def test_output_transform(self):
"""Check output for extrinsic normalizer with exponential transform."""
df = self.normalizer(exp=True, **self.kwargs).normalize(self.df.copy())
self.assertEqual(len(df), len(self.df))
for speaker in self.df['speaker'].unique():
actual_df = df[df['speaker'] == speaker]
expected_df = self.df[self.df['speaker'] == speaker]
for formant in self.formants:
mu_log = np.log(expected_df[self.formants]).mean(axis=0).mean()
actual = actual_df[formant]
expected = np.exp(np.log(expected_df[formant]) - mu_log)
assert_series_equal(actual, expected)
def test_output(self):
"""Check output."""
df = self.normalizer(**self.kwargs).normalize(self.df.copy())
self.assertEqual(len(df), len(self.df))
for speaker in self.df['speaker'].unique():
actual_df = df[df['speaker'] == speaker]
expected_df = self.df[self.df['speaker'] == speaker]
for formant in self.formants:
mu_log = np.log(expected_df[formant]).mean(axis=0)
actual = actual_df[formant]
expected = np.log(expected_df[formant]) - mu_log
assert_series_equal(actual, expected)
def test_output(self):
"""Check output."""
df = self.normalizer().normalize(self.df.copy(), **self.kwargs)
self.assertEqual(len(df), len(self.df))
for speaker in self.df['speaker'].unique():
actual_df = df[df['speaker'] == speaker]
expected_df = self.df[self.df['speaker'] == speaker]
for formant in self.formants:
fmin = expected_df[formant].min(axis=0)
fmax = expected_df[formant].max(axis=0)
actual = actual_df[formant]
expected = 999 * (expected_df[formant] - fmin) / (fmax - fmin)
assert_series_equal(actual, expected)
def test_output(self):
"""Check output."""
df = self.normalizer().normalize(self.df.copy(), **self.kwargs)
self.assertEqual(len(df), len(self.df))
for speaker in self.df['speaker'].unique():
actual_df = df[df['speaker'] == speaker]
expected_df = self.df[self.df['speaker'] == speaker]
for formant in self.formants:
mu = expected_df[formant].mean()
sigma = expected_df[formant].std() or 0.
actual = actual_df[formant]
expected = (expected_df[formant] - mu) / sigma if sigma else 0.
assert_series_equal(actual, expected)
def test_get_centroid(self):
"""Test the get_centroid method."""
df = DataFrame(dict(
speaker=['s1', 's1'],
vowel=['e', 'e'],
f1=[100., 200.],
f2=[400., 500.]
))
actual = self.normalizer.get_centroid(
df, dict(letter='e'), vowel='vowel', formants=['f1', 'f2'])
expected = Series(
dict(
f1=(100. + 200.) / 2,
f2=(400. + 500.) / 2),
dtype=actual.dtype)
assert_series_equal(actual, expected)
def test_get_centroid(self):
"""Test get_centroid method."""
df = DataFrame(dict(
speaker=['s1', 's1', 's1', 's1'],
vowel=['fleece', 'fleece', 'trap', 'trap'],
f1=[100., 200., 200., 300.],
f2=[400., 500., 500., 600.]
))
actual = self.normalizer.get_centroid(
df, dict(fleece='fleece', trap='trap'),
vowel='vowel', formants=['f1', 'f2'])
expected = Series(
dict(
f1=(150. + 250. + 150.) / 3,
f2=(450. + 550. + 150.) / 3),
dtype=actual.dtype)
assert_series_equal(actual, expected)