priorityx 0.2.3

Entity prioritization and escalation detection using GLMM statistical models

Priorityx: Entity prioritization and escalation detection using GLMM statistical models

Entity prioritization and escalation detection using GLMM statistical models

Installation

pip install priorityx

Quick Start

Deterministic runs

Set PRIORITYX_GLMM_SEED (or call set_glmm_random_seed()) before running analyses to obtain repeatable GLMM estimates during debugging or CI checks:

PRIORITYX_GLMM_SEED=1234 python your_script.py

import pandas as pd
from priorityx.core.glmm import fit_priority_matrix
from priorityx.viz.matrix import plot_priority_matrix

df = pd.read_csv("data.csv")

results, stats = fit_priority_matrix(
    df,
    entity_col="service",
    timestamp_col="date",
    temporal_granularity="quarterly"
)

plot_priority_matrix(results, entity_name="Service", save_plot=True)

Features

• GLMM-based priority matrix (Q1–Q4) with entity-level intercept/slope insights
• Priority-based transition timeline (Crisis / Investigate / Monitor / Low) with spike markers (*X, *Y, *XY)
• Cumulative movement tracking and trajectory visualizations
• Transition driver analysis that surfaces top subcategories causing quadrant shifts with spike summaries
• Deterministic seeding option for reproducible GLMM runs (set PRIORITYX_GLMM_SEED)

Use Cases

IT incidents, software bugs, compliance violations, performance monitoring.

Documentation

• Quick Start
• API Reference
• Methodology
• Priority Classification