Pin dependencies, add unit tests and run the pipeline in CI

.github/workflows/ci.yml

@@ -0,0 +1,29 @@
+name: ci
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  pipeline:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+          cache-dependency-path: requirements*.txt
+
+      - name: Install dependencies
+        run: pip install -r requirements-dev.txt
+
+      - name: Data, train, evaluate, sanity check
+        run: make all
+
+      - name: Data is reproducible from the seed
+        run: git diff --exit-code data/
+
+      - name: Unit tests
+        run: make test

Makefile

@@ -1,4 +1,4 @@
-.PHONY: data train evaluate sanity app all clean
+.PHONY: data train evaluate sanity test app all clean
 
 PYTHON ?= python
 
@@ -14,6 +14,9 @@ evaluate:
 sanity:
 	$(PYTHON) -m src.models.sanity_check
 
+test:
+	$(PYTHON) -m pytest -q tests
+
 app:
 	streamlit run app.py
 

README.md

@@ -2,6 +2,7 @@
 
 <p align="left">
   <a href="https://dynamic-pricing-0trm.streamlit.app/"><img src="https://img.shields.io/badge/Live%20demo-Streamlit-ff4b4b?logo=streamlit&logoColor=white" alt="Live demo"></a>
+  <a href="https://github.com/0trm/dynamic-pricing/actions/workflows/ci.yml"><img src="https://github.com/0trm/dynamic-pricing/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
   <img src="https://img.shields.io/badge/ML-Supervised-lightgrey" alt="ML">
   <img src="https://img.shields.io/badge/Models-Prophet%20%2B%20XGBoost-lightgrey" alt="Models">
 </p>
@@ -17,11 +18,14 @@ An ML-powered dynamic pricing and decision support system for ticket pricing in
 ## Quickstart
 
 ```bash
-pip install -r requirements.txt
+pip install -r requirements-dev.txt   # Python 3.12, pinned versions
 make all          # synthetic data, train, holdout evaluation, elasticity sanity check
+make test         # unit tests (optimizer bounds, cold start, save/load)
 make app          # open the human-in-the-loop Streamlit page locally
 ```
 
+CI runs `make all` and `make test` on every push. The live demo sleeps after 12 hours without traffic (a Streamlit Community Cloud rule), so the first visit may need a click to wake it and about a minute to train.
+
 ---
 
 ## The problem
@@ -83,7 +87,7 @@ The **Dynamic Pricing Engine** ingests historical data from the **Club's Data Sy
 | Naive: last 7 days per series | 37.4% | 0.544 | 8.7 | 15.2 | 1.00 |
 | Naive: global mean | 79.7% | -0.428 | 18.6 | 27.0 | 2.13 |
 
-The baseline that matters is the last-7-days naive: the forecast anyone could make at the cutoff without a model. The ensemble's WAPE is **29% lower** than it. The XGBoost stage does the heavy lifting: on its own, trained directly on sales, it beats the ensemble on WAPE and MAE, while the ensemble edges it on R² and RMSE (fewer large misses). Prophet alone is worse than the naive. Reproducible with `RANDOM_SEED=42` in `src/data/make_dataset.py`.
+The baseline that matters is the last-7-days naive: the forecast anyone could make at the cutoff without a model. The ensemble's WAPE is **29% lower** than it. The XGBoost stage does the heavy lifting: on its own, trained directly on sales, it beats the ensemble on WAPE and MAE, while the ensemble edges it on R² and RMSE (fewer large misses). Prophet alone is worse than the naive. Reproducible with `RANDOM_SEED=42` in `src/data/make_dataset.py` and the pinned versions in `requirements.txt`.
 
 #### Cold start
 
@@ -218,7 +222,9 @@ dynamic-pricing/
 ├── README.md
 ├── app.py                              # Streamlit HiTL page
 ├── config.py                           # Paths and reference dates
-├── requirements.txt
+├── requirements.txt                    # Pinned runtime dependencies
+├── requirements-dev.txt                # + pytest
+├── tests/                              # Unit tests: optimizer bounds, cold start, save/load
 ├── assets/                             # Diagrams and images
 ├── data/
 │   └── 03_synthetic/
@@ -248,9 +254,10 @@ dynamic-pricing/
 ### Reproducing the results
 
 ```bash
-pip install -r requirements.txt
+pip install -r requirements-dev.txt
 make clean                              # remove cached artifacts
-make all                                # data + train + evaluate (accuracy + decision quality) + sanity check
+make all                                # data + train + evaluate (accuracy, decisions, cold start) + sanity check
+make test                               # unit tests
 make app                                # launch the Streamlit page
 
 # Or run the CLI examples directly:

app.py

@@ -2,7 +2,7 @@
 
 import json
 import os
-from datetime import datetime
+from datetime import UTC, datetime
 from pathlib import Path
 
 import pandas as pd
@@ -128,7 +128,7 @@ def load_data() -> pd.DataFrame:
 st.divider()
 if st.button("✓ Approve recommended price", type="primary"):
     proposal = {
-        'timestamp': datetime.utcnow().isoformat(),
+        'timestamp': datetime.now(UTC).isoformat(),
         'match_id': int(match_id),
         'seat_zone': seat_zone,
         'days_until_match': int(days_until_match),

requirements-dev.txt

@@ -0,0 +1,2 @@
+-r requirements.txt
+pytest==9.1.1

requirements.txt

@@ -1,10 +1,8 @@
-pandas>=2.2.0
-numpy>=2.0.0
-joblib>=1.4.0
-scikit-learn>=1.5.0
-prophet>=1.1.5
-xgboost>=2.0.0
-streamlit>=1.36.0
-jupyterlab==4.2.1
-matplotlib
-seaborn
+# Pinned to the versions the README numbers were produced with (Python 3.12).
+pandas==3.0.6
+numpy==2.5.3
+joblib==1.6.0
+scikit-learn==1.9.1
+prophet==1.5.0
+xgboost==3.4.1
+streamlit==1.65.0

tests/conftest.py

@@ -0,0 +1,21 @@
+import os
+import sys
+
+import pandas as pd
+import pytest
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import config
+from src.models.train_demand_model import train_ensemble
+
+
+@pytest.fixture(scope='session')
+def data() -> pd.DataFrame:
+    return pd.read_csv(config.SYNTHETIC_DATA_PATH)
+
+
+@pytest.fixture(scope='session')
+def small_model(data):
+    """Ensemble trained on three matches: enough series to exercise every path, fast enough for CI."""
+    return train_ensemble(data[data['match_id'].isin([1, 2, 3])])

tests/test_engine.py

@@ -0,0 +1,56 @@
+import numpy as np
+import pytest
+
+import config
+from src.data.make_dataset import price_effect
+from src.decision_engine.constants import ZONE_BASE_PRICES
+from src.decision_engine.optimize import OptimizationEngine
+from src.models.evaluate import true_revenue_share
+from src.models.train_demand_model import price_support
+
+
+def test_true_optimum_is_one_and_a_quarter_times_base():
+    ratios = np.linspace(0.5, 2.5, 2001)
+    assert ratios[np.argmax(ratios * price_effect(ratios))] == pytest.approx(1.25, abs=1e-3)
+
+
+def test_price_support_is_inside_observed_prices(data):
+    for zone, (lo, hi) in price_support(data).items():
+        prices = data.loc[data['seat_zone'] == zone, 'ticket_price']
+        assert prices.min() <= lo < hi <= prices.max() + 1
+
+
+def test_recommendations_stay_inside_the_band(data, small_model):
+    rows = data[data['match_id'].isin([1, 2, 3])].drop(columns=[config.TARGET_COLUMN]).sample(30, random_state=0)
+    prices = OptimizationEngine(model=small_model).recommend_prices(rows)
+    for price, zone in zip(prices, rows['seat_zone']):
+        lo, hi = small_model.price_support[zone]
+        assert lo <= price <= hi
+
+
+def test_batch_recommendation_matches_single_row_curve(data, small_model):
+    engine = OptimizationEngine(model=small_model)
+    row = data[data['match_id'] == 2].drop(columns=[config.TARGET_COLUMN]).iloc[[10]].reset_index(drop=True)
+    curve = engine.revenue_curve(row)
+    assert engine.recommend_prices(row)[0] == curve.loc[curve['projected_revenue'].idxmax(), 'price']
+
+
+def test_unseen_match_uses_the_pooled_model(data, small_model):
+    unseen = data[data['match_id'] == 9].drop(columns=[config.TARGET_COLUMN])
+    with_fallback = small_model.predict(unseen)
+    pooled = np.clip(small_model.pooled_xgb.predict(small_model.feature_pipeline.transform(unseen)), 0, None)
+    np.testing.assert_allclose(with_fallback, pooled)
+
+
+def test_true_revenue_share_is_one_at_the_optimum(data):
+    zones = data['seat_zone'].drop_duplicates().reset_index(drop=True)
+    base = zones.map(ZONE_BASE_PRICES).to_numpy()
+    np.testing.assert_allclose(true_revenue_share(1.25 * base, zones), 1.0, atol=1e-3)
+
+
+def test_save_and_load_round_trip(tmp_path, data, small_model):
+    small_model.save(str(tmp_path))
+    loaded = type(small_model).load(str(tmp_path))
+    rows = data[data['match_id'] == 1].drop(columns=[config.TARGET_COLUMN]).head(20)
+    np.testing.assert_allclose(loaded.predict(rows), small_model.predict(rows))
+    assert loaded.price_support == small_model.price_support