def test_max_values_mapping(df_cyclical):
cyclical = CyclicalTransformer(variables="day", max_values={"day": 31})
X = cyclical.fit_transform(df_cyclical)
transf_df = df_cyclical.copy()
transf_df["day_sin"] = [
0.937752,
0.988468,
0.848644,
0.571268,
0.201298,
0.394355,
0.724792,
]
transf_df["day_cos"] = [
0.347305,
0.151428,
0.528964,
0.820763,
0.979530,
0.918958,
0.688967,
]
pd.testing.assert_frame_equal(X, transf_df)
def test_general_transformation_dropping_original_variables(df_cyclical):
# test case 1: just one variable, but dropping the variable after transformation
cyclical = CyclicalTransformer(variables=['day'], drop_original=True)
X = cyclical.fit_transform(df_cyclical)
transf_df = df_cyclical.copy()
# expected output
transf_df['day_sin'] = [
-0.78183,
0.0,
-0.97493,
0.43388,
0.78183,
0.97493,
-0.43388,
]
transf_df['day_cos'] = [
0.623490, 1.0, -0.222521, -0.900969, 0.623490, -0.222521, -0.900969
]
transf_df = transf_df.drop(columns='day')
# test init params
assert cyclical.variables == ['day']
# test fit attr
assert cyclical.input_shape_ == (7, 2)
assert cyclical.max_values_ == {
'day': 7,
}
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
def test_automatically_find_variables(df_cyclical):
# test case 2: automatically select variables
cyclical = CyclicalTransformer(variables=None, drop_original=True)
X = cyclical.fit_transform(df_cyclical)
transf_df = df_cyclical.copy()
# expected output
transf_df["day_sin"] = [
-0.78183,
0.0,
-0.97493,
0.43388,
0.78183,
0.97493,
-0.43388,
]
transf_df["day_cos"] = [
0.62349,
1.0,
-0.222521,
-0.900969,
0.62349,
-0.222521,
-0.900969,
]
transf_df["months_sin"] = [
1.0,
-0.5,
-1.0,
0.0,
0.86603,
0.0,
0.0,
]
transf_df["months_cos"] = [
0.0,
-0.86603,
-0.0,
1.0,
-0.5,
-1.0,
1.0,
]
transf_df = transf_df.drop(columns=["day", "months"])
# test init params
assert cyclical.variables is None
# test fit attr
assert cyclical.variables_ == ["day", "months"]
assert cyclical.n_features_in_ == 2
assert cyclical.max_values_ == {
"day": 7,
"months": 12,
}
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
def test_automatically_find_variables(df_cyclical):
# test case 2: automatically select variables
cyclical = CyclicalTransformer(variables=None, drop_original=True)
X = cyclical.fit_transform(df_cyclical)
transf_df = df_cyclical.copy()
# expected output
transf_df['day_sin'] = [
-0.78183,
0.0,
-0.97493,
0.43388,
0.78183,
0.97493,
-0.43388,
]
transf_df['day_cos'] = [
0.62349, 1.0, -0.222521, -0.900969, 0.62349, -0.222521, -0.900969
]
transf_df['months_sin'] = [
1.0,
-0.5,
-1.0,
0.0,
0.86603,
0.0,
0.0,
]
transf_df['months_cos'] = [
0.0,
-0.86603,
-0.0,
1.0,
-0.5,
-1.0,
1.0,
]
transf_df = transf_df.drop(columns=['day', 'months'])
# test init params
assert cyclical.variables == ['day', 'months']
# test fit attr
assert cyclical.input_shape_ == (7, 2)
assert cyclical.max_values_ == {
'day': 7,
'months': 12,
}
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
def test_general_transformation_dropping_original_variables(df_cyclical):
# test case 1: just one variable, but dropping the variable after transformation
cyclical = CyclicalTransformer(variables=["day"], drop_original=True)
X = cyclical.fit_transform(df_cyclical)
transf_df = df_cyclical.copy()
# expected output
transf_df["day_sin"] = [
-0.78183,
0.0,
-0.97493,
0.43388,
0.78183,
0.97493,
-0.43388,
]
transf_df["day_cos"] = [
0.623490,
1.0,
-0.222521,
-0.900969,
0.623490,
-0.222521,
-0.900969,
]
transf_df = transf_df.drop(columns="day")
# test init params
assert cyclical.variables == ["day"]
# test fit attr
assert cyclical.variables_ == ["day"]
assert cyclical.n_features_in_ == 2
assert cyclical.max_values_ == {
"day": 7,
}
# test transform output
pd.testing.assert_frame_equal(X, transf_df)
def test_check_validation_of_init_parameters(df_cyclical):
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables="day",
max_values=("dayi", 31))
transformer.fit(df_cyclical)
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables="day",
max_values={"day": "31"})
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables="day",
drop_original="True")
def test_check_validation_of_init_parameters(df_cyclical):
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables='day',
max_values=('dayi', 31))
transformer.fit(df_cyclical)
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables='day',
max_values={'day': '31'})
with pytest.raises(TypeError):
transformer = CyclicalTransformer(variables='day',
drop_original='True')
import pytest
from sklearn.utils.estimator_checks import check_estimator
from feature_engine.creation import (
CombineWithReferenceFeature,
CyclicalTransformer,
MathematicalCombination,
)
@pytest.mark.parametrize(
"Estimator",
[
MathematicalCombination(variables_to_combine=["0", "1"]),
CombineWithReferenceFeature(variables_to_combine=["0"],
reference_variables=["1"]),
CyclicalTransformer(),
],
)
def test_all_transformers(Estimator):
return check_estimator(Estimator)
def test_fit_raises_error_if_mapping_key_not_in_variables(df_cyclical):
# test case 3: when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = CyclicalTransformer(variables="day",
max_values={"dayi": 31})
transformer.fit(df_cyclical)
def test_fit_raises_error_if_na_in_df(df_na):
# test case 3: when dataset contains na, fit method
with pytest.raises(ValueError):
transformer = CyclicalTransformer()
transformer.fit(df_na)