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test_count.py
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"""Tests for the CountEncoder."""
from unittest import TestCase # or `from unittest import ...` if on Python 3.4+
import category_encoders as encoders
import numpy as np
import pandas as pd
X = pd.DataFrame(
{
'none': [
'A',
'A',
'B',
None,
None,
'C',
None,
'C',
None,
'B',
'A',
'A',
'C',
'B',
'B',
'A',
'A',
None,
'B',
None,
],
'na_categorical': [
'A',
'A',
'C',
'A',
'B',
'C',
'C',
'A',
np.nan,
'B',
'A',
'C',
'C',
'A',
'B',
'C',
np.nan,
'A',
np.nan,
np.nan,
],
}
)
X_t = pd.DataFrame(
{
'none': ['A', 'C', None, 'B', 'C', 'C', None, None, 'A', 'A', 'C', 'A', 'B', 'A', 'A'],
'na_categorical': [
'C',
'C',
'A',
'B',
'C',
'A',
np.nan,
'B',
'A',
'A',
'B',
np.nan,
'A',
np.nan,
'A',
],
}
)
class TestCountEncoder(TestCase):
"""Unit tests for the CountEncoder."""
def test_count_defaults(self):
"""Test the defaults are working as expected on 'none' and 'categorical'.
These are the most extreme edge cases for the count encoder.
"""
enc = encoders.CountEncoder(verbose=1)
enc.fit(X)
out = enc.transform(X_t)
self.assertTrue(pd.Series([5, 3, 6]).isin(out['none'].unique()).all())
self.assertTrue(out['none'].unique().shape == (3,))
self.assertTrue(out['none'].isna().sum() == 0)
self.assertTrue(pd.Series([6, 3]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (4,))
self.assertTrue(enc.mapping is not None)
def test_count_handle_missing_string(self):
"""Test the handle_missing string on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(handle_missing='return_nan')
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._handle_missing)
self.assertTrue(pd.Series([6, 5, 3]).isin(out['none']).all())
self.assertTrue(out['none'].unique().shape == (4,))
self.assertTrue(out['none'].isna().sum() == 3)
self.assertTrue(pd.Series([6, 7, 3]).isin(out['na_categorical']).all())
self.assertFalse(pd.Series([4]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (4,))
self.assertTrue(out['na_categorical'].isna().sum() == 3)
def test_count_handle_missing_dict(self):
"""Test the handle_missing dict on 'none' and 'na_categorical'.
We want to see differing behaviour between 'none' and 'na_cat' cols.
"""
enc = encoders.CountEncoder(handle_missing={'na_categorical': 'return_nan'})
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._handle_missing)
self.assertTrue(pd.Series([5, 3, 6]).isin(out['none']).all())
self.assertTrue(out['none'].unique().shape == (3,))
self.assertTrue(out['none'].isna().sum() == 0)
self.assertTrue(pd.Series([6, 7, 3]).isin(out['na_categorical']).all())
self.assertFalse(pd.Series([4]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (4,))
self.assertTrue(out['na_categorical'].isna().sum() == 3)
def test_count_handle_unknown_string(self):
"""Test the handle_unknown string on 'none' and 'na_categorical'.
The 'handle_missing' must be set to 'return_nan' in order to test
'handle_unkown' correctly.
"""
enc = encoders.CountEncoder(
handle_missing='return_nan',
handle_unknown='return_nan',
)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._handle_unknown)
self.assertTrue(pd.Series([6, 5, 3]).isin(out['none']).all())
self.assertTrue(out['none'].unique().shape == (4,))
self.assertTrue(out['none'].isna().sum() == 3)
self.assertTrue(pd.Series([3, 6, 7]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (4,))
self.assertTrue(out['na_categorical'].isna().sum() == 3)
def test_count_handle_unknown_dict(self):
"""Test the 'handle_unkown' dict with all non-default options."""
enc = encoders.CountEncoder(
handle_missing='return_nan',
handle_unknown={'none': -1, 'na_categorical': 'return_nan'},
)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._handle_unknown)
self.assertTrue(pd.Series([6, 5, 3, -1]).isin(out['none']).all())
self.assertTrue(out['none'].unique().shape == (4,))
self.assertTrue(out['none'].isna().sum() == 0)
self.assertTrue(pd.Series([3, 6, 7]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (4,))
self.assertTrue(out['na_categorical'].isna().sum() == 3)
def test_count_min_group_size_int(self):
"""Test the min_group_size int on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(min_group_size=7)
enc.fit(X)
out = enc.transform(X_t)
self.assertTrue(pd.Series([6, 5, 3]).isin(out['none']).all())
self.assertTrue(out['none'].unique().shape == (3,))
self.assertTrue(out['none'].isna().sum() == 0)
self.assertIn(np.nan, enc.mapping['none'])
self.assertTrue(pd.Series([13, 7]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (2,))
self.assertIn('B_C_nan', enc.mapping['na_categorical'])
self.assertFalse(np.nan in enc.mapping['na_categorical'])
def test_count_min_group_size_dict(self):
"""Test the min_group_size dict on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(min_group_size={'none': 6, 'na_categorical': 7})
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._min_group_size)
self.assertTrue(pd.Series([6, 8]).isin(out['none']).all())
self.assertEqual(out['none'].unique().shape[0], 2)
self.assertTrue(out['none'].isna().sum() == 0)
self.assertIn(np.nan, enc.mapping['none'])
self.assertTrue(pd.Series([13, 7]).isin(out['na_categorical']).all())
self.assertTrue(out['na_categorical'].unique().shape == (2,))
self.assertIn('B_C_nan', enc.mapping['na_categorical'])
self.assertFalse(np.nan in enc.mapping['na_categorical'])
def test_count_combine_min_nan_groups_bool(self):
"""Test the min_nan_groups_bool on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(min_group_size=7, combine_min_nan_groups=False)
enc.fit(X)
out = enc.transform(X_t)
self.assertTrue(pd.Series([6, 5, 3]).isin(out['none']).all())
self.assertEqual(out['none'].unique().shape[0], 3)
self.assertEqual(out['none'].isna().sum(), 0)
self.assertTrue(pd.Series([9, 7, 4]).isin(out['na_categorical']).all())
self.assertEqual(out['na_categorical'].unique().shape[0], 3)
self.assertTrue(enc.mapping is not None)
self.assertIn(np.nan, enc.mapping['na_categorical'])
def test_count_combine_min_nan_groups_dict(self):
"""Test the combine_min_nan_groups dict on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(
min_group_size={'none': 6, 'na_categorical': 7},
combine_min_nan_groups={'none': 'force', 'na_categorical': False},
)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._combine_min_nan_groups)
self.assertTrue(pd.Series([14, 6]).isin(out['none']).all())
self.assertEqual(out['none'].unique().shape[0], 2)
self.assertEqual(out['none'].isna().sum(), 0)
self.assertTrue(pd.Series([9, 7, 4]).isin(out['na_categorical']).all())
self.assertEqual(out['na_categorical'].unique().shape[0], 3)
self.assertTrue(enc.mapping is not None)
self.assertIn(np.nan, enc.mapping['na_categorical'])
def test_count_min_group_name_string(self):
"""Test the min_group_name string on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(min_group_size=6, min_group_name='dave')
enc.fit(X)
self.assertIn('dave', enc.mapping['none'])
self.assertEqual(enc.mapping['none']['dave'], 8)
self.assertIn('dave', enc.mapping['na_categorical'])
self.assertEqual(enc.mapping['na_categorical']['dave'], 7)
def test_count_min_group_name_dict(self):
"""Test the min_group_name dict on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(
min_group_size={'none': 6, 'na_categorical': 6},
min_group_name={'none': 'dave', 'na_categorical': None},
)
enc.fit(X)
self.assertIn('none', enc._min_group_name)
self.assertIn('dave', enc.mapping['none'])
self.assertEqual(enc.mapping['none']['dave'], 8)
self.assertIn('B_nan', enc.mapping['na_categorical'])
self.assertEqual(enc.mapping['na_categorical']['B_nan'], 7)
def test_count_normalize_bool(self):
"""Test the normalize bool on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(min_group_size=6, normalize=True)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._normalize)
self.assertTrue(out['none'].round(5).isin([0.3, 0.4]).all())
self.assertEqual(out['none'].unique().shape[0], 2)
self.assertEqual(out['none'].isna().sum(), 0)
self.assertTrue(pd.Series([0.3, 0.35]).isin(out['na_categorical']).all())
self.assertEqual(out['na_categorical'].unique().shape[0], 2)
self.assertTrue(enc.mapping is not None)
def test_count_normalize_dict(self):
"""Test the normalize dict on 'none' and 'na_categorical'."""
enc = encoders.CountEncoder(
min_group_size=7, normalize={'none': True, 'na_categorical': False}
)
enc.fit(X)
out = enc.transform(X_t)
self.assertIn('none', enc._normalize)
self.assertTrue(out['none'].round(5).isin([0.3, 0.15, 0.25]).all())
self.assertEqual(out['none'].unique().shape[0], 3)
self.assertEqual(out['none'].isna().sum(), 0)
self.assertTrue(pd.Series([13, 7]).isin(out['na_categorical']).all())
self.assertEqual(out['na_categorical'].unique().shape[0], 2)
self.assertTrue(enc.mapping is not None)