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Merge pull request #9 from VowpalWabbit/fix_embedding_w_indexes
proper embeddings and rolling window average
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,31 +1,66 @@ | ||
| from typing import TYPE_CHECKING, Dict, List, Optional, Union | ||
| from collections import deque | ||
| from typing import TYPE_CHECKING, Dict, List, Union | ||
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| if TYPE_CHECKING: | ||
| import pandas as pd | ||
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| class MetricsTracker: | ||
| class MetricsTrackerAverage: | ||
| def __init__(self, step: int): | ||
| self._history: List[Dict[str, Union[int, float]]] = [] | ||
| self._step: int = step | ||
| self._i: int = 0 | ||
| self._num: float = 0 | ||
| self._denom: float = 0 | ||
| 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 | ||
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| @property | ||
| def score(self) -> float: | ||
| return self._num / self._denom if self._denom > 0 else 0 | ||
| return self.num / self.denom if self.denom > 0 else 0 | ||
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| def on_decision(self) -> None: | ||
| self._denom += 1 | ||
| self.denom += 1 | ||
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| def on_feedback(self, score: Optional[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 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}) | ||
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| def to_pandas(self) -> "pd.DataFrame": | ||
| import pandas as pd | ||
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| return pd.DataFrame(self._history) | ||
| return pd.DataFrame(self.history) | ||
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| class MetricsTrackerRollingWindow: | ||
| 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 | ||
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| @property | ||
| def score(self) -> float: | ||
| return self.sum / len(self.queue) if len(self.queue) > 0 else 0 | ||
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| def on_decision(self) -> None: | ||
| pass | ||
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| def on_feedback(self, value: float) -> None: | ||
| self.sum += value | ||
| self.queue.append(value) | ||
| self.i += 1 | ||
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| if len(self.queue) > self.window_size: | ||
| old_val = self.queue.popleft() | ||
| self.sum -= old_val | ||
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| if self.step > 0 and self.i % self.step == 0: | ||
| self.history.append({"step": self.i, "score": self.sum / len(self.queue)}) | ||
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| def to_pandas(self) -> "pd.DataFrame": | ||
| import pandas as pd | ||
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| return pd.DataFrame(self.history) |
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