def test_mtry(self, causal_X, causal_y, causal_w, mtry): forest = GRFForestCausalRegressor(n_estimators=100, mtry=mtry) forest.fit(causal_X, causal_y, causal_w, causal_w) if mtry is not None: assert forest.mtry_ == mtry else: assert forest.mtry_ == 5
def test_honesty_prune_leaves(self, causal_X, causal_y, causal_w, honesty_prune_leaves): forest = GRFForestCausalRegressor( n_estimators=100, honesty=True, honesty_prune_leaves=honesty_prune_leaves) forest.fit(causal_X, causal_y, causal_w, causal_w)
def test_with_X_nan(self, causal_X, causal_y, causal_w): causal_X_nan = causal_X.copy() index = np.random.choice(causal_X_nan.size, 100, replace=False) causal_X_nan.ravel()[index] = np.nan assert np.sum(np.isnan(causal_X_nan)) == 100 forest = GRFForestCausalRegressor(n_estimators=100) forest.fit(causal_X_nan, causal_y, causal_w) pred = forest.predict(causal_X_nan) assert len(pred) == causal_X_nan.shape[0]
def test_fit(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=100) with pytest.raises(NotFittedError): check_is_fitted(forest) forest.fit(causal_X, causal_y, causal_w) check_is_fitted(forest) assert hasattr(forest, "grf_forest_") assert hasattr(forest, "mtry_") assert forest.criterion == "mse"
def test_equalize_cluster_weights( self, causal_X, causal_y, causal_w, causal_cluster, equalize_cluster_weights, ): forest = GRFForestCausalRegressor( n_estimators=100, equalize_cluster_weights=equalize_cluster_weights) forest.fit(causal_X, causal_y, causal_w, causal_w, cluster=causal_cluster) if equalize_cluster_weights: assert forest.samples_per_cluster_ == 20 else: assert forest.samples_per_cluster_ == causal_y.shape[0] - 20 if equalize_cluster_weights: with pytest.raises(ValueError): forest.fit( causal_X, causal_y, causal_w, causal_w, cluster=causal_cluster, sample_weight=causal_y, ) forest.fit(causal_X, causal_y, causal_w, causal_w, cluster=None) assert forest.samples_per_cluster_ == 0
def test_serialize(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=100) # not fitted tf = tempfile.TemporaryFile() pickle.dump(forest, tf) tf.seek(0) forest = pickle.load(tf) forest.fit(causal_X, causal_y, causal_w) # fitted tf = tempfile.TemporaryFile() pickle.dump(forest, tf) tf.seek(0) new_forest = pickle.load(tf) pred = new_forest.predict(causal_X) assert len(pred) == causal_X.shape[0]
def test_estimators_(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=10) with pytest.raises(AttributeError): _ = forest.estimators_ forest.fit(causal_X, causal_y, causal_w, causal_w) with pytest.raises(ValueError): _ = forest.estimators_ forest = GRFForestCausalRegressor(n_estimators=10, enable_tree_details=True) forest.fit(causal_X, causal_y, causal_w, causal_w) estimators = forest.estimators_ assert len(estimators) == 10 assert isinstance(estimators[0], GRFTreeCausalRegressor) check_is_fitted(estimators[0])
def test_alpha(self, causal_X, causal_y, causal_w, alpha): forest = GRFForestCausalRegressor(n_estimators=100, alpha=alpha) if alpha <= 0 or alpha >= 0.25: with pytest.raises(ValueError): forest.fit(causal_X, causal_y, causal_w, causal_w) else: forest.fit(causal_X, causal_y, causal_w, causal_w)
def test_get_kernel_weights(self, causal_X, causal_y, causal_w): X_train, X_test, y_train, y_test, w_train, w_test = train_test_split( causal_X, causal_y, causal_w, test_size=0.33, random_state=42) forest = GRFForestCausalRegressor() forest.fit(X_train, y_train, w_train) weights = forest.get_kernel_weights(X_test) assert weights.shape[0] == X_test.shape[0] assert weights.shape[1] == X_train.shape[0] oob_weights = forest.get_kernel_weights(X_train, True) assert oob_weights.shape[0] == X_train.shape[0] assert oob_weights.shape[1] == X_train.shape[0]
def test_honesty_fraction(self, causal_X, causal_y, causal_w, honesty_fraction): forest = GRFForestCausalRegressor( n_estimators=100, honesty=True, honesty_fraction=honesty_fraction, honesty_prune_leaves=True, ) if honesty_fraction <= 0 or honesty_fraction >= 1: with pytest.raises(RuntimeError): forest.fit(causal_X, causal_y, causal_w, causal_w) else: forest.fit(causal_X, causal_y, causal_w, causal_w)
def test_sample_fraction(self, causal_X, causal_y, causal_w, sample_fraction): # and ci_group_size forest = GRFForestCausalRegressor(n_estimators=100, sample_fraction=sample_fraction, ci_group_size=1) if sample_fraction <= 0 or sample_fraction > 1: with pytest.raises(ValueError): forest.fit(causal_X, causal_y, causal_w, causal_w) else: forest.fit(causal_X, causal_y, causal_w, causal_w) forest = GRFForestCausalRegressor(n_estimators=100, sample_fraction=sample_fraction, ci_group_size=2) if sample_fraction <= 0 or sample_fraction > 0.5: with pytest.raises(ValueError): forest.fit(causal_X, causal_y, causal_w, causal_w) else: forest.fit(causal_X, causal_y, causal_w, causal_w)
def test_clone(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=100) forest.fit(causal_X, causal_y, causal_w) clone(forest)
def test_honesty(self, causal_X, causal_y, causal_w, honesty): forest = GRFForestCausalRegressor(n_estimators=100, honesty=honesty) forest.fit(causal_X, causal_y, causal_w, causal_w)
def test_predict(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=100) forest.fit(causal_X, causal_y, causal_w) pred = forest.predict(causal_X) assert len(pred) == causal_X.shape[0]
def test_get_feature_importances(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor() forest.fit(causal_X, causal_y, causal_w) fi = forest.get_feature_importances() assert len(fi) == causal_X.shape[1]
def test_get_split_frequencies(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor() forest.fit(causal_X, causal_y, causal_w) sf = forest.get_split_frequencies() assert sf.shape[1] == causal_X.shape[1]
def test_get_estimator(self, causal_X, causal_y, causal_w): forest = GRFForestCausalRegressor(n_estimators=10) with pytest.raises(NotFittedError): _ = forest.get_estimator(idx=0) forest.fit(causal_X, causal_y, causal_w, causal_w) with pytest.raises(ValueError): _ = forest.get_estimator(idx=0) forest = GRFForestCausalRegressor(n_estimators=10, enable_tree_details=True) forest.fit(causal_X, causal_y, causal_w, causal_w) estimator = forest.get_estimator(idx=0) check_is_fitted(estimator) assert isinstance(estimator, GRFTreeCausalRegressor) with pytest.raises(IndexError): _ = forest.get_estimator(idx=20)
def test_orthogonal_boosting(self, causal_X, causal_y, causal_w, orthogonal_boosting): forest = GRFForestCausalRegressor( n_estimators=100, orthogonal_boosting=orthogonal_boosting) forest.fit(causal_X, causal_y, causal_w)
def test_init(self): _ = GRFForestCausalRegressor(n_estimators=100)