def test_LR_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):
linear_regression(data)
def test_LR_wrong_regressions(self):
"""
Negative test
data: Correct data frame (sales)
regressions: 'z' (not a valid value)
Checks that the function raises a ValueError if the value passed for
the parameter regressions is not valid.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
linear_regression(df, 'sales', ['advertising', 'year'], 'z')
def test_LR_wrong_predictor(self):
"""
Negative test
data: Correct data frame (sales)
predictors: ['advertising', 'z'] ('z' is not a column of sales)
Checks that the function raises a ValueError if one of the column
specified as the predictor variables doesn't exist in the data.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
linear_regression(df, 'sales', ['advertising', 'z'])
def test_LR_wrong_dependent(self):
"""
Negative test
data: Correct data frame (sales)
dependent: 'z' (not a column of sales)
Checks that the function raises a ValueError if the column specified as
the dependent variable doesn't exist in the data.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act & 3. Assert
with self.assertRaises(ValueError):
linear_regression(df, 'z', ['advertising', 'year'])
def test_LR_inplace(self):
"""
Positive test
data: Correct data frame (sales)
The data frame sales contains 4 NA values in the column 'sales'.
linear_regression() should impute 3 of them.
Checks that the data frame contains 1 NA value in the column 'sales'
after the operation.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act
linear_regression(df, 'sales', ['advertising', 'year'], inplace=True)
# 3. Assert
self.assertEqual(df['sales'].isna().sum(), 1)
def mice_one_imputation(data):
"""Auxiliary function that performs one MICE imputation, choosing the
order in which the columns are imputed at random.
:param data: The data on which to perform the imputation.
:type data: pandas.DataFrame
:return: The dataframe with one MICE imputation performed.
:rtype: pandas.DataFrame
"""
# This auxiliary function always returns a copy:
res = data.copy()
# Save the mask of missing values:
na_mask = pd.isna(data)
# Compute the list of columns with missing values
columns_with_na = []
for column in data.columns:
if data[column].isna().any():
columns_with_na.append(column)
# Shuffle the list of columns to impute:
shuffle(columns_with_na)
# Impute with mean substitution:
for column in columns_with_na:
if is_numeric_dtype(data[column]):
mean_substitution(res, columns=[column], inplace=True)
else:
random_sample_imputation(res, columns=[column], inplace=True)
# Compute which columns are numeric in order to use them as predictors:
numerics = [col for col in data.columns if is_numeric_dtype(data[col])]
# Impute each column:
for column in columns_with_na:
if is_numeric_dtype(data[column]):
res.loc[na_mask[column], column] = np.nan
linear_regression(res, column, predictors=numerics, inplace=True)
else:
res.loc[na_mask[column], column] = None
logistic_regression(res, column, inplace=True)
return res
def test_LR_returning(self):
"""
Positive test
data: Correct data frame (sales)
The data frame sales contains 4 NA values in the column 'sales'.
linear_regression() should impute 3 of them.
Checks that the original data frame remains unmodified and that the
returned series contains 1 NA value in the column 'sales'.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act
df2 = linear_regression(df, 'sales', ['advertising', 'year'])
# 3. Assert
self.assertEqual(df['sales'].isna().sum(), 4)
self.assertEqual(df2['sales'].isna().sum(), 1)
def test_LR_all_columns(self):
"""
Positive test
data: Correct data frame (sales)
dependent: None
The data frame sales contains 8 NA values.
linear_regression() should impute 5 of them.
Checks that the original data frame remains unmodified and that the
returned series contains 3 NA values.
"""
# 1. Arrange
df = generate_df_sales()
# 2. Act
df2 = linear_regression(df)
# 3. Assert
self.assertEqual(df.isna().sum().sum(), 8)
self.assertEqual(df2.isna().sum().sum(), 3)