plot_subsequence_frequencies()

plot_subsequence_frequencies() draws a frequency chart for subsequences.

Function Usage

plot_subsequence_frequencies(
    subsequence_results,
    frequency_values=None,
    text_scale=1.0,
    color="steelblue",
    figsize=(10, 6),
    title=None,
    fontsize=11,
    x_label=None,
    y_label=None,
    save_as=None,
    dpi=200,
    show=False
)

TraMineR Parameter Mapping

• subsequence_results -> TraMineR S3 plot.subseqelist() x
• frequency_values -> TraMineR S3 plot.subseqelist() freq
• text_scale -> TraMineR S3 plot.subseqelist() cex
• title -> TraMineR S3 plot.subseqelist() main
• x_label / y_label -> TraMineR S3 plot.subseqelist() xlab / ylab

Entry Parameters

Parameter	Required	Type	Description
subsequence_results	✓	SubsequenceList	The subsequences to visualize (usually from find_frequent_subsequences()).
frequency_values	✗	sequence[float]	Optional custom values (defaults to support).
x_label, y_label	✗	str	Axis label overrides.
save_as	✗	str	Save path; .png is auto-appended if no extension is given.
dpi	✗	int	Save resolution (default 200).
show	✗	bool	If True, calls plt.show() inside the function.

What It Does

• Displays how common each subsequence is.
• Makes it easier to compare top patterns at a glance.

Example

from sequenzo.event_sequences import plot_subsequence_frequencies

plot_subsequence_frequencies(
    fsubseq,
    x_label="Support",
    y_label="Subsequence",
    save_as="outputs/subsequence_support",
    dpi=300,
    show=True
)

R Counterpart

• Closest R method: TraMineR S3 plot.subseqelist()
• Mapping note: Both visualize subsequence frequencies/support from frequent-subsequence results.

See Also

• Event Sequences Introduction explains the workflow and data structures.
• Quickstart Example shows a complete event-mining run.
• Helper Functions lists supporting utilities.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

References

Ritschard, G., Bürgin, R., & Studer, M. (2013). Exploratory Mining of Life Event Histories. In J. J. McArdle & G. Ritschard (Eds.), Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (pp. 221-253). Routledge.