def train_with_reg_cv()

in utils.py [0:0]

• 17 lines of code
• 4 McCabe index (conditional complexity)

def train_with_reg_cv(trX, trY, vaX, vaY, teX=None, teY=None, penalty='l1',
        C=2**np.arange(-8, 1).astype(np.float), seed=42):
    scores = []
    for i, c in enumerate(C):
        model = LogisticRegression(C=c, penalty=penalty, random_state=seed+i)
        model.fit(trX, trY)
        score = model.score(vaX, vaY)
        scores.append(score)
    c = C[np.argmax(scores)]
    model = LogisticRegression(C=c, penalty=penalty, random_state=seed+len(C))
    model.fit(trX, trY)
    nnotzero = np.sum(model.coef_ != 0)
    if teX is not None and teY is not None:
        score = model.score(teX, teY)*100.
    else:
        score = model.score(vaX, vaY)*100.
    return score, c, nnotzero