def _sub_plot_multiple_twin(self, axis:plt.axis, columns:tuple):
'''
Create a sub-plot for the given columns.
Create a twin x-axis with new y scale for the second column.
Parameters
----------
ax : plt.axis
figure axis
column : tuple
2 column names
twin : bool
'''
# PLot first column
axis.plot(self.m_data_df[columns[0]], label=columns[0], color='tab:blue',linestyle='-',linewidth=0.1)
axis.tick_params(axis='y', labelcolor='tab:blue')
axis.set_xlabel('Rollouts')
axis.set_ylabel(columns[0])
axis.set_title(columns[0] + ' ~ ' + columns[1])
# PLot second column
ax = axis.twinx()
ax.plot(self.m_data_df[columns[1]], label=columns[1], color='tab:red',linestyle='-',linewidth=1)
ax.set_ylabel(columns[1])
ax.tick_params(axis='y', labelcolor='tab:red')
def plot(ax: plt.axis, R, data, title=""):
"""
Plot function for subtask 1
"""
ax.set_aspect('equal')
arrowed_spines(ax)
ax.set_title(title)
def plot(ax: plt.axis, x, y, u, v, trajectory, pause=0.2, title=""):
"""
Plot function for subtask 1
"""
ax.set_aspect('equal')
arrowed_spines(ax)
ax.set_xlim([min(x) * 1, max(x) * 1])
ax.set_ylim([min(y) * 1, max(y) * 1])
ax.set_title(title)
ax.streamplot(x, y, u, v)
ax.plot(trajectory[:, 0], trajectory[:, 1])
def add_descriptions(
self,
ax: plt.axis,
experiment: Union[str, None] = 'Belle II',
luminosity: Union[str, int, float, None] = '',
additional_info: Union[str, None] = None,
small_title: Union[bool, None] = False,
preliminary: Union[bool, None] = True,
title_indent: Union[int, None] = 0,
exp_nr: Union[int, None] = None,
proc_nr: Union[int, None] = None,
buck_nr: Union[int, None] = None,
):
# generate luminosity text
if type(luminosity) == int or type(luminosity) == float:
luminosity = r"$\int \mathcal{L} \,\mathrm{d}t=" + "{:.0f}".format(
np.round(luminosity, 0)) + "\,\mathrm{pb}^{-1}$"
# add some additional dataset meta data
if exp_nr:
luminosity += f"\nExp. {exp_nr}"
if proc_nr:
luminosity += f", Proc {proc_nr}"
if buck_nr:
luminosity += f", Buck. {buck_nr}"
if small_title:
ax.set_title('{}'.format(' ' * title_indent) + experiment +
'\n{}'.format(' ' * title_indent) +
'{}'.format('(Preliminary)' if preliminary else ''),
loc="left",
fontdict={
'style': 'normal',
'weight': 'bold'
})
ax.set_title(luminosity, loc="right")
else:
ax.set_title('{}'.format(' ' * title_indent) + experiment,
loc="left",
fontdict={
'size': 16,
'style': 'normal',
'weight': 'bold'
})
ax.set_title('{}'.format('(Preliminary) ' if preliminary else '') +
luminosity,
loc="right")
ax.annotate(additional_info, (0.02, 0.98),
xytext=(4, -4),
xycoords='axes fraction',
textcoords='offset points',
fontweight='bold',
ha='left',
va='top')
def _sub_plot_single_from_array(self, ax:plt.axis, data:np.array, x_title:str, title:str, legend=None):
'''
Create a sub-plot for a given column.
Parameters
----------
ax : plt.axis
figure axis
data : np.array
data to plot
'''
if legend == None:
ax.plot(data,linestyle='-',linewidth=1)
else:
ax.plot(data,linestyle='-',linewidth=1,label=legend)
ax.set_xlabel(x_title)
ax.set_title(title)
def _sub_plot_multiple(self, axis:plt.axis, columns:tuple):
'''
Create a sub-plot for the given columns.
Parameters
----------
ax : plt.axis
figure axis
columns : tuple
column names
'''
title = ''
for col in columns:
axis.plot(self.m_data_df[col], label=col,linestyle='-',linewidth=0.1)
title += col + ', '
axis.set_title(title)
axis.set_xlabel('Rollouts')
axis.legend()
def add_descriptions_to_plot(
ax: plt.axis,
experiment: Union[str, None] = None,
luminosity: Union[str, None] = None,
additional_info: Union[str, None] = None,
):
ax.set_title(experiment,
loc="left",
fontdict={
'size': 16,
'style': 'normal',
'weight': 'bold'
})
ax.set_title(luminosity, loc="right")
ax.annotate(additional_info, (0.02, 0.98),
xytext=(4, -4),
xycoords='axes fraction',
textcoords='offset points',
fontweight='bold',
ha='left',
va='top')
def _sub_plot_single(self, ax:plt.axis, column:str, legend=None):
'''
Create a sub-plot for a given column.
Parameters
----------
ax : :plt.axis
figure axis
column : str
column name
'''
if legend == None:
ax.plot(self.m_data_df[column],linestyle='-',linewidth=0.1)
else:
ax.plot(self.m_data_df[column],linestyle='-',linewidth=0.1,label=legend)
ax.set_xlabel('Rollouts')
if False and column == 'Num_timesteps':
column += ' (approx. run time: {0:.2f} hours)'.format(self.estimated_run_time())
ax.set_title(column)
def mpl_plot(self,
ax: plt.axis = None,
temp_unit: str = "GK",
**kwargs) -> plt.axis:
"""
Parameters
----------
ax : mpl.Axis
mpl Axis to plot onto, if none provided get current axis is used.
temp_unit : str {"Gk", "KeV"}
Tempreture units for x axis.
kwargs : key word args for mpl.plot
"""
ax = ax or plt.gca()
ax.set_title("Reaction Rate")
ax.set_ylabel(r"Rate ($cm^3\;mol^{-1}\;sec^{-1}$)")
if temp_unit is "GK":
ax.set_xlabel("Temperature ($GK$)")
return ax
def _plot_summary_on_axis(
self,
ax: plt.axis,
label_y_axis: bool,
use_title: bool,
):
"""used to plot summary on multi-axis figure, or in standalone figure"""
# axis
if use_title:
ax.set_title('Average', fontsize=configs.Figs.title_font_size)
y_axis_label = self.y_axis_label
else:
y_axis_label = f'Average {self.y_axis_label}'
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylim(self.y_lims)
# x-axis
ax.set_xticks([self.last_step])
ax.set_xticklabels([shorten_tick_label(self.last_step)],
fontsize=configs.Figs.tick_font_size)
ax.set_xlabel(self.x_axis_label, fontsize=configs.Figs.ax_font_size)
# y axis
if label_y_axis:
ax.set_ylabel(y_axis_label, fontsize=configs.Figs.ax_font_size)
ax.set_yticks(self.y_ticks)
ax.set_yticklabels(self.y_ticks,
fontsize=configs.Figs.tick_font_size)
else:
ax.set_ylabel('', fontsize=configs.Figs.ax_font_size)
ax.set_yticks([])
ax.set_yticklabels([], fontsize=configs.Figs.tick_font_size)
# collect curves for each replication across all paradigms
gn2rep2curves_by_pd = defaultdict(dict)
for pd in self.pds:
for gn, rep2curve in pd.group_name2rep2curve.items():
for rep, curve in rep2curve.items():
# this curve is performance collapsed across template and for a unique rep and paradigm
gn2rep2curves_by_pd[gn].setdefault(rep, []).append(curve)
# plot
for gn, rep2curves_by_pd in gn2rep2curves_by_pd.items():
# average across paradigms
rep2curve_avg_across_pds = {
rep: np.array(curves_by_pd).mean(axis=0)
for rep, curves_by_pd in rep2curves_by_pd.items()
}
curves = np.array([
rep2curve_avg_across_pds[rep]
for rep in rep2curve_avg_across_pds
]) # one for each rep
color = f'C{self.pds[0].group_names.index(gn)}'
x = np.arange(0, self.last_step + self.step_size, self.step_size)
# plot averages for BabyBERTa
y = np.array(curves).mean(axis=0)
ax.plot(x, y, linewidth=self.line_width, color=color)
# plot average for RoBERTa-base
y_roberta_base = np.repeat(
np.mean(list(self.paradigm2roberta_base_accuracy.values())),
len(x))
ax.plot(x,
y_roberta_base,
linewidth=self.line_width,
**self.ax_kwargs_roberta_base)
# plot average for frequency baseline
y_baseline = np.repeat(
np.mean(list(self.paradigm2baseline_accuracy.values())),
len(x))
ax.plot(x,
y_baseline,
linewidth=self.line_width,
**self.ax_kwargs_baseline)
# plot the margin of error (shaded region)
n = len(curves)
h = sem(curves, axis=0) * t.ppf(
(1 + self.confidence) / 2, n - 1) # margin of error
ax.fill_between(x, y + h, y - h, alpha=0.2, color=color)
# printout
if use_title: # to prevent printing summary twice
print(f'{gn} avg acc at step {self.last_step} = {y[-1]:.3f}')
if use_title:
y_roberta_base = np.mean(
list(self.paradigm2roberta_base_accuracy.values()))
print(
f'roberta-base Liu2019 avg acc at step {self.last_step} = {y_roberta_base:.3f}'
)
def plot_word_vectors(
transformed_word_embeddings: np.ndarray,
words: list,
title: str,
x_label: str,
y_label: str,
word_colors: np.ndarray = None,
ax: plt.axis = None,
show_plot: bool = True,
interactive: bool = False,
continuous_word_colors: bool = False,
) -> None:
"""
Plots word vectors in transformed 2D space.
Parameters
----------
transformed_word_embeddings : np.ndarray
Word embeddings transformed into 2D space.
words : list
List of words to plot
title : str,
Title to use for the plot.
x_label : str,
Label to use for the x-axis.
y_label : str
Label to use for the y-axis
word_colors : np.ndarray, optional
Numpy array consisting of unique labels for each word (i.e. cluster labels),
(defaults to None).
ax : plt.axis
Matplotlib axis (defaults to None)
show_plot : bool
Whether or not to call plt.show() (defaults to True)
interactive : bool
Whether or not to make the visualization interactive
using Plotly (defaults to False).
continuous_word_colors : bool
Whether or not to make the word color continuous (defaults to False).
"""
if interactive:
# Plot interactive plot
fig = px.scatter(
x=transformed_word_embeddings[:, 0],
y=transformed_word_embeddings[:, 1],
title=title,
labels={
"x": x_label,
"y": y_label
},
color=[
cluster_label if continuous_word_colors else str(cluster_label)
for cluster_label in word_colors
] if word_colors is not None else None,
hover_data={"word": words},
)
fig.show()
else:
if ax is None:
_, ax = plt.subplots()
ax.scatter(
transformed_word_embeddings[:, 0],
transformed_word_embeddings[:, 1],
c=word_colors,
)
ax.set_title(title)
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
if show_plot:
plt.show()
