From 82c954e6d227301a40dea075255df138d0a6d1c9 Mon Sep 17 00:00:00 2001
From: qingyangliu0065 <ciaraliuqy@gmail.com>
Date: Thu, 19 Sep 2024 15:17:15 -0400
Subject: [PATCH] toxicity evaluator added
---
llments/eval/toxicity.py | 183 ++++++++++++++++++
.../lm/base/{dataset_ml.py => dataset_lm.py} | 0
2 files changed, 183 insertions(+)
create mode 100644 llments/eval/toxicity.py
rename llments/lm/base/{dataset_ml.py => dataset_lm.py} (100%)
diff --git a/llments/eval/toxicity.py b/llments/eval/toxicity.py
new file mode 100644
index 0000000..bfa28a6
--- /dev/null
+++ b/llments/eval/toxicity.py
@@ -0,0 +1,183 @@
+"""Evaluators for toxicity analysis."""
+
+import os
+import requests # type: ignore
+import json
+import time
+import tqdm
+from typing import Dict
+from llments.eval.eval import Evaluator, EvalContext
+
+
+class ToxicityEvaluator(Evaluator):
+ """A class that defines an evaluation function for toxicity analysis."""
+
+ def __init__(self, api_key: str):
+ """Initializes the ToxicityEvaluator class.
+
+ Args:
+ api_key (str): API key for accessing the Perspective API.
+ """
+ self.api_key = api_key
+ self.url = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={api_key}"
+ self.categories = [
+ "toxicity",
+ "severe_toxicity",
+ "insult",
+ "flirtation",
+ "profanity",
+ "sexually_explicit",
+ "threat",
+ "identity_attack",
+ ]
+ self.languages = ["en"]
+
+ def _get_toxicity_scores(self, text: str) -> Dict[str, float]:
+ """Helper method to get toxicity scores for a single text input from the API.
+
+ Args:
+ text (str): The text to evaluate.
+
+ Returns:
+ Dict[str, float]: A dictionary containing toxicity scores for various categories.
+ """
+ if len(text.strip()) == 0:
+ # Return zeros for empty text
+ return {category: 0.0 for category in self.categories}
+
+ data_dict = {
+ "comment": {"text": text},
+ "languages": self.languages,
+ "requestedAttributes": {cat.upper(): {} for cat in self.categories},
+ }
+
+ # Make the API request
+ response = requests.post(url=self.url, data=json.dumps(data_dict))
+ response_dict = json.loads(response.content)
+
+ # Return scores or zeros if no valid response
+ if "attributeScores" in response_dict:
+ scores = {}
+ for category in self.categories:
+ score = response_dict["attributeScores"][category.upper()][
+ "spanScores"
+ ][0]["score"]["value"]
+ scores[category] = score
+ return scores
+ else:
+ # Return zeros for invalid responses
+ return {category: 0.0 for category in self.categories}
+
+ # override the evaluate method from the Evaluator class
+ # for better design logic, the evaluate() and evaluate_batch() methods
+ # only support the "toxicity" context
+ def evaluate(self, hyp: str, context: EvalContext | None = None) -> float:
+ """Returns the toxicity score for a given hypothesis.
+
+ Args:
+ hyp (str): The hypothesized string (e.g. a system output).
+ context (EvalContext | None): The reference context to condition on.
+
+ Returns:
+ float: The toxicity score, usually between 0 and 1 inclusive.
+ """
+ if context is not None:
+ raise Warning(
+ "This method only supports the 'toxicity' metric. \
+ Please use evaluate_multiple() for other metrics."
+ )
+
+ return self._get_toxicity_scores(hyp)["toxicity"]
+
+ # override the evaluate_batch method from the Evaluator class
+ def evaluate_batch(
+ self,
+ hyps: list[str],
+ contexts: list[EvalContext] | None = None,
+ minibatch_size: int | None = None,
+ show_progress: bool = False,
+ ) -> list[float]:
+ """Evaluate the toxicity of many hypotheses at once.
+
+ Args:
+ hyps (list[str]): A list of hypothesized strings (e.g. system outputs).
+ contexts (list[EvalContext] | None): The reference context to condition on.
+ minibatch_size (int | None): The size of the minibatch to use,
+ None guesses a good size automatically.
+ show_progress (bool): Whether to show a progress bar.
+
+ Returns:
+ list[float]: A list of toxicity scores, usually between 0 and 1 inclusive.
+ """
+ if contexts is not None:
+ # raise a warning that this method only supports the "toxicity" context
+ raise Warning(
+ "This method only supports the 'toxicity' metric. \
+ Please use evaluate_batch_multiple() for other metrics."
+ )
+
+ if show_progress:
+ hyps = tqdm.tqdm(hyps, desc="Evaluating")
+
+ res = []
+ for hyp in hyps:
+ # to avoid rate limiting
+ time.sleep(1.2)
+ res.append(self.evaluate(hyp, contexts))
+ return res
+
+ # override the evaluate_multiple method from the Evaluator class
+ # set the default contexts to ["toxicity"]
+
+ def evaluate_multiple(
+ self,
+ hyp: str,
+ metrics: list[str] = ["toxicity"],
+ show_progress: bool = False,
+ ) -> list[float]:
+ """Evaluate multiple metrics at once.
+
+ Args:
+ hyp (str): The hypothesized string.
+ metrics (list[str]): A list of metrics to evaluate.
+ show_progress (bool): Whether to show a progress bar.
+
+ Returns:
+ Dict[str, float]: A dictionary of evaluation scores, usually between 0 and 1 inclusive.
+ """
+ for metric in metrics:
+ if metric not in self.categories:
+ raise ValueError(f"Invalid metric: {metric}")
+
+ # get the toxicity scores based on the contexts
+ scores = self._get_toxicity_scores(hyp)
+
+ # return the scores for the specified contexts
+ return [scores[metric] for metric in metrics]
+
+ def evaluate_batch_multiple(
+ self,
+ hyps: list[str],
+ metrics: list[str] = ["toxicity"],
+ show_progress: bool = False,
+ ) -> list[list[float]]:
+ """Evaluate multiple metrics for many hypotheses at once.
+
+ Args:
+ hyps (list[str]): A list of hypothesized strings.
+ metrics (list[str]): A list of metrics to evaluate.
+ show_progress (bool): Whether to show a progress bar.
+
+ Returns:
+ list[list[float]]: A list of lists of evaluation scores, usually between 0 and 1 inclusive.
+ """
+ if show_progress:
+ hyps = tqdm.tqdm(hyps, desc="Evaluating")
+
+ res = []
+ for hyp in hyps:
+ # to avoid rate limiting
+ time.sleep(1.2)
+ res.append(self.evaluate_multiple(hyp, metrics))
+
+ return res
diff --git a/llments/lm/base/dataset_ml.py b/llments/lm/base/dataset_lm.py
similarity index 100%
rename from llments/lm/base/dataset_ml.py
rename to llments/lm/base/dataset_lm.py