LDA topic modeling

This example trains a latent Dirichlet allocation model with scikit-learn using abstracts from Neurosynth.

import os

import pandas as pd

from nimare import annotate
from nimare.dataset import Dataset
from nimare.utils import get_resource_path

Load dataset with abstracts

dset = Dataset(os.path.join(get_resource_path(), "neurosynth_laird_studies.json"))

Initialize LDA model

model = annotate.lda.LDAModel(n_topics=5, max_iter=1000, text_column="abstract")

Run model

new_dset = model.fit(dset)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.0.12rc1/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)

View results

This DataFrame is very large, so we will only show a slice of it.

new_dset.annotations[new_dset.annotations.columns[:10]].head(10)

	id	study_id	contrast_id	Neurosynth_TFIDF__001	Neurosynth_TFIDF__01	Neurosynth_TFIDF__05	Neurosynth_TFIDF__10	Neurosynth_TFIDF__100	Neurosynth_TFIDF__11	Neurosynth_TFIDF__12
0	17029760-1	17029760	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
1	18760263-1	18760263	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
2	19162389-1	19162389	1	0.0	0.0	0.0	0.000000	0.0	0.176321	0.0
3	19603407-1	19603407	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
4	20197097-1	20197097	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
5	22569543-1	22569543	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
6	22659444-1	22659444	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
7	23042731-1	23042731	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0
8	23702412-1	23702412	1	0.0	0.0	0.0	0.061006	0.0	0.000000	0.0
9	24681401-1	24681401	1	0.0	0.0	0.0	0.000000	0.0	0.000000	0.0

Given that this DataFrame is very wide (many terms), we will transpose it before presenting it.

model.distributions_["p_topic_g_word_df"].T.head(10)

	LDA5__1	LDA5__2	LDA5__3	LDA5__4	LDA5__5
10	0.001000	2.001000	0.001	0.001000	0.001000
abstract	1.000108	0.001000	0.001	0.001000	1.001892
action	1.000776	1.001224	0.001	0.001000	0.001000
active	4.001000	0.001000	0.001	0.001000	0.001000
addition	2.284153	1.201311	0.001	1.517536	0.001000
additionally	2.001000	0.001000	0.001	0.001000	0.001000
affective	3.192516	1.809065	0.001	0.001000	1.001420
affective processes	2.001000	0.001000	0.001	0.001000	0.001000
ale	0.001000	0.001000	0.001	2.001000	0.001000
altered	4.001000	0.001000	0.001	0.001000	0.001000

n_top_terms = 10
top_term_df = model.distributions_["p_topic_g_word_df"].T
temp_df = top_term_df.copy()
top_term_df = pd.DataFrame(columns=top_term_df.columns, index=range(n_top_terms))
top_term_df.index.name = "Token"
for col in top_term_df.columns:
    top_tokens = temp_df.sort_values(by=col, ascending=False).index.tolist()[:n_top_terms]
    top_term_df.loc[:, col] = top_tokens

top_term_df

	LDA5__1	LDA5__2	LDA5__3	LDA5__4	LDA5__5
Token
0	connectivity	cortex	functional	literature	social
1	functional	human	connectivity	talairach	motor
2	functional connectivity	lateral	anterior	error	functional
3	macm	prefrontal	networks	coordinate	task
4	anterior	frontal	indicate	suggest	cortex
5	cognitive	cognition	altered	indicated	network
6	structural	identified	functional networks	quantitative	reflecting
7	analytic	functional	structural	using	systems
8	modeling	prefrontal cortex	function	published	compare
9	posterior	maps	connectivity modeling	estimation	systematic