def test_merge_histogram(self):
data = pd.Series([], dtype=object)
profiler = FloatColumn(data.name)
profiler.update(data)
profiler._stored_histogram['histogram']['bin_counts'] = np.array(
[3, 2])
profiler._stored_histogram['histogram']['bin_edges'] = \
np.array([1.0, 3.0, 5.0])
input_array = [0.5, 1.0, 2.0, 5.0]
profiler._merge_histogram(input_array)
merged_hist = profiler._histogram_for_profile('sqrt')[0]
expected_bin_counts, expected_bin_edges = \
[5, 2, 2], [0.5, 2.0, 3.5, 5.0]
self.assertEqual(expected_bin_counts,
merged_hist['bin_counts'].tolist())
self.assertCountEqual(expected_bin_edges, merged_hist['bin_edges'])
def test_histogram_option_integration(self):
# test setting bin methods
options = FloatOptions()
options.histogram_and_quantiles.bin_count_or_method = "sturges"
num_profiler = FloatColumn(name="test", options=options)
self.assertIsNone(num_profiler.histogram_selection)
self.assertEqual(["sturges"], num_profiler.histogram_bin_method_names)
options.histogram_and_quantiles.bin_count_or_method = [
"sturges", "doane"
]
num_profiler = FloatColumn(name="test2", options=options)
self.assertIsNone(num_profiler.histogram_selection)
self.assertEqual(["sturges", "doane"],
num_profiler.histogram_bin_method_names)
# test histogram bin count set
options.histogram_and_quantiles.bin_count_or_method = 100
num_profiler = FloatColumn(name="test3", options=options)
self.assertIsNone(num_profiler.histogram_selection)
self.assertEqual(['custom'], num_profiler.histogram_bin_method_names)
# case when just 1 unique value, should just set bin size to be 1
num_profiler.update(pd.Series(['1', '1']))
self.assertEqual(
1,
len(num_profiler.histogram_methods['custom']['histogram']
['bin_counts']))
# case when more than 1 unique value, by virtue of a streaming update
num_profiler.update(pd.Series(['2']))
self.assertEqual(
100,
len(num_profiler._stored_histogram['histogram']['bin_counts']))
histogram, _ = num_profiler._histogram_for_profile('custom')
self.assertEqual(100, len(histogram['bin_counts']))
