Spaces:
Running
Running
File size: 7,057 Bytes
17e20d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
from river import stream
from river import cluster
class River:
def __init__(self, model):
self.model = model
def partial_fit(self, umap_embeddings):
for umap_embedding, _ in stream.iter_array(umap_embeddings):
self.model = self.model.learn_one(umap_embedding)
labels = []
for umap_embedding, _ in stream.iter_array(umap_embeddings):
label = self.model.predict_one(umap_embedding)
labels.append(label)
self.labels_ = labels
return self
import pandas as pd
from typing import List
import plotly.graph_objects as go
from sklearn.preprocessing import normalize
def visualize_topics_over_time(topic_model,
topics_over_time: pd.DataFrame,
top_n_topics: int = None,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
title: str = "<b>Topics over Time</b>",
width: int = 860,
height: int = 600) -> go.Figure:
"""
Based on BERTopic's funciton https://github.com/MaartenGr/BERTopic/blob/809414b88ca3f12a46728069d098d82345986489/bertopic/plotting/_topics_over_time.py
"""
#colors = ["#E69F00", "#56B4E9", "#009E73", "#F0E442", "#D55E00", "#0072B2", "#CC79A7"]
# Select topics based on top_n and topics args
freq_df = topic_model.get_topic_freq()
freq_df = freq_df.loc[freq_df.Topic != -1, :]
if topics is not None:
selected_topics = list(topics)
elif top_n_topics is not None:
selected_topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
else:
selected_topics = sorted(freq_df.Topic.to_list())
# Prepare data
if topic_model.custom_labels_ is not None and custom_labels:
topic_names = {key: topic_model.custom_labels_[key + topic_model._outliers] for key, _ in topic_model.topic_labels_.items()}
else:
topic_names = {key: value[:30] + "..." if len(value) > 30 else value
for key, value in topic_model.topic_labels_.items()}
topics_over_time["Name"] = topics_over_time.Topic.map(topic_names)
data = topics_over_time.loc[topics_over_time.Topic.isin(selected_topics), :].sort_values(["Topic", "Timestamp"])
# Add traces
fig = go.Figure()
for index, topic in enumerate(data.Topic.unique()):
trace_data = data.loc[data.Topic == topic, :]
topic_name = trace_data.Name.values[0]
words = trace_data.Words.values
if normalize_frequency:
y = normalize(trace_data.Frequency.values.reshape(1, -1))[0]
else:
y = trace_data.Frequency
fig.add_trace(go.Scatter(x=pd.to_datetime(trace_data.Timestamp), y=y,
mode='lines',
#marker_color=colors[index % 7],
hoverinfo="text",
name=topic_name,
hovertext=[f'<b>Topic {topic}</b><br>Words: {word}' for word in words]))
# Styling of the visualization
#fig.update_xaxes(
# dtick=7,
# tickformat="%b\n%Y"
# )
fig.update_layout(
yaxis_title="Normalized Frequency" if normalize_frequency else "Frequency",
title={'text':f'{title}',
'font': dict(size=22)
},
width=width,
height=height,
hoverlabel=dict(
bgcolor="white",
font_size=16,
#font_family="Rockwell"
),
legend=dict(
title="<b>Global Topic Representation",
orientation="h",
y = -.2,
x = 0
#yanchor="bottom",
#xanchor="left"
)
)
return fig
def visualize_topics_per_class(topic_model,
topics_per_class: pd.DataFrame,
top_n_topics: int = 10,
topics: List[int] = None,
normalize_frequency: bool = False,
custom_labels: bool = False,
title: str = "<b>Topics per Class</b>",
width: int = 900,
height: int = 900) -> go.Figure:
"""
Based on BERTopic's funciton https://github.com/MaartenGr/BERTopic/blob/809414b88ca3f12a46728069d098d82345986489/bertopic/plotting/_topics_per_class.py
"""
# Select topics based on top_n and topics args
freq_df = topic_model.get_topic_freq()
freq_df = freq_df.loc[freq_df.Topic != -1, :]
if topics is not None:
selected_topics = list(topics)
elif top_n_topics is not None:
#selected_topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
selected_topics = freq_df.Topic.to_list()[:top_n_topics]
else:
selected_topics = sorted(freq_df.Topic.to_list())
# Prepare data
if topic_model.custom_labels_ is not None and custom_labels:
topic_names = {key: topic_model.custom_labels_[key + topic_model._outliers] for key, _ in topic_model.topic_labels_.items()}
else:
topic_names = {key: value[:40] + "..." if len(value) > 40 else value
for key, value in topic_model.topic_labels_.items()}
topics_per_class["Name"] = topics_per_class.Topic.map(topic_names)
data = topics_per_class.loc[topics_per_class.Topic.isin(selected_topics), :]
# Add traces
fig = go.Figure()
for index, topic in enumerate(selected_topics):
if index == 0:
visible = True
else:
visible = "legendonly"
trace_data = data.loc[data.Topic == topic, :]
topic_name = trace_data.Name.values[0]
words = trace_data.Words.values
if normalize_frequency:
x = normalize(trace_data.Frequency.values.reshape(1, -1))[0]
else:
x = trace_data.Frequency
fig.add_trace(go.Bar(y=trace_data.Class,
x=x,
visible=visible,
hoverinfo="text",
name=topic_name,
orientation="h",
hovertext=[f'<b>Topic {topic}</b><br>Words: {word}' for word in words]))
# Styling of the visualization
fig.update_xaxes(showgrid=True)
fig.update_yaxes(showgrid=True)
fig.update_layout(
xaxis_title="Normalized Frequency" if normalize_frequency else "Frequency",
yaxis_title="Class",
title={
'text': f"{title}",
'font': dict(
size=22)
},
width=width,
height=height,
hoverlabel=dict(
bgcolor="white",
font_size=16,
),
legend=dict(
title="<b>Global Topic Representation",
)
)
return fig |