def get_interactive_vec_plot(visual_df):
"""
to visualize text data for better access and exploration
:param visual_df: pandas dataframe
:return: bokeh plot instance
"""
datasource = ColumnDataSource(visual_df)
colour_mapper = CategoricalColorMapper(factors=["0", "1", "2"],
palette=["red", "purple", "green"])
TOOLTIPS = [("id", "@index"), ("text", "@reviews"), ("label", "@labels")]
hover = HoverTool(tooltips=TOOLTIPS)
hover.attachment = 'right'
plot = figure(title='2D-Vector Representation of Reviews',
plot_width=600,
plot_height=400,
tools=('pan, wheel_zoom, reset', 'box_zoom', 'undo'))
plot.add_tools(hover)
plot.circle('x',
'y',
source=datasource,
color=dict(field="labels", transform=colour_mapper),
legend="labels")
return plot
def get_interactive_embedding_plot(embeddings: np.ndarray, df: pd.DataFrame):
pca = PCA(n_components=2)
pca_embeddings = pca.fit_transform(embeddings)
df_copy = df.copy()
df_copy["pca_x"] = pca_embeddings[:, 0]
df_copy["pca_y"] = pca_embeddings[:, 1]
hover = HoverTool(tooltips=[
("product name", "@product_name"),
("barcode", "@code"),
("categories", "@deepest_categories"),
("predicted categories", "@predicted_deepest_categories"),
])
hover.attachment = "right"
p = figure(
title="Embedding projection",
plot_width=1200,
plot_height=800,
tools=("pan,wheel_zoom,reset", "box_zoom", "undo"),
)
p.add_tools(hover)
for filter_, name, color in zip(
(
"is_correct",
"missing_cat_error",
"additional_cat_error",
"over_pred_cat_error",
"under_pred_cat_error",
),
[
"correct", "missing", "addition", "over-prediction",
"under-prediction"
],
Set2[5],
):
data_source = ColumnDataSource(df_copy[df_copy[filter_] == True])
p.circle(
"pca_x",
"pca_y",
source=data_source,
line_alpha=0,
fill_alpha=0.4,
size=5,
legend=name,
color=color,
)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
return p
def create_figure():
G1 = copy.deepcopy(G)
nx.set_node_attributes(G1, dict(G1.degree(weight='Weight')), 'WDegree')
nx.set_node_attributes(G1, dict(G1.degree()), 'Degree')
rem_edg = []
for u,v,d in G1.edges(data=True):
if d['Weight'] < edf.value:
rem_edg.append((u,v))
G1.remove_edges_from(rem_edg)
rem_node = []
for u in G1.nodes():
if G1.node[u]['WDegree'] < ndf.value:
rem_node.append(u)
rem_node = rem_node+list(nx.isolates(G1))
G1.remove_nodes_from(rem_node)
node_df = ColumnDataSource(nodes_df(G1))
edge_df = ColumnDataSource(edges_df(G1))
color_mapper = CategoricalColorMapper(factors=states, palette=Category20b[20] + Category20c[20] + Category20[20])
mapper = LogColorMapper(palette=['#FDFDFD', '#FDF5CA', '#FDF2B1', '#FDEE98', '#FDE665', '#FDDF33', '#FDD700'],
low=min(edge_df.data['weight']), high=max(edge_df.data['weight']))
p = figure(title="US Air Flights in 97", toolbar_location="left", plot_width=1100, plot_height=600,
match_aspect=True, aspect_scale=0.7, x_range=[-140, -50], y_range=[20, 55])
r1 = p.circle('x', 'y', source=node_df, size='size', level='overlay',
color={'field': 'state', 'transform': color_mapper},
alpha=0.8, line_color='#240606', line_width=1, line_alpha=0.3)
r2 = p.multi_line('xs', 'ys', line_width='line_width', alpha='alphas',
color={'field': 'weight', 'transform': mapper},
source=edge_df)
node_hover = HoverTool(tooltips=[('Name', '@Name'), ('State', '@state'), ('No. of Connections', '@Degree'),
('Total Frequency', '@WDegree')], renderers=[r1])
node_hover.attachment = 'right'
edge_hover = HoverTool(tooltips=[('Airports', '@city1'), ('', '@city2'), ('Frequency', '@weight')], renderers=[r2])
edge_hover.line_policy = 'interp'
edge_hover.attachment = 'left'
p.add_tools(node_hover)
p.add_tools(edge_hover)
return p
def histogram_plot(data, width=600, height=300):
'''
'''
if len(data) > 5:
data = data[:5]
ids = [d['id'] for d in data]
topics = ['Topic {}'.format(d['id']) for d in data]
dist = [d['dist'] for d in data]
labels = [', '.join(d['words']) for d in data]
source = ColumnDataSource(
data=dict(topics=topics, distribution=dist, ids=ids, labels=labels))
hover = HoverTool(tooltips="""
<div>
<div>
<span style="font-size: 20px;">Topics: <strong>@ids</strong></span><br>
<span style="font-size: 12px;">(Click to see more about the topic)</span><br>
<span style="font-size: 20px;">Words: @labels</span>
</div>
</div>
""")
hover.attachment = 'right'
plot = figure(x_range=topics,
width=width,
height=height,
tools=[hover, 'tap', 'save'],
title='Relevant topics')
plot.vbar(x='topics',
top='distribution',
width=0.8,
source=source,
line_color='white',
fill_color=factor_cmap('topics', palette=COLORS, factors=topics))
plot.y_range.start = 0
plot.xgrid.grid_line_color = None
plot.xaxis.axis_label = 'Topics'
plot.yaxis.axis_label = 'Proportion'
plot.title.text_font_size = '1.5em'
plot.xaxis.axis_label_text_font_size = "1.5em"
plot.yaxis.axis_label_text_font_size = "1.5em"
plot.xaxis.major_label_text_font_size = "1em"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url="/topic/@ids")
plot.toolbar.active_inspect = [hover]
return components(plot)