# Copyright (c) MONAI Consortium
# 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.
import logging
import time
from typing import Any, Dict
from monailabel.interfaces.datastore import Datastore
from monailabel.interfaces.tasks.strategy import Strategy
logger = logging.getLogger(__name__)
[docs]class Epistemic(Strategy):
"""
Epistemic as active learning strategy
"""
SECS_IN_DAY = 24 * 60 * 60
def __init__(
self, k=0, reset=SECS_IN_DAY, key="epistemic_entropy", desc="Get First Sample Based on Epistemic score"
):
self.k = k
self.reset = reset # Reset previously served samples after N seconds (ex: every day)
self.key = key
super().__init__(desc)
def __call__(self, request, datastore: Datastore):
label_tag = request.get("label_tag")
labels = request.get("labels")
images = datastore.get_unlabeled_images(label_tag, labels)
if not len(images):
return None
scores: Dict[str, Any] = {}
current_ts = int(time.time())
strategy = request["strategy"]
for image in images:
info = datastore.get_image_info(image)
score = info.get(self.key, 0)
ts = min(current_ts - info.get("strategy", {}).get(strategy, {}).get("ts", 0), self.reset)
scores[image] = {"score": score, "ts": ts}
scores = {k: v for k, v in sorted(scores.items(), key=lambda item: item[1]["score"], reverse=True)} # type: ignore
logger.info(f"{strategy}: Top-N: {scores}")
# Pick Top-N based on epistemic scores
top_k: Dict[str, Any] = {}
max_len = self.k if 0 < self.k < len(scores) else len(scores)
for k, v in scores.items():
if len(top_k) == max_len:
break
# Handle similar timestamps
top_k[k] = {
"score": v["score"],
"ts": v["ts"] - (pow(10, len(top_k)) if v["ts"] == self.reset else len(top_k)) * 10,
}
logger.info(f"{strategy}: Top-K: {top_k}")
# Pick the one which is least served recently among Top-N
top_k = {k: v for k, v in sorted(scores.items(), key=lambda item: item[1]["ts"], reverse=True)} # type: ignore
logger.info(f"{strategy}: Top-K (ts): {top_k};")
image = next(iter(top_k))
logger.info(f"{strategy}: Selected Image: {image}; epistemic_entropy: {top_k[image]}")
return {"id": image, "epistemic_entropy": top_k[image]}