| from typing import Callable, TypeVar, Mapping |
|
|
| import numpy as np |
|
|
|
|
| metric_registry = [] |
| NumMap = Mapping[str, float | int | bool] |
| NM = TypeVar("NM", bound=NumMap) |
|
|
|
|
| def register( |
| func: Callable[[list[np.ndarray]], NM] |
| ) -> Callable[[list[np.ndarray]], NM]: |
| metric_registry.append(func) |
| return func |
|
|
|
|
| @register |
| def basic(data: list[np.ndarray]) -> NumMap: |
| catted = np.concatenate(data) |
| return { |
| "Token Count": len(catted), |
| "Line Count": len(data), |
| "Tokens per Line": len(np.concatenate(data)) / len(data), |
| "Tokens per Line SD": float(np.std([len(x) for x in data])), |
| "Unique Tokens": len(np.unique(catted)), |
| "Unique Lines": len({hash(l.data.tobytes()) for l in data}), |
| } |
|
|
|
|
| @register |
| def entropy_1gram(data: list[np.ndarray]) -> NumMap: |
| catted = np.concatenate(data) |
| counts = np.unique(catted, return_counts=True)[1] |
| normed = counts / counts.sum() |
| ent = -(normed * np.log2(normed)).sum() |
| return { |
| "1-gram Entropy": ent, |
| "1-gram Normalized Entropy": ent / np.log2(len(counts)), |
| } |
|
|
|
|
| @register |
| def conditional_entropy_2gram(data: list[np.ndarray]) -> NumMap: |
| windows = [ |
| np.lib.stride_tricks.sliding_window_view(x, (2,), axis=-1) |
| for x in data |
| if len(x) >= 2 |
| ] |
| catted = np.concatenate(windows) |
| counts = np.unique(catted, axis=0, return_counts=True)[1] |
| normed = counts / counts.sum() |
| ent = -(normed * np.log2(normed)).sum() |
| return { |
| "2-gram Entropy": ent, |
| "2-gram Conditional Entropy": ent - entropy_1gram(data)["1-gram Entropy"], |
| } |
|
|
|
|
| @register |
| def entropy_per_line(data: list[np.ndarray]) -> NumMap: |
| catted = np.concatenate(data) |
| unique_values, counts_dense = np.unique(catted, return_counts=True) |
| counts = np.zeros(np.max(unique_values) + 1, dtype=float) |
| counts[unique_values] = counts_dense |
| |
| counts += 1e-10 |
| logged = np.log2(counts / counts.sum()) |
| bpm = -np.mean([logged[msg].sum() for msg in data]) |
| return {"Entropy per Line": bpm} |
|
|
|
|
| @register |
| def end_of_sentence(data: list[np.ndarray]) -> NumMap: |
| """Detect if end-of-sentence token present.""" |
| candidates = {l[-1] for l in data} |
| if len(candidates) == 1: |
| c = list(candidates)[0] |
|
|
| eos_only_at_end = lambda x: all(x[:-1] != c) |
| all_eos_rightward = lambda x: all( |
| (x[i] != c) or all(x[i:] == c) for i in range(len(x)) |
| ) |
| eos_present = all(eos_only_at_end(x) or all_eos_rightward(x) for x in data) |
|
|
| same_length = len({len(l) for l in data}) == 1 |
| padding_exists = any(any(x[:-1] == c) for x in data) |
| eos_padding = eos_present and same_length and padding_exists |
| else: |
| eos_present = False |
| eos_padding = False |
|
|
| return { |
| "EoS Token Present": eos_present, |
| "EoS Padding": eos_padding, |
| } |
|
|
