def test_SI_wrong_type(self):
"""
Negative test
data: array (unsupported type)
Checks that the function raises a TypeError if the data is passed as
an array.
"""
# 1. Arrange
data = [2, 4, np.nan, 1]
# 2. Act & 3. Assert
with self.assertRaises(TypeError):
seasonal_interpolation(data)
def test_SI_wrong_dec_model(self):
"""
Negative test
data: Correct data frame (example_df_ts)
dec_model='z' (not a valid decomposition model)
Checks that the function raises a ValueError if the value of
dec_model is not valid.
"""
# 1. Arrange
df = generate_example_df_ts()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
seasonal_interpolation(df, dec_model='z')
def test_SI_df_wrong_column(self):
"""
Negative test
data: Correct data frame (example_df_ts)
columns: ['z'] ('z' is not a column of example_df_ts)
Checks that the function raises a ValueError if one of the specified
columns doesn't exist in the data.
"""
# 1. Arrange
df = generate_example_df_ts()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
seasonal_interpolation(df, columns=['z'])
def test_SI_col_for_series(self):
"""
Negative test
data: Correct series (ts_airgap)
columns: ['a'] (series can't have columns)
Checks that the function raises a ValueError if a column is passed
for a series.
"""
# 1. Arrange
ser = generate_ts_airgap()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
seasonal_interpolation(ser, columns=['a'])
def test_SI_series_inplace(self):
"""
Positive test
data: Correct series (ts_airgap)
inplace: True
The series (ts_airgap) contains 13 NA values.
seasonal_interpolation() should impute all of them.
Checks that the series contains no NA values after the operation.
"""
# 1. Arrange
ts = generate_ts_airgap()
# 2. Act
seasonal_interpolation(ts, inplace=True)
# 3. Assert
self.assertEqual(ts.isna().sum().sum(), 0)
def test_SI_df_inplace(self):
"""
Positive test
data: Correct data frame (example_df_ts)
inplace: True
The data frame (example_df_ts) contains 144+13 NA values.
seasonal_interpolation() should impute 13 of them.
Checks that the data frame contains 144 NA values after the operation.
"""
# 1. Arrange
df = generate_example_df_ts()
# 2. Act
seasonal_interpolation(df, inplace=True)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 144 + 0)
def test_SI_series_returning(self):
"""
Positive test
data: Correct series (ts_airgap)
The series (ts_airgap) contains 13 NA values.
seasonal_interpolation() should impute all of them.
Checks that the original series remains unmodified and that the
returned series contains no NA values.
"""
# 1. Arrange
ts = generate_ts_airgap()
# 2. Act
ts2 = seasonal_interpolation(ts)
# 3. Assert
self.assertEqual(ts.isna().sum().sum(), 13)
self.assertEqual(ts2.isna().sum().sum(), 0)
def test_SI_df_returning(self):
"""
Positive test
data: Correct data frame (example_df_ts)
The data frame (example_df_ts) contains 144+13 NA values.
seasonal_interpolation() should impute 13 of them.
Checks that the original data frame remains unmodified and that the
returned data frame contains 144 NA values.
"""
# 1. Arrange
df = generate_example_df_ts()
# 2. Act
df2 = seasonal_interpolation(df)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 144 + 13)
self.assertEqual(df2.isna().sum().sum(), 144 + 0)
def test_SI_series_additive_model(self):
"""
Positive test
data: Correct series (ts_ausbeer)
dec_model='additive'
The series (ts_ausbeer) contains 9 NA values.
seasonal_interpolation() should impute all of them.
Checks that the original series remains unmodified and that the
returned series contains no NA values.
"""
# 1. Arrange
ts = generate_ts_ausbeer()
# 2. Act
ts2 = seasonal_interpolation(ts, dec_model='additive')
# 3. Assert
self.assertEqual(ts.isna().sum().sum(), 9)
self.assertEqual(ts2.isna().sum().sum(), 0)
def test_SI_series_cubic_interpolation(self):
"""
Positive test
data: Correct series (ts_airgap)
int_method: 'cubic'
The series (ts_airgap) contains 13 NA values.
seasonal_interpolation() should impute all but one of them, because
there is a NA in the fifth position of the timeseries, and 5 is less
then half of the window size (6).
Checks that the original series remains unmodified and that the
returned series contains no NA values.
"""
# 1. Arrange
ts = generate_ts_airgap()
# 2. Act
ts2 = seasonal_interpolation(ts, int_method='cubic')
# 3. Assert
self.assertEqual(ts.isna().sum().sum(), 13)
self.assertEqual(ts2.isna().sum().sum(), 1)
