def _parse_unigrams(self, path):
print('parsing unigram data...')
data = data2numpy(path)
for row in data:
word = row[0].lower()
unigram = float(row[1])
self.unigram[word] = unigram
print('done')
return
def __init__(self,
relatedness_paths,
homophone_unigram_paths,
puns_data_path,
self_relatedness=13.0):
self.self_relatedness = self_relatedness
self.use_trigrams = True
self.unigram = {}
self.trigram = {}
self.homo_unigram = {}
self.relatedness = {}
self.word_vector = {}
self.get_idx = map_puntypeID_to_idx(data2numpy(puns_data_path))
self._parse_relatedness(data2numpy(relatedness_paths[0]), 'near')
self._parse_relatedness(data2numpy(relatedness_paths[1]), 'identical')
self._parse_unigrams(data2numpy(homophone_unigram_paths[0]), 'near')
self._parse_unigrams(data2numpy(homophone_unigram_paths[1]),
'identical')
print('loaded data model v1')
def _parse_relatedness(self, path):
print('parsing relatedness data...')
data = data2numpy(path)
for row in data:
words = [row[0].lower(), row[1].lower()]
words.sort()
words = tuple(words)
relatedness = float(row[2])
self.relatedness[words] = relatedness
print('done')
return
def _parse_trigrams(self, path):
print('parsing trigram data...')
data = data2numpy(path)
for row in data:
idx = int(row[0])
word = row[1].lower()
m1_trigram = float(row[2])
m2_trigram = float(row[3])
# save trigram data
if idx not in self.trigram:
self.trigram[idx] = {}
self.word_vector[idx] = []
self.trigram[idx]['m1'] = []
self.trigram[idx]['m2'] = []
self.trigram[idx]['m1'].append(m1_trigram)
self.trigram[idx]['m2'].append(m2_trigram)
self.word_vector[idx].append(word)
print('done')
return
#!/usr/bin/env python3
from utils import data2numpy
near_unigram_datapath = './homophones_unigram_near.csv'
identical_unigram_datapath = './homophones_unigram_identical.csv'
# get the whole homophone unigram data in a single list
unigram_data = []
near_unigram_data = data2numpy(near_unigram_datapath)
for row in near_unigram_data:
unigram_data.append(row)
data = data2numpy(identical_unigram_datapath)
for row in data:
unigram_data.append(row)
# make word to unigram mapping
# ideally, it should be a one to one mapping
unigram = {}
for row in unigram_data:
w1 = row[2].lower()
w2 = row[3].lower()
# unigram probabilities
p_w1 = float(row[4])
p_w2 = float(row[5])
if w1 not in unigram:
unigram[w1] = []
if w2 not in unigram:
unigram[w2] = []
# log mutliple unigram values for a single key, if any
"""
This script merges two model output files for near and identical type puns
which are computed by author's original script 'computeMeasures.py'. Measures
are saved in a new csv file.
"""
from utils import data2numpy, map_puntypeID_to_idx, save_results
if __name__ == '__main__':
jokes = data2numpy("../../data/data-agg.csv")
get_idx = map_puntypeID_to_idx(jokes)
jokes_data = {}
# fill dictionary with model output on identical and near puns
jokes_data = save_results('../ModelOutputs/near_trigram_13_0.csv',
jokes_data, get_idx)
jokes_data = save_results('../ModelOutputs/identical_trigram_13_0.csv',
jokes_data, get_idx)
# write model outputs in a new csv file
output_path = "../ModelOutputs/data.csv"
f_v0 = open(output_path, "wr")
f_v0.write(
"idx,sentenceID,punType,sentenceType,ambiguity,distinctiveness," +
"sentence\n")
# parse measures from original data sheet pun-model/data/data-agg.csv and
# save it in the same format as data.csv for comparing model results
f_original = open("../ModelOutputs/data-agg-measures.csv", "wr")
f_original.write("idx,sentenceID,punType,sentenceType,ambiguity," +
"distinctiveness,sentence\n")
for idx in range(len(jokes_data)):
"""
Fits a linear regression model over the computed measures - ambiguity and
distinctiveness. Groundtruth funniness ratings are obtained from
pun-model/data/data-agg.csv. Regression model summary is printed on console.
"""
import statsmodels.api as sm
import numpy as np
from utils import data2numpy
if __name__ == '__main__':
# read groundtruth funniness ratings
jokes_data_path = "../../data/data-agg.csv"
jokes_data = data2numpy(jokes_data_path)
gt = np.array(jokes_data[:, -1]).astype(float)
# read ambiguity and distinctiveness measures computed by model
model_output = data2numpy("../results/data.csv")
amb = np.array(model_output[:, 4]).astype(float)
dist = np.array(model_output[:, 5]).astype(float)
# fit linear regression model
X = np.stack([amb, dist]).transpose()
y = gt
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
"""
Fits a linear regression model over the computed measures - ambiguity and
distinctiveness. Groundtruth funniness ratings are obtained from
pun-model/data/data-agg.csv. Regression model summary is printed on console.
"""
import statsmodels.api as sm
import numpy as np
from utils import data2numpy
if __name__ == '__main__':
# read ground truth funniness ratings
jokes_data_path = "../../data/data-agg.csv"
jokes_data = data2numpy(jokes_data_path)
gt = np.array(jokes_data[:, -1]).astype(float)
# read ambiguity and distinctiveness measures computed by model
model_output = data2numpy("../ModelOutputs/data.csv")
amb = np.array(model_output[:, 4]).astype(float)
dist = np.array(model_output[:, 5]).astype(float)
# fit linear regression model
X = np.stack([amb, dist]).transpose()
y = gt
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
#!/usr/bin/env python3
from utils import data2numpy, map_puntypeID_to_idx
relatedness_near_datapath = ("./wordPair_relatedness_" +
"smoothedTrigrams_near.csv")
relatedness_identical_datapath = ("./wordPair_relatedness_" +
"smoothedTrigrams_identical.csv")
# get m1 and m2 (two meanings/interpretations) for each sentence in the dataset
meanings = {}
puns_datapath = '../../data/data-agg.csv'
puns_data = data2numpy(puns_datapath)
for i, row in enumerate(puns_data):
meanings[i] = [row[-3], row[-2]]
# map puntype (near/identical) and puntypeID to index in puns dataset
# for example, m['near'][1] will yield index of the pun with 'near' homophone
# and having 'near' sentence ID = 1 in puns dataset [data-agg.csv]
get_idx = map_puntypeID_to_idx(data2numpy(puns_datapath))
# clean up relatedness data
relatedness = {}
data = data2numpy(relatedness_near_datapath)
for row in data:
id = int(row[0])
idx = get_idx['near'][id]
m1 = meanings[idx][0]
m2 = meanings[idx][1]
word = row[3]
m1_relatedness = float(row[4])
m2_relatedness = float(row[5])
