def test_mixed_categorical_col_integer_string(self):
dataset = self.aws_dataset["localeabbr"].dropna()
profile = CategoricalColumn(dataset.name)
profile.update(dataset)
categories = {
'36', 'OR', 'IL', '41', '51', '13', '21', 'WA',
'11', 'CA', '37', 'TX', '10', 'SPE', '34', '32', '35',
'23', 'NM', 'NV', '33', '44', '22', 'GR', '15', 'MI',
'43', 'FL', 'TA', 'KY', 'SP', 'SE', 'AZ', '42', 'NJ',
'DC', '77', '50', 'NGR', '31', 'DIF', '61', '45',
'NY', 'MH', 'ALT', 'CH', 'NSW', 'MS', '81', 'GP',
'KU', '14', '53', '64', 'AP', '38', 'IRK', 'CL',
'TXG', 'LUA', 'ANT', 'PA', 'QC', 'RS', 'MO', 'C',
'MOW', 'ENG', 'ON', 'CE', 'TN', 'PI', 'VLG', 'DL',
'VL', 'GE', 'WP', 'GO', 'BS', 'KEM', 'MA', 'BEL',
'LB', 'CU', 'EC', 'PB', 'RIX', 'B', 'RJ', 'VA',
'7', 'SL', 'BE', '47', 'RM', 'BIH', 'SD', 'OH',
'PR', 'M', 'SN', 'COR', '63', 'E', 'BD', 'VI',
'SAM', 'BA', 'WY', '62', '4', 'PER', 'WKO', 'KYA',
'6', 'MN', 'SA', '8', 'CO', 'IS', 'RIS', 'FS',
'IN', 'LIV', 'IA', '24', 'VIC', '27', '16', 'PK',
'WB', 'NH', 'DAS', 'CT', 'CN', 'BIR', 'NVS', 'MG',
'3', 'PH', 'TO', '1', 'HE', 'VGG', 'BU', 'AB',
'NIZ', '92', '46', 'MZ', 'FR'
}
self.assertEqual(2120, profile.sample_size)
six.assertCountEqual(self, categories, profile.categories)
def test_true_categorical_report(self):
df_categorical = pd.Series([
"a",
"a",
"a",
"b",
"b",
"b",
"b",
"c",
"c",
"c",
"c",
"c",
])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
report = profile.profile
six.assertCountEqual(self, ['categorical', 'statistics', 'times'],
report)
self.assertTrue(report["categorical"])
six.assertCountEqual(self,
['unique_count', 'unique_ratio', 'categories'],
report['statistics'])
self.assertEqual(3, report["statistics"]["unique_count"])
self.assertEqual(0.25, report["statistics"]["unique_ratio"])
self.assertCountEqual(["a", "b", "c"],
report["statistics"]["categories"])
def test_correct_categorical_model_string(self):
dataset = self.aws_dataset["host"].dropna()
profile = CategoricalColumn(dataset.name)
profile.update(dataset)
self.assertEqual(1.0, profile.is_match)
self.assertEqual(2997, profile.sample_size)
categories = {
'groucho-oregon', 'groucho-us-east', 'groucho-singapore',
'groucho-tokyo', 'groucho-sa', 'zeppo-norcal', 'groucho-norcal',
'groucho-eu', 'groucho-sydney'
}
six.assertCountEqual(self, categories, profile.categories)
def test_false_categorical_report(self):
df_non_categorical = pd.Series(list(map(str, range(0, 20))))
profile = CategoricalColumn(df_non_categorical.name)
profile.update(df_non_categorical)
report = profile.profile
six.assertCountEqual(self, ['categorical', 'statistics', 'times'],
report)
self.assertFalse(report["categorical"])
six.assertCountEqual(self, ['unique_count', 'unique_ratio'],
report['statistics'])
self.assertEqual(20, report["statistics"]["unique_count"])
self.assertEqual(1.0, report["statistics"]["unique_ratio"])
def test_false_categorical_report(self):
df_non_categorical = pd.Series(list(map(str, range(0, 20))))
profile = CategoricalColumn(df_non_categorical.name)
profile.update(df_non_categorical)
report = profile.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=False,
statistics=dict([
('unique_count', 20),
('unique_ratio', 1),
]),
)
self.assertEqual(report, expected_profile)
def test_true_categorical_report(self):
df_categorical = pd.Series([
"a",
"a",
"a",
"b",
"b",
"b",
"b",
"c",
"c",
"c",
"c",
"c",
])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
report = profile.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=True,
statistics=dict([
("unique_count", 3),
("unique_ratio", 0.25),
("categories", ["a", "b", "c"]),
("categorical_count", {
"a": 3,
"b": 4,
"c": 5
}),
("gini_impurity", (27 / 144) + (32 / 144) + (35 / 144)),
("unalikeability", 2 * (12 + 15 + 20) / 132),
]),
)
# We have to pop these values because sometimes the order changes
self.assertCountEqual(
expected_profile["statistics"].pop("categories"),
report["statistics"].pop("categories"),
)
self.assertCountEqual(
expected_profile["statistics"].pop("categorical_count"),
report["statistics"].pop("categorical_count"),
)
self.assertEqual(report, expected_profile)
def test_categorical_column_with_wrong_options(self):
with self.assertRaisesRegex(
ValueError,
"CategoricalColumn parameter 'options' must"
" be of type CategoricalOptions.",
):
profiler = CategoricalColumn("Categorical",
options="wrong_data_type")
def test_report(self):
df_non_categorical = pd.Series(list(map(str, range(0, 20))))
profile = CategoricalColumn(df_non_categorical.name)
profile.update(df_non_categorical)
report1 = profile.profile
report2 = profile.report(remove_disabled_flag=False)
report3 = profile.report(remove_disabled_flag=True)
self.assertDictEqual(report1, report2)
self.assertDictEqual(report1, report3)
def test_top_k_categories_change(self):
# Test if top_k_categories is None
options = CategoricalOptions()
df_series = pd.Series(
["a", "a", "b", "c", "d", "e", "e", "e", "f", "g"])
profile = CategoricalColumn(df_series.name, options)
profile.update(df_series)
self.assertEqual(
len(profile.profile['statistics']['categorical_count']), 7)
# Test if top_k_categories is less than the count of categories
profile._top_k_categories = 6
self.assertEqual(
len(profile.profile['statistics']['categorical_count']), 6)
# Test if top_k_categories is greater than the count of categories
options.top_k_categories = 6
df_series = pd.Series(["a", "a", "b", "c", "d"])
profile = CategoricalColumn(df_series.name, options)
profile.update(df_series)
self.assertEqual(
len(profile.profile['statistics']['categorical_count']), 4)
def test_categorical_merge(self):
df1 = pd.Series([
"abcd", "aa", "abcd", "aa", "b", "4", "3", "2", "dfd", "2", np.nan
])
df2 = pd.Series([
"1", "null", "ee", "NaN", "ff", "nan", "gg", "None", "aa", "b",
"ee"
])
# Expected is based off insertion order
expected_categories = [
'abcd', 'aa', 'b', '4', '3', '2', 'dfd', np.nan, '1', 'null', 'ee',
'NaN', 'ff', 'nan', 'gg', 'None'
]
profile = CategoricalColumn("Name")
profile.update(df1)
profile2 = CategoricalColumn("Name")
profile2.update(df2)
# Add profiles
profile3 = profile + profile2
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size,
profile.sample_size + profile2.sample_size)
self.assertEqual(profile3.is_match, False)
# Add again
profile3 = profile + profile3
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size, 33)
# Check is_match and unique_ratio if the sample size was small
self.assertEqual(profile3.is_match, False)
self.assertEqual(profile3.unique_ratio, 16 / 33)
# Check is_match and unique_ratio if the sample size was large
profile3.sample_size = 1000
self.assertEqual(profile3.is_match, True)
self.assertEqual(profile3.unique_ratio, 16 / 1000)
def test_timeit_profile(self):
dataset = self.aws_dataset["host"].dropna()
profile = CategoricalColumn(dataset.name)
time_array = [float(x) for x in range(17, 0, -1)]
with patch('time.time', side_effect=lambda: time_array.pop()):
# Validate the time in the column class is empty.
self.assertEqual(defaultdict(float), profile.profile['times'])
# Validate the time in the column class has the expected time.
profile.update(dataset)
expected = defaultdict(float, {'categories': 1.0})
self.assertEqual(expected, profile.profile['times'])
# Validate expected time after second update to profile
profile.update(dataset)
expected = defaultdict(float, {'categories': 2.0})
self.assertEqual(expected, profile.profile['times'])
def test_categorical_merge(self):
df1 = pd.Series([
"abcd", "aa", "abcd", "aa", "b", "4", "3", "2", "dfd", "2", np.nan
])
df2 = pd.Series([
"1", "null", "ee", "NaN", "ff", "nan", "gg", "None", "aa", "b",
"ee"
])
# Expected is based off insertion order
expected_categories = [
"abcd",
"aa",
"b",
"4",
"3",
"2",
"dfd",
np.nan,
"1",
"null",
"ee",
"NaN",
"ff",
"nan",
"gg",
"None",
]
profile = CategoricalColumn("Name")
profile.update(df1)
expected_dict = {
"abcd": 2,
"aa": 2,
"b": 1,
"4": 1,
"3": 1,
"2": 2,
"dfd": 1,
np.nan: 1,
}
self.assertDictEqual(expected_dict, profile._categories)
profile2 = CategoricalColumn("Name")
profile2.update(df2)
# Add profiles
profile3 = profile + profile2
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size,
profile.sample_size + profile2.sample_size)
self.assertEqual(profile3.is_match, False)
expected_dict = {
"abcd": 2,
"aa": 3,
"b": 2,
"4": 1,
"3": 1,
"2": 2,
np.nan: 1,
"dfd": 1,
"1": 1,
"ee": 2,
"ff": 1,
"gg": 1,
"NaN": 1,
"None": 1,
"nan": 1,
"null": 1,
}
self.assertDictEqual(expected_dict, profile3._categories)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=False,
statistics=dict([("unique_count", 16), ("unique_ratio", 16 / 22)]),
)
self.assertEqual(report, expected_profile)
# Add again
profile3 = profile + profile3
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size, 33)
# Check is_match and unique_ratio if the sample size was small
self.assertEqual(profile3.is_match, False)
self.assertEqual(profile3.unique_ratio, 16 / 33)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=False,
statistics=dict([
("unique_count", 16),
("unique_ratio", 16 / 33),
]),
)
self.assertEqual(report, expected_profile)
# Check is_match and unique_ratio if the sample size was large
profile3.sample_size = 1000
self.assertEqual(profile3.is_match, True)
self.assertEqual(profile3.unique_ratio, 16 / 1000)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
report_categories = report["statistics"].pop("categories")
report_count = report["statistics"].pop("categorical_count")
report_gini = report["statistics"].pop("gini_impurity")
expected_profile = dict(
categorical=True,
statistics=dict([
("unique_count", 16),
("unique_ratio", 16 / 1000),
("unalikeability", 32907 / (1000000 - 1000)),
]),
)
expected_gini = ((1 * ((5 / 1000) * (995 / 1000))) +
(2 * ((4 / 1000) * (996 / 1000))) +
(1 * ((3 / 1000) * (997 / 1000))) +
(5 * ((2 / 1000) * (998 / 1000))) +
(7 * ((1 / 1000) * (999 / 1000))))
self.assertAlmostEqual(report_gini, expected_gini)
self.assertEqual(report, expected_profile)
self.assertCountEqual(
report_categories,
[
"abcd",
"aa",
"2",
np.nan,
"4",
"b",
"3",
"dfd",
"ee",
"ff",
"nan",
"None",
"1",
"gg",
"null",
"NaN",
],
)
expected_dict = {
"aa": 5,
"2": 4,
"abcd": 4,
"b": 3,
np.nan: 2,
"dfd": 2,
"3": 2,
"4": 2,
"ee": 2,
"null": 1,
"ff": 1,
"NaN": 1,
"1": 1,
"nan": 1,
"gg": 1,
"None": 1,
}
self.assertCountEqual(report_count, expected_dict)
def test_gini_impurity(self):
# Normal test
df_categorical = pd.Series(["y", "y", "y", "y", "n", "n", "n"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
expected_val = ((4 / 7) * (3 / 7)) + ((4 / 7) * (3 / 7))
self.assertAlmostEqual(profile.gini_impurity, expected_val)
# One class only test
df_categorical = pd.Series(["y", "y", "y", "y", "y", "y", "y"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
expected_val = 0
self.assertEqual(profile.gini_impurity, expected_val)
# Empty test
df_categorical = pd.Series([])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(profile.gini_impurity, None)
def test_categorical_diff(self):
df_categorical = pd.Series(["y", "y", "y", "y", "n", "n", "n"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
df_categorical = pd.Series(["y", "maybe", "y", "y", "n", "n", "maybe"])
profile2 = CategoricalColumn(df_categorical.name)
profile2.update(df_categorical)
# chi2-statistic = sum((observed-expected)^2/expected for each category in each column)
# df = categories - 1
# p-value found through using chi2 CDF
expected_diff = {
"categorical": "unchanged",
"statistics": {
"unique_count": -1,
"unique_ratio": -0.14285714285714285,
"categories": [[], ["y", "n"], ["maybe"]],
"gini_impurity": -0.16326530612244894,
"unalikeability": -0.19047619047619047,
"categorical_count": {
"y": 1,
"n": 1,
"maybe": [None, 2]
},
},
"chi2-test": {
"chi2-statistic": 82 / 35,
"df": 2,
"p-value": 0.3099238764710244,
},
}
self.assertDictEqual(expected_diff, profile.diff(profile2))
# Test with one categorical column matching
df_not_categorical = pd.Series([
"THIS",
"is",
"not",
"a",
"categorical",
"column",
"for",
"testing",
"purposes",
"Bada",
"Bing",
"Badaboom",
])
profile2 = CategoricalColumn(df_not_categorical.name)
profile2.update(df_not_categorical)
expected_diff = {
"categorical": [True, False],
"statistics": {
"unique_count": -10,
"unique_ratio": -0.7142857142857143
},
}
self.assertDictEqual(expected_diff, profile.diff(profile2))
def test_mixed_categorical_col_integer_string(self):
dataset = self.aws_dataset["localeabbr"].dropna()
profile = CategoricalColumn(dataset.name)
profile.update(dataset)
categories = {
"36",
"OR",
"IL",
"41",
"51",
"13",
"21",
"WA",
"11",
"CA",
"37",
"TX",
"10",
"SPE",
"34",
"32",
"35",
"23",
"NM",
"NV",
"33",
"44",
"22",
"GR",
"15",
"MI",
"43",
"FL",
"TA",
"KY",
"SP",
"SE",
"AZ",
"42",
"NJ",
"DC",
"77",
"50",
"NGR",
"31",
"DIF",
"61",
"45",
"NY",
"MH",
"ALT",
"CH",
"NSW",
"MS",
"81",
"GP",
"KU",
"14",
"53",
"64",
"AP",
"38",
"IRK",
"CL",
"TXG",
"LUA",
"ANT",
"PA",
"QC",
"RS",
"MO",
"C",
"MOW",
"ENG",
"ON",
"CE",
"TN",
"PI",
"VLG",
"DL",
"VL",
"GE",
"WP",
"GO",
"BS",
"KEM",
"MA",
"BEL",
"LB",
"CU",
"EC",
"PB",
"RIX",
"B",
"RJ",
"VA",
"7",
"SL",
"BE",
"47",
"RM",
"BIH",
"SD",
"OH",
"PR",
"M",
"SN",
"COR",
"63",
"E",
"BD",
"VI",
"SAM",
"BA",
"WY",
"62",
"4",
"PER",
"WKO",
"KYA",
"6",
"MN",
"SA",
"8",
"CO",
"IS",
"RIS",
"FS",
"IN",
"LIV",
"IA",
"24",
"VIC",
"27",
"16",
"PK",
"WB",
"NH",
"DAS",
"CT",
"CN",
"BIR",
"NVS",
"MG",
"3",
"PH",
"TO",
"1",
"HE",
"VGG",
"BU",
"AB",
"NIZ",
"92",
"46",
"MZ",
"FR",
}
self.assertEqual(2120, profile.sample_size)
six.assertCountEqual(self, categories, profile.categories)
def test_categorical_merge(self):
df1 = pd.Series([
"abcd", "aa", "abcd", "aa", "b", "4", "3", "2", "dfd", "2", np.nan
])
df2 = pd.Series([
"1", "null", "ee", "NaN", "ff", "nan", "gg", "None", "aa", "b",
"ee"
])
# Expected is based off insertion order
expected_categories = [
'abcd', 'aa', 'b', '4', '3', '2', 'dfd', np.nan, '1', 'null', 'ee',
'NaN', 'ff', 'nan', 'gg', 'None'
]
profile = CategoricalColumn("Name")
profile.update(df1)
expected_dict = {
"abcd": 2,
"aa": 2,
"b": 1,
"4": 1,
"3": 1,
"2": 2,
"dfd": 1,
np.nan: 1
}
self.assertDictEqual(expected_dict, profile._categories)
profile2 = CategoricalColumn("Name")
profile2.update(df2)
# Add profiles
profile3 = profile + profile2
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size,
profile.sample_size + profile2.sample_size)
self.assertEqual(profile3.is_match, False)
expected_dict = {
"abcd": 2,
"aa": 3,
"b": 2,
"4": 1,
"3": 1,
"2": 2,
np.nan: 1,
"dfd": 1,
"1": 1,
"ee": 2,
"ff": 1,
"gg": 1,
"NaN": 1,
"None": 1,
"nan": 1,
"null": 1
}
self.assertDictEqual(expected_dict, profile3._categories)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=False,
statistics=dict([('unique_count', 16), ('unique_ratio', 16 / 22)]),
)
self.assertEqual(report, expected_profile)
# Add again
profile3 = profile + profile3
self.assertCountEqual(expected_categories, profile3.categories)
self.assertEqual(profile3.sample_size, 33)
# Check is_match and unique_ratio if the sample size was small
self.assertEqual(profile3.is_match, False)
self.assertEqual(profile3.unique_ratio, 16 / 33)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
expected_profile = dict(
categorical=False,
statistics=dict([
('unique_count', 16),
('unique_ratio', 16 / 33),
]),
)
self.assertEqual(report, expected_profile)
# Check is_match and unique_ratio if the sample size was large
profile3.sample_size = 1000
self.assertEqual(profile3.is_match, True)
self.assertEqual(profile3.unique_ratio, 16 / 1000)
report = profile3.profile
self.assertIsNotNone(report.pop("times", None))
report_categories = report['statistics'].pop('categories')
report_count = report['statistics'].pop('categorical_count')
report_gini = report['statistics'].pop('gini_impurity')
expected_profile = dict(categorical=True,
statistics=dict([('unique_count', 16),
('unique_ratio', 16 / 1000),
('unalikeability',
32907 / (1000000 - 1000))]))
expected_gini = (1*((5/1000) * (995/1000))) + \
(2*((4/1000) * (996/1000))) + \
(1*((3/1000) * (997/1000))) + \
(5*((2/1000) * (998/1000))) + \
(7*((1/1000) * (999/1000)))
self.assertAlmostEqual(report_gini, expected_gini)
self.assertEqual(report, expected_profile)
self.assertCountEqual(report_categories, [
'abcd', 'aa', '2', np.nan, '4', 'b', '3', 'dfd', 'ee', 'ff', 'nan',
'None', '1', 'gg', 'null', 'NaN'
])
expected_dict = {
'aa': 5,
'2': 4,
'abcd': 4,
'b': 3,
np.nan: 2,
'dfd': 2,
'3': 2,
'4': 2,
'ee': 2,
'null': 1,
'ff': 1,
'NaN': 1,
'1': 1,
'nan': 1,
'gg': 1,
'None': 1
}
self.assertCountEqual(report_count, expected_dict)
def test_categorical_diff(self):
df_categorical = pd.Series(["y", "y", "y", "y", "n", "n", "n"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
df_categorical = pd.Series(["y", "maybe", "y", "y", "n", "n", "maybe"])
profile2 = CategoricalColumn(df_categorical.name)
profile2.update(df_categorical)
# chi2-statistic = sum((observed-expected)^2/expected for each category in each column)
# df = categories - 1
# p-value found through using chi2 CDF
expected_diff = {
'categorical': 'unchanged',
'statistics': {
'unique_count': -1,
'unique_ratio': -0.14285714285714285,
'categories': [[], ['y', 'n'], ['maybe']],
'gini_impurity': -0.16326530612244894,
'unalikeability': -0.19047619047619047,
'categorical_count': {
'y': 1,
'n': 1,
'maybe': [None, 2]
}
},
'chi2-test': {
'chi2-statistic': 82 / 35,
'df': 2,
'p-value': 0.3099238764710244
}
}
self.assertDictEqual(expected_diff, profile.diff(profile2))
# Test with one categorical column matching
df_not_categorical = pd.Series([
"THIS", "is", "not", "a", "categorical", "column", "for",
"testing", "purposes", "Bada", "Bing", "Badaboom"
])
profile2 = CategoricalColumn(df_not_categorical.name)
profile2.update(df_not_categorical)
expected_diff = {
'categorical': [True, False],
'statistics': {
'unique_count': -10,
'unique_ratio': -0.7142857142857143
}
}
self.assertDictEqual(expected_diff, profile.diff(profile2))
def test_unalikeability(self):
df_categorical = pd.Series(["a", "a"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(profile.unalikeability, 0)
df_categorical = pd.Series(["a", "c", "b"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(profile.unalikeability, 1)
df_categorical = pd.Series(["a", "a", "a", "b", "b", "b"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(profile.unalikeability, 18 / 30)
df_categorical = pd.Series(
["a", "a", "b", "b", "b", "a", "c", "c", "a", "a"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(profile.unalikeability, 2 * (10 + 15 + 6) / 90)
df_categorical = pd.Series(["a"])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(0, profile.unalikeability)
df_categorical = pd.Series([])
profile = CategoricalColumn(df_categorical.name)
profile.update(df_categorical)
self.assertEqual(None, profile.unalikeability)
