# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# 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
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import copy
import logging
from collections import OrderedDict
import numpy as np
from scipy.stats import pearsonr, spearmanr
from libai.utils import distributed as dist
from .evaluator import DatasetEvaluator
logger = logging.getLogger(__name__)
[docs]class RegEvaluator(DatasetEvaluator):
def __init__(self):
self._predictions = []
[docs] def reset(self):
self._predictions = []
[docs] def process(self, inputs, outputs):
pred_logits = outputs["prediction_scores"]
labels = inputs["labels"]
# measure accuracy
preds = pred_logits.cpu().topk(1)[1].squeeze(1).numpy()
labels = labels.cpu().numpy()
self._predictions.append({"preds": preds, "labels": labels})
[docs] def evaluate(self):
if not dist.is_main_process():
return {}
else:
predictions = self._predictions
preds = np.array([])
labels = np.array([])
for prediction in predictions:
preds = np.concatenate((preds, prediction["preds"]))
labels = np.concatenate((labels, prediction["labels"]))
pearson_corr = pearsonr(preds, labels)[0]
spearman_corr = spearmanr(preds, labels)[0]
corr = (pearson_corr + spearman_corr) / 2
self._results = OrderedDict()
self._results["pearson"] = pearson_corr
self._results["spearman"] = spearman_corr
self._results["corr"] = corr
return copy.deepcopy(self._results)