dataeval-plots 0.0.5

DataEval companion package for plotting utilities

DataEval Plots

Multi-backend plotting utilities for DataEval outputs.

Installation

# Minimal - no plotting backend included
pip install dataeval-plots

# With matplotlib plotting (recommended)
pip install dataeval-plots[matplotlib]

# With multiple backends
pip install dataeval-plots[matplotlib,plotly]

# Everything
pip install dataeval-plots[all]

For development:

pip install -e dataeval-plots[all]

Available Backends

Backend	Status	Install With	Description
matplotlib	✅ Default	[matplotlib]	Standard publication-quality plots
seaborn	✅ Available	[seaborn]	Statistical data visualization
plotly	✅ Available	[plotly]	Interactive web-based plots
altair	✅ Available	[altair]	Declarative visualization grammar

Usage

Option 1: Import from dataeval-plots directly

from dataeval_plots import plot
from dataeval.metrics.bias import coverage

result = coverage(embeddings)
fig = plot(result, images=dataset, top_k=6)
fig.savefig("coverage.png")

Option 2: Import from dataeval core (convenience)

from dataeval import plotting
from dataeval.metrics.bias import coverage

result = coverage(embeddings)
fig = plotting.plot(result, images=dataset)

Option 3: Set default backend

from dataeval_plots import plot, set_default_backend

# Set seaborn as default
set_default_backend("seaborn")
fig = plot(result, images=dataset)  # Uses seaborn

# Override for a specific plot
fig = plot(result, backend="matplotlib", images=dataset)

Features

• Multi-backend architecture: Support for matplotlib (default), seaborn, plotly, and altair
• Optional dependencies: Install only the backends you need
• Clean separation: Core dataeval has zero plotting dependencies
• Protocol-based design: Loose coupling via structural typing (Plottable protocol)
• Extensible: Easy to add new backends via BasePlottingBackend or custom outputs via Plottable
• Lazy loading: Backends are only imported when first used
• Type safe: Static type checking with mypy/pyright via @runtime_checkable protocols
• DRY architecture: Centralized routing logic in BasePlottingBackend

Architecture

The package uses a protocol-based architecture for loose coupling between dataeval and dataeval-plots:

dataeval/                           # Core package
    outputs/
        _bias.py                    # CoverageOutput, BalanceOutput, DiversityOutput
        _stats.py                   # BaseStatsOutput
        _workflows.py               # SufficiencyOutput
        _drift.py                   # DriftMVDCOutput
    plotting.py                     # Convenience hook to dataeval-plots

dataeval-plots/                     # Separate plotting package
    src/dataeval_plots/
        __init__.py                 # Main plot() function
        _registry.py                # Backend registry with lazy loading
        protocols.py                # Protocol definitions (Plottable hierarchy)
        backends/
            _base.py                # BasePlottingBackend (abstract routing)
            _matplotlib.py          # MatplotlibBackend (default)
            _seaborn.py             # SeabornBackend
            _plotly.py              # PlotlyBackend
            _altair.py              # AltairBackend

Protocol-Based Design

All DataEval output classes implement the Plottable protocol, which requires:

• plot_type(): Returns a string identifying the plot type (e.g., "balance")
• meta(): Returns execution metadata

This enables:

• Loose coupling: dataeval-plots doesn't import concrete classes from dataeval
• Type safety: Static and runtime type checking via @runtime_checkable protocols
• Extensibility: Anyone can create custom outputs implementing Plottable
• Zero dependencies: Core dataeval has no plotting dependencies

Supported Output Types

Output Type	Plot Type	Description	Source
BalanceOutput	"balance"	Heatmap of class balance metrics	dataeval/_bias.py
DiversityOutput	"diversity"	Visualization of diversity indices	dataeval/_bias.py
SufficiencyOutput	"sufficiency"	Learning curves with extrapolation	dataeval/_workflows.py
BaseStatsOutput	"stats"	Statistical histograms and distributions	dataeval/_stats.py
DriftMVDCOutput	"drift_mvdc"	Drift detection plots (MVDC analysis)	dataeval/_drift.py

Each output type implements the Plottable protocol and can be plotted using any registered backend.

Extending the Package

Creating Custom Outputs

You can create custom output classes that work with the plotting system by implementing the Plottable protocol:

from dataclasses import dataclass
from numpy.typing import NDArray
from dataeval_plots.protocols import Plottable, ExecutionMetadata

@dataclass
class MyCustomOutput:
    """Custom output that reuses existing plot type."""
    uncovered_indices: NDArray

    def plot_type(self) -> str:
        return "balance"  # Reuse existing balance plotting

    def meta(self) -> ExecutionMetadata:
        return ExecutionMetadata.empty()

# Works seamlessly with existing backends
result = MyCustomOutput(uncovered_indices=my_data)
fig = plot(result, images=my_images)

Creating Custom Backends

Extend BasePlottingBackend to create a new plotting backend:

from dataeval_plots.backends._base import BasePlottingBackend
from dataeval_plots.protocols import PlottableBalance
from dataeval_plots import register_backend

class CustomBackend(BasePlottingBackend):
    """Custom plotting backend using your preferred library."""

    def _plot_balance(self, output: PlottableBalance, **kwargs):
        # Implement balance plotting
        return my_figure

    # Implement other _plot_* methods...

# Register and use
register_backend("custom", CustomBackend())
fig = plot(result, backend="custom")

The BasePlottingBackend class handles all routing logic automatically - you just implement the plot-type-specific methods (_plot_coverage, _plot_balance, etc.).