def test_encode_into_top_categories_plus_drop_binary(df_enc_binary):
# top_categories = 1
encoder = OneHotEncoder(top_categories=1,
variables=None,
drop_last=False,
drop_last_binary=True)
X = encoder.fit_transform(df_enc_binary)
# test fit attr
transf = {
"target": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],
"var_A_B":
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_B_A":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_C_A":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}
transf = pd.DataFrame(transf).astype("int32")
X = pd.DataFrame(X).astype("int32")
assert encoder.variables_ == ["var_A", "var_B", "var_C"]
assert encoder.n_features_in_ == 4
# test transform output
pd.testing.assert_frame_equal(X, transf)
assert "var_C_B" not in X.columns
# top_categories = 2
encoder = OneHotEncoder(top_categories=2,
variables=None,
drop_last=False,
drop_last_binary=True)
X = encoder.fit_transform(df_enc_binary)
# test fit attr
transf = {
"target": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],
"var_A_B":
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_A_A":
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_B_A":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_B_B":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_C_A":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}
transf = pd.DataFrame(transf).astype("int32")
X = pd.DataFrame(X).astype("int32")
assert encoder.variables_ == ["var_A", "var_B", "var_C"]
assert encoder.n_features_in_ == 4
# test transform output
pd.testing.assert_frame_equal(X, transf)
assert "var_C_B" not in X.columns
def test_encode_top_categories(df_enc_big):
# test case 3: encode only the most popular categories
encoder = OneHotEncoder(top_categories=4, variables=None, drop_last=False)
X = encoder.fit_transform(df_enc_big)
# test init params
assert encoder.top_categories == 4
# test fit attr
transf = {
"var_A_D": 10,
"var_A_B": 10,
"var_A_A": 6,
"var_A_G": 6,
"var_B_A": 10,
"var_B_D": 10,
"var_B_G": 6,
"var_B_B": 6,
"var_C_D": 10,
"var_C_C": 10,
"var_C_G": 6,
"var_C_B": 6,
}
assert encoder.n_features_in_ == 3
# test transform output
for col in transf.keys():
assert X[col].sum() == transf[col]
assert "var_B" not in X.columns
assert "var_B_F" not in X.columns
def test_encode_numerical_variables(df_enc_numeric):
encoder = OneHotEncoder(
top_categories=None,
variables=None,
drop_last=False,
ignore_format=True,
)
X = encoder.fit_transform(df_enc_numeric[["var_A", "var_B"]])
# test fit attr
transf = {
"var_A_1":
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_A_2":
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_A_3":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
"var_B_1":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_B_2":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_B_3":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
}
transf = pd.DataFrame(transf).astype("int32")
X = pd.DataFrame(X).astype("int32")
assert encoder.variables_ == ["var_A", "var_B"]
assert encoder.n_features_in_ == 2
# test transform output
pd.testing.assert_frame_equal(X, transf)
def test_encode_categories_in_k_minus_1_binary_plus_list_of_variables(df_enc_big):
# test case 2: encode all categories into k-1 binary variables,
# pass list of variables
encoder = OneHotEncoder(
top_categories=None, variables=["var_A", "var_B"], drop_last=True
)
X = encoder.fit_transform(df_enc_big)
# test init params
assert encoder.top_categories is None
assert encoder.variables == ["var_A", "var_B"]
assert encoder.drop_last is True
# test fit attr
transf = {
"var_A_A": 6,
"var_A_B": 10,
"var_A_C": 4,
"var_A_D": 10,
"var_A_E": 2,
"var_A_F": 2,
"var_B_A": 10,
"var_B_B": 6,
"var_B_C": 4,
"var_B_D": 10,
"var_B_E": 2,
"var_B_F": 2,
}
assert encoder.input_shape_ == (40, 3)
# test transform output
for col in transf.keys():
assert X[col].sum() == transf[col]
assert "var_B" not in X.columns
assert "var_B_G" not in X.columns
assert "var_C" in X.columns
def test_encode_categories_in_k_binary_plus_select_vars_automatically(
df_enc_big):
# test case 1: encode all categories into k binary variables, select variables
# automatically
encoder = OneHotEncoder(top_categories=None,
variables=None,
drop_last=False)
X = encoder.fit_transform(df_enc_big)
# test init params
assert encoder.top_categories is None
assert encoder.variables is None
assert encoder.drop_last is False
# test fit attr
transf = {
"var_A_A": 6,
"var_A_B": 10,
"var_A_C": 4,
"var_A_D": 10,
"var_A_E": 2,
"var_A_F": 2,
"var_A_G": 6,
"var_B_A": 10,
"var_B_B": 6,
"var_B_C": 4,
"var_B_D": 10,
"var_B_E": 2,
"var_B_F": 2,
"var_B_G": 6,
"var_C_A": 4,
"var_C_B": 6,
"var_C_C": 10,
"var_C_D": 10,
"var_C_E": 2,
"var_C_F": 2,
"var_C_G": 6,
}
assert encoder.variables_ == ["var_A", "var_B", "var_C"]
assert encoder.variables_binary_ == []
assert encoder.n_features_in_ == 3
assert encoder.encoder_dict_ == {
"var_A": ["A", "B", "C", "D", "E", "F", "G"],
"var_B": ["A", "B", "C", "D", "E", "F", "G"],
"var_C": ["A", "B", "C", "D", "E", "F", "G"],
}
# test transform output
assert X.sum().to_dict() == transf
assert "var_A" not in X.columns
def test_encode_top_categories():
# test case 3: encode only the most popular categories
df = pd.DataFrame({
"var_A": ["A"] * 5 + ["B"] * 11 + ["C"] * 4 + ["D"] * 9 + ["E"] * 2 +
["F"] * 2 + ["G"] * 7,
"var_B": ["A"] * 11 + ["B"] * 7 + ["C"] * 4 + ["D"] * 9 + ["E"] * 2 +
["F"] * 2 + ["G"] * 5,
"var_C": ["A"] * 4 + ["B"] * 5 + ["C"] * 11 + ["D"] * 9 + ["E"] * 2 +
["F"] * 2 + ["G"] * 7,
})
encoder = OneHotEncoder(top_categories=4, variables=None, drop_last=False)
X = encoder.fit_transform(df)
# test init params
assert encoder.top_categories == 4
# test fit attr
transf = {
"var_A_D": 9,
"var_A_B": 11,
"var_A_A": 5,
"var_A_G": 7,
"var_B_A": 11,
"var_B_D": 9,
"var_B_G": 5,
"var_B_B": 7,
"var_C_D": 9,
"var_C_C": 11,
"var_C_G": 7,
"var_C_B": 5,
}
# test fit attr
assert encoder.variables_ == ["var_A", "var_B", "var_C"]
assert encoder.variables_binary_ == []
assert encoder.n_features_in_ == 3
assert encoder.encoder_dict_ == {
"var_A": ["B", "D", "G", "A"],
"var_B": ["A", "D", "B", "G"],
"var_C": ["C", "D", "G", "B"],
}
# test transform output
for col in transf.keys():
assert X[col].sum() == transf[col]
assert "var_B" not in X.columns
assert "var_B_F" not in X.columns
def test_encode_into_k_dummy_plus_drop_binary(df_enc_binary):
encoder = OneHotEncoder(top_categories=None,
variables=None,
drop_last=False,
drop_last_binary=True)
X = encoder.fit_transform(df_enc_binary)
X = X.astype("int32")
# test fit attr
transf = {
"var_num":
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0],
"var_A_A":
[1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_A_B":
[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_A_C":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
"var_B_A":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"var_B_B":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
"var_B_C":
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1],
"var_C_AHA":
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
"var_D_OHO":
[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}
transf = pd.DataFrame(transf).astype("int32")
assert encoder.variables_ == ["var_A", "var_B", "var_C", "var_D"]
assert encoder.variables_binary_ == ["var_C", "var_D"]
assert encoder.n_features_in_ == 5
assert encoder.encoder_dict_ == {
"var_A": ["A", "B", "C"],
"var_B": ["A", "B", "C"],
"var_C": ["AHA"],
"var_D": ["OHO"],
}
# test transform output
pd.testing.assert_frame_equal(X, transf)
assert "var_C_B" not in X.columns