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_functional_connectivity_cbp	LDA5__2_cortex_identified_literature	LDA5__3_connectivity_functional_macm	LDA5__4_connectivity_functional_human	LDA5__5_social_network_task
10	1.001169	0.001000	0.001000	1.000831	0.001000
abstract	1.000657	0.001000	0.001000	0.001000	1.001343
action	0.001000	0.001000	1.001143	1.000857	0.001000
active	0.001000	1.000782	3.001218	0.001000	0.001000
addition	1.000590	3.001620	1.000789	0.001000	0.001000
additionally	1.001155	0.001000	0.001000	1.000845	0.001000
affective	1.000693	0.001000	0.001000	4.000538	1.001769
affective processes	0.001000	0.001000	0.001000	2.001000	0.001000
ale	0.001000	1.001233	0.001000	1.000767	0.001000
altered	1.000943	0.001000	0.001000	3.001057	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_functional_connectivity_cbp	LDA5__2_cortex_identified_literature	LDA5__3_connectivity_functional_macm	LDA5__4_connectivity_functional_human	LDA5__5_social_network_task
Token
0	functional	cortex	connectivity	connectivity	social
1	connectivity	identified	functional	functional	network
2	cbp	literature	macm	human	task
3	frontal	published	insula	functional connectivity	functional
4	cognitive	stimulation	networks	posterior	systems
5	functions	talairach	anterior	seed	research
6	parcellation	prefrontal	functional networks	cognitive	high
7	behavioral	lateral	methods	motor	cognitive
8	clusters	indicated	language	structural	modeling
9	analytic	addition	cognition	anterior	connected