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amazon_movie_drift.py
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import os
import sys
import pickle
from datetime import datetime, timedelta
import time
import random
random.seed(42)
import re
import yaml
mode = "bert_768"
#mode = "bow_50"
#mode = "bow_768"
#mode = "dummy"
num_samples = 2000 # Will be taken from each score class, e.g. 2,000*5=10,000 samples overall
year_min = 2011 # Minimum year to extract data from
year_max = 2012 # Maximum year to extract data from
# amazon_raw_file generated by amazon_movie_sorter.py
# gensim_model_files available at https://hobbitdata.informatik.uni-leipzig.de/EML4U/2021-05-17-Amazon-Doc2Vec/
# generated by word2vec/doc2vec.py
# embeddings_file is to be generated here
# sample_file also generated here, ensures same data for each model
config = yaml.safe_load(open("config.yaml", 'r'))
amazon_raw_file = os.path.join(config["AMAZON_MOVIE_REVIEWS_DIRECTORY"], "amazon_raw.pickle")
gensim_model_50_file = os.path.join(config["GENSIM_MODEL_DIRECTORY"], "amazonreviews_d.model")
gensim_model_768_file = os.path.join(config["GENSIM_MODEL_DIRECTORY"], "amazonreviews_e.model")
sample_file = os.path.join(config["AMAZON_MOVIE_REVIEWS_DIRECTORY"], "samples_" + str(num_samples*5) + "_" + str(year_min) + "-" + str(year_max) + ".pickle")
embeddings_file = os.path.join(config["EMBEDDINGS_DIRECTORY"], "amazon_drift_" + mode + ".pickle")
#target_percentages = [0.005, 0.01, 0.02, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0, 4.0, 10.0, 20.0, 50.0, 100.0]
target_percentages = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1] # From every 40th word to every second word
negative_words = ['waste', 'unwatchable', 'stinks']
if(True): # ratio at least 2.0
negative_words.extend(['atrocious', 'yawn', 'ugh', 'abomination', 'stupidest', 'garbage', 'laughably', 'worst', 'rubbish', 'defective', 'incoherent', 'ripoff', 'unconvincing', 'awful', 'dud', 'wasted', 'abysmal', 'travesty', 'wasting'])
if(False): # ratio at least 1.6
negative_words.extend(['poorly', 'disgrace', 'lame', 'insulting', 'lousy', 'disapointment', 'pointless', 'horrible', 'insult', 'laughable', 'terrible', 'moronic'])
if(False): # ratio at least 1.5
negative_words.extend(['sucks', 'idiotic', 'boycott', 'froze', 'nauseating', 'worthless', 'shoddy'])
if os.path.isfile(embeddings_file): # Do not overwrite
print("Embeddings file already exists, exiting.", embeddings_file)
exit()
time_begin = time.time()
print(datetime.fromtimestamp(time_begin))
# Configure model to use by mode
print("mode", mode)
print("amazon_raw_file", amazon_raw_file)
print("embeddings_file", embeddings_file)
print("sample_file", sample_file)
if(mode == "bert_768"):
from embedding import BertHuggingface
bert = BertHuggingface(8, batch_size=8)
embed = bert.embed
elif(mode == "bow_50"):
print("gensim_model_50_file", gensim_model_50_file)
from word2vec.Word2Vec import Word2Vec
word2vec = Word2Vec(gensim_model_50_file)
word2vec.prepare()
embed = word2vec.embed
elif(mode == "bow_768"):
print("gensim_model_768_file", gensim_model_768_file)
from word2vec.Word2Vec import Word2Vec
word2vec = Word2Vec(gensim_model_768_file)
word2vec.prepare()
embed = word2vec.embed
elif(mode == "dummy"):
print("Skipping model")
def justpass(x):
pass
embed = justpass
else:
raise ValueError("Unknown mode " + mode)
# Load data
print("Loading data")
if os.path.isfile(sample_file):
with open(sample_file, 'rb') as handle:
original_data = pickle.load(handle)
drift_data = pickle.load(handle)
train_data = pickle.load(handle)
print("Loaded", len(drift_data), "|", len(original_data), "|", len(train_data), "samples")
else:
docs_in_years = {}
with open(amazon_raw_file, 'rb') as handle:
texts, keys = pickle.load(handle)
for i in range(len(keys)):
keys[i][1] -= 1 # fix class names from 1..5 to 0..4 for easier 1-hot encoding
docs_in_years[keys[i][-2].year] = docs_in_years.get(keys[i][-2].year , 0) + 1
print("Docs in year overview:", docs_in_years)
print("Example keys", keys[0])
# Gather amazon reviews of year range only
# e.g. classes[2] = [..., ("text", ['7/15', 2, datetime.datetime(1997, 12, 19, 1, 0), 39862]), ...]
classes = [[] for x in range(5)]
for i in range(len(keys)):
if(keys[i][-2].year >= year_min and keys[i][-2].year <= year_max):
classes[keys[i][1]].append((texts[i],keys[i]))
# Randomize order and get samples
# Same amount for original data and drift data
for i in range(len(classes)):
random.shuffle(classes[i])
original_data = []
drift_data = []
train_data = []
for i in range(len(classes)):
original_data.extend(classes[i][:num_samples])
drift_data.extend(classes[i][num_samples:2*num_samples])
train_data.extend(classes[i][2*num_samples:3*num_samples])
#sample_data = {'original_data': original_data, 'drift_data': drift_data}
with open(sample_file, 'wb') as handle:
pickle.dump(original_data, handle)
pickle.dump(drift_data, handle)
pickle.dump(train_data, handle)
print("Created", len(drift_data), "|", len(original_data), "|", len(train_data), "samples")
# Injection
# 0.5 means insertion of 1 words in 50 percent of texts
# 2.0 means insertion of 2 words in each text
indices = []
more_than_one = 0
def inject(texts, percent):
""" texts will be injected so that they then are at a injection rate of #percent
"""
global indices
global more_than_one
num = int(percent*len(texts)) - (more_than_one*len(texts) + len(indices))
for _ in range(num):
# find a text which hasnt been injected yet
not_yet_injected = [x for x in range(len(texts)) if x not in indices]
if not not_yet_injected:
indices = []
more_than_one += 1
not_yet_injected = [x for x in range(len(texts))]
injection_index = random.choice(not_yet_injected)
indices.append(injection_index)
# actual injection process
spaces = [m.start() for m in re.finditer(' ', texts[injection_index])]
if spaces:
injection_point = random.choice(spaces)
else:
injection_point = len(texts[injection_index])
t = texts[injection_index]
t = t[:injection_point] + ' ' + random.choice(negative_words) + t[injection_point:]
texts[injection_index] = t
return texts
if(False): # Injection test
texts = ["0 0 0 0","1 1 1 1","2 2 2 2","3 3 3 3","4 4 4 4","5 5 5 5","6 6 6 6","7 7 7 7","8 8 8 8","9 9 9 9"]
print(inject(texts, 0.5))
print(inject(texts, 2))
exit()
# Inject percentages
drifted_embeddings = []
drifted_texts, drifted_keys = [list(t) for t in zip(*drift_data)]
drifted_embeddings.append(embed(drifted_texts))
for num, perc in enumerate(target_percentages):
print("Incecting", num, "|", perc)
drifted_texts = inject(drifted_texts, perc)
drifted_embeddings.append(embed(drifted_texts))
# Save
original_texts, original_keys = [list(t) for t in zip(*original_data)]
original_embs = embed(original_texts)
train_texts, train_keys = [list(t) for t in zip(*train_data)]
train_embs = embed(train_texts)
dump_data = {'orig': (original_embs, original_keys), 'drifted': (drifted_embeddings, drifted_keys), 'train': (train_embs, train_keys)}
with open(embeddings_file, 'wb') as handle:
pickle.dump(dump_data, handle)
# Stats
time_end = time.time()
runtime = time_end - time_begin;
print("Runtime: %s minutes" % (runtime/60))
# Example
if(True):
with open(embeddings_file, 'rb') as handle:
data = pickle.load(handle)
print(type(data), len(data))
for key, value in data.items() :
print (key, type(value), len(value))
for i in range(len(value)) :
print (value[i][0])
print()