def test_label_encode_drop_one():
random.seed(0)
np.random.seed(0)
df = DataFrame()
# initialize data frame
df["cats"] = np.random.randint(7, size=10, dtype=np.int32)
vals = list(df["cats"].unique())
# drop 1 randomly
del vals[random.randrange(len(vals))]
lab = dict(zip(vals, list(range(len(vals)))))
# label encode series
ncol = df["cats"].label_encoding(cats=vals, dtype="float32")
arr = ncol.to_array()
# verify labels of new column
for i in range(arr.size):
# assuming -1 is used for missing value
np.testing.assert_equal(arr[i], lab.get(df.cats[i], -1))
# label encode data frame
df2 = df.label_encoding(column="cats",
prefix="cats",
cats=vals,
dtype="float32")
assert df2.columns[0] == "cats"
assert df2.columns[1] == "cats_labels"
def test_label_encode_float_output():
random.seed(0)
np.random.seed(0)
df = DataFrame()
# initialize data frame
df["cats"] = arr = np.random.randint(7, size=10, dtype=np.int32)
cats = [1, 2, 3, 4]
encoder = {c: i for i, c in enumerate(cats)}
df2 = df.label_encoding(
column="cats",
prefix="cats",
cats=cats,
dtype=np.float32,
na_sentinel=np.nan,
)
got = df2["cats_labels"].to_array(fillna="pandas")
handcoded = np.array([encoder.get(v, np.nan) for v in arr])
np.testing.assert_equal(got, handcoded)
def test_label_encode(nelem, dtype):
df = DataFrame()
np.random.seed(0)
# initialize data frame
df["cats"] = _random(nelem, dtype)
vals = df["cats"].unique()
lab = dict(zip(vals, range(len(vals))))
# label encode series
ncol = df["cats"].label_encoding(cats=vals)
arr = ncol.to_array()
# verify labels of new column
for i in range(arr.size):
np.testing.assert_equal(arr[i], lab.get(df.cats[i], None))
# label encode data frame
df2 = df.label_encoding(column="cats", prefix="cats", cats=vals)
assert df2.columns[0] == "cats"
assert df2.columns[1] == "cats_labels"
