def test_correct_rows_ingested(self):
test_dict = {
'1': ['nan', 'null', None, None, ''],
1: ['nan', 'None', 'null', None, ''],
}
test_dataset = pd.DataFrame(data=test_dict)
profiler_options = ProfilerOptions()
profiler_options.set({'data_labeler.is_enabled': False})
trained_schema = dp.Profiler(test_dataset,
len(test_dataset),
profiler_options=profiler_options)
self.assertCountEqual(['', 'nan', 'None', 'null'],
trained_schema.profile['1'].null_types)
self.assertEqual(5, trained_schema.profile['1'].null_count)
self.assertEqual({
'': {4},
'nan': {0},
'None': {2, 3},
'null': {1}
}, trained_schema.profile['1'].null_types_index)
self.assertCountEqual(['', 'nan', 'None', 'null'],
trained_schema.profile[1].null_types)
self.assertEqual(5, trained_schema.profile[1].null_count)
self.assertEqual({
'': {4},
'nan': {0},
'None': {1, 3},
'null': {2}
}, trained_schema.profile[1].null_types_index)
def test_correct_total_sample_size_and_counts_and_mutability(self):
data = [['test1', 1.0], ['test2', 2.0], ['test3', 3.0], [None, None],
['test5', 5.0], ['test6', 6.0], [None, None], ['test7', 7.0]]
data = pd.DataFrame(data, columns=['NAME', 'VALUE'])
profiler_options = ProfilerOptions()
profiler_options.set({'data_labeler.is_enabled': False})
col_one_len = len(data['NAME'])
col_two_len = len(data['VALUE'])
# Test reloading data, ensuring immutable
for i in range(2):
# Profile Once
data.index = pd.RangeIndex(0, 8)
profile = dp.Profiler(data,
profiler_options=profiler_options,
samples_per_update=2)
# Profile Twice
data.index = pd.RangeIndex(8, 16)
profile.update_profile(data)
# rows sampled are [5, 6], [13, 14] (0 index)
self.assertEqual(16, profile.total_samples)
self.assertEqual(4, profile._max_col_samples_used)
self.assertEqual(2, profile.row_has_null_count)
self.assertEqual(0.5, profile._get_row_has_null_ratio())
self.assertEqual(2, profile.row_is_null_count)
self.assertEqual(0.5, profile._get_row_is_null_ratio())
self.assertEqual(0.4375, profile._get_unique_row_ratio())
self.assertEqual(9, profile._get_duplicate_row_count())
self.assertEqual(col_one_len, len(data['NAME']))
self.assertEqual(col_two_len, len(data['VALUE']))
def test_sample_size_passed_to_profile(self, *mocks):
update_mock = mocks[0]
# data setup
data = pd.DataFrame([0] * int(50e3))
# option setup
profiler_options = ProfilerOptions()
profiler_options.structured_options.multiprocess.is_enabled = False
profiler_options.set({'data_labeler.is_enabled': False})
# test data size < min_sample_size = 5000 by default
profiler = dp.Profiler(data[:1000], profiler_options=profiler_options)
profiler._min_sample_size = 5000
profiler._sampling_ratio = 0.2
self.assertEqual(1000, update_mock.call_args[0][1])
# test data size * 0.20 < min_sample_size < data size
profiler = dp.Profiler(data[:10000], profiler_options=profiler_options)
profiler._min_sample_size = 5000
profiler._sampling_ratio = 0.2
self.assertEqual(5000, update_mock.call_args[0][1])
# test min_sample_size > data size * 0.20
profiler = dp.Profiler(data, profiler_options=profiler_options)
profiler._min_sample_size = 5000
profiler._sampling_ratio = 0.2
self.assertEqual(10000, update_mock.call_args[0][1])
def setUpClass(cls):
cls.input_file_path = os.path.join(test_root_path, 'data',
'csv/aws_honeypot_marx_geo.csv')
cls.aws_dataset = pd.read_csv(cls.input_file_path)
profiler_options = ProfilerOptions()
profiler_options.set({'data_labeler.is_enabled': False})
cls.trained_schema = dp.Profiler(cls.aws_dataset,
len(cls.aws_dataset),
profiler_options=profiler_options)
def test_null_calculation_with_differently_sampled_cols(self):
opts = ProfilerOptions()
opts.structured_options.multiprocess.is_enabled = False
data = pd.DataFrame({"full": [1, 2, 3, 4, 5, 6, 7, 8, 9],
"sparse": [1, None, 3, None, 5, None, 7, None, 9]})
profile = dp.Profiler(data, samples_per_update=5, min_true_samples=5,
profiler_options=opts)
# Rows 2, 4, 5, 6, 7 are sampled in first column
# Therefore only those rows should be considered for null calculations
# The only null in those rows in second column in that subset are 5, 7
# Therefore only 2 rows have null according to row_has_null_count
self.assertEqual(0, profile.row_is_null_count)
self.assertEqual(2, profile.row_has_null_count)
# Accordingly, make sure ratio of null rows accounts for the fact that
# Only 5 total rows were sampled (5 in col 1, 9 in col 2)
self.assertEqual(0, profile._get_row_is_null_ratio())
self.assertEqual(0.4, profile._get_row_has_null_ratio())
data2 = pd.DataFrame(
{"sparse": [1, None, 3, None, 5, None, 7, None],
"sparser": [1, None, None, None, None, None, None, 8]})
profile2 = dp.Profiler(data2, samples_per_update=2, min_true_samples=2,
profiler_options=opts)
# Rows are sampled as follows: [6, 5], [1, 4], [2, 3], [0, 7]
# First column gets min true samples from ids 1, 4, 5, 6
# Second column gets completely sampled (has a null in 1, 4, 5, 6)
# rows 1 and 5 are completely null, 4 and 6 only null in col 2
self.assertEqual(2, profile2.row_is_null_count)
self.assertEqual(4, profile2.row_has_null_count)
# Only 4 total rows sampled, ratio accordingly
self.assertEqual(0.5, profile2._get_row_is_null_ratio())
self.assertEqual(1, profile2._get_row_has_null_ratio())
def test_correct_null_row_counts(self):
file_path = os.path.join(test_root_path, 'data', 'csv/empty_rows.txt')
data = pd.read_csv(file_path)
profiler_options = ProfilerOptions()
profiler_options.set({'data_labeler.is_enabled': False})
profile = dp.Profiler(data, profiler_options=profiler_options)
self.assertEqual(2, profile.row_has_null_count)
self.assertEqual(0.25, profile._get_row_has_null_ratio())
self.assertEqual(2, profile.row_is_null_count)
self.assertEqual(0.25, profile._get_row_is_null_ratio())
file_path = os.path.join(test_root_path, 'data','csv/iris-with-null-rows.csv')
data = pd.read_csv(file_path)
profile = dp.Profiler(data, profiler_options=profiler_options)
self.assertEqual(13, profile.row_has_null_count)
self.assertEqual(13/24, profile._get_row_has_null_ratio())
self.assertEqual(3, profile.row_is_null_count)
self.assertEqual(3/24, profile._get_row_is_null_ratio())
def test_validate(self):
# Valid cases should return None while invalid cases
# should return or throw a list of errors
option = self.get_options()
optpth = self.get_options_path()
# Default Configuration Is Valid
self.assertEqual(None, option.validate())
# Option is_enabled is not a boolean
for key in self.keys:
option.set({"{}.text.is_enabled".format(key): "Hello World"})
expected_error = [
"{}.{}.text.is_enabled must be a "
"Boolean.".format(optpth, key) for key in self.keys
]
expected_error = set(expected_error)
# Verify expected errors are a subset of all errors
with self.assertRaises(ValueError) as cm:
option.validate(raise_error=True)
raised_error = set(str(cm.exception).split("\n"))
self.assertSetEqual(expected_error,
expected_error.intersection(raised_error))
self.assertSetEqual(
expected_error,
expected_error.intersection(set(
option.validate(raise_error=False))),
)
# Wrong Class Type
option = self.get_options()
option.structured_options = ProfilerOptions()
option.unstructured_options = ProfilerOptions()
expected_error = [
"{}.structured_options must be a StructuredOptions.".format(
optpth),
"{}.unstructured_options must be an UnstructuredOptions.".format(
optpth),
]
with self.assertRaisesRegex(ValueError, "\n".join(expected_error)):
option.validate()
self.assertListEqual(expected_error,
option.validate(raise_error=False))
def test_null_in_file(self):
filename_null_in_file = os.path.join(
test_root_path, 'data', 'csv/sparse-first-and-last-column.txt')
profiler_options = ProfilerOptions()
profiler_options.set({'data_labeler.is_enabled': False})
data = dp.Data(filename_null_in_file)
profile = dp.Profiler(data, profiler_options=profiler_options)
report = profile.report(report_options={"output_format":"pretty"})
self.assertEqual(
report['data_stats']['COUNT']['statistics']['null_types_index'],
{'': '[2, 3, 4, 5, 7, 8]'}
)
self.assertEqual(
report['data_stats'][' NUMBERS']['statistics']['null_types_index'],
{'': '[5, 6, 8]', ' ': '[2, 4]'}
)
def test_validate_helper(self):
# Valid cases should return [] while invalid cases
# should return a list of errors
option = self.get_options()
optpth = self.get_options_path()
# Default Configuration Is Valid
self.assertEqual([], option._validate_helper())
# Variable Path Is Not A String
expected_error = "The variable path must be a string."
with self.assertRaisesRegex(ValueError, expected_error):
option._validate_helper(1)
# Option is_enabled is not a boolean
for key in self.keys:
option.set({'{}.text.is_enabled'.format(key): "Hello World"})
expected_error = [
'{}.{}.text.is_enabled must be a '
'Boolean.'.format(optpth, key) for key in self.keys
]
expected_error = set(expected_error)
# Verify expected errors are a subset of all errors
self.assertSetEqual(
expected_error,
expected_error.intersection(set(option._validate_helper())))
# Wrong Class Type
option = self.get_options()
option.structured_options = ProfilerOptions()
option.unstructured_options = ProfilerOptions()
expected_error = [
'{}.structured_options must be a StructuredOptions.'.format(
optpth),
'{}.unstructured_options must be an UnstructuredOptions.'.format(
optpth)
]
self.assertEqual(expected_error, option._validate_helper())
def test_validate(self):
# Valid cases should return None while invalid cases
# should return or throw a list of errors
option = self.get_options()
optpth = self.get_options_path()
# Default Configuration Is Valid
self.assertEqual(None, option.validate())
# Option is_enabled is not a boolean
for key in option.structured_options.properties:
option.set({'structured_options.{}.is_enabled' \
.format(key): "Hello World"})
expected_error = ['{}.structured_options.{}.is_enabled must be a ' \
'Boolean.'.format(optpth, key)
for key in option.structured_options.properties]
expected_error = set(expected_error)
# Verify expected errors are a subset of all errors
with self.assertRaises(ValueError) as cm:
option.validate(raise_error=True)
raised_error = set(str(cm.exception).split("\n"))
self.assertSetEqual(expected_error,
expected_error.intersection(raised_error))
self.assertSetEqual(expected_error,
expected_error \
.intersection(set(option \
.validate(raise_error=False))))
# Wrong Class Type
option = self.get_options()
option.structured_options = ProfilerOptions()
expected_error = '{}.structured_options must be a StructuredOptions.' \
.format(optpth,)
with self.assertRaisesRegex(ValueError, expected_error):
option.validate()
self.assertEqual([expected_error], option.validate(raise_error=False))
def get_options(self, **params):
options = ProfilerOptions()
options.set(params)
return options
def test_null_row_stats_correct_after_updates(self, *mocks):
data1 = pd.DataFrame([[1, None], [1, 1], [None, None], [None, 1]])
data2 = pd.DataFrame([[None, None], [1, None], [None, None], [None,
1]])
opts = ProfilerOptions()
opts.structured_options.multiprocess.is_enabled = False
# When setting min true samples/samples per update
profile = dp.Profiler(data1,
min_true_samples=2,
samples_per_update=2,
profiler_options=opts)
self.assertEqual(3, profile.row_has_null_count)
self.assertEqual(1, profile.row_is_null_count)
self.assertEqual(0.75, profile._get_row_has_null_ratio())
self.assertEqual(0.25, profile._get_row_is_null_ratio())
self.assertEqual(4, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2},
profile._profile[1].null_types_index['nan'])
profile.update_profile(data2, min_true_samples=2, sample_size=2)
self.assertEqual(7, profile.row_has_null_count)
self.assertEqual(3, profile.row_is_null_count)
self.assertEqual(0.875, profile._get_row_has_null_ratio())
self.assertEqual(0.375, profile._get_row_is_null_ratio())
self.assertEqual(4, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3, 4, 6, 7},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2, 4, 5, 6},
profile._profile[1].null_types_index['nan'])
# When not setting min true samples/samples per update
opts = ProfilerOptions()
opts.structured_options.multiprocess.is_enabled = False
profile = dp.Profiler(data1, profiler_options=opts)
self.assertEqual(3, profile.row_has_null_count)
self.assertEqual(1, profile.row_is_null_count)
self.assertEqual(0.75, profile._get_row_has_null_ratio())
self.assertEqual(0.25, profile._get_row_is_null_ratio())
self.assertEqual(4, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2},
profile._profile[1].null_types_index['nan'])
profile.update_profile(data2)
self.assertEqual(7, profile.row_has_null_count)
self.assertEqual(3, profile.row_is_null_count)
self.assertEqual(0.875, profile._get_row_has_null_ratio())
self.assertEqual(0.375, profile._get_row_is_null_ratio())
self.assertEqual(4, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3, 4, 6, 7},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2, 4, 5, 6},
profile._profile[1].null_types_index['nan'])
# Test that update with emtpy data doesn't change stats
profile.update_profile(pd.DataFrame([]))
self.assertEqual(7, profile.row_has_null_count)
self.assertEqual(3, profile.row_is_null_count)
self.assertEqual(0.875, profile._get_row_has_null_ratio())
self.assertEqual(0.375, profile._get_row_is_null_ratio())
self.assertEqual(0, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3, 4, 6, 7},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2, 4, 5, 6},
profile._profile[1].null_types_index['nan'])
# Test one row update
profile.update_profile(pd.DataFrame([[1, None]]))
self.assertEqual(8, profile.row_has_null_count)
self.assertEqual(3, profile.row_is_null_count)
self.assertEqual(8 / 9, profile._get_row_has_null_ratio())
self.assertEqual(3 / 9, profile._get_row_is_null_ratio())
self.assertEqual(1, profile._min_sampled_from_batch)
self.assertSetEqual({2, 3, 4, 6, 7},
profile._profile[0].null_types_index['nan'])
self.assertSetEqual({0, 2, 4, 5, 6},
profile._profile[1].null_types_index['nan'])
# Weird pandas behavior makes this None since this column will be
# recognized as object, not float64
self.assertSetEqual({8}, profile._profile[1].null_types_index['None'])
