139 lines
4.0 KiB
Python
139 lines
4.0 KiB
Python
import os
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from collections import defaultdict
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from pathlib import Path
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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: [
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"delta_train",
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"stdev_train",
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"train_table",
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"shift",
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"shift_table",
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"diagonal",
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]
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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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"train_table",
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"shift",
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"shift_table",
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"delta_test",
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"stdev_test",
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"test_table",
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]
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def create_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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plot_view=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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estimators = CE.name[estimators]
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if mode is None:
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mode = valid_plot_modes[plot_view][0]
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_dpi = 112
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match (plot_view, mode):
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case ("avg", "train_table"):
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_data = (
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dr.data(metric=metric, estimators=estimators).groupby(level=1).mean()
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)
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return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
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case ("avg", "test_table"):
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_data = (
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dr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
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)
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return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
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case ("avg", "shift_table"):
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_data = (
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dr.shift_data(metric=metric, estimators=estimators)
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.groupby(level=0)
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.mean()
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)
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return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
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case ("avg", _ as plot_mode):
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_plot = 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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return (
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pn.pane.Matplotlib(
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_plot,
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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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if _plot is not None
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else None
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)
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case (_, "train_table"):
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cr = dr.crs[_prevs.index(int(plot_view))]
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_data = (
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cr.data(metric=metric, estimators=estimators).groupby(level=0).mean()
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)
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return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
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case (_, "shift_table"):
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cr = dr.crs[_prevs.index(int(plot_view))]
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_data = (
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cr.shift_data(metric=metric, estimators=estimators)
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.groupby(level=0)
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.mean()
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)
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return pn.pane.DataFrame(_data, align="center") if not _data.empty else None
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case (_, _ as plot_mode):
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cr = dr.crs[_prevs.index(int(plot_view))]
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_plot = cr.get_plots(
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mode=plot_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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return (
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pn.pane.Matplotlib(
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_plot,
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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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if _plot is not None
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else None
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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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