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_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_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_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_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_bad_shrinkage_value_error():
with pytest.raises(ValueError) as e:
df = load_chicken(as_frame=True)
mod = GroupedEstimator(estimator=LinearRegression(),
groups="diet",
shrinkage="dinosaurhead")
mod.fit(df[["time", "diet"]], df["weight"])
assert "shrinkage function" in str(e)
def test_bad_shrinkage_value_error():
with pytest.raises(ValueError) as e:
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(
estimator=LinearRegression(),
groups="diet",
shrinkage="dinosaurhead",
)
mod.fit(df[['time', 'diet']], df['weight'])
assert "shrinkage function" in str(e)
def test_estimator_checks(test_fn):
clf = GroupedEstimator(estimator=LinearRegression(),
groups=[0],
use_fallback=True)
test_fn(GroupedEstimator.__name__ + "_fallback", clf)
clf = GroupedEstimator(estimator=LinearRegression(),
groups=[0],
use_fallback=False)
test_fn(GroupedEstimator.__name__ + "_nofallback", clf)
def test_chickweigt_string_groups():
df = load_chicken(as_frame=True)
df["diet"] = ["omgomgomg" + s for s in df["diet"].astype(str)]
X = df[["time", "diet"]]
X_np = np.array(X)
y = df["weight"]
# This should NOT raise errors
GroupedEstimator(LinearRegression(), groups=["diet"]).fit(X, y).predict(X)
GroupedEstimator(LinearRegression(), groups=1).fit(X_np, y).predict(X_np)
def test_chickweigt_string_groups():
df = load_chicken(give_pandas=True)
df['diet'] = ['omgomgomg' + s for s in df['diet'].astype(str)]
X = df[['time', 'diet']]
X_np = np.array(X)
y = df['weight']
# This should NOT raise errors
GroupedEstimator(LinearRegression(), groups=['diet']).fit(X, y).predict(X)
GroupedEstimator(LinearRegression(), groups=1).fit(X_np, y).predict(X_np)
def test_invalid_shrinkage(shrinkage_data, wrong_func):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
with pytest.raises(ValueError) as e:
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage=wrong_func,
use_global_model=False,
)
shrink_est.fit(X, y)
assert "Invalid shrinkage specified." in str(e)
def test_shrinkage_single_group_no_global(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df['Target']
with pytest.raises(ValueError) as e:
shrink_est = GroupedEstimator(DummyRegressor(),
'Country',
value_columns=[],
shrinkage="constant",
use_global_model=False,
alpha=0.1)
shrink_est.fit(X, y)
assert "Cannot do shrinkage with a single group if use_global_model is False" in str(
e)
def test_unexisting_shrinkage_func(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
with pytest.raises(ValueError) as e:
unexisting_func = "some_highly_unlikely_function_name"
shrink_est = GroupedEstimator(
estimator=DummyRegressor(),
groups=["Planet", "Country"],
shrinkage=unexisting_func,
)
shrink_est.fit(X, y)
assert "shrinkage function" in str(e)
def test_min_n_obs_shrinkage_too_little_obs(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df['Target']
too_big_n_obs = X.shape[0] + 1
shrink_est = GroupedEstimator(DummyRegressor(),
["Planet", 'Country', 'City'],
shrinkage="min_n_obs",
use_global_model=False,
min_n_obs=too_big_n_obs)
with pytest.raises(ValueError) as e:
shrink_est.fit(X, y)
assert f"There is no group with size greater than or equal to {too_big_n_obs}" 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_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_custom_shrinkage_wrong_return_type(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
def shrinkage_func(group_sizes):
return group_sizes
with pytest.raises(ValueError) as e:
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage=shrinkage_func,
use_global_model=False,
)
shrink_est.fit(X, y)
assert "should return an np.ndarray" in str(e)
def test_custom_shrinkage_wrong_length(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 + 1)
with pytest.raises(ValueError) as e:
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage=shrinkage_func,
use_global_model=False,
)
shrink_est.fit(X, y)
assert ".shape should be " in str(e)
def test_custom_shrinkage_raises_error(shrinkage_data):
df, means = shrinkage_data
X, y = df.drop(columns="Target"), df["Target"]
def shrinkage_func(group_sizes):
raise KeyError("This function is bad and you should feel bad")
with pytest.raises(ValueError) as e:
shrink_est = GroupedEstimator(
DummyRegressor(),
["Planet", "Country", "City"],
shrinkage=shrinkage_func,
use_global_model=False,
)
shrink_est.fit(X, y)
assert "you should feel bad" in str(
e) and "while checking the shrinkage function" in str(e)
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_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_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_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_chickweight_np_keys():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups=[1, 2])
mod.fit(df[['time', 'chick', 'diet']].values, df['weight'].values)
# there should still only be 50 groups on this dataset
assert len(mod.estimators_.keys()) == 50
def test_chickweight_df2_keys():
df = load_chicken(give_pandas=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="chick")
mod.fit(df[['time', 'chick']], df['weight'])
assert set(mod.estimators_.keys()) == set(range(1, 50 + 1))
def test_chickweight_df1_keys():
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}
def test_chickweight_df2_keys():
df = load_chicken(as_frame=True)
mod = GroupedEstimator(estimator=LinearRegression(), groups="chick")
mod.fit(df[["time", "chick"]], df["weight"])
assert set(mod.estimators_.keys()) == set(range(1, 50 + 1))
