a71e59578a0ba78b81b40678ddd99e53edfc5fce,tests/tabular/test_tabular_supervised.py,,test_fit_predict_evalute_regression,#,56

Before Change

    assert score >= 0.0
    // test different model loading in predict
    clf.clf = None
    results = clf.predict(train_x)
    assert len(results) == len(train_x)
    clf.clf = None
    clf.save_filename = None
    results = clf.predict(train_x)

After Change

    clf = TabularRegressor(path=TEST_TEMP_DIR)
    nsample = 10000
    [ntime, nnum, ncat] = [3, 15, 3]
    datainfo = np.array(["TIME"] * ntime + ["NUM"] * nnum + ["CAT"] * ncat)
    x_num = np.random.random([nsample, nnum])
    x_time = np.random.random([nsample, ntime])
    x_cat = np.random.randint(0, 200, [nsample, ncat])
    train_x = np.concatenate([x_num, x_time, x_cat], axis=1)

Instances