train.py

"""Script to create the model artifact

Trains a simple logistic regression with grid search on a synthetic dataset and
stores the model in a pickle file.

"""

import joblib
from sklearn.datasets import make_classification
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV


SEED = 0
FILENAME = 'sklearn_model.joblib'


def get_data():
    X, y = make_classification(n_samples=2000, random_state=SEED)
    return X, y


def get_model(**kwargs):
    model = SGDClassifier(random_state=SEED)
    model.set_params(**kwargs)
    return model


def get_hparams():
    hparams = {
        'penalty': ['l1', 'l2'],
        'alpha': [0.00001, 0.0001, 0.001],
    }
    return hparams


def grid_search(model, X, y, hparams):
    search = GridSearchCV(model, hparams, cv=5, scoring='accuracy')
    search.fit(X, y)
    return search


def train(model, X, y, hparams):
    search = grid_search(model, X, y, hparams=hparams)
    print(f"Best accuracy: {100 * search.best_score_:.1f}%")
    print(f"Best parameters: {search.best_params_}")
    return search.best_estimator_


def save_model(model, filename):
    joblib.dump(model, filename)
    print(f"Stored model in '{filename}'")


def main():
    X, y = get_data()
    model = get_model()
    hparams = get_hparams()
    model_trained = train(model, X, y, hparams=hparams)
    save_model(model_trained, FILENAME)


if __name__ == '__main__':
    main()