02-lda.py 5.35 KB
import gensim
import os
import sys
import pickle
from gensim.models.ldamodel import  LdaModel
from gensim.models.ldamulticore import LdaMulticore
from collections import Counter
import numpy as np
import codecs
import shelve
import logging
import dill
from tinydb import TinyDB, where, Query
import time
from joblib import Parallel, delayed

def calc_perp(models,train):

    name = models[0]

    logging.warning(" go {} ".format(name))
    logging.warning("TRS  to be done")
    entry = Query()
    value=db.search(entry.name == name)
    if len(value) > 0 :
        logging.warning("{} already done".format(name))

    dev_trs=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["DEV"]]
    lda_trs = models[2]
    perp_trs = lda_trs.log_perplexity(dev_trs)

    logging.warning("ASR  to be done")
    dev_asr = [ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["DEV"]]
    lda_asr = models[5]
    perp_asr = lda_asr.log_perplexity(dev_asr)
    logging.warning("ASR  saving")
    res_dict = {"name" : name, "asr" : perp_asr, "trs" : perp_trs }
    return res_dict

def train_lda(out_dir,train,size,it,sw_size,alpha,eta,passes,chunk):
    name = "s{}_it{}_sw{}_a{}_e{}_p{}_c{}".format(size,it,sw_size,alpha,eta,passes,chunk)
    deep_out_dir = out_dir+"/"+name
    if os.path.isdir(deep_out_dir):
        logging.error(name+" already done")
    logging.warning(name+" to be done")
    asr_count=Counter([ x for y in train["ASR_wid"]["TRAIN"] for x in y])
    trs_count=Counter([ x for y in train["TRS_wid"]["TRAIN"] for x in y])
    asr_sw = [ x[0] for x in asr_count.most_common(sw_size) ]
    trs_sw = [ x[0] for x in trs_count.most_common(sw_size) ]
    stop_words=set(asr_sw) | set(trs_sw)

    logging.warning("TRS  to be done")

    lda_trs = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["TRS_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=chunk,iterations=it,alpha=alpha,eta=eta,passes=passes)

    logging.warning("ASR  to be done")
    lda_asr = LdaModel(corpus=[ [ (x,y) for x,y in Counter(z).items() if x not in stop_words] for z in train["ASR_wid"]["TRAIN"]], id2word=train["vocab"], num_topics=int(size), chunksize=chunk,iterations=it,alpha=alpha,eta=eta,passes=passes)

    dico = train["vocab"]
    word_list =  [ dico[x] for x in range(len(train["vocab"]))]
    asr_probs = []
    for line in lda_asr.expElogbeta:
        nline = line / np.sum(line)
        asr_probs.append([ str(x) for x in nline])
    trs_probs = []
    for line in lda_trs.expElogbeta:
        nline = line / np.sum(line)
        trs_probs.append([str(x) for x in nline])

    K = lda_asr.num_topics
    topicWordProbMat_asr = lda_asr.print_topics(K,10)

    K = lda_trs.num_topics
    topicWordProbMat_trs = lda_trs.print_topics(K,10)
    dill.dump([x for x in stop_words],open(deep_out_dir+"/stopwords.dill","w"))
    dill.dump([x for x in asr_probs],open(deep_out_dir+"/lda_asr_probs.dill","w"))
    dill.dump([x for x in trs_probs],open(deep_out_dir+"/lda_trs_probs.dill","w"))

    return [name, stop_words, lda_asr , asr_probs , topicWordProbMat_asr, lda_trs, trs_probs, topicWordProbMat_trs]

def train_one(name,train,s,i,sw,a,e,p,c):
    logging.warning(" ; ".join([str(x) for x in [s,i,sw,a,e,p,c]]))
    models = train_lda(name,train,s,i,sw,a,e,p,c)
    if models:
        m = calc_perp(models,train)
    else : 
        m = None
    e = time.time()
    logging.warning("fin en : {}".format(e-st))
    return m

if __name__ == "__main__": 
    logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.WARNING)

    input_shelve = sys.argv[1]
    db_path = sys.argv[2]
    size = [ int(x) for x in sys.argv[3].split("_")]
    workers = int(sys.argv[4])
    name = sys.argv[5]
    it = [ int(x) for x in sys.argv[6].split("_")]
    sw_size = [ int(x) for x in sys.argv[7].split("_")]
    if sys.argv[8] != "None" :
        alpha = [ "symmetric", "auto" ] + [ float(x) for x in sys.argv[8].split("_")]
        eta = ["auto"] + [ float(x) for x in sys.argv[9].split("_")]
    else :
        alpha = ["symmetric"]
        eta = ["auto"]
    passes = [ int(x) for x in sys.argv[10].split("_")]
    chunk = [ int(x) for x in sys.argv[11].split("_")]

    train = shelve.open(input_shelve)
    try :
    except :
        logging.warning(" folder already existe " )
    db  = TinyDB(db_path)
    nb_model = len(passes) * len(chunk) * len(it) * len(sw_size) * len(alpha) * len(eta) * len(size)
    logging.warning(" hey will train {} models ".format(nb_model))

    for p in passes:
        for c in chunk: 
            for i in it :
                for sw in sw_size:
                    for a in alpha:
                        for e in eta:
                            for s in size: 
    res_list= Parallel(n_jobs=15)(delayed(train_one)(*args) for args in args_list)
    for m in res_list :