panel implemented

qcpanel/run.py

@@ -0,0 +1,253 @@
+import argparse
+import os
+from pathlib import Path
+
+import panel as pn
+
+from quacc.evaluation.comp import CE
+from quacc.evaluation.report import DatasetReport
+
+pn.extension(design="bootstrap")
+
+
+def create_cr_plots(
+    dr: DatasetReport,
+    mode="delta",
+    metric="acc",
+    estimators=None,
+    prev=None,
+):
+    idx = [round(cr.train_prev[1] * 100) for cr in dr.crs].index(prev)
+    cr = dr.crs[idx]
+    estimators = CE.name[estimators]
+    _dpi = 112
+    return pn.pane.Matplotlib(
+        cr.get_plots(
+            mode=mode,
+            metric=metric,
+            estimators=estimators,
+            conf="panel",
+            return_fig=True,
+        ),
+        tight=True,
+        format="png",
+        sizing_mode="scale_height",
+        # sizing_mode="scale_both",
+    )
+
+
+def create_avg_plots(
+    dr: DatasetReport,
+    mode="delta",
+    metric="acc",
+    estimators=None,
+    prev=None,
+):
+    estimators = CE.name[estimators]
+    return pn.pane.Matplotlib(
+        dr.get_plots(
+            mode=mode,
+            metric=metric,
+            estimators=estimators,
+            conf="panel",
+            return_fig=True,
+        ),
+        tight=True,
+        format="png",
+        sizing_mode="scale_height",
+        # sizing_mode="scale_both",
+    )
+
+
+def build_cr_tab(dr: DatasetReport):
+    _data = dr.data()
+    _metrics = _data.columns.unique(0)
+    _estimators = _data.columns.unique(1)
+
+    valid_metrics = [m for m in _metrics if not m.endswith("_score")]
+    metric_widget = pn.widgets.Select(
+        name="metric",
+        value="acc",
+        options=valid_metrics,
+        align="center",
+    )
+
+    valid_estimators = [e for e in _estimators if e != "ref"]
+    estimators_widget = pn.widgets.CheckButtonGroup(
+        name="estimators",
+        options=valid_estimators,
+        value=valid_estimators,
+        button_style="outline",
+        button_type="primary",
+        align="center",
+        orientation="vertical",
+        sizing_mode="scale_width",
+    )
+
+    valid_plot_modes = ["delta", "delta_stdev", "diagonal", "shift"]
+    plot_mode_widget = pn.widgets.RadioButtonGroup(
+        name="mode",
+        value=valid_plot_modes[0],
+        options=valid_plot_modes,
+        button_style="outline",
+        button_type="primary",
+        align="center",
+        orientation="vertical",
+        sizing_mode="scale_width",
+    )
+
+    valid_prevs = [round(cr.train_prev[1] * 100) for cr in dr.crs]
+    prevs_widget = pn.widgets.RadioButtonGroup(
+        name="train prevalence",
+        value=valid_prevs[0],
+        options=valid_prevs,
+        button_style="outline",
+        button_type="primary",
+        align="center",
+        orientation="vertical",
+    )
+
+    plot_pane = pn.bind(
+        create_cr_plots,
+        dr=dr,
+        mode=plot_mode_widget,
+        metric=metric_widget,
+        estimators=estimators_widget,
+        prev=prevs_widget,
+    )
+
+    return pn.Row(
+        pn.Spacer(width=20),
+        pn.Column(
+            metric_widget,
+            pn.Row(
+                prevs_widget,
+                plot_mode_widget,
+            ),
+            estimators_widget,
+            align="center",
+        ),
+        pn.Spacer(sizing_mode="scale_width"),
+        plot_pane,
+    )
+
+
+def build_avg_tab(dr: DatasetReport):
+    _data = dr.data()
+    _metrics = _data.columns.unique(0)
+    _estimators = _data.columns.unique(1)
+
+    valid_metrics = [m for m in _metrics if not m.endswith("_score")]
+    metric_widget = pn.widgets.Select(
+        name="metric",
+        value="acc",
+        options=valid_metrics,
+        align="center",
+    )
+
+    valid_estimators = [e for e in _estimators if e != "ref"]
+    estimators_widget = pn.widgets.CheckButtonGroup(
+        name="estimators",
+        options=valid_estimators,
+        value=valid_estimators,
+        button_style="outline",
+        button_type="primary",
+        align="center",
+        orientation="vertical",
+        sizing_mode="scale_width",
+    )
+
+    valid_plot_modes = [
+        "delta_train",
+        "stdev_train",
+        "delta_test",
+        "stdev_test",
+        "shift",
+    ]
+    plot_mode_widget = pn.widgets.RadioButtonGroup(
+        name="mode",
+        value=valid_plot_modes[0],
+        options=valid_plot_modes,
+        button_style="outline",
+        button_type="primary",
+        align="center",
+        orientation="vertical",
+        sizing_mode="scale_width",
+    )
+
+    plot_pane = pn.bind(
+        create_avg_plots,
+        dr=dr,
+        mode=plot_mode_widget,
+        metric=metric_widget,
+        estimators=estimators_widget,
+    )
+
+    return pn.Row(
+        pn.Spacer(width=20),
+        pn.Column(
+            metric_widget,
+            plot_mode_widget,
+            estimators_widget,
+            align="center",
+        ),
+        pn.Spacer(sizing_mode="scale_width"),
+        plot_pane,
+    )
+
+
+def build_dataset(dataset_path: Path):
+    dr: DatasetReport = DatasetReport.unpickle(dataset_path)
+
+    prevs_tab = ("train prevs.", build_cr_tab(dr))
+    avg_tab = ("avg", build_avg_tab(dr))
+
+    app = pn.Tabs(objects=[avg_tab, prevs_tab], dynamic=False)
+    app.servable()
+    return app
+
+
+def explore_datasets(root: Path | str):
+    if isinstance(root, str):
+        root = Path(root)
+
+    drs = []
+    for f in os.listdir(root):
+        if (root / f).is_dir():
+            drs += explore_datasets(root / f)
+        elif f == f"{root.name}.pickle":
+            drs.append((str(root), build_dataset(root / f)))
+            # drs.append((str(root),))
+
+    return drs
+
+
+def serve(address="localhost"):
+    # app = build_dataset(Path("output/rcv1_CCAT_9prevs/rcv1_CCAT_9prevs.pickle"))
+    app = pn.Tabs(
+        objects=explore_datasets("output"),
+        tabs_location="left",
+        dynamic=False,
+    )
+
+    __port = 33420
+    pn.serve(
+        app,
+        autoreload=True,
+        port=__port,
+        show=False,
+        address=address,
+        websocket_origin=f"{address}:{__port}",
+    )
+
+
+def run():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--address",
+        action="store",
+        dest="address",
+        default="localhost",
+    )
+    args = parser.parse_args()
+    serve(address=args.address)