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sliding_windows_frequent_directions.py
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from line_profiler import profile
from frequent_directions import (
FrequentDirectionsWithDump,
FasterFrequentDirectionsWithDump,
)
from fastfdwithdump import FastFrequentDirectionsWithDump
from dump_fd import DumpFdBase, OptDumpFd
import numpy as np
import numpy.typing as npt
from dataclasses import dataclass, field
from scipy.io import savemat
from tqdm import tqdm
from scipy import linalg
from collections import deque
from abc import ABC, abstractmethod
@dataclass
class FDSnapshot:
v: npt.NDArray
s: np.uint64
t: np.uint64
@dataclass
class DumpFDwithSnapshotQueue:
C: (
DumpFdBase
| FrequentDirectionsWithDump
| FastFrequentDirectionsWithDump
| FasterFrequentDirectionsWithDump
)
queue: deque[FDSnapshot] = field(default_factory=deque)
last_dump_time: np.uint64 = np.uint64(1)
size: float = 0.0
class SlidingWindowFdBase(ABC):
@abstractmethod
def fit(self, X: npt.NDArray, time: int | None) -> None:
pass
@abstractmethod
def get_sketch(self) -> npt.NDArray:
pass
class SlidingWindowFD:
def __init__(
self,
N: int,
d: int,
sketch_dim: int,
C: int = 1,
faster=FrequentDirectionsWithDump,
**kwargs,
):
"""Sliding Window on Frequent Directions
Args:
N (int): Sliding window size.
d (int): Vector dimension.
sketch_dim (int): Sketch dimension.
error_threshold (float, optional): Dump threshold, default as `1.0`.
"""
if "error_threshold" in kwargs:
self.error = kwargs["error_threshold"]
else:
self.error = N * 1.0 / sketch_dim
self.faster = faster
self.fd = DumpFDwithSnapshotQueue(C=self.faster(d, sketch_dim * C, self.error))
self.fd_aux = DumpFDwithSnapshotQueue(
C=self.faster(d, sketch_dim * C, self.error)
)
self.period = 0
self.N = np.uint64(N)
self.d = d
self.C = C
self.sketch_dim = sketch_dim
self.time = np.uint64(0)
self.size = 0.0
self.real_size = 0.0
def append(self, X: npt.NDArray, t=None):
if t != None:
self.time = np.uint64(t)
else:
self.time += np.uint64(1)
# if self.fd.size > 2 * self.error * self.sketch_dim:
# if self.time % self.N == 1:
while self.period < self.time // self.N:
self.fd = self.fd_aux
self.fd_aux = DumpFDwithSnapshotQueue(
C=self.faster(self.d, self.sketch_dim * self.C, self.error),
last_dump_time=self.time,
)
self.period += 1
s = self.fd.last_dump_time
self.fd.last_dump_time = self.time + 1
self.fd.queue.append(FDSnapshot(v=X, s=s, t=self.time))
s = self.fd_aux.last_dump_time
self.fd_aux.last_dump_time = self.time + 1
self.fd_aux.queue.append(FDSnapshot(v=X, s=s, t=self.time))
@profile
def fit(self, X: npt.NDArray, t=None):
"""Handle the input vector
Args:
X (npt.NDArray): Arriving vector at the time. (row vector: (1, n)-shape)
"""
if t != None:
self.time = np.uint64(t)
else:
self.time += np.uint64(1)
# self.fd.size += [email protected]
# self.fd_aux.size += [email protected]
# if self.fd.size > 2 * self.error * self.sketch_dim:
# if self.time % self.N == 1:
while self.period < self.time // self.N:
self.fd = self.fd_aux
self.fd_aux = DumpFDwithSnapshotQueue(
C=self.faster(self.d, self.sketch_dim * self.C, self.error),
last_dump_time=self.time,
)
self.period += 1
while len(self.fd.queue) != 0:
head_snapshot = self.fd.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd.queue.popleft()
else:
break
while len(self.fd_aux.queue) != 0:
head_snapshot = self.fd_aux.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd_aux.queue.popleft()
else:
break
# with energy optimization
self.fd.C.fit(X)
dumped = self.fd.C.dump()
if dumped is not None:
s = self.fd.last_dump_time
self.fd.last_dump_time = self.time + 1
self.fd.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
self.fd_aux.C.fit(X)
dumped = self.fd_aux.C.dump()
if dumped is not None:
s = self.fd_aux.last_dump_time
self.fd_aux.last_dump_time = self.time + 1
self.fd_aux.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
# without energy
# sketch, sigma_squared, Vt, delta = self.fd.get()
# if sigma_squared[0] >= (self.N * 1.0 * self.R)/(self.C * self.sketch_dim):
# v = np.sqrt(sigma_squared[0]) * Vt[0:1]
# t = self.time
# self.snapshots.append(FDSnapshot(v, t))
# sketch[0, :] = 0
# sigma_squared[0] = 0
# Vt[0, :] = 0
# np.roll(sketch, -1)
# np.roll(sigma_squared, -1)
# np.roll(Vt, -1)
def update(self, X: npt.NDArray):
sq = self.fd
q = sq.queue
C = sq.C
while len(q) != 0:
head_snapshot = q[0]
if len(q) > self.queue_capacity or head_snapshot.t + self.N <= self.time:
q.popleft()
else:
break
sq = self.fd_aux
q = sq.queue
C = sq.C
while len(q) != 0:
head_snapshot = q[0]
if len(q) > self.queue_capacity or head_snapshot.t + self.N <= self.time:
q.popleft()
else:
break
error = C.get_error()
if X @ X.T >= error:
self.append(X)
else:
self.fit(X)
def get(self):
q = self.fd.queue
C = self.fd.C.get_sketch()
if len(q) != 0:
ret = np.vstack([C, *(s.v for s in q)])
_, s, Vt = linalg.svd(ret, full_matrices=False, lapack_driver="gesvd")
s = s**2
if s.shape[0] > self.sketch_dim:
s = s[: self.sketch_dim] - s[self.sketch_dim]
Vt = Vt[: self.sketch_dim]
# else:
# s = np.pad(s, (0, self.sketch_dim-s.shape[0]), 'constant')
# Vt = np.pad(
# Vt, ((0, self.sketch_dim-Vt.shape[0]), (0, 0)), 'constant')
sketch = Vt * np.sqrt(s).reshape(-1, 1)
return sketch, s, Vt, 0.0
else:
return self.fd.C.get()
# @profile
def get_sketch(self):
q = self.fd.queue
ret = self.fd.C.sketch
if len(q) != 0:
ret = np.vstack([ret, *(s.v for s in q)])
if self.fd.C.buffer is not None:
ret = np.vstack([ret, self.fd.C.buffer])
if len(ret) > self.sketch_dim:
_, s, Vt = linalg.svd(ret, full_matrices=False, lapack_driver="gesvd")
s = s**2
s = s[: self.sketch_dim] - s[self.sketch_dim]
Vt = Vt[: self.sketch_dim]
sketch = Vt * np.sqrt(s).reshape(-1, 1)
return sketch
else:
return ret
def get_size(self):
return len(self.fd.queue) + len(self.fd_aux.queue) + 2 * self.sketch_dim
class OptSwfd(SlidingWindowFdBase):
def __init__(
self,
N: int,
d: int,
sketch_dim: int,
**kwargs,
):
"""Sliding Window on Frequent Directions
Args:
N (int): Sliding window size.
d (int): Vector dimension.
sketch_dim (int): Sketch dimension.
error_threshold (float, optional): Dump threshold, default as `1.0`.
"""
if "error_threshold" in kwargs:
self.error = kwargs["error_threshold"]
else:
self.error = N * 1.0 / sketch_dim
self.fd = DumpFDwithSnapshotQueue(C=OptDumpFd(d, sketch_dim, self.error))
self.fd_aux = DumpFDwithSnapshotQueue(C=OptDumpFd(d, sketch_dim, self.error))
self.period = 0
self.N = np.uint64(N)
self.d = d
# self.C = C
self.sketch_dim = sketch_dim
self.time = np.uint64(0)
self.size = 0.0
self.real_size = 0.0
def fit(self, X: npt.NDArray, t=None):
"""Handle the input vector
Args:
X (npt.NDArray): Arriving vector at the time. (row vector: (1, n)-shape)
"""
if t != None:
self.time = np.uint64(t)
else:
self.time += np.uint64(1)
# self.fd.size += [email protected]
# self.fd_aux.size += [email protected]
# if self.fd.size > 2 * self.error * self.sketch_dim:
# if self.time % self.N == 1:
while self.period < self.time // self.N:
self.fd = self.fd_aux
self.fd_aux = DumpFDwithSnapshotQueue(
C=OptDumpFd(self.d, self.sketch_dim, self.error),
last_dump_time=self.time,
)
self.period += 1
while len(self.fd.queue) != 0:
head_snapshot = self.fd.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd.queue.popleft()
else:
break
while len(self.fd_aux.queue) != 0:
head_snapshot = self.fd_aux.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd_aux.queue.popleft()
else:
break
# with energy optimization
dumped = self.fd.C.fit(X)
if dumped is not None:
s = self.fd.last_dump_time
self.fd.last_dump_time = self.time + 1
self.fd.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
dumped = self.fd_aux.C.fit(X)
if dumped is not None:
s = self.fd_aux.last_dump_time
self.fd_aux.last_dump_time = self.time + 1
self.fd_aux.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
def get(self):
q = self.fd.queue
ret = self.fd.C.get_sketch()
if len(q) != 0:
ret = np.vstack([ret, *(s.v for s in q)])
return ret, None, None, None
# @profile
def get_sketch(self):
q = self.fd.queue
ret = self.fd.C.sketch
if len(q) != 0:
ret = np.vstack([ret, *(s.v for s in q)])
return ret
def get_size(self):
return len(self.fd.queue) + len(self.fd_aux.queue) + 2 * self.sketch_dim
class FastSlidingWindowFD:
def __init__(self, N: int, d: int, sketch_dim: int, C: int = 1, **kwargs):
"""Sliding Window on Frequent Directions
Args:
N (int): Sliding window size.
d (int): Vector dimension.
sketch_dim (int): Sketch dimension.
error_threshold (float, optional): Dump threshold, default as `1.0`.
"""
if "error_threshold" in kwargs:
self.error = kwargs["error_threshold"]
else:
self.error = N * 1.0 / sketch_dim
self.fd = DumpFDwithSnapshotQueue(
C=FastFrequentDirectionsWithDump(d, sketch_dim * C, self.error)
)
self.fd_aux = DumpFDwithSnapshotQueue(
C=FastFrequentDirectionsWithDump(d, sketch_dim * C, self.error)
)
self.period = 0
self.N = np.uint64(N)
self.d = d
self.C = C
self.sketch_dim = sketch_dim
self.time = np.uint64(0)
def append(self, X: npt.NDArray, t=None):
if t != None:
self.time = np.uint64(t)
else:
self.time += np.uint64(1)
# self.fd.size += [email protected]
# self.fd_aux.size += [email protected]
# if self.fd.size > 2 * self.error * self.sketch_dim:
while self.period < self.time // self.N:
self.fd = self.fd_aux
self.fd_aux = DumpFDwithSnapshotQueue(
C=FastFrequentDirectionsWithDump(
self.d, self.sketch_dim * self.C, self.error
),
last_dump_time=self.time,
)
self.period += 1
s = self.fd.last_dump_time
self.fd.last_dump_time = self.time + 1
self.fd.queue.append(FDSnapshot(v=X, s=s, t=self.time))
s = self.fd_aux.last_dump_time
self.fd_aux.last_dump_time = self.time + 1
self.fd_aux.queue.append(FDSnapshot(v=X, s=s, t=self.time))
@profile
def fit(self, X: npt.NDArray, t=None):
"""Handle the input vector
Args:
X (npt.NDArray): Arriving vector at the time. (row vector: (1, n)-shape)
"""
if t != None:
self.time = np.uint64(t)
else:
self.time += np.uint64(1)
# self.fd.size += [email protected]
# self.fd_aux.size += [email protected]
# if self.fd.size > 2 * self.error * self.sketch_dim:
while self.period < self.time // self.N:
self.fd = self.fd_aux
self.fd_aux = DumpFDwithSnapshotQueue(
C=FastFrequentDirectionsWithDump(
self.d, self.sketch_dim * self.C, self.error
),
last_dump_time=self.time,
)
self.period += 1
while len(self.fd.queue) != 0:
head_snapshot = self.fd.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd.queue.popleft()
else:
break
while len(self.fd_aux.queue) != 0:
head_snapshot = self.fd_aux.queue[0]
if head_snapshot.t + self.N <= self.time:
self.fd_aux.queue.popleft()
else:
break
# with energy optimization
dumped = self.fd.C.fit(X)
if dumped is not None:
s = self.fd.last_dump_time
self.fd.last_dump_time = self.time + 1
self.fd.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
dumped = self.fd_aux.C.fit(X)
if dumped is not None:
s = self.fd_aux.last_dump_time
self.fd_aux.last_dump_time = self.time + 1
self.fd_aux.queue.append(FDSnapshot(v=dumped, s=s, t=self.time))
def get(self):
q = self.fd.queue
C = self.fd.C.get_sketch()
if len(q) != 0:
ret = np.vstack([C, *(s.v for s in q)])
_, s, Vt = linalg.svd(ret, full_matrices=False, lapack_driver="gesvd")
s = s**2
s = s[: self.sketch_dim] - s[self.sketch_dim]
Vt = Vt[: self.sketch_dim]
sketch = Vt * np.sqrt(s).reshape(-1, 1)
return sketch, s, Vt, 0.0
else:
return self.fd.C.get()
# @profile
def get_sketch(self):
q = self.fd.queue
ret = self.fd.C.sketch
if len(q) != 0:
ret = np.vstack([ret, *(s.v for s in q)])
if self.fd.C.buffer is not None:
ret = np.vstack([ret, self.fd.C.buffer])
if len(ret) > self.sketch_dim:
_, s, Vt = linalg.svd(ret, full_matrices=False, lapack_driver="gesvd")
s = s**2
s = s[: self.sketch_dim] - s[self.sketch_dim]
Vt = Vt[: self.sketch_dim]
sketch = Vt * np.sqrt(s).reshape(-1, 1)
return sketch
else:
return ret
def get_size(self):
return len(self.fd.queue) + len(self.fd_aux.queue) + 2 * self.sketch_dim
def run(l, N, d, epochs):
A = np.empty((epochs, d))
sw_fd = SlidingWindowFD(N, d, l)
B = np.empty((epochs, l, d))
deltas = np.empty((epochs))
count = 0
for t in tqdm(range(epochs)):
random_vector = np.random.randn(1, d)
a = random_vector / np.linalg.norm(random_vector, ord=2)
# a = np.sqrt(R/(l-3)) * np.eye(1, d, k=(t//N) % d)
# if (t // 128) % 2 == 0:
# a /= 100
# count += 1
# a = random_vector * np.sqrt(R)/d
# while linalg.norm(a, ord=2) ** 2 > R:
# random_vector = np.random.randn(1, d)
# a = random_vector * np.sqrt(R)/d
A[t : t + 1, :] = a
sw_fd.fit(a)
B_t, _, _, delta = sw_fd.get()
B[t] = B_t
deltas[t] = delta
mdic = {
"l": l,
"N": N,
"d": d,
"R": 1.0,
"epochs": epochs,
"A": A,
"B": B,
"deltas": deltas,
}
mat_path = f"results/result_l={l},N={N},d={d},epochs={epochs}.mat"
savemat(mat_path, mdic)
print(mat_path)
print(count)
if __name__ == "__main__":
l = 16
N = 16**3
d = 32
epochs = N * 16
for i in range(1):
run(l, N, d, epochs)
l, N, d, epochs = l * 2, N * 2, d * 2, epochs * 2