plot_label_distributions

plot_label_distributions(partitioner: Partitioner, label_name: str, plot_type: str = 'bar', size_unit: str = 'absolute', max_num_partitions: int | None = None, partition_id_axis: str = 'x', axis: Axes | None = None, figsize: tuple[float, float] | None = None, title: str = 'Per Partition Label Distribution', cmap: str | Colormap | None = None, legend: bool = False, legend_title: str | None = None, verbose_labels: bool = True, plot_kwargs: dict[str, Any] | None = None, legend_kwargs: dict[str, Any] | None = None) → tuple[Figure, Axes, DataFrame][source]

Plot the label distribution of the partitions.

Parameters:

  • partitioner (Partitioner) – Partitioner with an assigned dataset.

  • label_name (str) – Column name identifying label based on which the plot will be created.

  • plot_type (str) – Type of plot, either “bar” or “heatmap”.

  • size_unit (str) – “absolute” or “percent”. “absolute” - (number of samples). “percent” - normalizes each value, so they sum up to 100%.

  • max_num_partitions (Optional[int]) – The number of partitions that will be used. If left None, then all partitions will be used.

  • partition_id_axis (str) – “x” or “y”. The axis on which the partition_id will be marked.

  • axis (Optional[Axes]) – Matplotlib Axes object to plot on.

  • figsize (Optional[Tuple[float, float]]) – Size of the figure.

  • title (str) – Title of the plot.

  • cmap (Optional[Union[str, mcolors.Colormap]]) – Colormap for determining the colorspace of the plot.

  • legend (bool) – Include the legend.

  • legend_title (Optional[str]) – Title for the legend. If None, the defaults will be takes based on the type of plot.

  • verbose_labels (bool) – Whether to use verbose versions of the labels. These values are used as columns of the returned dataframe and as labels on the legend in a bar plot and columns/ rows ticks in a heatmap plot.

  • plot_kwargs (Optional[Dict[str, Any]]) – Any key value pair that can be passed to a plot function that are not supported directly. In case of the parameter doubling (e.g. specifying cmap here too) the chosen value will be taken from the explicit arguments (e.g. cmap specified as an argument to this function not the value in this dictionary).

  • legend_kwargs (Optional[Dict[str, Any]]) – Any key value pair that can be passed to a figure.legend in case of bar plot or cbar_kws in case of heatmap that are not supported directly. In case of the parameter doubling (e.g. specifying legend_title here too) the chosen value will be taken from the explicit arguments (e.g. legend_title specified as an argument to this function not the value in this dictionary).

Returns:

  • fig (Figure) – The figure object.

  • axis (Axes) – The Axes object with the plot.

  • dataframe (pd.DataFrame) – The DataFrame where each row represents the partition id and each column represents the class.

Examples

Visualize the label distribution resulting from DirichletPartitioner.

>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import DirichletPartitioner
>>> from flwr_datasets.visualization import plot_label_distributions
>>>
>>> fds = FederatedDataset(
>>>     dataset="cifar10",
>>>     partitioners={
>>>         "train": DirichletPartitioner(
>>>             num_partitions=20,
>>>             partition_by="label",
>>>             alpha=0.3,
>>>             min_partition_size=0,
>>>         ),
>>>     },
>>> )
>>> partitioner = fds.partitioners["train"]
>>> figure, axis, dataframe = plot_label_distributions(
>>>     partitioner=partitioner,
>>>     label_name="label",
>>>     legend=True,
>>>     verbose_labels=True,
>>> )

Alternatively you can visualize each partition in terms of fraction of the data available on that partition instead of the absolute count

>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import DirichletPartitioner
>>> from flwr_datasets.visualization import plot_label_distributions
>>>
>>> fds = FederatedDataset(
>>>     dataset="cifar10",
>>>     partitioners={
>>>         "train": DirichletPartitioner(
>>>             num_partitions=20,
>>>             partition_by="label",
>>>             alpha=0.3,
>>>             min_partition_size=0,
>>>         ),
>>>     },
>>> )
>>> partitioner = fds.partitioners["train"]
>>> figure, axis, dataframe = plot_label_distributions(
>>>     partitioner=partitioner,
>>>     label_name="label",
>>>     size_unit="percent",
>>>     legend=True,
>>>     verbose_labels=True,
>>> )
>>>

You can also visualize the data as a heatmap by changing the plot_type from default “bar” to “heatmap”

>>> from flwr_datasets import FederatedDataset
>>> from flwr_datasets.partitioner import DirichletPartitioner
>>> from flwr_datasets.visualization import plot_label_distributions
>>>
>>> fds = FederatedDataset(
>>>     dataset="cifar10",
>>>     partitioners={
>>>         "train": DirichletPartitioner(
>>>             num_partitions=20,
>>>             partition_by="label",
>>>             alpha=0.3,
>>>             min_partition_size=0,
>>>         ),
>>>     },
>>> )
>>> partitioner = fds.partitioners["train"]
>>> figure, axis, dataframe = plot_label_distributions(
>>>     partitioner=partitioner,
>>>     label_name="label",
>>>     size_unit="percent",
>>>     plot_type="heatmap",
>>>     legend=True,
>>>     plot_kwargs={"annot": True},
>>> )

You can also visualize the returned DataFrame in Jupyter Notebook >>> dataframe.style.background_gradient(axis=None)