def get_mean_relationship(wiki, n, m, freq_func):
subsamples1 = (Sentences.subsample(wiki, n) for _ in range(m))
subsamples2 = (Sentences.subsample(wiki, n) for _ in range(m))
ranks = [compute_ranks(sub) for sub in subsamples1]
ranks_joined = pool_ranks(ranks)
mean_ranks = reduce_pooled(ranks_joined)
freqs = [freq_func(sub) for sub in subsamples2]
freqs_joined = pool_freqs(freqs)
mean_freqs = reduce_pooled(freqs_joined)
return mean_ranks, mean_freqs
def variance_main(wiki, n, m, save_dir="./"):
subsamples1 = (Sentences.subsample(wiki, n) for _ in range(m))
subsamples2 = (Sentences.subsample(wiki, n) for _ in range(m))
ranks = [compute_ranks(sub) for sub in subsamples1]
ranks_joined = pool_ranks(ranks)
freqs = [compute_freqs(sub) for sub in subsamples2]
freqs_joined = pool_freqs(freqs)
mean_vs_pooled(ranks_joined, freqs_joined, save_dir)
do_mles(ranks, freqs, save_dir)
covariance_across_words(ranks_joined, freqs_joined, save_dir)
def heap(corp, rng):
vocab_sizes = []
for ntoks in rng:
subsample = Sentences(sent_subsample(corp, ntoks))
vocab_size = compute_vocab_size(subsample)
vocab_sizes.append(vocab_size)
return vocab_sizes
def get_model(corpus, n):
big_ranks = compute_ranks(Sentences.subsample(corpus, n))
freqs = compute_freqs(Sentences.subsample(corpus, n))
joint = merge_to_joint(big_ranks, freqs)
xs, ys = list(zip(*sorted(joint.values())))
mandelbrot = Mandelbrot(ys, xs)
mandelbrot_fit = mandelbrot.fit(start_params=np.asarray([1.0, 1.0]),
method="powell",
full_output=True)
mandelbrot.register_fit(mandelbrot_fit)
mandelbrot.print_result()
auto_typ = typicality(mandelbrot, joint)
return big_ranks, mandelbrot, auto_typ
def heap(corp, rng):
vocab_sizes = []
for i, ntoks in enumerate(rng):
if i % 10 == 0:
print(i, ntoks)
subsample = Sentences.subsample(corp, ntoks)
vocab_size = compute_vocab_size(subsample)
vocab_sizes.append(vocab_size)
return vocab_sizes
def get_filters(filter_dir, k, names, param_name, param_ls):
filters_dict = {}
for param in param_ls:
all_samples = corpora_from_pickles(filter_dir, names=names)
cur_param_filters = [Sentences(c) for name_d, c in all_samples if
name_d["k"] == k and name_d[param_name] == param]
filters_dict[param] = cur_param_filters
return filters_dict
def filter_worker(i):
print("started ", i)
cur_seed = int.from_bytes(os.urandom(4), byteorder='little')
rand.seed(cur_seed)
filtered = list(filter_typicality_incremental(mp_array, zipf_model,
rank_dict, auto_typ, n, factor*epsilon_f_minus, lt))
filtered_freqs = compute_freqs(Sentences(filtered))
print("filtered ", i, " typicality: ",
typicality(zipf_model, merge_to_joint(rank_dict, filtered_freqs)))
name = "_".join((str(n), str(factor), str(i)))
corpus_to_pickle(filtered, "results/" + lang + "/TF", name)
def establish_typical_set(corpus, rank_dict, zipf_model, n, m):
typicalities = []
for i in range(m):
sub = Sentences.subsample(corpus, n)
sub_freqs = compute_freqs(sub)
sub_joints = merge_to_joint(rank_dict, sub_freqs)
sub_typicality = typicality(zipf_model, sub_joints)
typicalities.append(sub_typicality)
mean_typ, std_typ = np.mean(typicalities), np.var(typicalities)**.5
return mean_typ, std_typ
def get_reference_dist(wiki):
n = int(10e6)
m = 10
wiki_ls = list(wiki)
subsamples = [Sentences.subsample(wiki_ls, n) for _ in range(m)]
mean_ranks, mean_freqs = mean_rank_freq_from_samples(subsamples)
joints = merge_to_joint(mean_ranks, mean_freqs)
xs, ys = list(zip(*sorted(joints.values())))
mandelbrot = Mandelbrot(ys, xs)
mandelbrot_fit = mandelbrot.fit(start_params=np.asarray([1.0, 1.0]),
method="powell",
full_output=True)
mandelbrot.register_fit(mandelbrot_fit)
mandelbrot.print_result()
return mandelbrot, mean_ranks
setup_m = 100
m = 10
wiki = list(wiki_from_pickles("data/"+lang+"_pkl"))
sents = [s for a in wiki for s in a]
zipf_model, rank_dict, mean_typ, std_typ, auto_typ = setup_filtering(wiki,
big_n(wiki),
n,
setup_m)
mean_corrected = abs(mean_typ - auto_typ)
epsilon_f_plus = mean_corrected + std_typ*factor
epsilon_f_minus = - epsilon_f_plus
print("\nModel and Epsilon established")
print(auto_typ, mean_typ, std_typ)
print(epsilon_f_minus, epsilon_f_plus)
for m_i in range(m):
print("started ", m_i)
filtered = list(filter_typicality_incremental(sents, zipf_model,
rank_dict, auto_typ, n, epsilon_f_minus, lt))
filtered_freqs = compute_freqs(Sentences(filtered))
print("filtered ", m_i, " typicality: ",
typicality(zipf_model, merge_to_joint(rank_dict, filtered_freqs)))
name = "_".join((str(n), str(factor), str(m_i)))
corpus_to_pickle(filtered, "results/" + lang + "/TF", name)
print("ARGS: ", lang, factors, hist_lens, "\n")
d = "results/" + lang + "/"
results_d = d + "evaluation/"
k = 1000000
srfs = get_filters(d + "SRF/", k, ["k", "h", "i"], "h", hist_lens)
tfs = get_filters(d + "TF/", k, ["k", "f", "i"], "f", factors)
highest_three_factors = factors[-3:]
three_tfs = {k: tfs[k] for k in highest_three_factors}
highest_three_hist_lens = hist_lens[-3:]
three_srfs = {k: srfs[k] for k in highest_three_hist_lens}
unis = [
Sentences(c)
for _, c in corpora_from_pickles(d + "UNI", names=["k", "i"])
]
uni_mean_ranks, uni_mean_freqs = mean_rank_freq_from_samples(unis)
uni_joints = merge_to_joint(uni_mean_ranks, uni_mean_freqs)
uni_xs, uni_ys = list(zip(*sorted(uni_joints.values())))
print("filters loaded", flush=True)
# MLEs
tf_mles, srf_mles, uni_mandel = do_mles(tfs, srfs, unis)
with open(results_d + "mle_mandelbrot.txt", "w") as handle:
for param, mandel in tf_mles.items():
handle.write("TF " + str(param))
if __name__ == "__main__":
n = 100000
d = "results/ALS/"
# GET UNIVERSE
wiki = list(wiki_from_pickles("data/ALS_pkl"))
sent_d, label_f = number_sents((s for a in wiki for s in a))
word_d, word_label_f = number_words((w for a in wiki for s in a
for w in s))
## LOAD CORPORA
# SRFs
srf_samples = list(corpora_from_pickles(d + "SRF", names=["n", "h", "i"]))
srf_10 = [
Sentences(c) for name_d, c in srf_samples
if name_d["n"] == n and name_d["h"] == 10
]
srf_20 = [
Sentences(c) for name_d, c in srf_samples
if name_d["n"] == n and name_d["h"] == 20
]
srf_30 = [
Sentences(c) for name_d, c in srf_samples
if name_d["n"] == n and name_d["h"] == 30
]
#TFs
tf_samples = list(corpora_from_pickles(d + "TF", names=["n", "f", "i"]))
tf_50 = [
Sentences(c) for name_d, c in tf_samples
if name_d["n"] == n and name_d["f"] == 50
# -*- coding: utf-8 -*-
from data.reader import wiki_from_pickles, corpus_to_pickle
from data.corpus import Sentences
from stats.stat_functions import compute_freqs, merge_to_joint
import argparse
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--lang", type=str)
p.add_argument("--n_tokens", type=int)
args = p.parse_args()
return args.lang, args.n_tokens
if __name__ == "__main__":
lang, n = parse_args()
m = 10
wiki = list(wiki_from_pickles("data/" + lang + "_pkl"))
for i in range(m):
sampled = Sentences.subsample(wiki, n)
sampled_sents = list(sampled.sentences())
name = "_".join((str(n), str(i)))
corpus_to_pickle(sampled_sents, "results/" + lang + "/UNI", name)
hexbin_plot(xs, ys, xlbl="$\log$ $r(w)$", ylbl="$\log$ $f(w)$")
plt.savefig(d + "rank_freq_" + str(n) + "_piantadosi.png", dpi=300)
plt.close()
if __name__ == "__main__":
lang = parse_args()
d = "results/" + lang + "/plots/"
wiki = list(wiki_from_pickles("data/" + lang + "_pkl"))
n = int(10e6)
m = 10
zipf_wrong(wiki, n, d)
zipf_piantadosi(wiki, n, d)
subsamples1 = (Sentences.subsample(wiki, n) for _ in range(m))
subsamples2 = (Sentences.subsample(wiki, n) for _ in range(m))
ranks = [compute_ranks(sub) for sub in subsamples1]
ranks_joined = pool_ranks(ranks)
mean_ranks = reduce_pooled(ranks_joined)
freqs = [compute_freqs(sub) for sub in subsamples2]
freqs_joined = pool_freqs(freqs)
mean_freqs = reduce_pooled(freqs_joined)
print("subsampling done")
joints = merge_to_joint(mean_ranks, mean_freqs)
xs, ys = list(zip(*sorted(joints.values())))
c_vec1, c_vec2 = [cs1[x] for x in sorted(universe)], [cs2[x] for x in sorted(universe)]
return sum(min(one, two) for one, two in zip(c_vec1, c_vec2))/sum(max(one, two) for one, two in zip(c_vec1, c_vec2))
if __name__ == "__main__":
n = 100000
d = "results/ALS/"
wiki = list(wiki_from_pickles("data/ALS_pkl"))
print("Total num sents", len([s for a in wiki for s in a]))
srf_samples = corpora_from_pickles(d + "SRF", names=["n", "h", "i"])
srf_30 = [Sentences(c) for name_d, c in srf_samples if name_d["n"] == n and
name_d["h"] == 30]
tf_samples = corpora_from_pickles(d + "TF", names=["n", "f", "i"])
tf_100 = [Sentences(c) for name_d, c in tf_samples if name_d["n"] == n and
name_d["f"] == 100]
uni_samples = corpora_from_pickles(d + "UNI", names=["n", "i"])
uni = [Sentences(c) for name_d, c in uni_samples if name_d["n"] == n]
for subcorp_set, name in zip([srf_30, tf_100, uni], ["SRF", "TF", "UNI"]):
print("\n", name)
shuffled_sents = rand.permutation([s for subcorp in subcorp_set
