def test_dataframe_to_array_all_categorical_with_missing_vals():
s_1 = pd.Series([-1, 0, 2, 1, float('NaN')])
s_2 = pd.Series(['one', 'two', 'three', 'four', float('NaN')])
df = pd.concat([s_1, s_2], axis=1)
dtypes = ['categorical'] * 2
metadata = dict()
valmaps = du.gen_valmaps(df, dtypes, metadata)
data = du.dataframe_to_array(df, valmaps)
assert data.shape == df.shape
assert 'float' in str(data.dtype)
assert data[0, 0] == 0
assert data[1, 0] == 1
assert data[2, 0] == 3
assert data[3, 0] == 2
assert np.isnan(data[4, 0])
assert data[0, 1] == 1
assert data[1, 1] == 3
assert data[2, 1] == 2
assert data[3, 1] == 0
assert np.isnan(data[4, 1])
def test_dataframe_to_array_all_categorical_with_missing_vals():
s_1 = pd.Series([-1, 0, 2, 1, float('NaN')])
s_2 = pd.Series(['one', 'two', 'three', 'four', float('NaN')])
df = pd.concat([s_1, s_2], axis=1)
dtypes = ['categorical']*2
metadata = dict()
valmaps = du.gen_valmaps(df, dtypes, metadata)
data = du.dataframe_to_array(df, valmaps)
assert data.shape == df.shape
assert 'float' in str(data.dtype)
assert data[0, 0] == 0
assert data[1, 0] == 1
assert data[2, 0] == 3
assert data[3, 0] == 2
assert np.isnan(data[4, 0])
assert data[0, 1] == 1
assert data[1, 1] == 3
assert data[2, 1] == 2
assert data[3, 1] == 0
assert np.isnan(data[4, 1])
def test_dataframe_to_array_all_continuous():
n_cols = 5
df = pd.DataFrame(np.random.rand(30, n_cols))
valmaps = dict()
data = du.dataframe_to_array(df, valmaps)
assert data.shape == df.shape
assert 'float' in str(data.dtype)
def test_dataframe_to_array_all_continuous():
n_cols = 5
df = pd.DataFrame(np.random.rand(30, n_cols))
valmaps = dict()
data = du.dataframe_to_array(df, valmaps)
assert data.shape == df.shape
assert 'float' in str(data.dtype)