7.5 KiB
7.5 KiB
Test Coverage Report (ONNX-ML Operators)
Outlines
Node Test Coverage
Summary
Node tests have covered 4/19 (21.05%, 0 generators excluded) common operators.
Node tests have covered 0/0 (N/A) experimental operators.
- Covered Common Operators
- No Cover Common Operators
- Covered Experimental Operators
- No Cover Experimental Operators
💚Covered Common Operators
ArrayFeatureExtractor
There are 1 test cases, listed as following:
arrayfeatureextractor
node = onnx.helper.make_node(
"ArrayFeatureExtractor",
inputs=["x", "y"],
outputs=["z"],
domain="ai.onnx.ml",
)
x = np.arange(12).reshape((3, 4)).astype(np.float32)
y = np.array([0, 1], dtype=np.int64)
z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T
expect(
node,
inputs=[x, y],
outputs=[z],
name="test_ai_onnx_ml_array_feature_extractor",
)
Binarizer
There are 1 test cases, listed as following:
binarizer
threshold = 1.0
node = onnx.helper.make_node(
"Binarizer",
inputs=["X"],
outputs=["Y"],
threshold=threshold,
domain="ai.onnx.ml",
)
x = np.random.randn(3, 4, 5).astype(np.float32)
y = compute_binarizer(x, threshold)[0]
expect(node, inputs=[x], outputs=[y], name="test_ai_onnx_ml_binarizer")
LabelEncoder
There are 2 test cases, listed as following:
string_int_label_encoder
node = onnx.helper.make_node(
"LabelEncoder",
inputs=["X"],
outputs=["Y"],
domain="ai.onnx.ml",
keys_strings=["a", "b", "c"],
values_int64s=[0, 1, 2],
default_int64=42,
)
x = np.array(["a", "b", "d", "c", "g"]).astype(object)
y = np.array([0, 1, 42, 2, 42]).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y],
name="test_ai_onnx_ml_label_encoder_string_int",
)
node = onnx.helper.make_node(
"LabelEncoder",
inputs=["X"],
outputs=["Y"],
domain="ai.onnx.ml",
keys_strings=["a", "b", "c"],
values_int64s=[0, 1, 2],
)
x = np.array(["a", "b", "d", "c", "g"]).astype(object)
y = np.array([0, 1, -1, 2, -1]).astype(np.int64)
expect(
node,
inputs=[x],
outputs=[y],
name="test_ai_onnx_ml_label_encoder_string_int_no_default",
)
tensor_based_label_encoder
tensor_keys = make_tensor(
"keys_tensor", onnx.TensorProto.STRING, (3,), ["a", "b", "c"]
)
repeated_string_keys = ["a", "b", "c"]
x = np.array(["a", "b", "d", "c", "g"]).astype(object)
y = np.array([0, 1, 42, 2, 42]).astype(np.int16)
node = onnx.helper.make_node(
"LabelEncoder",
inputs=["X"],
outputs=["Y"],
domain="ai.onnx.ml",
keys_tensor=tensor_keys,
values_tensor=make_tensor(
"values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2]
),
default_tensor=make_tensor(
"default_tensor", onnx.TensorProto.INT16, (1,), [42]
),
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_ai_onnx_ml_label_encoder_tensor_mapping",
)
node = onnx.helper.make_node(
"LabelEncoder",
inputs=["X"],
outputs=["Y"],
domain="ai.onnx.ml",
keys_strings=repeated_string_keys,
values_tensor=make_tensor(
"values_tensor", onnx.TensorProto.INT16, (3,), [0, 1, 2]
),
default_tensor=make_tensor(
"default_tensor", onnx.TensorProto.INT16, (1,), [42]
),
)
expect(
node,
inputs=[x],
outputs=[y],
name="test_ai_onnx_ml_label_encoder_tensor_value_only_mapping",
)
TreeEnsemble
There are 2 test cases, listed as following:
tree_ensemble_set_membership
node = onnx.helper.make_node(
"TreeEnsemble",
["X"],
["Y"],
domain="ai.onnx.ml",
n_targets=4,
aggregate_function=1,
membership_values=make_tensor(
"membership_values",
onnx.TensorProto.FLOAT,
(8,),
[1.2, 3.7, 8, 9, np.nan, 12, 7, np.nan],
),
nodes_missing_value_tracks_true=None,
nodes_hitrates=None,
post_transform=0,
tree_roots=[0],
nodes_modes=make_tensor(
"nodes_modes",
onnx.TensorProto.UINT8,
(3,),
np.array([0, 6, 6], dtype=np.uint8),
),
nodes_featureids=[0, 0, 0],
nodes_splits=make_tensor(
"nodes_splits",
onnx.TensorProto.FLOAT,
(3,),
np.array([11, 232344.0, np.nan], dtype=np.float32),
),
nodes_trueleafs=[0, 1, 1],
nodes_truenodeids=[1, 0, 1],
nodes_falseleafs=[1, 0, 1],
nodes_falsenodeids=[2, 2, 3],
leaf_targetids=[0, 1, 2, 3],
leaf_weights=make_tensor(
"leaf_weights", onnx.TensorProto.FLOAT, (4,), [1, 10, 1000, 100]
),
)
x = np.array([1.2, 3.4, -0.12, np.nan, 12, 7], np.float32).reshape(-1, 1)
expected = np.array(
[
[1, 0, 0, 0],
[0, 0, 0, 100],
[0, 0, 0, 100],
[0, 0, 1000, 0],
[0, 0, 1000, 0],
[0, 10, 0, 0],
],
dtype=np.float32,
)
expect(
node,
inputs=[x],
outputs=[expected],
name="test_ai_onnx_ml_tree_ensemble_set_membership",
)
tree_ensemble_single_tree
node = onnx.helper.make_node(
"TreeEnsemble",
["X"],
["Y"],
domain="ai.onnx.ml",
n_targets=2,
membership_values=None,
nodes_missing_value_tracks_true=None,
nodes_hitrates=None,
aggregate_function=1,
post_transform=0,
tree_roots=[0],
nodes_modes=make_tensor(
"nodes_modes",
onnx.TensorProto.UINT8,
(3,),
np.array([0, 0, 0], dtype=np.uint8),
),
nodes_featureids=[0, 0, 0],
nodes_splits=make_tensor(
"nodes_splits",
onnx.TensorProto.DOUBLE,
(3,),
np.array([3.14, 1.2, 4.2], dtype=np.float64),
),
nodes_truenodeids=[1, 0, 1],
nodes_trueleafs=[0, 1, 1],
nodes_falsenodeids=[2, 2, 3],
nodes_falseleafs=[0, 1, 1],
leaf_targetids=[0, 1, 0, 1],
leaf_weights=make_tensor(
"leaf_weights",
onnx.TensorProto.DOUBLE,
(4,),
np.array([5.23, 12.12, -12.23, 7.21], dtype=np.float64),
),
)
x = np.array([1.2, 3.4, -0.12, 1.66, 4.14, 1.77], np.float64).reshape(3, 2)
y = np.array([[5.23, 0], [5.23, 0], [0, 12.12]], dtype=np.float64)
expect(
node,
inputs=[x],
outputs=[y],
name="test_ai_onnx_ml_tree_ensemble_single_tree",
)
💔No Cover Common Operators
CastMap (call for test cases)
CategoryMapper (call for test cases)
DictVectorizer (call for test cases)
FeatureVectorizer (call for test cases)
Imputer (call for test cases)
LinearClassifier (call for test cases)
LinearRegressor (call for test cases)
Normalizer (call for test cases)
OneHotEncoder (call for test cases)
SVMClassifier (call for test cases)
SVMRegressor (call for test cases)
Scaler (call for test cases)
TreeEnsembleClassifier (call for test cases)
TreeEnsembleRegressor (call for test cases)
ZipMap (call for test cases)
💚Covered Experimental Operators
💔No Cover Experimental Operators
Model Test Coverage
No model tests present for selected domain