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prediction.py
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prediction.py
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#
# Copyright (c) 2022 IBM Corp.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from dataclasses import dataclass
from typing import Mapping
@dataclass
class Prediction:
"""
An object containing model predictions for a single element.
Each model.infer method should return at least the label and score fields. In order for a model to return
additional fields (e.g., model embeddings), a different dataclass that inherits from this one, and adds the desired
fields, can be used.
"""
label: bool
score: float
def __post_init__(self):
# Since many models return numpy objects, which are not json-serializable, we convert them here
self.label = bool(self.label)
self.score = float(self.score)
if self.score < 0 or self.score > 1:
raise Exception(f'Model score {self.score} is outside the range [0-1]')
@dataclass
class MulticlassPrediction:
"""
An object containing multiclass model predictions for a single element.
Each model.infer method should return at least the label and scores fields. In order for a model to return
additional fields (e.g., model embeddings), a different dataclass that inherits from this one, and adds the desired
fields, can be used.
"""
label: int
scores: Mapping[int, float]
def __post_init__(self):
# Since many models return numpy objects, which are not json-serializable, we convert them here
self.label = int(self.label)
if type(self.scores) == dict: # for backward compatibility, can be removed in future
self.scores = {int(k): float(v) for k, v in self.scores.items()}
for score in self.scores.values():
if score < 0 or score > 1:
raise Exception(f'Model score {score} is outside the range [0-1]')