priorityx 0.4.0

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

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",
    # customize weights (default shown)
    w_volume=0.4, w_growth=0.4, w_severity=0.2,
    w_resolution=0.5, w_recovery=0.5,
    w_risk=0.7, w_quality=0.3,
)

# 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 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