mirror of https://github.com/hpcaitech/ColossalAI
220 lines
11 KiB
Python
220 lines
11 KiB
Python
import os
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from typing import Any, Dict, List
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import gpt_evaluate
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import metrics
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import pandas as pd
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import unieval
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from utils import analyze_automatic_results, get_data_per_category, save_automatic_results
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class Evaluator(object):
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"""
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A class named Evaluator includes GPT-3.5/GPT-4 evaluation
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and automatic evaluation
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"""
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def __init__(self, params: Dict[str, Any], battle_prompt: Dict[str, Any], gpt_evaluation_prompt: Dict[str, Any],
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gpt_model: str, language: str, path_for_UniEval: Dict[str, str], gpt_with_reference: bool) -> None:
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self.params = params
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self.battle_prompt = battle_prompt
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self.gpt_evaluation_prompt = gpt_evaluation_prompt
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self.gpt_model = gpt_model
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self.language = language
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self.path_for_UniEval = path_for_UniEval
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self.gpt_with_reference = gpt_with_reference
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self.automatic_metric_stats = dict()
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self.unieval_metric_stats = dict()
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self.gpt_evaluation_results = dict()
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self.battle_results = []
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def battle(self, answers1: List[Dict], answers2: List[Dict]) -> None:
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"""
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Comparison between two models using GPT-4 as the reviewer.
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"""
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self.battle_results = gpt_evaluate.battle(answers1, answers2, self.battle_prompt)
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def evaluate(self, answers: List[Dict], targets: List[Dict]) -> None:
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"""
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A comprehensive evaluation of the answers from the model.
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The function evaluates the model's performance from different perspectives
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using GPT-3.5, GPT-4, and off-the-shelf evaluation metrics.
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The metrics will be decided by the config file.
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"""
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def switch(metric, language):
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if metric == "BLEU":
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return metrics.bleu_score(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "ROUGE":
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return metrics.rouge_score(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "Distinct":
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return metrics.distinct_score(preds=predicts_list, language=language)
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elif metric == "BERTScore":
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return metrics.bert_score(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "Precision":
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return metrics.precision(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "Recall":
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return metrics.recall(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "F1 score":
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return metrics.F1_score(preds=predicts_list, targets=targets_list, language=language)
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elif metric == "CHRF":
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return metrics.chrf_score(preds=predicts_list, targets=targets_list, language=language)
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else:
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raise ValueError(f"Unexpected metric")
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answers_per_category = get_data_per_category(answers, list(self.params.keys()))
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targets_per_category = get_data_per_category(targets, list(self.params.keys()))
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# automatic evaluation
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for category in self.params:
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if len(answers_per_category[category]) == 0:
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print(f"Category {category} specified in your config doesn't have corresponding answers!")
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continue
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if self.params[category].get("Metrics", None) is None:
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continue
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category_metrics = self.params[category]["Metrics"]
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self.automatic_metric_stats[category] = {}
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targets_list = [
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target["target"] if target["target"] else target["output"] for target in targets_per_category[category]
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]
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predicts_list = [answer["output"] for answer in answers_per_category[category]]
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for metric in category_metrics:
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self.automatic_metric_stats[category].update(switch(metric=metric, language=self.language))
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# UniEval evaluation
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# self.unieval_metric_stats's key is "task" instead of "category".
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# Iterating "task" first will avoid repeated loading models because one task corresponds to one UniEval model.
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# If key is "category", different models will be loaded for multiple times across categories because the user may require different task(models) to evaluate one category.
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for category in self.params:
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if len(answers_per_category[category]) == 0:
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print(f"Category {category} specified in your config doesn't have corresponding answers!")
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continue
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if self.params[category].get("UniEval", None) is None:
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continue
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if self.params[category]["UniEval"] and self.language == "cn":
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raise Exception(
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"UniEval doesn't support Chinese! Please remove UniEval config in your Chinese config file.")
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category_metrics = self.params[category]["UniEval"]
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for task, metric in [tuple(category_metric.split("-")) for category_metric in category_metrics]:
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if self.unieval_metric_stats.get(task, None) is None:
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self.unieval_metric_stats[task] = {category: {metric: 0}}
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elif self.unieval_metric_stats[task].get(category, None) is None:
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self.unieval_metric_stats[task][category] = {metric: 0}
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else:
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self.unieval_metric_stats[task][category][metric] = 0
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for task in self.unieval_metric_stats:
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if self.path_for_UniEval is None:
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raise Exception(f"Please specify the path for UniEval model in the config file!")
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if self.path_for_UniEval.get(task, None) is None:
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raise Exception(f"Please specify the model path for task {task} in the config file!")
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print(f"Load UniEval model for task {task}.")
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uni_evaluator = unieval.get_evaluator(task, model_name_or_path=self.path_for_UniEval[task])
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for category in self.unieval_metric_stats[task]:
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targets_list = [
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target["target"] if target["target"] else target["output"]
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for target in targets_per_category[category]
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]
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predicts_list = [answer["output"] for answer in answers_per_category[category]]
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sources_list = [answer["instruction"] + answer["input"] for answer in answers_per_category[category]]
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data = unieval.convert_data_to_unieval_format(predicts_list, sources_list, targets_list)
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scores = uni_evaluator.evaluate(data,
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category,
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dims=list(self.unieval_metric_stats[task][category].keys()),
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overall=False)
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avg_scores = unieval.calculate_average_score(scores)
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self.unieval_metric_stats[task][category].update(avg_scores)
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# gpt evaluation
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for category in self.params:
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if len(answers_per_category[category]) == 0:
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print(f"Category {category} specified in your config doesn't have corresponding answers!")
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continue
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if self.params[category].get("GPT", None) is None:
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continue
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category_metrics = self.params[category]["GPT"]
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prompt = self.gpt_evaluation_prompt.get(category, None)
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if prompt is None:
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print(f"No prompt for category {category}! Use prompt for category general now.")
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prompt = self.gpt_evaluation_prompt["general"]
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self.gpt_evaluation_results[category] = gpt_evaluate.evaluate(
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answers_per_category[category],
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prompt,
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category_metrics,
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category,
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self.gpt_model,
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self.language,
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references=targets_per_category[category] if self.gpt_with_reference else None)
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def save(self, path: str, model_name_list: List[str]) -> None:
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"""
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Save evaluation results of GPT-3.5, GPT-4, and off-the-shelf evaluation metrics.
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"""
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if len(model_name_list) == 2:
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save_path = os.path.join(path, "gpt_evaluate", "battle_results")
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gpt_evaluate.save_battle_results(self.battle_results, model_name_list[0], model_name_list[1], save_path)
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else:
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if self.automatic_metric_stats:
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# Save evaluation results for automatic metrics
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automatic_base_save_path = os.path.join(path, "automatic_results")
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automatic_results_save_path = os.path.join(automatic_base_save_path, "evaluation_results")
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save_automatic_results(model_name_list[0], self.automatic_metric_stats, automatic_results_save_path)
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# Save charts and csv.
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automatic_analyses_save_path = os.path.join(automatic_base_save_path, "evaluation_analyses")
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analyze_automatic_results(automatic_results_save_path, automatic_analyses_save_path)
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if self.unieval_metric_stats:
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# Save evaluation results for UniEval metrics
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unieval_base_save_path = os.path.join(path, "unieval_results")
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unieval_results_save_path = os.path.join(unieval_base_save_path, "evaluation_results")
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unieval.save_unieval_results(model_name_list[0], self.unieval_metric_stats, unieval_results_save_path)
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# Save charts and csv.
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unieval_analyses_save_path = os.path.join(unieval_base_save_path, "evaluation_analyses")
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unieval.analyze_unieval_results(unieval_results_save_path, unieval_analyses_save_path)
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if self.gpt_evaluation_results:
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# Save evaluation results for GPT evaluation metrics.
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gpt_base_save_path = os.path.join(path, "gpt_evaluate", "gpt_evaluate_results")
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gpt_evaluation_results_save_path = os.path.join(gpt_base_save_path, "evaluation_results")
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all_evaluations = gpt_evaluate.save_gpt_evaluation_results(model_name_list[0],
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self.gpt_evaluation_results,
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gpt_evaluation_results_save_path)
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# Start to calculate scores and save statistics.
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gpt_evaluation_statistics_save_path = os.path.join(gpt_base_save_path, "evaluation_statistics")
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gpt_evaluate.save_gpt_evaluation_statistics(model_name_list[0], all_evaluations,
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gpt_evaluation_statistics_save_path)
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# Save charts and csv.
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gpt_evaluation_analyses_save_path = os.path.join(gpt_base_save_path, "evaluation_analyses")
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gpt_evaluate.analyze_gpt_evaluation_statistics(gpt_evaluation_statistics_save_path,
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gpt_evaluation_analyses_save_path)
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