visualize | AutoML | teradataml - visualize - Teradata VantageCloud Lake

Use the visualize function to visualize the data using various plots such as heatmap, pair plot, histogram, univariate plot, count plot, box plot, and target distribution.

Required Parameters

data

Specifies the input teradataml DataFrame for plotting

target_column

Specifies the name of the target column in "data".

"target_column" must be of numeric type.

Optional Parameters

plot_type

Specifies the type of plot to be displayed.

Permitted values:

• "heatmap": Displays a heatmap of feature correlations.
• "pair": Displays a pair plot of features.
• "density": Displays a density plot of features.
• "count": Displays a count plot of categorical features.
• "box": Displays a box plot of numerical features.
• "target": Displays the distribution of the target variable.
• "all": Displays all the plots.

Default value: "target"

length

Specifies the length of the plot.

Default value: 10

breadth

Specifies the breadth of the plot.

Default value: 8

columns

Specifies the column names to be used for plotting.

max_features

Specifies the maximum number of features to be used for plotting.

Default value: 10

It applies separately to categorical and numerical features.

problem_type

Specifies the type of problem.

Permitted values:

• 'regression'
• 'classification'

Example setup

The following examples can be run with either AutoML, AutoClassifier, AutoRegressor, or Autodataprep. Import a preferred module to run the example.

The following examples use AutoML module.

>>> from teradataml import AutoML
>>> from teradataml import DataFrame
>>> load_example_data("teradataml", "titanic")
>>> titanic_data = DataFrame("titanic")

Example 1: Visualize the data using AutoML class

>>> AutoML.visualize(data = titanic_data,
...                  target_column = 'survived',
...                  plot_type = ['heatmap', 'pair', 'histogram', 'target'],
...                  length = 10,
...                  breadth = 8,
...                  max_features = 10,
...                  problem_type = 'classification')

Example 2: Visualize the data using AutoDataPrep class

>>> from teradataml import AutoDataPrep
>>> obj = AutoDataPrep(task_type="classification")
>>> obj.fit(data = titanic_data, target_column = 'survived')

Retrieve the data from AutoDataPrep object

>>> datas = obj.get_data()
>>> AutoDataPrep.visualize(data = datas['lasso_train'],
...                        target_column = 'survived',
...                        plot_type = 'all'
...                        length = 20,
...                        breadth = 15)