from Bio import Entrez

import json

import numpy as np

import pandas as pd

from sklearn.metrics.pairwise import cosine_similarity

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn import decomposition

from sklearn.cluster.bicluster import SpectralCoclustering

def fetch_details(id_list):

return results

def search(query,N):

return results

def mergeAbstract(abstract):

return("".join(abstract))

results = search('zebrafish',500)

id_list = results['IdList']

papers = fetch_details(id_list)

listOfTitles = [paper['MedlineCitation']['Article']['ArticleTitle'] for paper in papers]

listOfAbstracts = []

for paper in papers:

if 'Abstract' in paper['MedlineCitation']['Article'].keys():

listOfAbstracts.append(mergeAbstract(paper['MedlineCitation']['Article']['Abstract']['AbstractText']))

# Create TF-IDF matrix

vect = TfidfVectorizer(max_df = 1)

tfidf = vect.fit_transform(listOfAbstracts)

# Non-negative Matrix Factorization

num_topics = 2

num_top_words = 5

nmf = decomposition.NMF(n_components=num_topics, random_state=1)

doctopic = nmf.fit_transform(tfidf)

topic_words = []

vocab = np.array(vect.get_feature_names())

for topic in nmf.components_:

word_idx = np.argsort(topic)[::-1][0:num_top_words]

topic_words.append([vocab[i] for i in word_idx])

# Coclustering

cocluster = SpectralCoclustering(n_clusters=5,svd_method='arpack', random_state=0)

cocluster.fit(tfidf)

y_cocluster = cocluster.row_labels_

x_cocluster = cocluster.column_labels_

# print(np.array(vect.get_feature_names())[x_cocluster == 4])