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vastorbit.machine_learning.vast.automl.AutoDataPrep

class vastorbit.machine_learning.vast.automl.AutoDataPrep(name: str | None = None, overwrite_model: bool | None = False, cat_method: Literal['label', 'ooe'] = 'ooe', num_method: Literal['same_freq', 'same_width', 'none'] = 'none', nbins: int = 20, outliers_threshold: float = 4.0, na_method: Literal['auto', 'drop'] = 'auto', cat_topk: int = 10, standardize: bool = True, standardize_min_cat: int = 6, id_method: Literal['none', 'drop'] = 'drop', apply_pca: bool = False, rule: Annotated[str | timedelta, 'Time Interval'] = 'auto', identify_ts: bool = True, save: bool = True)

Automatically find relations between the different features to preprocess the data according to each column type.

Parameters:

  • name (str, optional) – Name of the model in which to store the output relation in the VAST DataBase.

  • overwrite_model (bool, optional) – If set to True, training a model with the same name as an existing model overwrites the existing model.

  • cat_method (str, optional) – Method for encoding categorical features. This can be set to ‘label’ for label encoding and ‘ooe’ for One-Hot Encoding.

  • num_method (str, optional) – [Only used for non-time series datasets] Method for encoding numerical features. This can be set to ‘same_freq’ to encode using frequencies, ‘same_width’ to encode using regular bins, or ‘none’ to not encode numerical features.

  • nbins (int, optional) – [Only used for non-time series datasets] Number of bins used to discretize numerical features.

  • outliers_threshold (float, optional) – [Only used for non-time series datasets] Method for dealing with outliers. If a number is used, all elements with an absolute z-score greater than the threshold are converted to NULL values. Otherwise, outliers are treated as regular values.

  • na_method (str, optional) –

    Method for handling missing values.
    auto: Mean for the numerical features and

    creates a new category for the categorical VastColumns. For time series datasets, ‘constant’ interpolation is used for categorical features and ‘linear’ for the others.

    drop: Drops the missing values.

  • cat_topk (int, optional) – Keeps the top-k most frequent categories and merges the others into one unique category. If unspecified, all categories are kept.

  • standardize (bool, optional) – If True, the data is standardized. The ‘num_method’ parameter must be set to ‘none’.

  • standardize_min_cat (int, optional) – Minimum feature cardinality before using standardization.

  • id_method (str, optional) –

    Method for handling ID features.

    drop: Drops any feature detected as ID. none: Does not change ID features.

  • apply_pca (bool, optional) – [Only used for non-time series datasets] If True, a PCA is applied at the end of the preprocessing.

  • rule (TimeInterval, optional) – [Only used for time series datasets] Interval used to slice the time. For example, setting to ‘5 minutes’ creates records separated by ‘5 minutes’ time interval. If set to auto, the rule is detected using aggregations.

  • identify_ts (bool, optional) – If True and parameter ‘ts’ is undefined when fitting the model, the function tries to automatically detect the parameter ‘ts’.

  • save (bool, optional) – If True, saves the final relation inside the database.

Variables:

  • X_in_ (list) – Variables used to fit the model.

  • X_out_ (list) – Variables created by the model.

  • sql_ (str) – SQL needed to deploy the model.

  • final_relation_ (VastFrame) – Relation created after fitting the model.

__init__(name: str | None = None, overwrite_model: bool | None = False, cat_method: Literal['label', 'ooe'] = 'ooe', num_method: Literal['same_freq', 'same_width', 'none'] = 'none', nbins: int = 20, outliers_threshold: float = 4.0, na_method: Literal['auto', 'drop'] = 'auto', cat_topk: int = 10, standardize: bool = True, standardize_min_cat: int = 6, id_method: Literal['none', 'drop'] = 'drop', apply_pca: bool = False, rule: Annotated[str | timedelta, 'Time Interval'] = 'auto', identify_ts: bool = True, save: bool = True) → None

Methods

__init__([name, overwrite_model, ...])

contour([nbins, chart])

Draws the model's contour plot.

deploySQL([X])

Returns the SQL code needed to deploy the model.

drop()

Drops the model from the VAST DataBase.

export_models(name, path[, kind])

Exports machine learning models.

fit(input_relation[, X, ts, by, return_report])

Trains the model.

get_attributes([attr_name])

Returns the model attributes.

get_match_index(x, col_list[, str_check])

Returns the matching index.

get_params()

Returns the parameters of the model.

get_plotting_lib([class_name, chart, ...])

Returns the first available library (Plotly, Matplotlib) to draw a specific graphic.

import_models(path[, schema, kind])

Imports machine learning models.

set_params([parameters])

Sets the parameters of the model.

summarize()

Summarizes the model.

to_binary(path)

Exports the model to the VAST Binary format.

to_python([return_proba, ...])

Returns the Python function needed for in-memory scoring without using built-in VAST functions.

to_sql([X, return_proba, ...])

Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes

object_type