def _get_data(
df_main: pd.DataFrame,
gce_prefix: str,
family_order: List[str],
rescaling_methods: Optional[List[str]] = None,
dataset_name: str = "imagenet(split='validation[20%:]')"
) -> Tuple[pd.DataFrame, mpl.colors.ListedColormap]:
"""Selects data for plotting."""
# Select data:
mask = df_main.Metric.str.startswith(gce_prefix)
mask &= df_main.ModelName.isin(display.get_standard_model_list())
mask &= df_main.DatasetName.isin([dataset_name])
mask &= df_main.rescaling_method.isin(rescaling_methods
or ["temperature_scaling"])
mask &= df_main.ModelFamily.isin(family_order)
df_plot = df_main[mask].copy()
df_plot, cmap = display.add_display_data(df_plot, family_order)
return df_plot, cmap
def plot_alternative_metrics(
df_main: pd.DataFrame,
xs: List[str], ys: List[str],
gce_prefix: str = plotting.STD_GCE_PREFIX,
add_legend: bool = True,
add_metric_description: bool = False,
) -> sns.axisgrid.FacetGrid:
"""Plots acc/calib for ImageNet and natural OOD datasets."""
# Settings:
rescaling_method = "temperature_scaling"
family_order = display.get_model_families_sorted()
dataset_order = (["imagenet(split='validation[20%:]')"] +
display.OOD_DATASET_ORDER)
# Select data:
mask = df_main.Metric.str.startswith(gce_prefix)
mask &= df_main.ModelName.isin(display.get_standard_model_list())
mask &= df_main.DatasetName.isin(dataset_order)
mask &= df_main.rescaling_method.isin([rescaling_method])
mask &= ~(df_main.DatasetName.str.startswith("imagenet_a") &
(~df_main.use_dataset_labelset.eq(True)))
mask &= ~(df_main.DatasetName.str.startswith("imagenet_r") &
(~df_main.use_dataset_labelset.eq(True)))
family_order = display.get_model_families_sorted()
mask &= df_main.ModelFamily.isin(family_order)
mask &= df_main.DatasetName.isin(dataset_order)
df_plot = df_main[mask].copy()
df_plot, cmap = display.add_display_data(df_plot, family_order)
# Remove "use_dataset_labelset=True" to have uniform metric name:
df_plot.Metric = df_plot.Metric.str.replace("use_dataset_labelset=True,", "")
# Repeat dataframe with rows:
df_orig = df_plot
df_plot = pd.DataFrame()
assert not ((len(set(xs)) > 1) and (len(set(ys)) > 1)), (
"One of x and y must contain a constant value.")
varying_set = xs if (len(set(xs)) > 1) else ys
for x, y, varying in zip(xs, ys, varying_set):
df_here = df_orig.copy()
df_here["x"] = x
df_here["y"] = y
df_here["row"] = varying
df_plot = pd.concat([df_plot, df_here])
def get_residual(classification_error, metric):
reg = linear_model.LinearRegression()
metric = metric.to_numpy()
not_nan = ~np.isnan(metric)
x = classification_error.to_numpy()[:, None]
reg.fit(x[not_nan, :], metric[not_nan])
return metric - reg.predict(x)
def subplot_fn(data, **kwargs):
del kwargs
ax = plt.gca()
x = ax.my_xlabel = utils.assert_and_get_constant(data.x)
y = ax.my_ylabel = utils.assert_and_get_constant(data.y)
if y.endswith("_residual"):
# Plot residual w.r.t. accuracy:
y = y.replace("_residual", "")
data = data.copy()
data.loc[:, y] = get_residual(data["downstream_error"], data[y])
for marker in data.family_marker.unique():
data_sub = data[data.family_marker == marker]
ax.scatter(
data_sub[x],
data_sub[y],
s=plotting.model_to_scatter_size(data_sub.model_size),
c=data_sub.family_index,
cmap=cmap,
vmin=0,
vmax=len(family_order),
marker=marker,
alpha=0.7,
zorder=30,
label=utils.assert_and_get_constant(data_sub.ModelFamily),
linewidth=0.0)
g = plotting.FacetGrid(
data=df_plot,
sharex=False,
sharey=False,
dropna=False,
col="DatasetName",
col_order=dataset_order,
row="row",
row_order=varying_set,
height=1.05,
aspect=1.3,
margin_titles=True)
g.map_dataframe(subplot_fn)
g.set_titles(col_template="{col_name}", row_template="",
size=mpl.rcParams["axes.titlesize"])
for ax in g.axes.flat:
plotting.show_spines(ax)
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.1))
col = plotting.col_num(ax)
if dataset_order[col] == "imagenet_v2(variant='MATCHED_FREQUENCY')":
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(0.1))
if dataset_order[col] == "imagenet_r":
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(0.2))
if dataset_order[col] == "imagenet_a":
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(0.2))
if varying_set == xs:
ax.set_xlabel(ax.my_xlabel)
if ax.is_first_col():
ax.set_ylabel(ax.my_ylabel)
elif varying_set == ys:
if ax.is_last_row():
ax.set_xlabel(ax.my_xlabel)
else:
ax.set_xticklabels([])
if ax.is_first_col():
ax.set_ylabel(ax.my_ylabel)
ax.grid(True, axis="x", which="minor")
ax.grid(True, axis="both")
if add_metric_description:
plotting.add_metric_description_title(df_plot, g.fig, y=1.05)
def prettify(x):
x = display.prettify(x)
if x == "downstream_error": return display.XLABEL_CLASSIFICATION_ERROR
if x == "MetricValue": return display.YLABEL_ECE_TEMP_SCALED_SHORT
if x == "brier": return "Brier score\n(temp.-scaled)"
if x == "brier_div_error": return "Brier / class. error"
if x == "brier_residual": return "Brier (residual)"
if x == "nll": return "NLL\n(temp.-scaled)"
if x == "nll_div_error": return "NLL / class. error"
if x == "nll_residual": return "NLL (residual)"
return x
plotting.apply_to_fig_text(g.fig, prettify)
g.fig.tight_layout()
g.fig.subplots_adjust(wspace=0.4, hspace=0.1 if varying_set == ys else 0.5)
# Model family legend below plot:
if add_legend:
handles, labels = plotting.get_model_family_legend(
g.axes.flat[0], family_order)
legend = g.axes.flat[0].legend(
handles=handles, labels=labels, loc="upper center",
title="Model family", bbox_to_anchor=(0.5, 0), frameon=True,
bbox_transform=g.fig.transFigure, ncol=len(family_order),
handletextpad=0.1)
legend.get_frame().set_linewidth(mpl.rcParams["axes.linewidth"])
legend.get_frame().set_edgecolor("lightgray")
plotting.apply_to_fig_text(g.fig, prettify)
return g
def plot(df_main: pd.DataFrame,
gce_prefix: str = plotting.STD_GCE_PREFIX,
rescaling_methods: Optional[List[str]] = None,
plot_confidence: bool = False,
add_legend: bool = True,
legend_position: str = "right",
height: float = 1.05,
aspect: float = 1.2,
add_metric_description: bool = False,
) -> sns.axisgrid.FacetGrid:
"""Plots acc/calib for ImageNet and natural OOD datasets."""
# Settings:
rescaling_methods = rescaling_methods or ["none", "temperature_scaling"]
if plot_confidence:
rescaling_methods += ["tau"]
family_order = display.get_model_families_sorted()
dataset_order = display.OOD_DATASET_ORDER
# Select data:
mask = df_main.Metric.str.startswith(gce_prefix)
mask &= df_main.ModelName.isin(display.get_standard_model_list())
mask &= df_main.DatasetName.isin(dataset_order)
mask &= df_main.rescaling_method.isin(rescaling_methods)
mask &= ~(df_main.DatasetName.str.startswith("imagenet_a") &
(~df_main.use_dataset_labelset.eq(True)))
mask &= ~(df_main.DatasetName.str.startswith("imagenet_r") &
(~df_main.use_dataset_labelset.eq(True)))
family_order = display.get_model_families_sorted()
mask &= df_main.ModelFamily.isin(family_order)
mask &= df_main.DatasetName.isin(dataset_order)
df_plot = df_main[mask].copy()
df_plot, cmap = display.add_display_data(df_plot, family_order)
# Remove "use_dataset_labelset=True" to have uniform metric name:
df_plot.Metric = df_plot.Metric.str.replace("use_dataset_labelset=True,", "")
# Add "optimal temperature" as another rescaling method, so that seaborn can
# plot it as a third row:
df_tau = df_plot[df_plot.rescaling_method == "temperature_scaling"].copy()
df_tau.rescaling_method = "tau"
df_tau.MetricValue = df_tau.tau_on_eval_data
df_plot = pd.concat([df_plot, df_tau])
def subplot_fn(data, x, y, **kwargs):
del kwargs
ax = plt.gca()
for marker in data.family_marker.unique():
data_sub = data[data.family_marker == marker]
ax.scatter(
data_sub[x],
data_sub[y],
s=plotting.model_to_scatter_size(data_sub.model_size),
c=data_sub.family_index,
cmap=cmap,
vmin=0,
vmax=len(family_order),
marker=marker,
linewidth=0,
alpha=0.7,
zorder=30,
label=utils.assert_and_get_constant(data_sub.ModelFamily))
g = plotting.FacetGrid(
data=df_plot,
sharex=False,
sharey=False,
dropna=False,
col="DatasetName",
col_order=dataset_order,
row="rescaling_method",
row_order=rescaling_methods,
height=height,
aspect=aspect,
margin_titles=True)
g.map_dataframe(subplot_fn, x="downstream_error", y="MetricValue")
g.set_titles(col_template="{col_name}", row_template="",
size=mpl.rcParams["axes.titlesize"])
for ax in g.axes.flat:
plotting.show_spines(ax)
ax.grid(True, axis="both")
ax.grid(True, axis="both", which="minor")
ax.set_xlim(left=0.1)
ax.set_ylim(bottom=0.0)
ax.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.1))
if rescaling_methods[plotting.row_num(ax)] != "tau":
ax.set_yticks([], minor=True)
ax.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(0.02))
if dataset_order[plotting.col_num(
ax)] == "imagenet(split='validation[20%:]')":
if rescaling_methods[plotting.row_num(ax)] == "none":
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.04))
if dataset_order[plotting.col_num(
ax)] == "imagenet_v2(variant='MATCHED_FREQUENCY')":
ax.set_ylim(top=0.14)
if rescaling_methods[plotting.row_num(ax)] == "none":
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.02))
if rescaling_methods[plotting.row_num(ax)] == "temperature_scaling":
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.02))
if dataset_order[plotting.col_num(ax)] == "imagenet_r":
ax.set_ylim(top=0.3)
ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(0.1))
if dataset_order[plotting.col_num(
ax)] == "imagenet_v2(variant='MATCHED_FREQUENCY')":
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(0.1))
if dataset_order[plotting.col_num(ax)] == "imagenet_a":
if rescaling_methods[plotting.row_num(ax)] != "tau":
ax.set_ylim(top=0.6)
ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(0.2))
# Labels:
if ax.is_first_col():
if rescaling_methods[plotting.row_num(ax)] == "none":
ax.set_ylabel(display.YLABEL_ECE_UNSCALED)
elif rescaling_methods[plotting.row_num(ax)] == "temperature_scaling":
ax.set_ylabel(display.YLABEL_ECE_TEMP_SCALED)
elif rescaling_methods[plotting.row_num(ax)] == "beta_scaling":
ax.set_ylabel(display.YLABEL_ECE_BETA_SCALED)
elif rescaling_methods[plotting.row_num(ax)] == "tau":
ax.set_ylabel(display.YLABEL_TEMP_FACTOR)
else:
ax.set_ylabel(rescaling_methods[plotting.row_num(ax)])
if ax.is_last_row():
ax.set_xlabel(display.XLABEL_CLASSIFICATION_ERROR)
else:
ax.set_xticklabels("")
if add_metric_description:
plotting.add_metric_description_title(df_plot, g.fig, y=1.05)
plotting.apply_to_fig_text(g.fig, display.prettify)
g.fig.tight_layout(pad=0)
g.fig.subplots_adjust(wspace=0.2, hspace=0.2)
for ax in g.axes.flat:
if rescaling_methods[plotting.row_num(ax)] == "tau":
ax.set_ylim(0.5, 2.5)
plotting.annotate_confidence_plot(ax)
if add_legend:
handles, labels = plotting.get_model_family_legend(
g.axes.flat[0], family_order)
if legend_position == "below":
# Model family legend below plot:
legend = g.axes.flat[0].legend(
handles=handles, labels=labels, loc="upper center",
title="Model family", bbox_to_anchor=(0.5, 0.0), frameon=True,
bbox_transform=g.fig.transFigure, ncol=len(family_order),
handletextpad=0.1)
elif legend_position == "right":
# Model family legend next to plot:
legend = g.axes.flat[0].legend(
handles=handles, labels=labels, loc="center left",
title="Model family", bbox_to_anchor=(1.00, 0.53), frameon=True,
bbox_transform=g.fig.transFigure, ncol=1,
handletextpad=0.1)
legend.get_frame().set_linewidth(mpl.rcParams["axes.linewidth"])
legend.get_frame().set_edgecolor("lightgray")
plotting.apply_to_fig_text(g.fig, display.prettify)
return g
def plot(df_main: pd.DataFrame,
family_order: Optional[List[str]] = None,
rescaling_method: str = "temperature_scaling",
gce_prefix: str = plotting.STD_GCE_PREFIX) -> sns.axisgrid.FacetGrid:
"""Plots a regression of accuracy and calibration, split by model family."""
# Settings:
if family_order is None:
family_order = display.get_model_families_sorted()
dataset_order = [
"imagenet(split='validation[20%:]')",
"imagenet_v2(variant='MATCHED_FREQUENCY')",
"imagenet_r",
"imagenet_a",
]
# Select data:
mask = df_main.Metric.str.startswith(gce_prefix)
mask &= df_main.ModelName.isin(display.get_standard_model_list())
mask &= df_main.rescaling_method == rescaling_method
mask &= df_main.ModelFamily.isin(family_order)
mask &= ~(df_main.DatasetName.str.startswith("imagenet_a") &
~df_main.use_dataset_labelset.eq(True))
mask &= ~(df_main.DatasetName.str.startswith("imagenet_r") &
~df_main.use_dataset_labelset.eq(True))
df_plot = df_main[mask].copy()
# Select OOD datasets:
mask = df_plot.DatasetName.isin(dataset_order)
mask |= df_plot.DatasetName.str.startswith("imagenet_c")
df_plot = df_plot[mask].copy()
# Add plotting-related data:
df_plot, _ = display.add_display_data(df_plot, family_order)
df_plot["dataset_group"] = "others"
im_c_mask = df_plot.DatasetName.str.startswith("imagenet_c")
df_plot.loc[im_c_mask, "dataset_group"] = "imagenet_c"
dataset_group_order = ["imagenet_c", "others"]
def subplot_fn(**kwargs):
x = kwargs["x"]
y = kwargs["y"]
data = kwargs["data"]
utils.assert_no_duplicates_in_condition(
data, group_by=["DatasetName", "ModelName"])
ax = plt.gca()
kwargs["color"] = utils.assert_and_get_constant(data.family_color)
ax = sns.regplot(ax=ax, **kwargs)
plotter = RegressionPlotter(x, y, data=data)
# Get regression parameters and show in plot:
grid = np.linspace(data[x].min(), data[x].max(), 100)
beta_plot, beta_boots = plotter.get_params(grid)
beta_plot = np.array(beta_plot)
beta_boots = np.array(beta_boots)
intercept = 10 ** np.median(beta_boots[0, :])
intercept_ci = 10 ** sns_utils.ci(beta_boots[0, :])
slope = np.median(beta_boots[1, :])
slope_ci = sns_utils.ci(beta_boots[1, :])
s = (f"a = {intercept:1.2f} ({intercept_ci[0]:1.2f}, "
f"{intercept_ci[1]:1.2f})\nk = {slope:1.2f} ({slope_ci[0]:1.2f}, "
f"{slope_ci[1]:1.2f})")
ax.text(0.04, 0.96, s, va="top", ha="left", transform=ax.transAxes,
fontsize=4, color=(0.3, 0.3, 0.3),
bbox=dict(facecolor="w", alpha=0.8, boxstyle="square,pad=0.1"))
df_plot["MetricValue_log"] = np.log10(df_plot.MetricValue)
df_plot["downstream_error_log"] = np.log10(df_plot.downstream_error)
g = plotting.FacetGrid(
data=df_plot,
sharex=False,
sharey=False,
col="ModelFamily",
col_order=family_order,
hue="ModelFamily",
hue_order=family_order,
row="dataset_group",
row_order=dataset_group_order,
height=1.0,
aspect=0.9,)
g.map_dataframe(subplot_fn, x="downstream_error_log", y="MetricValue_log",
scatter_kws={"alpha": 0.5, "linewidths": 0.0, "s": 2})
g.set_titles(template="{col_name}", size=mpl.rcParams["axes.titlesize"])
for ax in g.axes.flat:
plotting.show_spines(ax)
ax.set_xlim(np.log10(0.1), np.log10(1.0))
xticks = np.arange(0.1, 1.0 + 0.001, 0.1)
ax.set_xticks(np.log10(xticks))
if ax.is_last_row():
show = [0.1, 0.2, 0.4, 1.0]
xticklabels = [f"{x:0.1f}"if x in show else "" for x in xticks]
ax.set_xticklabels(xticklabels)
ax.set_title("")
else:
ax.set_xticklabels([])
ax.set_ylim(np.log10(0.01), np.log10(0.8))
yticks = np.arange(0.05, 0.8 + 0.001, 0.05)
ax.set_yticks(np.log10(yticks))
if ax.is_first_col():
show = [0.1, 0.2, 0.4, 0.8]
yticklabels = [f"{x:0.1f}"if x in show else "" for x in yticks]
ax.set_yticklabels(yticklabels)
else:
ax.set_yticklabels([])
ax.grid(True, axis="both")
# Labels:
if ax.is_last_row():
ax.set_xlabel(display.XLABEL_CLASSIFICATION_ERROR)
if ax.is_first_col():
ax.set_ylabel(display.YLABEL_ECE_TEMP_SCALED_SHORT)
plotting.apply_to_fig_text(g.fig, display.prettify)
g.fig.tight_layout(w_pad=0)
return g