{% if action == 'overview' %}

Basic Info

{{basic_info}}

Training Info

{{training_info}}

Initial Parameters

{% for i, (k, v) in enumerate(getattr(model, '_init_params', {}).items()) %} {% if k in init_param_desc %} {% else %} {% end %}
{% end %}

Parameters

{{params_info}}

Topics

{{topics_info}}
{% elif action == 'document' %}
{% if filtered_docs is None %}

All documents in the model ({{total_docs}})

{% else %}

Filtered documents ({{filtered_docs}} / {{total_docs}})

{% end %}
{% if available.get('metadata') %}
{% end %} Keywords:
Show only documents where the proportion of >= %
    {% for doc in documents %}
  • {% for i, v in enumerate(doc.topic_dist) %}
    {% end %}
  • {% end %}
{% if total_pages > 1 %} {% set args = f'&s={sort_key}&t={filter_target}&v={filter_value}&sq={filter_keyword}&m={filter_metadata}' %}
{% end %}
{% elif action == 'topic' %}

Top words in each topic

    {% for topic in topics %}
  • {{get_topic_label(topic.topic_id, "Topic ")}} {% if not read_only %} {% end %}

    {% for word, id, dist in topic.words %} {% end %}
    {{word}}
    {{dist:.3%}}
  • {% end %}
{% set col = "col-md-6" if top1_topic_dist_by_metadata else "" %}

Distribution of Document Top1 Topics

{% if top1_topic_dist_by_metadata %}

Distribution of Document Top1 Topics by Metadata

{% end %}
{% elif action == 'topic-rel' %}

Word overlap between topics

{% for i in range(len(overlaps)) %} {% end %} {% for j in range(len(overlaps)) %} {% for i in range(len(overlaps)) %} {% end %} {% end %}
{{i}}
{{j}}
{% for n, (i, j) in enumerate(similar_pairs) %} = len(overlaps) else ""}}">
{{get_topic_label(i, id_suffix=True)}} - {{get_topic_label(j, id_suffix=True)}}: {{overlaps[i, j]:.2%}}
{{get_topic_label(i, prefix="Topic ", id_suffix=True)}}: {% for word, p in model.get_topic_words(i, top_n=10) %} {{word}}({{p:.2%}}) {% end %} {{get_topic_label(j, prefix="Topic ", id_suffix=True)}}: {% for word, p in model.get_topic_words(j, top_n=10) %} {{word}}({{p:.2%}}) {% end %}
{% end %}
{% elif action == 'metadata' %}
Confidence
{% for i, (s, e) in enumerate(numeric_metadata) %}
Numeric Metadata #{{i}}: Range({{s}} - {{e}})
- {% if i == len(numeric_metadata) - 1 %} {% end %}
{% end %}
{% for topic in range(cats.shape[2]) %}

{{get_topic_label(topic, prefix="Topic ", id_suffix=True)}}: {{", ".join(w for w, _ in model.get_topic_words(topic, 5))}}

{% end %}
{% elif action == 'tdf-map' %}

Topic Distribution Function Map

{% for topic in range(model.k) %}

{{get_topic_label(topic, prefix="Topic ", id_suffix=True)}}

Map for {{get_topic_label(topic, prefix=
{% end %}
{% end %}