def test_seed_per_observation_plus_product_of_seeding_variables(df_na):
# test case 3: observation seed, 2 variables as seed, product of seed variables
imputer = RandomSampleImputer(
variables=["City", "Studies"],
random_state=["Marks", "Age"],
seed="observation",
seeding_method="multiply",
)
df_na[["Marks", "Age"]] = df_na[["Marks", "Age"]].fillna(1)
X_transformed = imputer.fit_transform(df_na)
# expected output
ref = {
"Name":
["tom", "nick", "krish", np.nan, "peter", np.nan, "fred", "sam"],
"City": [
"London",
"Manchester",
"London",
"Manchester",
"London",
"London",
"Bristol",
"Manchester",
],
"Studies": [
"Bachelor",
"Bachelor",
"Bachelor",
"Masters",
"Bachelor",
"PhD",
"None",
"Masters",
],
"Age": [20, 21, 19, np.nan, 23, 40, 41, 37],
"Marks": [0.9, 0.8, 0.7, np.nan, 0.3, np.nan, 0.8, 0.6],
"dob":
pd.date_range("2020-02-24", periods=8, freq="T"),
}
ref = pd.DataFrame(ref)
assert imputer.variables == ["City", "Studies"]
assert imputer.random_state == ["Marks", "Age"]
assert imputer.seed == "observation"
pd.testing.assert_frame_equal(imputer.X_[["City", "Studies"]],
df_na[["City", "Studies"]])
pd.testing.assert_frame_equal(
X_transformed[["City", "Studies"]],
ref[["City", "Studies"]],
check_dtype=False,
)
def test_general_seed_plus_automatically_select_variables(df_na):
# set up transformer
imputer = RandomSampleImputer(variables=None,
random_state=5,
seed="general")
X_transformed = imputer.fit_transform(df_na)
# expected output:
# fillna based on seed used (found experimenting on Jupyter notebook)
ref = {
"Name":
["tom", "nick", "krish", "peter", "peter", "sam", "fred", "sam"],
"City": [
"London",
"Manchester",
"London",
"Manchester",
"London",
"London",
"Bristol",
"Manchester",
],
"Studies": [
"Bachelor",
"Bachelor",
"PhD",
"Masters",
"Bachelor",
"PhD",
"None",
"Masters",
],
"Age": [20, 21, 19, 23, 23, 40, 41, 37],
"Marks": [0.9, 0.8, 0.7, 0.3, 0.3, 0.6, 0.8, 0.6],
"dob":
pd.date_range("2020-02-24", periods=8, freq="T"),
}
ref = pd.DataFrame(ref)
# test init params
assert imputer.variables == [
"Name", "City", "Studies", "Age", "Marks", "dob"
]
assert imputer.random_state == 5
assert imputer.seed == "general"
# test fit attr
assert imputer.input_shape_ == (8, 6)
pd.testing.assert_frame_equal(imputer.X_, df_na)
# test transform output
pd.testing.assert_frame_equal(X_transformed, ref, check_dtype=False)
def test_seed_per_observation_with_only_1_variable_as_seed(df_na):
# test case 4: observation seed, only variable indicated as seed, method: addition
# Note the variable used as seed should not have missing data
imputer = RandomSampleImputer(variables=["City", "Studies"],
random_state="Age",
seed="observation")
df_na["Age"] = df_na["Age"].fillna(1)
X_transformed = imputer.fit_transform(df_na)
# expected output
ref = {
"Name":
["tom", "nick", "krish", np.nan, "peter", np.nan, "fred", "sam"],
"City": [
"London",
"Manchester",
"Manchester",
"Manchester",
"London",
"London",
"Bristol",
"Manchester",
],
"Studies": [
"Bachelor",
"Bachelor",
"Masters",
"Masters",
"Bachelor",
"PhD",
"None",
"Masters",
],
"Age": [20, 21, 19, np.nan, 23, 40, 41, 37],
"Marks": [0.9, 0.8, 0.7, np.nan, 0.3, np.nan, 0.8, 0.6],
"dob":
pd.date_range("2020-02-24", periods=8, freq="T"),
}
ref = pd.DataFrame(ref)
assert imputer.random_state == ["Age"]
pd.testing.assert_frame_equal(imputer.X_[["City", "Studies"]],
df_na[["City", "Studies"]])
pd.testing.assert_frame_equal(
X_transformed[["City", "Studies"]],
ref[["City", "Studies"]],
check_dtype=False,
)
def test_seed_per_observation_and_multiple_variables_in_random_state(df_na):
# test case 2: imputer seed per observation using multiple variables to determine
# the random_state
# Note the variables used as seed should not have missing data
imputer = RandomSampleImputer(variables=["City", "Studies"],
random_state=["Marks", "Age"],
seed="observation")
df_na[["Marks", "Age"]] = df_na[["Marks", "Age"]].fillna(1)
X_transformed = imputer.fit_transform(df_na)
# expected output
ref = {
"Name":
["tom", "nick", "krish", np.nan, "peter", np.nan, "fred", "sam"],
"City": [
"London",
"Manchester",
"London",
"London",
"London",
"London",
"Bristol",
"Manchester",
],
"Studies": [
"Bachelor",
"Bachelor",
"PhD",
"Bachelor",
"Bachelor",
"PhD",
"None",
"Masters",
],
"Age": [20, 21, 19, np.nan, 23, 40, 41, 37],
"Marks": [0.9, 0.8, 0.7, np.nan, 0.3, np.nan, 0.8, 0.6],
"dob":
pd.date_range("2020-02-24", periods=8, freq="T"),
}
ref = pd.DataFrame(ref)
assert imputer.variables == ["City", "Studies"]
assert imputer.random_state == ["Marks", "Age"]
assert imputer.seed == "observation"
pd.testing.assert_frame_equal(imputer.X_[["City", "Studies"]],
df_na[["City", "Studies"]])
pd.testing.assert_frame_equal(X_transformed[["City", "Studies"]],
ref[["City", "Studies"]])
def test_error_if_random_state_is_string(df_na):
with pytest.raises(ValueError):
imputer = RandomSampleImputer(seed="observation",
random_state="arbitrary")
imputer.fit(df_na)
def test_error_if_random_state_is_none_when_seed_is_observation():
with pytest.raises(ValueError):
RandomSampleImputer(seed="observation", random_state=None)
def test_error_if_random_state_takes_not_permitted_value():
with pytest.raises(ValueError):
RandomSampleImputer(seed="general", random_state="arbitrary")
def test_error_if_seeding_method_not_permitted_value():
with pytest.raises(ValueError):
RandomSampleImputer(seeding_method="arbitrary")
LogTransformer,
PowerTransformer,
ReciprocalTransformer,
YeoJohnsonTransformer,
)
from feature_engine.wrappers import SklearnTransformerWrapper
# imputation
@parametrize_with_checks([
MeanMedianImputer(),
ArbitraryNumberImputer(),
CategoricalImputer(fill_value=0, ignore_format=True),
EndTailImputer(),
AddMissingIndicator(),
RandomSampleImputer(),
DropMissingData(),
])
def test_sklearn_compatible_imputer(estimator, check):
check(estimator)
# encoding
@parametrize_with_checks([
CountFrequencyEncoder(ignore_format=True),
DecisionTreeEncoder(regression=False, ignore_format=True),
MeanEncoder(ignore_format=True),
OneHotEncoder(ignore_format=True),
OrdinalEncoder(ignore_format=True),
RareLabelEncoder(
tol=0.00000000001,
def imputeField36Field40(numpy_array):
dataFrame = pd.DataFrame(numpy_array[:, 47:49])
dateImputer = RandomSampleImputer()
dateImputer.fit(dataFrame)
convertedField39_Field40 = dateImputer.transform(dataFrame)
numpy_array[:, 47:49] = convertedField39_Field40
def imputeField26Ffield35(array):
dataFrame = pd.DataFrame(array[:, 35:41])
stringImputer = RandomSampleImputer()
stringImputer.fit(dataFrame)
convertedField35_Field40 = stringImputer.transform(dataFrame)
array[:, 35:41] = convertedField35_Field40