def _get_contour_subplot(
info: _SubContourInfo,
reverse_scale: bool,
target_name: str = "Objective Value",
) -> Tuple["Contour", "Scatter"]:
x_indices = info.xaxis.indices
y_indices = info.yaxis.indices
x_values = []
y_values = []
for x_value, y_value in zip(info.xaxis.values, info.yaxis.values):
if x_value is not None and y_value is not None:
x_values.append(x_value)
y_values.append(y_value)
z_values = [[float("nan") for _ in range(len(info.xaxis.indices))]
for _ in range(len(info.yaxis.indices))]
for (x_i, y_i), z_value in info.z_values.items():
z_values[y_i][x_i] = z_value
if len(x_indices) < 2 or len(y_indices) < 2:
return go.Contour(), go.Scatter()
contour = go.Contour(
x=x_indices,
y=y_indices,
z=z_values,
colorbar={"title": target_name},
colorscale=COLOR_SCALE,
connectgaps=True,
contours_coloring="heatmap",
hoverinfo="none",
line_smoothing=1.3,
reversescale=reverse_scale,
)
scatter = go.Scatter(
x=x_values,
y=y_values,
marker={
"line": {
"width": 2.0,
"color": "Grey"
},
"color": "black"
},
mode="markers",
showlegend=False,
)
return contour, scatter
def _generate_contour_subplot(
trials: List[FrozenTrial],
x_param: str,
y_param: str,
reverse_scale: bool,
param_values_range: Optional[Dict[str, Tuple[float, float]]] = None,
target: Optional[Callable[[FrozenTrial], float]] = None,
target_name: str = "Objective Value",
) -> Tuple["Contour", "Scatter"]:
if param_values_range is None:
param_values_range = {}
x_indices = sorted(set(_get_param_values(trials, x_param)))
y_indices = sorted(set(_get_param_values(trials, y_param)))
if len(x_indices) < 2:
_logger.warning(
"Param {} unique value length is less than 2.".format(x_param))
return go.Contour(), go.Scatter()
if len(y_indices) < 2:
_logger.warning(
"Param {} unique value length is less than 2.".format(y_param))
return go.Contour(), go.Scatter()
# Padding to the plot for non-categorical params.
x_range = param_values_range[x_param]
if _is_numerical(trials, x_param):
x_indices = [x_range[0]] + x_indices + [x_range[1]]
y_range = param_values_range[y_param]
if _is_numerical(trials, y_param):
y_indices = [y_range[0]] + y_indices + [y_range[1]]
z = [[float("nan") for _ in range(len(x_indices))]
for _ in range(len(y_indices))]
x_values = []
y_values = []
for trial in trials:
if x_param not in trial.params or y_param not in trial.params:
continue
x_value = trial.params[x_param]
y_value = trial.params[y_param]
if not _is_numerical(trials, x_param):
x_value = str(x_value)
if not _is_numerical(trials, y_param):
y_value = str(y_value)
x_values.append(x_value)
y_values.append(y_value)
x_i = x_indices.index(x_value)
y_i = y_indices.index(y_value)
if target is None:
value = trial.value
else:
value = target(trial)
if isinstance(value, int):
value = float(value)
elif not isinstance(value, float):
raise ValueError(
f"Trial{trial.number} has COMPLETE state, but its target value is non-numeric."
)
z[y_i][x_i] = value
contour = go.Contour(
x=x_indices,
y=y_indices,
z=z,
colorbar={"title": target_name},
colorscale=COLOR_SCALE,
connectgaps=True,
contours_coloring="heatmap",
hoverinfo="none",
line_smoothing=1.3,
reversescale=reverse_scale,
)
scatter = go.Scatter(
x=x_values,
y=y_values,
marker={
"line": {
"width": 2.0,
"color": "Grey"
},
"color": "black"
},
mode="markers",
showlegend=False,
)
return (contour, scatter)
def _generate_contour_subplot(
trials: List[FrozenTrial],
x_param: str,
y_param: str,
direction: StudyDirection,
param_values_range: Optional[Dict[str, Tuple[float, float]]] = None,
) -> Tuple["Contour", "Scatter"]:
if param_values_range is None:
param_values_range = {}
x_indices = sorted(set(_get_param_values(trials, x_param)))
y_indices = sorted(set(_get_param_values(trials, y_param)))
if len(x_indices) < 2:
_logger.warning("Param {} unique value length is less than 2.".format(x_param))
return go.Contour(), go.Scatter()
if len(y_indices) < 2:
_logger.warning("Param {} unique value length is less than 2.".format(y_param))
return go.Contour(), go.Scatter()
# Padding to the plot for non-categorical params.
x_range = param_values_range[x_param]
if not _is_categorical(trials, x_param):
x_indices = [x_range[0]] + x_indices + [x_range[1]]
y_range = param_values_range[y_param]
if not _is_categorical(trials, y_param):
y_indices = [y_range[0]] + y_indices + [y_range[1]]
z = [[float("nan") for _ in range(len(x_indices))] for _ in range(len(y_indices))]
x_values = []
y_values = []
for trial in trials:
if x_param not in trial.params or y_param not in trial.params:
continue
x_value = trial.params[x_param]
y_value = trial.params[y_param]
if _is_categorical(trials, x_param):
x_value = str(x_value)
if _is_categorical(trials, y_param):
y_value = str(y_value)
x_values.append(x_value)
y_values.append(y_value)
x_i = x_indices.index(x_value)
y_i = y_indices.index(y_value)
if isinstance(trial.value, int):
value = float(trial.value)
elif isinstance(trial.value, float):
value = trial.value
else:
raise ValueError(
"Trial{} has COMPLETE state, but its value is non-numeric.".format(trial.number)
)
z[y_i][x_i] = value
# TODO(Yanase): Use reversescale argument to reverse colorscale if Plotly's bug is fixed.
# If contours_coloring='heatmap' is specified, reversescale argument of go.Contour does not
# work correctly. See https://github.com/pfnet/optuna/issues/606.
colorscale = plotly.colors.PLOTLY_SCALES["Blues"]
if direction == StudyDirection.MAXIMIZE:
colorscale = [[1 - t[0], t[1]] for t in colorscale]
colorscale.reverse()
contour = go.Contour(
x=x_indices,
y=y_indices,
z=z,
colorbar={"title": "Objective Value"},
colorscale=colorscale,
connectgaps=True,
contours_coloring="heatmap",
hoverinfo="none",
line_smoothing=1.3,
)
scatter = go.Scatter(
x=x_values,
y=y_values,
marker={"line": {"width": 0.5, "color": "Grey"}, "color": "black"},
mode="markers",
showlegend=False,
)
return (contour, scatter)
def _generate_contour_subplot(
trials: List[FrozenTrial], x_param: str, y_param: str,
direction: StudyDirection) -> Tuple["Contour", "Scatter"]:
x_indices = sorted(
list({t.params[x_param]
for t in trials if x_param in t.params}))
y_indices = sorted(
list({t.params[y_param]
for t in trials if y_param in t.params}))
if len(x_indices) < 2:
_logger.warning(
"Param {} unique value length is less than 2.".format(x_param))
return go.Contour(), go.Scatter()
if len(y_indices) < 2:
_logger.warning(
"Param {} unique value length is less than 2.".format(y_param))
return go.Contour(), go.Scatter()
z = [[float("nan") for _ in range(len(x_indices))]
for _ in range(len(y_indices))]
x_values = []
y_values = []
for trial in trials:
if x_param not in trial.params or y_param not in trial.params:
continue
x_values.append(trial.params[x_param])
y_values.append(trial.params[y_param])
x_i = x_indices.index(trial.params[x_param])
y_i = y_indices.index(trial.params[y_param])
if isinstance(trial.value, int):
value = float(trial.value)
elif isinstance(trial.value, float):
value = trial.value
else:
raise ValueError(
"Trial{} has COMPLETE state, but its value is non-numeric.".
format(trial.number))
z[y_i][x_i] = value
# TODO(Yanase): Use reversescale argument to reverse colorscale if Plotly's bug is fixed.
# If contours_coloring='heatmap' is specified, reversesecale argument of go.Contour does not
# work correctly. See https://github.com/pfnet/optuna/issues/606.
colorscale = plotly.colors.PLOTLY_SCALES["Blues"]
if direction == StudyDirection.MINIMIZE:
colorscale = [[1 - t[0], t[1]] for t in colorscale]
colorscale.reverse()
contour = go.Contour(
x=x_indices,
y=y_indices,
z=z,
colorbar={"title": "Objective Value"},
colorscale=colorscale,
connectgaps=True,
contours_coloring="heatmap",
hoverinfo="none",
line_smoothing=1.3,
)
scatter = go.Scatter(x=x_values,
y=y_values,
marker={"color": "black"},
mode="markers",
showlegend=False)
return (contour, scatter)
