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PriorityX

Entity prioritization and escalation detection using GLMM statistical models

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Installation

pip install priorityx

Quick Start

import pandas as pd
import priorityx as px

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

# Default: volume x growth (single GLMM)
results, stats = px.fit_priority_matrix(
    df,
    entity_col="service",
    timestamp_col="date",
    temporal_granularity="quarterly",
)
# Returns: entity, x_score, y_score, count, quadrant

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

Custom Axes

# Custom Y axis: volume × resolution_days (two GLMMs)
results, _ = px.fit_priority_matrix(
    df,
    entity_col="service",
    timestamp_col="date",
    y_metric="resolution_days",
)

# Custom both axes: disputed_amount × paid_amount
results, _ = px.fit_priority_matrix(
    df,
    entity_col="service",
    timestamp_col="date",
    x_metric="disputed_amount",
    y_metric="paid_amount",
)

Composite Indices

# Add entity metrics
metrics = px.aggregate_entity_metrics(
    df,
    entity_col="service",
    duration_start_col="opened_at",
    duration_end_col="closed_at",
    primary_col="exposure",
    secondary_col="recovery",
)
results = results.merge(metrics, left_on="entity", right_on="service", how="left")

# Add weighted indices: RI (Risk), SQI (Service Quality), EWI (Early Warning)
results = px.add_priority_indices(
    results,
    volume_col="count",
    growth_col="y_score",
    severity_col="total_primary",
    resolution_col="mean_duration",
    recovery_col="secondary_to_primary_ratio",
)

# Top priority entities
top_risks = results.nlargest(10, "EWI")

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
  • Deterministic seeding option for reproducible GLMM runs (set PRIORITYX_GLMM_SEED)

Use Cases

  • Consumer complaint prioritization (financial services, regulatory)
  • IT incident triage
  • Software bug prioritization
  • Compliance violation monitoring
  • Performance monitoring and escalation detection

Next Steps