import os
from pathlib import Path
import numpy as np
import pandas as pd
import panel as pn
import param
from qcpanel.util import create_plots, explore_datasets, valid_plot_modes
from quacc.evaluation.estimators import CE
from quacc.evaluation.report import DatasetReport
class QuaccTestViewer(param.Parameterized):
__base_path = "output"
dataset = param.Selector()
metric = param.Selector()
estimators = param.ListSelector()
plot_view = param.Selector()
mode = param.Selector()
modal_estimators = param.ListSelector()
modal_plot_view = param.ListSelector()
modal_mode_prev = param.ListSelector(
objects=valid_plot_modes[0], default=valid_plot_modes[0]
)
modal_mode_avg = param.ListSelector(
objects=valid_plot_modes["avg"], default=valid_plot_modes["avg"]
)
param_pane = param.Parameter()
plot_pane = param.Parameter()
modal_pane = param.Parameter()
root = param.String()
def __init__(self, param_init=None, **params):
super().__init__(**params)
self.param_init = param_init
self.__setup_watchers()
self.update_datasets()
# self._update_on_dataset()
self.__create_param_pane()
self.__create_modal_pane()
def __get_param_init(self, val):
__b = val in self.param_init
if __b:
setattr(self, val, self.param_init[val])
del self.param_init[val]
return __b
def __save_callback(self, event):
_home = Path("output")
_save_input_val = self.save_input.value_input
_config = "default" if len(_save_input_val) == 0 else _save_input_val
base_path = _home / self.dataset / _config
os.makedirs(base_path, exist_ok=True)
base_plot = base_path / "plot"
os.makedirs(base_plot, exist_ok=True)
l_dr = self.datasets_[self.dataset]
res = l_dr.to_md(
conf=_config,
metric=self.metric,
estimators=CE.name[self.modal_estimators],
dr_modes=self.modal_mode_avg,
cr_modes=self.modal_mode_prev,
cr_prevs=self.modal_plot_view,
plot_path=base_plot,
)
with open(base_path / f"{self.metric}.md", "w") as f:
f.write(res)
pn.state.notifications.success(f'"{_config}" successfully saved')
def __create_param_pane(self):
self.dataset_widget = pn.Param(
self,
show_name=False,
parameters=["dataset"],
widgets={"dataset": {"widget_type": pn.widgets.Select}},
)
self.metric_widget = pn.Param(
self,
show_name=False,
parameters=["metric"],
widgets={"metric": {"widget_type": pn.widgets.Select}},
)
self.estimators_widgets = pn.Param(
self,
show_name=False,
parameters=["estimators"],
widgets={
"estimators": {
"widget_type": pn.widgets.MultiChoice,
# "orientation": "vertical",
"sizing_mode": "scale_width",
# "button_type": "primary",
# "button_style": "outline",
"solid": True,
"search_option_limit": 1000,
"option_limit": 1000,
"max_items": 1000,
}
},
)
self.plot_view_widget = pn.Param(
self,
show_name=False,
parameters=["plot_view"],
widgets={
"plot_view": {
"widget_type": pn.widgets.RadioButtonGroup,
"orientation": "vertical",
"button_type": "primary",
"button_style": "outline",
}
},
)
self.mode_widget = pn.Param(
self,
show_name=False,
parameters=["mode"],
widgets={
"mode": {
"widget_type": pn.widgets.RadioButtonGroup,
"orientation": "vertical",
"sizing_mode": "scale_width",
"button_type": "primary",
"button_style": "outline",
}
},
align="center",
)
self.param_pane = pn.Column(
self.dataset_widget,
self.metric_widget,
pn.Row(
self.plot_view_widget,
self.mode_widget,
),
self.estimators_widgets,
)
def __create_modal_pane(self):
self.modal_estimators_widgets = pn.Param(
self,
show_name=False,
parameters=["modal_estimators"],
widgets={
"modal_estimators": {
"widget_type": pn.widgets.CheckButtonGroup,
"orientation": "vertical",
"sizing_mode": "scale_width",
"button_type": "primary",
"button_style": "outline",
}
},
)
self.modal_plot_view_widget = pn.Param(
self,
show_name=False,
parameters=["modal_plot_view"],
widgets={
"modal_plot_view": {
"widget_type": pn.widgets.CheckButtonGroup,
"orientation": "vertical",
"button_type": "primary",
"button_style": "outline",
}
},
)
self.modal_mode_prev_widget = pn.Param(
self,
show_name=False,
parameters=["modal_mode_prev"],
widgets={
"modal_mode_prev": {
"widget_type": pn.widgets.CheckButtonGroup,
"orientation": "vertical",
"sizing_mode": "scale_width",
"button_type": "primary",
"button_style": "outline",
}
},
align="center",
)
self.modal_mode_avg_widget = pn.Param(
self,
show_name=False,
parameters=["modal_mode_avg"],
widgets={
"modal_mode_avg": {
"widget_type": pn.widgets.CheckButtonGroup,
"orientation": "vertical",
"sizing_mode": "scale_width",
"button_type": "primary",
"button_style": "outline",
}
},
align="center",
)
self.save_input = pn.widgets.TextInput(
name="Configuration Name", placeholder="default", sizing_mode="scale_width"
)
self.save_button = pn.widgets.Button(
name="Save",
sizing_mode="scale_width",
button_style="solid",
button_type="success",
)
self.save_button.on_click(self.__save_callback)
_title_styles = {
"font-size": "14pt",
"font-weight": "bold",
}
self.modal_pane = pn.Column(
pn.Column(
pn.pane.Str("Avg. configuration", styles=_title_styles),
self.modal_mode_avg_widget,
pn.pane.Str("Train prevs. configuration", styles=_title_styles),
pn.Row(
self.modal_plot_view_widget,
self.modal_mode_prev_widget,
),
pn.pane.Str("Estimators configuration", styles=_title_styles),
self.modal_estimators_widgets,
self.save_input,
self.save_button,
pn.Spacer(height=20),
width=450,
align="center",
scroll=True,
),
sizing_mode="stretch_both",
)
def update_datasets(self):
if not self.__get_param_init("root"):
self.root = self.__base_path
dataset_paths = sorted(
explore_datasets(self.root), key=lambda t: (-len(t.parts), t)
)
self.datasets_ = {
str(dp.parent.relative_to(Path(self.root))): DatasetReport.unpickle(dp)
for dp in dataset_paths
}
self.available_datasets = list(self.datasets_.keys())
_old_dataset = self.dataset
self.param["dataset"].objects = self.available_datasets
if not self.__get_param_init("dataset"):
self.dataset = (
_old_dataset
if _old_dataset in self.available_datasets
else self.available_datasets[0]
)
def __setup_watchers(self):
self.param.watch(
self._update_on_dataset,
["dataset"],
queued=True,
precedence=0,
)
self.param.watch(self._update_on_view, ["plot_view"], queued=True, precedence=1)
self.param.watch(self._update_on_metric, ["metric"], queued=True, precedence=2)
self.param.watch(
self._update_plot,
["dataset", "metric", "estimators", "plot_view", "mode"],
# ["metric", "estimators", "mode"],
onlychanged=False,
precedence=3,
)
self.param.watch(
self._update_on_estimators,
["estimators"],
queued=True,
precedence=4,
)
def _update_on_dataset(self, *events):
l_dr = self.datasets_[self.dataset]
l_data = l_dr.data()
l_metrics = l_data.columns.unique(0)
l_valid_metrics = [m for m in l_metrics if not m.endswith("_score")]
_old_metric = self.metric
self.param["metric"].objects = l_valid_metrics
if not self.__get_param_init("metric"):
self.metric = (
_old_metric if _old_metric in l_valid_metrics else l_valid_metrics[0]
)
_old_estimators = self.estimators
l_valid_estimators = l_dr.data(metric=self.metric).columns.unique(0).to_numpy()
_new_estimators = l_valid_estimators[
np.isin(l_valid_estimators, _old_estimators)
].tolist()
self.param["estimators"].objects = l_valid_estimators
if not self.__get_param_init("estimators"):
self.estimators = _new_estimators
l_valid_views = [str(round(cr.train_prev[1] * 100)) for cr in l_dr.crs]
l_valid_views = ["avg"] + l_valid_views
_old_view = self.plot_view
self.param["plot_view"].objects = l_valid_views
if not self.__get_param_init("plot_view"):
self.plot_view = _old_view if _old_view in l_valid_views else "avg"
self.param["mode"].objects = valid_plot_modes[self.plot_view]
if not self.__get_param_init("mode"):
_old_mode = self.mode
if _old_mode in valid_plot_modes[self.plot_view]:
self.mode = _old_mode
else:
self.mode = valid_plot_modes[self.plot_view][0]
self.param["modal_estimators"].objects = l_valid_estimators
self.modal_estimators = []
self.param["modal_plot_view"].objects = l_valid_views
self.modal_plot_view = l_valid_views.copy()
def _update_on_view(self, *events):
_old_mode = self.mode
self.param["mode"].objects = valid_plot_modes[self.plot_view]
if _old_mode in valid_plot_modes[self.plot_view]:
self.mode = _old_mode
else:
self.mode = valid_plot_modes[self.plot_view][0]
def _update_on_metric(self, *events):
_old_estimators = self.estimators
l_dr = self.datasets_[self.dataset]
l_data: pd.DataFrame = l_dr.data(metric=self.metric)
l_valid_estimators: np.ndarray = l_data.columns.unique(0).to_numpy()
_new_estimators = l_valid_estimators[
np.isin(l_valid_estimators, _old_estimators)
].tolist()
self.param["estimators"].objects = l_valid_estimators
self.estimators = _new_estimators
def _update_on_estimators(self, *events):
self.modal_estimators = self.estimators.copy()
def _update_plot(self, *events):
__svg = pn.pane.SVG(
"""<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-area-filled" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
<path stroke="none" d="M0 0h24v24H0z" fill="none" />
<path d="M20 18a1 1 0 0 1 .117 1.993l-.117 .007h-16a1 1 0 0 1 -.117 -1.993l.117 -.007h16z" stroke-width="0" fill="currentColor" />
<path d="M15.22 5.375a1 1 0 0 1 1.393 -.165l.094 .083l4 4a1 1 0 0 1 .284 .576l.009 .131v5a1 1 0 0 1 -.883 .993l-.117 .007h-16.022l-.11 -.009l-.11 -.02l-.107 -.034l-.105 -.046l-.1 -.059l-.094 -.07l-.06 -.055l-.072 -.082l-.064 -.089l-.054 -.096l-.016 -.035l-.04 -.103l-.027 -.106l-.015 -.108l-.004 -.11l.009 -.11l.019 -.105c.01 -.04 .022 -.077 .035 -.112l.046 -.105l.059 -.1l4 -6a1 1 0 0 1 1.165 -.39l.114 .05l3.277 1.638l3.495 -4.369z" stroke-width="0" fill="currentColor" />
</svg>""",
sizing_mode="stretch_both",
)
if len(self.estimators) == 0:
self.plot_pane = __svg
else:
_dr = self.datasets_[self.dataset]
__plot = create_plots(
_dr,
mode=self.mode,
metric=self.metric,
estimators=self.estimators,
plot_view=self.plot_view,
)
self.plot_pane = __svg if __plot is None else __plot
def get_plot(self):
return self.plot_pane
def get_param_pane(self):
return self.param_pane