launcher_func.py

import os
from sklearn.metrics import classification_report

from mlp import DumbModel, Dataset

def train_model(dataset_dir, model_file, vocab_size):
    print(f'Training model from directory {dataset_dir}')
    print(f'Vocabulary size: {vocab_size}')

    train_dir = os.path.join(dataset_dir, 'train')
    test_dir = os.path.join(dataset_dir, 'test')
    dset = Dataset(train_dir, test_dir)
    X, y = dset.get_train_set()

    model = DumbModel(vocab_size=vocab_size)
    model.train(X, y)

    print(f'Storing model to {model_file}')
    model.serialize(model_file)

    X_test, y_test = dset.get_test_set()
    y_pred = model.predict(X_test)

    print(classification_report(y_test, y_pred))

def ask_model(model_file, question):
    print(f'Asking model {model_file} about "{question}"')

    model = DumbModel.deserialize(model_file)

    y_pred = model.predict_proba([question])
    print(y_pred[0])