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chripiermarini merged 1 commit into from
Jun 22, 2026
Merged

simple formatting test change #16

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109 changes: 71 additions & 38 deletions scripts/generate_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -203,41 +203,61 @@ def _experiment_synthetic_v2() -> None:
start = datetime(2024, 1, 1)
dates = pl.date_range(
start=start,
end=datetime(2024, 12, 30), # 2024 is a leap year; end on Dec 30 for exactly 365 days
end=datetime(
2024, 12, 30
), # 2024 is a leap year; end on Dec 30 for exactly 365 days
interval="1d",
eager=True,
)
n_days = len(dates)
day_of_week = [d.weekday() for d in dates] # 0=Mon … 6=Sun

# --- origins ---
origins = pl.DataFrame({
"origin_id": ["O1", "O2", "O3"],
"daily_capacity": [300.0, 250.0, 200.0],
})
origins = pl.DataFrame(
{
"origin_id": ["O1", "O2", "O3"],
"daily_capacity": [300.0, 250.0, 200.0],
}
)

# --- destinations ---
destinations = pl.DataFrame({
"destination_id": ["D1", "D2", "D3", "D4", "D5", "D6"],
"holding_cost": [0.50, 0.75, 1.00, 0.40, 0.60, 0.90],
})
destinations = pl.DataFrame(
{
"destination_id": ["D1", "D2", "D3", "D4", "D5", "D6"],
"holding_cost": [0.50, 0.75, 1.00, 0.40, 0.60, 0.90],
}
)

# --- lanes (hand-assigned, fully connected 3×6) ---
lane_costs = {
# O1: cheapest (2.0–4.0)
("O1", "D1"): 2.0, ("O1", "D2"): 2.5, ("O1", "D3"): 3.0,
("O1", "D4"): 2.2, ("O1", "D5"): 3.5, ("O1", "D6"): 4.0,
("O1", "D1"): 2.0,
("O1", "D2"): 2.5,
("O1", "D3"): 3.0,
("O1", "D4"): 2.2,
("O1", "D5"): 3.5,
("O1", "D6"): 4.0,
# O2: mid-range (3.5–6.0)
("O2", "D1"): 3.5, ("O2", "D2"): 4.0, ("O2", "D3"): 4.5,
("O2", "D4"): 5.0, ("O2", "D5"): 5.5, ("O2", "D6"): 6.0,
("O2", "D1"): 3.5,
("O2", "D2"): 4.0,
("O2", "D3"): 4.5,
("O2", "D4"): 5.0,
("O2", "D5"): 5.5,
("O2", "D6"): 6.0,
# O3: most expensive (5.0–8.0)
("O3", "D1"): 5.0, ("O3", "D2"): 5.5, ("O3", "D3"): 6.0,
("O3", "D4"): 6.5, ("O3", "D5"): 7.0, ("O3", "D6"): 8.0,
("O3", "D1"): 5.0,
("O3", "D2"): 5.5,
("O3", "D3"): 6.0,
("O3", "D4"): 6.5,
("O3", "D5"): 7.0,
("O3", "D6"): 8.0,
}
lanes = pl.DataFrame([
{"origin_id": o, "destination_id": d, "unit_cost": c}
for (o, d), c in lane_costs.items()
])
lanes = pl.DataFrame(
[
{"origin_id": o, "destination_id": d, "unit_cost": c}
for (o, d), c in lane_costs.items()
]
)

# --- demand history ---
def seasonal_mult(pattern: list[float]) -> np.ndarray:
Expand All @@ -246,33 +266,45 @@ def seasonal_mult(pattern: list[float]) -> np.ndarray:
t = np.arange(n_days)

# D1: strong weekly seasonality, level ~80, no trend, low noise
d1 = 80.0 * seasonal_mult([1.20, 1.15, 1.00, 0.90, 0.85, 0.70, 0.75]) \
+ np.random.normal(0, 4, n_days)
d1 = 80.0 * seasonal_mult(
[1.20, 1.15, 1.00, 0.90, 0.85, 0.70, 0.75]
) + np.random.normal(0, 4, n_days)

# D2: flat, no seasonality, level ~60, low noise
d2 = 60.0 + np.random.normal(0, 3, n_days)

# D3: upward trend, weak seasonality, level ~50, moderate noise
d3 = (50.0 + 0.1 * t) * seasonal_mult([1.05, 1.02, 1.00, 0.98, 0.97, 0.99, 1.00]) \
+ np.random.normal(0, 7, n_days)
d3 = (50.0 + 0.1 * t) * seasonal_mult(
[1.05, 1.02, 1.00, 0.98, 0.97, 0.99, 1.00]
) + np.random.normal(0, 7, n_days)

# D4: strong weekly seasonality + upward trend, level ~90, moderate noise
d4 = (90.0 + 0.08 * t) * seasonal_mult([1.25, 1.20, 1.05, 0.95, 0.85, 0.70, 0.80]) \
+ np.random.normal(0, 8, n_days)
d4 = (90.0 + 0.08 * t) * seasonal_mult(
[1.25, 1.20, 1.05, 0.95, 0.85, 0.70, 0.80]
) + np.random.normal(0, 8, n_days)

# D5: high noise, no pattern, level ~70
d5 = 70.0 + np.random.normal(0, 20, n_days)

# D6: weekly seasonality + mild downward trend, level ~100, low noise
d6 = (100.0 - 0.05 * t) * seasonal_mult([1.10, 1.05, 1.00, 0.95, 0.90, 0.95, 1.00]) \
+ np.random.normal(0, 5, n_days)
d6 = (100.0 - 0.05 * t) * seasonal_mult(
[1.10, 1.05, 1.00, 0.95, 0.90, 0.95, 1.00]
) + np.random.normal(0, 5, n_days)

demand_rows = []
for dest_id, raw in [("D1", d1), ("D2", d2), ("D3", d3),
("D4", d4), ("D5", d5), ("D6", d6)]:
for dest_id, raw in [
("D1", d1),
("D2", d2),
("D3", d3),
("D4", d4),
("D5", d5),
("D6", d6),
]:
clipped = np.clip(raw, 0, None).round(1)
for date, val in zip(dates, clipped):
demand_rows.append({"date": date, "destination_id": dest_id, "demand": float(val)})
demand_rows.append(
{"date": date, "destination_id": dest_id, "demand": float(val)}
)

demand_history = pl.DataFrame(demand_rows).with_columns(
pl.col("date").cast(pl.Date)
Expand Down Expand Up @@ -304,14 +336,15 @@ def seasonal_mult(pattern: list[float]) -> np.ndarray:
print(f" {row['destination_id']}: {row['holding_cost']}")
print("\nDemand statistics per destination:")
stats = (
demand_history
.group_by("destination_id")
.agg([
pl.col("demand").mean().round(2).alias("mean"),
pl.col("demand").std().round(2).alias("std"),
pl.col("demand").min().round(2).alias("min"),
pl.col("demand").max().round(2).alias("max"),
])
demand_history.group_by("destination_id")
.agg(
[
pl.col("demand").mean().round(2).alias("mean"),
pl.col("demand").std().round(2).alias("std"),
pl.col("demand").min().round(2).alias("min"),
pl.col("demand").max().round(2).alias("max"),
]
)
.sort("destination_id")
)
print(stats)
Expand Down
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