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"""Script to produce radial plots."""
from functools import partial
import plotly.graph_objects as go
import json
import numpy as np
from collections import defaultdict
import pandas as pd
from pydantic import BaseModel
import gradio as gr
import requests
import random
import logging
import datetime as dt
import scipy.stats as stats
import itertools as it
fmt = "%(asctime)s [%(levelname)s] <%(name)s> %(message)s"
logging.basicConfig(level=logging.INFO, format=fmt)
logger = logging.getLogger("radial_plot_generator")
INTRO_MARKDOWN = """
# Radial Plot Generator
This demo allows you to generate a radial plot comparing the performance of different
language models on different tasks. It is based on the generative results from the
[ScandEval benchmark](https://scandeval.com).
"""
ABOUT_MARKDOWN = """
## About the ScandEval Benchmark
The [ScandEval benchmark](https://scandeval.com) is used compare pretrained language
models on tasks in Danish, Swedish, Norwegian Bokmål, Norwegian Nynorsk, Icelandic,
Faroese, German, Dutch and English. The benchmark supports both encoder models (such as
BERT) and generative models (such as GPT), and leaderboards for both kinds [are
available](https://scandeval.com).
The generative models are evaluated using in-context learning with few-shot prompts.
The few-shot examples are sampled randomly from the training split, and we benchmark
the models 10 times with bootstrapped test sets and different few-shot examples in each
iteration. This allows us to better measure the uncertainty of the results. We use the
uncertainty in the radial plot when we compute the rank scores for the models. Namely,
we compute the rank score by firstly computing the rank of the model on each task,
where two models are considered to have the same rank if there is not a statistically
significant difference between their scores (one-tailed t-test with p < 0.05). We next
apply a logaritmic transformation to the ranks, to downplay the importance of the
poorly performing models. Lastly, we invert and normalise the logaritmic ranks to the
range [0, 1], resulting in the best performing models having rank scores close to 1 and
the worst performing models having rank scores close to 0.
## The Benchmark Datasets
The ScandEval generative benchmark currently covers the languages Danish, Swedish,
Norwegian, Icelandic, German, Dutch and English. For each language, the benchmark
consists of 7 different tasks, each of which consists of 1-2 datasets. The tasks are
the following:
### Text Classification
Given a piece of text, classify it into a number of classes. For this task we extract
the first token of the possible labels, and choose the label whose first token has the
highest probability. All datasets in this category are currently trinary sentiment
classification datasets. We use the Matthews Correlation Coefficient (MCC) as the
evaluation metric.
### Information Extraction
Given a piece of text, extract a number of entities from the text. As the model needs
to extract multiple entities, we use [structured
generation](https://github.com/noamgat/lm-format-enforcer) to make the model generate a
JSON dictionary with keys being the entity categories and values being lists of the
identified entities. All datasets in this task are named entity recognition datasets.
We use the micro-averaged F1 score as the evaluation metric, where we ignore the
Miscellaneous category.
### Grammar
Given a piece of text, determine whether it is grammatically correct or not. All
datasets in this task are built from the dependency treebanks of the languages, where
words are removed or swapped, in a way that makes the sentence ungrammatical. We use
the Matthews Correlation Coefficient (MCC) as the evaluation metric.
### Question Answering
Given a question and a piece of text, extract the answer to the question from the text.
All datasets in this task are extractive question answering datasets. We use the exact
match (EM) score as the evaluation metric.
### Summarisation
Given a piece of text, generate a summary of the text. All the datasets come from
either news articles or WikiHow articles. We use the BERTScore metric as the evaluation
metric, where the encoder model used is
[microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base).
### Knowledge
Given a trivia-style question with multiple choice answers, choose the correct answer.
As with text classification, we use the probabilities of the answer letter (a, b, c or
d) to choose the answer. The datasets in this task are machine translated versions of
the [MMLU](https://doi.org/10.48550/arXiv.2009.03300) and
[ARC](https://allenai.org/data/arc) datasets. We use the Matthews Correlation
Coefficient (MCC) as the evaluation metric.
### Reasoning
Given a scenario and multiple possible endings, choose the correct ending. As with text
classification, we use the probabilities of the answer letter (a, b, c or d) to choose
the answer. The datasets in this task are machine translated versions of the
[HellaSwag](https://rowanzellers.com/hellaswag/) dataset. We use the Matthews
Correlation Coefficient (MCC) as the evaluation metric.
## Citation
If you use the ScandEval benchmark in your work, please cite [the
paper](https://aclanthology.org/2023.nodalida-1.20):
```
@inproceedings{nielsen2023scandeval,
title={ScandEval: A Benchmark for Scandinavian Natural Language Processing},
author={Nielsen, Dan},
booktitle={Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
pages={185--201},
year={2023}
}
```
"""
UPDATE_FREQUENCY_MINUTES = 5
MIN_COLOUR_DISTANCE_BETWEEN_MODELS = 200
class Task(BaseModel):
"""Class to hold task information."""
name: str
metric: str
def __hash__(self):
return hash(self.name)
class Language(BaseModel):
"""Class to hold language information."""
code: str
name: str
def __hash__(self):
return hash(self.code)
class Dataset(BaseModel):
"""Class to hold dataset information."""
name: str
language: Language
task: Task
def __hash__(self):
return hash(self.name)
SUMMARISATION = Task(name="summarisation", metric="bertscore")
KNOWLEDGE = Task(name="knowledge", metric="mcc")
REASONING = Task(name="reasoning", metric="mcc")
GRAMMAR = Task(name="grammar", metric="mcc")
QUESTION_ANSWERING = Task(name="question answering", metric="em")
TEXT_CLASSIFICATION = Task(name="text classification", metric="mcc")
INFORMATION_EXTRACTION = Task(name="information extraction", metric="micro_f1_no_misc")
ALL_TASKS = [obj for obj in globals().values() if isinstance(obj, Task)]
DANISH = Language(code="da", name="Danish")
NORWEGIAN = Language(code="no", name="Norwegian")
SWEDISH = Language(code="sv", name="Swedish")
ICELANDIC = Language(code="is", name="Icelandic")
GERMAN = Language(code="de", name="German")
DUTCH = Language(code="nl", name="Dutch")
ENGLISH = Language(code="en", name="English")
ALL_LANGUAGES = {
obj.name: obj for obj in globals().values() if isinstance(obj, Language)
}
DATASETS = [
Dataset(name="swerec", language=SWEDISH, task=TEXT_CLASSIFICATION),
Dataset(name="angry-tweets", language=DANISH, task=TEXT_CLASSIFICATION),
Dataset(name="norec", language=NORWEGIAN, task=TEXT_CLASSIFICATION),
Dataset(name="sb10k", language=GERMAN, task=TEXT_CLASSIFICATION),
Dataset(name="dutch-social", language=DUTCH, task=TEXT_CLASSIFICATION),
Dataset(name="sst5", language=ENGLISH, task=TEXT_CLASSIFICATION),
Dataset(name="suc3", language=SWEDISH, task=INFORMATION_EXTRACTION),
Dataset(name="dansk", language=DANISH, task=INFORMATION_EXTRACTION),
Dataset(name="norne-nb", language=NORWEGIAN, task=INFORMATION_EXTRACTION),
Dataset(name="norne-nn", language=NORWEGIAN, task=INFORMATION_EXTRACTION),
Dataset(name="mim-gold-ner", language=ICELANDIC, task=INFORMATION_EXTRACTION),
Dataset(name="germeval", language=GERMAN, task=INFORMATION_EXTRACTION),
Dataset(name="conll-nl", language=DUTCH, task=INFORMATION_EXTRACTION),
Dataset(name="conll-en", language=ENGLISH, task=INFORMATION_EXTRACTION),
Dataset(name="scala-sv", language=SWEDISH, task=GRAMMAR),
Dataset(name="scala-da", language=DANISH, task=GRAMMAR),
Dataset(name="scala-nb", language=NORWEGIAN, task=GRAMMAR),
Dataset(name="scala-nn", language=NORWEGIAN, task=GRAMMAR),
Dataset(name="scala-is", language=ICELANDIC, task=GRAMMAR),
Dataset(name="scala-de", language=GERMAN, task=GRAMMAR),
Dataset(name="scala-nl", language=DUTCH, task=GRAMMAR),
Dataset(name="scala-en", language=ENGLISH, task=GRAMMAR),
Dataset(name="scandiqa-da", language=DANISH, task=QUESTION_ANSWERING),
Dataset(name="norquad", language=NORWEGIAN, task=QUESTION_ANSWERING),
Dataset(name="scandiqa-sv", language=SWEDISH, task=QUESTION_ANSWERING),
Dataset(name="nqii", language=ICELANDIC, task=QUESTION_ANSWERING),
Dataset(name="germanquad", language=GERMAN, task=QUESTION_ANSWERING),
Dataset(name="squad", language=ENGLISH, task=QUESTION_ANSWERING),
Dataset(name="squad-nl", language=DUTCH, task=QUESTION_ANSWERING),
Dataset(name="nordjylland-news", language=DANISH, task=SUMMARISATION),
Dataset(name="mlsum", language=GERMAN, task=SUMMARISATION),
Dataset(name="rrn", language=ICELANDIC, task=SUMMARISATION),
Dataset(name="no-sammendrag", language=NORWEGIAN, task=SUMMARISATION),
Dataset(name="wiki-lingua-nl", language=DUTCH, task=SUMMARISATION),
Dataset(name="swedn", language=SWEDISH, task=SUMMARISATION),
Dataset(name="cnn-dailymail", language=ENGLISH, task=SUMMARISATION),
Dataset(name="danish-citizen-tests", language=DANISH, task=KNOWLEDGE),
Dataset(name="danske-talemaader", language=DANISH, task=KNOWLEDGE),
Dataset(name="mmlu-no", language=NORWEGIAN, task=KNOWLEDGE),
Dataset(name="mmlu-sv", language=SWEDISH, task=KNOWLEDGE),
Dataset(name="mmlu-is", language=ICELANDIC, task=KNOWLEDGE),
Dataset(name="mmlu-de", language=GERMAN, task=KNOWLEDGE),
Dataset(name="mmlu-nl", language=DUTCH, task=KNOWLEDGE),
Dataset(name="mmlu", language=ENGLISH, task=KNOWLEDGE),
Dataset(name="hellaswag-da", language=DANISH, task=REASONING),
Dataset(name="hellaswag-no", language=NORWEGIAN, task=REASONING),
Dataset(name="hellaswag-sv", language=SWEDISH, task=REASONING),
Dataset(name="hellaswag-is", language=ICELANDIC, task=REASONING),
Dataset(name="hellaswag-de", language=GERMAN, task=REASONING),
Dataset(name="hellaswag-nl", language=DUTCH, task=REASONING),
Dataset(name="hellaswag", language=ENGLISH, task=REASONING),
]
def main() -> None:
"""Produce a radial plot."""
global last_fetch
results_dfs = fetch_results()
last_fetch = dt.datetime.now()
all_languages = sorted(
[language.name for language in ALL_LANGUAGES.values()],
key=lambda language_name: language_name.lower(),
)
danish_models = sorted(
list({model_id for model_id in results_dfs[DANISH].index}),
key=lambda model_id: model_id.lower(),
)
global colour_mapping
global seed
seed = 4242
update_colour_mapping(results_dfs=results_dfs)
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr.Markdown(INTRO_MARKDOWN)
with gr.Tab(label="Build a Radial Plot"):
with gr.Column():
with gr.Row():
language_names_dropdown = gr.Dropdown(
choices=all_languages,
multiselect=True,
label="Languages",
value=["Danish"],
interactive=True,
scale=2,
)
model_ids_dropdown = gr.Dropdown(
choices=danish_models,
multiselect=True,
label="Models",
value=["gpt-4-0613", "mistralai/Mistral-7B-v0.1"],
interactive=True,
scale=2,
)
with gr.Row():
use_rank_score_checkbox = gr.Checkbox(
label="Compare models with rank scores (as opposed to raw "
"scores)",
value=True,
interactive=True,
scale=1,
)
show_scale_checkbox = gr.Checkbox(
label="Show the scale on the plot (always 0-100)",
value=False,
interactive=True,
scale=1,
)
plot_width_slider = gr.Slider(
label="Plot width",
minimum=600,
maximum=1000,
step=10,
value=800,
interactive=True,
scale=1,
)
plot_height_slider = gr.Slider(
label="Plot height",
minimum=300,
maximum=700,
step=10,
value=500,
interactive=True,
scale=1,
)
update_colours_button = gr.Button(
value="Update colours",
interactive=True,
scale=1,
)
with gr.Row():
plot = gr.Plot(
value=produce_radial_plot(
model_ids_dropdown.value,
language_names=language_names_dropdown.value,
use_rank_score=use_rank_score_checkbox.value,
show_scale=show_scale_checkbox.value,
plot_width=plot_width_slider.value,
plot_height=plot_height_slider.value,
results_dfs=results_dfs,
),
)
with gr.Tab(label="About"):
gr.Markdown(ABOUT_MARKDOWN)
gr.Markdown(
"<center>Made with ❤️ by the <a href=\"https://alexandra.dk\">"
"Alexandra Institute</a>.</center>"
)
language_names_dropdown.change(
fn=partial(update_model_ids_dropdown, results_dfs=results_dfs),
inputs=[language_names_dropdown, model_ids_dropdown],
outputs=model_ids_dropdown,
)
# Update plot when anything changes
update_plot_kwargs = dict(
fn=partial(
produce_radial_plot,
results_dfs=results_dfs,
),
inputs=[
model_ids_dropdown,
language_names_dropdown,
use_rank_score_checkbox,
show_scale_checkbox,
plot_width_slider,
plot_height_slider,
],
outputs=plot,
)
language_names_dropdown.change(**update_plot_kwargs)
model_ids_dropdown.change(**update_plot_kwargs)
use_rank_score_checkbox.change(**update_plot_kwargs)
show_scale_checkbox.change(**update_plot_kwargs)
plot_width_slider.change(**update_plot_kwargs)
plot_height_slider.change(**update_plot_kwargs)
# Update colours when the button is clicked
update_colours_button.click(
fn=partial(update_colour_mapping, results_dfs=results_dfs),
).then(**update_plot_kwargs)
demo.launch()
def update_model_ids_dropdown(
language_names: list[str],
model_ids: list[str],
results_dfs: dict[Language, pd.DataFrame] | None,
) -> dict:
"""When the language names are updated, update the model ids dropdown.
Args:
language_names:
The names of the languages to include in the plot.
model_ids:
The ids of the models to include in the plot.
results_dfs:
The results dataframes for each language.
Returns:
The Gradio update to the model ids dropdown.
"""
global last_fetch
minutes_since_last_fetch = (dt.datetime.now() - last_fetch).total_seconds() / 60
if minutes_since_last_fetch > UPDATE_FREQUENCY_MINUTES:
results_dfs = fetch_results()
last_fetch = dt.datetime.now()
if results_dfs is None or len(language_names) == 0:
if results_dfs is None:
logger.info("No results fetched yet. Resetting model ids dropdown.")
else:
logger.info("No languages selected. Resetting model ids dropdown.")
return gr.update(choices=[], value=[])
tasks = [
task
for task in ALL_TASKS
if all(
task in df.columns
for language, df in results_dfs.items()
if language.name in language_names
)
]
filtered_results_dfs = {
language: df[tasks]
for language, df in results_dfs.items()
if language.name in language_names
}
unique_models: set[str] = {
str(model_id)
for df in filtered_results_dfs.values()
for model_id in df.index
}
filtered_models: list[str] = sorted(
[
model_id
for model_id in unique_models
if all(model_id in df.index for df in filtered_results_dfs.values())
],
key=lambda model_id: model_id.lower(),
)
if len(filtered_models) == 0:
logger.info(
"No valid models for the selected languages. Resetting model ids dropdown."
)
return gr.update(choices=[], value=[])
valid_selected_models: list[str] = [
model_id for model_id in model_ids if model_id in filtered_models
]
if not valid_selected_models:
if len(filtered_models) > 1:
valid_selected_models = random.sample(population=filtered_models, k=2)
elif len(filtered_models) == 1:
valid_selected_models = random.sample(population=filtered_models, k=1)
logger.info(
f"Updated model ids dropdown with {len(filtered_models):,} valid models for "
f"the selected languages, with {valid_selected_models} selected."
)
return gr.update(choices=filtered_models, value=valid_selected_models)
def produce_radial_plot(
model_ids: list[str],
language_names: list[str],
use_rank_score: bool,
show_scale: bool,
plot_width: int,
plot_height: int,
results_dfs: dict[Language, pd.DataFrame] | None,
) -> go.Figure:
"""Produce a radial plot as a plotly figure.
Args:
model_ids:
The ids of the models to include in the plot.
language_names:
The names of the languages to include in the plot.
use_rank_score:
Whether to use rank scores (as opposed to raw scores).
show_scale:
Whether to show the scale on the plot.
plot_width:
The width of the plot.
plot_height:
The height of the plot.
results_dfs:
The results dataframes for each language.
Returns:
A plotly figure.
"""
global last_fetch
minutes_since_last_fetch = (dt.datetime.now() - last_fetch).total_seconds() / 60
if minutes_since_last_fetch > UPDATE_FREQUENCY_MINUTES:
results_dfs = fetch_results()
last_fetch = dt.datetime.now()
if results_dfs is None or len(language_names) == 0 or len(model_ids) == 0:
if results_dfs is None:
logger.info("No results fetched yet. Resetting plot.")
elif len(language_names) == 0:
logger.info("No languages selected. Resetting plot.")
else:
logger.info("No models selected. Resetting plot.")
return go.Figure()
logger.info(
f"Producing radial plot for models {model_ids!r} on languages "
f"{language_names!r}..."
)
languages = [ALL_LANGUAGES[language_name] for language_name in language_names]
results_dfs_filtered = {
language: df
for language, df in results_dfs.items()
if language.name in language_names
}
tasks = [
task
for task in ALL_TASKS
if all(task in df.columns for df in results_dfs_filtered.values())
]
logger.info("Computing rank scores...")
all_rank_scores: dict[Task, dict[Language, dict[str, float]]] = {
task: {
language: dict()
for language in languages
}
for task in tasks
}
for task in tasks:
for language in languages:
df = results_dfs_filtered[language][task].dropna()
model_ids_sorted: list[str] = (
df.map(np.mean).sort_values(ascending=False).index.tolist()
)
ranks = list()
rank = 0
best_scores = None
for model_id in model_ids_sorted:
if best_scores is None:
best_scores = df.loc[model_id]
rank = 1
else:
scores = df.loc[model_id]
worse_than_previous_models = stats.ttest_rel(
a=best_scores, b=scores, alternative="greater"
).pvalue < 0.05
if worse_than_previous_models:
rank += 1
best_scores = scores
ranks.append(rank)
log_ranks = np.log(ranks)
scores = 1 - (log_ranks / log_ranks.max())
for model_id, score in zip(model_ids_sorted, scores):
all_rank_scores[task][language][model_id] = score
logger.info("Successfully computed rank scores.")
# Add all the evaluation results for each model
results: list[list[float]] = list()
for model_id in model_ids:
result_list = list()
for task in tasks:
rank_scores = list()
scores = list()
for language in languages:
if model_id not in results_dfs_filtered[language].index:
continue
rank_score = 100 * all_rank_scores[task][language][model_id]
rank_scores.append(rank_score)
score_arr = np.array(results_dfs_filtered[language].loc[model_id][task])
if score_arr.mean() < 1:
score_arr *= 100
scores.append(score_arr.mean())
if use_rank_score:
result_list.append(np.mean(rank_scores))
else:
result_list.append(np.mean(scores))
results.append(result_list)
# Get a matrix of shape [num_models, num_tasks], where entry (i, j) indicates how
# many models that model i has beaten on task j
result_matrix = np.array(results)
num_models = result_matrix.shape[0]
num_tasks = result_matrix.shape[1]
num_models_beaten = np.zeros((num_models, num_tasks))
for i in range(num_models):
for j in range(num_tasks):
num_models_beaten[i, j] = np.sum(
result_matrix[i, j] > result_matrix[:, j]
)
# Sort the models (and their results) such that the model who beats most other
# models first. This will result in the "smaller areas" being on top of the "larger
# areas", which is more aesthetically pleasing.
sorted_idxs = num_models_beaten.sum(axis=1).argsort()[::-1]
model_ids = np.asarray(model_ids)[sorted_idxs].tolist()
results = result_matrix[sorted_idxs].tolist()
# Add the results to a plotly figure
fig = go.Figure()
for model_id, result_list in zip(model_ids, results):
r, g, b = colour_mapping[model_id]
fig.add_trace(go.Scatterpolar(
r=result_list,
theta=[task.name for task in tasks],
name=model_id,
fill='toself',
fillcolor=f'rgba({r}, {g}, {b}, 0.6)',
line=dict(color=f'rgb({r}, {g}, {b})'),
))
languages_str = ""
if len(languages) > 1:
languages_str = ", ".join([language.name for language in languages[:-1]])
languages_str += " and "
languages_str += languages[-1].name
if use_rank_score:
title = f'Rank Score on on {languages_str} Language Tasks'
else:
title = f'Raw Score on on {languages_str} Language Tasks'
# Builds the radial plot from the results
fig.update_layout(
polar=dict(radialaxis=dict(visible=show_scale, range=[0, 100])),
showlegend=True,
title=title,
width=plot_width,
height=plot_height,
)
logger.info("Successfully produced radial plot.")
return fig
def fetch_results() -> dict[Language, pd.DataFrame]:
"""Fetch the results from the ScandEval benchmark.
Returns:
A dictionary of languages -> results-dataframes, whose indices are the
models and columns are the tasks.
"""
logger.info("Fetching results from ScandEval benchmark...")
response = requests.get(
"https://www.scandeval.com/scandeval_benchmark_results.jsonl"
)
response.raise_for_status()
records = [
json.loads(dct_str)
for dct_str in response.text.split("\n")
if dct_str.strip("\n")
]
# Build a dictionary of languages -> results-dataframes, whose indices are the
# models and columns are the tasks.
results_dfs = dict()
for language in {dataset.language for dataset in DATASETS}:
possible_dataset_names = {
dataset.name for dataset in DATASETS if dataset.language == language
}
data_dict = defaultdict(dict)
for record in records:
model_name = record["model"]
# Manual fix for OpenAI models: Only keep the validation split results
if "gpt-3.5" in model_name or "gpt-4" in model_name:
if not record.get("validation_split", False):
continue
dataset_name = record["dataset"]
if dataset_name in possible_dataset_names:
dataset = next(
dataset for dataset in DATASETS if dataset.name == dataset_name
)
scores = [
test_score_dict.get(
f"test_{dataset.task.metric}",
test_score_dict.get(dataset.task.metric)
)
for test_score_dict in record["results"]["raw"]["test"]
]
if dataset.task in data_dict[model_name]:
data_dict[model_name][dataset.task].append(scores)
else:
data_dict[model_name][dataset.task] = [scores]
results_df = pd.DataFrame(data_dict).T.map(
lambda lists_or_nan:
list(it.chain(lists_or_nan))
if lists_or_nan == lists_or_nan
else lists_or_nan
).dropna().map(lambda lst: lst[0])
results_dfs[language] = results_df
logger.info("Successfully fetched results from ScandEval benchmark.")
return results_dfs
def update_colour_mapping(results_dfs: dict[Language, pd.DataFrame]) -> None:
"""Get a mapping from model ids to RGB triplets.
Args:
results_dfs:
The results dataframes for each language.
"""
global colour_mapping
global seed
seed += 1
gr.Info(f"Updating colour mapping...")
# Get distinct RGB values for all models
all_models = list(
{model_id for df in results_dfs.values() for model_id in df.index}
)
colour_mapping = dict()
for i in it.count():
min_colour_distance = MIN_COLOUR_DISTANCE_BETWEEN_MODELS - i
retries_left = 10 * len(all_models)
for model_id in all_models:
random.seed(hash(model_id) + i + seed)
r, g, b = 0, 0, 0
too_bright, similar_to_other_model = True, True
while (too_bright or similar_to_other_model) and retries_left > 0:
r, g, b = tuple(random.randint(0, 255) for _ in range(3))
too_bright = np.min([r, g, b]) > 200
similar_to_other_model = any(
np.abs(
np.array(colour) - np.array([r, g, b])
).sum() < min_colour_distance
for colour in colour_mapping.values()
)
retries_left -= 1
colour_mapping[model_id] = (r, g, b)
if retries_left:
logger.info(
f"Successfully found a colour mapping with min colour distance "
f"{min_colour_distance}."
)
break
if __name__ == "__main__":
main()