def test_onehot_generic_index():
np.random.seed(0)
size = 33
indices = np.random.randint(low=0, high=100, size=size)
df = DataFrame()
values = np.random.randint(low=0, high=4, size=size)
df["fo"] = Series(values, index=Index(indices))
out = df.one_hot_encoding(
"fo", cats=df.fo.unique(), prefix="fo", dtype=np.int32
)
assert set(out.columns) == {"fo", "fo_0", "fo_1", "fo_2", "fo_3"}
np.testing.assert_array_equal(values == 0, out.fo_0.to_array())
np.testing.assert_array_equal(values == 1, out.fo_1.to_array())
np.testing.assert_array_equal(values == 2, out.fo_2.to_array())
np.testing.assert_array_equal(values == 3, out.fo_3.to_array())
def test_onehot_random():
df = DataFrame()
low = 10
high = 17
size = 10
df["src"] = src = np.random.randint(low=low, high=high, size=size)
df2 = df.one_hot_encoding(
column="src", prefix="out_", cats=tuple(range(10, 17))
)
mat = df2.as_matrix(columns=df2.columns[1:])
for val in range(low, high):
colidx = val - low
arr = mat[:, colidx]
mask = src == val
np.testing.assert_equal(arr, mask)
def test_onehot_simple():
np.random.seed(0)
df = DataFrame()
# Populate with data [0, 10)
df["vals"] = np.arange(10, dtype=np.int32)
# One Hot (Series)
for i, col in enumerate(df["vals"].one_hot_encoding(list(range(10)))):
arr = col.to_array()
# Verify 1 in the right position
np.testing.assert_equal(arr[i], 1)
# Every other slots are 0s
np.testing.assert_equal(arr[:i], 0)
np.testing.assert_equal(arr[i + 1 :], 0)
# One Hot (DataFrame)
df2 = df.one_hot_encoding(
column="vals", prefix="vals", cats=list(range(10))
)
assert df2.columns[0] == "vals"
for i in range(1, len(df2.columns)):
assert df2.columns[i] == "vals_%s" % (i - 1)
got = df2.as_matrix(columns=df2.columns[1:])
expect = np.identity(got.shape[0])
np.testing.assert_equal(got, expect)
def test_onehot_masked():
np.random.seed(0)
high = 5
size = 100
arr = np.random.randint(low=0, high=high, size=size)
bitmask = utils.random_bitmask(size)
bytemask = np.asarray(
utils.expand_bits_to_bytes(bitmask)[:size], dtype=np.bool_
)
arr[~bytemask] = -1
df = DataFrame()
df["a"] = Series(arr).set_mask(bitmask)
out = df.one_hot_encoding(
"a", cats=list(range(high)), prefix="a", dtype=np.int32
)
assert tuple(out.columns) == ("a", "a_0", "a_1", "a_2", "a_3", "a_4")
np.testing.assert_array_equal((out["a_0"] == 1).to_array(), arr == 0)
np.testing.assert_array_equal((out["a_1"] == 1).to_array(), arr == 1)
np.testing.assert_array_equal((out["a_2"] == 1).to_array(), arr == 2)
np.testing.assert_array_equal((out["a_3"] == 1).to_array(), arr == 3)
np.testing.assert_array_equal((out["a_4"] == 1).to_array(), arr == 4)
