def test_schema_enforcement_named_tensor_schema_1d():
m = Model()
input_schema = Schema([
TensorSpec(np.dtype(np.uint64), (-1, ), "a"),
TensorSpec(np.dtype(np.float32), (-1, ), "b")
])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
pdf = pd.DataFrame(data=[[0, 0], [1, 1]], columns=["a", "b"])
pdf["a"] = pdf["a"].astype(np.uint64)
pdf["b"] = pdf["a"].astype(np.float32)
d_inp = {
"a": np.array(pdf["a"], dtype=np.uint64),
"b": np.array(pdf["b"], dtype=np.float32),
}
# test dataframe input works for 1d tensor specs and input is converted to dict
res = pyfunc_model.predict(pdf)
assert _compare_exact_tensor_dict_input(res, d_inp)
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test that dictionary works too
res = pyfunc_model.predict(d_inp)
assert res == d_inp
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
def test_schema_enforcement_single_named_tensor_schema():
m = Model()
input_schema = Schema([TensorSpec(np.dtype(np.uint64), (-1, 2), "a")])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
inp = {
"a": np.array([[0, 0], [1, 1]], dtype=np.uint64),
}
# sanity test that dictionary with correct input works
res = pyfunc_model.predict(inp)
assert res == inp
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test single np.ndarray input works and is converted to dictionary
res = pyfunc_model.predict(inp["a"])
assert res == inp
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test list does not work
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict([[0, 0], [1, 1]])
assert "Model is missing inputs ['a']" in str(ex)
def test_parse_with_schema(pandas_df_with_all_types):
schema = Schema([ColSpec(c, c) for c in pandas_df_with_all_types.columns])
df = _shuffle_pdf(pandas_df_with_all_types)
json_str = json.dumps(df.to_dict(orient="split"), cls=NumpyEncoder)
df = pyfunc_scoring_server.parse_json_input(json_str,
orient="split",
schema=schema)
json_str = json.dumps(df.to_dict(orient="records"), cls=NumpyEncoder)
df = pyfunc_scoring_server.parse_json_input(json_str,
orient="records",
schema=schema)
assert schema == infer_signature(df[schema.input_names()]).inputs
# The current behavior with pandas json parse with type hints is weird. In some cases, the
# types are forced ignoting overflow and loss of precision:
bad_df = """{
"columns":["bad_integer", "bad_float", "bad_string", "bad_boolean"],
"data":[
[9007199254740991.0, 1.1, 1, 1.5],
[9007199254740992.0, 9007199254740992.0, 2, 0],
[9007199254740994.0, 3.3, 3, "some arbitrary string"]
]
}"""
schema = Schema([
ColSpec("integer", "bad_integer"),
ColSpec("float", "bad_float"),
ColSpec("float", "good_float"),
ColSpec("string", "bad_string"),
ColSpec("boolean", "bad_boolean"),
])
df = pyfunc_scoring_server.parse_json_input(bad_df,
orient="split",
schema=schema)
# Unfortunately, the current behavior of pandas parse is to force numbers to int32 even if
# they don't fit:
assert df["bad_integer"].dtype == np.int32
assert all(df["bad_integer"] == [-2147483648, -2147483648, -2147483648])
# The same goes for floats:
assert df["bad_float"].dtype == np.float32
assert all(df["bad_float"] == np.array([1.1, 9007199254740992, 3.3],
dtype=np.float32))
# However bad string is recognized as int64:
assert all(df["bad_string"] == np.array([1, 2, 3], dtype=np.object))
# Boolean is forced - zero and empty string is false, everything else is true:
assert df["bad_boolean"].dtype == np.bool
assert all(df["bad_boolean"] == [True, False, True])
def parse_csv_input(csv_input, schema: Schema = None):
"""
:param csv_input: A CSV-formatted string representation of a Pandas DataFrame, or a stream
containing such a string representation.
:param schema: Optional schema specification to be used during parsing.
"""
try:
if schema is None:
return pd.read_csv(csv_input)
else:
dtypes = dict(zip(schema.input_names(), schema.pandas_types()))
return pd.read_csv(csv_input, dtype=dtypes)
except Exception:
_handle_serving_error(
error_message=
("Failed to parse input as a Pandas DataFrame. Ensure that the input is"
" a valid CSV-formatted Pandas DataFrame produced using the"
" `pandas.DataFrame.to_csv()` method."),
error_code=BAD_REQUEST,
)
def test_tensor_multi_named_schema_enforcement():
m = Model()
input_schema = Schema([
TensorSpec(np.dtype(np.uint64), (-1, 5), "a"),
TensorSpec(np.dtype(np.short), (-1, 2), "b"),
TensorSpec(np.dtype(np.float32), (2, -1, 2), "c"),
])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
inp = {
"a": np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], dtype=np.uint64),
"b": np.array([[0, 0], [1, 1], [2, 2]], dtype=np.short),
"c": np.array([[[0, 0], [1, 1]], [[2, 2], [3, 3]]], dtype=np.float32),
}
# test that missing column raises
inp1 = {k: v for k, v in inp.items()}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp1.pop("b"))
assert "Model is missing inputs" in str(ex)
# test that extra column is ignored
inp2 = {k: v for k, v in inp.items()}
inp2["x"] = 1
# test that extra column is removed
res = pyfunc_model.predict(inp2)
assert res == {k: v for k, v in inp.items() if k in {"a", "b", "c"}}
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test that variable axes are supported
inp3 = {
"a":
np.array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]],
dtype=np.uint64),
"b":
np.array([[0, 0], [1, 1]], dtype=np.short),
"c":
np.array([[[0, 0]], [[2, 2]]], dtype=np.float32),
}
res = pyfunc_model.predict(inp3)
assert _compare_exact_tensor_dict_input(res, inp3)
expected_types = dict(
zip(input_schema.input_names(), input_schema.input_types()))
actual_types = {k: v.dtype for k, v in res.items()}
assert expected_types == actual_types
# test that type casting is not supported
inp4 = {k: v for k, v in inp.items()}
inp4["a"] = inp4["a"].astype(np.int32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp4)
assert "dtype of input int32 does not match expected dtype uint64" in str(
ex)
# test wrong shape
inp5 = {
"a": np.array([[0, 0, 0, 0]], dtype=np.uint),
"b": np.array([[0, 0], [1, 1]], dtype=np.short),
"c": np.array([[[0, 0]]], dtype=np.float32),
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp5)
assert "Shape of input (1, 4) does not match expected shape (-1, 5)" in str(
ex)
# test non-dictionary input
inp6 = [
np.array([[0, 0, 0, 0, 0]], dtype=np.uint64),
np.array([[0, 0], [1, 1]], dtype=np.short),
np.array([[[0, 0]]], dtype=np.float32),
]
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp6)
assert "Model is missing inputs ['a', 'b', 'c']." in str(ex)
# test empty ndarray does not work
inp7 = {k: v for k, v in inp.items()}
inp7["a"] = np.array([])
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp7)
assert "Shape of input (0,) does not match expected shape" in str(ex)
# test dictionary of str -> list does not work
inp8 = {k: list(v) for k, v in inp.items()}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(inp8)
assert "This model contains a tensor-based model signature with input names" in str(
ex)
assert (
"suggests a dictionary input mapping input name to a numpy array, but a dict"
" with value type <class 'list'> was found") in str(ex)
# test dataframe input fails at shape enforcement
pdf = pd.DataFrame(
data=[[1, 2, 3]],
columns=["a", "b", "c"],
)
pdf["a"] = pdf["a"].astype(np.uint64)
pdf["b"] = pdf["b"].astype(np.short)
pdf["c"] = pdf["c"].astype(np.float32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Shape of input (1,) does not match expected shape (-1, 5)" in str(
ex)
def test_column_schema_enforcement():
m = Model()
input_schema = Schema([
ColSpec("integer", "a"),
ColSpec("long", "b"),
ColSpec("float", "c"),
ColSpec("double", "d"),
ColSpec("boolean", "e"),
ColSpec("string", "g"),
ColSpec("binary", "f"),
ColSpec("datetime", "h"),
])
m.signature = ModelSignature(inputs=input_schema)
pyfunc_model = PyFuncModel(model_meta=m, model_impl=TestModel())
pdf = pd.DataFrame(
data=[[
1, 2, 3, 4, True, "x",
bytes([1]), "2021-01-01 00:00:00.1234567"
]],
columns=["b", "d", "a", "c", "e", "g", "f", "h"],
dtype=np.object,
)
pdf["a"] = pdf["a"].astype(np.int32)
pdf["b"] = pdf["b"].astype(np.int64)
pdf["c"] = pdf["c"].astype(np.float32)
pdf["d"] = pdf["d"].astype(np.float64)
pdf["h"] = pdf["h"].astype(np.datetime64)
# test that missing column raises
with pytest.raises(MlflowException) as ex:
res = pyfunc_model.predict(pdf[["b", "d", "a", "e", "g", "f", "h"]])
assert "Model is missing inputs" in str(ex)
# test that extra column is ignored
pdf["x"] = 1
# test that columns are reordered, extra column is ignored
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
expected_types = dict(
zip(input_schema.input_names(), input_schema.pandas_types()))
# MLflow datetime type in input_schema does not encode precision, so add it for assertions
expected_types["h"] = np.dtype("datetime64[ns]")
actual_types = res.dtypes.to_dict()
assert expected_types == actual_types
# Test conversions
# 1. long -> integer raises
pdf["a"] = pdf["a"].astype(np.int64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
# 2. integer -> long works
pdf["b"] = pdf["b"].astype(np.int32)
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
pdf["b"] = pdf["b"].astype(np.int64)
# 3. unsigned int -> long works
pdf["b"] = pdf["b"].astype(np.uint32)
res = pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
pdf["b"] = pdf["b"].astype(np.int64)
# 4. unsigned int -> int raises
pdf["a"] = pdf["a"].astype(np.uint32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
# 5. double -> float raises
pdf["c"] = pdf["c"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 6. float -> double works, double -> float does not
pdf["d"] = pdf["d"].astype(np.float32)
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
assert "Incompatible input types" in str(ex)
pdf["d"] = pdf["d"].astype(np.float64)
pdf["c"] = pdf["c"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 7. int -> float raises
pdf["c"] = pdf["c"].astype(np.int32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["c"] = pdf["c"].astype(np.float32)
# 8. int -> double works
pdf["d"] = pdf["d"].astype(np.int32)
pyfunc_model.predict(pdf)
assert all((res == pdf[input_schema.input_names()]).all())
assert res.dtypes.to_dict() == expected_types
# 9. long -> double raises
pdf["d"] = pdf["d"].astype(np.int64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["d"] = pdf["d"].astype(np.float64)
# 10. any float -> any int raises
pdf["a"] = pdf["a"].astype(np.float32)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
# 10. any float -> any int raises
pdf["a"] = pdf["a"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["a"] = pdf["a"].astype(np.int32)
pdf["b"] = pdf["b"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
assert "Incompatible input types" in str(ex)
pdf["b"] = pdf["b"].astype(np.int64)
pdf["b"] = pdf["b"].astype(np.float64)
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf)
pdf["b"] = pdf["b"].astype(np.int64)
assert "Incompatible input types" in str(ex)
# 11. objects work
pdf["b"] = pdf["b"].astype(np.object)
pdf["d"] = pdf["d"].astype(np.object)
pdf["e"] = pdf["e"].astype(np.object)
pdf["f"] = pdf["f"].astype(np.object)
pdf["g"] = pdf["g"].astype(np.object)
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
# 12. datetime64[D] (date only) -> datetime64[x] works
pdf["h"] = pdf["h"].astype("datetime64[D]")
res = pyfunc_model.predict(pdf)
assert res.dtypes.to_dict() == expected_types
pdf["h"] = pdf["h"].astype("datetime64[s]")
# 13. np.ndarrays can be converted to dataframe but have no columns
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(pdf.values)
assert "Model is missing inputs" in str(ex)
# 14. dictionaries of str -> list/nparray work
arr = np.array([1, 2, 3])
d = {
"a":
arr.astype("int32"),
"b":
arr.astype("int64"),
"c":
arr.astype("float32"),
"d":
arr.astype("float64"),
"e": [True, False, True],
"g": ["a", "b", "c"],
"f": [bytes(0), bytes(1), bytes(1)],
"h":
np.array(["2020-01-01", "2020-02-02", "2020-03-03"],
dtype=np.datetime64),
}
res = pyfunc_model.predict(d)
assert res.dtypes.to_dict() == expected_types
# 15. dictionaries of str -> list[list] fail
d = {
"a": [arr.astype("int32")],
"b": [arr.astype("int64")],
"c": [arr.astype("float32")],
"d": [arr.astype("float64")],
"e": [[True, False, True]],
"g": [["a", "b", "c"]],
"f": [[bytes(0), bytes(1), bytes(1)]],
"h": [
np.array(["2020-01-01", "2020-02-02", "2020-03-03"],
dtype=np.datetime64)
],
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(d)
assert "Incompatible input types" in str(ex)
# 16. conversion to dataframe fails
d = {
"a": [1],
"b": [1, 2],
"c": [1, 2, 3],
}
with pytest.raises(MlflowException) as ex:
pyfunc_model.predict(d)
assert "This model contains a column-based signature, which suggests a DataFrame input." in str(
ex)
