def predict(*args):
model = SparkModelCache.get_or_load(archive_path)
schema = {str(i): arg for i, arg in enumerate(args)}
pdf = None
for x in args:
if type(x) == pandas.DataFrame:
if len(args) != 1:
raise Exception(
"If passing a StructType column, there should be only one "
"input column, but got %d" % len(args))
pdf = x
if pdf is None:
# Explicitly pass order of columns to avoid lexicographic ordering (i.e., 10 < 2)
columns = [str(i) for i, _ in enumerate(args)]
pdf = pandas.DataFrame(schema, columns=columns)
result = model.predict(pdf)
if not isinstance(result, pandas.DataFrame):
result = pandas.DataFrame(data=result)
elif type(elem_type) == IntegerType:
result = result.select_dtypes(
[np.byte, np.ubyte, np.short, np.ushort,
np.int32]).astype(np.int32)
elif type(elem_type) == LongType:
result = result.select_dtypes(
[np.byte, np.ubyte, np.short, np.ushort, np.int, np.long])
elif type(elem_type) == FloatType:
result = result.select_dtypes(include=(np.number, )).astype(
np.float32)
elif type(elem_type) == DoubleType:
result = result.select_dtypes(include=(np.number, )).astype(
np.float64)
if len(result.columns) == 0:
raise MlflowException(
message=
"The the model did not produce any values compatible with the requested "
"type '{}'. Consider requesting udf with StringType or "
"Arraytype(StringType).".format(str(elem_type)),
error_code=INVALID_PARAMETER_VALUE)
if type(elem_type) == StringType:
result = result.applymap(str)
if type(result_type) == ArrayType:
return pandas.Series([row[1].values for row in result.iterrows()])
else:
return result[result.columns[0]]
def predict(*args):
model = SparkModelCache.get_or_load(archive_path)
input_schema = model.metadata.get_input_schema()
pdf = None
for x in args:
if type(x) == pandas.DataFrame:
if len(args) != 1:
raise Exception(
"If passing a StructType column, there should be only one "
"input column, but got %d" % len(args))
pdf = x
if pdf is None:
args = list(args)
if input_schema is None:
names = [str(i) for i in range(len(args))]
else:
names = input_schema.column_names()
if len(args) > len(names):
args = args[:len(names)]
if len(args) < len(names):
message = (
"Model input is missing columns. Expected {0} input columns {1},"
" but the model received only {2} unnamed input columns"
" (Since the columns were passed unnamed they are expected to be in"
" the order specified by the schema).".format(
len(names), names, len(args)))
raise MlflowException(message)
pdf = pandas.DataFrame(
data={names[i]: x
for i, x in enumerate(args)}, columns=names)
result = model.predict(pdf)
if not isinstance(result, pandas.DataFrame):
result = pandas.DataFrame(data=result)
elem_type = result_type.elementType if isinstance(
result_type, ArrayType) else result_type
if type(elem_type) == IntegerType:
result = result.select_dtypes(
[np.byte, np.ubyte, np.short, np.ushort,
np.int32]).astype(np.int32)
elif type(elem_type) == LongType:
result = result.select_dtypes(
[np.byte, np.ubyte, np.short, np.ushort, np.int, np.long])
elif type(elem_type) == FloatType:
result = result.select_dtypes(include=(np.number, )).astype(
np.float32)
elif type(elem_type) == DoubleType:
result = result.select_dtypes(include=(np.number, )).astype(
np.float64)
if len(result.columns) == 0:
raise MlflowException(
message=
"The the model did not produce any values compatible with the requested "
"type '{}'. Consider requesting udf with StringType or "
"Arraytype(StringType).".format(str(elem_type)),
error_code=INVALID_PARAMETER_VALUE,
)
if type(elem_type) == StringType:
result = result.applymap(str)
if type(result_type) == ArrayType:
return pandas.Series(result.to_numpy().tolist())
else:
return result[result.columns[0]]
