TableReportDisplay#

class skore.TableReportDisplay(summary)[source]#

Display reporting information about a given dataset.

This display summarizes the dataset and provides a way to visualize the distribution of its columns.

Parameters:
summarydict

The summary of the dataset, as returned by summarize_dataframe.

Attributes:
ax_matplotlib axes

The axes of the figure.

figure_matplotlib figure.

The figure of the plot.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.data.analyze()
>>> display.plot(kind="corr")
frame(*, kind='dataset')[source]#

Get the data related to the table report.

Parameters:
kind{‘dataset’, ‘top-associations’}

The kind of data to return.

Returns:
DataFrame

The dataset used to create the table report.

help()[source]#

Display available attributes and methods using rich.

plot(*, x=None, y=None, hue=None, kind='dist', top_k_categories=20, scatterplot_kwargs=None, stripplot_kwargs=None, boxplot_kwargs=None, heatmap_kwargs=None, histplot_kwargs=None)[source]#

Plot distribution or correlation of the columns from the dataset.

Parameters:
xstr, default=None

The name of the column to use for the x-axis of the plot. Mandatory when kind='dist'.

ystr, default=None

The name of the column to use for the y-axis of the plot. Only used when kind='dist'.

huestr, default=None

The name of the column to use for the color or hue axis of the plot. Only used when kind='dist'.

kind{‘dist’, ‘corr’}, default=’dist’

The kind of plot drawn.

  • If 'dist', plot a distribution parametrized by x, y and hue. When only x is defined, the distribution is 1d. When y is also defined, the plot is the 2d. Finally, when the color is set using hue, the distribution is 2d, with a color per data-point based on hue. This mode handle both numeric and categorical columns.

  • If 'corr', plot Cramer’s V correlation among all columns. This option doesn’t take any x, y or hue argument.

top_k_categoriesint, default=20

For categorical columns, the number of most frequent elements to display. Only used when kind='dist'.

scatterplot_kwargs: dict, default=None

Keyword arguments to be passed to seaborn’s seaborn.scatterplot() for rendering the distribution 2D plot, when both x and y are numeric.

stripplot_kwargs: dict, default=None

Keyword arguments to be passed to seaborn’s searborn.stripplot() for rendering the distribution 2D plot, when either x or y is numeric, and the other is categorical. This plot is drawn on top of the boxplot.

boxplot_kwargs: dict, default=None

Keyword arguments to be passed to seaborn’s seaborn.boxplot() for rendering the distribution 2D plot, when either x or y is numeric, and the other is categorical. This plot is drawn below the stripplot.

heatmap_kwargs: dict, default=None

Keyword arguments to be passed to seaborn’s seaborn.heatmap() for rendering Cramer’s V correlation matrix, when kind='corr' or when kind='dist' and both x and y are categorical.

histplot_kwargs: dict, default=None

Keyword arguments to be passed to seaborn’s seaborn.histplot() for rendering the distribution 1D plot, when only x is provided.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.data.analyze()
>>> display.plot(kind="corr")
set_style(**kwargs)[source]#

Set the style parameters for the display.

Parameters:
**kwargsdict

Style parameters to set. Each parameter name should correspond to a a style attribute passed to the plot method of the display.

Returns:
selfobject

Returns the instance itself.

Raises:
ValueError

If a style parameter is unknown.

static style_plot(plot_func)[source]#

Apply consistent style to skore displays.

This decorator: 1. Applies default style settings 2. Executes plot_func

Parameters:
plot_funccallable

The plot function to be decorated.

Returns:
callable

The decorated plot function.