QuantileTransform

class hana_ml.algorithms.pal.preprocessing.QuantileTransform(num_quantiles=None, output_distribution=None)

Python wrapper for PAL Quantile Transformer.

Parameters:

num_quantilesint, optional

Specifies the number of quantiles to be computed.

Defaults to 100.

output_distribution{'uniform', 'normal'}, optional

Specifies the marginal distribution of the quantile-transformed data.

'uniform': Uniform distribution
'normal': normal distribution

Defaults to 'uniform'.

Examples

>>> qt = QuantileTransform(num_quantiles=200, output_distribution='uniform')
>>> qt.fit(data=df, key='ID', features=['X2', 'X6'], categorical_variable='X5')
>>> qt.result_.collect()

Attributes:

result_DataFrame

Training data with selected features quantile-transformed.

model_list of DataFrames

The model for transforming subsequent data, consisted of 2 DataFrames:

DataFrame 1: Quantiles for the output distribution.
DataFrame 2: Other model info for the Quantile Transformer.

Methods

`fit`(data[, key, features, categorical_variable])	Quantile transformation to numerical features.
`fit_transform`(data[, key, features, ...])	Fit a Quantile Transformer, in the meantime transform the training data and return the result.
`transform`(data[, key])	Transform the test data using a fitted QuantileTransformer.

fit(data, key=None, features=None, categorical_variable=None)

Quantile transformation to numerical features.

Parameters:

dataDataFrame

Input data for fitting a quantile-transformation model(Quantile-Transformer).

keystr, optional

Specifies the name of the ID column in data.

Mandatory if data is not indexed by a single column; otherwise defaults to the index column of data.

featuresstr or list of strings, optional

Specifies the names of columns in data for which quantile-transformation should be applied. However, categorical columns in features are ignored since only numerical columns can be quantile-transformed.

Defaults to all numerical columns in data``(except ``key).

categorical_variablestr or a list of str, optional

Specifies which INTEGER columns should be treated as categorical, with all other INTEGER columns treated as continuous.

No default value.

Returns:

A fitted object of class "QuantileTransform".

fit_transform(data, key=None, features=None, categorical_variable=None)

Fit a Quantile Transformer, in the meantime transform the training data and return the result.

Parameters:

dataDataFrame

Input data for fitting a quantile-transformation model(Quantile-Transformer).

keystr, optional

Specifies the name of the ID column in data.

Mandatory if data is not indexed by a single column; otherwise defaults to the index column of data.

featuresstr or list of strings, optional

Specifies the names of columns in data for which quantile-transformation should be applied. However, categorical columns in features are ignored since only numerical columns can be quantile-transformed.

Defaults to all numerical columns in data``(except ``key).

categorical_variablestr or a list of str, optional

Specifies which INTEGER columns should be treated as categorical, with all other INTEGER columns treated as continuous.

No default value.

Returns:

DataFrame: The data with selected features being quantile-transformed.

transform(data, key=None)

Transform the test data using a fitted QuantileTransformer.

Parameters:

dataDataFrame

Input data for applying a trained quantile-transformation model(Quantile-Transformer).

Should be structured the same as the data used in the model training phase.

keystr, optional

Specifies the name of the ID column in data.

Mandatory if data is not indexed by a single column; otherwise defaults to the index column of data.

Returns:

DataFrame: Quantile-transformed data w.r.t. selected(numerical) features.

Inherited Methods from PALBase

Besides those methods mentioned above, the QuantileTransform class also inherits methods from PALBase class, please refer to PAL Base for more details.