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Krzysztof kuhy Rudnicki
cc06bc0cd8
749 findings across six labs and two projects. The mechanical half was scripted; everything below is what the scripts got wrong or could not do, and how it was caught. Three regressions I introduced and then found by RUNNING the code, not by reading it: - Converting print() to logger.info() made the programs SILENT. logging's last-resort handler only emits WARNING and above, so with no basicConfig call `lab2/main.py -t 1 1` printed nothing at all. Fifteen files across the repo now configure logging where they log. - The same conversion dropped `end=""`. lab2 draws its board one square at a time and lab1 draws the maze one cell at a time; both came out one glyph per line. Those thirteen call sites are sys.stdout.write again, and the rendered board now diffs byte-identically against the pre-change output. - The B007 fix renamed `col` to `_col` in the FIRST loop it matched, which was print_letters -- where the variable IS used. That would have been a NameError on the first call. And one the scripts caused silently: annotating 110 signatures dropped the DEFAULT VALUES from 22 of them, so `preprocessing(a, b, USER_THRESHOLD, ANIME_THRESHOLD)` would have raised TypeError. Every signature is now diffed against the pre-change version, name by name and default by default: zero mismatches. Real defects the lint surfaced: - `alpha_beta` ended with an unreachable `return None` that violated its own return type. Its maximising and minimising halves were mirror images, so they are one `_search_branch` now (C901 11 -> ok). - `get_data`'s `gpu` switch, `predict`'s `auto`, `create_model`'s `rows_number` and five of `preprocess_model_predict`'s fifteen parameters were never read. Gone, with their call sites. - np.random.* -> Generator throughout (NPY002), `.values` -> `.to_numpy()`, `datetime.now()` with a tzinfo, `inplace=True` removed, `except:` given a type, `random.*` -> `SystemRandom` (S311). - print_board's three nested helpers became methods (C901 17 -> ok); user_input's nine-branch if-ladder became a table (C901 12 -> ok). Verification, all by running: lab1 `-t` and lab2 `-t 1 1` produce output byte-identical to the pre-change code; lab2's rendered 8x8 board diffs clean; lab3's argument parsing returns the same dict for the same argv; lab4 fails identically before and after (no sklearn here). Also included: the logging configuration for NLP, MOM and ECRYPT_PROJECT, which had the same silence bug. Repo-wide: 2,775 findings -> 1,883. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
169 lines
5.0 KiB
Python
Executable File
169 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Bi-criteria distribution model for MOM project 3 (data set 1.6).
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A PuLP transport problem over two factories, four warehouses and six
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customers, minimising distribution cost while maximising customer
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satisfaction. It is solved three ways: once on cost alone, then swept over
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alpha in 0.0..1.0 to trace the efficient frontier, then at the five
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weightings the report tabulates.
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"""
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import logging
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import sys
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import pulp
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logger = logging.getLogger(__name__)
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logging.basicConfig(format="%(message)s", level=logging.INFO)
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# alpha runs over 0.0, 0.1 ... 1.0, so eleven steps.
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ALPHA_STEPS = 11
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# Create a problem variable:
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model = pulp.LpProblem("Optimal_Distribution", pulp.LpMinimize)
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# Zdefiniowanie dostawcy (fabryki and magazyny)
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fabryki = ["F1", "F2"]
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magazyny = ["M1", "M2", "M3", "M4"]
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dostawcy = fabryki + magazyny # Combining both lists
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# Zdefiniowanie klientów
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klienci = ["K1", "K2", "K3", "K4", "K5", "K6"]
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koszt = {
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"F1": {
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"M1": 0.3,
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"M2": 0.5,
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"M3": 1.2,
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"M4": 0.8,
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"K1": 1.2,
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"K2": 999,
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"K3": 1.2,
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"K4": 2.0,
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"K5": 999,
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"K6": 1.1,
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},
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"F2": {
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"M1": 999,
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"M2": 0.4,
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"M3": 0.5,
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"M4": 0.3,
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"K1": 1.8,
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"K2": 999,
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"K3": 999,
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"K4": 999,
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"K5": 999,
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"K6": 999,
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},
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"M1": {"K1": 999, "K2": 1.2, "K3": 0.2, "K4": 1.7, "K5": 999, "K6": 2.0},
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"M2": {"K1": 1.4, "K2": 0.3, "K3": 1.8, "K4": 1.3, "K5": 0.5, "K6": 999},
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"M3": {"K1": 999, "K2": 1.3, "K3": 2.0, "K4": 999, "K5": 0.3, "K6": 1.4},
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"M4": {"K1": 999, "K2": 999, "K3": 0.4, "K4": 2.0, "K5": 0.5, "K6": 1.6},
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}
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P = pulp.LpVariable.dicts(
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"P", [(i, j) for i in dostawcy for j in klienci], cat="Binary"
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)
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maksymalne_zamowienie = 60
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poziom_satysfakcji = {
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"K1": 50 / maksymalne_zamowienie,
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"K2": 10 / maksymalne_zamowienie,
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"K3": 40 / maksymalne_zamowienie,
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"K4": 35 / maksymalne_zamowienie,
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"K5": 60 / maksymalne_zamowienie,
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"K6": 20 / maksymalne_zamowienie,
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}
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preferencja_klienta = {
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"K1": ["F2"],
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"K2": ["M1"],
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"K3": ["M2", "M3"],
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"K4": ["F1"],
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"K5": [],
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"K6": ["M3", "M4"],
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}
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suma_satysfakcji = pulp.lpSum(
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[poziom_satysfakcji[j] * P[(i, j)] for i in dostawcy for j in klienci]
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)
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# Zmienne decyzyjne
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x = pulp.LpVariable.dicts(
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"x", [(i, j) for i in dostawcy for j in klienci], lowBound=0, cat="Integer"
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)
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y = pulp.LpVariable.dicts(
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"y", [(i, k) for i in fabryki for k in magazyny], lowBound=0, cat="Integer"
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)
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# Funkcje Celu
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koszt_dystrybucji = pulp.lpSum(
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[koszt[i][j] * x[(i, j)] for i in dostawcy for j in klienci]
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)
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koszt_magazynowania = pulp.lpSum(
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[koszt[i][k] * y[(i, k)] for i in fabryki for k in magazyny]
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)
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alpha = 0.5
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beta = 0.5
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model += alpha * (koszt_dystrybucji + koszt_magazynowania) - beta * suma_satysfakcji
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mozliwosci_fabryki = {"F1": 150, "F2": 200}
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pojemnosc_magazynu = {"M1": 70, "M2": 50, "M3": 100, "M4": 40}
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zamowienia_klientow = {"K1": 50, "K2": 10, "K3": 40, "K4": 35, "K5": 60, "K6": 20}
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for i in fabryki:
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model += (
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pulp.lpSum([x[(i, j)] for j in klienci] + [y[(i, k)] for k in magazyny])
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<= mozliwosci_fabryki[i]
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)
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for k in magazyny:
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model += pulp.lpSum([x[(k, j)] for j in klienci]) <= pojemnosc_magazynu[k]
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for j in klienci:
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model += pulp.lpSum([x[(i, j)] for i in dostawcy]) == zamowienia_klientow[j]
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model.solve()
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for v in model.variables():
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logger.info("%s = %s", v.name, v.varValue)
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koszt_wyniki = []
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zadowolenie_wyniki = []
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wyniki_funkcji = []
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maksymalny_wynik = 0
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for step in range(ALPHA_STEPS):
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alpha = step / 10.0
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beta = (ALPHA_STEPS - 1 - step + sys.float_info.epsilon) / 10.0
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# Update objective function
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model.objective = alpha * koszt_dystrybucji - beta * suma_satysfakcji
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# Solve the model
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model.solve()
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logger.info("%s", alpha)
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# Record the wyniki
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calkowity_koszt = pulp.value(koszt_dystrybucji)
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calkowite_zadowolenie = pulp.value(suma_satysfakcji)
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wynik_funkcji = pulp.value(alpha * koszt_dystrybucji - beta * suma_satysfakcji)
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koszt_wyniki.append(calkowity_koszt)
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zadowolenie_wyniki.append(calkowite_zadowolenie)
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maksymalny_wynik = max(maksymalny_wynik, wynik_funkcji)
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wyniki_funkcji.append(wynik_funkcji)
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logger.info("maksymalny_wynik %s %s", maksymalny_wynik, wyniki_funkcji)
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# Pseudo-code, assuming model setup as previously discussed
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scemariusze = [
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(1.0, sys.float_info.epsilon),
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(0.8, 0.2),
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(0.5, 0.5),
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(0.2, 0.8),
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(sys.float_info.epsilon, 1.0),
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]
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wyniki = []
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for alpha, beta in scemariusze:
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model.objective = alpha * koszt_dystrybucji - beta * suma_satysfakcji
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model.solve()
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calkowity_koszt = pulp.value(koszt_dystrybucji)
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calkowite_zadowolenie = pulp.value(suma_satysfakcji)
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wyniki.append((alpha, beta, calkowity_koszt, calkowite_zadowolenie))
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for idx, (alpha, beta, koszt, zadowolenie) in enumerate(wyniki):
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logger.info("Krok %d:", idx + 1)
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logger.info(" koszt: %s, zadowolenie: %s", alpha, beta)
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logger.info(" Calkowity koszt: %s, zadowolenie klienta: %s\n", koszt, zadowolenie)
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