add experiment.py

SobolevNS/docs/data/task1_data_structures/experiment.py

@@ -0,0 +1,194 @@
+"""
+experiment.py
+
+Замеры производительности трёх структур данных на одних и тех же данных:
+    LinkedList, HashTable, BST
+в двух режимах:
+    случайный порядок (shuffled), отсортированный порядок (sorted)
+для трёх операций:
+    insert N записей, find 110 раз, delete 50 раз
+Каждый эксперимент повторяется TRIALS раз. Сохраняем все замеры + средние
+в CSV. Для BST на отсортированных данных снижаем N - иначе эксперимент
+длится десятки минут (вырожденное дерево, O(N^2) вставка).
+"""
+
+import csv
+import os
+import random
+import time
+
+import phonebook as pb
+
+
+# ---------- параметры эксперимента ----------
+N = 10_000               # число записей в основном эксперименте
+N_BST_SORTED = 2_000     # для BST на отсортированных данных - меньше (O(N^2))
+TRIALS = 5               # количество повторов каждого замера
+N_FIND_EXISTING = 100
+N_FIND_MISSING = 10
+N_DELETE = 50
+HT_SIZE = 2048           # размер хеш-таблицы
+RNG_SEED = 42
+OUT_CSV = os.path.join("docs", "data", "results.csv")
+# --------------------------------------------
+
+
+def gen_records(n):
+    """Генерирует n записей вида ('User_00001', '555-0001-...')."""
+    return [(f"User_{i:05d}", f"555-{i:07d}") for i in range(n)]
+
+
+def pick_keys(records, k_exist, k_miss, rng):
+    """Выбирает k_exist существующих имён и k_miss отсутствующих."""
+    existing = [name for name, _ in rng.sample(records, k_exist)]
+    missing = [f"None_{i}" for i in range(k_miss)]
+    return existing + missing
+
+
+# ---------- замеры по структурам ----------
+
+def measure_linked_list(records, find_keys, delete_keys):
+    # вставка
+    t0 = time.perf_counter()
+    head = pb.ll_create()
+    for name, phone in records:
+        head = pb.ll_insert(head, name, phone)
+    t_insert = time.perf_counter() - t0
+
+    # поиск
+    t0 = time.perf_counter()
+    for name in find_keys:
+        pb.ll_find(head, name)
+    t_find = time.perf_counter() - t0
+
+    # удаление
+    t0 = time.perf_counter()
+    for name in delete_keys:
+        head = pb.ll_delete(head, name)
+    t_delete = time.perf_counter() - t0
+
+    return t_insert, t_find, t_delete
+
+
+def measure_hash_table(records, find_keys, delete_keys):
+    t0 = time.perf_counter()
+    ht = pb.ht_create(size=HT_SIZE)
+    for name, phone in records:
+        pb.ht_insert(ht, name, phone)
+    t_insert = time.perf_counter() - t0
+
+    t0 = time.perf_counter()
+    for name in find_keys:
+        pb.ht_find(ht, name)
+    t_find = time.perf_counter() - t0
+
+    t0 = time.perf_counter()
+    for name in delete_keys:
+        pb.ht_delete(ht, name)
+    t_delete = time.perf_counter() - t0
+
+    return t_insert, t_find, t_delete
+
+
+def measure_bst(records, find_keys, delete_keys):
+    t0 = time.perf_counter()
+    root = pb.bst_create()
+    for name, phone in records:
+        root = pb.bst_insert(root, name, phone)
+    t_insert = time.perf_counter() - t0
+
+    t0 = time.perf_counter()
+    for name in find_keys:
+        pb.bst_find(root, name)
+    t_find = time.perf_counter() - t0
+
+    t0 = time.perf_counter()
+    for name in delete_keys:
+        root = pb.bst_delete(root, name)
+    t_delete = time.perf_counter() - t0
+
+    return t_insert, t_find, t_delete
+
+
+# ---------- запуск ----------
+
+def run_one(structure_name, mode, n, rng_seed):
+    """Готовит данные, прогоняет TRIALS раз и возвращает список (insert, find, delete)."""
+    base_records = gen_records(n)
+
+    runs = []
+    for trial in range(TRIALS):
+        # отдельный rng для воспроизводимости и независимости попыток
+        rng = random.Random(rng_seed + trial)
+
+        if mode == "shuffled":
+            records = base_records[:]
+            rng.shuffle(records)
+        elif mode == "sorted":
+            records = sorted(base_records, key=lambda x: x[0])
+        else:
+            raise ValueError(mode)
+
+        find_keys = pick_keys(records, N_FIND_EXISTING, N_FIND_MISSING, rng)
+        delete_keys = [name for name, _ in rng.sample(records, N_DELETE)]
+
+        if structure_name == "LinkedList":
+            r = measure_linked_list(records, find_keys, delete_keys)
+        elif structure_name == "HashTable":
+            r = measure_hash_table(records, find_keys, delete_keys)
+        elif structure_name == "BST":
+            r = measure_bst(records, find_keys, delete_keys)
+        else:
+            raise ValueError(structure_name)
+
+        runs.append(r)
+    return runs
+
+
+def main():
+    os.makedirs(os.path.dirname(OUT_CSV), exist_ok=True)
+
+    rows = [["Структура", "Режим", "Операция", "N", "Trial", "Время (сек)"]]
+    summary = []   # (structure, mode, op, n, mean, all_trials)
+
+    configs = [
+        ("LinkedList", "shuffled", N),
+        ("LinkedList", "sorted",   N),
+        ("HashTable",  "shuffled", N),
+        ("HashTable",  "sorted",   N),
+        ("BST",        "shuffled", N),
+        ("BST",        "sorted",   N_BST_SORTED),   # вырожденный случай
+    ]
+
+    for structure, mode, n in configs:
+        print(f"==> {structure:10s} | {mode:9s} | N={n}")
+        runs = run_one(structure, mode, n, RNG_SEED)
+        # runs = [(insert, find, delete), ...]
+        ops = ["insert", "find", "delete"]
+        for op_idx, op in enumerate(ops):
+            vals = [r[op_idx] for r in runs]
+            mean = sum(vals) / len(vals)
+            for trial_idx, v in enumerate(vals):
+                rows.append([structure, mode, op, n, trial_idx + 1, f"{v:.6f}"])
+            summary.append((structure, mode, op, n, mean, vals))
+            print(f"    {op:7s}: mean={mean*1000:.3f} ms  "
+                  f"runs={[f'{v*1000:.3f}' for v in vals]}")
+
+    # сводная строка со средними
+    rows.append([])
+    rows.append(["--- СРЕДНИЕ ---"])
+    rows.append(["Структура", "Режим", "Операция", "N",
+                 "Среднее (сек)", "Все замеры (сек)"])
+    for s, mode, op, n, mean, vals in summary:
+        rows.append([s, mode, op, n, f"{mean:.6f}",
+                     ";".join(f"{v:.6f}" for v in vals)])
+
+    with open(OUT_CSV, "w", newline="", encoding="utf-8") as f:
+        writer = csv.writer(f)
+        writer.writerows(rows)
+
+    print(f"\nГотово. Результаты записаны в {OUT_CSV}")
+
+
+if __name__ == "__main__":
+    main()