def test_global_model_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
shrink_est_without_global = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage="min_n_obs",
use_global_model=False,
min_n_obs=2,
)
shrink_est_with_global = GroupedEstimator(
DummyRegressor(),
["Country", "City"],
value_columns=[],
shrinkage="min_n_obs",
use_global_model=True,
min_n_obs=2,
)
shrink_est_without_global.fit(X, y)
shrink_est_with_global.fit(X, y)
pd.testing.assert_series_equal(shrink_est_with_global.predict(X),
shrink_est_without_global.predict(X))
def test_chickweight_raise_error_cols_missing2():
df = load_chicken(as_frame=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="diet")
mod.fit(df[["time", "diet"]], df["weight"])
with pytest.raises(ValueError) as e:
mod.predict(df[["diet", "chick"]])
assert "not in columns" in str(e)
def test_chickweight_raise_error_cols_missing2():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="diet")
mod.fit(df[['time', 'diet']], df['weight'])
with pytest.raises(ValueError) as e:
mod.predict(df[['diet', 'chick']])
assert "not in columns" in str(e)
def test_chickweight_can_do_fallback():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="diet")
mod.fit(df[['time', 'diet']], df['weight'])
assert set(mod.estimators_.keys()) == {1, 2, 3, 4}
to_predict = pd.DataFrame({"time": [21, 21], "diet": [5, 6]})
assert mod.predict(to_predict).shape == (2, )
assert mod.predict(to_predict)[0] == mod.predict(to_predict)[1]
def test_fallback_can_raise_error():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(),
groups="diet",
use_fallback=False)
mod.fit(df[['time', 'diet']], df['weight'])
to_predict = pd.DataFrame({"time": [21, 21], "diet": [5, 6]})
with pytest.raises(ValueError):
mod.predict(to_predict)
def test_fallback_can_raise_error():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(),
groups="diet",
use_global_model=False,
shrinkage=None)
mod.fit(df[['time', 'diet']], df['weight'])
to_predict = pd.DataFrame({"time": [21, 21], "diet": [5, 6]})
with pytest.raises(ValueError) as e:
mod.predict(to_predict)
assert "found a group" in str(e)
def test_shrinkage_single_group(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
shrink_est = GroupedEstimator(
DummyRegressor(),
"Country",
value_columns=[],
shrinkage="constant",
use_global_model=True,
alpha=0.1,
)
shrinkage_factors = np.array([0.1, 0.9])
shrink_est.fit(X, y)
expected_prediction = [
np.array([means["Earth"], means["NL"]]) @ shrinkage_factors,
np.array([means["Earth"], means["NL"]]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"]]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"]]) @ shrinkage_factors,
]
assert expected_prediction == shrink_est.predict(X).tolist()
def test_custom_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
def shrinkage_func(group_sizes):
n = len(group_sizes)
return np.repeat(1 / n, n)
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage=shrinkage_func,
use_global_model=False,
)
shrinkage_factors = np.array([1, 1, 1]) / 3
shrink_est.fit(X, y)
expected_prediction = [
np.array([means["Earth"], means["NL"], means["Amsterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["NL"], means["Rotterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Antwerp"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Brussels"]])
@ shrinkage_factors,
]
assert expected_prediction == shrink_est.predict(X).tolist()
def test_relative_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage="relative",
use_global_model=False,
)
shrinkage_factors = np.array([4, 2, 1]) / 7
shrink_est.fit(X, y)
expected_prediction = [
np.array([means["Earth"], means["NL"], means["Amsterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["NL"], means["Rotterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Antwerp"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Brussels"]])
@ shrinkage_factors,
]
assert expected_prediction == shrink_est.predict(X).tolist()
def test_predict_missing_group_column(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df['Target']
shrink_est = GroupedEstimator(DummyRegressor(),
["Planet", 'Country', 'City'],
shrinkage="constant",
use_global_model=False,
alpha=0.1)
shrink_est.fit(X, y)
with pytest.raises(ValueError) as e:
shrink_est.predict(X.drop(columns=['Country']))
assert "group columns" in str(e)
def test_constant_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df['Target']
shrink_est = GroupedEstimator(DummyRegressor(),
["Planet", 'Country', 'City'],
shrinkage="constant",
use_global_model=False,
alpha=0.1)
shrinkage_factors = np.array([0.01, 0.09, 0.9])
shrink_est.fit(X, y)
expected_prediction = [
np.array([means["Earth"], means["NL"], means["Amsterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["NL"], means["Rotterdam"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Antwerp"]
]) @ shrinkage_factors,
np.array([means["Earth"], means["BE"], means["Brussels"]])
@ shrinkage_factors,
]
assert expected_prediction == shrink_est.predict(X).tolist()
def test_predict_missing_value_column(shrinkage_data):
df, means = shrinkage_data
value_column = "predictor"
X, y = df.drop(columns="Target"), df['Target']
X = X.assign(**{value_column: np.random.normal(size=X.shape[0])})
shrink_est = GroupedEstimator(LinearRegression(),
["Planet", 'Country', 'City'],
shrinkage="constant",
use_global_model=False,
alpha=0.1)
shrink_est.fit(X, y)
with pytest.raises(ValueError) as e:
shrink_est.predict(X.drop(columns=[value_column]))
assert "columns to use" in str(e)
def test_unseen_groups_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
shrink_est = GroupedEstimator(DummyRegressor(),
["Planet", "Country", "City"],
shrinkage="constant",
alpha=0.1)
shrink_est.fit(X, y)
unseen_group = pd.DataFrame({
"Planet": ["Earth"],
"Country": ["DE"],
"City": ["Hamburg"]
})
with pytest.raises(ValueError) as e:
shrink_est.predict(X=pd.concat([unseen_group] * 4, axis=0))
assert "found a group" in str(e)
def test_min_n_obs_shrinkage(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage="min_n_obs",
use_global_model=False,
min_n_obs=2,
)
shrink_est.fit(X, y)
expected_prediction = [means["NL"], means["NL"], means["BE"], means["BE"]]
assert expected_prediction == shrink_est.predict(X).tolist()
def test_chickweight_raise_error_cols_missing1():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="diet")
mod.fit(df[['time', 'diet']], df['weight'])
with pytest.raises(KeyError):
mod.predict(df[['time', 'chick']])
