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83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | from __future__ import annotations
import itertools
import logging
import random
from collections import defaultdict
from typing import Any, Dict, Optional
import numpy as np
import sklearn
import sklearn.cluster
from datasets import Dataset, DatasetDict
from sklearn.metrics.cluster import v_measure_score
from ..MTEBResults import HFSubset
from .AbsTask import AbsTask
logger = logging.getLogger(__name__)
MultilingualDataset = Dict[HFSubset, DatasetDict]
def evaluate_clustering_bootstrapped(
embeddings: np.ndarray,
labels: list[list[str]],
n_clusters: int,
cluster_size: int,
kmean_batch_size: int,
max_depth: Optional[int],
rng_state: random.Random = random.Random(),
) -> dict[str, list[float]]:
"""Bootstrapped evaluation of clustering performance using V-measure.
The bootstrapping is done by sampling N samples from the corpus and clustering them. It is done without replacement to get a diverse set of
samples.
"""
n_embeddings = embeddings.shape[0]
v_measures = defaultdict(list)
if max_depth is not None:
max_depth = min(max_depth, max(map(len, labels)))
else:
max_depth = max(map(len, labels))
# Evaluate on each level til max depth
for i_level in range(max_depth):
level_labels = []
# Assign -1 to gold label if the level is not there
for label in labels:
if len(label) > i_level:
level_labels.append(label[i_level])
else:
level_labels.append(-1)
level_labels = np.array(level_labels)
valid_idx = level_labels != -1
level_labels = level_labels[valid_idx]
level_embeddings = embeddings[valid_idx]
clustering_model = sklearn.cluster.MiniBatchKMeans(
n_clusters=len(set(level_labels)),
batch_size=kmean_batch_size,
n_init="auto",
)
for _ in range(n_clusters):
# sample N samples from the corpus with replacement
n_embeddings = len(level_embeddings)
cluster_indices = rng_state.choices(range(n_embeddings), k=cluster_size)
_embeddings = level_embeddings[cluster_indices]
_labels = level_labels[cluster_indices]
cluster_assignment = clustering_model.fit_predict(_embeddings)
v_measure = v_measure_score(_labels, cluster_assignment)
v_measures[f"Level {i_level}"].append(v_measure)
return v_measures
class AbsTaskClusteringFast(AbsTask):
"""Abstract class for Clustering tasks.
This class embeds the corpus sentences then samples N samples from the corpus and clusters them.
The similarity then is calculated using the V-measure metric, which is invariant to the permutation of the labels.
This approach is then repeated K times.
If the clustering is hieararchical, and more than one label is specified in order for each observation,
V-measures are calculated in the outlined way on each of the levels separately.
self.load_data() must generate a huggingface dataset with a split matching self.metadata_dict["eval_splits"], and assign it to self.dataset.
It must contain the following columns:
sentences: list[str]
labels: list[str] | list[list[str]]
"""
max_documents_to_embed = 16_384
max_documents_per_cluster = 2048
n_clusters = 10
k_mean_batch_size = 512
max_depth = None
def __init__(self, **kwargs):
super().__init__(**kwargs)
def _add_main_score(self, scores):
if self.metadata_dict["main_score"] in scores:
scores["main_score"] = scores[self.metadata_dict["main_score"]]
else:
logger.warn(
f"main score {self.metadata_dict['main_score']} not found in scores {scores.keys()}"
)
def _evaluate_subset(
self, model, dataset: DatasetDict, **kwargs: Any
) -> dict[str, float | dict[str, list[float]]]:
rng_state = random.Random(self.seed)
if len(dataset) > self.max_documents_to_embed:
example_indices = rng_state.sample(
range(len(dataset)), k=self.max_documents_to_embed
)
downsampled_dataset = dataset.select(example_indices)
else:
downsampled_dataset = dataset
logger.info(f"Encoding {len(downsampled_dataset)} sentences...")
embeddings = model.encode(downsampled_dataset["sentences"])
labels = []
for label in downsampled_dataset["labels"]:
if not isinstance(label, list):
label = [label]
labels.append(label)
v_measures = evaluate_clustering_bootstrapped(
embeddings,
labels,
n_clusters=self.n_clusters,
cluster_size=self.max_documents_per_cluster,
kmean_batch_size=self.k_mean_batch_size,
max_depth=self.max_depth,
rng_state=rng_state,
)
all_v_scores = itertools.chain.from_iterable(v_measures.values())
mean_v_measure = np.mean(list(all_v_scores))
scores = {"v_measures": v_measures, "v_measure": float(mean_v_measure)}
self._add_main_score(scores)
return scores
def clustering_downsample(
dataset: DatasetDict, seed: int, max_samples_in_cluster: int = 2048
) -> DatasetDict:
"""In cases where it is not possible to convert the dataset to a fast version, we can downsample the dataset to speed up the evaluation.
This might be necessary when the clusters in the dataset is not sampled from the same distribution.
"""
rng_state = random.Random(seed)
ds = {}
for split in dataset:
_docs = []
_labels = []
n_clusters = len(dataset[split])
for i in range(n_clusters):
labels = dataset[split]["labels"][i]
sentences = dataset[split]["sentences"][i]
n_sample = min(max_samples_in_cluster, len(sentences))
# sample n_sample from each cluster
idxs = rng_state.sample(range(len(sentences)), n_sample)
_docs.append([sentences[idx] for idx in idxs])
_labels.append([labels[idx] for idx in idxs])
ds[split] = Dataset.from_dict({"sentences": _docs, "labels": _labels})
return DatasetDict(ds)
def convert_to_fast(
dataset: DatasetDict, seed: int, max_size: int = 100_000
) -> DatasetDict:
"""Converts a clustering dataset to a fast version. This concats the cluster into two columns, sentences and labels.
It additionally downsamples the dataset to max_size.
"""
categories = None
rng_state = random.Random(seed)
ds = {}
for split in dataset:
sent_set = set()
labels = []
sentences = []
n_clusters = len(dataset[split])
for i in range(n_clusters):
lab = dataset[split]["labels"][i]
sents = dataset[split]["sentences"][i]
for l, s in zip(lab, sents):
if s not in sent_set:
labels.append(l)
sentences.append(s)
sent_set.add(s) # ensuring no duplicates
# check that it is the same distribution
if categories is None:
categories = set(labels)
else:
assert (
categories == set(labels)
), "The clusters are not sampled from the same distribution as they have different labels."
ds[split] = Dataset.from_dict({"sentences": sentences, "labels": labels})
if len(ds[split]) > max_size:
idxs = rng_state.sample(range(len(ds[split])), max_size)
ds[split] = ds[split].select(idxs)
return DatasetDict(ds)
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