def test_classification_ignore_format(df_enc_numeric):
encoder = DecisionTreeEncoder(regression=False, 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"]])
transf_df = df_enc_numeric.copy()
transf_df["var_A"] = [0.25] * 16 + [0.5] * 4 # Tree: var_A <= 1.5 -> 0.25 else 0.5
transf_df["var_B"] = [0.2] * 10 + [0.4] * 10 # Tree: var_B <= 0.5 -> 0.2 else 0.4
pd.testing.assert_frame_equal(X, transf_df[["var_A", "var_B"]])
def test_variables_cast_as_category(df_enc_category_dtypes):
df = df_enc_category_dtypes.copy()
encoder = DecisionTreeEncoder(regression=False)
encoder.fit(df[["var_A", "var_B"]], df["target"])
X = encoder.transform(df[["var_A", "var_B"]])
transf_df = df.copy()
transf_df["var_A"] = [0.25] * 16 + [0.5] * 4 # Tree: var_A <= 1.5 -> 0.25 else 0.5
transf_df["var_B"] = [0.2] * 10 + [0.4] * 10 # Tree: var_B <= 0.5 -> 0.2 else 0.4
pd.testing.assert_frame_equal(X, transf_df[["var_A", "var_B"]], check_dtype=False)
assert X["var_A"].dtypes == float
def test_regression(df_enc, df_enc_na):
random = np.random.RandomState(42)
y = random.normal(0, 0.1, len(df_enc))
encoder = DecisionTreeEncoder(regression=True, random_state=random)
encoder.fit(df_enc[["var_A", "var_B"]], y)
X = encoder.transform(df_enc[["var_A", "var_B"]])
transf_df = df_enc.copy()
transf_df["var_A"] = ([0.034348] * 6 + [-0.024679] * 10 + [-0.075473] * 4
) # Tree: var_A <= 1.5 -> 0.25 else 0.5
transf_df["var_B"] = [0.044806] * 10 + [-0.079066] * 10
pd.testing.assert_frame_equal(X.round(6), transf_df[["var_A", "var_B"]])
def test_encoding_method_param(df_enc):
# defaults
encoder = DecisionTreeEncoder()
encoder.fit(df_enc, df_enc["target"])
assert encoder.encoder_[0].encoding_method == "arbitrary"
# ordered encoding
encoder = DecisionTreeEncoder(encoding_method="ordered")
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
assert encoder.encoder_[0].encoding_method == "ordered"
# incorrect input
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder(encoding_method="other")
encoder.fit(df_enc, df_enc["target"])
def test_transform_raises_error_if_df_contains_na(df_enc, df_enc_na):
# test case 4: when dataset contains na, transform method
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder()
encoder.fit(df_enc_na[["var_A", "var_B"]], df_enc_na["target"])
encoder.transform(df_enc_na)
def test_fit_raises_error_if_df_contains_na(df_enc_na):
# test case 4: when dataset contains na, fit method
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder()
encoder.fit(df_enc_na)
def test_non_fitted_error(df_enc):
with pytest.raises(NotFittedError):
encoder = DecisionTreeEncoder()
encoder.transform(df_enc)
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,
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)
CountFrequencyEncoder,
DecisionTreeEncoder,
MeanEncoder,
OneHotEncoder,
OrdinalEncoder,
PRatioEncoder,
RareLabelEncoder,
WoEEncoder,
)
@pytest.mark.parametrize(
"Estimator",
[
CountFrequencyEncoder(ignore_format=True),
DecisionTreeEncoder(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_all_transformers(Estimator):
return check_estimator(Estimator)
def test_fit_raises_error_if_df_contains_na(df_enc_na):
# test case 4: when dataset contains na, fit method
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder(regression=False)
encoder.fit(df_enc_na[["var_A", "var_B"]], df_enc_na["target"])
def test_error_when_regression_is_false_and_target_is_continuous(df_enc):
random = np.random.RandomState(42)
y = random.normal(0, 10, len(df_enc))
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder(regression=False)
encoder.fit(df_enc[["var_A", "var_B"]], y)
def test_error_when_regression_is_true_and_target_is_binary(df_enc):
with pytest.raises(ValueError):
encoder = DecisionTreeEncoder(regression=True)
encoder.fit(df_enc[["var_A", "var_B"]], df_enc["target"])
