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3d viz

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This commit is contained in:
Azeem Fidahusein
2025-08-11 02:49:41 +01:00
parent 4ca7e8ab61
commit 647111e9d3
3 changed files with 111 additions and 47 deletions
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139
apps/cluster_map/visualization.py
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@@ -47,7 +47,7 @@ def calculate_point_sizes(reduced_embeddings, density_based_sizing=False,
def create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, hover_text,
point_sizes, point_opacity=DEFAULT_POINT_OPACITY, method="PCA"):
point_sizes, point_opacity=DEFAULT_POINT_OPACITY, method="PCA", enable_3d=False):
"""Create a plot colored by clusters"""
fig = go.Figure()
@@ -63,26 +63,43 @@ def create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels, hover
cluster_name = f"Cluster {cluster_id}" if cluster_id != -1 else "Noise"
fig.add_trace(go.Scatter(
x=cluster_embeddings[:, 0],
y=cluster_embeddings[:, 1],
mode='markers',
name=cluster_name,
marker=dict(
size=cluster_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=cluster_hover
))
if enable_3d:
fig.add_trace(go.Scatter3d(
x=cluster_embeddings[:, 0],
y=cluster_embeddings[:, 1],
z=cluster_embeddings[:, 2],
mode='markers',
name=cluster_name,
marker=dict(
size=cluster_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=cluster_hover
))
else:
fig.add_trace(go.Scatter(
x=cluster_embeddings[:, 0],
y=cluster_embeddings[:, 1],
mode='markers',
name=cluster_name,
marker=dict(
size=cluster_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=cluster_hover
))
return fig
def create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources, hover_text,
point_sizes, point_opacity=DEFAULT_POINT_OPACITY):
point_sizes, point_opacity=DEFAULT_POINT_OPACITY, enable_3d=False):
"""Create a plot colored by source files"""
fig = go.Figure()
colors = px.colors.qualitative.Set1
@@ -94,20 +111,37 @@ def create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources
source_hover = [hover_text[j] for j, mask in enumerate(source_mask) if mask]
source_sizes = [point_sizes[j] for j, mask in enumerate(source_mask) if mask]
fig.add_trace(go.Scatter(
x=source_embeddings[:, 0],
y=source_embeddings[:, 1],
mode='markers',
name=source,
marker=dict(
size=source_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=source_hover
))
if enable_3d:
fig.add_trace(go.Scatter3d(
x=source_embeddings[:, 0],
y=source_embeddings[:, 1],
z=source_embeddings[:, 2],
mode='markers',
name=source,
marker=dict(
size=source_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=source_hover
))
else:
fig.add_trace(go.Scatter(
x=source_embeddings[:, 0],
y=source_embeddings[:, 1],
mode='markers',
name=source,
marker=dict(
size=source_sizes,
color=colors[i % len(colors)],
opacity=point_opacity,
line=dict(width=1, color='white')
),
hovertemplate='%{hovertext}<extra></extra>',
hovertext=source_hover
))
return fig
@@ -115,7 +149,7 @@ def create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources
def create_visualization_plot(reduced_embeddings, filtered_df, cluster_labels=None,
selected_sources=None, method="PCA", clustering_method="None",
point_size=DEFAULT_POINT_SIZE, point_opacity=DEFAULT_POINT_OPACITY,
density_based_sizing=False, size_variation=2.0):
density_based_sizing=False, size_variation=2.0, enable_3d=False):
"""Create the main visualization plot"""
# Create hover text
@@ -128,23 +162,37 @@ def create_visualization_plot(reduced_embeddings, filtered_df, cluster_labels=No
# Create plot based on coloring strategy
if cluster_labels is not None:
fig = create_clustered_plot(reduced_embeddings, filtered_df, cluster_labels,
hover_text, point_sizes, point_opacity, method)
hover_text, point_sizes, point_opacity, method, enable_3d)
else:
if selected_sources is None:
selected_sources = filtered_df['source_file'].unique()
fig = create_source_colored_plot(reduced_embeddings, filtered_df, selected_sources,
hover_text, point_sizes, point_opacity)
hover_text, point_sizes, point_opacity, enable_3d)
# Update layout
title_suffix = f" with {clustering_method}" if clustering_method != "None" else ""
fig.update_layout(
title=f"Discord Chat Messages - {method} Visualization{title_suffix}",
xaxis_title=f"{method} Component 1",
yaxis_title=f"{method} Component 2",
hovermode='closest',
width=1000,
height=700
)
dimension_text = "3D" if enable_3d else "2D"
if enable_3d:
fig.update_layout(
title=f"Discord Chat Messages - {method} {dimension_text} Visualization{title_suffix}",
scene=dict(
xaxis_title=f"{method} Component 1",
yaxis_title=f"{method} Component 2",
zaxis_title=f"{method} Component 3"
),
width=1000,
height=700
)
else:
fig.update_layout(
title=f"Discord Chat Messages - {method} {dimension_text} Visualization{title_suffix}",
xaxis_title=f"{method} Component 1",
yaxis_title=f"{method} Component 2",
hovermode='closest',
width=1000,
height=700
)
return fig
@@ -182,7 +230,7 @@ def display_summary_stats(filtered_df, selected_sources):
st.metric("Source Files", len(selected_sources))
def display_clustering_results(filtered_df, cluster_labels, reduced_embeddings, method, clustering_method):
def display_clustering_results(filtered_df, cluster_labels, reduced_embeddings, method, clustering_method, enable_3d=False):
"""Display clustering results and export options"""
if cluster_labels is None:
return
@@ -195,16 +243,21 @@ def display_clustering_results(filtered_df, cluster_labels, reduced_embeddings,
export_df['x_coordinate'] = reduced_embeddings[:, 0]
export_df['y_coordinate'] = reduced_embeddings[:, 1]
# Add z coordinate if 3D
if enable_3d and reduced_embeddings.shape[1] >= 3:
export_df['z_coordinate'] = reduced_embeddings[:, 2]
# Show cluster distribution
cluster_dist = pd.Series(cluster_labels).value_counts().sort_index()
st.bar_chart(cluster_dist)
# Download option
csv_data = export_df.to_csv(index=False)
dimension_text = "3D" if enable_3d else "2D"
st.download_button(
label="📥 Download Clustering Results (CSV)",
data=csv_data,
file_name=f"chat_clusters_{method}_{clustering_method}.csv",
file_name=f"chat_clusters_{method}_{clustering_method}_{dimension_text}.csv",
mime="text/csv"
)