ccurtis7 / diff_classifier

    # assert len(xy[1]) == 250

def test_feature_plot_3D():

    np.random.seed(seed=1)

    dataset = {'label': 250*['yes'] + 250*['no'],

               0: np.random.normal(0.5, 1, size=500),

               1: np.random.normal(1, 2, size=500),

               2: np.random.normal(3, 10, size=500)

               }

    df = pd.DataFrame(data=dataset)

    xy = pca.feature_plot_3D(df, label='label', features=[0, 1, 2],

                             lvals=['yes', 'no'], randsel=True,

                             fname='test1.png')

    # assert len(xy[1]) == 200

    # assert os.path.isfile('test1.png')

    #

    assert np.round(np.mean(to_violin['Feature Value']), 1) == 2.1

def test_feature_plot_2D():

    np.random.seed(seed=1)

    dataset = {'label': 250*['yes'] + 250*['no'],

               0: np.random.normal(0.5, 1, size=500),

               1: np.random.normal(1, 2, size=500),

               2: np.random.normal(3, 10, size=500)

               }

    df = pd.DataFrame(data=dataset)

    xy = pca.feature_plot_2D(df, label='label', features=[0, 1], randsel=True,

                             lvals=['yes', 'no'], fname='test1.png')

    # assert len(xy[1]) == 200

    # assert os.path.isfile('test1.png')

    #