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from collections import deque
from typing import TYPE_CHECKING, Dict, List, Union
if TYPE_CHECKING:
import pandas as pd
class MetricsTrackerAverage:
"""Metrics Tracker Average."""
def __init__(self, step: int):
self.history: List[Dict[str, Union[int, float]]] = [{"step": 0, "score": 0}]
self.step: int = step
self.i: int = 0
self.num: float = 0
self.denom: float = 0
@property
def score(self) -> float:
return self.num / self.denom if self.denom > 0 else 0
def on_decision(self) -> None:
self.denom += 1
def on_feedback(self, score: float) -> None:
self.num += score or 0
self.i += 1
if self.step > 0 and self.i % self.step == 0:
self.history.append({"step": self.i, "score": self.score})
def to_pandas(self) -> "pd.DataFrame":
import pandas as pd
return pd.DataFrame(self.history)
class MetricsTrackerRollingWindow:
"""Metrics Tracker Rolling Window."""
def __init__(self, window_size: int, step: int):
self.history: List[Dict[str, Union[int, float]]] = [{"step": 0, "score": 0}]
self.step: int = step
self.i: int = 0
self.window_size: int = window_size
self.queue: deque = deque()
self.sum: float = 0.0
@property
def score(self) -> float:
return self.sum / len(self.queue) if len(self.queue) > 0 else 0
def on_decision(self) -> None:
pass
def on_feedback(self, value: float) -> None:
self.sum += value
self.queue.append(value)
self.i += 1
if len(self.queue) > self.window_size:
old_val = self.queue.popleft()
self.sum -= old_val
if self.step > 0 and self.i % self.step == 0:
self.history.append({"step": self.i, "score": self.sum / len(self.queue)})
def to_pandas(self) -> "pd.DataFrame":
import pandas as pd
return pd.DataFrame(self.history)
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