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pmi.py
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pmi.py
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# pmi.py compute normalized pointwise mutual information
import roles_config
import pnames
import os
import sys
import re
import codecs
import pdb
import math
import copy
from collections import defaultdict
import es_np_query
# pmi.gram_tuple2string(gtuple)
# convert a tuple into the equivalent string
def gram_tuple2string(gram_tuple):
gram_string = " ".join(gram_tuple)
return(gram_string)
# reduce a part of speech tag string to a single word made up
# of the fist letter of each tag.
# pmi.abbrev_pos("JJ NN NNS") => "JNN"
def abbrev_pos(pos_string):
l_pos = pos_string.split(" ")
return("".join(map(lambda x: x[0], l_pos)))
# convert number to string. All else return as is.
def num2str(unknown):
if isinstance(unknown, (int, long, float)):
return(str(unknown))
else:
return(unknown)
# This does two things: (1) abbreviates the pos string to the first char of each pos tag
# (2) sums up the instances of canonical n-grams to get a total frequency across corpus and docs.
# The frequency is instance frequency since the file data has one entry per gram occurrence.
# create a subset for testing:
# cat trigrams.inst.filt | sort > trigrams.inst.filt.sorted
# cat trigrams.inst.filt.sorted | head -100000 | tail -10000 > trigrams.inst.filt.sorted.test
# pmi.dump_canon_gram_freq("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.inst.filt"
# creates a file .freq with fields: term, corpus_freq, doc_freq, pos (term is canonical np)
def dump_canon_gram_freq(gram_file):
freq_file = gram_file + ".freq"
s_gram = codecs.open(gram_file, encoding='utf-8')
s_freq = codecs.open(freq_file, "w", encoding='utf-8')
# sum instances to get corpus freq
d_gram2freq = defaultdict(int)
# keep the abbreviated part of speech signature
d_gram2pos = {}
# use the set of doc_ids to get doc freq
d_gram2doc_ids = defaultdict(set)
for line in s_gram:
line = line.strip()
l_line = line.split("\t")
canonical_form = l_line[0]
doc_id = l_line[2]
pos = abbrev_pos(l_line[3])
d_gram2freq[canonical_form] += 1
d_gram2doc_ids[canonical_form].add(doc_id)
d_gram2pos[canonical_form] = pos
for term in d_gram2freq:
corpus_freq = str(d_gram2freq[term])
doc_freq = str(len(d_gram2doc_ids[term]))
pos = d_gram2pos[term]
output_string = "\t".join([term, corpus_freq, doc_freq, pos])
s_freq.write("%s\n" % output_string)
s_gram.close()
s_freq.close()
"""
# SUbsumed by NCAssoc
# mutual information class
class MI():
def __init__(self):
self.d_AB2freq = defaultdict(int)
self.d_A2freq = defaultdict(int)
self.d_B2freq = defaultdict(int)
self.all_AB_freq = 0
# AB is a tuple containing two strings
# input to load is a tuple and its frequency (typically the number of docs the pair appears in)
# If the same tuple is loaded multiple times, we compute the sum of individual frequencies.
# Note that we have to load all bigrams in the collection, even we are only interested in a subset, in
# order to obtain the corpus statistics needed for denominators (e.g. freq(term_A, any_term_B)
def load(self, AB, freq):
# AB term_tuple is a tuple containing two ordered terms A, B
A = AB[0]
B = AB[1]
# increment freq count for tuple AB and its components
self.d_AB2freq[AB] += freq
self.d_A2freq[A] += freq
self.d_B2freq[B] += freq
# total # of pairs
self.all_AB_freq += freq
# bigram_file has records of the form: freq\tterm
# e.g.
# 20734 room temperature
def load_bigram_file(self, bigram_file):
s_pair_data = codecs.open(bigram_file, encoding='utf-8')
for line in s_pair_data:
line = line.strip()
l_fields = line.split("\t")
freq = int(l_fields[0])
AB = tuple(l_fields[1].split(" "))
self.load(AB, freq)
s_pair_data.close()
def compute_mi(self, AB, verbose_p=False):
# default value in case the pmi cannot be computed due to 0 freq somewhere
pmi = -1000
mi = -1000
norm_pmi = -1000
A = AB[0]
B = AB[1]
A_prob = float(self.d_A2freq[A])/self.all_AB_freq
B_prob = float(self.d_B2freq[B])/self.all_AB_freq
AB_prob = float(self.d_AB2freq[AB])/self.all_AB_freq
# compute normalized pmi
# Check for odd cases where a term prob of 0 arises
# It shouldn't happen but it does
denom = A_prob * B_prob
if denom == 0:
if verbose_p:
print "0 probability for term A: [%s, %f] or term B: [%s, %s]" % (A, A_prob, B, B_prob)
pass
elif AB_prob == 0:
if verbose_p:
print "0 probability for pair AB: %s, %s" % (A, B)
pass
else:
pmi = math.log(AB_prob/(A_prob * B_prob),2)
mi = AB_prob * pmi
norm_pmi = pmi / (-1 * math.log(AB_prob, 2))
if verbose_p:
print "[pmi]npmi for %s %s: %f, freq/probs: %i/%f, %i/%f, %i/%f" % (A, B, norm_pmi, fpmi, self.d_A2freq[A], A_prob, self.d_B2freq[B], self.B_prob, self.d_AB2freq[AB], AB_prob)
return([pmi, norm_pmi, mi])
def dump_mi(self, outfile):
s_out = codecs.open(outfile, "w", encoding='utf-8')
for AB in self.d_AB2freq.keys():
AB_freq = self.d_AB2freq[AB]
l_mi = self.compute_mi(AB)
pmi = l_mi[0]
npmi = l_mi[1]
mi = l_mi[2]
A = AB[0]
B = AB[1]
A_freq = self.d_A2freq[A]
B_freq = self.d_B2freq[B]
AB_string = gram_tuple2string(AB)
s_out.write("%s\t%i\t%i\t%i\t%.4f\t%.4f\t%.4f\n" % (AB_string, AB_freq, A_freq, B_freq, pmi, npmi, mi))
s_out.close()
"""
# class to compute all NC association measures
# subsumes class MI, which just computes mutual information variants
# measures come from Nakov:
# freq
# conditional_prob (Lauer)
# MI
# x2
class NCAssoc():
def __init__(self):
self.d_AB2freq = defaultdict(int)
self.d_A2freq = defaultdict(int)
self.d_B2freq = defaultdict(int)
self.d_AB2cprob = defaultdict(float)
self.d_AB2npmi = defaultdict(float)
# note that d_AB2mi could return an empty list if AB does not occur in corpus
self.d_AB2mi = defaultdict(list)
self.d_AB2chi2 = defaultdict(float)
# total doc_freq of all bigrams
self.all_AB_freq = 0
# external access to bigram raw data
# AB is a tuple with two words (comprising a bigram)
# NOTE that because they are defaultdict, if a bigram does not exist
# the value returned will be 0.
def bg2freq(self, AB):
return(self.d_AB2freq[AB])
def bg2cprob(self, AB):
return(self.d_AB2cprob[AB])
def bg2npmi(self, AB):
return(self.d_AB2npmi[AB])
def bg2chi2(self, AB):
return(self.d_AB2chi2[AB])
# AB is a tuple containing two strings
# input to load is a tuple and its frequency (typically the number of docs the pair appears in)
# If the same tuple is loaded multiple times, we compute the sum of individual frequencies.
# Note that we have to load all bigrams in the collection, even we are only interested in a subset, in
# order to obtain the corpus statistics needed for denominators (e.g. freq(term_A, any_term_B)
def load(self, AB, doc_freq):
# AB term_tuple is a tuple containing two ordered terms A, B
A = AB[0]
B = AB[1]
# increment freq count for tuple AB and its components
self.d_AB2freq[AB] += doc_freq
# note that the frequencies are based solely on bigrams
# AX freq
self.d_A2freq[A] += doc_freq
# XB freq
self.d_B2freq[B] += doc_freq
# total # of pairs
self.all_AB_freq += doc_freq
# bigram_file has records of the form: freq\tterm
# e.g.
# 20734 room temperature
# format of bigram file changed 6/17 to: term corpus_freq doc_freq pos_signature (.freq file)
def load_bigram_file(self, bigram_file):
s_bigram_data = codecs.open(bigram_file, encoding='utf-8')
for line in s_bigram_data:
line = line.strip()
l_fields = line.split("\t")
corpus_freq = int(l_fields[1])
doc_freq = int(l_fields[2])
pos_sig = l_fields[3]
# we split into a tuple so that the load method can index
# by the tuple and also compute freq for AX and XB occurrences
AB = tuple(l_fields[0].split(" "))
# for now, we ignore the corpus_freq and pos_sig for bigrams and just keep the doc freq
self.load(AB, doc_freq)
s_bigram_data.close()
# conditional prob of AB given XB (B as head of any bigram)
def compute_cprob(self, AB):
# freq(AB) / freq(B)
# If AB is a phrase, then B should exist
B = AB[1]
AB_B_cprob = float(self.d_AB2freq[AB])/float(self.d_B2freq[B])
return(AB_B_cprob)
def compute_chi2(self, AB):
# chi2 has four boxes:
# a: freq(AB)
# b: freq(A, !B) = freq(AX) - freq(AB)
# c: freq(!A, B) = freq(XB) - freq(AB)
# d: freq(!A, !B) = total bigrams -a -b -c
# chi2 = N(a*d - b*c)^2 / (a + c) (b + d) (a + b) (c + d)
A = AB[0]
B = AB[1]
N = self.all_AB_freq
a = self.d_AB2freq[AB]
b = self.d_A2freq[A] - a
c = self.d_B2freq[B] - a
d = N - (a + b + c)
chi2 = (N * math.pow( ((a * d) - (b * c)), 2) ) / float( (a + c) * (b + d) * (a + b ) * (c + d))
#pdb.set_trace()
return(chi2)
def compute_mi(self, AB, verbose_p=False):
# default value in case the pmi cannot be computed due to 0 freq somewhere
pmi = -1000
mi = -1000
norm_pmi = -1000
A = AB[0]
B = AB[1]
A_prob = float(self.d_A2freq[A])/self.all_AB_freq
B_prob = float(self.d_B2freq[B])/self.all_AB_freq
AB_prob = float(self.d_AB2freq[AB])/self.all_AB_freq
# compute normalized pmi
# Check for odd cases where a term prob of 0 arises
# It shouldn't happen but it does
denom = A_prob * B_prob
if denom == 0:
if verbose_p:
print "0 probability for term A: [%s, %f] or term B: [%s, %s]" % (A, A_prob, B, B_prob)
pass
elif AB_prob == 0:
if verbose_p:
print "0 probability for pair AB: %s, %s" % (A, B)
pass
else:
pmi = math.log(AB_prob/(A_prob * B_prob),2)
mi = AB_prob * pmi
norm_pmi = pmi / (-1 * math.log(AB_prob, 2))
if verbose_p:
print "[pmi]npmi for %s %s: %f, freq/probs: %i/%f, %i/%f, %i/%f" % (A, B, norm_pmi, self.d_A2freq[A], A_prob, self.d_B2freq[B], self.B_prob, self.d_AB2freq[AB], AB_prob)
return([pmi, norm_pmi, mi])
# compute and store association metrics
def compute_assoc(self):
for AB in self.d_AB2freq.keys():
AB_freq = self.d_AB2freq[AB]
l_mi = self.compute_mi(AB)
pmi = l_mi[0]
npmi = l_mi[1]
mi = l_mi[2]
A = AB[0]
B = AB[1]
A_freq = self.d_A2freq[A]
B_freq = self.d_B2freq[B]
cprob = self.compute_cprob(AB)
chi2 = self.compute_chi2(AB)
# store the computed values in dicts
self.d_AB2cprob[AB] = cprob
self.d_AB2mi[AB] = l_mi
# AB2npmi is redundant with AB2mi but will return 0 for a non-occurring AB
# So for convenience, we keep an extra dict.
self.d_AB2npmi[AB] = npmi
self.d_AB2chi2[AB] = chi2
# .assoc file format: AB_string, AB_freq, A_freq, B_freq, mi, pmi, npmi, cprob, chi2
def dump_assoc(self, outfile):
s_out = codecs.open(outfile, "w", encoding='utf-8')
for AB in self.d_AB2freq.keys():
AB_string = gram_tuple2string(AB)
AB_freq = self.d_AB2freq[AB]
l_mi = self.d_AB2mi[AB]
pmi = l_mi[0]
npmi = l_mi[1]
mi = l_mi[2]
A = AB[0]
B = AB[1]
A_freq = self.d_A2freq[A]
B_freq = self.d_B2freq[B]
cprob = self.compute_cprob(AB)
chi2 = self.compute_chi2(AB)
s_out.write("%s\t%i\t%i\t%i\t%.4f\t%.4f\t%.4f\t%.4f\t%.4f\n" % (AB_string, AB_freq, A_freq, B_freq, mi, pmi, npmi, cprob, chi2))
s_out.close()
class Trigram():
# pass in ncassoc containing all the bigram data for the corpus
def __init__(self, ncassoc):
self.ncassoc = ncassoc
self.d_trigram2corpus_freq = {}
self.d_trigram2doc_freq = {}
self.d_trigram2pos_sig = {}
self.d_trigram2predictions = {}
self.d_trigram2bigram_data = {}
self.d_trigram2summary = {}
"""
# bigram association metrics (precomputed)
self.d_bg2freq = defaultdict(int)
self.d_bg2npmi = defaultdict(float)
self.d_bg2cprob = defaultdict(float)
self.d_bg2chi2 = defaultdict(float)
"""
"""
def load_bigram_assoc(self, assoc_file):
# .assoc file contains: AB_string, AB_freq, A_freq, B_freq, pmi, npmi, mi, cprob, chi2
s_assoc = codecs.open(assoc_file, encoding='utf-8')
for line in s_assoc:
line = line.strip()
l_fields = line.split("\t")
bigram_str = l_fields[0]
# use tuple of words as the key for bigram dictionaries
bigram = tuple(bigram_str.split(" "))
self.d_bg2freq[bigram] = l_fields[1]
self.d_bg2npmi[bigram] = l_fields[5]
self.d_bg2cprob[bigram] = l_fields[7]
self.d_bg2chi2[bigram] = l_fields[8]
s_assoc.close()
"""
# trigram bracketing predictions
def load_trigram_file(self, trigram_file):
# file contains: trigram corpus_freq doc_freq pos_signature
s_trigram_data = codecs.open(trigram_file, encoding='utf-8')
for line in s_trigram_data:
line = line.strip()
l_fields = line.split("\t")
trigram = l_fields[0]
corpus_freq = int(l_fields[1])
doc_freq = int(l_fields[2])
pos_sig = l_fields[3]
self.load_trigram(trigram, corpus_freq, doc_freq, pos_sig)
s_trigram_data.close()
def dump_trigram(self, output_file, verbose_p=False):
s_output = codecs.open(output_file, "w", encoding='utf-8')
summary_file = output_file + ".sum"
s_summary = codecs.open(summary_file, "w", encoding='utf-8')
for trigram in self.d_trigram2corpus_freq:
cf = self.d_trigram2corpus_freq[trigram]
df = self.d_trigram2doc_freq[trigram]
ps = self.d_trigram2pos_sig[trigram]
l_pred = self.d_trigram2predictions[trigram]
l_bigram_data = self.d_trigram2bigram_data[trigram]
bigram_str = "\t".join(map(lambda x: num2str(x), l_bigram_data))
pred_string = "\t".join(map(lambda x: num2str(x), l_pred))
trigram_string = "\t".join(map(lambda x: num2str(x), [trigram, cf, df, ps]))
s_output.write("%s\t%s\t%s\n" % (trigram_string, pred_string, bigram_str))
s_summary.write("%s\n" % "\t".join(map(lambda x: num2str(x), self.d_trigram2summary[trigram])))
s_output.close()
s_summary.close()
def load_trigram(self,trigram, corpus_freq, doc_freq, pos_sig, verbose_p=False):
self.d_trigram2corpus_freq[trigram] = corpus_freq
self.d_trigram2doc_freq[trigram] = doc_freq
self.d_trigram2pos_sig[trigram] = pos_sig
# bracketing predictions based on Nakov thesis p. 49
# given a triple (string), predict bracketing based on several association models
ABC = trigram.split(" ")
AB = tuple( ABC[0:2] )
BC = tuple( ABC[1:3] )
AC = tuple( [ABC[0], ABC[2] ] )
#print "phrase: %s, AB: %s, BC: %s, AC: %s" % (ABC, AB, BC, AC)
# compute assoc measures for AB, BC, AC
fr_AB = self.ncassoc.bg2freq(AB)
fr_BC = self.ncassoc.bg2freq(BC)
fr_AC = self.ncassoc.bg2freq(AC)
"""
cp_AB = self.compute_cprob(AB)
cp_BC = self.compute_cprob(BC)
cp_AC = self.compute_cprob(AC)
npmi_AB = self.compute_mi(AB)[1]
npmi_BC = self.compute_mi(BC)[1]
npmi_AC = self.compute_mi(AC)[1]
x2_AB = self.compute_chi2(AB)
x2_BC = self.compute_chi2(BC)
x2_AC = self.compute_chi2(AC)
"""
cp_AB = self.ncassoc.bg2cprob(AB)
cp_BC = self.ncassoc.bg2cprob(BC)
cp_AC = self.ncassoc.bg2cprob(AC)
npmi_AB = self.ncassoc.bg2npmi(AB)
npmi_BC = self.ncassoc.bg2npmi(BC)
npmi_AC = self.ncassoc.bg2npmi(AC)
x2_AB = self.ncassoc.bg2chi2(AB)
x2_BC = self.ncassoc.bg2chi2(BC)
x2_AC = self.ncassoc.bg2chi2(AC)
# save bigram data as list and string
l_bigram_data = [fr_AB, fr_BC, fr_AC, cp_AB, cp_BC, cp_AC, npmi_AB, npmi_BC, npmi_AC, x2_AB, x2_BC, x2_AC]
str_bigram_data = "\t".join(map(lambda x: str(x), l_bigram_data))
self.d_trigram2bigram_data[trigram] = l_bigram_data
if verbose_p:
print "for phrase: %s" % trigram
print "[load_trigram]bigram_data: %s" % str_bigram_data
#print "fr_AB\tfr_BC\tfr_AC\tcp_AB\tcp_BC\tcp_AC\tnpmi_AB\tnpmi_BC\tnpmi_AC\tx2_AB\tx2_BC\tx2_AC"
#print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (fr_AB, fr_BC, fr_AC, cp_AB, cp_BC, cp_AC, npmi_AB, npmi_BC, npmi_AC, x2_AB, x2_BC, x2_AC)
# set default predictions to "L"
adj_fr = "L"
adj_cp = "L"
adj_npmi = "L"
adj_x2 = "L"
dep_fr = "L"
dep_cp = "L"
dep_npmi = "L"
dep_x2 = "L"
# for adjacency model, we compare AB to BC
if fr_BC > fr_AB:
adj_fr = "R"
elif fr_BC == fr_AB:
adj_fr = "U"
if cp_BC > cp_AB:
adj_cp = "R"
elif cp_BC == cp_AB:
adj_cp = "U"
if npmi_BC > npmi_AB:
adj_npmi = "R"
elif npmi_BC == npmi_AB:
adj_npmi = "U"
if x2_BC > x2_AB:
adj_x2 = "R"
elif x2_BC == x2_AB:
adj_x2 = "U"
# for dependencency model, we compare AC to AB
if fr_AC > fr_AB:
dep_fr = "R"
elif fr_AC == fr_AB:
dep_fr = "U"
if cp_AC > cp_AB:
dep_cp = "R"
elif cp_AC == cp_AB:
dep_cp = "U"
if npmi_AC > npmi_AB:
dep_npmi = "R"
elif npmi_AC == npmi_AB:
dep_npmi = "U"
if x2_AC > x2_AB:
dep_x2 = "R"
elif x2_AC == x2_AB:
dep_x2 = "U"
# a shortened set of useful predictions (to compare frequency based adj/dep, and chi2-based)
# a field is created of the form fAD xAD where A and D are the respective predictions of adj and dep models.
pred_summary = "".join(["f", adj_fr, dep_fr, " x", adj_x2, dep_x2])
l_summary = [trigram, pos_sig, doc_freq, fr_AB, fr_BC, fr_AC, pred_summary]
self.d_trigram2summary[trigram] = l_summary
l_prediction = [adj_fr, adj_cp, adj_npmi, adj_x2, dep_fr, dep_cp, dep_npmi, dep_x2]
self.d_trigram2predictions[trigram] = l_prediction
prediction_string = "\t".join([trigram, pos_sig, str(corpus_freq), str(doc_freq), str(fr_AB), str(fr_BC), adj_fr, adj_cp, adj_npmi, adj_x2, dep_fr, dep_cp, dep_npmi, dep_x2])
#print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % ...
return([prediction_string, l_prediction])
"""
# pmi.run_mi()
def run_mi():
mi = MI()
#mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.inst.filt.su.f1.uc1.nr.1k.site")
#mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.inst.filt.su.f1.uc1.nr.1k")
#mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.inst.filt.su.f1.uc1.nr.10k")
# for 2003
mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.inst.filt.su.f1.uc1.nr")
mi.dump_mi("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.mi")
# for 1997
# full set of bigrams:
mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.su.f1.uc1.nr")
mi.dump_mi("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.mi")
# subset for testing:
mi.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.su.f1.uc1.nr.10k")
mi.dump_mi("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.10k.mi")
"""
# l_assoc97 = pmi.run_assoc97()
# NOTE: Make sure each year uses a separate class instance or they will accumulate data from multiple years!
def run_assoc97():
#"""
# for 1997
# full set of bigrams:
assoc97 = NCAssoc()
#assoc.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.su.f1.uc1.nr")
assoc97.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.freq")
assoc97.compute_assoc()
#assoc97.dump_assoc("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.assoc")
tg97 = Trigram(assoc97)
tg97.load_trigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.inst.filt.freq")
tg97.dump_trigram("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.pred")
return([assoc97, tg97])
#"""
# l_assoc03 = pmi.run_assoc03()
# NOTE: Make sure each year uses a separate class instance or they will accumulate data from multiple years!
def run_assoc03():
# for 2003
assoc03 = NCAssoc()
assoc03.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.inst.filt.freq")
assoc03.compute_assoc()
#assoc03.dump_assoc("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/bigrams.assoc")
tg03 = Trigram(assoc03)
tg03.load_trigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/trigrams.inst.filt.freq")
tg03.dump_trigram("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/trigrams.pred")
return([assoc03, tg03])
"""
# ass = pmi.test_assoc()
def test_assoc():
# subset for testing:
assoc = NCAssoc()
assoc.load(("cell", "line"), 2346)
assoc.load(("human", "cell"), 702)
assoc.load(("stem", "cell"), 472)
assoc.load(("human", "line"), 6)
#assoc.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.su.f1.uc1.nr.10k")
assoc.compute_assoc()
assoc.dump_assoc("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.test.assoc")
tg = Trigram(assoc)
tg.load_trigram("human cell line", 357, 234, "JNN")
tg.load_trigram("stem cell line", 6, 6, "NNN")
tg.dump_trigram("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.test.pred")
return(assoc)
"""
# test loading trigrams individually using load_trigram.
# cass = pmi.test_assoc_cell()
# cat bigrams.inst.filt.freq | grep cell > bigrams.inst.filt.freq.cell
def test_assoc_cell():
assoc = NCAssoc()
assoc.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.freq.cell")
assoc.compute_assoc()
assoc.dump_assoc("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.assoc.cell")
tg = Trigram(assoc)
#tg.load_trigram_file("trigrams.inst.filt.freq.cell")
tg.load_trigram("human cell line", 357, 234, "JNN")
tg.load_trigram("stem cell line", 6, 6, "NNN")
tg.load_trigram("ceramide treated cell", 2, 2, "NJN")
tg.load_trigram("cell structure test", 1, 1, "NNN")
tg.load_trigram("cerebellar purkinje cell", 13, 13, "NNN")
tg.dump_trigram("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.test.cell.pred")
return(assoc)
# l_cass = pmi.test_assoc_cell_file()
# cat bigrams.inst.filt.freq | grep cell > bigrams.inst.filt.freq.cell
# cat trigrams.inst.filt.freq | grep " cell" > trigrams.inst.filt.freq.cell
# test loading from a trigram file
def test_assoc_cell_file():
assoc = NCAssoc()
assoc.load_bigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.inst.filt.freq.cell")
assoc.compute_assoc()
assoc.dump_assoc("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/bigrams.assoc.cell")
tg = Trigram(assoc)
tg.load_trigram_file("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.inst.filt.freq.cell")
tg.dump_trigram("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.test.cell.pred")
return([assoc, tg])
# track freq diff
# Z1 means that prediction changed, Z0 means no change
# SP1G4 started at freq 1 and grew from 5 to 9
# SP1G9 started at freq 1 and grew to 10
# SP1L5 started at freq 1 and grew less than 5
# SP11 stayed at 1
# SP grew in doc freq
# SN shrank
# SE (equal) no change
# output: term pos f1,diff b1f1,diff b2f1,diff b3f1,diff fAD xAD gAD yAD (Z1|Z0) (S1G5,S1G10,S+,S-)
def compare_pred_by_time(pred_file1, pred_file2, out_file):
s_pred1 = codecs.open(pred_file1, encoding='utf-8')
s_pred2 = codecs.open(pred_file2, encoding='utf-8')
s_out = codecs.open(out_file, "w", encoding='utf-8')
# map trigram to pred1 list
d_tg2pred1 = {}
# store the first set of predictions by trigram
for line in s_pred1:
line = line.strip()
l_line = line.split("\t")
trigram = l_line[0]
d_tg2pred1[trigram] = l_line
for line in s_pred2:
line = line.strip()
l_pred2 = line.split("\t")
trigram = l_pred2[0]
# create an output line if the trigram matches one in pred1 data
if trigram in d_tg2pred1:
l_pred1 = d_tg2pred1[trigram]
# extract elements from the pred1 list
pos = l_pred1[1]
tf1 = int(l_pred1[2])
# bigram freq (AB, BC, AC)
AB1 = int(l_pred1[3])
BC1 = int(l_pred1[4])
AC1 = int(l_pred1[5])
pred1 = l_pred1[6]
# extract equivalent elements from pred2 (l_pred2)
tf2 = int(l_pred2[2])
# bigram freq (AB, BC, AC)
AB2 = int(l_pred2[3])
BC2 = int(l_pred2[4])
AC2 = int(l_pred2[5])
pred2 = l_pred2[6]
# compute prediction differences
# pred field is of the form: fLU xLR
# f = freq, x = chi2
# {RLU} are adjcency followed by dependency
# Hence we can use indices into the string to pull out specific values
# 1: fr adj
# 2: fr dep
# 5: x2 adj
# 6: x2 dep
xd = "xd" + pred1[6] + pred2[6]
xa = "xa" + pred1[5] + pred2[5]
fd = "fd" + pred1[2] + pred2[2]
fa = "fa" + pred1[1] + pred2[1]
# compute their frequency differences
AB_diff = AB2 - AB1
BC_diff = BC2 - BC1
AC_diff = AC2 - AC1
# set growth value to one of S1G4, S1G9, S1L5, SP, SN, SE
# default growth to SX (unknown)
# S1 means doc freq was 1 at start time.
# SP,N,E indicate that doc freq at start exceeded 1 and growth was positve, negative, or none (equal)
growth = "SX"
diff = tf2 - tf1
if tf1 == 1:
if diff > 9:
growth = "SP1G9"
elif diff > 4:
growth = "SP1G4"
elif diff < 5:
growth = "SP1L5"
if diff == 0:
growth = "SP10"
else:
# tf1 is not 1
if diff > 0:
growth = "SP"
elif diff < 0:
growth = "SN"
elif diff == 0:
growth = "SE"
else:
print "[compare_pred_by_time]ERROR: growth value unknown for %s" % l_line
if pred1 == pred2:
pred_diff = "PS"
else:
pred_diff = "PD"
# replace the key initials in pred2 f=>g, x=y
# to make them easier to extract using grep
new_pred2 = pred2.replace("f", "g").replace("x", "y")
# append the fields to l_output, converting them to string
l_output = [trigram, pos, str(tf1), str(AB1), str(BC1), str(AC1), growth, str(diff), str(AB_diff), str(BC_diff), str(AC_diff), pred1, new_pred2, pred_diff, fa, xa, fd, xd
]
output_string = "\t".join(l_output)
s_out.write("%s\n" % output_string)
s_pred1.close()
s_pred2.close()
s_out.close()
# Take two years of data and compare trigram parameters and predictions
# pmi.run_compare()
def run_compare():
pred_file1 = "/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.pred.sum"
pred_file2 = "/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_2003/trigrams.pred.sum"
out_file = "/home/j/anick/patent-classifier/ontology/roles/data/nc/eval/pred_bio_1997_2003.diff"
compare_pred_by_time(pred_file1, pred_file2, out_file)
# extract bigrams of trigrams from index, given trigram and doc_id
# r = es_np_query.qmamf(l_query_must=[["spv", "increase"], ["sp", "cost ]"] ],l_fields=["loc", "cphr", "section"], query_type="search", index_name="i_bio_1997", doc_type="np")
# find whether a document containing a trigram contains any component bigrams.
# pmi.doc_bigrams("plant variety protection", "US5880348A", "i_bio2_1997")
def doc_bigrams(trigram, doc_id, index):
# split trigram into its 3 bigrams
ABC = trigram.split(" ")
AB = " ".join( ABC[0:2] )
BC = " ".join( ABC[1:3] )
AC = " ".join( [ABC[0], ABC[2] ] )
for bigram in [AB, BC, AC]:
sp_pattern = es_np_query.phr2sp(bigram, phr_subset="f")
r = es_np_query.qmamf(l_query_must=[ ["sp", sp_pattern], ["doc_id", doc_id] ], l_fields=["loc", "cphr", "section"], query_type="search", index_name=index, doc_type="np")
print "bigram: %s, res: %s" % (bigram, r)
# pmi.pred_cprobs("/home/j/anick/patent-classifier/ontology/roles/data/nc/bio_1997/trigrams.pred.sum")
# example input line: pooled peak fraction JNN 3 5 89 104 fRR xRR
def pred_cprobs(sum_file):
cprob_file = sum_file + ".cprob"
s_sum = codecs.open(sum_file, encoding='utf-8')
s_cprob = codecs.open(cprob_file, "w", encoding='utf-8')
# count the number of different phrases containing term as word1,2,3
# key is tuple of word, word_position (1,2,3) and initial pos (N, J)
d_w2freq = defaultdict(int)
# key is a tuple of word, word_position (1,2,3) initial pos and prediction where prediction is one of (fXX, xXX)
d_w_pred2freq = defaultdict(int)
# key is initial_POS and pred (fXX or xXX)
d_pred2freq = defaultdict(int)
for line in s_sum:
line = line.strip()
l_line = line.split("\t")
trigram = l_line[0]
initial_pos = l_line[1][0]
trigram_freq = int(l_line[2])
(fpred, xpred) = l_line[6].split(" ")
l_words = trigram.split(" ")
# update dictionaries
for word_index in [0, 1, 2]:
word = l_words[word_index]
# key is tuple of word, word_index (0,1,2) and initial pos (N, J)
d_w2freq[(word, word_index, initial_pos)] += 1
# add prediction to key
# for frequency based prediction
d_w_pred2freq[(word, word_index, initial_pos, fpred)] += 1
# for chi2 based prediction
d_w_pred2freq[(word, word_index, initial_pos, xpred)] += 1
# count number of predictions given the initial_pos and pred
d_pred2freq[(initial_pos, fpred)] += 1
d_pred2freq[(initial_pos, xpred)] += 1
# generate output (prob prediction given word index and initial pos of trigram
for key in d_w_pred2freq.keys():
word = key[0]
word_index = key[1]
initial_pos = key[2]
pred = key[3]
word_freq = d_w2freq[(word, word_index, initial_pos)]
w_pred_freq = d_w_pred2freq[key]
cprob = w_pred_freq / float(word_freq)
output_list = [word, "P" + str(word_index), initial_pos, pred, str(w_pred_freq), str(word_freq), str(cprob)]
output_string = "\t".join(output_list)
s_cprob.write("%s\n" % output_string)
s_sum.close()
s_cprob.close()