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name: plotting-and-utilities description: "Use mlxtend plotting helpers, packaged data loaders, file IO, text, math, and small utility helpers." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Plotting and utilities

Use this sub-skill when the task is to create Matplotlib visualizations with mlxtend.plotting, load bundled or local toy datasets from mlxtend.data, group local files, tokenize or generalize text/name strings, run combinatorics or vector-space helpers, or use small utilities such as Counter, check_Xy, and format_kwarg_dictionaries.

Read first

• references/api-reference.md for verified signatures, return objects, and API-specific constraints.
• references/workflows.md for headless plotting, decision regions, confusion matrices, heatmaps, scatter/PCA/SFS plots, dataset loaders, file grouping, text, math, and utility recipes.
• references/data-formats.md for accepted plotting inputs, dataset shapes, local MNIST files, file-group schemas, tokenizer/name outputs, and utility array contracts.
• references/troubleshooting.md for display/backend failures, figure/Axes return pitfalls, decision-region filler errors, dataset path/shape problems, file glob quirks, tokenizer/name edge cases, and math/counting validation gaps.
• scripts/plotting_utilities_smoke.py to smoke-test the installed plotting/data/file/text/math/utils APIs on deterministic CPU examples.

Route here for

• mlxtend.plotting: decision regions, confusion-matrix plots, learning curves, heatmaps, checkerboards, ECDFs, scatter plots, scatterplot matrices, PCA correlation graphs, linear-regression plots, SFS metric plots, enrichment plots, stacked bars, and border cleanup.
• mlxtend.data: iris_data, wine_data, autompg_data, boston_housing_data, mnist_data, loadlocal_mnist, three_blobs_data, and make_multiplexer_dataset.
• mlxtend.file_io: find_files and find_filegroups on local directories.
• mlxtend.text: word/emoticon tokenizers plus generalize_names and generalize_names_duplcheck.
• mlxtend.math: factorial, num_combinations, num_permutations, vectorspace_orthonormalization, and vectorspace_dimensionality.
• mlxtend.utils: Counter, check_Xy, format_kwarg_dictionaries, and the small testing helper assert_raises when diagnosing expected exceptions.

Boundaries and sibling routes

• Do not teach estimator fitting, stacking, voting, or Kmeans here. Route classifier/regressor/cluster construction to ../estimators-and-ensembles/SKILL.md; plotting recipes may assume a fitted estimator or use a tiny dummy predictor only to demonstrate a plotting API.
• Do not choose evaluation metrics, statistical tests, validation splitters, or produce confusion matrices here. Route metric/test choice and mlxtend.evaluate arrays to ../evaluation-and-validation/SKILL.md, then return here only to visualize an existing matrix or curve.
• Do not produce selector metric dictionaries here. Route feature selection and SequentialFeatureSelector.get_metric_dict() creation to ../feature-workflows/SKILL.md, then return here for plot_sequential_feature_selection.
• Do not route market-basket mining here; plotting or file helpers around mined outputs are in scope, but mining itself belongs to ../frequent-patterns/SKILL.md.

Operating rules

1. In headless jobs, set MPLBACKEND=Agg or call matplotlib.use("Agg") before importing matplotlib.pyplot; save figures instead of calling plt.show().
2. Check each plotting helper's return type before saving or modifying a plot: some return Axes, some return (fig, ax), some return Figure, and scatter_hist returns the scatter artist.
3. For decision regions, use a fitted object with .predict, numeric 2D X, 1D integer y, and explicit filler_feature_values for any non-plotted features.
4. Dataset helpers return NumPy arrays and never download remote files; loadlocal_mnist requires local IDX/ubyte image and label files.
5. Keep file IO examples in a temporary directory and expect returned paths, not open file handles.

Safe smoke

From this sub-skill directory or any environment with mlxtend installed:

MPLBACKEND=Agg python scripts/plotting_utilities_smoke.py --task all

The script uses tiny arrays, packaged datasets, and temporary files only; it writes no persistent artifacts and does not depend on a source checkout.