# 💕 Relationship Longevity Predictor

**An ensemble ML model that predicts relationship compatibility and longevity based on personal and professional profiles of two individuals.**

This is a **predictability engine** — not a matchmaking system. Given two individuals' personal attributes, values, interests, and personality traits, it predicts how likely their relationship is to succeed.

## Model Architecture

**Ensemble of 3 gradient-boosted tree models** with 113 engineered dyadic features:

| Model | Weight | AUC-ROC | F1 |
|-------|--------|---------|----|
| XGBoost | 0.40 | 0.8852 | 0.6013 |
| **LightGBM** | **0.35** | **0.8912** | **0.6351** |
| CatBoost | 0.25 | 0.8661 | 0.5974 |
| **Ensemble** | — | **0.8842** | **0.6198** |

**Best single model: LightGBM (AUC-ROC = 0.891, F1 = 0.635)**

## Performance Metrics (5-Fold Cross-Validation)

| Metric | Value |
|--------|-------|
| **AUC-ROC** | 0.891 |
| **AUC-PR** | 0.699 |
| **Accuracy** | 85.3% |
| **F1 Score** | 0.635 |
| **Precision** | 56.8% |
| **Recall** | 72.0% |
| **Brier Score** | 0.101 |

## What the Model Learns

### Top Predictive Features (from SHAP analysis)

1. **Attractiveness Perception Product** — Mutual physical attraction between partners
2. **Probability Partner Wants to Date** — Perceived reciprocal interest
3. **Humor Perception Product** — Shared sense of humor (mutual ratings)
4. **Total Self-Awareness Gap** — How accurately people perceive themselves vs how partners see them
5. **Interest Correlation** — Overlap in hobbies and interests
6. **Shared Interests Score** — Partner's rating of shared interests
7. **Interest Diversity** — Breadth of the dater's interests
8. **Confidence Calibration** — How well people predict their own attractiveness to others
9. **Intelligence Value Fulfillment** — Whether the partner meets intelligence expectations
10. **Expectation Meets Reality** — Gap between expected and actual satisfaction

### Key Insights

- **Mutual attraction matters most** — but it's the *product* (both people finding each other attractive) that predicts success, not just one-sided attraction
- **Humor compatibility** ranks #3 — couples who both rate each other as funny are much more likely to match
- **Self-awareness** is a strong predictor — people who accurately assess how others see them tend to form better partnerships
- **Shared interests** matter significantly — the correlation between interests is more predictive than any single interest
- **Value alignment** (what you care about vs what your partner delivers) drives long-term compatibility

## Feature Engineering

113 features in 10 categories, engineered from raw dyadic profiles:

| Category | Features | Description |
|----------|----------|-------------|
| **Perception Gap** | 5 | How you rate your partner vs how they rate you (per trait) |
| **Mutual Scores** | 5 | Average of both partners' ratings (per trait) |
| **Perception Products** | 5 | Multiplicative interaction of mutual ratings |
| **Value Fulfillment** | 5 | Does your partner deliver what you value most? |
| **Self-Awareness** | 5 | Self-perception vs partner perception gap |
| **Age Features** | 4 | Gap, gap², is_older, combined age |
| **Interest Features** | 5 | Diversity, intensity, range, correlation |
| **Importance Alignment** | 8 | Do both people value the same traits? |
| **Expectation Features** | 2 | Expectation calibration, meets-reality score |
| **Demographics** | 4 | Race match, gender, same race importance |
| **Raw Profiles** | ~65 | Original personality ratings, interests, preferences |

## Training Data

**Fisman Speed Dating Experiment** ([mstz/speeddating](https://hf.co/datasets/mstz/speeddating))
- 1,048 speed-dating encounters between participants
- Columbia Business School, 2002-2004
- 17.7% positive match rate (class imbalance handled via scale_pos_weight + balanced weighting)

## Usage

```python
import joblib
import json
import numpy as np
from catboost import CatBoostClassifier

# Load models
xgb = joblib.load("xgboost_model.joblib")
lgb = joblib.load("lightgbm_model.joblib")
cat = CatBoostClassifier()
cat.load_model("catboost_model.cbm")
feature_cols = joblib.load("feature_columns.joblib")

with open("ensemble_config.json") as f:
    config = json.load(f)

# Prepare feature vector (113 features — see feature_columns.joblib)
# features = pd.DataFrame([your_feature_vector], columns=feature_cols)

# Predict
xgb_prob = xgb.predict_proba(features)[:, 1]
lgb_prob = lgb.predict_proba(features)[:, 1]
cat_prob = cat.predict_proba(features)[:, 1]

# Ensemble
score = 0.4 * xgb_prob + 0.35 * lgb_prob + 0.25 * cat_prob

# Interpret
if score >= 0.7:
    print("High Compatibility ❤️")
elif score >= 0.4:
    print("Moderate Compatibility 💛")
else:
    print("Low Compatibility 💔")
```

## Visualizations

### ROC Curves
![ROC Curves](figures/roc_curves.png)

### Feature Importance
![Feature Importance](figures/feature_importance.png)

### SHAP Summary
![SHAP Summary](figures/shap_summary.png)

### SHAP Dependence (Top 6 Features)
![SHAP Dependence](figures/shap_dependence.png)

### Confusion Matrix
![Confusion Matrix](figures/confusion_matrix.png)

### Prediction Distribution
![Probability Distribution](figures/probability_distribution.png)

## Literature Basis

| Paper | Contribution |
|-------|-------------|
| Grinsztajn et al. (NeurIPS 2022) — *"Why do tree-based models still outperform deep learning on tabular data?"* | Validated XGBoost/LightGBM as SOTA for tabular data with <100K rows |
| Fisman et al. (QJE 2006) — *"Gender Differences in Mate Selection"* | Original speed dating experiment; ~70% accuracy with logistic regression |
| Gorishniy et al. (NeurIPS 2021) — *"Revisiting Deep Learning Models for Tabular Data"* | FT-Transformer architecture for tabular; confirmed tree superiority on small datasets |
| Savcisens et al. (Nature Human Behaviour 2024) — *"Using Sequences of Life-events to Predict Human Lives"* | life2vec — longitudinal life-event prediction; architecture reusable for dyadic temporal modeling |

## Limitations

- **Short-term proxy**: The training data captures initial match decisions (4-minute speed dates), not long-term relationship outcomes. The model predicts initial compatibility, which is a proxy for — but not equivalent to — relationship longevity.
- **Sample demographics**: Columbia University students (2002-2004) — may not generalize to all demographics/cultures.
- **Static features only**: No temporal/interaction data. Adding communication patterns, life events, or behavioral signals would significantly improve longevity prediction (see life2vec approach).
- **Class imbalance**: 17.7% match rate means the model is well-calibrated for rejection but less certain for positive predictions.

## Files

| File | Description |
|------|-------------|
| `xgboost_model.joblib` | XGBoost classifier (2000 trees) |
| `lightgbm_model.joblib` | LightGBM classifier (2000 trees) |
| `catboost_model.cbm` | CatBoost classifier (2000 iterations) |
| `ensemble_config.json` | Ensemble weights, threshold, feature list, metrics |
| `feature_columns.joblib` | Ordered list of 113 feature column names |
| `race_encoder.joblib` | LabelEncoder for race categories |
| `evaluation_results.csv` | Full evaluation metrics table |
| `feature_importance.csv` | Feature importance rankings from XGB + LGB |
| `predictor.py` | Prediction interface class |
| `train_relationship_predictor.py` | Full training script (reproducible) |
| `figures/` | All visualizations (ROC, SHAP, confusion matrix, etc.) |

## License

Research use. Based on publicly available academic dataset.

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*Built with XGBoost, LightGBM, CatBoost, SHAP, and scikit-learn.*