| 1 | # SPDX-License-Identifier: Apache-2.0 |
| 2 | # SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | import argparse |
| 4 | import json |
| 5 | from abc import ABC, abstractmethod |
| 6 | from concurrent.futures import ProcessPoolExecutor |
| 7 | from dataclasses import dataclass |
| 8 | from functools import partial |
| 9 | from pathlib import Path |
| 10 | from types import TracebackType |
| 11 | from typing import TYPE_CHECKING, ClassVar |
| 12 | |
| 13 | from typing_extensions import Self, override |
| 14 | |
| 15 | from vllm.utils.argparse_utils import FlexibleArgumentParser |
| 16 | from vllm.utils.collection_utils import full_groupby |
| 17 | from vllm.utils.import_utils import PlaceholderModule |
| 18 | |
| 19 | from .utils import sanitize_filename |
| 20 | |
| 21 | if TYPE_CHECKING: |
| 22 | import pandas as pd |
| 23 | |
| 24 | |
| 25 | @dataclass |
| 26 | class PlotFilterBase(ABC): |
| 27 | var: str |
| 28 | target: str |
| 29 | |
| 30 | @classmethod |
| 31 | def parse_str(cls, s: str): |
| 32 | for op_key in PLOT_FILTERS: |
| 33 | if op_key in s: |
| 34 | key, value = s.split(op_key) |
| 35 | return PLOT_FILTERS[op_key]( |
| 36 | key, |
| 37 | value.removeprefix(op_key).strip("'").strip('"'), |
| 38 | ) |
| 39 | else: |
| 40 | raise ValueError( |
| 41 | f"Invalid operator for plot filter '{s}'. " |
| 42 | f"Valid operators are: {sorted(PLOT_FILTERS)}", |
| 43 | ) |
| 44 | |
| 45 | @abstractmethod |
| 46 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 47 | """Applies this filter to a DataFrame.""" |
| 48 | raise NotImplementedError |
| 49 | |
| 50 | |
| 51 | @dataclass |
| 52 | class PlotEqualTo(PlotFilterBase): |
| 53 | @override |
| 54 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 55 | target: float | str |
| 56 | try: |
| 57 | target = float(self.target) |
| 58 | except ValueError: |
| 59 | target = self.target |
| 60 | |
| 61 | return df[df[self.var] == target] |
| 62 | |
| 63 | |
| 64 | @dataclass |
| 65 | class PlotNotEqualTo(PlotFilterBase): |
| 66 | @override |
| 67 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 68 | target: float | str |
| 69 | try: |
| 70 | target = float(self.target) |
| 71 | except ValueError: |
| 72 | target = self.target |
| 73 | |
| 74 | return df[df[self.var] != target] |
| 75 | |
| 76 | |
| 77 | @dataclass |
| 78 | class PlotLessThan(PlotFilterBase): |
| 79 | @override |
| 80 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 81 | return df[df[self.var] < float(self.target)] |
| 82 | |
| 83 | |
| 84 | @dataclass |
| 85 | class PlotLessThanOrEqualTo(PlotFilterBase): |
| 86 | @override |
| 87 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 88 | return df[df[self.var] <= float(self.target)] |
| 89 | |
| 90 | |
| 91 | @dataclass |
| 92 | class PlotGreaterThan(PlotFilterBase): |
| 93 | @override |
| 94 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 95 | return df[df[self.var] > float(self.target)] |
| 96 | |
| 97 | |
| 98 | @dataclass |
| 99 | class PlotGreaterThanOrEqualTo(PlotFilterBase): |
| 100 | @override |
| 101 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 102 | return df[df[self.var] >= float(self.target)] |
| 103 | |
| 104 | |
| 105 | # NOTE: The ordering is important! Match longer op_keys first |
| 106 | PLOT_FILTERS: dict[str, type[PlotFilterBase]] = { |
| 107 | "==": PlotEqualTo, |
| 108 | "!=": PlotNotEqualTo, |
| 109 | "<=": PlotLessThanOrEqualTo, |
| 110 | ">=": PlotGreaterThanOrEqualTo, |
| 111 | "<": PlotLessThan, |
| 112 | ">": PlotGreaterThan, |
| 113 | } |
| 114 | |
| 115 | |
| 116 | class PlotFilters(list[PlotFilterBase]): |
| 117 | @classmethod |
| 118 | def parse_str(cls, s: str): |
| 119 | if not s: |
| 120 | return cls() |
| 121 | |
| 122 | return cls(PlotFilterBase.parse_str(e) for e in s.split(",")) |
| 123 | |
| 124 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 125 | for item in self: |
| 126 | df = item.apply(df) |
| 127 | |
| 128 | return df |
| 129 | |
| 130 | |
| 131 | @dataclass |
| 132 | class PlotBinner: |
| 133 | var: str |
| 134 | bin_size: float |
| 135 | |
| 136 | @classmethod |
| 137 | def parse_str(cls, s: str): |
| 138 | for op_key in PLOT_BINNERS: |
| 139 | if op_key in s: |
| 140 | key, value = s.split(op_key) |
| 141 | return PLOT_BINNERS[op_key](key, float(value.removeprefix(op_key))) |
| 142 | else: |
| 143 | raise ValueError( |
| 144 | f"Invalid operator for plot binner '{s}'. " |
| 145 | f"Valid operators are: {sorted(PLOT_BINNERS)}", |
| 146 | ) |
| 147 | |
| 148 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 149 | """Applies this binner to a DataFrame.""" |
| 150 | df = df.copy() |
| 151 | df[self.var] = df[self.var] // self.bin_size * self.bin_size |
| 152 | return df |
| 153 | |
| 154 | |
| 155 | PLOT_BINNERS: dict[str, type[PlotBinner]] = { |
| 156 | "%": PlotBinner, |
| 157 | } |
| 158 | |
| 159 | |
| 160 | class PlotBinners(list[PlotBinner]): |
| 161 | @classmethod |
| 162 | def parse_str(cls, s: str): |
| 163 | if not s: |
| 164 | return cls() |
| 165 | |
| 166 | return cls(PlotBinner.parse_str(e) for e in s.split(",")) |
| 167 | |
| 168 | def apply(self, df: "pd.DataFrame") -> "pd.DataFrame": |
| 169 | for item in self: |
| 170 | df = item.apply(df) |
| 171 | |
| 172 | return df |
| 173 | |
| 174 | |
| 175 | def _json_load_bytes(path: Path) -> list[dict[str, object]]: |
| 176 | with path.open("rb") as f: |
| 177 | return json.load(f) |
| 178 | |
| 179 | |
| 180 | def _convert_inf_nan_strings(data: list[dict[str, object]]) -> list[dict[str, object]]: |
| 181 | """Convert string values "inf", "-inf", and "nan" to their float equivalents. |
| 182 | |
| 183 | This handles the case where JSON serialization represents inf/nan as strings. |
| 184 | """ |
| 185 | converted_data = [] |
| 186 | for record in data: |
| 187 | converted_record: dict[str, object] = {} |
| 188 | for key, value in record.items(): |
| 189 | if isinstance(value, str): |
| 190 | if value in ["inf", "-inf", "nan"]: |
| 191 | converted_record[key] = float(value) |
| 192 | else: |
| 193 | converted_record[key] = value |
| 194 | else: |
| 195 | converted_record[key] = value |
| 196 | converted_data.append(converted_record) |
| 197 | return converted_data |
| 198 | |
| 199 | |
| 200 | def _get_metric(run_data: dict[str, object], metric_key: str): |
| 201 | try: |
| 202 | return run_data[metric_key] |
| 203 | except KeyError as exc: |
| 204 | raise ValueError(f"Cannot find metric {metric_key!r} in {run_data=}") from exc |
| 205 | |
| 206 | |
| 207 | def _get_group(run_data: dict[str, object], group_keys: list[str]): |
| 208 | return tuple((k, str(_get_metric(run_data, k))) for k in group_keys) |
| 209 | |
| 210 | |
| 211 | def _get_fig_path(fig_dir: Path, group: tuple[tuple[str, str], ...], fig_name: str): |
| 212 | parts = list[str]() |
| 213 | |
| 214 | # Start with figure name (always provided, defaults to "FIGURE") |
| 215 | parts.append(fig_name) |
| 216 | |
| 217 | # Always append group data if present |
| 218 | if group: |
| 219 | parts.extend(f"{k}={v}" for k, v in group) |
| 220 | |
| 221 | return fig_dir / sanitize_filename("-".join(parts) + ".png") |
| 222 | |
| 223 | |
| 224 | class DummyExecutor: |
| 225 | map = map |
| 226 | |
| 227 | def __enter__(self) -> Self: |
| 228 | return self |
| 229 | |
| 230 | def __exit__( |
| 231 | self, |
| 232 | exc_type: type[BaseException] | None, |
| 233 | exc_value: BaseException | None, |
| 234 | exc_traceback: TracebackType | None, |
| 235 | ) -> None: |
| 236 | return None |
| 237 | |
| 238 | |
| 239 | def _plot_fig( |
| 240 | fig_dir: Path, |
| 241 | fig_group_data: tuple[tuple[tuple[str, str], ...], list[dict[str, object]]], |
| 242 | row_by: list[str], |
| 243 | col_by: list[str], |
| 244 | curve_by: list[str], |
| 245 | *, |
| 246 | var_x: str, |
| 247 | var_y: str, |
| 248 | filter_by: PlotFilters, |
| 249 | bin_by: PlotBinners, |
| 250 | scale_x: str | None, |
| 251 | scale_y: str | None, |
| 252 | dry_run: bool, |
| 253 | fig_name: str, |
| 254 | error_bars: bool, |
| 255 | fig_height: float, |
| 256 | fig_dpi: int, |
| 257 | ): |
| 258 | # Lazy-import matplotlib/pandas/seaborn |
| 259 | try: |
| 260 | import matplotlib.pyplot as plt |
| 261 | except ImportError: |
| 262 | plt = PlaceholderModule("matplotlib").placeholder_attr("pyplot") |
| 263 | try: |
| 264 | import pandas as pd |
| 265 | except ImportError: |
| 266 | pd = PlaceholderModule("pandas") |
| 267 | try: |
| 268 | import seaborn as sns |
| 269 | except ImportError: |
| 270 | sns = PlaceholderModule("seaborn") |
| 271 | |
| 272 | fig_group, fig_data = fig_group_data |
| 273 | |
| 274 | row_groups = full_groupby( |
| 275 | fig_data, |
| 276 | key=lambda item: _get_group(item, row_by), |
| 277 | ) |
| 278 | num_rows = len(row_groups) |
| 279 | num_cols = max( |
| 280 | len(full_groupby(row_data, key=lambda item: _get_group(item, col_by))) |
| 281 | for _, row_data in row_groups |
| 282 | ) |
| 283 | |
| 284 | fig_path = _get_fig_path(fig_dir, fig_group, fig_name) |
| 285 | |
| 286 | print("[BEGIN FIGURE]") |
| 287 | print(f"Group: {dict(fig_group)}") |
| 288 | print(f"Grid: {num_rows} rows x {num_cols} cols") |
| 289 | print(f"Output file: {fig_path}") |
| 290 | |
| 291 | if dry_run: |
| 292 | print("[END FIGURE]") |
| 293 | return |
| 294 | |
| 295 | # Convert string "inf", "-inf", and "nan" to their float equivalents |
| 296 | fig_data = _convert_inf_nan_strings(fig_data) |
| 297 | df = pd.DataFrame.from_records(fig_data) |
| 298 | |
| 299 | if var_x not in df.columns: |
| 300 | raise ValueError( |
| 301 | f"Cannot find {var_x=!r} in parameter sweep results. " |
| 302 | f"Available variables: {df.columns.tolist()}" |
| 303 | ) |
| 304 | if var_y not in df.columns: |
| 305 | raise ValueError( |
| 306 | f"Cannot find {var_y=!r} in parameter sweep results. " |
| 307 | f"Available variables: {df.columns.tolist()}" |
| 308 | ) |
| 309 | for k in row_by: |
| 310 | if k not in df.columns: |
| 311 | raise ValueError( |
| 312 | f"Cannot find row_by={k!r} in parameter sweep results. " |
| 313 | f"Available variables: {df.columns.tolist()}" |
| 314 | ) |
| 315 | for k in col_by: |
| 316 | if k not in df.columns: |
| 317 | raise ValueError( |
| 318 | f"Cannot find col_by={k!r} in parameter sweep results. " |
| 319 | f"Available variables: {df.columns.tolist()}" |
| 320 | ) |
| 321 | for k in curve_by: |
| 322 | if k not in df.columns: |
| 323 | raise ValueError( |
| 324 | f"Cannot find curve_by={k!r} in parameter sweep results. " |
| 325 | f"Available variables: {df.columns.tolist()}" |
| 326 | ) |
| 327 | |
| 328 | df = filter_by.apply(df) |
| 329 | df = bin_by.apply(df) |
| 330 | |
| 331 | if len(df) == 0: |
| 332 | print(f"No data to plot. Filters: {filter_by}") |
| 333 | print("[END FIGURE]") |
| 334 | return |
| 335 | |
| 336 | # Sort by curve_by columns alphabetically for consistent legend ordering |
| 337 | if curve_by: |
| 338 | df = df.sort_values(by=curve_by) |
| 339 | |
| 340 | df["row_group"] = ( |
| 341 | pd.concat( |
| 342 | [k + "=" + df[k].astype(str) for k in row_by], |
| 343 | axis=1, |
| 344 | ).agg("\n".join, axis=1) |
| 345 | if row_by |
| 346 | else "(All)" |
| 347 | ) |
| 348 | |
| 349 | df["col_group"] = ( |
| 350 | pd.concat( |
| 351 | [k + "=" + df[k].astype(str) for k in col_by], |
| 352 | axis=1, |
| 353 | ).agg("\n".join, axis=1) |
| 354 | if col_by |
| 355 | else "(All)" |
| 356 | ) |
| 357 | |
| 358 | if len(curve_by) <= 3: |
| 359 | hue, style, size, *_ = (*curve_by, None, None, None) |
| 360 | |
| 361 | g = sns.relplot( |
| 362 | df, |
| 363 | x=var_x, |
| 364 | y=var_y, |
| 365 | hue=hue, |
| 366 | style=style, |
| 367 | size=size, |
| 368 | markers=True, |
| 369 | errorbar="sd" if error_bars else None, |
| 370 | kind="line", |
| 371 | row="row_group", |
| 372 | col="col_group", |
| 373 | height=fig_height, |
| 374 | ) |
| 375 | else: |
| 376 | df["curve_group"] = ( |
| 377 | pd.concat( |
| 378 | [k + "=" + df[k].astype(str) for k in curve_by], |
| 379 | axis=1, |
| 380 | ).agg("\n".join, axis=1) |
| 381 | if curve_by |
| 382 | else "(All)" |
| 383 | ) |
| 384 | |
| 385 | g = sns.relplot( |
| 386 | df, |
| 387 | x=var_x, |
| 388 | y=var_y, |
| 389 | hue="curve_group", |
| 390 | markers=True, |
| 391 | errorbar="sd" if error_bars else None, |
| 392 | kind="line", |
| 393 | row="row_group", |
| 394 | col="col_group", |
| 395 | height=fig_height, |
| 396 | ) |
| 397 | |
| 398 | if row_by and col_by: |
| 399 | g.set_titles("{row_name}\n{col_name}") |
| 400 | elif row_by: |
| 401 | g.set_titles("{row_name}") |
| 402 | elif col_by: |
| 403 | g.set_titles("{col_name}") |
| 404 | else: |
| 405 | g.set_titles("") |
| 406 | |
| 407 | if scale_x: |
| 408 | g.set(xscale=scale_x) |
| 409 | if scale_y: |
| 410 | g.set(yscale=scale_y) |
| 411 | |
| 412 | g.savefig(fig_path, dpi=fig_dpi) |
| 413 | plt.close(g.figure) |
| 414 | |
| 415 | print("[END FIGURE]") |
| 416 | |
| 417 | |
| 418 | def plot( |
| 419 | output_dir: Path, |
| 420 | fig_dir: Path, |
| 421 | fig_by: list[str], |
| 422 | row_by: list[str], |
| 423 | col_by: list[str], |
| 424 | curve_by: list[str], |
| 425 | *, |
| 426 | var_x: str, |
| 427 | var_y: str, |
| 428 | filter_by: PlotFilters, |
| 429 | bin_by: PlotBinners, |
| 430 | scale_x: str | None, |
| 431 | scale_y: str | None, |
| 432 | dry_run: bool, |
| 433 | fig_name: str = "FIGURE", |
| 434 | error_bars: bool = True, |
| 435 | fig_height: float = 6.4, |
| 436 | fig_dpi: int = 300, |
| 437 | ): |
| 438 | all_data = [ |
| 439 | run_data |
| 440 | for path in output_dir.rglob("**/summary.json") |
| 441 | for run_data in _json_load_bytes(path) |
| 442 | ] |
| 443 | |
| 444 | if not all_data: |
| 445 | raise ValueError(f"Did not find any parameter sweep results under {output_dir}") |
| 446 | |
| 447 | fig_dir.mkdir(parents=True, exist_ok=True) |
| 448 | |
| 449 | fig_groups = full_groupby( |
| 450 | all_data, |
| 451 | key=lambda item: _get_group(item, fig_by), |
| 452 | ) |
| 453 | |
| 454 | with DummyExecutor() if len(fig_groups) <= 1 else ProcessPoolExecutor() as executor: |
| 455 | # Resolve the iterable to ensure that the workers are run |
| 456 | all( |
| 457 | executor.map( |
| 458 | partial( |
| 459 | _plot_fig, |
| 460 | fig_dir, |
| 461 | row_by=row_by, |
| 462 | col_by=col_by, |
| 463 | curve_by=curve_by, |
| 464 | var_x=var_x, |
| 465 | var_y=var_y, |
| 466 | filter_by=filter_by, |
| 467 | bin_by=bin_by, |
| 468 | scale_x=scale_x, |
| 469 | scale_y=scale_y, |
| 470 | dry_run=dry_run, |
| 471 | fig_name=fig_name, |
| 472 | error_bars=error_bars, |
| 473 | fig_height=fig_height, |
| 474 | fig_dpi=fig_dpi, |
| 475 | ), |
| 476 | fig_groups, |
| 477 | ) |
| 478 | ) |
| 479 | |
| 480 | |
| 481 | @dataclass |
| 482 | class SweepPlotArgs: |
| 483 | output_dir: Path |
| 484 | fig_dir: Path |
| 485 | fig_by: list[str] |
| 486 | row_by: list[str] |
| 487 | col_by: list[str] |
| 488 | curve_by: list[str] |
| 489 | var_x: str |
| 490 | var_y: str |
| 491 | filter_by: PlotFilters |
| 492 | bin_by: PlotBinners |
| 493 | scale_x: str | None |
| 494 | scale_y: str | None |
| 495 | dry_run: bool |
| 496 | fig_name: str = "FIGURE" |
| 497 | error_bars: bool = True |
| 498 | fig_height: float = 6.4 |
| 499 | fig_dpi: int = 300 |
| 500 | |
| 501 | parser_name: ClassVar[str] = "plot" |
| 502 | parser_help: ClassVar[str] = "Plot performance curves from parameter sweep results." |
| 503 | |
| 504 | @classmethod |
| 505 | def from_cli_args(cls, args: argparse.Namespace): |
| 506 | output_dir = Path(args.EXPERIMENT_DIR) |
| 507 | if not output_dir.exists(): |
| 508 | raise ValueError(f"No parameter sweep results under {output_dir}") |
| 509 | |
| 510 | curve_by = [] if not args.curve_by else args.curve_by.split(",") |
| 511 | row_by = [] if not args.row_by else args.row_by.split(",") |
| 512 | col_by = [] if not args.col_by else args.col_by.split(",") |
| 513 | fig_by = [] if not args.fig_by else args.fig_by.split(",") |
| 514 | |
| 515 | return cls( |
| 516 | output_dir=output_dir, |
| 517 | fig_dir=output_dir / args.fig_dir, |
| 518 | fig_by=fig_by, |
| 519 | row_by=row_by, |
| 520 | col_by=col_by, |
| 521 | curve_by=curve_by, |
| 522 | var_x=args.var_x, |
| 523 | var_y=args.var_y, |
| 524 | filter_by=PlotFilters.parse_str(args.filter_by), |
| 525 | bin_by=PlotBinners.parse_str(args.bin_by), |
| 526 | scale_x=args.scale_x, |
| 527 | scale_y=args.scale_y, |
| 528 | dry_run=args.dry_run, |
| 529 | fig_name=args.fig_name, |
| 530 | error_bars=not args.no_error_bars, |
| 531 | fig_height=args.fig_height, |
| 532 | fig_dpi=args.fig_dpi, |
| 533 | ) |
| 534 | |
| 535 | @classmethod |
| 536 | def add_cli_args(cls, parser: FlexibleArgumentParser) -> FlexibleArgumentParser: |
| 537 | parser.add_argument( |
| 538 | "EXPERIMENT_DIR", |
| 539 | type=str, |
| 540 | help="The directory containing the sweep results to plot.", |
| 541 | ) |
| 542 | parser.add_argument( |
| 543 | "--fig-dir", |
| 544 | type=str, |
| 545 | default="", |
| 546 | help="The directory to save the figures, relative to `OUTPUT_DIR`. " |
| 547 | "By default, the same directory is used.", |
| 548 | ) |
| 549 | parser.add_argument( |
| 550 | "--fig-by", |
| 551 | type=str, |
| 552 | default="", |
| 553 | help="A comma-separated list of variables, such that a separate figure " |
| 554 | "is created for each combination of these variables.", |
| 555 | ) |
| 556 | parser.add_argument( |
| 557 | "--row-by", |
| 558 | type=str, |
| 559 | default="", |
| 560 | help="A comma-separated list of variables, such that a separate row " |
| 561 | "is created for each combination of these variables.", |
| 562 | ) |
| 563 | parser.add_argument( |
| 564 | "--col-by", |
| 565 | type=str, |
| 566 | default="", |
| 567 | help="A comma-separated list of variables, such that a separate column " |
| 568 | "is created for each combination of these variables.", |
| 569 | ) |
| 570 | parser.add_argument( |
| 571 | "--curve-by", |
| 572 | type=str, |
| 573 | default=None, |
| 574 | help="A comma-separated list of variables, such that a separate curve " |
| 575 | "is created for each combination of these variables.", |
| 576 | ) |
| 577 | parser.add_argument( |
| 578 | "--var-x", |
| 579 | type=str, |
| 580 | default="total_token_throughput", |
| 581 | help="The variable for the x-axis.", |
| 582 | ) |
| 583 | parser.add_argument( |
| 584 | "--var-y", |
| 585 | type=str, |
| 586 | default="median_ttft_ms", |
| 587 | help="The variable for the y-axis", |
| 588 | ) |
| 589 | parser.add_argument( |
| 590 | "--filter-by", |
| 591 | type=str, |
| 592 | default="", |
| 593 | help="A comma-separated list of statements indicating values to filter by. " |
| 594 | "This is useful to remove outliers. " |
| 595 | "Example: `max_concurrency<1000,max_num_batched_tokens<=4096` means " |
| 596 | "plot only the points where `max_concurrency` is less than 1000 and " |
| 597 | "`max_num_batched_tokens` is no greater than 4096.", |
| 598 | ) |
| 599 | parser.add_argument( |
| 600 | "--bin-by", |
| 601 | type=str, |
| 602 | default="", |
| 603 | help="A comma-separated list of statements indicating values to bin by. " |
| 604 | "This is useful to avoid plotting points that are too close together. " |
| 605 | "Example: `request_throughput%%1` means " |
| 606 | "use a bin size of 1 for the `request_throughput` variable.", |
| 607 | ) |
| 608 | parser.add_argument( |
| 609 | "--scale-x", |
| 610 | type=str, |
| 611 | default=None, |
| 612 | help="The scale to use for the x-axis. " |
| 613 | "Currently only accepts string values such as 'log' and 'sqrt'. " |
| 614 | "See also: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html", |
| 615 | ) |
| 616 | parser.add_argument( |
| 617 | "--scale-y", |
| 618 | type=str, |
| 619 | default=None, |
| 620 | help="The scale to use for the y-axis. " |
| 621 | "Currently only accepts string values such as 'log' and 'sqrt'. " |
| 622 | "See also: https://seaborn.pydata.org/generated/seaborn.objects.Plot.scale.html", |
| 623 | ) |
| 624 | parser.add_argument( |
| 625 | "--fig-name", |
| 626 | type=str, |
| 627 | default="FIGURE", |
| 628 | help="Name prefix for the output figure file. " |
| 629 | "Group data is always appended when present. " |
| 630 | "Default: 'FIGURE'. Example: --fig-name my_performance_plot", |
| 631 | ) |
| 632 | parser.add_argument( |
| 633 | "--no-error-bars", |
| 634 | action="store_true", |
| 635 | help="If set, disables error bars on the plot. " |
| 636 | "By default, error bars are shown.", |
| 637 | ) |
| 638 | parser.add_argument( |
| 639 | "--fig-height", |
| 640 | type=float, |
| 641 | default=6.4, |
| 642 | help="Height of each subplot in inches. Default: 6.4", |
| 643 | ) |
| 644 | parser.add_argument( |
| 645 | "--fig-dpi", |
| 646 | type=int, |
| 647 | default=300, |
| 648 | help="Resolution of the output figure in dots per inch. Default: 300", |
| 649 | ) |
| 650 | parser.add_argument( |
| 651 | "--dry-run", |
| 652 | action="store_true", |
| 653 | help="If set, prints the information about each figure to plot, " |
| 654 | "then exits without drawing them.", |
| 655 | ) |
| 656 | |
| 657 | return parser |
| 658 | |
| 659 | |
| 660 | def run_main(args: SweepPlotArgs): |
| 661 | return plot( |
| 662 | output_dir=args.output_dir, |
| 663 | fig_dir=args.fig_dir, |
| 664 | fig_by=args.fig_by, |
| 665 | row_by=args.row_by, |
| 666 | col_by=args.col_by, |
| 667 | curve_by=args.curve_by, |
| 668 | var_x=args.var_x, |
| 669 | var_y=args.var_y, |
| 670 | filter_by=args.filter_by, |
| 671 | bin_by=args.bin_by, |
| 672 | scale_x=args.scale_x, |
| 673 | scale_y=args.scale_y, |
| 674 | dry_run=args.dry_run, |
| 675 | fig_name=args.fig_name, |
| 676 | error_bars=args.error_bars, |
| 677 | fig_height=args.fig_height, |
| 678 | fig_dpi=args.fig_dpi, |
| 679 | ) |
| 680 | |
| 681 | |
| 682 | def main(args: argparse.Namespace): |
| 683 | run_main(SweepPlotArgs.from_cli_args(args)) |
| 684 | |
| 685 | |
| 686 | if __name__ == "__main__": |
| 687 | parser = FlexibleArgumentParser(description=SweepPlotArgs.parser_help) |
| 688 | SweepPlotArgs.add_cli_args(parser) |
| 689 | |
| 690 | main(parser.parse_args()) |