Example #1
0
def run(*args):
    #print "# Running data"

    global hypotheses

    data_size = args[0]

    p_representation = defaultdict(int) # how often do you get the right representation
    p_response = defaultdict(int) # how often do you get the right response?
    p_representation_literal = defaultdict(int) # how often do you get the right representation
    p_response_literal = defaultdict(int)  # how often do you get the right response?
    p_representation_presup = defaultdict(int) # how often do you get the right representation
    p_response_presup = defaultdict(int) # how often do you get the right response?

    #print "# Generating data"
    data = generate_data(data_size)

    # recompute these
    #print "# Computing posterior"
    #[ x.unclear_functions() for x in hypotheses ]
    [ x.compute_posterior(data) for x in hypotheses ]

    # normalize the posterior in fs
    #print "# Computing normalizer"
    Z = logsumexp([x.posterior_score for x in hypotheses])

    # and output the top hypotheses
    qq = FiniteBestSet(max=True, N=25)
    for h in hypotheses: qq.push(h, h.posterior_score) # get the tops
    for i, h in enumerate(qq.get_all(sorted=True)):
        for w in h.all_words():
            fprintn(8, data_size, i, w, h.posterior_score, q(h.value[w]), f=options.OUT_PATH+"-hypotheses."+str(get_rank())+".txt")

    # and compute the probability of being correct
    #print "# Computing correct probability"
    for h in hypotheses:
        hstr = str(h)
        #print data_size, len(data), exp(h.posterior_score), correct[ str(h)+":"+w ]
        for w in words:
            p = exp(h.posterior_score - Z)
            key = w + ":" + hstr

            p_representation[w] += p * (agree_pct[key] == 1.)
            p_representation_presup[w]  += p * (agree_pct_presup[key] == 1.) # if we always agree with the target, then we count as the right rep.
            p_representation_literal[w] += p * (agree_pct_literal[key] == 1.)

            # and just how often does the hypothesis agree?
            p_response[w] += p * agree_pct[key]
            p_response_presup[w]  += p * agree_pct_presup[key]
            p_response_literal[w] += p * agree_pct_literal[key]

    #print "# Outputting"


    for w in words:
        fprintn(10, str(get_rank()), q(w), data_size, p_representation[w], p_representation_presup[w], p_representation_literal[w], p_response[w], p_response_presup[w], p_response_literal[w], f=options.OUT_PATH+"-stats."+str(get_rank())+".txt")

    return 0
Example #2
0
def run(*args):
    #print "# Running data"

    global hypotheses

    data_size = args[0]

    p_representation = defaultdict(
        int)  # how often do you get the right representation
    p_response = defaultdict(int)  # how often do you get the right response?
    p_representation_literal = defaultdict(
        int)  # how often do you get the right representation
    p_response_literal = defaultdict(
        int)  # how often do you get the right response?
    p_representation_presup = defaultdict(
        int)  # how often do you get the right representation
    p_response_presup = defaultdict(
        int)  # how often do you get the right response?

    #print "# Generating data"
    data = generate_data(data_size)

    # recompute these
    #print "# Computing posterior"
    #[ x.unclear_functions() for x in hypotheses ]
    [x.compute_posterior(data) for x in hypotheses]

    # normalize the posterior in fs
    #print "# Computing normalizer"
    Z = logsumexp([x.posterior_score for x in hypotheses])

    # and output the top hypotheses
    qq = FiniteBestSet(max=True, N=25)
    for h in hypotheses:
        qq.push(h, h.posterior_score)  # get the tops
    for i, h in enumerate(qq.get_all(sorted=True)):
        for w in h.all_words():
            fprintn(8,
                    data_size,
                    i,
                    w,
                    h.posterior_score,
                    q(h.value[w]),
                    f=options.OUT_PATH + "-hypotheses." + str(get_rank()) +
                    ".txt")

    # and compute the probability of being correct
    #print "# Computing correct probability"
    for h in hypotheses:
        hstr = str(h)
        #print data_size, len(data), exp(h.posterior_score), correct[ str(h)+":"+w ]
        for w in words:
            p = exp(h.posterior_score - Z)
            key = w + ":" + hstr

            p_representation[w] += p * (agree_pct[key] == 1.)
            p_representation_presup[w] += p * (
                agree_pct_presup[key] == 1.
            )  # if we always agree with the target, then we count as the right rep.
            p_representation_literal[w] += p * (agree_pct_literal[key] == 1.)

            # and just how often does the hypothesis agree?
            p_response[w] += p * agree_pct[key]
            p_response_presup[w] += p * agree_pct_presup[key]
            p_response_literal[w] += p * agree_pct_literal[key]

    #print "# Outputting"

    for w in words:
        fprintn(10,
                str(get_rank()),
                q(w),
                data_size,
                p_representation[w],
                p_representation_presup[w],
                p_representation_literal[w],
                p_response[w],
                p_response_presup[w],
                p_response_literal[w],
                f=options.OUT_PATH + "-stats." + str(get_rank()) + ".txt")

    return 0
Example #3
0

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# MPI interface

if True:  #not is_master_process(): # only load if you aren't zero (else we must wait for zero to load!!

    from LOTlib.Serialization import *
    fs = file2object(options.LOAD_HYPOTHESES_PATH)
    ## The finite set of samples
    #inh = open(options.LOAD_HYPOTHESES_PATH)
    #fs = pickle.load(inh)

    hypotheses = fs.get_all()

    print "#", get_rank(), ": Loaded pickle. ", len(hypotheses), " hypotheses."

    # get all the words
    words = hypotheses[0].all_words(
    )  # just get the words from the first hypothesis

    # now figure out how often each meaning is right for each word
    agree_pct = dict(
    )  # how often does each part of meaning agree with each word?
    agree_pct_presup = dict()
    agree_pct_literal = dict()
    for h in hypotheses:
        for w in words:
            tresp = [target.value[w](t) for t in TESTING_SET]
            hresp = [h.value[w](t) for t in TESTING_SET]
Example #4
0
    return 0

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# MPI interface

if True: #not is_master_process(): # only load if you aren't zero (else we must wait for zero to load!!

    from LOTlib.Serialization import *
    fs = file2object(options.LOAD_HYPOTHESES_PATH)
    ## The finite set of samples
    #inh = open(options.LOAD_HYPOTHESES_PATH)
    #fs = pickle.load(inh)

    hypotheses = fs.get_all()

    print "#", get_rank(), ": Loaded pickle. ", len(hypotheses), " hypotheses."

    # get all the words
    words = hypotheses[0].all_words() # just get the words from the first hypothesis

    # now figure out how often each meaning is right for each word
    agree_pct = dict()  # how often does each part of meaning agree with each word?
    agree_pct_presup = dict()
    agree_pct_literal = dict()
    for h in hypotheses:
        for w in words:
            tresp = [ target.value[w](t) for t in TESTING_SET]
            hresp = [ h.value[w](t)      for t in TESTING_SET]

            key = w+":"+str(h)
            agree_pct[key]         = np.mean( collapse_undefs(tresp) == collapse_undefs(hresp) )