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interactions.py
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interactions.py
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"""
Classes describing datasets of user-item interactions. Instances of these
are returned by dataset-fetching and dataset-processing functions.
"""
import numpy as np
import scipy.sparse as sp
class Interactions(object):
"""
Interactions object. Contains (at a minimum) pair of user-item
interactions. This is designed only for implicit feedback scenarios.
Parameters
----------
file_path: file contains (user,item,rating) triplets
user_map: dict of user mapping
item_map: dict of item mapping
"""
def __init__(self, user_item_sequence, num_users, num_items):
user_ids, item_ids = [], []
for uid, item_seq in enumerate(user_item_sequence):
for iid in item_seq:
user_ids.append(uid)
item_ids.append(iid)
user_ids = np.asarray(user_ids)
item_ids = np.asarray(item_ids)
self.num_users = num_users
self.num_items = num_items
self.user_ids = user_ids
self.item_ids = item_ids
self.sequences = None
self.test_sequences = None
def __len__(self):
return len(self.user_ids)
def tocoo(self):
"""
Transform to a scipy.sparse COO matrix.
"""
row = self.user_ids
col = self.item_ids
data = np.ones(len(self))
return sp.coo_matrix((data, (row, col)),
shape=(self.num_users, self.num_items))
def tocsr(self):
"""
Transform to a scipy.sparse CSR matrix.
"""
return self.tocoo().tocsr()
def to_sequence(self, sequence_length=5, target_length=1):
"""
Transform to sequence form.
Valid subsequences of users' interactions are returned. For
example, if a user interacted with items [1, 2, 3, 4, 5, 6, 7, 8, 9], the
returned interactions matrix at sequence length 5 and target length 3
will be be given by:
sequences:
[[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7]]
targets:
[[6, 7],
[7, 8],
[8, 9]]
sequence for test (the last 'sequence_length' items of each user's sequence):
[[5, 6, 7, 8, 9]]
Parameters
----------
sequence_length: int
Sequence length. Subsequences shorter than this
will be left-padded with zeros.
target_length: int
Sequence target length.
"""
# # change the item index start from 1 as 0 is used for padding in sequences
# for k, v in self.item_map.items():
# self.item_map[k] = v + 1
# self.item_ids = self.item_ids + 1
# self.num_items += 1
max_sequence_length = sequence_length + target_length
# Sort first by user id
sort_indices = np.lexsort((self.user_ids,))
user_ids = self.user_ids[sort_indices]
item_ids = self.item_ids[sort_indices]
user_ids, indices, counts = np.unique(user_ids,
return_index=True,
return_counts=True)
# print(np.where(counts>50))
# print(np.mean(counts))
num_subsequences = sum([c - max_sequence_length + 1 if c >= max_sequence_length else 1 for c in counts])
sequences = np.zeros((num_subsequences, sequence_length),
dtype=np.int64)
sequences_targets = np.zeros((num_subsequences, target_length),
dtype=np.int64)
sequence_users = np.empty(num_subsequences,
dtype=np.int64)
test_sequences = np.zeros((self.num_users, sequence_length),
dtype=np.int64)
test_users = np.empty(self.num_users,
dtype=np.int64)
_uid = None
for i, (uid,
item_seq) in enumerate(_generate_sequences(user_ids,
item_ids,
indices,
max_sequence_length)):
if uid != _uid:
test_sequences[uid][:] = item_seq[-sequence_length:]
test_users[uid] = uid
_uid = uid
sequences_targets[i][:] = item_seq[-target_length:]
sequences[i][:] = item_seq[:sequence_length]
sequence_users[i] = uid
self.sequences = SequenceInteractions(sequence_users, sequences, sequences_targets)
self.test_sequences = SequenceInteractions(test_users, test_sequences)
def to_newsequence(self, sequence_length=5, target_length=1):
"""
Transform to sequence form.
Valid subsequences of users' interactions are returned. For
example, if a user interacted with items [1, 2, 3, 4, 5, 6, 7, 8, 9], the
returned interactions matrix at sequence length 5 and target length 3
will be be given by:
sequences:
[[1, 2, 3, 4, 5],
[2, 3, 4, 5, 6],
[3, 4, 5, 6, 7]]
targets:
[[6, 7],
[7, 8],
[8, 9]]
sequence for test (the last 'sequence_length' items of each user's sequence):
[[5, 6, 7, 8, 9]]
Parameters
----------
sequence_length: int
Sequence length. Subsequences shorter than this
will be left-padded with zeros.
target_length: int
Sequence target length.
"""
max_sequence_length = sequence_length + target_length
# Sort first by user id
sort_indices = np.lexsort((self.user_ids,))
user_ids = self.user_ids[sort_indices]
item_ids = self.item_ids[sort_indices]
user_ids, indices, counts = np.unique(user_ids,
return_index=True,
return_counts=True)
num_subsequences = sum([ 1 for c in counts])
_uid = None
seq_user =[]
sequences =[]
sequences_targets =[]
sequences_length =[]
sequences_targetlen= []
test_users = []
test_sequences = []
test_seq_length=[]
for i,user_id in enumerate(user_ids):
start_idx = indices[i]
try:
stop_idx = indices[i + 1]
except:
stop_idx = None
one_sequence = item_ids[start_idx:stop_idx]
if len(one_sequence)<=0:
print(user_id,one_sequence,indices[i])
if len(one_sequence)>sequence_length:
one_sequence = one_sequence[-sequence_length:]
test_train_seq = np.pad(one_sequence, (0, sequence_length-len(one_sequence)), 'constant')
test_users.append(user_id)
test_sequences.append(test_train_seq)
test_seq_length.append(len(one_sequence))
for train_len in range(len(one_sequence)-1):
sub_seq = one_sequence[0:train_len+1]
sequences_length.append(train_len+1)
num_paddings = sequence_length-train_len-1
sub_seq = np.pad(sub_seq, (0, num_paddings), 'constant')
target_sub = one_sequence[train_len+1:train_len+1+target_length]
target_len = len(target_sub)
target_sub = np.pad(target_sub, (0, target_length-len(target_sub)), 'constant')
seq_user.append(user_id)
sequences.append(sub_seq)
sequences_targetlen.append(target_len)
sequences_targets.append(target_sub)
sequence_users =np.array(seq_user)
sequences =np.array(sequences)
sequences_targetlen = np.array(sequences_targetlen)
sequences_targets =np.array(sequences_targets)
test_users = np.array(test_users)
test_sequences = np.array(test_sequences)
sequences_length = np.array(sequences_length)
#print(sequences_length.shape)
test_seq_length=np.array(test_seq_length)
self.sequences = SequenceInteractions(sequence_users, sequences, targets=sequences_targets,length=sequences_length,tar_len=sequences_targetlen)
self.test_sequences = SequenceInteractions(test_users, test_sequences,length=test_seq_length)
class SequenceInteractions(object):
"""
Interactions encoded as a sequence matrix.
Parameters
----------
user_ids: np.array
sequence users
sequences: np.array
The interactions sequence matrix, as produced by
:func:`~Interactions.to_sequence`
targets: np.array
sequence targets
"""
def __init__(self,
user_ids,
sequences,
targets=None,
length=None,
tar_len=None):
self.user_ids = user_ids
self.sequences = sequences
self.targets = targets
self.length = length
self.tarlen = tar_len
self.L = sequences.shape[1]
self.T = None
if np.any(targets):
self.T = targets.shape[1]
def _sliding_window(tensor, window_size, step_size=1):
if len(tensor) - window_size >= 0:
# for i in range(len(tensor), 0, -step_size):
# if i - window_size >= 0:
# yield tensor[i - window_size:i]
i = len(tensor)
yield tensor[i - window_size:i]
# else:
# break
else:
num_paddings = window_size - len(tensor)
# Pad sequence with 0s if it is shorter than windows size.
yield np.pad(tensor, (0, num_paddings), 'constant')
def _generate_sequences(user_ids, item_ids,
indices,
max_sequence_length):
for i in range(len(indices)):
start_idx = indices[i]
if i >= len(indices) - 1:
stop_idx = None
else:
stop_idx = indices[i + 1]
for seq in _sliding_window(item_ids[start_idx:stop_idx],
max_sequence_length):
yield (user_ids[i], seq)