes.
OneHotEncoder.get_match_index(x, col_list[, ...])	Returns the matching index.
OneHotEncoder.get_params()	Returns the parameters of the model.
OneHotEncoder.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
OneHotEncoder.import_models(path[, schema, kind])	Imports machine learning models.
OneHotEncoder.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
OneHotEncoder.set_params([parameters])	Sets the parameters of the model.
OneHotEncoder.summarize()	Summarizes the model.
OneHotEncoder.to_binary(path)	Exports the model to the VAST Binary format.
OneHotEncoder.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
OneHotEncoder.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
OneHotEncoder.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
OneHotEncoder.transform([vdf, X])	Applies the model on a VastFrame.

Attributes:

OneHotEncoder.object_type

---

Scaler

preprocessing.Scaler([name, ...])

Creates a VAST Scaler object.

Methods:

Scaler.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
Scaler.deploySQL([X, key_columns, ...])	Returns the SQL code needed to deploy the model.
Scaler.drop()	Drops the model from the VAST DataBase.
Scaler.export_models(name, path[, kind])	Exports machine learning models.
Scaler.fit(input_relation[, X, return_report])	Trains the model.
Scaler.get_attributes([attr_name])	Returns the model attributes.
Scaler.get_match_index(x, col_list[, str_check])	Returns the matching index.
Scaler.get_params()	Returns the parameters of the model.
Scaler.get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
Scaler.import_models(path[, schema, kind])	Imports machine learning models.
Scaler.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
Scaler.set_params([parameters])	Sets the parameters of the model.
Scaler.summarize()	Summarizes the model.
Scaler.to_binary(path)	Exports the model to the VAST Binary format.
Scaler.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
Scaler.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
Scaler.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
Scaler.transform([vdf, X])	Applies the model on a VastFrame.

Attributes:

Scaler.object_type

Standard Scaler

preprocessing.StandardScaler([name, ...])

i.e. Scaler with param method = 'zscore'.

Methods:

StandardScaler.deployInverseSQL([...])	Returns the SQL code needed to deploy the inverse model.
StandardScaler.deploySQL([X, key_columns, ...])	Returns the SQL code needed to deploy the model.
StandardScaler.drop()	Drops the model from the VAST DataBase.
StandardScaler.export_models(name, path[, kind])	Exports machine learning models.
StandardScaler.fit(input_relation[, X, ...])	Trains the model.
StandardScaler.get_attributes([attr_name])	Returns the model attributes.
StandardScaler.get_match_index(x, col_list)	Returns the matching index.
StandardScaler.get_params()	Returns the parameters of the model.
StandardScaler.get_plotting_lib([...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
StandardScaler.import_models(path[, schema, ...])	Imports machine learning models.
StandardScaler.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
StandardScaler.set_params([parameters])	Sets the parameters of the model.
StandardScaler.summarize()	Summarizes the model.
StandardScaler.to_binary(path)	Exports the model to the VAST Binary format.
StandardScaler.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
StandardScaler.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
StandardScaler.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
StandardScaler.transform([vdf, X])	Applies the model on a VastFrame.

Attributes:

StandardScaler.object_type

Min Max Scaler

preprocessing.MinMaxScaler([name, ...])

i.e. Scaler with param method = 'minmax'.

Methods:

MinMaxScaler.contour([nbins, chart])	Draws the model's contour plot.
MinMaxScaler.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
MinMaxScaler.deploySQL([X, key_columns, ...])	Returns the SQL code needed to deploy the model.
MinMaxScaler.drop()	Drops the model from the VAST DataBase.
MinMaxScaler.export_models(name, path[, kind])	Exports machine learning models.
MinMaxScaler.fit(input_relation[, X, ...])	Trains the model.
MinMaxScaler.get_attributes([attr_name])	Returns the model attributes.
MinMaxScaler.get_match_index(x, col_list[, ...])	Returns the matching index.
MinMaxScaler.get_params()	Returns the parameters of the model.
MinMaxScaler.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
MinMaxScaler.import_models(path[, schema, kind])	Imports machine learning models.
MinMaxScaler.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
MinMaxScaler.set_params([parameters])	Sets the parameters of the model.
MinMaxScaler.summarize()	Summarizes the model.
MinMaxScaler.to_binary(path)	Exports the model to the VAST Binary format.
MinMaxScaler.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
MinMaxScaler.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
MinMaxScaler.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
MinMaxScaler.transform([vdf, X])	Applies the model on a VastFrame.

Attributes:

MinMaxScaler.object_type

Robust Scaler

preprocessing.RobustScaler([name, ...])

i.e. Scaler with param method = 'robust_zscore'.

Methods:

RobustScaler.contour([nbins, chart])	Draws the model's contour plot.
RobustScaler.deployInverseSQL([key_columns, ...])	Returns the SQL code needed to deploy the inverse model.
RobustScaler.deploySQL([X, key_columns, ...])	Returns the SQL code needed to deploy the model.
RobustScaler.drop()	Drops the model from the VAST DataBase.
RobustScaler.export_models(name, path[, kind])	Exports machine learning models.
RobustScaler.fit(input_relation[, X, ...])	Trains the model.
RobustScaler.get_attributes([attr_name])	Returns the model attributes.
RobustScaler.get_match_index(x, col_list[, ...])	Returns the matching index.
RobustScaler.get_params()	Returns the parameters of the model.
RobustScaler.get_plotting_lib([class_name, ...])	Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.
RobustScaler.import_models(path[, schema, kind])	Imports machine learning models.
RobustScaler.inverse_transform(vdf[, X])	Applies the Inverse Model on a VastFrame.
RobustScaler.set_params([parameters])	Sets the parameters of the model.
RobustScaler.summarize()	Summarizes the model.
RobustScaler.to_binary(path)	Exports the model to the VAST Binary format.
RobustScaler.to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
RobustScaler.to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
RobustScaler.to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.
RobustScaler.transform([vdf, X])	Applies the model on a VastFrame.

Attributes:

RobustScaler.object_type

---

Balance

preprocessing.balance(name, input_relation, y)

Creates a view with an equal distribution of the input data based on the response_column.