3RNN/Lib/site-packages/sklearn/utils/_fast_dict.pyx
2024-05-26 19:49:15 +02:00

138 lines
4.5 KiB
Cython

"""
Uses C++ map containers for fast dict-like behavior with keys being
integers, and values float.
"""
# Author: Gael Varoquaux
# License: BSD
# C++
from cython.operator cimport dereference as deref, preincrement as inc
from libcpp.utility cimport pair
from libcpp.map cimport map as cpp_map
import numpy as np
from ._typedefs cimport float64_t, intp_t
###############################################################################
# An object to be used in Python
# Lookup is faster than dict (up to 10 times), and so is full traversal
# (up to 50 times), and assignment (up to 6 times), but creation is
# slower (up to 3 times). Also, a large benefit is that memory
# consumption is reduced a lot compared to a Python dict
cdef class IntFloatDict:
def __init__(
self,
intp_t[:] keys,
float64_t[:] values,
):
cdef int i
cdef int size = values.size
# Should check that sizes for keys and values are equal, and
# after should boundcheck(False)
for i in range(size):
self.my_map[keys[i]] = values[i]
def __len__(self):
return self.my_map.size()
def __getitem__(self, int key):
cdef cpp_map[intp_t, float64_t].iterator it = self.my_map.find(key)
if it == self.my_map.end():
# The key is not in the dict
raise KeyError('%i' % key)
return deref(it).second
def __setitem__(self, int key, float value):
self.my_map[key] = value
# Cython 0.20 generates buggy code below. Commenting this out for now
# and relying on the to_arrays method
# def __iter__(self):
# cdef cpp_map[intp_t, float64_t].iterator it = self.my_map.begin()
# cdef cpp_map[intp_t, float64_t].iterator end = self.my_map.end()
# while it != end:
# yield deref(it).first, deref(it).second
# inc(it)
def __iter__(self):
cdef int size = self.my_map.size()
cdef intp_t [:] keys = np.empty(size, dtype=np.intp)
cdef float64_t [:] values = np.empty(size, dtype=np.float64)
self._to_arrays(keys, values)
cdef int idx
cdef intp_t key
cdef float64_t value
for idx in range(size):
key = keys[idx]
value = values[idx]
yield key, value
def to_arrays(self):
"""Return the key, value representation of the IntFloatDict
object.
Returns
=======
keys : ndarray, shape (n_items, ), dtype=int
The indices of the data points
values : ndarray, shape (n_items, ), dtype=float
The values of the data points
"""
cdef int size = self.my_map.size()
keys = np.empty(size, dtype=np.intp)
values = np.empty(size, dtype=np.float64)
self._to_arrays(keys, values)
return keys, values
cdef _to_arrays(self, intp_t [:] keys, float64_t [:] values):
# Internal version of to_arrays that takes already-initialized arrays
cdef cpp_map[intp_t, float64_t].iterator it = self.my_map.begin()
cdef cpp_map[intp_t, float64_t].iterator end = self.my_map.end()
cdef int index = 0
while it != end:
keys[index] = deref(it).first
values[index] = deref(it).second
inc(it)
index += 1
def update(self, IntFloatDict other):
cdef cpp_map[intp_t, float64_t].iterator it = other.my_map.begin()
cdef cpp_map[intp_t, float64_t].iterator end = other.my_map.end()
while it != end:
self.my_map[deref(it).first] = deref(it).second
inc(it)
def copy(self):
cdef IntFloatDict out_obj = IntFloatDict.__new__(IntFloatDict)
# The '=' operator is a copy operator for C++ maps
out_obj.my_map = self.my_map
return out_obj
def append(self, intp_t key, float64_t value):
# Construct our arguments
cdef pair[intp_t, float64_t] args
args.first = key
args.second = value
self.my_map.insert(args)
###############################################################################
# operation on dict
def argmin(IntFloatDict d):
cdef cpp_map[intp_t, float64_t].iterator it = d.my_map.begin()
cdef cpp_map[intp_t, float64_t].iterator end = d.my_map.end()
cdef intp_t min_key = -1
cdef float64_t min_value = np.inf
while it != end:
if deref(it).second < min_value:
min_value = deref(it).second
min_key = deref(it).first
inc(it)
return min_key, min_value