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vastorbit.machine_learning.vast.preprocessing.Scaler

class vastorbit.machine_learning.vast.preprocessing.Scaler(name: str = None, overwrite_model: bool = False, method: Literal['zscore', 'robust_zscore', 'minmax'] = 'zscore')

Creates a VAST Scaler object.

Attributes

Many attributes are created during the fitting phase.

For StandardScaler:

mean_: numpy.array

Model’s features means.

std_: numpy.array

Model’s features standard deviation.

For MinMaxScaler:

min_: numpy.array

Model’s features minimums.

max_: numpy.array

Model’s features maximums.

For RobustScaler:

median_: numpy.array

Model’s features medians.

mad_: numpy.array

Model’s features median absolute deviations.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_attributes() method.

Parameters:

  • name (str, optional) – Name of the model.

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

  • method (str, optional) –

    Method used to scale the data.

    • zscore:

    Scaling using the Z-Score

    \[Z_score = (x - avg) / std\]

    • robust_zscore:

    Scaling using the Robust Z-Score.

    \[Z_rscore = (x - median) / (1.4826 * mad)\]

    • minmax:

    Normalization using the Min & Max.

    \[Z_minmax = (x - min) / (max - min)\]

Examples

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Load data for machine learning

We import vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use a dummy dataset.

data = vo.VastFrame(
    {
        "values": [1, 1.01, 1.02, 1.05, 1.024],
    }
)

Note

vastorbit offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the vastorbit environment.

Model Initialization

First we import the Scaler model:

from vastorbit.machine_learning.vast import Scaler

Then we can create the model:

model = Scaler(method = "zscore")

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Fitting

We can now fit the model:

model.fit(data)

Important

To fit a model, you can directly use the VastFrame or the name of the relation stored in the database.

Model Parameters

To fetch the model parameter (mean) you can use:

model.mean_

Similarly for standard deviation:

model.std_

Conversion/Transformation

To get the scaled dataset, we can use the transform method. Let us transform the data:

model.transform(data)
123
values
Decimal(38, 10)
1-0.642493021
21.7371107604
3-0.0475920756
40.1903683025
5-1.2373939663
Rows: 1-5 | Column: values | Type: decimal(38, 10)

Please refer to transform() for more details on transforming a VastFrame.

Similarly, you can perform the inverse transform to get the original features using:

model.inverse_transform(data_transformed)

The variable data_transformed is the scaled dataset.

Model Exporting

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The preceding methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

SQL

To get the SQL query use below:

model.to_sql()

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[1]]
model.to_python()(X)

Hint

The to_python() method is used to scale the data. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

See also

StandardScaler : Scalar with method set as zscore.
RobustScaler : Scalar with method set as robust_zscore.
MinMaxScaler : Scalar with method set as minmax.
__init__(name: str = None, overwrite_model: bool = False, method: Literal['zscore', 'robust_zscore', 'minmax'] = 'zscore') → None

Methods

__init__([name, overwrite_model, method])

contour([nbins, chart])

Draws the model's contour plot.

deployInverseSQL([key_columns, ...])

Returns the SQL code needed to deploy the inverse model.

deploySQL([X, key_columns, exclude_columns])

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, 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.

inverse_transform(vdf[, X])

Applies the Inverse Model on a VastFrame.

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_memmodel()

Converts the model to an InMemory object that can be used for different types of predictions.

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.

transform([vdf, X])

Applies the model on a VastFrame.

Attributes

object_type