File size: 12,911 Bytes
40e38d3 |
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 |
import gradio as gr
from src.logic.data_fetching import fetch_datasets, fetch_groups, fetch_metrics, load_data, reverse_search, reverse_search_add
from src.logic.data_processing import export_data
from src.logic.plotting import plot_data
from src.logic.utils import get_desc
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import os
import re
METRICS_LOCATION_DEFAULT = os.getenv("METRICS_LOCATION_DEFAULT", "hf://datasets/HuggingFaceFW-Dev/summary-stats-files")
def update_graph(
base_folder,
datasets,
metric_name,
grouping,
log_scale_x,
log_scale_y,
rounding,
normalization,
top_k,
direction,
regex,
cumsum,
perc,
progress=gr.Progress(),
):
if len(datasets) <= 0 or not metric_name or not grouping:
return None
with ThreadPoolExecutor() as pool:
data = list(
progress.tqdm(
pool.map(
partial(load_data, base_folder=base_folder, metric_name=metric_name, grouping=grouping),
datasets,
),
total=len(datasets),
desc="Loading data...",
)
)
data = {path: result for path, result in zip(datasets, data)}
return plot_data(data, metric_name, normalization, rounding, grouping, top_k, direction, regex, log_scale_x,
log_scale_y, cumsum, perc, progress), data, export_data(data, metric_name), get_desc(data)
def create_interface():
with gr.Blocks() as demo:
datasets = gr.State([])
exported_data = gr.State([])
metrics_headline = gr.Markdown(value="# Metrics Exploration")
with gr.Tabs():
with gr.TabItem("Help"):
gr.Markdown(
label="Readme",
value="""
## How to use:
1) Specify Metrics location (Stats block `output_folder` without the last path segment) and click "Fetch Datasets"
2) Select datasets you are interested in using the dropdown or regex filter
3) Specify Grouping (global average/value/fqdn/suffix) and Metric name
4) Click "Render Metric"
## Groupings:
- **histogram**: Creates a line plot of values with their frequencies. If normalization is on, the frequencies sum to 1.
* normalize:
- **(fqdn/suffix)**: Creates a bar plot of the avg. values of the metric for full qualifed domain name/suffix of domain.
* k: the number of groups to show
* Top/Bottom/Most frequent (n_docs): Groups with the top/bottom k values/most prevalant docs are shown
- **none**: Shows the average value of given metric
## Reverse search:
To search for datasets containing a grouping and certain metric, use the Reverse search section.
Specify the search parameters and click "Search". This will show you found datasets in the "Found datasets" textbox. You can modify the selection after search by removing unwanted lines and clicking "Add to selection".
## Note:
The data might not be 100% representative, due to the sampling and optimistic merging of the metrics (fqdn/suffix).
""",
)
with gr.TabItem("Metric View"):
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
with gr.Column(scale=1):
base_folder = gr.Textbox(
label="Metrics Location",
value=METRICS_LOCATION_DEFAULT,
)
datasets_refetch = gr.Button("Fetch Datasets")
with gr.Column(scale=1):
regex_select = gr.Text(label="Regex filter", value=".*")
regex_button = gr.Button("Search")
with gr.Row():
datasets_selected = gr.Dropdown(
choices=[],
label="Datasets",
multiselect=True,
)
with gr.Column(scale=1):
grouping_dropdown = gr.Dropdown(
choices=[],
label="Grouping",
multiselect=False,
)
metric_name_dropdown = gr.Dropdown(
choices=[],
label="Metric name",
multiselect=False,
)
render_button = gr.Button("Render Metric", variant="primary")
with gr.Tabs():
with gr.TabItem("Graph Settings"):
log_scale_x_checkbox = gr.Checkbox(
label="Log scale x",
value=False,
)
log_scale_y_checkbox = gr.Checkbox(
label="Log scale y",
value=False,
)
rounding = gr.Number(
label="Rounding",
value=2,
)
normalization_checkbox = gr.Checkbox(
label="Normalize",
value=True,
visible=False
)
with gr.Row():
export_data_json = gr.File(visible=False)
with gr.TabItem("Grouping Settings"):
with gr.Row(visible=False) as group_choices:
with gr.Column(scale=2):
group_regex = gr.Text(
label="Group Regex",
value=None,
)
with gr.Row():
top_select = gr.Number(
label="N Groups",
value=100,
interactive=True,
)
direction_checkbox = gr.Radio(
label="Partition",
choices=[
"Top",
"Bottom",
"Most frequent (n_docs)",
],
value="Most frequent (n_docs)",
)
with gr.TabItem("Histogram Settings") as histogram_settings:
cdf_checkbox = gr.Checkbox(
label="CDF",
value=False,
)
perc_checkbox = gr.Checkbox(
label="%",
value=False,
)
with gr.Column(visible=False) as min_max_hist:
min_max_hist_data = gr.Markdown()
with gr.Row():
graph_output = gr.Plot(label="Graph")
with gr.TabItem("Reverse Metrics Search"):
reverse_search_headline = gr.Markdown(value="# Reverse Metrics Search")
with gr.Row():
with gr.Column(scale=1):
reverse_grouping_dropdown = gr.Dropdown(
choices=[],
label="Grouping",
multiselect=False,
)
reverse_metric_name_dropdown = gr.Dropdown(
choices=[],
label="Metric Name",
multiselect=False,
)
reverse_search_button = gr.Button("Search")
reverse_search_add_button = gr.Button("Add to selection")
with gr.Column(scale=2):
reverse_search_results = gr.Textbox(
label="Found datasets",
lines=10,
placeholder="Found datasets containing the group/metric name. You can modify the selection after search by removing unwanted lines and clicking Add to selection"
)
render_button.click(
fn=update_graph,
inputs=[
base_folder,
datasets_selected,
metric_name_dropdown,
grouping_dropdown,
log_scale_x_checkbox,
log_scale_y_checkbox,
rounding,
normalization_checkbox,
top_select,
direction_checkbox,
group_regex,
cdf_checkbox,
perc_checkbox
],
outputs=[graph_output, exported_data, export_data_json, min_max_hist_data],
)
gr.on(
triggers=[normalization_checkbox.change, rounding.change, group_regex.change, direction_checkbox.change,
top_select.change, log_scale_x_checkbox.change,
log_scale_y_checkbox.change, cdf_checkbox.change, perc_checkbox.change],
fn=plot_data,
inputs=[
exported_data,
metric_name_dropdown,
normalization_checkbox,
rounding,
grouping_dropdown,
top_select,
direction_checkbox,
group_regex,
log_scale_x_checkbox,
log_scale_y_checkbox,
cdf_checkbox,
perc_checkbox
],
outputs=[graph_output],
)
datasets_selected.change(
fn=fetch_groups,
inputs=[base_folder, datasets_selected, grouping_dropdown],
outputs=grouping_dropdown,
)
grouping_dropdown.change(
fn=fetch_metrics,
inputs=[base_folder, datasets_selected, grouping_dropdown, metric_name_dropdown],
outputs=metric_name_dropdown,
)
reverse_grouping_dropdown.select(
fn=partial(fetch_metrics, type="union"),
inputs=[base_folder, datasets, reverse_grouping_dropdown, reverse_metric_name_dropdown],
outputs=reverse_metric_name_dropdown,
)
reverse_search_button.click(
fn=reverse_search,
inputs=[base_folder, datasets, reverse_grouping_dropdown, reverse_metric_name_dropdown],
outputs=reverse_search_results,
)
reverse_search_add_button.click(
fn=reverse_search_add,
inputs=[datasets_selected, reverse_search_results],
outputs=datasets_selected,
)
datasets_refetch.click(
fn=fetch_datasets,
inputs=[base_folder],
outputs=[datasets, datasets_selected, reverse_grouping_dropdown],
)
def update_datasets_with_regex(regex, selected_runs, all_runs):
if not regex:
return
new_dsts = {run for run in all_runs if re.search(regex, run)}
if not new_dsts:
return gr.update(value=list(selected_runs))
dst_union = new_dsts.union(selected_runs or [])
return gr.update(value=sorted(list(dst_union)))
regex_button.click(
fn=update_datasets_with_regex,
inputs=[regex_select, datasets_selected, datasets],
outputs=datasets_selected,
)
def update_grouping_options(grouping):
if grouping == "histogram":
return {
normalization_checkbox: gr.Column(visible=True),
group_choices: gr.Column(visible=False),
min_max_hist: gr.Column(visible=True),
histogram_settings: gr.TabItem(visible=True),
}
else:
return {
normalization_checkbox: gr.Column(visible=False),
group_choices: gr.Column(visible=True),
min_max_hist: gr.Column(visible=False),
histogram_settings: gr.TabItem(visible=False),
}
grouping_dropdown.change(
fn=update_grouping_options,
inputs=[grouping_dropdown],
outputs=[normalization_checkbox, group_choices, min_max_hist, histogram_settings],
)
return demo |