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)

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	0.001	0.001000	2.001000	0.001000	0.001000
abstract	0.001	0.001000	2.001000	0.001000	0.001000
action	0.001	0.001000	2.001000	0.001000	0.001000
active	0.001	2.004606	0.998147	0.001000	1.000248
addition	0.001	0.001000	1.999720	1.001564	2.001717
additionally	0.001	0.001000	1.000581	0.001000	1.001419
affective	0.001	0.001000	6.001000	0.001000	0.001000
affective processes	0.001	0.001000	2.001000	0.001000	0.001000
ale	0.001	0.001000	1.000565	0.001000	1.001435
altered	0.001	0.001000	3.000817	0.001000	1.001183

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	resonance	frontal	connectivity	cortex	motor
1	altered	cortex	functional	lateral	literature
2	cluster	lobe	human	level	identified
3	differential connectivity	pole	functional connectivity	mode	stimulation
4	maps	frontal pole	cognitive	default	published
5	applied	cognition	macm	default mode	functional
6	indicated	prefrontal	anterior	network	error
7	common	prefrontal cortex	networks	thalamus	talairach
8	indicate	active	social	anterior	cortex
9	quantitative	parietal	analytic	systems	magnetic