def test_logratio_error_if_numerator_probability_zero():
# test case 5: when the numerator probability is zero, only applies to log_ratio
with pytest.raises(ValueError):
df = {
"var_A": ["A"] * 6 + ["B"] * 10 + ["C"] * 4,
"var_B": ["A"] * 10 + ["B"] * 6 + ["C"] * 4,
"target": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],
}
df = pd.DataFrame(df)
encoder = PRatioEncoder(encoding_method="log_ratio")
encoder.fit(df[["var_A", "var_B"]], df["target"])
def test_ratio_error_if_denominator_probability_zero():
# test case 3: when the denominator probability is zero, ratio
with pytest.raises(ValueError):
df = {
"var_A": ["A"] * 6 + ["B"] * 10 + ["C"] * 4,
"var_B": ["A"] * 10 + ["B"] * 6 + ["C"] * 4,
"target": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],
}
df = pd.DataFrame(df)
encoder = PRatioEncoder(encoding_method="ratio")
encoder.fit(df[["var_A", "var_B"]], df["target"])
def test_ratio_with_one_variable(df_enc):
# test case 1: 1 variable, ratio
encoder = PRatioEncoder(encoding_method="ratio", variables=["var_A"])
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
X = encoder.transform(df_enc[["var_A", "var_B"]])
# transformed dataframe
transf_df = df_enc.copy()
transf_df["var_A"] = [
0.49999999999999994,
0.49999999999999994,
0.49999999999999994,
0.49999999999999994,
0.49999999999999994,
0.49999999999999994,
0.25,
0.25,
0.25,
0.25,
0.25,
0.25,
0.25,
0.25,
0.25,
0.25,
1.0,
1.0,
1.0,
1.0,
]
# init params
assert encoder.encoding_method == "ratio"
assert encoder.variables == ["var_A"]
# fit params
assert encoder.variables_ == ["var_A"]
assert encoder.encoder_dict_ == {
"var_A": {
"A": 0.49999999999999994,
"B": 0.25,
"C": 1.0
}
}
assert encoder.n_features_in_ == 2
# transform params
pd.testing.assert_frame_equal(X, transf_df[["var_A", "var_B"]])
def test_logratio_and_automaticcally_select_variables(df_enc):
# test case 2: automatically select variables, log_ratio
encoder = PRatioEncoder(encoding_method="log_ratio", variables=None)
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
X = encoder.transform(df_enc[["var_A", "var_B"]])
# transformed dataframe
transf_df = df_enc.copy()
transf_df["var_A"] = [
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
0.0,
0.0,
0.0,
0.0,
]
transf_df["var_B"] = [
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
0.0,
0.0,
0.0,
0.0,
]
# init params
assert encoder.encoding_method == "log_ratio"
assert encoder.variables is None
# fit params
assert encoder.variables_ == ["var_A", "var_B"]
assert encoder.encoder_dict_ == {
"var_A": {"A": -0.6931471805599454, "B": -1.3862943611198906, "C": 0.0},
"var_B": {"A": -1.3862943611198906, "B": -0.6931471805599454, "C": 0.0},
}
assert encoder.n_features_in_ == 2
# transform params
pd.testing.assert_frame_equal(X, transf_df[["var_A", "var_B"]])
# test error raise
with pytest.raises(ValueError):
PRatioEncoder(encoding_method="other")
def test_error_if_rare_labels_not_permitted_value():
with pytest.raises(ValueError):
PRatioEncoder(errors="empanada")
def test_warn_if_transform_df_contains_categories_not_seen_in_fit(df_enc, df_enc_rare):
# test case 3: when dataset to be transformed contains categories not present
# in training dataset
msg = "During the encoding, NaN values were introduced in the feature(s) var_A."
# check for warning when rare_labels equals 'ignore'
with pytest.warns(UserWarning) as record:
encoder = PRatioEncoder(errors="ignore")
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
encoder.transform(df_enc_rare[["var_A", "var_B"]])
# check that only one warning was raised
assert len(record) == 1
# check that the message matches
assert record[0].message.args[0] == msg
# check for error when rare_labels equals 'raise'
with pytest.raises(ValueError) as record:
encoder = PRatioEncoder(errors="raise")
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
encoder.transform(df_enc_rare[["var_A", "var_B"]])
# check that the error message matches
assert str(record.value) == msg
def test_logratio_on_numerical_variables(df_enc_numeric):
# test ignore_format
encoder = PRatioEncoder(
encoding_method="log_ratio", variables=None, ignore_format=True
)
encoder.fit(df_enc_numeric[["var_A", "var_B"]], df_enc_numeric["target"])
X = encoder.transform(df_enc_numeric[["var_A", "var_B"]])
# transformed dataframe
transf_df = df_enc_numeric.copy()
transf_df["var_A"] = [
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
0.0,
0.0,
0.0,
0.0,
]
transf_df["var_B"] = [
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-1.3862943611198906,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
-0.6931471805599454,
0.0,
0.0,
0.0,
0.0,
]
# init params
assert encoder.encoding_method == "log_ratio"
assert encoder.variables is None
# fit params
assert encoder.variables_ == ["var_A", "var_B"]
assert encoder.encoder_dict_ == {
"var_A": {1: -0.6931471805599454, 2: -1.3862943611198906, 3: 0.0},
"var_B": {1: -1.3862943611198906, 2: -0.6931471805599454, 3: 0.0},
}
assert encoder.n_features_in_ == 2
# transform params
pd.testing.assert_frame_equal(X, transf_df[["var_A", "var_B"]])
# test error raise
with pytest.raises(ValueError):
PRatioEncoder(encoding_method="other")
def test_error_if_df_contains_na_in_transform(df_enc, df_enc_na):
# test case 8: when dataset contains na, transform method
with pytest.raises(ValueError):
encoder = PRatioEncoder(encoding_method="ratio")
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
encoder.transform(df_enc_na)
def test_raises_non_fitted_error(df_enc):
# test case 6: non fitted error
with pytest.raises(NotFittedError):
imputer = PRatioEncoder()
imputer.transform(df_enc)
# 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,
n_categories=100000000000,
replace_with=10,
ignore_format=True,
),
WoEEncoder(ignore_format=True),
PRatioEncoder(ignore_format=True),
])
def test_sklearn_compatible_encoder(estimator, check):
check(estimator)
# outliers
@parametrize_with_checks([
ArbitraryOutlierCapper(max_capping_dict={"0": 10}),
OutlierTrimmer(),
Winsorizer(),
])
def test_sklearn_compatible_outliers(estimator, check):
check(estimator)