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()
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'
