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2021-10-04 18:34:15 +08:00
# Marcus Vinicius Sousa Leite de Carvalho
# marcus.decarvalho@ntu.edu.sg
# ivsucram@gmail.com
#
# NANYANG TECHNOLOGICAL UNIVERSITY - NTUITIVE PTE LTD Dual License Agreement
# Non-Commercial Use Only
# This NTUITIVE License Agreement, including all exhibits ("NTUITIVE-LA") is a legal agreement between you and NTUITIVE (or “we”) located at 71 Nanyang Drive, NTU Innovation Centre, #01-109, Singapore 637722, a wholly owned subsidiary of Nanyang Technological University (“NTU”) for the software or data identified above, which may include source code, and any associated materials, text or speech files, associated media and "online" or electronic documentation and any updates we provide in our discretion (together, the "Software").
#
# By installing, copying, or otherwise using this Software, found at https://github.com/Ivsucram/ATL_Matlab, you agree to be bound by the terms of this NTUITIVE-LA. If you do not agree, do not install copy or use the Software. The Software is protected by copyright and other intellectual property laws and is licensed, not sold. If you wish to obtain a commercial royalty bearing license to this software please contact us at marcus.decarvalho@ntu.edu.sg.
#
# SCOPE OF RIGHTS:
# You may use, copy, reproduce, and distribute this Software for any non-commercial purpose, subject to the restrictions in this NTUITIVE-LA. Some purposes which can be non-commercial are teaching, academic research, public demonstrations and personal experimentation. You may also distribute this Software with books or other teaching materials, or publish the Software on websites, that are intended to teach the use of the Software for academic or other non-commercial purposes.
# You may not use or distribute this Software or any derivative works in any form for commercial purposes. Examples of commercial purposes would be running business operations, licensing, leasing, or selling the Software, distributing the Software for use with commercial products, using the Software in the creation or use of commercial products or any other activity which purpose is to procure a commercial gain to you or others.
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#
# Copyright (c) NTUITIVE. All rights reserved.
import numpy as np
import pandas
import pandas as pd
import torch
import torchvision
import ssl
import gzip
import json
from tqdm import tqdm
from torchvision.datasets.utils import download_url
from MySingletons import MyWord2Vec
from nltk.tokenize import TweetTokenizer
import os
import tarfile
from lxml import etree
class MyCustomBikeSharingDataLoader(torch.utils.data.Dataset):
path = 'data/BikeSharing/'
df = None
@property
def datasets(self):
return self.df
def __init__(self, london_or_washignton : str = 'london'):
if london_or_washignton.lower() == 'london':
self.base_files = ['london_merged.csv']
self.base_url = 'https://www.kaggle.com/marklvl/bike-sharing-dataset'
elif london_or_washignton.lower() == 'washington':
self.base_files = ['hour.csv', 'day.csv']
self.base_url = 'https://www.kaggle.com/hmavrodiev/london-bike-sharing-dataset'
if not os.path.exists(self.path):
os.makedirs(self.path)
for file in self.base_files:
if os.path.isfile(f'{self.path}{file}'):
if self.df is None:
self.df = pandas.read_csv(f'{self.path}{file}')
else:
df = pandas.read_csv(f'{self.path}{file}')
self.df = pd.concat([self.df, df], sort=True)
else:
print(f'Please, manually download file {file} from url {self.base_url} and put it at path {self.path}')
exit()
if london_or_washignton.lower() == 'london':
self.df['demand'] = (self.df['cnt'] <= self.df['cnt'].median()).astype(int)
self.df.drop(columns=['timestamp', 'cnt'], inplace=True)
self.df = self.df[
['t1', 't2', 'hum', 'wind_speed', 'weather_code', 'is_holiday', 'is_weekend', 'season', 'demand']]
elif london_or_washignton.lower() == 'washington':
self.df['demand'] = (self.df['cnt'] <= self.df['cnt'].median()).astype(int)
self.df.drop(columns=['casual', 'dteday', 'holiday', 'hr', 'instant', 'mnth', 'registered','yr', 'cnt'],
inplace=True)
self.df.rename(
columns={'temp': 't1', 'atemp': 't2', 'windspeed': 'wind_speed', 'weathersit': 'weather_code',
'workingday': 'is_holiday', 'weekday': 'is_weekend'}, inplace=True)
self.df['is_weekend'] = ((self.df['is_weekend'] == 0) | (self.df['is_weekend'] == 6)).astype(int)
self.df['is_holiday'] = (self.df['is_holiday'] == 0).astype(int)
self.df['season'] = self.df['season'] - 1
self.df = self.df[
['t1', 't2', 'hum', 'wind_speed', 'weather_code', 'is_holiday', 'is_weekend', 'season', 'demand']]
self.normalize()
def __len__(self):
return len(self.df)
def __getitem__(self, idx: int):
item = self.df.iloc[idx]
if idx < len(self):
try:
return {'x': item.drop('demand').to_numpy(), 'y': item['demand']}
except:
return self.__getitem__(idx - 1)
else:
return None
def normalize(self, a: int = 0, b: int = 1):
assert a < b
for feature_name in self.df.drop('demand', axis=1).columns:
max_value = self.df[feature_name].max()
min_value = self.df[feature_name].min()
self.df[feature_name] = (b - a) * (self.df[feature_name] - min_value) / (max_value - min_value) + a
class MyCustomAmazonReviewDataLoader(torch.utils.data.Dataset):
base_url = 'http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/'
path = 'data/AmazonReview/'
df = None
@property
def datasets(self):
return self.df
def __init__(self, filename):
torchvision.datasets.utils.download_url(self.base_url + filename, self.path)
self.df = self.get_df(self.path + filename)
self.normalize()
def __len__(self):
return len(self.df)
def __getitem__(self, idx: int):
item = self.df.iloc[idx]
if idx < len(self):
try:
return {'x': item.drop('overall').to_numpy(), 'y': item['overall']}
except:
return self.__getitem__(idx - 1)
else:
return None
def normalize(self, a: int = 0, b: int = 1):
assert a < b
for feature_name in self.df.drop('overall', axis=1).columns:
max_value = self.df[feature_name].max()
min_value = self.df[feature_name].min()
self.df[feature_name] = (b - a) * (self.df[feature_name] - min_value) / (max_value - min_value) + a
@staticmethod
def parse(path):
g = gzip.open(path, 'r')
for l in g:
yield json.loads(l)
def get_df(self, path, high_bound=500000):
try:
print('Trying to load processed file %s.h5 from disc...' % path)
df = pd.read_hdf(path_or_buf=os.path.join(os.path.dirname(__file__), path + '.h5'),
key='df')
except:
print('Processed file does not exists')
print('Reading dataset into memory and applying Word2Vec...')
print('\nWe will save a maximum of half million samples because of memory constraints')
print('and because that is more than sufficient samples to test transfer learning models\n')
i = 0
df = {}
if path == 'data/AmazonReview/AMAZON_FASHION_5.json.gz':
total = 3176
elif path == 'data/AmazonReview/All_Beauty_5.json.gz':
total = 5269
elif path == 'data/AmazonReview/Appliances_5.json.gz':
total = 2277
elif path == 'data/AmazonReview/Arts_Crafts_and_Sewing_5.json.gz':
total = 494485
elif path == 'data/AmazonReview/Automotive_5.json.gz':
total = 1711519
elif path == 'data/AmazonReview/Books_5.json.gz':
total = 27164983
elif path == 'data/AmazonReview/CDs_and_Vinyl_5.json.gz':
total = 1443755
elif path == 'data/AmazonReview/Cell_Phones_and_Accessories_5.json.gz':
total = 1128437
elif path == 'data/AmazonReview/Clothing_Shoes_and_Jewelry_5.json.gz':
total = 11285464
elif path == 'data/AmazonReview/Digital_Music_5.json.gz':
total = 169781
elif path == 'data/AmazonReview/Electronics_5.json.gz':
total = 6739590
elif path == 'data/AmazonReview/Gift_Cards_5.json.gz':
total = 2972
elif path == 'data/AmazonReview/Grocery_and_Gourmet_Food_5.json.gz':
total = 1143860
elif path == 'data/AmazonReview/Home_and_Kitchen_5.json.gz':
total = 6898955
elif path == 'data/AmazonReview/Industrial_and_Scientific_5.json.gz':
total = 77071
elif path == 'data/AmazonReview/Kindle_Store_5.json.gz':
total = 2222983
elif path == 'data/AmazonReview/Luxury_Beauty_5.json.gz':
total = 34278
elif path == 'data/AmazonReview/Magazine_Subscriptions_5.json.gz':
total = 2375
elif path == 'data/AmazonReview/Movies_and_TV_5.json.gz':
total = 3410019
elif path == 'data/AmazonReview/Musical_Instruments_5.json.gz':
total = 231392
elif path == 'data/AmazonReview/Office_Products_5.json.gz':
total = 800357
elif path == 'data/AmazonReview/Patio_Lawn_and_Garden_5.json.gz':
total = 798415
elif path == 'data/AmazonReview/Pet_Supplies_5.json.gz':
total = 2098325
elif path == 'data/AmazonReview/Prime_Pantry_5.json.gz':
total = 137788
elif path == 'data/AmazonReview/Software_5.json.gz':
total = 12805
elif path == 'data/AmazonReview/Sports_and_Outdoors_5.json.gz':
total = 2839940
elif path == 'data/AmazonReview/Tools_and_Home_Improvement_5.json.gz':
total = 2070831
elif path == 'data/AmazonReview/Toys_and_Games_5.json.gz':
total = 1828971
elif path == 'data/AmazonReview/Video_Games_5.json.gz':
total = 497577
MyWord2Vec().get()
pbar = tqdm(unit=' samples', total=np.min([total, high_bound]))
tokenizer = TweetTokenizer()
for d in self.parse(path):
if i >= 500000:
break
try:
reviewText = d['reviewText']
try:
word_count = 0
vector = np.zeros(MyWord2Vec().get().vector_size)
for word in tokenizer.tokenize(reviewText):
try:
vector += MyWord2Vec().get()[word]
word_count += 1
except:
pass
if word_count > 1:
try:
overall = d['overall']
df[i] = {'overall': overall, 'reviewText': vector / word_count}
pbar.update(1)
i += 1
except:
pass
except:
pass
except:
pass
pbar.close()
print('Saving processed tokenized dataset in disc for future usage...')
df = pd.DataFrame.from_dict(df, orient='index')
df = pd.DataFrame([{x: y for x, y in enumerate(item)}
for item in df['reviewText'].values.tolist()]).assign(overall=df.overall.tolist())
df.to_hdf(path_or_buf=os.path.join(os.path.dirname(__file__), path + '.h5'),
key='df',
mode='w',
format='table',
complevel=9,
complib='bzip2')
df = pd.read_hdf(path_or_buf=os.path.join(os.path.dirname(__file__), path + '.h5'),
key='df')
return df
class MyCustomAmazonReviewNIPSDataLoader(torch.utils.data.Dataset):
dataset_url = 'https://www.cs.jhu.edu/~mdredze/datasets/sentiment/processed_stars.tar.gz'
path = 'data/AmazonReviewNIPS/'
compressed_filename = path + 'processed_stars.tar.gz'
books_file_path = path + 'processed_stars/books/all_balanced.review'
dvd_file_path = path + 'processed_stars/dvd/all_balanced.review'
electronics_file_path = path + 'processed_stars/electronics/all_balanced.review'
kitchen_file_path = path + 'processed_stars/kitchen/all_balanced.review'
@property
def datasets(self):
return self.df
def __init__(self, folder):
torchvision.datasets.utils.download_url(self.dataset_url, self.path)
tar = tarfile.open(self.compressed_filename)
tar.extractall(self.path)
tar.close()
if folder == 'books':
filename_path = self.books_file_path
elif folder == 'dvd':
filename_path = self.dvd_file_path
elif folder == 'electronics':
filename_path = self.electronics_file_path
elif folder == 'kitchen':
filename_path = self.kitchen_file_path
self.df = self.get_df(filename_path)
def __len__(self):
return len(self.df)
def __getitem__(self, idx: int):
item = self.df.iloc[idx]
if idx < len(self):
return {'x': item['x'], 'y': item['targets']}
else:
return None
def get_df(self, path):
try:
print('Trying to load processed file %s.h5 from disc...' % path)
df = pd.read_hdf(path_or_buf=path + '.h5',
key='df')
except:
print('Processed file does not exists')
print('Reading dataset into memory and applying Word2Vec...')
if path == self.books_file_path:
total = 5501
elif path == self.dvd_file_path:
total = 5518
elif path == self.electronics_file_path:
total = 5901
elif path == self.kitchen_file_path:
total = 5149
line_count = 0
df = {}
MyWord2Vec().get()
pbar = tqdm(unit=' samples', total=total)
for line in open(path, 'rb'):
word_count = 0
vector = np.zeros(MyWord2Vec().get().vector_size)
for word in line.decode('utf-8').split(' '):
x, y = word.split(':')
if x != '#label#':
for j in range(int(y)):
for xx in x.split('_'):
try:
vector += MyWord2Vec().get()[xx]
word_count += 1
except:
pass
else:
try:
df[line_count] = {'x': vector / word_count, 'targets': int(float(y.replace('\n', '')))}
except:
df[line_count] = {'x': vector / word_count, 'targets': int(float(y))}
line_count += 1
pbar.update(1)
pbar.close()
print('Saving processed tokenized dataset in disc for future usage...')
df = pd.DataFrame.from_dict(df, orient='index')
df.to_hdf(path_or_buf=path + '.h5',
key='df',
mode='w',
format='table',
complevel=9,
complib='bzip2')
df = pd.read_hdf(path_or_buf=path + '.h5',
key='df')
return df
class MyCustomNewsPopularityDataLoader(torch.utils.data.Dataset):
dataset_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00432/Data/News_Final.csv'
path = 'data/UCIMultiSourceNews/'
filename = 'News_Final.csv'
@property
def datasets(self):
return self.df
def __init__(self, topic: str, social_feed: str):
torchvision.datasets.utils.download_url(self.dataset_url, self.path)
path = (self.path + topic + '_' + social_feed + '.h5').lower()
try:
print('Trying to load processed file %s from disc...' % path)
self.df = pd.read_hdf(path_or_buf=path,
key='df')
except:
print('Processed file does not exists')
print('Reading dataset into memory and applying Word2Vec...')
self.df = {}
df = pd.read_csv(self.path + self.filename)
if social_feed == 'all':
df = df.loc[df['Topic'] == topic][['Title', 'Headline', 'Facebook', 'GooglePlus', 'LinkedIn']]
else:
df = df.loc[df['Topic'] == topic][['Title', 'Headline', social_feed]]
df['targets'] = df[df.columns[2:]].sum(axis=1)
df = df[['Title', 'Headline', 'targets']]
df.loc[df['targets'] <= 10, 'targets'] = 0
df.loc[df['targets'] > 10, 'targets'] = 1
df['fullText'] = df['Title'].astype(str) + ' ' + df['Headline'].astype(str)
tokenizer = TweetTokenizer()
MyWord2Vec().get()
sample_count = 0
pbar = tqdm(unit=' samples', total=len(df))
for _, row in df.iterrows():
word_counter = 0
vector = np.zeros(MyWord2Vec().get().vector_size)
try:
for word in tokenizer.tokenize(row['fullText']):
vector += MyWord2Vec().get()[word]
word_counter += 1
except:
pass
if word_counter > 0:
self.df[sample_count] = {'x': vector / word_counter, 'targets': int(row['targets'])}
sample_count += 1
pbar.update(1)
pbar.close()
print('Saving processed tokenized dataset in disc for future usage...')
self.df = pd.DataFrame.from_dict(self.df, orient='index')
self.df = pd.DataFrame([{x: y for x, y in enumerate(item)} for item in self.df['x'].values.tolist()],
index=self.df.index).assign(targets=self.df['targets'].tolist())
self.df.to_hdf(path_or_buf=path,
key='df',
mode='w',
format='table',
complevel=9,
complib='bzip2')
self.df = pd.read_hdf(path_or_buf=path,
key='df')
def __len__(self):
return len(self.df)
def __getitem__(self, idx: int):
item = self.df.iloc[idx]
if idx < len(self):
return {'x': item.drop('targets').to_numpy(), 'y': int(item['targets'])}
else:
return None
class MyCustomAmazonReviewACLDataLoader(torch.utils.data.Dataset):
dataset_url = 'https://www.cs.jhu.edu/~mdredze/datasets/sentiment/unprocessed.tar.gz'
path = 'data/AmazonReviewACL/'
compressed_filename = path + 'unprocessed.tar.gz'
apparel_file_path = path + 'sorted_data/apparel/all.review'
automotive_file_path = path + 'sorted_data/automotive/all.review'
baby_file_path = path + 'sorted_data/baby/all.review'
beauty_file_path = path + 'sorted_data/beauty/all.review'
books_file_path = path + 'sorted_data/books/all.review'
camera_photo_file_path = path + 'sorted_data/camera_&_photo/all.review'
cell_phones_service_file_path = path + 'sorted_data/cell_phones_&_service/all.review'
computer_video_games_file_path = path + 'sorted_data/computer_&_video_games/all.review'
dvd_file_path = path + 'sorted_data/dvd/all.review'
electronics_file_path = path + 'sorted_data/electronics/all.review'
gourmet_food_file_path = path + 'sorted_data/gourmet_food/all.review'
grocery_file_path = path + 'sorted_data/grocery/all.review'
health_personal_care_file_path = path + 'sorted_data/health_&_personal_care/all.review'
jewelry_watches_file_path = path + 'sorted_data/jewelry_&_watches/all.review'
kitchen_housewares_file_path = path + 'sorted_data/kitchen_&_housewares/all.review'
magazines_file_path = path + 'sorted_data/magazines/all.review'
music_file_path = path + 'sorted_data/music/all.review'
musical_instruments_file_path = path + 'sorted_data/musical_instruments/all.review'
office_products_file_path = path + 'sorted_data/office_products/all.review'
outdoor_living_file_path = path + 'sorted_data/outdoor_living/all.review'
software_file_path = path + 'sorted_data/software/all.review'
sports_outdoors_file_path = path + 'sorted_data/sports_&_outdoors/all.review'
tools_hardware_file_path = path + 'sorted_data/tools_&_hardware/all.review'
toys_games_file_path = path + 'sorted_data/toys_&_games/all.review'
video_file_path = path + 'sorted_data/video/all.review'
@property
def datasets(self):
return self.df
def __init__(self, folder):
torchvision.datasets.utils.download_url(self.dataset_url, self.path)
tar = tarfile.open(self.compressed_filename)
tar.extractall(self.path)
tar.close()
if folder == 'apparel': filename_path = self.apparel_file_path
if folder == 'automotive': filename_path = self.automotive_file_path
if folder == 'baby': filename_path = self.baby_file_path
if folder == 'beauty': filename_path = self.beauty_file_path
if folder == 'books': filename_path = self.books_file_path
if folder == 'camera_photo': filename_path = self.camera_photo_file_path
if folder == 'cell_phones_service': filename_path = self.cell_phones_service_file_path
if folder == 'computer_video_games': filename_path = self.computer_video_games_file_path
if folder == 'dvd': filename_path = self.dvd_file_path
if folder == 'electronics': filename_path = self.electronics_file_path
if folder == 'gourmet_food': filename_path = self.gourmet_food_file_path
if folder == 'grocery': filename_path = self.grocery_file_path
if folder == 'health_personal_care': filename_path = self.health_personal_care_file_path
if folder == 'jewelry_watches': filename_path = self.jewelry_watches_file_path
if folder == 'kitchen_housewares': filename_path = self.kitchen_housewares_file_path
if folder == 'magazines': filename_path = self.magazines_file_path
if folder == 'music': filename_path = self.music_file_path
if folder == 'musical_instruments': filename_path = self.musical_instruments_file_path
if folder == 'office_products': filename_path = self.office_products_file_path
if folder == 'outdoor_living': filename_path = self.outdoor_living_file_path
if folder == 'software': filename_path = self.software_file_path
if folder == 'sports_outdoors': filename_path = self.sports_outdoors_file_path
if folder == 'tools_hardware': filename_path = self.tools_hardware_file_path
if folder == 'toys_games': filename_path = self.toys_games_file_path
if folder == 'video': filename_path = self.video_file_path
self.df = self.get_df(filename_path)
def __len__(self):
return len(self.df)
def __getitem__(self, idx: int):
item = self.df.iloc[idx]
if idx < len(self):
return {'x': item['x'], 'y': item['targets']}
else:
return None
def get_df(self, path):
try:
os.remove(path + '.xml')
except:
pass
try:
print('Trying to load processed file %s.h5 from disc...' % path)
df = pd.read_hdf(path_or_buf=path + '.h5',
key='df')
except:
print('Processed file does not exists')
print('Reading dataset into memory and applying Word2Vec...')
with open(path + '.xml', 'w', encoding='utf-8-sig') as f:
f.write('<amazonreview>')
for line in open(path, 'rb'):
f.write(line.decode(encoding='utf-8-sig', errors='ignore'))
f.write('</amazonreview>')
parser = etree.XMLParser(recover=True)
with open(path + '.xml', 'r', encoding='utf-8-sig') as f:
contents = f.read()
tree = etree.fromstring(contents, parser=parser)
df = {}
tokenizer = TweetTokenizer()
MyWord2Vec().get()
line_count = 0
pbar = tqdm(unit=' samples', total=len(tree.findall('review')) - 1)
for review in tree.findall('review'):
word_count = 0
vector = np.zeros(MyWord2Vec().get().vector_size)
try:
for word in tokenizer.tokenize(review.find('review_text').text):
try:
vector += MyWord2Vec().get()[word]
word_count += 1
except:
pass
if word_count > 0:
try:
score = int(float(review.find('rating').text.replace('\n', '')))
if type(score) is int:
df[line_count] = {'x': vector / word_count, 'targets': score}
line_count += 1
pbar.update(1)
except:
pass
except:
pass
pbar.close()
print('Saving processed tokenized dataset in disc for future usage...')
df = pd.DataFrame.from_dict(df, orient='index')
df.to_hdf(path_or_buf=path + '.h5',
key='df',
mode='w',
format='table',
complevel=9,
complib='bzip2')
df = pd.read_hdf(path_or_buf=path + '.h5',
key='df')
try:
os.remove(path + '.xml')
except:
pass
return df
class MyCustomMNISTUSPSDataLoader(torch.utils.data.Dataset):
datasets = []
transforms = None
def __init__(self, datasets, transforms: torchvision.transforms = None):
self.datasets = datasets
self.transforms = transforms
def __len__(self):
return sum(len(d) for d in self.datasets)
def __getitem__(self, idx: int):
if torch.is_tensor(idx):
idx = idx.tolist()
offset = 0
dataset_idx = 0
sample = None
if idx < len(self):
while sample is None:
if idx < (offset + len(self.datasets[dataset_idx])):
sample = self.datasets[dataset_idx][idx - offset]
else:
offset += len(self.datasets[dataset_idx])
dataset_idx += 1
else:
return None
x = sample[0]
for transform in self.transforms:
x = transform(x)
return {'x': x, 'y': sample[1]}
class MyCustomCIFAR10STL10DataLoader(torch.utils.data.Dataset):
datasets = []
transforms = None
resnet = None
samples = None
def __init__(self, datasets, transforms: torchvision.transforms = None):
self.datasets = []
self.resnet = torchvision.models.resnet18(pretrained=True)
self.resnet.eval()
self.resnet.fc_backup = self.resnet.fc
self.resnet.fc = torch.nn.Sequential()
if isinstance(self, CIFAR10):
for dataset in datasets:
idx_to_delete = np.where(np.array([dataset.targets]) == 6)[1]
dataset.targets = list(np.delete(np.array(dataset.targets), idx_to_delete))
dataset.data = np.delete(dataset.data, idx_to_delete, 0)
self.datasets.append(dataset)
elif isinstance(self, STL10):
for dataset in datasets:
idx_to_delete = np.where(np.array([dataset.labels]) == 7)[1]
dataset.labels = list(np.delete(np.array(dataset.labels), idx_to_delete))
dataset.data = np.delete(dataset.data, idx_to_delete, 0)
self.datasets.append(dataset)
self.transforms = transforms
def __len__(self):
return sum(len(d) for d in self.datasets)
def __getitem__(self, idx: int):
if torch.is_tensor(idx):
idx = idx.tolist()
offset = 0
dataset_idx = 0
sample = None
if idx < len(self):
while sample is None:
if idx < (offset + len(self.datasets[dataset_idx])):
sample = self.datasets[dataset_idx][idx - offset]
else:
offset += len(self.datasets[dataset_idx])
dataset_idx += 1
else:
return None
x = sample[0]
for transform in self.transforms:
x = transform(x)
x = x.unsqueeze(0)
if torch.cuda.is_available():
x = x.to('cuda')
self.resnet.to('cuda')
if isinstance(self, CIFAR10):
if sample[1] == 0:
y = 0 # Airplane
elif sample[1] == 1:
y = 1 # Automobile
elif sample[1] == 2:
y = 2 # Bird
elif sample[1] == 3:
y = 3 # Cat
elif sample[1] == 4:
y = 4 # Deer
elif sample[1] == 5:
y = 5 # Dog
elif sample[1] == 7:
y = 6 # Horse
elif sample[1] == 8:
y = 7 # Ship
elif sample[1] == 9:
y = 8 # Truck
elif isinstance(self, STL10):
if sample[1] == 0:
y = 0 # Airplane
elif sample[1] == 1:
y = 2 # Bird
elif sample[1] == 2:
y = 1 # Car
elif sample[1] == 3:
y = 3 # Cat
elif sample[1] == 4:
y = 4 # Deer
elif sample[1] == 5:
y = 5 # Dog
elif sample[1] == 6:
y = 6 # Horse
elif sample[1] == 8:
y = 7 # Ship
elif sample[1] == 9:
y = 8 # Truck
with torch.no_grad():
x = self.resnet(x)[0].to('cpu')
return {'x': x, 'y': y}
class USPS(MyCustomMNISTUSPSDataLoader):
def __init__(self, transform: torchvision.transforms = None):
ssl._create_default_https_context = ssl._create_unverified_context
datasets = []
datasets.append(torchvision.datasets.USPS(root='./data', train=True, download=True))
datasets.append(torchvision.datasets.USPS(root='./data', train=False, download=True))
MyCustomMNISTUSPSDataLoader.__init__(self, datasets, transform)
class MNIST(MyCustomMNISTUSPSDataLoader):
def __init__(self, transform: torchvision.transforms = None):
ssl._create_default_https_context = ssl._create_unverified_context
datasets = []
datasets.append(torchvision.datasets.MNIST(root='./data', train=True, download=True))
datasets.append(torchvision.datasets.MNIST(root='./data', train=False, download=True))
MyCustomMNISTUSPSDataLoader.__init__(self, datasets, transform)
class CIFAR10(MyCustomCIFAR10STL10DataLoader):
def __init__(self, transform: torchvision.transforms = None):
ssl._create_default_https_context = ssl._create_unverified_context
datasets = []
datasets.append(torchvision.datasets.CIFAR10(root='./data', train=True, download=True))
datasets.append(torchvision.datasets.CIFAR10(root='./data', train=False, download=True))
MyCustomCIFAR10STL10DataLoader.__init__(self, datasets, transform)
class STL10(MyCustomCIFAR10STL10DataLoader):
def __init__(self, transform: torchvision.transforms = None):
ssl._create_default_https_context = ssl._create_unverified_context
datasets = []
datasets.append(torchvision.datasets.STL10(root='./data', split='train', download=True))
datasets.append(torchvision.datasets.STL10(root='./data', split='test', download=True))
MyCustomCIFAR10STL10DataLoader.__init__(self, datasets, transform)
class DataManipulator:
data = None
__number_samples = None
__number_features = None
__number_classes = None
__padding = 0
concept_drift_noise = None
n_concept_drifts = 1
def concept_drift(self, x, idx):
if idx == 0:
return x
def normalize(x, a: int = 0, b: int = 1):
assert a < b
return (b - a) * (x - np.min(x)) / (np.max(x) - np.min(x)) + a
if self.concept_drift_noise is None:
self.concept_drift_noise = []
for i in range(self.n_concept_drifts - 1):
np.random.seed(seed=self.n_concept_drifts * self.n_concept_drifts + i)
self.concept_drift_noise.append((np.random.rand(self.number_features())) + 1) # Random on range [0, 2)
np.random.seed(seed=None)
return normalize(x * self.concept_drift_noise[idx - 1], np.min(x), np.max(x))
def number_classes(self, force_count: bool = False):
if self.__number_classes is None or force_count:
try:
self.__min_class = int(np.min([np.min(d.targets) for d in self.data.datasets]))
self.__max_class = int(np.max([np.max(d.targets) for d in self.data.datasets]))
except TypeError:
self.__min_class = int(np.min([np.min(d.targets.numpy()) for d in self.data.datasets]))
self.__max_class = int(np.max([np.max(d.targets.numpy()) for d in self.data.datasets]))
except AttributeError:
try:
self.__min_class = int(np.min(self.data.datasets.overall.values))
self.__max_class = int(np.max(self.data.datasets.overall.values))
except:
try:
self.__min_class = int(np.min(self.data.datasets.demand.values))
self.__max_class = int(np.max(self.data.datasets.demand.values))
except:
try:
self.__min_class = int(np.min(self.data.datasets.targets.values))
self.__max_class = int(np.max(self.data.datasets.targets.values))
except:
self.__min_class = int(np.min([np.min(d.labels) for d in self.data.datasets]))
self.__max_class = int(np.max([np.max(d.labels) for d in self.data.datasets]))
self.__number_classes = len(range(self.__min_class, self.__max_class + 1))
if isinstance(self.data, CIFAR10) or isinstance(self.data, STL10):
self.__number_classes = self.__number_classes - 1
return self.__number_classes
def number_features(self, force_count: bool = False, specific_sample: int = None):
if self.__number_features is None or force_count or specific_sample is not None:
if specific_sample is None:
idx = 0
else:
idx = specific_sample
self.__number_features = int(np.prod(self.get_x(idx).shape))
return self.__number_features
def number_samples(self, force_count: bool = False):
if self.__number_samples is None or force_count:
self.__number_samples = len(self.data)
return self.__number_samples
def get_x_from_y(self, y: int, idx: int = 0, random_idx: bool = False):
x = None
if random_idx:
while x is None:
idx = np.random.randint(0, self.number_samples())
temp_x, temp_y = self.get_x_y(idx)
if np.argmax(temp_y) == y:
x = temp_x
else:
while x is None:
temp_x, temp_y = self.get_x_y(idx)
if np.argmax(temp_y) == y:
x = temp_x
else:
idx += 1
return x
def get_x_y(self, idx: int):
data = self.data[idx]
if self.__padding > 0:
m = torch.nn.ConstantPad2d(self.__padding, 0)
x = m(data['x']).flatten().numpy()
else:
if type(data['x']) is np.ndarray:
x = data['x']
else:
x = data['x'].flatten().numpy()
y = np.zeros(self.number_classes())
y[int((data['y'] - self.__min_class))] = 1
x = self.concept_drift(x, int(idx / (self.number_samples() / self.n_concept_drifts)))
return x, y
def get_x(self, idx: int):
x, _ = self.get_x_y(idx)
return x
def get_y(self, idx: int):
_, y = self.get_x_y(idx)
return y
def load_mnist(self, resize: int = None, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
if resize is None:
self.data = MNIST([torchvision.transforms.ToTensor()])
else:
self.data = MNIST([torchvision.transforms.Resize(resize),
torchvision.transforms.ToTensor()])
def load_usps(self, resize: int = None, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
if resize is None:
self.data = USPS([torchvision.transforms.ToTensor()])
else:
self.data = USPS([torchvision.transforms.Resize(resize),
torchvision.transforms.ToTensor()])
def load_cifar10(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = CIFAR10([torchvision.transforms.Resize(224),
torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
])
def load_stl10(self, resize: int = None, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
if resize is None:
self.data = STL10([torchvision.transforms.Resize(224),
torchvision.transforms.ToTensor(),
# torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
])
def load_london_bike_sharing(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomBikeSharingDataLoader('london')
def load_washington_bike_sharing(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomBikeSharingDataLoader('washington')
def load_amazon_review_fashion(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('AMAZON_FASHION_5.json.gz')
def load_amazon_review_all_beauty(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('All_Beauty_5.json.gz')
def load_amazon_review_appliances(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Appliances_5.json.gz')
def load_amazon_review_arts_crafts_sewing(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Arts_Crafts_and_Sewing_5.json.gz')
def load_amazon_review_automotive(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Automotive_5.json.gz')
def load_amazon_review_books(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Books_5.json.gz')
def load_amazon_review_cds_vinyl(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('CDs_and_Vinyl_5.json.gz')
def load_amazon_review_cellphones_accessories(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Cell_Phones_and_Accessories_5.json.gz')
def load_amazon_review_clothing_shoes_jewelry(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Clothing_Shoes_and_Jewelry_5.json.gz')
def load_amazon_review_digital_music(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Digital_Music_5.json.gz')
def load_amazon_review_electronics(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Electronics_5.json.gz')
def load_amazon_review_gift_card(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Gift_Cards_5.json.gz')
def load_amazon_review_grocery_gourmet_food(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Grocery_and_Gourmet_Food_5.json.gz')
def load_amazon_review_home_kitchen(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Home_and_Kitchen_5.json.gz')
def load_amazon_review_industrial_scientific(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Industrial_and_Scientific_5.json.gz')
def load_amazon_review_kindle_store(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Kindle_Store_5.json.gz')
def load_amazon_review_luxury_beauty(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Luxury_Beauty_5.json.gz')
def load_amazon_review_magazine_subscription(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Magazine_Subscriptions_5.json.gz')
def load_amazon_review_movies_tv(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Movies_and_TV_5.json.gz')
def load_amazon_review_musical_instruments(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Musical_Instruments_5.json.gz')
def load_amazon_review_office_products(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Office_Products_5.json.gz')
def load_amazon_review_patio_lawn_garden(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Patio_Lawn_and_Garden_5.json.gz')
def load_amazon_review_pet_supplies(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Pet_Supplies_5.json.gz')
def load_amazon_review_prime_pantry(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Prime_Pantry_5.json.gz')
def load_amazon_review_software(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Software_5.json.gz')
def load_amazon_review_sports_outdoors(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Sports_and_Outdoors_5.json.gz')
def load_amazon_review_tools_home_improvements(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Tools_and_Home_Improvement_5.json.gz')
def load_amazon_review_toys_games(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Toys_and_Games_5.json.gz')
def load_amazon_review_video_games(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewDataLoader('Video_Games_5.json.gz')
def load_amazon_review_nips_books(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewNIPSDataLoader('books')
def load_amazon_review_nips_dvd(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewNIPSDataLoader('dvd')
def load_amazon_review_nips_electronics(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewNIPSDataLoader('electronics')
def load_amazon_review_nips_kitchen(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewNIPSDataLoader('kitchen')
def load_amazon_review_acl_apparel(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('apparel')
def load_amazon_review_acl_automotive(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('automotive')
def load_amazon_review_acl_baby(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('baby')
def load_amazon_review_acl_beauty(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('beauty')
def load_amazon_review_acl_books(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('books')
def load_amazon_review_acl_camera_photo(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('camera_photo')
def load_amazon_review_acl_cell_phones_service(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('cell_phones_service')
def load_amazon_review_acl_computer_video_games(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('computer_video_games')
def load_amazon_review_acl_dvd(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('dvd')
def load_amazon_review_acl_electronics(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('electronics')
def load_amazon_review_acl_gourmet_food(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('gourmet_food')
def load_amazon_review_acl_grocery(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('grocery')
def load_amazon_review_acl_health_personal_care(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('health_personal_care')
def load_amazon_review_acl_jewelry_watches(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('jewelry_watches')
def load_amazon_review_acl_kitchen_housewares(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('kitchen_housewares')
def load_amazon_review_acl_magazines(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('magazines')
def load_amazon_review_acl_music(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('music')
def load_amazon_review_acl_musical_instruments(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('musical_instruments')
def load_amazon_review_acl_office_products(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('office_products')
def load_amazon_review_acl_outdoor_living(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('outdoor_living')
def load_amazon_review_acl_software(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('software')
def load_amazon_review_acl_sports_outdoors(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('sports_outdoors')
def load_amazon_review_acl_tools_hardware(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('tools_hardware')
def load_amazon_review_acl_toys_games(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('toys_games')
def load_amazon_review_acl_video(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomAmazonReviewACLDataLoader('video')
def load_news_popularity_obama_all(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('obama', 'all')
def load_news_popularity_economy_all(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('economy', 'all')
def load_news_popularity_microsoft_all(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('microsoft', 'all')
def load_news_popularity_palestine_all(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('palestine', 'all')
def load_news_popularity_obama_facebook(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('obama', 'Facebook')
def load_news_popularity_economy_facebook(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('economy', 'Facebook')
def load_news_popularity_microsoft_facebook(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('microsoft', 'Facebook')
def load_news_popularity_palestine_facebook(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('palestine', 'Facebook')
def load_news_popularity_obama_googleplus(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('obama', 'GooglePlus')
def load_news_popularity_economy_googleplus(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('economy', 'GooglePlus')
def load_news_popularity_microsoft_googleplus(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('microsoft', 'GooglePlus')
def load_news_popularity_palestine_googleplus(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('palestine', 'GooglePlus')
def load_news_popularity_obama_linkedin(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('obama', 'LinkedIn')
def load_news_popularity_economy_linkedin(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('economy', 'LinkedIn')
def load_news_popularity_microsoft_linkedin(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('microsoft', 'LinkedIn')
def load_news_popularity_palestine_linkedin(self, n_concept_drifts: int = 1):
self.n_concept_drifts = n_concept_drifts
self.data = MyCustomNewsPopularityDataLoader('palestine', 'LinkedIn')