docs/README.md

metadata

title: KPI Score Correlation Analysis
emoji: 📊
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.33.0
app_file: kpi_correlation_app.py
pinned: false

KPI Score Correlation Analysis

This directory contains tools for analyzing correlations between IPM scores and axiia scores.

Architecture

The code has been refactored to share common functionality:

• correlation_analysis_core.py: Core analysis module with shared functions
• csv_utils.py: Utilities for loading CSV/Excel files
• analyze_correlations_v2.py: Command-line interface (CLI)
• kpi_correlation_app.py: Gradio web interface

Installation

Ensure you have the required dependencies:

pip install pandas numpy scipy matplotlib seaborn gradio pyyaml openpyxl xlrd

Usage

Command-Line Interface

The CLI tool is best for batch processing and automation:

# Basic usage
python3 analyze_correlations_v2.py -k kpi_file.csv -s scores_file.csv

# With custom output file
python3 analyze_correlations_v2.py -k kpi_file.csv -s scores_file.csv -o results.yaml

# With plots
python3 analyze_correlations_v2.py -k kpi_file.csv -s scores_file.csv -p

# Full example
python3 analyze_correlations_v2.py \
  -k ../../data/lenovo_kpi.csv \
  -s ../../data/lenovo-scores-0603.csv \
  -o score_corr.yaml \
  -p

Options:

• -k, --kpi: Path to KPI file (CSV or Excel)
• -s, --scores: Path to scores file (CSV)
• -o, --output: Output YAML file (default: score_corr.yaml)
• -p, --plot: Generate correlation plots

Gradio Web Interface

The Gradio app provides an interactive UI with parameterized analysis:

# Basic usage (uses default scores file in same directory)
python3 kpi_correlation_app.py

# With custom scores file
python3 kpi_correlation_app.py --scores-file path/to/scores.csv

# Share publicly
python3 kpi_correlation_app.py --share

# Custom port
python3 kpi_correlation_app.py --port 8080

# Full example
python3 kpi_correlation_app.py \
  --scores-file ../../data/lenovo-scores-0603.csv \
  --port 7860

Options:

• --scores-file: Path to scores CSV file (default: lenovo-scores-0603.csv)
• --share: Create a public link
• --port: Port to run on (default: 7860)

The Gradio interface provides the following parameterized features:

1. Data Selection:

   • Choose between different KPI files (CSV/Excel)
   • Select specific score columns for analysis
   • Filter data by manager status and other criteria

2. Analysis Parameters:

   • Correlation method selection (Pearson/Spearman)
   • Confidence level adjustment
   • Sample size requirements
   • Outlier detection thresholds

3. Visualization Options:

   • Plot type selection (scatter, regression, etc.)
   • Color scheme customization
   • Figure size and DPI settings
   • Trend line display options

4. Output Configuration:

   • Export format selection (YAML/CSV/Excel)
   • Custom output file naming
   • Detailed vs. summary report options
   • Plot export settings

All parameters can be adjusted in real-time through the web interface, with immediate updates to the analysis results and visualizations.

Input File Requirements

KPI File

• Must contain an email column (case-insensitive)
• Must contain IPM columns for FY23/24 and FY24/25
• Supports CSV and Excel formats

Scores File

• Must contain columns: email, problem_score, ability_score
• CSV format

Output

CLI Output

• Console output with data quality report and correlation analysis
• YAML file with detailed results
• Optional PNG plots (individual and combined)

Gradio Output

• Interactive web interface
• Real-time analysis results
• Interactive scatter plots with trend lines
• Data quality statistics

Correlation Pairs Analyzed

• AC: problem_score vs FY23/24 IPM
• AD: problem_score vs FY24/25 IPM
• BC: ability_score vs FY23/24 IPM
• BD: ability_score vs FY24/25 IPM

Each pair shows:

• Pearson correlation coefficient
• Spearman correlation coefficient
• Number of valid samples
• Data quality metrics

HF deployment

git subtree push --prefix=data-analysis/kpi_score_analysis hfspace main