IBM Model 1 Machine Translation in Python

translations = [
    (
        "hallo",
        "hello",
    ),
    (
        "guten morgen",
        "good morning",
    ),
    (
        "auf wiedersehen",
        "goodbye",
    ),
    (
        "danke",
        "thank you",
    ),
    (
        "bitte",
        "please",
    ),
    (
        "ich esse gerne pizza.",
        "i enjoy eating pizza.",
    ),
    (
        "das ist ein leckeres gericht.",
        "this is a delicious dish.",
    ),
    (
        "möchtest du zum abendessen gehen?",
        "would you like to go for dinner?",
    ),
    (
        "ich trinke gerne kaffee am morgen.",
        "i like to have coffee in the morning.",
    ),
    (
        "dieses restaurant serviert köstliche pasta.",
        "this restaurant serves delicious pasta.",
    ),
    (
        "ich liebe schokoladeneis.",
        "i love chocolate ice cream.",
    ),
    (
        "kannst du mir bitte das salz reichen?",
        "can you please pass me the salt?",
    ),
    (
        "wir haben gestern abend sushi gegessen.",
        "we had sushi last night.",
    ),
    (
        "das restaurant hat eine umfangreiche speisekarte.",
        "the restaurant has an extensive menu.",
    ),
    (
        "ich mag frisches gemüse in meinem salat.",
        "i like fresh vegetables in my salad.",
    ),
    (
        "kannst du bitte das dessert bestellen?",
        "can you please order dessert?",
    ),
    (
        "das brot ist frisch aus dem ofen.",
        "the bread is fresh from the oven.",
    ),
    (
        "meine oma macht die besten pfannkuchen.",
        "my grandma makes the best pancakes.",
    ),
    (
        "wir brauchen noch einige zutaten für das abendessen.",
        "we need a few more ingredients for dinner.",
    ),
    (
        "das ist ein traditionelles deutsches gericht.",
        "this is a traditional german dish.",
    ),
    (
        "ich koche gerne italienische gerichte.",
        "i enjoy cooking italian dishes.",
    ),
    (
        "der kellner brachte uns die speisekarte.",
        "the waiter brought us the menu.",
    ),
    (
        "ich habe eine allergie gegen erdnüsse.",
        "i have an allergy to peanuts.",
    ),
    (
        "heute abend machen wir grillen im garten.",
        "tonight, we're having a barbecue in the garden.",
    ),
    (
        "ich probiere gerne exotische gerichte.",
        "i enjoy trying exotic dishes.",
    ),
    (
        "die suppe ist noch heiß.",
        "the soup is still hot.",
    ),
    (
        "ich habe lust auf ein stück schokoladenkuchen.",
        "i'm in the mood for a piece of chocolate cake.",
    ),
    (
        "die kellnerin empfahl den fisch des tages.",
        "the waitress recommended the fish of the day.",
    ),
    (
        "wir haben gestern ein neues restaurant ausprobiert.",
        "we tried a new restaurant yesterday.",
    ),
    (
        "die pizza ist mit peperoni und oliven belegt.",
        "the pizza is topped with pepperoni and olives.",
    ),
    (
        "ich esse lieber sushi mit stäbchen.",
        "i prefer eating sushi with chopsticks.",
    ),
    (
        "das restaurant serviert authentische mexikanische tacos.",
        "the restaurant serves authentic mexican tacos.",
    ),
    (
        "möchtest du etwas zum knabbern während des films?",
        "would you like something to snack on during the movie?",
    ),
    (
        "ich habe gestern abend spaghetti bolognese gekocht.",
        "i cooked spaghetti bolognese last night.",
    ),
    (
        "das brot schmeckt besonders gut mit butter.",
        "the bread tastes especially good with butter.",
    ),
    (
        "das restaurant hat eine tolle auswahl an vorspeisen.",
        "the restaurant has a great selection of appetizers.",
    ),
    (
        "in der bäckerei gibt es frische croissants.",
        "there are fresh croissants available at the bakery.",
    ),
    (
        "die suppe ist gut gewürzt und heiß.",
        "the soup is well-seasoned and hot.",
    ),
    (
        "ich habe einen tisch für zwei personen reserviert.",
        "i've reserved a table for two people.",
    ),
    (
        "heute abend bestellen wir chinesisches essen.",
        "tonight, we're ordering chinese food.",
    ),
    (
        "kannst du mir bitte das salatdressing reichen?",
        "can you please pass me the salad dressing?",
    ),
    (
        "ich habe eine vorliebe für scharfe gerichte.",
        "i have a preference for spicy dishes.",
    ),
    (
        "das restaurant bietet vegetarische optionen an.",
        "the restaurant offers vegetarian options.",
    ),
    (
        "die kuchen in dieser bäckerei sind unwiderstehlich.",
        "the cakes at this bakery are irresistible.",
    ),
    (
        "wir planen eine grillparty am samstag.",
        "we're planning a barbecue party on saturday.",
    ),
]

# do some simple normalization and tokenization
def pre(s: str) -> list[str]:
    return s.replace(",", " ").replace(".", "").replace("?", "").lower().split()

# we also swap the tuples so e=english and f=foreign (as is customary for IBM models)
translations = [(pre(e), pre(d)) for d, e in translations]

from collections import defaultdict
t = defaultdict(lambda: defaultdict(lambda: 0.1))

for _ in range(100):
    count = defaultdict(lambda: defaultdict(float))
    total = defaultdict(float)

    all_words_e = set(word for sentence, _ in translations for word in sentence)
    all_words_f = set(word for _, sentence in translations for word in sentence)
    s_total = defaultdict(float)

    for e, f in translations:

        # calculate how much probability mass each english word will recieve
        for we in e:
            for wf in f:
                s_total[we] += t[we][wf]

        # collect
        for we in e:
            for wf in f:
                # give each english word its probability mass
                count[we][wf] += t[we][wf] / s_total[we]

                # keep track how much probability mass each foreign word gave away
                total[wf] += t[we][wf] / s_total[we]

    # estimate probabilities
    for wf in all_words_f:
        for we in all_words_e:
            # normalize t(e|f), so that \sum_f t(e|f) = 1 for all e, ie we get proper probability distributions.
            t[we][wf] = count[we][wf] / total[wf]

words_f = set()
words_e = set()

for e, f in translations:
    for we in e:
        words_e.add(we)
    for wf in f:
        words_f.add(wf)

for we in words_e:
    prob, wf = max((t[we][wf], wf) for wf in words_f)
    if prob < 0.9:
        # skip unconverged words
        continue
    print(f"{we} -> {wf} \t ({prob:.2f})")