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data_handler.py
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import math
import pickle
import random
from random import shuffle
from operator import itemgetter
import numpy as np
import pandas as pd
import yaml
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from collections import Counter, defaultdict
from sklearn.utils import shuffle
import torch
# seed =0
# random.seed(seed)
# np.random.seed(seed)
import copy
# from train.visualisations import vis_by_person
# from train.visualisations.training_visualizer import plot_heatmap
# from sklearn.preprocessing import LabelEncoder
class PrototypicalBatchSampler(object):
'''
PrototypicalBatchSampler: yield a batch of indexes at each iteration.
Indexes are calculated by keeping in account 'classes_per_it' and 'num_samples',
In fact at every iteration the batch indexes will refer to 'num_support' + 'num_query' samples
for 'classes_per_it' random classes.
__len__ returns the number of episodes per epoch (same as 'self.iterations').
'''
def __init__(self, labels, classes_per_it, num_samples, iterations):
'''
Initialize the PrototypicalBatchSampler object
Args:
- labels: an iterable containing all the labels for the current dataset
samples indexes will be infered from this iterable.
- classes_per_it: number of random classes for each iteration
- num_samples: number of samples for each iteration for each class (support + query)
- iterations: number of iterations (episodes) per epoch
'''
super(PrototypicalBatchSampler, self).__init__()
self.labels = labels
self.classes_per_it = classes_per_it
self.sample_per_class = num_samples
self.iterations = iterations
self.classes, self.counts = np.unique(self.labels, return_counts=True)
self.classes = torch.LongTensor(self.classes)
# create a matrix, indexes, of dim: classes X max(elements per class)
# fill it with nans
# for every class c, fill the relative row with the indices samples belonging to c
# in numel_per_class we store the number of samples for each class/row
self.idxs = range(len(self.labels))
self.indexes = np.empty((len(self.classes), max(self.counts)), dtype=int) * np.nan
self.indexes = torch.Tensor(self.indexes)
self.numel_per_class = torch.zeros_like(self.classes)
for idx, label in enumerate(self.labels):
label_idx = np.argwhere(self.classes == label).item()
self.indexes[label_idx, np.where(np.isnan(self.indexes[label_idx]))[0][0]] = idx
self.numel_per_class[label_idx] += 1
def __iter__(self):
'''
yield a batch of indexes
'''
spc = self.sample_per_class
cpi = self.classes_per_it
for it in range(self.iterations):
batch_size = spc * cpi
batch = torch.LongTensor(batch_size)
c_idxs = torch.randperm(len(self.classes))[:cpi]
for i, c in enumerate(self.classes[c_idxs]):
s = slice(i * spc, (i + 1) * spc)
# FIXME when torch.argwhere will exists
label_idx = torch.arange(len(self.classes)).long()[self.classes == c].item()
sample_idxs = torch.randperm(self.numel_per_class[label_idx])[:spc]
#import pdb; pdb.set_trace()
batch[s] = self.indexes[label_idx][sample_idxs]
batch = batch[torch.randperm(len(batch))]
yield batch
def __len__(self):
'''
returns the number of iterations (episodes) per epoch
'''
return self.iterations
class DataHandler:
def __init__(self, nb_baseClasses, seed, train, ClassPercentage):
self.nb_baseClasses = nb_baseClasses
self.seed = seed
#self.seed_randomness()
self.train = train
self.ClassPercentage = ClassPercentage
self.counter = 0
self.base_done = False
def seed_randomness(self):
np.random.seed(self.seed)
random.seed()
def streaming_data(self,nb_NewClasses = 0):
_labels = sorted(set(self.train['label']))
print(_labels, self.nb_baseClasses)
# if ordered_labels:
# self.baseClasses = list(range(self.nb_baseClasses))
# #self.baseClasses = np.random.choice(_labels, self.nb_baseClasses, replace=False).tolist()
# else:
print(self.nb_baseClasses, len(np.unique(_labels)))
#np.random.seed(1)
self.baseClasses = np.random.choice(_labels, self.nb_baseClasses, replace=False).tolist()
# self.baseClasses = [2,3,4]
#np.random.seed(self.seed)
self.nb_NewClasses = nb_NewClasses
_labels = sorted(set(self.train['label']))
_labelsNew = np.setdiff1d(_labels,self.baseClasses)
self.NewClasses = np.random.choice(_labelsNew, nb_NewClasses, replace=False).tolist()
# self.NewClasses = [12] # [14, 18] for DSADS#[14,17]
self.baseData = dict()
self.baseData['data'] = []
self.baseData['label'] = []
self.remData = dict()
self.remData['data'] = []
self.remData['label'] = []
self.remSizePerBaseClass = dict()
self.remSizePerNewClass = dict()
for c in _labels:
d,l = copy.deepcopy(self.train['data'][self.train['label'] == c,:]), copy.deepcopy(self.train['label'][self.train['label'] == c])
if c in self.baseClasses:
self.baseIdx = np.random.choice(np.arange(len(d)),int(self.ClassPercentage*len(d)),replace=False)
self.baseData['data'].extend(d[self.baseIdx,:])
self.baseData['label'].extend(l[self.baseIdx])
self.remIdx = np.setdiff1d(np.arange(len(d)),self.baseIdx)
self.remData['data'].extend(d[self.remIdx,:])
self.remData['label'].extend(l[self.remIdx])
self.remSizePerBaseClass[c] = len(self.remIdx)
elif c in self.NewClasses:
#d,l = self.train['data'][self.train['label'] == c,:], self.train['label'][self.train['label'] == c]
self.remData['data'].extend(d[:])
self.remData['label'].extend(l[:])
self.remSizePerNewClass[c] = len(l[:])
self.maxSize = len(self.remData['data'])
self.shuffleRemData()
def streaming_data_IgnoreNewClassInStreaming(self,nb_NewClasses = 0):
_labels = sorted(set(self.train['label']))
print(_labels, self.nb_baseClasses)
# if ordered_labels:
# self.baseClasses = list(range(self.nb_baseClasses))
# #self.baseClasses = np.random.choice(_labels, self.nb_baseClasses, replace=False).tolist()
# else:
print(self.nb_baseClasses, len(np.unique(_labels)))
#np.random.seed(1)
self.baseClasses = np.random.choice(_labels, self.nb_baseClasses, replace=False).tolist()
# self.baseClasses = [2,4,6,9,1,3]
#np.random.seed(self.seed)
self.nb_NewClasses = nb_NewClasses
_labels = sorted(set(self.train['label']))
_labelsNew = np.setdiff1d(_labels,self.baseClasses)
# self.NewClasses = np.random.choice(_labelsNew, nb_NewClasses, replace=False).tolist()
self.NewClasses = [1,7] #[14, 18] for DSADS
self.baseData = dict()
self.baseData['data'] = []
self.baseData['label'] = []
self.remData = dict()
self.remData['data'] = []
self.remData['label'] = []
self.remSizePerBaseClass = dict()
self.remSizePerNewClass = dict()
for c in _labels:
d,l = copy.deepcopy(self.train['data'][self.train['label'] == c,:]), copy.deepcopy(self.train['label'][self.train['label'] == c])
if c in self.baseClasses:
self.baseIdx = np.random.choice(np.arange(len(d)),int(self.ClassPercentage*len(d)),replace=False)
self.baseData['data'].extend(d[self.baseIdx,:])
self.baseData['label'].extend(l[self.baseIdx])
self.remIdx = np.setdiff1d(np.arange(len(d)),self.baseIdx)
self.remData['data'].extend(d[self.remIdx,:])
self.remData['label'].extend(l[self.remIdx])
self.remSizePerBaseClass[c] = len(self.remIdx)
self.maxSize = len(self.remData['data'])
self.shuffleRemData()
def NIC_generation(self, N):
if self.counter == 0:
num_batches = math.floor(self.maxSize/N)
self.insertion_point = np.random.choice(int(3*num_batches/4),self.nb_NewClasses,replace=False)
#get_new = np.random.choice([0,1])
data, labels = [],[]
print(self.counter/N, self.insertion_point)
print('Old Classes {}, New Classes {}'.format(self.remSizePerBaseClass, self.remSizePerNewClass))
if int(self.counter/N) in self.insertion_point and self.remSizePerNewClass: #get_new or self.base_done:
# if self.base_done:
# # nb_NewClasses = np.random.choice(5)
# # classes = []
# # if self.remSizePerNewClass:
# # new_classes = np.random.choice(self.remSizePerNewClass.keys(), min(len(self.remSizePerNewClass),nb_NewClasses), replace=False)
# # if self.base_done:
# # classes = new_classes
# # else:
# # if self.remSizePerBaseClass.keys():
# # old_classes = np.random.choice(self.remSizePerBaseClass.keys(), self.nb_baseClasses-nb_NewClasses, replace=False)
# # classes = np.append(old_classes, new_classes)
# # #samples_per_class = N/len(classes)
# if self.remSizePerNewClass:
# classes = np.random.choice(list(self.remSizePerNewClass.keys()), min(len(self.remSizePerNewClass),5), replace=False)
# indxs = []
# for c in classes:
# ii = np.where(self.remData['label'] == c)[0]
# indxs.extend(ii)
# #print(np.shape(indxs))
# if len(indxs) > 0:
# selected_indxs = np.random.choice(indxs, min(N, len(indxs)), replace=False)
# data = copy.deepcopy((np.array(self.remData['data'])[selected_indxs]).tolist())
# labels = copy.deepcopy((np.array(self.remData['label'])[selected_indxs]).tolist())
# self.updateDict(self.remSizePerBaseClass, selected_indxs)
# self.updateDict(self.remSizePerNewClass, selected_indxs, move=self.remSizePerBaseClass)
# self.remData['data'] = np.delete(np.array(self.remData['data']),selected_indxs, axis=0).tolist()
# self.remData['label'] = np.delete(np.array(self.remData['label']), selected_indxs, axis=0).tolist()
# self.updateCounter(len(selected_indxs))
# else:
indxs = []
old_classes = []
new_classes = []
nb_newClass = np.random.choice(np.arange(1,min(len(self.remSizePerNewClass.keys())+1,3)))
new_classes = np.random.choice(list(self.remSizePerNewClass.keys()), nb_newClass, replace=False)
if self.remSizePerBaseClass:
old_classes = np.random.choice(list(self.remSizePerBaseClass.keys()), self.nb_baseClasses-len(new_classes), replace=False)
classes = np.append(old_classes,new_classes)
print('Classes Selected {}'.format(classes))
for c in classes:
ii = np.where(self.remData['label'] == c)[0]
indxs.extend(ii)
#print(np.shape(indxs))
if len(indxs) > 0:
selected_indxs = np.random.choice(indxs, min(N, len(indxs)), replace=False)
data = copy.deepcopy((np.array(self.remData['data'])[selected_indxs]).tolist())
labels = copy.deepcopy((np.array(self.remData['label'])[selected_indxs]).tolist())
self.updateDict(self.remSizePerBaseClass, selected_indxs)
self.updateDict(self.remSizePerNewClass, selected_indxs, move=self.remSizePerBaseClass)
self.remData['data'] = np.delete(np.array(self.remData['data']),selected_indxs, axis=0).tolist()
self.remData['label'] = np.delete(np.array(self.remData['label']), selected_indxs, axis=0).tolist()
self.updateCounter(len(selected_indxs))
else:
indxs = []
classes = np.random.choice(list(self.remSizePerBaseClass.keys()), min(len(self.remSizePerBaseClass.keys()),5), replace=False)
for c in classes:
indxs.extend(np.where(self.remData['label'] == c)[0])
#print(np.shape(indxs))
#print(len(indxs))
selected_indxs = np.random.choice(indxs, min(N, len(indxs)), replace=False)
data = copy.deepcopy((np.array(self.remData['data'])[selected_indxs]).tolist())
labels = copy.deepcopy((np.array(self.remData['label'])[selected_indxs]).tolist())
self.updateDict(self.remSizePerBaseClass, selected_indxs)
self.remData['data'] = np.delete(np.array(self.remData['data']),selected_indxs, axis=0).tolist()
self.remData['label'] = np.delete(np.array(self.remData['label']), selected_indxs, axis=0).tolist()
self.updateCounter(len(selected_indxs))
return data,labels
def shuffleRemData(self):
self.remData['data'],self.remData['label'] = shuffle(self.remData['data'],self.remData['label'])
def endOfStream(self):
# if self.counter + 1 >= 200: ## for debugging
# return True
return self.counter+1 >= self.maxSize
def getNextBatch_controlled(self, N):
#print(np.shape(self.remData['data']), np.shape(self.remData['label']))
classes = np.random.choice(list(self.remSizePerNewClass.keys()) + list(self.remSizePerBaseClass.keys()), min(len(list(self.remSizePerNewClass.keys()) + list(self.remSizePerBaseClass.keys())), self.nb_baseClasses), replace=False)
indxs = []
for c in classes:
indxs.extend(np.where(self.remData['label'] == c)[0])
#print(np.shape(indxs))
selected_indxs = np.random.choice(indxs, min(N, len(indxs)), replace=False)
data = copy.deepcopy((np.array(self.remData['data'])[selected_indxs]).tolist())
labels = copy.deepcopy((np.array(self.remData['label'])[selected_indxs]).tolist())
self.updateDict(self.remSizePerBaseClass, selected_indxs)
self.updateDict(self.remSizePerNewClass, selected_indxs, move=self.remSizePerBaseClass)
self.remData['data'] = np.delete(np.array(self.remData['data']),selected_indxs, axis=0).tolist()
self.remData['label'] = np.delete(np.array(self.remData['label']), selected_indxs, axis=0).tolist()
self.updateCounter(len(selected_indxs))
return data,labels
def task_incremental(self, N):
raise NotImplementedError()
def updateDict(self, oldDict, selected_indxs, move=None):
keys = list(oldDict.keys())
for c in keys:
count = len(np.where(np.array(self.remData['label'])[selected_indxs] == c)[0])
found = count > 0
oldDict[c] -= count
if oldDict[c] == 0:
del oldDict[c]
elif found and move:
move[c] = copy.deepcopy(oldDict[c])
del oldDict[c]
def getNextData(self):
d,l = copy.deepcopy(self.remData['data'][self.counter]), copy.deepcopy(self.remData['label'][self.counter])
self.updateCounter()
return d,l
def updateCounter(self, count=1):
self.counter = self.counter + count
def resetCounter(self):
self.counter = 0
def getBaseData(self):
return copy.deepcopy(self.baseData)