Spaces:
Sleeping
Sleeping
File size: 13,858 Bytes
f9b063b 8b0cc53 f9b063b 4a784da f9b063b 8b0cc53 f9b063b 4a784da f9b063b 4a784da f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b f129974 e581b39 f129974 f9b063b 8b0cc53 69dd8d4 e7f8388 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 69dd8d4 8b0cc53 f9b063b f129974 8b0cc53 f129974 8b0cc53 f129974 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 69dd8d4 f129974 f9b063b 8b0cc53 94b0814 8b0cc53 94b0814 8b0cc53 69dd8d4 8b0cc53 f9b063b 8b0cc53 f9b063b 8b0cc53 f129974 8b0cc53 f129974 f9b063b 8b0cc53 f9b063b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 |
import gradio as gr
import pandas as pd
import numpy as np
import os
from utils import (
plot_distances_tsne,
plot_distances_umap,
cluster_languages_hdbscan,
cluster_languages_kmeans,
plot_mst,
cluster_languages_by_families,
cluster_languages_by_subfamilies,
filter_languages_by_families,
)
from functools import partial
import datasets
dataset = datasets.load_dataset(
"mshamrai/language-metric-data", split="train", trust_remote_code=True
)
languages = dataset["languages_list"][0]
average_distances_matrix = np.array(dataset["average_distances_matrix"][0])
DATASETS = dataset["distances_matrices"][0]["dataset_name"]
MODELS = dataset["distances_matrices"][0]["models"][0]["model_name"]
distance_matrices = {
DATASETS[i]: {
MODELS[j]: np.array(dataset["distances_matrices"][0]["models"][i]["matrix"][j])
for j in range(len(MODELS))
}
for i in range(len(DATASETS))
}
def filter_languages_nan(model, dataset, use_average):
if use_average:
matrix = average_distances_matrix
else:
matrix = distance_matrices[dataset][model]
vector = matrix[0]
updated_languages = np.array(languages)[~np.isnan(vector)]
updated_matrix = matrix[~np.isnan(vector), :][:, ~np.isnan(vector)]
return updated_matrix, updated_languages
def get_similar_languages(model, dataset, selected_language, use_average, n):
"""
Retrieves the distances for the selected language from the chosen model and dataset,
sorts them by similarity (lowest distance first), and returns a DataFrame.
"""
if use_average:
matrix = average_distances_matrix
else:
matrix = distance_matrices[dataset][model]
selected_language_index = languages.index(selected_language)
distances = matrix[selected_language_index]
df = pd.DataFrame({"Language": languages, "Distance": distances})
sorted_distances = df.sort_values(by="Distance")
sorted_distances.drop(index=selected_language_index, inplace=True)
sorted_distances.reset_index(drop=True, inplace=True)
sorted_distances.reset_index(inplace=True)
sorted_distances["Distance"] = sorted_distances["Distance"].round(4)
return sorted_distances.head(n)
def update_languages(model, dataset):
"""
Returns the language list based on the given model and dataset.
"""
matrix = distance_matrices[dataset][model]
vector = matrix[0]
updated_languages = np.array(languages)[~np.isnan(vector)]
return list(updated_languages)
def update_language_options(model, dataset, language, use_average):
if use_average:
updated_languages = languages
else:
updated_languages = update_languages(model, dataset)
if language not in updated_languages:
language = updated_languages[0]
return gr.Dropdown(label="Language", choices=updated_languages, value=language)
def toggle_inputs(use_average):
if use_average:
return gr.update(interactive=False, visible=False), gr.update(
interactive=False, visible=False
)
else:
return gr.update(interactive=True, visible=True), gr.update(
interactive=True, visible=True
)
plot_path = "plots/last_plot.pdf"
os.makedirs("plots", exist_ok=True)
def plot_distances(
model,
dataset,
use_average,
cluster_method,
cluster_method_param,
figsize_h,
figsize_w,
plot_fn,
):
"""
Plots all languages from the distances matrix using t-SNE.
"""
updated_matrix, updated_languages = filter_languages_nan(
model, dataset, use_average
)
if cluster_method == "HDBSCAN":
filtered_matrix, filtered_languages, clusters = cluster_languages_hdbscan(
updated_matrix, updated_languages, min_cluster_size=cluster_method_param
)
legends = None
elif cluster_method == "KMeans":
filtered_matrix, filtered_languages, clusters = cluster_languages_kmeans(
updated_matrix, updated_languages, n_clusters=cluster_method_param
)
legends = None
elif cluster_method == "Family":
clusters, legends = cluster_languages_by_families(updated_languages)
filtered_matrix = updated_matrix
filtered_languages = updated_languages
elif cluster_method == "Subfamily":
clusters, legends = cluster_languages_by_subfamilies(updated_languages)
filtered_matrix = updated_matrix
filtered_languages = updated_languages
else:
raise ValueError("Invalid cluster method")
fig = plot_fn(
filtered_matrix,
filtered_languages,
clusters,
legends,
fig_size=(figsize_w, figsize_h),
)
fig.tight_layout()
fig.savefig(plot_path, format="pdf")
return fig, gr.DownloadButton(label="Download Plot", value=plot_path)
def plot_families_subfamilies(
families, model, dataset, use_average, figsize_h, figsize_w
):
updated_matrix, updated_languages = filter_languages_nan(
model, dataset, use_average
)
updated_matrix, updated_languages = filter_languages_by_families(
updated_matrix, updated_languages, families
)
clusters, legends = cluster_languages_by_subfamilies(updated_languages)
fig = plot_mst(
updated_matrix,
updated_languages,
clusters,
legends,
fig_size=(figsize_w, figsize_h),
)
fig.tight_layout()
fig.savefig(plot_path, format="pdf")
return fig, gr.DownloadButton(label="Download Plot", value=plot_path)
with gr.Blocks() as demo:
gr.Markdown("## Language Distance Explorer")
average_checkbox = gr.Checkbox(label="Use Average Distances", value=False)
with gr.Row():
model_input = gr.Dropdown(label="Model", choices=MODELS, value=MODELS[0])
dataset_input = gr.Dropdown(
label="Dataset", choices=DATASETS, value=DATASETS[0]
)
with gr.Tab(label="Closest Languages Table"):
with gr.Row():
language_input = gr.Dropdown(
label="Language", choices=languages, value=languages[0]
)
top_n_input = gr.Slider(
label="Top N", minimum=1, maximum=30, step=1, value=10
)
output_table = gr.Dataframe(label="Similar Languages")
model_input.change(
fn=update_language_options,
inputs=[model_input, dataset_input, language_input, average_checkbox],
outputs=language_input,
)
dataset_input.change(
fn=update_language_options,
inputs=[model_input, dataset_input, language_input, average_checkbox],
outputs=language_input,
)
language_input.change(
fn=get_similar_languages,
inputs=[
model_input,
dataset_input,
language_input,
average_checkbox,
top_n_input,
],
outputs=output_table,
)
model_input.change(
fn=get_similar_languages,
inputs=[
model_input,
dataset_input,
language_input,
average_checkbox,
top_n_input,
],
outputs=output_table,
)
dataset_input.change(
fn=get_similar_languages,
inputs=[
model_input,
dataset_input,
language_input,
average_checkbox,
top_n_input,
],
outputs=output_table,
)
top_n_input.change(
fn=get_similar_languages,
inputs=[
model_input,
dataset_input,
language_input,
average_checkbox,
top_n_input,
],
outputs=output_table,
)
average_checkbox.change(
fn=toggle_inputs,
inputs=[average_checkbox],
outputs=[model_input, dataset_input],
)
average_checkbox.change(
fn=update_language_options,
inputs=[model_input, dataset_input, language_input, average_checkbox],
outputs=language_input,
)
average_checkbox.change(
fn=get_similar_languages,
inputs=[
model_input,
dataset_input,
language_input,
average_checkbox,
top_n_input,
],
outputs=output_table,
)
with gr.Tab(label="Distance Plot"):
with gr.Row():
cluster_method_input = gr.Dropdown(
label="Cluster Method",
choices=["HDBSCAN", "KMeans", "Family", "Subfamily"],
value="HDBSCAN",
)
clusters_input = gr.Slider(
label="Minimum Elements in a Cluster",
minimum=2,
maximum=10,
step=1,
value=2,
)
def update_clusters_input_option(cluster_method):
if cluster_method == "HDBSCAN":
return gr.Slider(
label="Minimum Elements in a Cluster",
minimum=2,
maximum=10,
step=1,
value=2,
visible=True,
interactive=True,
)
elif cluster_method == "KMeans":
return gr.Slider(
label="Number of Clusters",
minimum=2,
maximum=20,
step=1,
value=2,
visible=True,
interactive=True,
)
else:
return gr.update(interactive=False, visible=False)
cluster_method_input.change(
fn=update_clusters_input_option,
inputs=[cluster_method_input],
outputs=clusters_input,
)
with gr.Row():
plot_tsne_button = gr.Button("Plot t-SNE")
plot_umap_button = gr.Button("Plot UMAP")
plot_mst_button = gr.Button("Plot MST")
with gr.Row():
plot_figsize_dist_h_input = gr.Slider(
label="Figure Height", minimum=5, maximum=30, step=1, value=15
)
plot_figsize_dist_w_input = gr.Slider(
label="Figure Width", minimum=5, maximum=30, step=1, value=15
)
with gr.Row():
download_plot_button = gr.DownloadButton("Download Plot")
with gr.Row():
plot_output = gr.Plot(label="Distance Plot")
plot_tsne_button.click(
fn=partial(plot_distances, plot_fn=plot_distances_tsne),
inputs=[
model_input,
dataset_input,
average_checkbox,
cluster_method_input,
clusters_input,
plot_figsize_dist_h_input,
plot_figsize_dist_w_input,
],
outputs=[plot_output, download_plot_button],
)
plot_umap_button.click(
fn=partial(plot_distances, plot_fn=plot_distances_umap),
inputs=[
model_input,
dataset_input,
average_checkbox,
cluster_method_input,
clusters_input,
plot_figsize_dist_h_input,
plot_figsize_dist_w_input,
],
outputs=[plot_output, download_plot_button],
)
plot_mst_button.click(
fn=partial(plot_distances, plot_fn=plot_mst),
inputs=[
model_input,
dataset_input,
average_checkbox,
cluster_method_input,
clusters_input,
plot_figsize_dist_h_input,
plot_figsize_dist_w_input,
],
outputs=[plot_output, download_plot_button],
)
with gr.Tab(label="Language Families Subplot"):
checked_families_input = gr.CheckboxGroup(
label="Language Families",
choices=[
"Afroasiatic",
"Austroasiatic",
"Austronesian",
"Constructed",
"Creole",
"Dravidian",
"Germanic",
"Indo-European",
"Japonic",
"Kartvelian",
"Koreanic",
"Language Isolate",
"Niger-Congo",
"Northeast Caucasian",
"Romance",
"Sino-Tibetan",
"Turkic",
"Uralic",
],
value=["Indo-European"],
)
with gr.Row():
plot_family_button = gr.Button("Plot Families")
plot_figsize_h_input = gr.Slider(
label="Figure Height", minimum=5, maximum=30, step=1, value=15
)
plot_figsize_w_input = gr.Slider(
label="Figure Width", minimum=5, maximum=30, step=1, value=15
)
with gr.Row():
download_families_plot_button = gr.DownloadButton(
"Download Plot", value=plot_path
)
plot_family_output = gr.Plot(label="Families Plot")
plot_family_button.click(
fn=plot_families_subfamilies,
inputs=[
checked_families_input,
model_input,
dataset_input,
average_checkbox,
plot_figsize_h_input,
plot_figsize_w_input,
],
outputs=[plot_family_output, download_families_plot_button],
)
demo.launch(share=True)
|