LDA topic modeling

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	0.0010	0.001000	1.001557	1.000443	0.001000
abstract	0.0010	1.000667	0.001000	0.001000	1.001333
action	0.0010	1.001116	0.001000	1.000884	0.001000
active	2.0057	0.998523	0.001000	0.998777	0.001000
addition	0.0010	2.001224	0.001000	2.000237	1.001539
additionally	0.0010	1.001089	0.001000	1.000911	0.001000
affective	0.0010	1.096106	1.001683	3.905211	0.001000
affective processes	0.0010	0.001000	0.001000	2.001000	0.001000
ale	0.0010	0.001000	0.001000	2.001000	0.001000
altered	0.0010	1.000802	0.001000	3.001198	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	cortex	connectivity	functional	connectivity	motor
1	prefrontal	social	frontal	functional	connectivity
2	frontal	functional	parcellation	human	cortex
3	prefrontal cortex	methods	cbp	anterior	functions
4	lobe	network	processes	functional connectivity	cognitive
5	cognition	stimulation	brainmap	networks	functional
6	language	identified	analytic	seed	functional connectivity
7	active	task	clusters	posterior	dorsal
8	parietal	connections	ventromedial	approaches	reflecting
9	non	cortex	human	structural	cognitive functions