"""
==================================================
MILP Solve Time vs. Feature & Sample Dimensions
==================================================

This example evaluates how execution time scales with feature count :math:`p`
and sample size :math:`n` when optimizing exact ternary weights via MILP.
"""

import time
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from tlmnet import TlmMilpClassifier

feature_counts = [5, 10, 15, 20, 25]
sample_sizes = [50, 100, 200]
results: dict[int, list[float]] = {n: [] for n in sample_sizes}

for n in sample_sizes:
    for p in feature_counts:
        X, y = make_classification(
            n_samples=n, n_features=p, n_informative=min(p, 3), random_state=42
        )
        clf = TlmMilpClassifier(max_features=5, C=1.0)

        start = time.time()
        clf.fit(X, y)
        elapsed = time.time() - start

        results[n].append(elapsed)

plt.figure(figsize=(8, 5))
for n in sample_sizes:
    plt.plot(feature_counts, results[n], marker="o", label=f"n = {n} samples")

plt.xlabel("Number of Features (p)")
plt.ylabel("MILP Solve Time (seconds)")
plt.title("TlmMilpClassifier Execution Time Scaling")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()