src/bin/semantic_similarity_infer.py

#!/usr/bin/env python
# coding: utf-8
import os
script_dir = os.path.dirname(os.path.abspath(__file__))

import pandas as pd
import numpy as np
import itertools
import multiprocessing
from scipy.spatial.distance import cdist
from numpy.linalg import norm
from scipy.stats import spearmanr
from tqdm import tqdm

manager = multiprocessing.Manager()
similarity_list = manager.list()
proteinListNew = manager.list()

representation_dataframe = ""
protein_names = ""
representation_name = ""
similarity_tasks = ""
detailed_output = False

def parallelSimilarity(paramList):
    protein_embedding_dataframe = representation_dataframe
    i = paramList[0]
    j = paramList[1]
    if j > i:
        protein1 = proteinListNew[i]
        protein2 = proteinListNew[j]
        if protein1 in protein_names and protein2 in protein_names:
            prot1vec = np.asarray(protein_embedding_dataframe.query("Entry == @protein1")['Vector'].item())
            prot2vec = np.asarray(protein_embedding_dataframe.query("Entry == @protein2")['Vector'].item())
            
            # Calculate Manhattan Distance and normalize
            manhattanDist = cdist(prot1vec.reshape(1,-1), prot2vec.reshape(1,-1), 'cityblock')
            manhattanDistNorm = manhattanDist / (norm(prot1vec,1) + norm(prot2vec,1))
            manhattanSim = 1 - manhattanDistNorm.item()
            
            if norm(prot1vec, 1) == 0 and norm(prot2vec, 1) == 0:
                manhattanSim = 1.0

            real = paramList[2]
            similarity_list.append((real, manhattanSim))
    return similarity_list

def calculateCorrelationforOntology(aspect, matrix_type):
    similarity_list[:] = []
    proteinListNew[:] = []
    
    similarityMatrixNameDict = {
        "All": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix.csv"),
        "500": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"),
        "sparse": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_500_proteins.csv"),
        "200": os.path.join(script_dir, "../data/preprocess/human_" + aspect + "_proteinSimilarityMatrix_for_highest_annotated_200_proteins.csv")
    }

    similarityMatrixFileName = similarityMatrixNameDict[matrix_type]
    human_proteinSimilarityMatrix = pd.read_csv(similarityMatrixFileName)
    human_proteinSimilarityMatrix.set_index(human_proteinSimilarityMatrix.columns, inplace=True)
    proteinList = human_proteinSimilarityMatrix.columns

    for prot in proteinList:
        proteinListNew.append(prot)

    if matrix_type == "sparse":
        sparsified_path = os.path.join(script_dir, "../data/auxilary_input/SparsifiedSimilarityCoordinates_" + aspect + "_for_highest_500.npy")
        sparsified_similarity_coordinates = np.load(sparsified_path)
        protParamList = sparsified_similarity_coordinates
    else:
        i = range(len(proteinList))
        j = range(len(proteinList))
        protParamList = list(itertools.product(i, j))

    protParamListNew = []
    for tup in tqdm(protParamList):
        i = tup[0]
        j = tup[1]
        if matrix_type == "sparse":
            protein1 = proteinListNew[i]
            protein2 = proteinListNew[j]
            real = human_proteinSimilarityMatrix.loc[protein1, protein2]
            tupNew = (tup[0],tup[1],real)
            protParamListNew.append(tupNew)
        else:
            if j > i:
                protein1 = proteinListNew[i]
                protein2 = proteinListNew[j]
                real = human_proteinSimilarityMatrix.loc[protein1, protein2]
                tupNew = (tup[0],tup[1],real)
                protParamListNew.append(tupNew)

    pool = multiprocessing.Pool()
    similarity_listRet = []
    for similarity_listRet in tqdm(pool.imap_unordered(parallelSimilarity, protParamListNew), total=len(protParamListNew), position=0, leave=True):
        pass
    pool.close()
    pool.join()

    real_distance_list = [value[0] for value in similarity_listRet]
    manhattan_distance_list = [value[1] for value in similarity_listRet]

    manhattanCorr = spearmanr(real_distance_list, manhattan_distance_list)

    return (manhattanCorr[0], manhattanCorr[1])

def calculate_all_correlations():
    results = {}
    for similarity_matrix_type in similarity_tasks:
        matrix_results = {}
        for aspect in ["MF", "BP", "CC"]:
            corr, p_value = calculateCorrelationforOntology(aspect, similarity_matrix_type)
            
            corr_key = f"{similarity_matrix_type}_{aspect}_correlation"
            p_value_key = f"{similarity_matrix_type}_{aspect}_pvalue"
            
            results[corr_key] = corr
            results[p_value_key] = p_value

    return results