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']])