def plot_all_trajs(ax: plt.axis, df: pd.DataFrame, radii: dict,
plot_rings=False, relative=False):
"""Plot all of the trajectories in the input DataFrame on the given axis.
Args:
ax: Matplotlib axis on which we want to plot.
df: DataFrame containing trajectory data.
radii: Dictionary with inner and outer radii of N-ring.
plot_rings: Boolean flag that plots outer and inner circles of N-ring if True.
"""
cmap = sns.cubehelix_palette(dark=.3, light=.8, as_cmap=True)
outer_rad, inner_rad = extract_radii(radii)
kwargs = {'data': df, 'legend': False, 'palette': cmap, 'edgecolor': None, 's': 20}
if relative is False:
ax = sns.scatterplot(x='X_1', y='Y_1', hue='Trial', **kwargs)
sns.scatterplot(x='X_2', y='Y_2', hue='Trial', **kwargs)
else:
ax = sns.scatterplot(x='rel_X', y='rel_Y', hue='Trial', **kwargs)
if plot_rings is True:
outer_circle = plt.Circle((0, 0), outer_rad, color='blue', fill=False)
ax.add_artist(outer_circle)
inner_circle = plt.Circle((0, 0), inner_rad, color='blue', fill=False)
ax.add_artist(inner_circle)
ax.axis('equal')
# ax.set_xlim([-2.0, 2.0])
# ax.set_ylim([-2.0, 2.0])
ax.set_xlabel('relative x position' if relative is True else 'x position')
ax.set_ylabel('relative y position' if relative is True else 'y position')
def plot_areas_society_progress(ax: plt.axis, time_array: List,
society_snapshot: Dict,
society_progress: Dict):
previous_status = None
ax.set_xlabel('time [days]')
ax.set_ylabel('population percentage')
for st in Status:
society_progress[st.name].append(society_snapshot[st.name])
if previous_status:
lower_limit = society_progress[previous_status.name]
else:
lower_limit = [0]
ax.fill_between(x=time_array,
y1=lower_limit,
y2=society_progress[st.name],
color=st.value,
label=st.name,
alpha=0.25)
ax.text(x=time_array[-1],
y=1 / 2 * (lower_limit[-1] + society_snapshot[st.name]),
s=r"{0:.2f}".format(society_snapshot[st.name] -
lower_limit[-1]),
size=10,
color=st.value)
previous_status = st
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_on(
self,
ax: plt.axis,
draw_legend: bool = True,
legend_inside: bool = True,
legend_kwargs: dict = {},
yaxis_scale=1.3,
hide_labels: bool = False,
) -> plt.axis:
for component in self.components:
if component.style == 'point':
ax.errorbar(
component.data.x_values,
component.data.y_values,
yerr=component.data.y_errors,
xerr=component.data.x_errors,
label=component.label,
color=component.color,
ls=component.ls,
marker=component.marker,
)
elif component.style == 'box':
ax.bar(component.data.x_values,
2 * component.data.y_errors,
width=2 * component.data.x_errors,
bottom=component.data.y_values -
component.data.y_errors,
label=component.label,
color=component.color,
alpha=0.5)
else:
raise NotImplementedError(
"Options: point|box. If you require a new kind of plot, report a feature request"
)
if not hide_labels:
ax.set_xlabel(self.variable.x_label, plot_style.xlabel_pos)
ax.set_ylabel(self.variable.y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False, **legend_kwargs)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], yaxis_scale * ylims[1])
else:
ax.legend(frameon=False,
bbox_to_anchor=(1, 1),
**legend_kwargs)
return ax
def plot_cluster_sizes(cluster_labels: list,
ax: plt.axis = None) -> np.ndarray:
"""
Plots cluster sizes using a histogram and returns a list of most frequent
cluster sizes.
Parameters
----------
cluster_labels : list
List of cluster labels
ax : plt.axis
Matplotlib axis (default None)
Returns
-------
most_common_cluster_sizes : np.ndarray
Numpy array containing the most common cluster sizes
"""
if ax is None:
_, ax = plt.subplots()
# Print cluster size ratio (max / min)
labels_unique, labels_counts = np.unique(cluster_labels,
return_counts=True)
cluster_sizes, cluster_size_counts = np.unique(labels_counts,
return_counts=True)
num_clusters = len(labels_unique)
max_cluster_size = max(labels_counts)
min_cluster_size = min(labels_counts)
cluster_size_ratio = max_cluster_size / min_cluster_size
print(
f"{num_clusters} clusters: max={max_cluster_size}, min={min_cluster_size}, ratio={cluster_size_ratio}"
)
# Plot distribution of cluster sizes
sns.histplot(labels_counts, bins=max_cluster_size, ax=ax)
ax.set_xlabel("Cluster size")
ax.set_ylabel("Number of words in cluster")
plt.show()
# Sort cluster sizes by frequency
most_common_cluster_sizes = cluster_sizes[np.argsort(cluster_size_counts)
[::-1]]
return most_common_cluster_sizes
def plot_lines_society_progress(ax: plt.axis, time_array: List,
society_snapshot: Dict,
society_progress: Dict):
ax.set_xlabel('time [days]')
ax.set_ylabel('population percentage')
for st in Status:
society_progress[st.name].append(society_snapshot[st.name] /
society_snapshot["Total"])
ax.plot(time_array,
society_progress[st.name],
c=st.value,
ls='-',
label=st.name,
alpha=0.5)
ax.text(x=time_array[-1],
y=society_progress[st.name][-1],
s=r"{0:.2f}".format(society_progress[st.name][-1]),
size=10,
color=st.value)
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_on(self,
ax: plt.axis,
ylabel="Events",
draw_legend=True,
legend_inside=True,
hide_labels: bool = False):
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
ax.hist(
x=[comp.data for comp in self._mc_components['stacked']],
bins=bin_edges,
weights=[comp.weights for comp in self._mc_components['stacked']],
stacked=True,
edgecolor="black",
lw=0.3,
color=[comp.color for comp in self._mc_components['stacked']],
label=[comp.label for comp in self._mc_components['stacked']],
histtype='stepfilled')
if not hide_labels:
ax.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
y_label = self._get_y_label(False, bin_width, ylabel)
ax.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], 1.4 * ylims[1])
else:
ax.legend(frameon=False, bbox_to_anchor=(1, 1))
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_on(
self,
ax1: plt.axis,
ax2,
style="stacked",
ylabel="Events",
sum_color=plot_style.KITColors.kit_purple,
draw_legend: bool = True,
legend_inside: bool = True,
):
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
sum_w = np.sum(np.array([
binned_statistic(comp.data,
comp.weights,
statistic="sum",
bins=bin_edges)[0]
for comp in self._mc_components["MC"]
]),
axis=0)
sum_w2 = np.sum(np.array([
binned_statistic(comp.data,
comp.weights**2,
statistic="sum",
bins=bin_edges)[0]
for comp in self._mc_components["MC"]
]),
axis=0)
hdata, _ = np.histogram(self._data_component.data, bins=bin_edges)
if style.lower() == "stacked":
ax1.hist(
x=[comp.data for comp in self._mc_components['MC']],
bins=bin_edges,
weights=[comp.weights for comp in self._mc_components['MC']],
stacked=True,
edgecolor="black",
lw=0.3,
color=[comp.color for comp in self._mc_components['MC']],
label=[comp.label for comp in self._mc_components['MC']],
histtype='stepfilled')
ax1.bar(x=bin_mids,
height=2 * np.sqrt(sum_w2),
width=self.bin_width,
bottom=sum_w - np.sqrt(sum_w2),
color="black",
hatch="///////",
fill=False,
lw=0,
label="MC stat. unc.")
if style.lower() == "summed":
ax1.bar(x=bin_mids,
height=2 * np.sqrt(sum_w2),
width=self.bin_width,
bottom=sum_w - np.sqrt(sum_w2),
color=sum_color,
lw=0,
label="MC")
ax1.errorbar(x=bin_mids,
y=hdata,
yerr=np.sqrt(hdata),
ls="",
marker=".",
color="black",
label=self._data_component.label)
y_label = self._get_y_label(False, bin_width, evts_or_cand=ylabel)
# ax1.legend(loc=0, bbox_to_anchor=(1,1))
ax1.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax1.legend(frameon=False)
ylims = ax1.get_ylim()
ax1.set_ylim(ylims[0], 1.4 * ylims[1])
else:
ax1.legend(frameon=False, bbox_to_anchor=(1, 1))
ax2.set_ylabel(r"$\frac{\mathrm{Data - MC}}{\mathrm{Data}}$")
ax2.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
ax2.set_ylim((-1, 1))
try:
uhdata = unp.uarray(hdata, np.sqrt(hdata))
uhmc = unp.uarray(sum_w, np.sqrt(sum_w2))
ratio = (uhdata - uhmc) / uhdata
ax2.axhline(y=0, color=plot_style.KITColors.dark_grey, alpha=0.8)
ax2.errorbar(bin_mids,
unp.nominal_values(ratio),
yerr=unp.std_devs(ratio),
ls="",
marker=".",
color=plot_style.KITColors.kit_black)
except ZeroDivisionError:
ax2.axhline(y=0, color=plot_style.KITColors.dark_grey, alpha=0.8)
plt.subplots_adjust(hspace=0.08)
def plot_on(self,
ax: plt.axis,
draw_legend: bool = True,
legend_inside: bool = True,
yaxis_scale=1.3,
normed: bool = False,
ylabel="Events",
hide_labels: bool = False) -> plt.axis:
"""
Plots the component on a given matplotlib.pyplot.axis
:param ax: matplotlib.pyplot.axis where the histograms will be drawn
on.
:param draw_legend: Draw legend on axis if True.
:param normed: If true the histograms are normalized.
:return: matplotlib.pyplot.axis with histogram drawn on it
"""
bin_edges, bin_mids, bin_width = self._get_bin_edges()
self._bin_edges = bin_edges
self._bin_mids = bin_mids
self._bin_width = bin_width
for component in self._mc_components['single']:
if component.histtype == 'stepfilled':
alpha = 0.6
edge_color = 'black'
else:
edge_color = None
alpha = 1.0
ax.hist(x=component.data,
bins=bin_edges,
density=normed,
weights=component.weights,
histtype=component.histtype,
label=component.label,
edgecolor=edge_color
if edge_color is not None else component.color,
alpha=alpha,
lw=1.5,
ls=component.ls,
color=component.color)
if not hide_labels:
ax.set_xlabel(self._variable.x_label, plot_style.xlabel_pos)
y_label = self._get_y_label(normed=normed,
bin_width=bin_width,
evts_or_cand=ylabel)
ax.set_ylabel(y_label, plot_style.ylabel_pos)
if draw_legend:
if legend_inside:
ax.legend(frameon=False)
ylims = ax.get_ylim()
ax.set_ylim(ylims[0], yaxis_scale * ylims[1])
else:
ax.legend(frameon=False, bbox_to_anchor=(1, 1))
return ax
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()
def plot_cluster_metric_scores(
metric_scores: list,
hyperparameters: list,
best_score_idx: int,
metric_name: str,
scatter: bool = True,
set_xticks: bool = True,
set_xtickslabels: bool = True,
xtickslabels_rotation: int = 90,
ax: plt.axis = None,
xlabel: str = "Hyperparameters",
xrange: range = None,
show_plot: bool = True,
) -> None:
"""
Plots internal cluster validation metric scores
Parameters
----------
metric_scores : list
List of scores computed using metric
hyperparameters : list
List of hyperparameters used to compute the scores
best_score_idx : int
Best score index
metric_name : str
Name of the internal cluster validation metric
scatter : bool
Whether or not to scatter points (defaults to True)
set_xticks : bool
Whether or not to set the ticks on the x-axis
set_xtickslabels : bool
Whether or not to set the labels on the x-axis
xtickslabels_rotation : int
Sets the xticks labels rotation (defaults to 90), set_xtickslabels
must be set to True to have an effect.
ax : plt.axis
Matplotlib axis (defaults to None)
xlabel : str
X-axis label (defaults to "Hyperparameters")
xrange : range
Range to use for the x-axis (default starts from 0 to)
show_plot : bool
Whether or not to call plt.show() (defaults to True)
"""
if ax is None:
_, ax = plt.subplots()
if xrange is None:
xrange = range(len(hyperparameters))
ax.plot(xrange, metric_scores)
if scatter:
ax.scatter(xrange, metric_scores)
ax.scatter(xrange[best_score_idx],
metric_scores[best_score_idx],
c="r",
s=72,
zorder=10)
if set_xticks:
ax.set_xticks(xrange)
if set_xtickslabels:
ax.set_xticklabels(hyperparameters,
rotation=xtickslabels_rotation,
ha="center")
ax.set_xlabel(xlabel)
ax.set_ylabel(f"{metric_name} score")
if show_plot:
plt.tight_layout()
plt.show()
def word_group_visualization(
transformed_word_embeddings: np.ndarray,
words: np.ndarray,
word_groups: dict,
xlabel: str,
ylabel: str,
emphasis_words: list = None,
alpha: float = 1,
non_group_words_color: str = "#ccc",
scatter_set_rasterized: bool = False,
rasterization_threshold: int = 1000,
ax: plt.axis = None,
show_plot: bool = True,
) -> None:
"""
Visualizes one or more word groups by plotting its word embeddings in 2D.
Parameters
----------
transformed_word_embeddings : np.ndarray
Transformed word embeddings.
words : np.ndarray
Numpy array containing all words from vocabulary.
word_groups : dict
Dictionary containing word groups to visualize.
xlabel : str
X-axis label.
ylabel : str
Y-axis label.
emphasis_words : list, optional
List representing words to emphasize in the visualization (defaults to None).
Entries can be either be strings (words) or tuples, consisting of the word, x-offset
and y-offset.
alpha : float
Scatter plot alpha value (defaults to 1).
non_group_words_color : str
Color for words outside groups (defaults to #ccc).
scatter_set_rasterized : bool
Whether or not to enable rasterization on scatter plotting (defaults to False).
rasterization_threshold : int
The least number of data points to enable rasterization, given that
`scatter_set_rasterized` is set to True (defaults to 1000).
ax : plt.axis
Axis (defaults to None).
show_plot : bool
Whether or not to call plt.show() (defaults to True).
"""
# Filter and restrict words in word groups
word_group_words_restricted = {}
for group_key, group_data in word_groups.items():
group_words = group_data["words"]
group_words = np.array([word for word in group_words if word in words])
group_words_indices = np.array(
[np.where(words == word)[0][0] for word in group_words])
group_word_embeddings = transformed_word_embeddings[
group_words_indices]
boundaries = group_data.get("boundaries", {})
if boundaries.get("xmin") is None:
boundaries["xmin"] = group_word_embeddings[:, 0].min()
if boundaries.get("xmax") is None:
boundaries["xmax"] = group_word_embeddings[:, 0].max()
if boundaries.get("ymin") is None:
boundaries["ymin"] = group_word_embeddings[:, 1].min()
if boundaries.get("ymax") is None:
boundaries["ymax"] = group_word_embeddings[:, 1].max()
group_word_embeddings_boundaries_mask = [
(boundaries["xmin"] <= word_vec[0] <= boundaries["xmax"])
and (boundaries["ymin"] <= word_vec[1] <= boundaries["ymax"])
for i, word_vec in enumerate(group_word_embeddings)
]
word_group_words_restricted[group_key] = group_words[
group_word_embeddings_boundaries_mask]
# Find words not in groups
words_not_in_groups_mask = [
i for i, word in enumerate(words)
for group_words in word_group_words_restricted.values()
if word not in group_words
]
if ax is None:
_, ax = plt.subplots(figsize=(12, 7))
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
# Plot non-group words
non_grp_scatter_handle = ax.scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:, 0],
y=transformed_word_embeddings[words_not_in_groups_mask][:, 1],
s=10,
alpha=alpha,
c=non_group_words_color,
)
if (scatter_set_rasterized
and len(words_not_in_groups_mask) >= rasterization_threshold):
non_grp_scatter_handle.set_rasterized(True)
# Plot group words
for group_key, group_words in word_group_words_restricted.items():
group_words_indices = np.array(
[np.where(words == word)[0][0] for word in group_words])
group_word_embeddings = transformed_word_embeddings[
group_words_indices]
grp_scatter_handle = ax.scatter(
x=group_word_embeddings[:, 0],
y=group_word_embeddings[:, 1],
s=15,
alpha=alpha,
c=word_groups[group_key]["color"],
label=word_groups[group_key]["label"],
)
if (scatter_set_rasterized
and len(group_word_embeddings) >= rasterization_threshold):
grp_scatter_handle.set_rasterized(True)
# Visualize emphasized words
if emphasis_words is not None:
emphasis_words = [(entry, 0, 0) if type(entry) == str else entry
for entry in emphasis_words]
for emphasis_word, x_offset, y_offset in emphasis_words:
word_group_key = None
for group_key, group_data in word_groups.items():
if emphasis_word in group_data["words"]:
word_group_key = group_key
break
if word_group_key is None:
word_color = non_group_words_color
else:
word_color = word_groups[group_key]["color"]
word_idx = [
i for i, word in enumerate(words) if word == emphasis_word
][0]
emphasis_scatter_handle = ax.scatter(
x=transformed_word_embeddings[word_idx, 0],
y=transformed_word_embeddings[word_idx, 1],
s=40,
alpha=alpha,
c=word_color,
)
if (scatter_set_rasterized
and len(emphasis_words) >= rasterization_threshold):
emphasis_scatter_handle.set_rasterized(True)
# Annotate emphasis word with a text box
offsetbox = TextArea(emphasis_word)
ab = AnnotationBbox(
offsetbox,
tuple(transformed_word_embeddings[word_idx]),
xybox=(x_offset, 40 + y_offset),
xycoords="data",
boxcoords="offset points",
arrowprops=dict(arrowstyle="->", color="black", linewidth=2),
)
ax.add_artist(ab)
ax.legend()
if show_plot:
plt.show()
def visualize_word_cluster_groups(
transformed_word_embeddings: np.ndarray,
words: np.ndarray,
word_groups: dict,
visualize_non_group_words: bool,
xlabel: str,
ylabel: str,
non_group_words_color: str = "#ccc",
ax: plt.axis = None,
show_plot: bool = True,
alpha: float = 1,
interactive: bool = False,
) -> None:
"""
Visualizes word cluster groups.
Parameters
----------
transformed_word_embeddings : np.ndarray
Transformed word embeddings.
words : np.ndarray
Numpy array containing all words from vocabulary.
word_groups : dict
Dictionary containing word groups to visualize.
visualize_non_group_words : bool
Whether or not to visualize words outside word groups
xlabel : str
X-axis label
ylabel : str
Y-axis label
non_group_words_color : str
Color for words outside groups (defaults to #ccc)
ax : plt.axis
Matplotlib axis (defaults to None)
show_plot : bool
Whether or not to call plt.show() (defaults to True)
alpha : float
Scatter plot alpha value (defaults to 1)
interactive : bool
Whether or not to make the visualization interactive
using Plotly (defaults to False).
"""
if ax is None and not interactive:
_, ax = plt.subplots(figsize=(12, 7))
if interactive:
fig = go.Figure(
layout=dict(xaxis=dict(title=xlabel), yaxis=dict(title=ylabel)))
if visualize_non_group_words:
# Create boolean mask for words outside groups
words_in_groups = []
for group_name in word_groups.keys():
words_in_groups.extend(word_groups[group_name]["words"])
words_not_in_groups_mask = [
word not in words_in_groups for word in words
]
words_not_in_groups_sorted = [
word for word in words if word not in words_in_groups
]
# Plot words outside word group
if interactive:
fig.add_trace(
go.Scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:,
0],
y=transformed_word_embeddings[words_not_in_groups_mask][:,
1],
mode="markers",
marker=dict(color=non_group_words_color),
hovertext=words_not_in_groups_sorted,
hoverinfo="x+y+text",
name="Non group words",
opacity=alpha,
))
else:
ax.scatter(
x=transformed_word_embeddings[words_not_in_groups_mask][:, 0],
y=transformed_word_embeddings[words_not_in_groups_mask][:, 1],
c=non_group_words_color,
alpha=alpha,
)
# Visualize words in groups
for group_name, word_group in word_groups.items():
words_in_group = word_group["words"]
words_in_group_mask = [word in words_in_group for word in words]
words_in_group_sorted = [
word for word in words if word in words_in_group
]
word_group_color = word_group["color"]
# Plot words inside word group
if interactive:
fig.add_trace(
go.Scatter(
x=transformed_word_embeddings[words_in_group_mask][:, 0],
y=transformed_word_embeddings[words_in_group_mask][:, 1],
mode="markers",
marker=dict(color=word_group_color),
hovertext=words_in_group_sorted,
hoverinfo="x+y+text",
name=f"Words in {group_name}",
opacity=alpha,
))
else:
ax.scatter(
x=transformed_word_embeddings[words_in_group_mask][:, 0],
y=transformed_word_embeddings[words_in_group_mask][:, 1],
c=word_group_color,
alpha=alpha,
)
if interactive:
fig.show()
else:
ax_legends = ["Non group words"]
ax_legends.extend([
f"Words which are {group_name}"
for group_name in word_groups.keys()
])
ax.legend(ax_legends)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if show_plot:
plt.show()