146 lines
3.6 KiB
Python
146 lines
3.6 KiB
Python
import os
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List
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import panel as pn
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from quacc.evaluation.comp import CE
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from quacc.evaluation.report import DatasetReport
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_plot_sizing_mode = "stretch_both"
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valid_plot_modes = defaultdict(
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lambda: ["delta", "delta_stdev", "diagonal", "shift", "table", "shift_table"]
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)
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valid_plot_modes["avg"] = [
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"delta_train",
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"stdev_train",
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"delta_test",
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"stdev_test",
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"shift",
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"train_table",
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"test_table",
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"shift_table",
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]
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def create_cr_plots(
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dr: DatasetReport,
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mode="delta",
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metric="acc",
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estimators=None,
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prev=None,
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):
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_prevs = [round(cr.train_prev[1] * 100) for cr in dr.crs]
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idx = _prevs.index(prev)
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cr = dr.crs[idx]
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estimators = CE.name[estimators]
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if mode is None:
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mode = valid_plot_modes[str(prev)][0]
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_dpi = 112
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if mode == "table":
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return pn.pane.DataFrame(
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cr.data(metric=metric, estimators=estimators).groupby(level=0).mean(),
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align="center",
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)
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elif mode == "shift_table":
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return pn.pane.DataFrame(
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cr.shift_data(metric=metric, estimators=estimators).groupby(level=0).mean(),
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align="center",
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)
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else:
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return pn.pane.Matplotlib(
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cr.get_plots(
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mode=mode,
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metric=metric,
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estimators=estimators,
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conf="panel",
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return_fig=True,
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),
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tight=True,
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format="png",
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sizing_mode=_plot_sizing_mode,
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# sizing_mode="scale_height",
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# sizing_mode="scale_both",
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)
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def create_avg_plots(
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dr: DatasetReport,
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mode="delta",
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metric="acc",
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estimators=None,
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):
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estimators = CE.name[estimators]
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if mode is None:
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mode = valid_plot_modes["avg"][0]
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if mode == "train_table":
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return pn.pane.DataFrame(
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dr.data(metric=metric, estimators=estimators).groupby(level=1).mean(),
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align="center",
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)
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elif mode == "test_table":
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return pn.pane.DataFrame(
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dr.data(metric=metric, estimators=estimators).groupby(level=0).mean(),
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align="center",
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)
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elif mode == "shift_table":
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return pn.pane.DataFrame(
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dr.shift_data(metric=metric, estimators=estimators).groupby(level=0).mean(),
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align="center",
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)
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return pn.pane.Matplotlib(
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dr.get_plots(
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mode=mode,
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metric=metric,
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estimators=estimators,
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conf="panel",
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return_fig=True,
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),
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tight=True,
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format="png",
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# sizing_mode="scale_height",
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sizing_mode=_plot_sizing_mode,
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# sizing_mode="scale_both",
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)
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def build_plot(
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datasets: Dict[str, DatasetReport],
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dst: str,
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metric: str,
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estimators: List[str],
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view: str,
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mode: str,
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):
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_dr = datasets[dst]
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if view == "avg":
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return create_avg_plots(_dr, mode=mode, metric=metric, estimators=estimators)
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else:
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prev = int(view)
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return create_cr_plots(
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_dr, mode=mode, metric=metric, estimators=estimators, prev=prev
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)
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def explore_datasets(root: Path | str):
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if isinstance(root, str):
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root = Path(root)
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if root.name == "plot":
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return []
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if not root.exists():
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return []
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drs = []
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for f in os.listdir(root):
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if (root / f).is_dir():
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drs += explore_datasets(root / f)
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elif f == f"{root.name}.pickle":
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drs.append(root / f)
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# drs.append((str(root),))
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return drs
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